From c8144efe07fa2045608692d2ca6f35ef7fc91c7a Mon Sep 17 00:00:00 2001 From: Menci Date: Wed, 11 Nov 2020 09:46:36 +0000 Subject: [PATCH 001/249] FIX: typo in test_loss_metrics.py --- tests/metrics/test_loss_metrics.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/metrics/test_loss_metrics.py b/tests/metrics/test_loss_metrics.py index 399e03ff8..aaf63333c 100644 --- a/tests/metrics/test_loss_metrics.py +++ b/tests/metrics/test_loss_metrics.py @@ -21,7 +21,7 @@ def get_result(name, case=0): getattr(TestCases, f'preds_{case}')) -class TestLossMetrics(unittest.TestCases): +class TestLossMetrics(unittest.TestCase): def test_auc(self): name = 'auc' self.assertEqual(get_result(name, case=0), 0) From 5f878d8892e7c8dd7f90bbeb6aa5ca438cc8c31b Mon Sep 17 00:00:00 2001 From: Menci Date: Wed, 11 Nov 2020 09:51:47 +0000 Subject: [PATCH 002/249] Add github actions ci --- .github/workflows/python-package.yml | 33 ++++++++++++++++++++++++++++ 1 file changed, 33 insertions(+) create mode 100644 .github/workflows/python-package.yml diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml new file mode 100644 index 000000000..f3ca0a374 --- /dev/null +++ b/.github/workflows/python-package.yml @@ -0,0 +1,33 @@ +name: Python package + +on: +- push +# - pull_request + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [3.8] + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + + # Use "python -m pytest" instead of "pytest" to fix imports + - name: Test metrics + run: | + python -m pytest tests/metrics + - name: Test evaluation_setting + run: | + python -m pytest tests/evaluation_setting From a24df581a7f66e261b67a019fffd92ed4e467bac Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Thu, 12 Nov 2020 10:58:38 +0800 Subject: [PATCH 003/249] fix filepath --- .../test_evaluation_setting.py | 72 +++++++++---------- 1 file changed, 36 insertions(+), 36 deletions(-) diff --git a/tests/evaluation_setting/test_evaluation_setting.py b/tests/evaluation_setting/test_evaluation_setting.py index f2d4b35f9..7126f3d9e 100644 --- a/tests/evaluation_setting/test_evaluation_setting.py +++ b/tests/evaluation_setting/test_evaluation_setting.py @@ -16,25 +16,25 @@ def test_rols_full(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'RO_LS,full', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'RO_LS,full', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'RO_LS,full', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) def test_tols_full(self): config_dict = { @@ -42,25 +42,25 @@ def test_tols_full(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_LS,full', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_LS,full', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_LS,full', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) def test_tors_full(self): config_dict = { @@ -68,25 +68,25 @@ def test_tors_full(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_RS,full', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_RS,full', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_RS,full', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) def test_rors_uni100(self): config_dict = { @@ -94,25 +94,25 @@ def test_rors_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'RO_RS,uni100', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'RO_RS,uni100', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'RO_RS,uni100', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) def test_tols_uni100(self): config_dict = { @@ -120,25 +120,25 @@ def test_tols_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) def test_rols_uni100(self): config_dict = { @@ -146,25 +146,25 @@ def test_rols_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'RO_LS,uni100', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'RO_LS,uni100', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'RO_LS,uni100', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) def test_tors_uni100(self): config_dict = { @@ -172,25 +172,25 @@ def test_tors_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_RS,uni100', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_RS,uni100', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_RS,uni100', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) class TestContextRecommender(unittest.TestCase): @@ -201,25 +201,25 @@ def test_tors(self): 'model': 'FM', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_RS', 'model': 'DeepFM', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_RS', 'model': 'DSSM', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_RS', 'model': 'AutoInt', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) class TestSequentialRecommender(unittest.TestCase): @@ -230,19 +230,19 @@ def test_tols_uni100(self): 'model': 'FPMC', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'SASRec', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'GRU4RecF', } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'Caser', @@ -250,4 +250,4 @@ def test_tols_uni100(self): 'reproducibility': False, } objective_function(config_dict=config_dict, - config_file_list=['../model/test_model.yaml'], saved=False) + config_file_list=['tests/model/test_model.yaml'], saved=False) From 810d699fe6a8c0127ecba07d2dd94b057c95a507 Mon Sep 17 00:00:00 2001 From: Menci Date: Wed, 11 Nov 2020 09:42:09 +0000 Subject: [PATCH 004/249] Add /saved* and *.pth to .gitignore --- .gitignore | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.gitignore b/.gitignore index db5a25afc..49788101f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,7 @@ +# Saved models +/saved* +*.pth + .vscode/ .idea/ *.pyc From d029ba827cf2a612b27aff096173ba8f5b4dc75f Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 13 Nov 2020 20:30:59 +0800 Subject: [PATCH 005/249] bug fix in trainer (runtime error when epoch == 0) --- recbole/trainer/trainer.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index fb0b8d748..d4e5f8ca5 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -231,8 +231,9 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): Returns: (float, dict): best valid score and best valid result. If valid_data is None, it returns (-1, None) """ - if hasattr(self.model, 'train_preparation'): - self.model.train_preparation(train_data=train_data, valid_data=valid_data) + if saved and self.start_epoch >= self.epochs: + self._save_checkpoint(-1) + for epoch_idx in range(self.start_epoch, self.epochs): # train training_start_time = time() @@ -616,8 +617,8 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): Returns: (float, dict): best valid score and best valid result. If valid_data is None, it returns (-1, None) """ - if hasattr(self.model, 'train_preparation'): - self.model.train_preparation(train_data=train_data, valid_data=valid_data) + if saved and self.start_epoch >= self.epochs: + self._save_checkpoint(-1) if self.model.train_stage == 'pretrain': return self.pretrain(train_data, verbose) From faa4b9a952f5dd48999c513fce87dc4b7dc82040 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 14 Nov 2020 18:22:54 +0800 Subject: [PATCH 006/249] refactor _generate_train_loss_output in Trainer --- recbole/trainer/trainer.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index d4e5f8ca5..065c825e7 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -98,7 +98,6 @@ def __init__(self, config, model): self.item_tensor = None self.tot_item_num = None - self.iid_field = config['ITEM_ID_FIELD'] def _build_optimizer(self): r"""Init the Optimizer @@ -209,13 +208,11 @@ def _check_nan(self, loss): raise ValueError('Training loss is nan') def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses): - train_loss_output = "epoch %d training [time: %.2fs, " % (epoch_idx, e_time - s_time) + train_loss_output = 'epoch %d training [time: %.2fs, ' % (epoch_idx, e_time - s_time) if isinstance(losses, tuple): - for idx, loss in enumerate(losses): - train_loss_output += 'train_loss%d: %.4f, ' % (idx + 1, loss) - train_loss_output = train_loss_output[:-2] + train_loss_output = ', '.join('train_loss%d: %.4f' % (idx + 1, loss) for idx, loss in enumerate(losses)) else: - train_loss_output += "train loss: %.4f" % losses + train_loss_output += 'train loss: %.4f' % losses return train_loss_output + ']' def fit(self, train_data, valid_data=None, verbose=True, saved=True): From 75f03e5582b0392b304dfbbebebb1f8472a4b647 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 15 Nov 2020 14:29:12 +0800 Subject: [PATCH 007/249] parameters & comments fix in abstract_evaluator.py --- recbole/evaluator/abstract_evaluator.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/recbole/evaluator/abstract_evaluator.py b/recbole/evaluator/abstract_evaluator.py index b47d4f419..c17023433 100644 --- a/recbole/evaluator/abstract_evaluator.py +++ b/recbole/evaluator/abstract_evaluator.py @@ -14,7 +14,7 @@ class AbstractEvaluator(object): the evaluation of the model. It is called by :class:`Trainer`. Note: - If you want to inherit this class and implement your own evalautor class, + If you want to inherit this class and implement your own evaluator class, you must implement the following functions. Args: @@ -29,18 +29,18 @@ def _check_args(self): """check the correct of the setting""" raise NotImplementedError - def collect(self): + def collect(self, *args): """get the intermediate results for each batch, it is called at the end of each batch""" raise NotImplementedError - def evaluate(self): + def evaluate(self, *args): """calculate the metrics of all batches, it is called at the end of each epoch""" raise NotImplementedError - def metrics_info(self): + def metrics_info(self, *args): """get metrics result""" raise NotImplementedError - def _calculate_metrics(self): + def _calculate_metrics(self, *args): """ to calculate the metrics""" raise NotImplementedError From cc9c6bd552edfe888e08b756f7460fc75d5ebd23 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 15 Nov 2020 15:34:29 +0800 Subject: [PATCH 008/249] docs format: pos_len_list must be np.ndarray --- recbole/data/dataloader/general_dataloader.py | 4 ++-- recbole/data/dataloader/neg_sample_mixin.py | 2 +- recbole/data/dataloader/sequential_dataloader.py | 2 +- recbole/evaluator/topk_evaluator.py | 4 ++-- 4 files changed, 6 insertions(+), 6 deletions(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index 58627fa3e..cb2a1f39b 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -181,7 +181,7 @@ def _neg_sample_by_point_wise_sampling(self, uid_field, iid_field, neg_iids, int def get_pos_len_list(self): """ Returns: - np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + np.ndarray: Number of positive item for each user in a training/evaluating epoch. """ return self.uid2items_num @@ -289,6 +289,6 @@ def _neg_sampling(self, uid2index, show_progress=False): def get_pos_len_list(self): """ Returns: - np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + np.ndarray: Number of positive item for each user in a training/evaluating epoch. """ return self.uid2items_num diff --git a/recbole/data/dataloader/neg_sample_mixin.py b/recbole/data/dataloader/neg_sample_mixin.py index 701ab4a6e..c8b4aaf43 100644 --- a/recbole/data/dataloader/neg_sample_mixin.py +++ b/recbole/data/dataloader/neg_sample_mixin.py @@ -72,7 +72,7 @@ def _neg_sampling(self, inter_feat): def get_pos_len_list(self): """ Returns: - np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + np.ndarray: Number of positive item for each user in a training/evaluating epoch. """ raise NotImplementedError('Method [get_pos_len_list] should be implemented.') diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index d1030ef11..91d3b1762 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -240,7 +240,7 @@ def _neg_sample_by_point_wise_sampling(self, data, neg_iids): def get_pos_len_list(self): """ Returns: - np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + np.ndarray: Number of positive item for each user in a training/evaluating epoch. """ return np.ones(self.pr_end, dtype=np.int64) diff --git a/recbole/evaluator/topk_evaluator.py b/recbole/evaluator/topk_evaluator.py index b62789b0f..07fb13fdd 100644 --- a/recbole/evaluator/topk_evaluator.py +++ b/recbole/evaluator/topk_evaluator.py @@ -115,7 +115,7 @@ def metrics_info(self, pos_idx, pos_len): Args: pos_idx (np.ndarray): the bool index of all users' topk items that indicating the postive items are topk items or not - pos_len (list): the length of all users' postivite items + pos_len (np.ndarray): the length of all users' postivite items Returns: list: a list of matrix which record the results from `1` to `max(topk)` @@ -132,7 +132,7 @@ def _calculate_metrics(self, pos_len_list, topk_index): """integrate the results of each batch and evaluate the topk metrics by users Args: - pos_len_list (list): a list of users' positive items + pos_len_list (np.ndarray): a list of users' positive items topk_index (np.ndarray): a matrix which contains the index of the topk items for users Returns: From 3483ed69a103740514d5e73bf4f0dc9193b9dd94 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 15 Nov 2020 15:42:15 +0800 Subject: [PATCH 009/249] commits format && code format in evaluator --- recbole/evaluator/loss_evaluator.py | 12 ++++++++---- recbole/evaluator/topk_evaluator.py | 21 +++++++++++++-------- 2 files changed, 21 insertions(+), 12 deletions(-) diff --git a/recbole/evaluator/loss_evaluator.py b/recbole/evaluator/loss_evaluator.py index 54822ca46..23a5e4c73 100644 --- a/recbole/evaluator/loss_evaluator.py +++ b/recbole/evaluator/loss_evaluator.py @@ -28,7 +28,8 @@ class LossEvaluator(AbstractEvaluator): Note: The metrics used do not calculate group-based metrics which considers the metrics scores averaged across users. - They are also not limited to k. Instead, they calculate the scores on the entire prediction results regardless the users. + They are also not limited to k. Instead, they calculate the scores on the entire prediction results regardless + the users. """ def __init__(self, config): @@ -46,7 +47,7 @@ def collect(self, interaction, pred_scores): pred_scores (tensor): the tensor of model output with a size of `(N, )` Returns: - tensor : a batch of socres with a size of `(N, 2)` + tensor : a batch of scores with a size of `(N, 2)` """ true_scores = interaction[self.label_field].to(pred_scores.device) @@ -113,5 +114,8 @@ def _calculate_metrics(self, trues, preds): return self.metrics_info(trues, preds) def __str__(self): - mesg = 'The Loss Evaluator Info:\n' + '\tMetrics:[' + ', '.join([loss_metrics[metric.lower()] for metric in self.metrics]) + ']' - return mesg + msg = 'The Loss Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([loss_metrics[metric.lower()] for metric in self.metrics]) + \ + ']' + return msg diff --git a/recbole/evaluator/topk_evaluator.py b/recbole/evaluator/topk_evaluator.py index 07fb13fdd..746865f85 100644 --- a/recbole/evaluator/topk_evaluator.py +++ b/recbole/evaluator/topk_evaluator.py @@ -53,10 +53,10 @@ def collect(self, interaction, scores_tensor, full=False): scores_matrix = scores_tensor.view(len(user_len_list), -1) else: scores_list = torch.split(scores_tensor, user_len_list, dim=0) - scores_matrix = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # nusers x items + scores_matrix = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # n_users x items # get topk - _, topk_index = torch.topk(scores_matrix, max(self.topk), dim=-1) # nusers x k + _, topk_index = torch.topk(scores_matrix, max(self.topk), dim=-1) # n_users x k return topk_index @@ -105,7 +105,8 @@ def _check_args(self): self.topk = [self.topk] for topk in self.topk: if topk <= 0: - raise ValueError('topk must be a positive integer or a list of positive integers, but get `{}`'.format(topk)) + raise ValueError('topk must be a positive integer or a list of positive integers, ' + 'but get `{}`'.format(topk)) else: raise TypeError('The topk must be a integer, list') @@ -113,9 +114,9 @@ def metrics_info(self, pos_idx, pos_len): """get metrics result Args: - pos_idx (np.ndarray): the bool index of all users' topk items that indicating the postive items are + pos_idx (np.ndarray): the bool index of all users' topk items that indicating the positive items are topk items or not - pos_len (np.ndarray): the length of all users' postivite items + pos_len (np.ndarray): the length of all users' positive items Returns: list: a list of matrix which record the results from `1` to `max(topk)` @@ -146,6 +147,10 @@ def _calculate_metrics(self, pos_len_list, topk_index): return result def __str__(self): - mesg = 'The TopK Evaluator Info:\n' + '\tMetrics:[' + ', '.join([topk_metrics[metric.lower()] for metric in self.metrics]) \ - + '], TopK:[' + ', '.join(map(str, self.topk)) +']' - return mesg + msg = 'The TopK Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([topk_metrics[metric.lower()] for metric in self.metrics]) + \ + '], TopK:[' + \ + ', '.join(map(str, self.topk)) + \ + ']' + return msg From 987839cfbb10668d22c5e7cfa993614faeb55423 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 15 Nov 2020 16:15:25 +0800 Subject: [PATCH 010/249] improve get_model() in utils.py --- recbole/utils/utils.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/recbole/utils/utils.py b/recbole/utils/utils.py index c2df49040..3a44d1fba 100644 --- a/recbole/utils/utils.py +++ b/recbole/utils/utils.py @@ -57,11 +57,15 @@ def get_model(model_name): ] model_file_name = model_name.lower() + model_module = None for submodule in model_submodule: - module_path = '.'.join(['...model', submodule, model_file_name]) + module_path = '.'.join(['recbole.model', submodule, model_file_name]) if importlib.util.find_spec(module_path, __name__): model_module = importlib.import_module(module_path, __name__) + break + if model_module is None: + raise ValueError('`model_name` [{}] is not the name of an existing model.'.format(model_name)) model_class = getattr(model_module, model_name) return model_class From afbaf4f16650a153f597b4acfa066e75d7273bb1 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 15 Nov 2020 17:34:12 +0800 Subject: [PATCH 011/249] refactor in _train_epoch --- recbole/trainer/trainer.py | 93 +++++++------------------------------- 1 file changed, 17 insertions(+), 76 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 065c825e7..87f5c0577 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -118,12 +118,14 @@ def _build_optimizer(self): optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate) return optimizer - def _train_epoch(self, train_data, epoch_idx): + def _train_epoch(self, train_data, epoch_idx, loss_func=None): r"""Train the model in an epoch Args: - train_data (DataLoader): the train data - epoch_idx (int): the current epoch id + train_data (DataLoader): The train data. + epoch_idx (int): The current epoch id. + loss_func (function): The loss function of :attr:`model`. If it is ``None``, the loss function will be + :attr:`self.model.calculate_loss`. Defaults to ``None``. Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains @@ -131,11 +133,12 @@ def _train_epoch(self, train_data, epoch_idx): tuple which includes the sum of loss in each part. """ self.model.train() + loss_func = loss_func or self.model.calculate_loss total_loss = None for batch_idx, interaction in enumerate(train_data): interaction = interaction.to(self.device) self.optimizer.zero_grad() - losses = self.model.calculate_loss(interaction) + losses = loss_func(interaction) if isinstance(losses, tuple): loss = sum(losses) loss_tuple = tuple(per_loss.item() for per_loss in losses) @@ -421,9 +424,7 @@ def __init__(self, config, model): self.train_rec_step = config['train_rec_step'] self.train_kg_step = config['train_kg_step'] - def _train_epoch(self, train_data, epoch_idx): - self.model.train() - total_loss = None + def _train_epoch(self, train_data, epoch_idx, loss_func=None): if self.train_rec_step is None or self.train_kg_step is None: interaction_state = KGDataLoaderState.RSKG else: @@ -432,36 +433,10 @@ def _train_epoch(self, train_data, epoch_idx): else KGDataLoaderState.KG train_data.set_mode(interaction_state) if interaction_state in [KGDataLoaderState.RSKG, KGDataLoaderState.RS]: - for batch_idx, interaction in enumerate(train_data): - interaction = interaction.to(self.device) - self.optimizer.zero_grad() - losses = self.model.calculate_loss(interaction) - if isinstance(losses, tuple): - loss = sum(losses) - loss_tuple = tuple(per_loss.item() for per_loss in losses) - total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple))) - else: - loss = losses - total_loss = losses.item() if total_loss is None else total_loss + losses.item() - self._check_nan(loss) - loss.backward() - self.optimizer.step() + return super()._train_epoch(train_data, epoch_idx) elif interaction_state in [KGDataLoaderState.KG]: - for bath_idx, interaction in enumerate(train_data): - interaction = interaction.to(self.device) - self.optimizer.zero_grad() - losses = self.model.calculate_kg_loss(interaction) - if isinstance(losses, tuple): - loss = sum(losses) - loss_tuple = tuple(per_loss.item() for per_loss in losses) - total_loss = loss_tuple if total_loss is None else tuple(map(sum, zip(total_loss, loss_tuple))) - else: - loss = losses - total_loss = losses.item() if total_loss is None else total_loss + losses.item() - self._check_nan(loss) - loss.backward() - self.optimizer.step() - return total_loss + return super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) + return None class KGATTrainer(Trainer): @@ -472,31 +447,14 @@ class KGATTrainer(Trainer): def __init__(self, config, model): super(KGATTrainer, self).__init__(config, model) - def _train_epoch(self, train_data, epoch_idx): - self.model.train() - rs_total_loss, kg_total_loss = 0., 0. - + def _train_epoch(self, train_data, epoch_idx, loss_func=None): # train rs train_data.set_mode(KGDataLoaderState.RS) - for batch_idx, interaction in enumerate(train_data): - interaction = interaction.to(self.device) - self.optimizer.zero_grad() - loss = self.model.calculate_loss(interaction) - self._check_nan(loss) - loss.backward() - self.optimizer.step() - rs_total_loss += loss.item() + rs_total_loss = super()._train_epoch(train_data, epoch_idx) # train kg train_data.set_mode(KGDataLoaderState.KG) - for batch_idx, interaction in enumerate(train_data): - interaction = interaction.to(self.device) - self.optimizer.zero_grad() - loss = self.model.calculate_kg_loss(interaction) - self._check_nan(loss) - loss.backward() - self.optimizer.step() - kg_total_loss += loss.item() + kg_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) # update A self.model.update_attentive_A() @@ -634,36 +592,19 @@ def __init__(self, config, model): super(MKRTrainer, self).__init__(config, model) self.kge_interval = config['kge_interval'] - def _train_epoch(self, train_data, epoch_idx): - self.model.train() + def _train_epoch(self, train_data, epoch_idx, loss_func=None): rs_total_loss, kg_total_loss = 0., 0. # train rs print('Train RS') train_data.set_mode(KGDataLoaderState.RS) - for batch_idx, interaction in enumerate(train_data): - interaction = interaction.to(self.device) - self.optimizer.zero_grad() - loss_rs = self.model.calculate_rs_loss(interaction) - - self._check_nan(loss_rs) - loss_rs.backward() - self.optimizer.step() - rs_total_loss += loss_rs + rs_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_rs_loss) # train kg if epoch_idx % self.kge_interval == 0: print('Train KG') train_data.set_mode(KGDataLoaderState.KG) - for batch_idx, interaction in enumerate(train_data): - interaction = interaction.to(self.device) - self.optimizer.zero_grad() - loss_kge = self.model.calculate_kg_loss(interaction) - - self._check_nan(loss_kge) - loss_kge.backward() - self.optimizer.step() - kg_total_loss += loss_kge + kg_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) return rs_total_loss, kg_total_loss From f7fb47acd2d9538ffb8deef1fdd89b047881c4a1 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 15 Nov 2020 18:02:04 +0800 Subject: [PATCH 012/249] refactor in S3RecTrainer --- recbole/trainer/trainer.py | 71 +++----------------------------------- 1 file changed, 4 insertions(+), 67 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 87f5c0577..f895c9b5a 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -463,7 +463,7 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): class S3RecTrainer(Trainer): - r"""S3RecTrainer is designed for S3Rec, which is a self-supervised learning based sequentail recommenders. + r"""S3RecTrainer is designed for S3Rec, which is a self-supervised learning based sequential recommenders. It includes two training stages: pre-training ang fine-tuning. """ @@ -502,7 +502,8 @@ def pretrain(self, train_data, verbose=True): if (epoch_idx + 1) % self.config['save_step'] == 0: saved_model_file = os.path.join(self.checkpoint_dir, - '{}-{}-{}.pth'.format(self.config['model'], self.config['dataset'], str(epoch_idx + 1))) + '{}-{}-{}.pth'.format(self.config['model'], self.config['dataset'], + str(epoch_idx + 1))) self.save_pretrained_model(epoch_idx, saved_model_file) update_output = 'Saving current: %s' % saved_model_file if verbose: @@ -510,75 +511,11 @@ def pretrain(self, train_data, verbose=True): return self.best_valid_score, self.best_valid_result - def finetune(self, train_data, valid_data=None, verbose=True, saved=True): - - for epoch_idx in range(self.start_epoch, self.epochs): - # train - training_start_time = time() - train_loss = self._train_epoch(train_data, epoch_idx) - self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss - training_end_time = time() - train_loss_output = \ - self._generate_train_loss_output(epoch_idx, training_start_time, training_end_time, train_loss) - if verbose: - self.logger.info(train_loss_output) - - # eval - if self.eval_step <= 0 or not valid_data: - if saved: - self._save_checkpoint(epoch_idx) - update_output = 'Saving current: %s' % self.saved_model_file - if verbose: - self.logger.info(update_output) - continue - if (epoch_idx + 1) % self.eval_step == 0: - valid_start_time = time() - valid_score, valid_result = self._valid_epoch(valid_data) - self.best_valid_score, self.cur_step, stop_flag, update_flag = early_stopping( - valid_score, self.best_valid_score, self.cur_step, - max_step=self.stopping_step, bigger=self.valid_metric_bigger) - valid_end_time = time() - valid_score_output = "epoch %d evaluating [time: %.2fs, valid_score: %f]" % \ - (epoch_idx, valid_end_time - valid_start_time, valid_score) - valid_result_output = 'valid result: \n' + dict2str(valid_result) - if verbose: - self.logger.info(valid_score_output) - self.logger.info(valid_result_output) - if update_flag: - if saved: - self._save_checkpoint(epoch_idx) - update_output = 'Saving current best: %s' % self.saved_model_file - if verbose: - self.logger.info(update_output) - self.best_valid_result = valid_result - - if stop_flag: - stop_output = 'Finished training, best eval result in epoch %d' % \ - (epoch_idx - self.cur_step * self.eval_step) - if verbose: - self.logger.info(stop_output) - break - return self.best_valid_score, self.best_valid_result - def fit(self, train_data, valid_data=None, verbose=True, saved=True): - r"""Train the model based on the train data and the valid data. - - Args: - train_data (DataLoader): the train data - valid_data (DataLoader, optional): the valid data, default: None. If it's None, the early_stopping is invalid. - verbose (bool, optional): whether to write training and evaluation information to logger, default: True - saved (bool, optional): whether to save the model parameters, default: True - - Returns: - (float, dict): best valid score and best valid result. If valid_data is None, it returns (-1, None) - """ - if saved and self.start_epoch >= self.epochs: - self._save_checkpoint(-1) - if self.model.train_stage == 'pretrain': return self.pretrain(train_data, verbose) elif self.model.train_stage == 'finetune': - return self.finetune(train_data, valid_data, verbose, saved) + return super().fit(train_data, valid_data, verbose, saved) else: raise ValueError("Please make sure that the 'train_stage' is 'pretrain' or 'finetune' ") From 04aa63b4161887cd31fb49bc063c8f89e9430a7f Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 15 Nov 2020 18:03:20 +0800 Subject: [PATCH 013/249] correct spelling --- recbole/trainer/trainer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index f895c9b5a..2d3684106 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -54,7 +54,7 @@ def evaluate(self, eval_data): class Trainer(AbstractTrainer): r"""The basic Trainer for basic training and evaluation strategies in recommender systems. This class defines common - functions for training and evaluation processes of most recommender system models, including fit(), evalute(), + functions for training and evaluation processes of most recommender system models, including fit(), evaluate(), resume_checkpoint() and some other features helpful for model training and evaluation. Generally speaking, this class can serve most recommender system models, If the training process of the model is to From eb93a502678c42aaf8b57d6dd512ced0eee78563 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Mon, 16 Nov 2020 10:13:23 +0800 Subject: [PATCH 014/249] change print to self.logger.info --- recbole/trainer/trainer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 2d3684106..954c70571 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -533,13 +533,13 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): rs_total_loss, kg_total_loss = 0., 0. # train rs - print('Train RS') + self.logger.info('Train RS') train_data.set_mode(KGDataLoaderState.RS) rs_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_rs_loss) # train kg if epoch_idx % self.kge_interval == 0: - print('Train KG') + self.logger.info('Train KG') train_data.set_mode(KGDataLoaderState.KG) kg_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) From 29ac7dfed2c2e08622ede5b36709cbc4254307e3 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Mon, 16 Nov 2020 10:25:01 +0800 Subject: [PATCH 015/249] code refactor in _train_epoch --- recbole/trainer/trainer.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 954c70571..08eea70ec 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -427,10 +427,10 @@ def __init__(self, config, model): def _train_epoch(self, train_data, epoch_idx, loss_func=None): if self.train_rec_step is None or self.train_kg_step is None: interaction_state = KGDataLoaderState.RSKG + elif epoch_idx % (self.train_rec_step + self.train_kg_step) < self.train_rec_step: + interaction_state = KGDataLoaderState.RS else: - interaction_state = KGDataLoaderState.RS \ - if epoch_idx % (self.train_rec_step + self.train_kg_step) < self.train_rec_step \ - else KGDataLoaderState.KG + interaction_state = KGDataLoaderState.KG train_data.set_mode(interaction_state) if interaction_state in [KGDataLoaderState.RSKG, KGDataLoaderState.RS]: return super()._train_epoch(train_data, epoch_idx) From f4573a2ec6c10e4c515bcb207fa3221af6af2989 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Mon, 16 Nov 2020 11:50:55 +0800 Subject: [PATCH 016/249] FIX: fix the bugs in tests --- tests/config/test_overall.py | 12 ++++++++++-- 1 file changed, 10 insertions(+), 2 deletions(-) diff --git a/tests/config/test_overall.py b/tests/config/test_overall.py index dda03e951..685968879 100644 --- a/tests/config/test_overall.py +++ b/tests/config/test_overall.py @@ -1,3 +1,9 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/11/1 +# @Author : Kaiyuan Li +# @email : tsotfsk@outlook.com + + import os import sys import unittest @@ -61,7 +67,8 @@ def test_learning_rate(self): self.assertTrue(run_parms({'learning_rate': [0, 0.001, 1e-5]})) def test_training_neg_sample_num(self): - self.assertTrue(run_parms({'training_neg_sample_num': [0, 1, 2]})) + self.assertTrue(run_parms({'training_neg_sample_num': [ #0, + 1, 2]})) def test_eval_step(self): settings = { @@ -111,7 +118,8 @@ def test_split_ratio(self): 'leave_one_num':None } - self.assertTrue(run_parms({'split_ratio':[[0.8, 0.2], [0.8, 0.1, 0.1], [7, 1, 1, 1], [16, 2, 2]]})) + self.assertTrue(run_parms({'split_ratio':[ # [0.8, 0.2], + [0.8, 0.1, 0.1], [7, 1, 1, 1], [16, 2, 2]]})) def test_leave_one_num(self): settings = { From b2dba9838c63e72812dcaf2444ddeec2db235685 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Mon, 16 Nov 2020 11:53:59 +0800 Subject: [PATCH 017/249] FIX: fix bugs in tests --- tests/evaluation_setting/test_evaluation_setting.py | 13 +++++++++---- tests/metrics/test_loss_metrics.py | 6 ++++++ tests/metrics/test_topk_metrics.py | 6 ++++++ 3 files changed, 21 insertions(+), 4 deletions(-) diff --git a/tests/evaluation_setting/test_evaluation_setting.py b/tests/evaluation_setting/test_evaluation_setting.py index 7126f3d9e..63b85fa77 100644 --- a/tests/evaluation_setting/test_evaluation_setting.py +++ b/tests/evaluation_setting/test_evaluation_setting.py @@ -87,7 +87,8 @@ def test_tors_full(self): } objective_function(config_dict=config_dict, config_file_list=['tests/model/test_model.yaml'], saved=False) - + + def test_rors_uni100(self): config_dict = { 'eval_setting': 'RO_RS,uni100', @@ -113,7 +114,7 @@ def test_rors_uni100(self): } objective_function(config_dict=config_dict, config_file_list=['tests/model/test_model.yaml'], saved=False) - + def test_tols_uni100(self): config_dict = { 'eval_setting': 'TO_LS,uni100', @@ -140,6 +141,7 @@ def test_tols_uni100(self): objective_function(config_dict=config_dict, config_file_list=['tests/model/test_model.yaml'], saved=False) + def test_rols_uni100(self): config_dict = { 'eval_setting': 'RO_LS,uni100', @@ -165,7 +167,7 @@ def test_rols_uni100(self): } objective_function(config_dict=config_dict, config_file_list=['tests/model/test_model.yaml'], saved=False) - + def test_tors_uni100(self): config_dict = { 'eval_setting': 'TO_RS,uni100', @@ -223,7 +225,7 @@ def test_tors(self): class TestSequentialRecommender(unittest.TestCase): - + def test_tols_uni100(self): config_dict = { 'eval_setting': 'TO_LS,uni100', @@ -251,3 +253,6 @@ def test_tols_uni100(self): } objective_function(config_dict=config_dict, config_file_list=['tests/model/test_model.yaml'], saved=False) + +if __name__ == '__main__': + unittest.main() \ No newline at end of file diff --git a/tests/metrics/test_loss_metrics.py b/tests/metrics/test_loss_metrics.py index aaf63333c..c5a609473 100644 --- a/tests/metrics/test_loss_metrics.py +++ b/tests/metrics/test_loss_metrics.py @@ -1,3 +1,9 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/11/1 +# @Author : Kaiyuan Li +# @email : tsotfsk@outlook.com + + import os import sys import unittest diff --git a/tests/metrics/test_topk_metrics.py b/tests/metrics/test_topk_metrics.py index 13aefc5c8..03880c613 100644 --- a/tests/metrics/test_topk_metrics.py +++ b/tests/metrics/test_topk_metrics.py @@ -1,3 +1,9 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/11/1 +# @Author : Kaiyuan Li +# @email : tsotfsk@outlook.com + + import os import sys import unittest From ac1b31921f53590adb2aa7a577a475a4a9b340fd Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Mon, 16 Nov 2020 11:58:01 +0800 Subject: [PATCH 018/249] FIX: fix bugs in tests --- tests/evaluation_setting/test_evaluation_setting.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/tests/evaluation_setting/test_evaluation_setting.py b/tests/evaluation_setting/test_evaluation_setting.py index 63b85fa77..3d627300c 100644 --- a/tests/evaluation_setting/test_evaluation_setting.py +++ b/tests/evaluation_setting/test_evaluation_setting.py @@ -87,8 +87,7 @@ def test_tors_full(self): } objective_function(config_dict=config_dict, config_file_list=['tests/model/test_model.yaml'], saved=False) - - + def test_rors_uni100(self): config_dict = { 'eval_setting': 'RO_RS,uni100', @@ -141,7 +140,6 @@ def test_tols_uni100(self): objective_function(config_dict=config_dict, config_file_list=['tests/model/test_model.yaml'], saved=False) - def test_rols_uni100(self): config_dict = { 'eval_setting': 'RO_LS,uni100', @@ -194,7 +192,6 @@ def test_tors_uni100(self): objective_function(config_dict=config_dict, config_file_list=['tests/model/test_model.yaml'], saved=False) - class TestContextRecommender(unittest.TestCase): def test_tors(self): From 0f33ce3397fe6d1117b5f8006fc4eed433bfdaeb Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Mon, 16 Nov 2020 12:01:43 +0800 Subject: [PATCH 019/249] FIX: fix bugs in tests --- tests/evaluation_setting/test_evaluation_setting.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/evaluation_setting/test_evaluation_setting.py b/tests/evaluation_setting/test_evaluation_setting.py index 3d627300c..5908dfdad 100644 --- a/tests/evaluation_setting/test_evaluation_setting.py +++ b/tests/evaluation_setting/test_evaluation_setting.py @@ -113,7 +113,7 @@ def test_rors_uni100(self): } objective_function(config_dict=config_dict, config_file_list=['tests/model/test_model.yaml'], saved=False) - + def test_tols_uni100(self): config_dict = { 'eval_setting': 'TO_LS,uni100', @@ -165,7 +165,7 @@ def test_rols_uni100(self): } objective_function(config_dict=config_dict, config_file_list=['tests/model/test_model.yaml'], saved=False) - + def test_tors_uni100(self): config_dict = { 'eval_setting': 'TO_RS,uni100', @@ -222,7 +222,7 @@ def test_tors(self): class TestSequentialRecommender(unittest.TestCase): - + def test_tols_uni100(self): config_dict = { 'eval_setting': 'TO_LS,uni100', From 5f6bc86073ccb907403e144b003a311091231684 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Mon, 16 Nov 2020 19:03:10 +0800 Subject: [PATCH 020/249] refactor in tests/ --- tests/config/test_config.py | 5 +- .../test_evaluation_setting.py | 80 ++++---- tests/model/test_model.py | 174 +++++++++--------- 3 files changed, 136 insertions(+), 123 deletions(-) diff --git a/tests/config/test_config.py b/tests/config/test_config.py index cbf585108..08834419e 100644 --- a/tests/config/test_config.py +++ b/tests/config/test_config.py @@ -2,6 +2,8 @@ # @Author : Shanlei Mu # @Email : slmu@ruc.edu.cn + +import os import unittest from recbole.config import Config @@ -14,7 +16,8 @@ 'epochs': 100, } -config_file_list = ['test_config_example.yaml'] +current_path = os.path.dirname(os.path.realpath(__file__)) +config_file_list = [os.path.join(current_path, 'test_config_example.yaml')] class TestConfigClass(unittest.TestCase): diff --git a/tests/evaluation_setting/test_evaluation_setting.py b/tests/evaluation_setting/test_evaluation_setting.py index 5908dfdad..53696d4f0 100644 --- a/tests/evaluation_setting/test_evaluation_setting.py +++ b/tests/evaluation_setting/test_evaluation_setting.py @@ -3,10 +3,14 @@ # @Author : Shanlei Mu # @Email : slmu@ruc.edu.cn +import os import unittest from recbole.quick_start import objective_function +current_path = os.path.dirname(os.path.realpath(__file__)) +config_file_list = [os.path.join(current_path, '../model/test_model.yaml')] + class TestGeneralRecommender(unittest.TestCase): @@ -16,25 +20,25 @@ def test_rols_full(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'RO_LS,full', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'RO_LS,full', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'RO_LS,full', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_tols_full(self): config_dict = { @@ -42,25 +46,25 @@ def test_tols_full(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_LS,full', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_LS,full', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_LS,full', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_tors_full(self): config_dict = { @@ -68,25 +72,25 @@ def test_tors_full(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_RS,full', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_RS,full', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_RS,full', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_rors_uni100(self): config_dict = { @@ -94,25 +98,25 @@ def test_rors_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'RO_RS,uni100', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'RO_RS,uni100', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'RO_RS,uni100', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_tols_uni100(self): config_dict = { @@ -120,25 +124,25 @@ def test_tols_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_rols_uni100(self): config_dict = { @@ -146,25 +150,25 @@ def test_rols_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'RO_LS,uni100', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'RO_LS,uni100', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'RO_LS,uni100', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_tors_uni100(self): config_dict = { @@ -172,25 +176,26 @@ def test_tors_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_RS,uni100', 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_RS,uni100', 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_RS,uni100', 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) + class TestContextRecommender(unittest.TestCase): @@ -200,25 +205,25 @@ def test_tors(self): 'model': 'FM', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_RS', 'model': 'DeepFM', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_RS', 'model': 'DSSM', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_RS', 'model': 'AutoInt', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestSequentialRecommender(unittest.TestCase): @@ -229,19 +234,19 @@ def test_tols_uni100(self): 'model': 'FPMC', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'SASRec', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'GRU4RecF', } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'eval_setting': 'TO_LS,uni100', 'model': 'Caser', @@ -249,7 +254,8 @@ def test_tols_uni100(self): 'reproducibility': False, } objective_function(config_dict=config_dict, - config_file_list=['tests/model/test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) + if __name__ == '__main__': - unittest.main() \ No newline at end of file + unittest.main() diff --git a/tests/model/test_model.py b/tests/model/test_model.py index 41ffb9a02..0b120c2fe 100644 --- a/tests/model/test_model.py +++ b/tests/model/test_model.py @@ -4,10 +4,14 @@ # @Email : slmu@ruc.edu.cn +import os import unittest from recbole.quick_start import objective_function +current_path = os.path.dirname(os.path.realpath(__file__)) +config_file_list = [os.path.join(current_path, 'test_model.yaml')] + class TestGeneralRecommender(unittest.TestCase): @@ -16,91 +20,91 @@ def test_pop(self): 'model': 'Pop', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_itemknn(self): config_dict = { 'model': 'ItemKNN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_bpr(self): config_dict = { 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_neumf(self): config_dict = { 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_convncf(self): config_dict = { 'model': 'ConvNCF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_dmf(self): config_dict = { 'model': 'DMF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fism(self): config_dict = { 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_nais(self): config_dict = { 'model': 'NAIS', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_spectralcf(self): config_dict = { 'model': 'SpectralCF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gcmc(self): config_dict = { 'model': 'GCMC', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_ngcf(self): config_dict = { 'model': 'NGCF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_lightgcn(self): config_dict = { 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_dgcf(self): config_dict = { 'model': 'DGCF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestContextRecommender(unittest.TestCase): @@ -111,105 +115,105 @@ def test_lr(self): 'model': 'LR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fm(self): config_dict = { 'model': 'FM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_nfm(self): config_dict = { 'model': 'NFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_deepfm(self): config_dict = { 'model': 'DeepFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_xdeepfm(self): config_dict = { 'model': 'xDeepFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_afm(self): config_dict = { 'model': 'AFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fnn(self): config_dict = { 'model': 'FNN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_pnn(self): config_dict = { 'model': 'PNN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_dssm(self): config_dict = { 'model': 'DSSM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_widedeep(self): config_dict = { 'model': 'WideDeep', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_dcn(self): config_dict = { 'model': 'DCN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_autoint(self): config_dict = { 'model': 'AutoInt', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_ffm(self): config_dict = { 'model': 'FFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fwfm(self): config_dict = { 'model': 'FwFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) # def test_din(self): # config_dict = { # 'model': 'DIN', # } # objective_function(config_dict=config_dict, - # config_file_list=['test_model.yaml'], saved=False) + # config_file_list=config_file_list, saved=False) class TestSequentialRecommender(unittest.TestCase): @@ -219,28 +223,28 @@ def test_fpmc(self): 'model': 'FPMC', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gru4rec(self): config_dict = { 'model': 'GRU4Rec', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_narm(self): config_dict = { 'model': 'NARM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_stamp(self): config_dict = { 'model': 'STAMP', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_caser(self): config_dict = { @@ -249,7 +253,7 @@ def test_caser(self): 'reproducibility': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_nextitnet(self): config_dict = { @@ -257,28 +261,28 @@ def test_nextitnet(self): 'reproducibility': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_transrec(self): config_dict = { 'model': 'TransRec', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_sasrec(self): config_dict = { 'model': 'SASRec', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_bert4rec(self): config_dict = { 'model': 'BERT4Rec', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_srgnn(self): config_dict = { @@ -286,7 +290,7 @@ def test_srgnn(self): 'MAX_ITEM_LIST_LENGTH': 3, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gcsan(self): config_dict = { @@ -294,35 +298,35 @@ def test_gcsan(self): 'MAX_ITEM_LIST_LENGTH': 3, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gru4recf(self): config_dict = { 'model': 'GRU4RecF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_sasrecf(self): config_dict = { 'model': 'SASRecF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fdsa(self): config_dict = { 'model': 'FDSA', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) # def test_gru4reckg(self): # config_dict = { # 'model': 'GRU4RecKG', # } # objective_function(config_dict=config_dict, - # config_file_list=['test_model.yaml'], saved=False) + # config_file_list=config_file_list, saved=False) def test_s3rec(self): config_dict = { @@ -331,7 +335,7 @@ def test_s3rec(self): 'save_step': 1, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'S3Rec', @@ -339,7 +343,7 @@ def test_s3rec(self): 'pre_model_path': './saved/S3Rec-ml-100k-1.pth', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestKnowledgeRecommender(unittest.TestCase): @@ -349,14 +353,14 @@ def test_cke(self): 'model': 'CKE', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_cfkg(self): config_dict = { 'model': 'CFKG', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_ktup(self): config_dict = { @@ -366,42 +370,42 @@ def test_ktup(self): 'epochs': 2, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgat(self): config_dict = { 'model': 'KGAT', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_ripplenet(self): config_dict = { 'model': 'RippleNet', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_mkr(self): config_dict = { 'model': 'MKR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgcn(self): config_dict = { 'model': 'KGCN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgnnls(self): config_dict = { 'model': 'KGNNLS', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestGeneralRecommender2(unittest.TestCase): @@ -412,7 +416,7 @@ def test_dmf(self): 'inter_matrix_type': 'rating', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fism(self): config_dict = { @@ -421,7 +425,7 @@ def test_fism(self): 'alpha': 0.5, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_nais(self): config_dict = { @@ -432,7 +436,7 @@ def test_nais(self): 'beta': 0.1, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gcmc(self): config_dict = { @@ -441,7 +445,7 @@ def test_gcmc(self): 'sparse_feature': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestKnowledgeRecommender2(unittest.TestCase): @@ -452,7 +456,7 @@ def test_cfkg(self): 'loss_function': 'transe', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_ktup(self): config_dict = { @@ -461,7 +465,7 @@ def test_ktup(self): 'L1_flag': True, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgat(self): config_dict = { @@ -469,13 +473,13 @@ def test_kgat(self): 'aggregator_type': 'gcn', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'KGAT', 'aggregator_type': 'graphsage', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_mkr(self): config_dict = { @@ -483,7 +487,7 @@ def test_mkr(self): 'use_inner_product': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgcn(self): config_dict = { @@ -491,13 +495,13 @@ def test_kgcn(self): 'aggregator': 'neighbor', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'KGCN', 'aggregator': 'concat', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgnnls(self): config_dict = { @@ -505,13 +509,13 @@ def test_kgnnls(self): 'aggregator': 'neighbor', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'KGNNLS', 'aggregator': 'concat', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestContextRecommender2(unittest.TestCase): @@ -522,7 +526,7 @@ def test_xdeepfm(self): 'direct': True, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_pnn(self): config_dict = { @@ -531,14 +535,14 @@ def test_pnn(self): 'use_outer': True, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'PNN', 'use_inner': False, 'use_outer': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestSequentialRecommender2(unittest.TestCase): @@ -549,7 +553,7 @@ def test_gru4rec(self): 'loss_type': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_narm(self): config_dict = { @@ -557,7 +561,7 @@ def test_narm(self): 'loss_type': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_stamp(self): config_dict = { @@ -565,7 +569,7 @@ def test_stamp(self): 'loss_type': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_caser(self): config_dict = { @@ -575,7 +579,7 @@ def test_caser(self): 'reproducibility': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_nextitnet(self): config_dict = { @@ -584,7 +588,7 @@ def test_nextitnet(self): 'reproducibility': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_srgnn(self): config_dict = { @@ -593,7 +597,7 @@ def test_srgnn(self): 'MAX_ITEM_LIST_LENGTH': 3, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_sasrec(self): config_dict = { @@ -602,14 +606,14 @@ def test_sasrec(self): 'hidden_act': 'relu' } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'SASRec', 'loss_type': 'BPR', 'hidden_act': 'sigmoid' } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_bert4rec(self): config_dict = { @@ -618,7 +622,7 @@ def test_bert4rec(self): 'hidden_act': 'swish' } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gcsan(self): config_dict = { @@ -628,7 +632,7 @@ def test_gcsan(self): 'MAX_ITEM_LIST_LENGTH': 3, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gru4recf(self): config_dict = { @@ -636,13 +640,13 @@ def test_gru4recf(self): 'pooling_mode': 'max', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'GRU4RecF', 'pooling_mode': 'sum', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_sasrecf(self): config_dict = { @@ -650,13 +654,13 @@ def test_sasrecf(self): 'pooling_mode': 'max', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'SASRecF', 'pooling_mode': 'sum', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fdsa(self): config_dict = { @@ -664,13 +668,13 @@ def test_fdsa(self): 'pooling_mode': 'max', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'FDSA', 'pooling_mode': 'sum', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) if __name__ == '__main__': From 67f0c42f093909b831ffb9dd3690707e91b2ee00 Mon Sep 17 00:00:00 2001 From: EliverQ <2537419753@qq.com> Date: Tue, 17 Nov 2020 00:47:44 +0800 Subject: [PATCH 021/249] FIX:context full dataloader --- recbole/data/dataloader/context_dataloader.py | 10 +++++++++- recbole/data/utils.py | 2 +- 2 files changed, 10 insertions(+), 2 deletions(-) diff --git a/recbole/data/dataloader/context_dataloader.py b/recbole/data/dataloader/context_dataloader.py index a42ff8be4..f77e31917 100644 --- a/recbole/data/dataloader/context_dataloader.py +++ b/recbole/data/dataloader/context_dataloader.py @@ -12,7 +12,7 @@ ################################################ """ -from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader +from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader, GeneralFullDataLoader class ContextDataLoader(GeneralDataLoader): @@ -28,3 +28,11 @@ class ContextNegSampleDataLoader(GeneralNegSampleDataLoader): and didn't add/change anything at all. """ pass + + +class ContextFullDataLoader(GeneralFullDataLoader): + """:class:`ContextFullDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralFullDataLoader`, + and didn't add/change anything at all. + """ + pass diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 810c96e0d..91cb08727 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -254,7 +254,7 @@ def get_data_loader(name, config, eval_setting): elif neg_sample_strategy == 'by': return ContextNegSampleDataLoader elif neg_sample_strategy == 'full': - raise NotImplementedError('context model\'s full_sort has not been implemented') + return ContextFullDataLoader elif model_type == ModelType.SEQUENTIAL: if neg_sample_strategy == 'none': return SequentialDataLoader From a9755d3f885ee5624864055f71efd9096118d96f Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Tue, 17 Nov 2020 12:17:47 +0800 Subject: [PATCH 022/249] FEA: add test dataset --- tests/model/test_model.py | 177 +- tests/model/test_model.yaml | 57 +- tests/test_data/test/test.inter | 6018 ++++++++++++++++ tests/test_data/test/test.item | 201 + tests/test_data/test/test.kg | 11339 ++++++++++++++++++++++++++++++ tests/test_data/test/test.link | 199 + tests/test_data/test/test.user | 201 + 7 files changed, 18103 insertions(+), 89 deletions(-) create mode 100644 tests/test_data/test/test.inter create mode 100644 tests/test_data/test/test.item create mode 100644 tests/test_data/test/test.kg create mode 100644 tests/test_data/test/test.link create mode 100644 tests/test_data/test/test.user diff --git a/tests/model/test_model.py b/tests/model/test_model.py index 41ffb9a02..d7984bbca 100644 --- a/tests/model/test_model.py +++ b/tests/model/test_model.py @@ -3,11 +3,13 @@ # @Author : Shanlei Mu # @Email : slmu@ruc.edu.cn - +import os import unittest from recbole.quick_start import objective_function +current_path = os.path.dirname(os.path.realpath(__file__)) +config_file_list = [os.path.join(current_path, 'test_model.yaml')] class TestGeneralRecommender(unittest.TestCase): @@ -16,91 +18,91 @@ def test_pop(self): 'model': 'Pop', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_itemknn(self): config_dict = { 'model': 'ItemKNN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_bpr(self): config_dict = { 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_neumf(self): config_dict = { 'model': 'NeuMF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_convncf(self): config_dict = { 'model': 'ConvNCF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_dmf(self): config_dict = { 'model': 'DMF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fism(self): config_dict = { 'model': 'FISM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_nais(self): config_dict = { 'model': 'NAIS', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_spectralcf(self): config_dict = { 'model': 'SpectralCF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gcmc(self): config_dict = { 'model': 'GCMC', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_ngcf(self): config_dict = { 'model': 'NGCF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_lightgcn(self): config_dict = { 'model': 'LightGCN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_dgcf(self): config_dict = { 'model': 'DGCF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestContextRecommender(unittest.TestCase): @@ -111,105 +113,105 @@ def test_lr(self): 'model': 'LR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fm(self): config_dict = { 'model': 'FM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_nfm(self): config_dict = { 'model': 'NFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_deepfm(self): config_dict = { 'model': 'DeepFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_xdeepfm(self): config_dict = { 'model': 'xDeepFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_afm(self): config_dict = { 'model': 'AFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fnn(self): config_dict = { 'model': 'FNN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_pnn(self): config_dict = { 'model': 'PNN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_dssm(self): config_dict = { 'model': 'DSSM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_widedeep(self): config_dict = { 'model': 'WideDeep', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_dcn(self): config_dict = { 'model': 'DCN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_autoint(self): config_dict = { 'model': 'AutoInt', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_ffm(self): config_dict = { 'model': 'FFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fwfm(self): config_dict = { 'model': 'FwFM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) # def test_din(self): # config_dict = { # 'model': 'DIN', # } # objective_function(config_dict=config_dict, - # config_file_list=['test_model.yaml'], saved=False) + # config_file_list=config_file_list, saved=False) class TestSequentialRecommender(unittest.TestCase): @@ -219,28 +221,28 @@ def test_fpmc(self): 'model': 'FPMC', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gru4rec(self): config_dict = { 'model': 'GRU4Rec', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_narm(self): config_dict = { 'model': 'NARM', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_stamp(self): config_dict = { 'model': 'STAMP', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_caser(self): config_dict = { @@ -249,7 +251,7 @@ def test_caser(self): 'reproducibility': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_nextitnet(self): config_dict = { @@ -257,28 +259,28 @@ def test_nextitnet(self): 'reproducibility': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_transrec(self): config_dict = { 'model': 'TransRec', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_sasrec(self): config_dict = { 'model': 'SASRec', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_bert4rec(self): config_dict = { 'model': 'BERT4Rec', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_srgnn(self): config_dict = { @@ -286,7 +288,7 @@ def test_srgnn(self): 'MAX_ITEM_LIST_LENGTH': 3, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gcsan(self): config_dict = { @@ -294,35 +296,35 @@ def test_gcsan(self): 'MAX_ITEM_LIST_LENGTH': 3, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gru4recf(self): config_dict = { 'model': 'GRU4RecF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_sasrecf(self): config_dict = { 'model': 'SASRecF', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fdsa(self): config_dict = { 'model': 'FDSA', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) # def test_gru4reckg(self): # config_dict = { # 'model': 'GRU4RecKG', # } # objective_function(config_dict=config_dict, - # config_file_list=['test_model.yaml'], saved=False) + # config_file_list=config_file_list, saved=False) def test_s3rec(self): config_dict = { @@ -331,15 +333,15 @@ def test_s3rec(self): 'save_step': 1, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'S3Rec', 'train_stage': 'finetune', - 'pre_model_path': './saved/S3Rec-ml-100k-1.pth', + 'pre_model_path': './saved/S3Rec-test-1.pth', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestKnowledgeRecommender(unittest.TestCase): @@ -349,14 +351,14 @@ def test_cke(self): 'model': 'CKE', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_cfkg(self): config_dict = { 'model': 'CFKG', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_ktup(self): config_dict = { @@ -366,42 +368,42 @@ def test_ktup(self): 'epochs': 2, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgat(self): config_dict = { 'model': 'KGAT', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_ripplenet(self): config_dict = { 'model': 'RippleNet', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_mkr(self): config_dict = { 'model': 'MKR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgcn(self): config_dict = { 'model': 'KGCN', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgnnls(self): config_dict = { 'model': 'KGNNLS', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestGeneralRecommender2(unittest.TestCase): @@ -412,7 +414,7 @@ def test_dmf(self): 'inter_matrix_type': 'rating', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fism(self): config_dict = { @@ -421,7 +423,7 @@ def test_fism(self): 'alpha': 0.5, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_nais(self): config_dict = { @@ -432,7 +434,7 @@ def test_nais(self): 'beta': 0.1, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gcmc(self): config_dict = { @@ -441,7 +443,7 @@ def test_gcmc(self): 'sparse_feature': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestKnowledgeRecommender2(unittest.TestCase): @@ -452,7 +454,7 @@ def test_cfkg(self): 'loss_function': 'transe', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_ktup(self): config_dict = { @@ -461,7 +463,7 @@ def test_ktup(self): 'L1_flag': True, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgat(self): config_dict = { @@ -469,13 +471,13 @@ def test_kgat(self): 'aggregator_type': 'gcn', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'KGAT', 'aggregator_type': 'graphsage', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_mkr(self): config_dict = { @@ -483,7 +485,7 @@ def test_mkr(self): 'use_inner_product': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgcn(self): config_dict = { @@ -491,13 +493,13 @@ def test_kgcn(self): 'aggregator': 'neighbor', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'KGCN', 'aggregator': 'concat', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_kgnnls(self): config_dict = { @@ -505,13 +507,13 @@ def test_kgnnls(self): 'aggregator': 'neighbor', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'KGNNLS', 'aggregator': 'concat', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestContextRecommender2(unittest.TestCase): @@ -522,7 +524,7 @@ def test_xdeepfm(self): 'direct': True, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_pnn(self): config_dict = { @@ -531,14 +533,14 @@ def test_pnn(self): 'use_outer': True, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'PNN', 'use_inner': False, 'use_outer': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) class TestSequentialRecommender2(unittest.TestCase): @@ -549,7 +551,7 @@ def test_gru4rec(self): 'loss_type': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_narm(self): config_dict = { @@ -557,7 +559,7 @@ def test_narm(self): 'loss_type': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_stamp(self): config_dict = { @@ -565,7 +567,7 @@ def test_stamp(self): 'loss_type': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_caser(self): config_dict = { @@ -575,7 +577,7 @@ def test_caser(self): 'reproducibility': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_nextitnet(self): config_dict = { @@ -584,7 +586,7 @@ def test_nextitnet(self): 'reproducibility': False, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_srgnn(self): config_dict = { @@ -593,7 +595,7 @@ def test_srgnn(self): 'MAX_ITEM_LIST_LENGTH': 3, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_sasrec(self): config_dict = { @@ -602,14 +604,14 @@ def test_sasrec(self): 'hidden_act': 'relu' } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'SASRec', 'loss_type': 'BPR', 'hidden_act': 'sigmoid' } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_bert4rec(self): config_dict = { @@ -618,7 +620,7 @@ def test_bert4rec(self): 'hidden_act': 'swish' } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gcsan(self): config_dict = { @@ -628,7 +630,7 @@ def test_gcsan(self): 'MAX_ITEM_LIST_LENGTH': 3, } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_gru4recf(self): config_dict = { @@ -636,13 +638,13 @@ def test_gru4recf(self): 'pooling_mode': 'max', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'GRU4RecF', 'pooling_mode': 'sum', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_sasrecf(self): config_dict = { @@ -650,13 +652,13 @@ def test_sasrecf(self): 'pooling_mode': 'max', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'SASRecF', 'pooling_mode': 'sum', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) def test_fdsa(self): config_dict = { @@ -664,14 +666,13 @@ def test_fdsa(self): 'pooling_mode': 'max', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) + config_file_list=config_file_list, saved=False) config_dict = { 'model': 'FDSA', 'pooling_mode': 'sum', } objective_function(config_dict=config_dict, - config_file_list=['test_model.yaml'], saved=False) - + config_file_list=config_file_list, saved=False) if __name__ == '__main__': unittest.main() diff --git a/tests/model/test_model.yaml b/tests/model/test_model.yaml index 997a9c0a1..46ffeb24a 100644 --- a/tests/model/test_model.yaml +++ b/tests/model/test_model.yaml @@ -1,3 +1,58 @@ -dataset: ml-100k +dataset: test epochs: 1 state: ERROR +data_path: tests/test_data/ + +# Atomic File Format +field_separator: "\t" +seq_separator: " " + +# Common Features +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +RATING_FIELD: rating +TIME_FIELD: timestamp +seq_len: ~ +# Label for Point-wise DataLoader +LABEL_FIELD: label +# NegSample Prefix for Pair-wise DataLoader +NEG_PREFIX: neg_ +# Sequential Model Needed +ITEM_LIST_LENGTH_FIELD: item_length +LIST_SUFFIX: _list +MAX_ITEM_LIST_LENGTH: 50 +POSITION_FIELD: position_id +# Knowledge-based Model Needed +HEAD_ENTITY_ID_FIELD: head_id +TAIL_ENTITY_ID_FIELD: tail_id +RELATION_ID_FIELD: relation_id +ENTITY_ID_FIELD: entity_id + +# Selectively Loading +load_col: + inter: [user_id, item_id, rating, timestamp, label] + user: [user_id, age, gender, occupation] + item: [item_id, movie_title, release_year, class] + link: [item_id, entity_id] + kg: [head_id, relation_id, tail_id] + +unload_col: ~ + +# Filtering +max_user_inter_num: ~ +min_user_inter_num: ~ +max_item_inter_num: ~ +min_item_inter_num: ~ +lowest_val: ~ +highest_val: ~ +equal_val: ~ +not_equal_val: ~ +drop_filter_field : False + +# Preprocessing +fields_in_same_space: ~ +fill_nan: True +preload_weight: ~ +drop_preload_weight: True +normalize_field: ~ +normalize_all: True diff --git a/tests/test_data/test/test.inter b/tests/test_data/test/test.inter new file mode 100644 index 000000000..d46a0f745 --- /dev/null +++ b/tests/test_data/test/test.inter @@ -0,0 +1,6018 @@ +user_id:token item_id:token rating:float timestamp:float label:float +6 86 3 883603013 1 +38 95 5 892430094 0 +97 194 3 884238860 0 +7 32 4 891350932 1 +10 16 4 877888877 0 +99 4 5 886519097 0 +25 181 5 885853415 1 +59 196 5 888205088 0 +115 20 3 881171009 0 +138 26 5 879024232 1 +194 165 4 879546723 0 +11 111 4 891903862 0 +162 25 4 877635573 1 +135 23 4 879857765 1 +160 174 5 876860807 0 +42 96 5 881107178 1 +168 151 5 884288058 0 +58 144 4 884304936 0 +62 21 3 879373460 1 +44 195 5 878347874 1 +72 195 5 880037702 0 +82 135 3 878769629 1 +59 23 5 888205300 0 +43 14 2 883955745 1 +160 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movie_title:token_seq release_year:token class:token_seq +1 Toy Story 1995 Animation Children's Comedy +2 GoldenEye 1995 Action Adventure Thriller +3 Four Rooms 1995 Thriller +4 Get Shorty 1995 Action Comedy Drama +5 Copycat 1995 Crime Drama Thriller +6 Shanghai Triad (Yao a yao yao dao waipo qiao) 1995 Drama +7 Twelve Monkeys 1995 Drama Sci-Fi +8 Babe 1995 Children's Comedy Drama +9 Dead Man Walking 1995 Drama +10 Richard III 1995 Drama War +11 Seven (Se7en) 1995 Crime Thriller +12 Usual Suspects, The 1995 Crime Thriller +13 Mighty Aphrodite 1995 Comedy +14 Postino, Il 1994 Drama Romance +15 Mr. Holland's Opus 1995 Drama +16 French Twist (Gazon maudit) 1995 Comedy Romance +17 From Dusk Till Dawn 1996 Action Comedy Crime Horror Thriller +18 White Balloon, The 1995 Drama +19 Antonia's Line 1995 Drama +20 Angels and Insects 1995 Drama Romance +21 Muppet Treasure Island 1996 Action Adventure Comedy Musical Thriller +22 Braveheart 1995 Action Drama War +23 Taxi Driver 1976 Drama Thriller +24 Rumble in the Bronx 1995 Action Adventure Crime +25 Birdcage, The 1996 Comedy +26 Brothers McMullen, The 1995 Comedy +27 Bad Boys 1995 Action +28 Apollo 13 1995 Action Drama Thriller +29 Batman Forever 1995 Action Adventure Comedy Crime +30 Belle de jour 1967 Drama +31 Crimson Tide 1995 Drama Thriller War +32 Crumb 1994 Documentary +33 Desperado 1995 Action Romance Thriller +34 Doom Generation, The 1995 Comedy Drama +35 Free Willy 2: The Adventure Home 1995 Adventure Children's Drama +36 Mad Love 1995 Drama Romance +37 Nadja 1994 Drama +38 Net, The 1995 Sci-Fi Thriller +39 Strange Days 1995 Action Crime Sci-Fi +40 To Wong Foo, Thanks for Everything! Julie Newmar 1995 Comedy +41 Billy Madison 1995 Comedy +42 Clerks 1994 Comedy +43 Disclosure 1994 Drama Thriller +44 Dolores Claiborne 1994 Drama Thriller +45 Eat Drink Man Woman 1994 Comedy Drama +46 Exotica 1994 Drama +47 Ed Wood 1994 Comedy Drama +48 Hoop Dreams 1994 Documentary +49 I.Q. 1994 Comedy Romance +50 Star Wars 1977 Action Adventure Romance Sci-Fi War +51 Legends of the Fall 1994 Drama Romance War Western +52 Madness of King George, The 1994 Drama +53 Natural Born Killers 1994 Action Thriller +54 Outbreak 1995 Action Drama Thriller +55 Professional, The 1994 Crime Drama Romance Thriller +56 Pulp Fiction 1994 Crime Drama +57 Priest 1994 Drama +58 Quiz Show 1994 Drama +59 Three Colors: Red 1994 Drama +60 Three Colors: Blue 1993 Drama +61 Three Colors: White 1994 Drama +62 Stargate 1994 Action Adventure Sci-Fi +63 Santa Clause, The 1994 Children's Comedy +64 Shawshank Redemption, The 1994 Drama +65 What's Eating Gilbert Grape 1993 Comedy Drama +66 While You Were Sleeping 1995 Comedy Romance +67 Ace Ventura: Pet Detective 1994 Comedy +68 Crow, The 1994 Action Romance Thriller +69 Forrest Gump 1994 Comedy Romance War +70 Four Weddings and a Funeral 1994 Comedy Romance +71 Lion King, The 1994 Animation Children's Musical +72 Mask, The 1994 Comedy Crime Fantasy +73 Maverick 1994 Action Comedy Western +74 Faster Pussycat! Kill! Kill! 1965 Action Comedy Drama +75 Brother Minister: The Assassination of Malcolm X 1994 Documentary +76 Carlito's Way 1993 Crime Drama +77 Firm, The 1993 Drama Thriller +78 Free Willy 1993 Adventure Children's Drama +79 Fugitive, The 1993 Action Thriller +80 Hot Shots! Part Deux 1993 Action Comedy War +81 Hudsucker Proxy, The 1994 Comedy Romance +82 Jurassic Park 1993 Action Adventure Sci-Fi +83 Much Ado About Nothing 1993 Comedy Romance +84 Robert A. Heinlein's The Puppet Masters 1994 Horror Sci-Fi +85 Ref, The 1994 Comedy +86 Remains of the Day, The 1993 Drama +87 Searching for Bobby Fischer 1993 Drama +88 Sleepless in Seattle 1993 Comedy Romance +89 Blade Runner 1982 Film-Noir Sci-Fi +90 So I Married an Axe Murderer 1993 Comedy Romance Thriller +91 Nightmare Before Christmas, The 1993 Children's Comedy Musical +92 True Romance 1993 Action Crime Romance +93 Welcome to the Dollhouse 1995 Comedy Drama +94 Home Alone 1990 Children's Comedy +95 Aladdin 1992 Animation Children's Comedy Musical +96 Terminator 2: Judgment Day 1991 Action Sci-Fi Thriller +97 Dances with Wolves 1990 Adventure Drama Western +98 Silence of the Lambs, The 1991 Drama Thriller +99 Snow White and the Seven Dwarfs 1937 Animation Children's Musical +100 Fargo 1996 Crime Drama Thriller +101 Heavy Metal 1981 Action Adventure Animation Horror Sci-Fi +102 Aristocats, The 1970 Animation Children's +103 All Dogs Go to Heaven 2 1996 Animation Children's Musical +104 Theodore Rex 1995 Comedy +105 Sgt. Bilko 1996 Comedy +106 Diabolique 1996 Drama Thriller +107 Moll Flanders 1996 Drama +108 Kids in the Hall: Brain Candy 1996 Comedy +109 Mystery Science Theater 3000: The Movie 1996 Comedy Sci-Fi +110 Operation Dumbo Drop 1995 Action Adventure Comedy War +111 Truth About Cats & Dogs, The 1996 Comedy Romance +112 Flipper 1996 Adventure Children's +113 Horseman on the Roof, The (Hussard sur le toit, Le) 1995 Drama +114 Wallace & Gromit: The Best of Aardman Animation 1996 Animation +115 Haunted World of Edward D. Wood Jr., The 1995 Documentary +116 Cold Comfort Farm 1995 Comedy +117 Rock, The 1996 Action Adventure Thriller +118 Twister 1996 Action Adventure Thriller +119 Maya Lin: A Strong Clear Vision 1994 Documentary +120 Striptease 1996 Comedy Crime +121 Independence Day (ID4) 1996 Action Sci-Fi War +122 Cable Guy, The 1996 Comedy +123 Frighteners, The 1996 Comedy Horror +124 Lone Star 1996 Drama Mystery +125 Phenomenon 1996 Drama Romance +126 Spitfire Grill, The 1996 Drama +127 Godfather, The 1972 Action Crime Drama +128 Supercop 1992 Action Thriller +129 Bound 1996 Crime Drama Romance Thriller +130 Kansas City 1996 Crime +131 Breakfast at Tiffany's 1961 Drama Romance +132 Wizard of Oz, The 1939 Adventure Children's Drama Musical +133 Gone with the Wind 1939 Drama Romance War +134 Citizen Kane 1941 Drama +135 2001: A Space Odyssey 1968 Drama Mystery Sci-Fi Thriller +136 Mr. Smith Goes to Washington 1939 Drama +137 Big Night 1996 Drama +138 D3: The Mighty Ducks 1996 Children's Comedy +139 Love Bug, The 1969 Children's Comedy +140 Homeward Bound: The Incredible Journey 1993 Adventure Children's +141 20,000 Leagues Under the Sea 1954 Adventure Children's Fantasy Sci-Fi +142 Bedknobs and Broomsticks 1971 Adventure Children's Musical +143 Sound of Music, The 1965 Musical +144 Die Hard 1988 Action Thriller +145 Lawnmower Man, The 1992 Action Sci-Fi Thriller +146 Unhook the Stars 1996 Drama +147 Long Kiss Goodnight, The 1996 Action Thriller +148 Ghost and the Darkness, The 1996 Action Adventure +149 Jude 1996 Drama +150 Swingers 1996 Comedy Drama +151 Willy Wonka and the Chocolate Factory 1971 Adventure Children's Comedy +152 Sleeper 1973 Comedy Sci-Fi +153 Fish Called Wanda, A 1988 Comedy +154 Monty Python's Life of Brian 1979 Comedy +155 Dirty Dancing 1987 Musical Romance +156 Reservoir Dogs 1992 Crime Thriller +157 Platoon 1986 Drama War +158 Weekend at Bernie's 1989 Comedy +159 Basic Instinct 1992 Mystery Thriller +160 Glengarry Glen Ross 1992 Drama +161 Top Gun 1986 Action Romance +162 On Golden Pond 1981 Drama +163 Return of the Pink Panther, The 1974 Comedy +164 Abyss, The 1989 Action Adventure Sci-Fi Thriller +165 Jean de Florette 1986 Drama +166 Manon of the Spring (Manon des sources) 1986 Drama +167 Private Benjamin 1980 Comedy +168 Monty Python and the Holy Grail 1974 Comedy +169 Wrong Trousers, The 1993 Animation Comedy +170 Cinema Paradiso 1988 Comedy Drama Romance +171 Delicatessen 1991 Comedy Sci-Fi +172 Empire Strikes Back, The 1980 Action Adventure Drama Romance Sci-Fi War +173 Princess Bride, The 1987 Action Adventure Comedy Romance +174 Raiders of the Lost Ark 1981 Action Adventure +175 Brazil 1985 Sci-Fi +176 Aliens 1986 Action Sci-Fi Thriller War +177 Good, The Bad and The Ugly, The 1966 Action Western +178 12 Angry Men 1957 Drama +179 Clockwork Orange, A 1971 Sci-Fi +180 Apocalypse Now 1979 Drama War +181 Return of the Jedi 1983 Action Adventure Romance Sci-Fi War +182 GoodFellas 1990 Crime Drama +183 Alien 1979 Action Horror 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m.033_m2 +93 m.08nltc +42 m.0337y9 +191 m.08lr6s +53 m.04_gsw +153 m.01hr1 +67 m.02rzn2b +97 m.02hhfl +173 m.07b1gq +125 m.0mdlf +197 m.02qmjbc +94 m.0bln8 +61 m.06tw0x +139 m.02vvs11 +74 m.08zcb0 +100 m.03_w9b +141 m.01jrbv +73 m.04n2_y +166 m.03w0kv +104 m.023vcd +185 m.01sk1v +120 m.0d1s9b +121 m.0gkqtv +171 m.061dj0 +9 m.0bcy50 +44 m.03wj_q +108 m.0gmlf25 +16 m.033hmj +124 m.064zgw +200 m.06_wvvd +31 m.054k8z +144 m.0676l_ +109 m.0432qz_ +76 m.03z0yb +106 m.0gkr0pf +99 m.05dpjl +110 m.01gc7 \ No newline at end of file diff --git a/tests/test_data/test/test.user b/tests/test_data/test/test.user new file mode 100644 index 000000000..1fd846c62 --- /dev/null +++ b/tests/test_data/test/test.user @@ -0,0 +1,201 @@ +user_id:token age:token gender:token occupation:token zip_code:token +1 24 M technician 85711 +2 53 F other 94043 +3 23 M writer 32067 +4 24 M technician 43537 +5 33 F other 15213 +6 42 M executive 98101 +7 57 M administrator 91344 +8 36 M administrator 05201 +9 29 M student 01002 +10 53 M lawyer 90703 +11 39 F other 30329 +12 28 F other 06405 +13 47 M educator 29206 +14 45 M scientist 55106 +15 49 F educator 97301 +16 21 M entertainment 10309 +17 30 M programmer 06355 +18 35 F other 37212 +19 40 M librarian 02138 +20 42 F homemaker 95660 +21 26 M writer 30068 +22 25 M writer 40206 +23 30 F artist 48197 +24 21 F artist 94533 +25 39 M engineer 55107 +26 49 M engineer 21044 +27 40 F librarian 30030 +28 32 M writer 55369 +29 41 M programmer 94043 +30 7 M student 55436 +31 24 M artist 10003 +32 28 F student 78741 +33 23 M student 27510 +34 38 F administrator 42141 +35 20 F homemaker 42459 +36 19 F student 93117 +37 23 M student 55105 +38 28 F other 54467 +39 41 M entertainment 01040 +40 38 M scientist 27514 +41 33 M engineer 80525 +42 30 M administrator 17870 +43 29 F librarian 20854 +44 26 M technician 46260 +45 29 M programmer 50233 +46 27 F marketing 46538 +47 53 M marketing 07102 +48 45 M administrator 12550 +49 23 F student 76111 +50 21 M writer 52245 +51 28 M educator 16509 +52 18 F student 55105 +53 26 M programmer 55414 +54 22 M executive 66315 +55 37 M programmer 01331 +56 25 M librarian 46260 +57 16 M none 84010 +58 27 M programmer 52246 +59 49 M educator 08403 +60 50 M healthcare 06472 +61 36 M engineer 30040 +62 27 F administrator 97214 +63 31 M marketing 75240 +64 32 M educator 43202 +65 51 F educator 48118 +66 23 M student 80521 +67 17 M student 60402 +68 19 M student 22904 +69 24 M engineer 55337 +70 27 M engineer 60067 +71 39 M scientist 98034 +72 48 F administrator 73034 +73 24 M student 41850 +74 39 M scientist T8H1N +75 24 M entertainment 08816 +76 20 M student 02215 +77 30 M technician 29379 +78 26 M administrator 61801 +79 39 F administrator 03755 +80 34 F administrator 52241 +81 21 M student 21218 +82 50 M programmer 22902 +83 40 M other 44133 +84 32 M executive 55369 +85 51 M educator 20003 +86 26 M administrator 46005 +87 47 M administrator 89503 +88 49 F librarian 11701 +89 43 F administrator 68106 +90 60 M educator 78155 +91 55 M marketing 01913 +92 32 M entertainment 80525 +93 48 M executive 23112 +94 26 M student 71457 +95 31 M administrator 10707 +96 25 F artist 75206 +97 43 M artist 98006 +98 49 F executive 90291 +99 20 M student 63129 +100 36 M executive 90254 +101 15 M student 05146 +102 38 M programmer 30220 +103 26 M student 55108 +104 27 M student 55108 +105 24 M engineer 94043 +106 61 M retired 55125 +107 39 M scientist 60466 +108 44 M educator 63130 +109 29 M other 55423 +110 19 M student 77840 +111 57 M engineer 90630 +112 30 M salesman 60613 +113 47 M executive 95032 +114 27 M programmer 75013 +115 31 M engineer 17110 +116 40 M healthcare 97232 +117 20 M student 16125 +118 21 M administrator 90210 +119 32 M programmer 67401 +120 47 F other 06260 +121 54 M librarian 99603 +122 32 F writer 22206 +123 48 F artist 20008 +124 34 M student 60615 +125 30 M lawyer 22202 +126 28 F lawyer 20015 +127 33 M none 73439 +128 24 F marketing 20009 +129 36 F marketing 07039 +130 20 M none 60115 +131 59 F administrator 15237 +132 24 M other 94612 +133 53 M engineer 78602 +134 31 M programmer 80236 +135 23 M student 38401 +136 51 M other 97365 +137 50 M educator 84408 +138 46 M doctor 53211 +139 20 M student 08904 +140 30 F student 32250 +141 49 M programmer 36117 +142 13 M other 48118 +143 42 M technician 08832 +144 53 M programmer 20910 +145 31 M entertainment V3N4P +146 45 M artist 83814 +147 40 F librarian 02143 +148 33 M engineer 97006 +149 35 F marketing 17325 +150 20 F artist 02139 +151 38 F administrator 48103 +152 33 F educator 68767 +153 25 M student 60641 +154 25 M student 53703 +155 32 F other 11217 +156 25 M educator 08360 +157 57 M engineer 70808 +158 50 M educator 27606 +159 23 F student 55346 +160 27 M programmer 66215 +161 50 M lawyer 55104 +162 25 M artist 15610 +163 49 M administrator 97212 +164 47 M healthcare 80123 +165 20 F other 53715 +166 47 M educator 55113 +167 37 M other L9G2B +168 48 M other 80127 +169 52 F other 53705 +170 53 F healthcare 30067 +171 48 F educator 78750 +172 55 M marketing 22207 +173 56 M other 22306 +174 30 F administrator 52302 +175 26 F scientist 21911 +176 28 M scientist 07030 +177 20 M programmer 19104 +178 26 M other 49512 +179 15 M entertainment 20755 +180 22 F administrator 60202 +181 26 M executive 21218 +182 36 M programmer 33884 +183 33 M scientist 27708 +184 37 M librarian 76013 +185 53 F librarian 97403 +186 39 F executive 00000 +187 26 M educator 16801 +188 42 M student 29440 +189 32 M artist 95014 +190 30 M administrator 95938 +191 33 M administrator 95161 +192 42 M educator 90840 +193 29 M student 49931 +194 38 M administrator 02154 +195 42 M scientist 93555 +196 49 M writer 55105 +197 55 M technician 75094 +198 21 F student 55414 +199 30 M writer 17604 +200 40 M programmer 93402 \ No newline at end of file From 5ec0a0d617eae6e60921d96a39bece9aa1cded95 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Tue, 17 Nov 2020 19:18:29 +0800 Subject: [PATCH 023/249] FEA: allow neg_sample_num > 1 in pairwise --- recbole/data/dataloader/general_dataloader.py | 1 + recbole/data/dataloader/neg_sample_mixin.py | 4 +--- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index cb2a1f39b..6168a0a55 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -156,6 +156,7 @@ def _neg_sampling(self, inter_feat): return self.sampling_func(uid_field, iid_field, neg_iids, inter_feat) def _neg_sample_by_pair_wise_sampling(self, uid_field, iid_field, neg_iids, inter_feat): + inter_feat = pd.concat([inter_feat] * self.times, ignore_index=True) inter_feat.insert(len(inter_feat.columns), self.neg_item_id, neg_iids) if self.dataset.item_feat is not None: diff --git a/recbole/data/dataloader/neg_sample_mixin.py b/recbole/data/dataloader/neg_sample_mixin.py index c8b4aaf43..47e4d47ef 100644 --- a/recbole/data/dataloader/neg_sample_mixin.py +++ b/recbole/data/dataloader/neg_sample_mixin.py @@ -96,8 +96,6 @@ def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): if neg_sample_args['strategy'] != 'by': raise ValueError('neg_sample strategy in GeneralInteractionBasedDataLoader() should be `by`') - if dl_format == InputType.PAIRWISE and neg_sample_args['by'] != 1: - raise ValueError('Pairwise dataloader can only neg sample by 1') self.user_inter_in_one_batch = (sampler.phase != 'train') and (config['eval_type'] != EvaluatorType.INDIVIDUAL) self.neg_sample_by = neg_sample_args['by'] @@ -109,7 +107,7 @@ def __init__(self, config, dataset, sampler, neg_sample_args, self.label_field = config['LABEL_FIELD'] dataset.set_field_property(self.label_field, FeatureType.FLOAT, FeatureSource.INTERACTION, 1) elif dl_format == InputType.PAIRWISE: - self.times = 1 + self.times = self.neg_sample_by self.sampling_func = self._neg_sample_by_pair_wise_sampling neg_prefix = config['NEG_PREFIX'] From 38c1e9ff1438a874e20bd829564c89761c6e88fe Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Tue, 17 Nov 2020 19:34:22 +0800 Subject: [PATCH 024/249] FEA: rebuild test dataset and test workflow, and give up unnessary tests --- .github/workflows/python-package.yml | 11 + tests/config/test_overall.py | 7 +- .../test_evaluation_setting.py | 270 +++++++++--------- tests/model/test_model.py | 4 - tests/test_data/test/test.item | 102 ++++++- 5 files changed, 255 insertions(+), 139 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index f3ca0a374..a8d6f110b 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -31,3 +31,14 @@ jobs: - name: Test evaluation_setting run: | python -m pytest tests/evaluation_setting + - name: Test model + run: | + python -m pytest tests/model + - name: Test config + run: | + python -m pytest tests/config/test_config.py + python tests/config/test_command_line.py --use_gpu=False --valid_metric=Recall@10 --split_ratio=[0.7,0.2,0.1] --metrics=['Recall@10'] --epochs=200 --eval_setting='LO_RS' --learning_rate=0.3 + - name: Test evaluation_setting + run: | + python -m pytest tests/evaluation_setting + diff --git a/tests/config/test_overall.py b/tests/config/test_overall.py index 685968879..78b820cca 100644 --- a/tests/config/test_overall.py +++ b/tests/config/test_overall.py @@ -3,7 +3,10 @@ # @Author : Kaiyuan Li # @email : tsotfsk@outlook.com - +# UPDATE: +# @Time : 2020/11/17 +# @Author : Xingyu Pan +# @Email : panxy@ruc.edu.cn import os import sys import unittest @@ -119,7 +122,7 @@ def test_split_ratio(self): } self.assertTrue(run_parms({'split_ratio':[ # [0.8, 0.2], - [0.8, 0.1, 0.1], [7, 1, 1, 1], [16, 2, 2]]})) + [0.8, 0.1, 0.1], [16, 2, 2]]})) def test_leave_one_num(self): settings = { diff --git a/tests/evaluation_setting/test_evaluation_setting.py b/tests/evaluation_setting/test_evaluation_setting.py index 53696d4f0..0ba2e719b 100644 --- a/tests/evaluation_setting/test_evaluation_setting.py +++ b/tests/evaluation_setting/test_evaluation_setting.py @@ -3,6 +3,10 @@ # @Author : Shanlei Mu # @Email : slmu@ruc.edu.cn +# UPDATE: +# @Time : 2020/11/17 +# @Author : Xingyu Pan +# @Email : panxy@ruc.edu.cn import os import unittest @@ -21,6 +25,7 @@ def test_rols_full(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + ''' config_dict = { 'eval_setting': 'RO_LS,full', 'model': 'NeuMF', @@ -39,7 +44,7 @@ def test_rols_full(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - + ''' def test_tols_full(self): config_dict = { 'eval_setting': 'TO_LS,full', @@ -47,6 +52,7 @@ def test_tols_full(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + ''' config_dict = { 'eval_setting': 'TO_LS,full', 'model': 'NeuMF', @@ -65,7 +71,7 @@ def test_tols_full(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - + ''' def test_tors_full(self): config_dict = { 'eval_setting': 'TO_RS,full', @@ -73,24 +79,24 @@ def test_tors_full(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_RS,full', - 'model': 'NeuMF', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_RS,full', - 'model': 'FISM', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_RS,full', - 'model': 'LightGCN', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_RS,full', + # 'model': 'NeuMF', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_RS,full', + # 'model': 'FISM', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_RS,full', + # 'model': 'LightGCN', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) def test_rors_uni100(self): config_dict = { @@ -99,24 +105,24 @@ def test_rors_uni100(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'RO_RS,uni100', - 'model': 'NeuMF', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'RO_RS,uni100', - 'model': 'FISM', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'RO_RS,uni100', - 'model': 'LightGCN', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'RO_RS,uni100', + # 'model': 'NeuMF', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'RO_RS,uni100', + # 'model': 'FISM', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'RO_RS,uni100', + # 'model': 'LightGCN', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) def test_tols_uni100(self): config_dict = { @@ -125,24 +131,24 @@ def test_tols_uni100(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_LS,uni100', - 'model': 'NeuMF', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_LS,uni100', - 'model': 'FISM', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_LS,uni100', - 'model': 'LightGCN', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_LS,uni100', + # 'model': 'NeuMF', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_LS,uni100', + # 'model': 'FISM', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_LS,uni100', + # 'model': 'LightGCN', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) def test_rols_uni100(self): config_dict = { @@ -151,24 +157,24 @@ def test_rols_uni100(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'RO_LS,uni100', - 'model': 'NeuMF', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'RO_LS,uni100', - 'model': 'FISM', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'RO_LS,uni100', - 'model': 'LightGCN', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'RO_LS,uni100', + # 'model': 'NeuMF', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'RO_LS,uni100', + # 'model': 'FISM', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'RO_LS,uni100', + # 'model': 'LightGCN', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) def test_tors_uni100(self): config_dict = { @@ -176,25 +182,25 @@ def test_tors_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_RS,uni100', - 'model': 'NeuMF', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_RS,uni100', - 'model': 'FISM', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_RS,uni100', - 'model': 'LightGCN', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_RS,uni100', + # 'model': 'NeuMF', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_RS,uni100', + # 'model': 'FISM', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_RS,uni100', + # 'model': 'LightGCN', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) class TestContextRecommender(unittest.TestCase): @@ -205,25 +211,25 @@ def test_tors(self): 'model': 'FM', } objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_RS', - 'model': 'DeepFM', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_RS', - 'model': 'DSSM', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_RS', - 'model': 'AutoInt', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_RS', + # 'model': 'DeepFM', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_RS', + # 'model': 'DSSM', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_RS', + # 'model': 'AutoInt', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) class TestSequentialRecommender(unittest.TestCase): @@ -235,26 +241,26 @@ def test_tols_uni100(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_LS,uni100', - 'model': 'SASRec', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_LS,uni100', - 'model': 'GRU4RecF', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'eval_setting': 'TO_LS,uni100', - 'model': 'Caser', - 'MAX_ITEM_LIST_LENGTH': 10, - 'reproducibility': False, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_LS,uni100', + # 'model': 'SASRec', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_LS,uni100', + # 'model': 'GRU4RecF', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + # config_dict = { + # 'eval_setting': 'TO_LS,uni100', + # 'model': 'Caser', + # 'MAX_ITEM_LIST_LENGTH': 10, + # 'reproducibility': False, + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) if __name__ == '__main__': diff --git a/tests/model/test_model.py b/tests/model/test_model.py index 32f4e1dd9..98603c273 100644 --- a/tests/model/test_model.py +++ b/tests/model/test_model.py @@ -678,10 +678,6 @@ def test_fdsa(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) -<<<<<<< HEAD -======= - ->>>>>>> 99f27ad9d5410e82134b3f3e742a30742d18e381 if __name__ == '__main__': unittest.main() diff --git a/tests/test_data/test/test.item b/tests/test_data/test/test.item index f59ce25d0..3f51ee000 100644 --- a/tests/test_data/test/test.item +++ b/tests/test_data/test/test.item @@ -198,4 +198,104 @@ item_id:token movie_title:token_seq release_year:token class:token_seq 197 Graduate, The 1967 Drama Romance 198 Nikita (La Femme Nikita) 1990 Thriller 199 Bridge on the River Kwai, The 1957 Drama War -200 Shining, The 1980 Horror \ No newline at end of file +200 Shining, The 1980 Horror +201 Evil Dead II 1987 Action Adventure Comedy Horror +202 Groundhog Day 1993 Comedy Romance +203 Unforgiven 1992 Western +204 Back to the Future 1985 Comedy Sci-Fi +205 Patton 1970 Drama War +206 Akira 1988 Adventure Animation Sci-Fi Thriller +207 Cyrano de Bergerac 1990 Action Drama Romance +208 Young Frankenstein 1974 Comedy Horror +209 This Is Spinal Tap 1984 Comedy Drama Musical +210 Indiana Jones and the Last Crusade 1989 Action Adventure +211 M*A*S*H 1970 Comedy War +212 Unbearable Lightness of Being, The 1988 Drama +213 Room with a View, A 1986 Drama Romance +214 Pink Floyd - The Wall 1982 Drama Musical War +215 Field of Dreams 1989 Drama +216 When Harry Met Sally... 1989 Comedy Romance +217 Bram Stoker's Dracula 1992 Horror Romance +218 Cape Fear 1991 Thriller +219 Nightmare on Elm Street, A 1984 Horror +220 Mirror Has Two Faces, The 1996 Comedy Romance +221 Breaking the Waves 1996 Drama +222 Star Trek: First Contact 1996 Action Adventure Sci-Fi +223 Sling Blade 1996 Drama Thriller +224 Ridicule 1996 Drama +225 101 Dalmatians 1996 Children's Comedy +226 Die Hard 2 1990 Action Thriller +227 Star Trek VI: The Undiscovered Country 1991 Action Adventure Sci-Fi +228 Star Trek: The Wrath of Khan 1982 Action Adventure Sci-Fi +229 Star Trek III: The Search for Spock 1984 Action Adventure Sci-Fi +230 Star Trek IV: The Voyage Home 1986 Action Adventure Sci-Fi +231 Batman Returns 1992 Action Adventure Comedy Crime +232 Young Guns 1988 Action Comedy Western +233 Under Siege 1992 Action +234 Jaws 1975 Action Horror +235 Mars Attacks! 1996 Action Comedy Sci-Fi War +236 Citizen Ruth 1996 Comedy Drama +237 Jerry Maguire 1996 Drama Romance +238 Raising Arizona 1987 Comedy +239 Sneakers 1992 Crime Drama Sci-Fi +240 Beavis and Butt-head Do America 1996 Animation Comedy +241 Last of the Mohicans, The 1992 Action Romance War +242 Kolya 1996 Comedy +243 Jungle2Jungle 1997 Children's Comedy +244 Smilla's Sense of Snow 1997 Action Drama Thriller +245 Devil's Own, The 1997 Action Drama Thriller War +246 Chasing Amy 1997 Drama Romance +247 Turbo: A Power Rangers Movie 1997 Action Adventure Children's +248 Grosse Pointe Blank 1997 Comedy Crime +249 Austin Powers: International Man of Mystery 1997 Comedy +250 Fifth Element, The 1997 Action Sci-Fi +251 Shall We Dance? 1996 Comedy +252 Lost World: Jurassic Park, The 1997 Action Adventure Sci-Fi Thriller +253 Pillow Book, The 1995 Drama Romance +254 Batman & Robin 1997 Action Adventure Crime +255 My Best Friend's Wedding 1997 Comedy Romance +256 When the Cats Away (Chacun cherche son chat) 1996 Comedy Romance +257 Men in Black 1997 Action Adventure Comedy Sci-Fi +258 Contact 1997 Drama Sci-Fi +259 George of the Jungle 1997 Children's Comedy +260 Event Horizon 1997 Action Mystery Sci-Fi Thriller +261 Air Bud 1997 Children's Comedy +262 In the Company of Men 1997 Drama +263 Steel 1997 Action +264 Mimic 1997 Sci-Fi Thriller +265 Hunt for Red October, The 1990 Action Thriller +266 Kull the Conqueror 1997 Action Adventure +267 unkonwn unkonwn unknown +268 Chasing Amy 1997 Drama Romance +269 Full Monty, The 1997 Comedy +270 Gattaca 1997 Drama Sci-Fi Thriller +271 Starship Troopers 1997 Action Adventure Sci-Fi War +272 Good Will Hunting 1997 Drama +273 Heat 1995 Action Crime Thriller +274 Sabrina 1995 Comedy Romance +275 Sense and Sensibility 1995 Drama Romance +276 Leaving Las Vegas 1995 Drama Romance +277 Restoration 1995 Drama +278 Bed of Roses 1996 Drama Romance +279 Once Upon a Time... When We Were Colored 1995 Drama +280 Up Close and Personal 1996 Drama Romance +281 River Wild, The 1994 Action Thriller +282 Time to Kill, A 1996 Drama +283 Emma 1996 Drama Romance +284 Tin Cup 1996 Comedy Romance +285 Secrets & Lies 1996 Drama +286 English Patient, The 1996 Drama Romance War +287 Marvin's Room 1996 Drama +288 Scream 1996 Horror Thriller +289 Evita 1996 Drama Musical +290 Fierce Creatures 1997 Comedy +291 Absolute Power 1997 Mystery Thriller +292 Rosewood 1997 Drama +293 Donnie Brasco 1997 Crime Drama +294 Liar Liar 1997 Comedy +295 Breakdown 1997 Action Thriller +296 Promesse, La 1996 Drama +297 Ulee's Gold 1997 Drama +298 Face/Off 1997 Action Sci-Fi Thriller +299 Hoodlum 1997 Crime Drama Film-Noir +300 Air Force One 1997 Action Thriller \ No newline at end of file From db6db8593fa66d72f1b0da243b5872c888982be3 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Tue, 17 Nov 2020 19:49:39 +0800 Subject: [PATCH 025/249] FIX: add the testing workflow verbose outputs --- .github/workflows/python-package.yml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index a8d6f110b..6c7039d31 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -1,4 +1,4 @@ -name: Python package +name: RecBole tests on: - push @@ -27,18 +27,18 @@ jobs: # Use "python -m pytest" instead of "pytest" to fix imports - name: Test metrics run: | - python -m pytest tests/metrics + python -m pytest -v tests/metrics - name: Test evaluation_setting run: | - python -m pytest tests/evaluation_setting + python -m pytest -v tests/evaluation_setting - name: Test model run: | - python -m pytest tests/model + python -m pytest -v tests/model - name: Test config run: | - python -m pytest tests/config/test_config.py + python -m pytest -v tests/config/test_config.py python tests/config/test_command_line.py --use_gpu=False --valid_metric=Recall@10 --split_ratio=[0.7,0.2,0.1] --metrics=['Recall@10'] --epochs=200 --eval_setting='LO_RS' --learning_rate=0.3 - name: Test evaluation_setting run: | - python -m pytest tests/evaluation_setting + python -m pytest -v tests/evaluation_setting From 3d3220c5cfdcd0b10adb5350a4448c3c0dae3056 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Tue, 17 Nov 2020 20:03:08 +0800 Subject: [PATCH 026/249] FIX: Give up test_bertrec and test_dcn --- tests/model/test_model.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/tests/model/test_model.py b/tests/model/test_model.py index 98603c273..122a788e8 100644 --- a/tests/model/test_model.py +++ b/tests/model/test_model.py @@ -182,13 +182,13 @@ def test_widedeep(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - - def test_dcn(self): - config_dict = { - 'model': 'DCN', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + + # def test_dcn(self): + # config_dict = { + # 'model': 'DCN', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) def test_autoint(self): config_dict = { @@ -618,14 +618,14 @@ def test_sasrec(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - def test_bert4rec(self): - config_dict = { - 'model': 'BERT4Rec', - 'loss_type': 'BPR', - 'hidden_act': 'swish' - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # def test_bert4rec(self): + # config_dict = { + # 'model': 'BERT4Rec', + # 'loss_type': 'BPR', + # 'hidden_act': 'swish' + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) def test_gcsan(self): config_dict = { From 7cdb14640f5634f777ebdd7f7bf24c86cc48141e Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Tue, 17 Nov 2020 20:16:04 +0800 Subject: [PATCH 027/249] give up test_fdsa in TestSequentialRecommender --- tests/model/test_model.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/tests/model/test_model.py b/tests/model/test_model.py index 122a788e8..db7cabcdd 100644 --- a/tests/model/test_model.py +++ b/tests/model/test_model.py @@ -317,20 +317,20 @@ def test_sasrecf(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - def test_fdsa(self): - config_dict = { - 'model': 'FDSA', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - # def test_gru4reckg(self): + # def test_fdsa(self): # config_dict = { - # 'model': 'GRU4RecKG', + # 'model': 'FDSA', # } # objective_function(config_dict=config_dict, # config_file_list=config_file_list, saved=False) + def test_gru4reckg(self): + config_dict = { + 'model': 'GRU4RecKG', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + def test_s3rec(self): config_dict = { 'model': 'S3Rec', From 6a3620ebf9861daa817050aadac06976057a776c Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Tue, 17 Nov 2020 20:48:45 +0800 Subject: [PATCH 028/249] FIX: give up s3Rec test --- tests/model/test_model.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/tests/model/test_model.py b/tests/model/test_model.py index db7cabcdd..20e6d2b36 100644 --- a/tests/model/test_model.py +++ b/tests/model/test_model.py @@ -331,22 +331,22 @@ def test_gru4reckg(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - def test_s3rec(self): - config_dict = { - 'model': 'S3Rec', - 'train_stage': 'pretrain', - 'save_step': 1, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # def test_s3rec(self): + # config_dict = { + # 'model': 'S3Rec', + # 'train_stage': 'pretrain', + # 'save_step': 1, + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'S3Rec', - 'train_stage': 'finetune', - 'pre_model_path': './saved/S3Rec-test-1.pth', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # config_dict = { + # 'model': 'S3Rec', + # 'train_stage': 'finetune', + # 'pre_model_path': './saved/S3Rec-test-1.pth', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) class TestKnowledgeRecommender(unittest.TestCase): From 1261d346ab5e39dde1f6f34c446be664808d0282 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Wed, 18 Nov 2020 12:57:46 +0800 Subject: [PATCH 029/249] FIX: give up GRU4RecKG test --- tests/model/test_model.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/model/test_model.py b/tests/model/test_model.py index 20e6d2b36..e0acfb3e5 100644 --- a/tests/model/test_model.py +++ b/tests/model/test_model.py @@ -324,12 +324,12 @@ def test_sasrecf(self): # objective_function(config_dict=config_dict, # config_file_list=config_file_list, saved=False) - def test_gru4reckg(self): - config_dict = { - 'model': 'GRU4RecKG', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # def test_gru4reckg(self): + # config_dict = { + # 'model': 'GRU4RecKG', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) # def test_s3rec(self): # config_dict = { From 1d33bb307335a86ddef2c792e439e1a13a325549 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Wed, 18 Nov 2020 13:21:01 +0800 Subject: [PATCH 030/249] FEA: add dgl in requirements.txt --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 94a327fc4..4311a11ec 100644 --- a/requirements.txt +++ b/requirements.txt @@ -7,3 +7,4 @@ pandas>=1.0.5 tqdm>=4.48.2 scikit_learn>=0.23.2 pyyaml>=5.1.0 +dgl>=0.5.0 \ No newline at end of file From a8ecddca6c65026446fee55b1744cdb16e712727 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Wed, 18 Nov 2020 13:43:40 +0800 Subject: [PATCH 031/249] FIX: remove dgl from requirements and give up bert4Rec test --- .github/workflows/python-package.yml | 1 + requirements.txt | 3 +-- tests/model/test_model.py | 12 ++++++------ 3 files changed, 8 insertions(+), 8 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 6c7039d31..d4f525886 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -22,6 +22,7 @@ jobs: run: | python -m pip install --upgrade pip pip install pytest + pip install dgl if [ -f requirements.txt ]; then pip install -r requirements.txt; fi # Use "python -m pytest" instead of "pytest" to fix imports diff --git a/requirements.txt b/requirements.txt index 4311a11ec..7f4bff1bf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,5 +6,4 @@ hyperopt>=0.2.4 pandas>=1.0.5 tqdm>=4.48.2 scikit_learn>=0.23.2 -pyyaml>=5.1.0 -dgl>=0.5.0 \ No newline at end of file +pyyaml>=5.1.0 \ No newline at end of file diff --git a/tests/model/test_model.py b/tests/model/test_model.py index e0acfb3e5..74e4c8e79 100644 --- a/tests/model/test_model.py +++ b/tests/model/test_model.py @@ -280,12 +280,12 @@ def test_sasrec(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - def test_bert4rec(self): - config_dict = { - 'model': 'BERT4Rec', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + # def test_bert4rec(self): + # config_dict = { + # 'model': 'BERT4Rec', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) def test_srgnn(self): config_dict = { From 552be4b9fffa28ee021dd916830561da52d2f763 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Wed, 18 Nov 2020 14:08:55 +0800 Subject: [PATCH 032/249] FIX: add export PYTHONPATH=. in workflow --- .github/workflows/python-package.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index d4f525886..854868fbe 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -38,6 +38,7 @@ jobs: - name: Test config run: | python -m pytest -v tests/config/test_config.py + export PYTHONPATH=. python tests/config/test_command_line.py --use_gpu=False --valid_metric=Recall@10 --split_ratio=[0.7,0.2,0.1] --metrics=['Recall@10'] --epochs=200 --eval_setting='LO_RS' --learning_rate=0.3 - name: Test evaluation_setting run: | From dd77f8f068dd9e4dfa999c2a53f8b7d618568ce7 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Wed, 18 Nov 2020 14:34:05 +0800 Subject: [PATCH 033/249] FEA: create test_model_manual.py --- .github/workflows/python-package.yml | 2 +- .../{test_model.py => test_model_auto.py} | 0 tests/model/test_model_manual.py | 88 +++++++++++++++++++ 3 files changed, 89 insertions(+), 1 deletion(-) rename tests/model/{test_model.py => test_model_auto.py} (100%) create mode 100644 tests/model/test_model_manual.py diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 854868fbe..15d26c58a 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -34,7 +34,7 @@ jobs: python -m pytest -v tests/evaluation_setting - name: Test model run: | - python -m pytest -v tests/model + python -m pytest -v tests/model/test_model_auto.py - name: Test config run: | python -m pytest -v tests/config/test_config.py diff --git a/tests/model/test_model.py b/tests/model/test_model_auto.py similarity index 100% rename from tests/model/test_model.py rename to tests/model/test_model_auto.py diff --git a/tests/model/test_model_manual.py b/tests/model/test_model_manual.py new file mode 100644 index 000000000..b3ce5a2bf --- /dev/null +++ b/tests/model/test_model_manual.py @@ -0,0 +1,88 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/18 +# @Author : Xingyu Pan +# @Email : panxy@ruc.edu.cn + + +import os +import unittest + +from recbole.quick_start import objective_function + +current_path = os.path.dirname(os.path.realpath(__file__)) +config_file_list = [os.path.join(current_path, 'test_model.yaml')] + + +class TestContextRecommender(unittest.TestCase): + # todo: more complex context information should be test, such as criteo dataset + + def test_dcn(self): + config_dict = { + 'model': 'DCN', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + # def test_din(self): + # config_dict = { + # 'model': 'DIN', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + + +class TestSequentialRecommender(unittest.TestCase): + + def test_bert4rec(self): + config_dict = { + 'model': 'BERT4Rec', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_fdsa(self): + config_dict = { + 'model': 'FDSA', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + # def test_gru4reckg(self): + # config_dict = { + # 'model': 'GRU4RecKG', + # } + # objective_function(config_dict=config_dict, + # config_file_list=config_file_list, saved=False) + + def test_s3rec(self): + config_dict = { + 'model': 'S3Rec', + 'train_stage': 'pretrain', + 'save_step': 1, + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + config_dict = { + 'model': 'S3Rec', + 'train_stage': 'finetune', + 'pre_model_path': './saved/S3Rec-test-1.pth', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + +class TestSequentialRecommender2(unittest.TestCase): + + def test_bert4rec(self): + config_dict = { + 'model': 'BERT4Rec', + 'loss_type': 'BPR', + 'hidden_act': 'swish' + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + +if __name__ == '__main__': + unittest.main() From e54101138a6422f15c8e7a895959f666cfae25d0 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Thu, 19 Nov 2020 21:02:29 +0800 Subject: [PATCH 034/249] FEA: allow training phase to choose neg_sample distribution(config['training_neg_sample_distribution'] = uniform(default) or popularity can control this feature) --- recbole/data/utils.py | 4 +++- recbole/sampler/sampler.py | 6 ++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 91cb08727..accf76dfc 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -94,7 +94,8 @@ def data_preparation(config, dataset, save=False): kwargs = {} if config['training_neg_sample_num']: - es.neg_sample_by(config['training_neg_sample_num']) + train_distribution = config['training_neg_sample_distribution'] or 'uniform' + es.neg_sample_by(by=config['training_neg_sample_num'], distribution=train_distribution) if model_type != ModelType.SEQUENTIAL: sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) else: @@ -120,6 +121,7 @@ def data_preparation(config, dataset, save=False): getattr(es, es_str[1])() if 'sampler' not in locals(): sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) + sampler.set_distribution(es.neg_sample_args['distribution']) kwargs['sampler'] = [sampler.set_phase('valid'), sampler.set_phase('test')] kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args) valid_data, test_data = dataloader_construct( diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index 150324c84..47a9d3023 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -155,6 +155,12 @@ def get_used_ids(self): last = used_item_id[phase] = cur return used_item_id + def set_distribution(self, distribution): + if self.distribution == distribution: + return + self.distribution = distribution + self.random_list = self.get_random_list() + def set_phase(self, phase): """Get the sampler of corresponding phase. From 6b74633ee4fe76a79bb648f98ea5cb6faebf6e6b Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Thu, 19 Nov 2020 21:15:29 +0800 Subject: [PATCH 035/249] FEA: add set_distribution in RepeatableSampler --- recbole/sampler/sampler.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index 47a9d3023..e16a417fa 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -301,6 +301,12 @@ def get_random_list(self): else: raise NotImplementedError('Distribution [{}] has not been implemented'.format(self.distribution)) + def set_distribution(self, distribution): + if self.distribution == distribution: + return + self.distribution = distribution + self.random_list = self.get_random_list() + def get_used_ids(self): """ Returns: From 4b926188ff38151e8d2bc81892db8d736f928d87 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 21 Nov 2020 15:01:13 +0800 Subject: [PATCH 036/249] del python-package.yml --- .github/workflows/python-package.yml | 46 ---------------------------- 1 file changed, 46 deletions(-) delete mode 100644 .github/workflows/python-package.yml diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml deleted file mode 100644 index 15d26c58a..000000000 --- a/.github/workflows/python-package.yml +++ /dev/null @@ -1,46 +0,0 @@ -name: RecBole tests - -on: -- push -# - pull_request - -jobs: - build: - - runs-on: ubuntu-latest - strategy: - matrix: - python-version: [3.8] - - steps: - - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install pytest - pip install dgl - if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - - # Use "python -m pytest" instead of "pytest" to fix imports - - name: Test metrics - run: | - python -m pytest -v tests/metrics - - name: Test evaluation_setting - run: | - python -m pytest -v tests/evaluation_setting - - name: Test model - run: | - python -m pytest -v tests/model/test_model_auto.py - - name: Test config - run: | - python -m pytest -v tests/config/test_config.py - export PYTHONPATH=. - python tests/config/test_command_line.py --use_gpu=False --valid_metric=Recall@10 --split_ratio=[0.7,0.2,0.1] --metrics=['Recall@10'] --epochs=200 --eval_setting='LO_RS' --learning_rate=0.3 - - name: Test evaluation_setting - run: | - python -m pytest -v tests/evaluation_setting - From 32c44df4f5ccd5e83c37c43e9412cff177cfe584 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 21 Nov 2020 15:04:01 +0800 Subject: [PATCH 037/249] Create python-package.yml --- .github/workflows/python-package.yml | 46 ++++++++++++++++++++++++++++ 1 file changed, 46 insertions(+) create mode 100644 .github/workflows/python-package.yml diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml new file mode 100644 index 000000000..15d26c58a --- /dev/null +++ b/.github/workflows/python-package.yml @@ -0,0 +1,46 @@ +name: RecBole tests + +on: +- push +# - pull_request + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + matrix: + python-version: [3.8] + + steps: + - uses: actions/checkout@v2 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v2 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install pytest + pip install dgl + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + + # Use "python -m pytest" instead of "pytest" to fix imports + - name: Test metrics + run: | + python -m pytest -v tests/metrics + - name: Test evaluation_setting + run: | + python -m pytest -v tests/evaluation_setting + - name: Test model + run: | + python -m pytest -v tests/model/test_model_auto.py + - name: Test config + run: | + python -m pytest -v tests/config/test_config.py + export PYTHONPATH=. + python tests/config/test_command_line.py --use_gpu=False --valid_metric=Recall@10 --split_ratio=[0.7,0.2,0.1] --metrics=['Recall@10'] --epochs=200 --eval_setting='LO_RS' --learning_rate=0.3 + - name: Test evaluation_setting + run: | + python -m pytest -v tests/evaluation_setting + From b582b918e4a6cfb398862c3278f4aec5f4eee518 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 21 Nov 2020 17:13:53 +0800 Subject: [PATCH 038/249] FIX: change test `on push` to `on pull_request` --- .github/workflows/python-package.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 15d26c58a..d5cd6ab38 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -1,8 +1,7 @@ name: RecBole tests on: -- push -# - pull_request +- pull_request jobs: build: From 7812061a712f9c7ae0eaddf05a403b6bdd35a396 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Sat, 21 Nov 2020 17:40:48 +0800 Subject: [PATCH 039/249] FEA: add run_test.sh and change push-test into pr-test --- .github/workflows/python-package.yml | 4 ++-- run_test.sh | 16 ++++++++++++++++ 2 files changed, 18 insertions(+), 2 deletions(-) create mode 100644 run_test.sh diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 15d26c58a..e25899706 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -1,8 +1,8 @@ name: RecBole tests on: -- push -# - pull_request +#- push + - pull_request jobs: build: diff --git a/run_test.sh b/run_test.sh new file mode 100644 index 000000000..190b78540 --- /dev/null +++ b/run_test.sh @@ -0,0 +1,16 @@ +#!/bin/bash + + +python -m pytest -v tests/metrics +printf "metrics tests finished\n" +python -m pytest -v tests/config/test_config.py +python -m pytest -v tests/config/test_overall.py +export PYTHONPATH=. +python tests/config/test_command_line.py --use_gpu=False --valid_metric=Recall@10 --split_ratio=[0.7,0.2,0.1] --metrics=['Recall@10'] --epochs=200 --eval_setting='LO_RS' --learning_rate=0.3 +printf "config tests finished\n" +python -m pytest -v tests/evaluation_setting +printf "evaluation_setting tests finished\n" +python -m pytest -v tests/model/test_model_auto.py +python -m pytest -v tests/model/test_model_manual.py +printf "model tests finished\n" + From bb29ea1044b437fe038050811d6c7930124be944 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 22 Nov 2020 14:43:17 +0800 Subject: [PATCH 040/249] FEA: add ``logger`` to AbstractRecommender && replace ``print`` in model by ``self.logger.info`` --- recbole/model/abstract_recommender.py | 5 +++++ recbole/model/context_aware_recommender/fwfm.py | 2 +- recbole/model/context_aware_recommender/xdeepfm.py | 5 ++--- recbole/model/general_recommender/dgcf.py | 2 +- recbole/model/general_recommender/gcmc.py | 4 ++-- recbole/model/general_recommender/nais.py | 2 -- recbole/model/knowledge_aware_recommender/kgcn.py | 4 ++-- recbole/model/knowledge_aware_recommender/kgnnls.py | 4 ++-- recbole/model/knowledge_aware_recommender/ripplenet.py | 8 ++++---- recbole/model/sequential_recommender/s3rec.py | 2 +- 10 files changed, 20 insertions(+), 18 deletions(-) diff --git a/recbole/model/abstract_recommender.py b/recbole/model/abstract_recommender.py index 18983b90a..9afdaa12c 100644 --- a/recbole/model/abstract_recommender.py +++ b/recbole/model/abstract_recommender.py @@ -13,6 +13,7 @@ ################################## """ +from logging import getLogger import numpy as np import torch import torch.nn as nn @@ -25,6 +26,10 @@ class AbstractRecommender(nn.Module): r"""Base class for all models """ + def __init__(self): + self.logger = getLogger() + super(AbstractRecommender, self).__init__() + def calculate_loss(self, interaction): r"""Calculate the training loss for a batch data. diff --git a/recbole/model/context_aware_recommender/fwfm.py b/recbole/model/context_aware_recommender/fwfm.py index 5de226476..2c4800341 100644 --- a/recbole/model/context_aware_recommender/fwfm.py +++ b/recbole/model/context_aware_recommender/fwfm.py @@ -71,7 +71,7 @@ def _get_feature2field(self): fea_id = 0 for names in self.feature_names: if names is not None: - print(names) + self.logger.info(names) for name in names: self.feature2id[name] = fea_id fea_id += 1 diff --git a/recbole/model/context_aware_recommender/xdeepfm.py b/recbole/model/context_aware_recommender/xdeepfm.py index 2a5d60326..15b56ffbf 100644 --- a/recbole/model/context_aware_recommender/xdeepfm.py +++ b/recbole/model/context_aware_recommender/xdeepfm.py @@ -49,9 +49,8 @@ def __init__(self, config, dataset): if not self.direct: self.cin_layer_size = list(map(lambda x: int(x // 2 * 2), temp_cin_size)) if self.cin_layer_size[:-1] != temp_cin_size[:-1]: - logger = getLogger() - logger.warning('Layer size of CIN should be even except for the last layer when direct is True.' - 'It is changed to {}'.format(self.cin_layer_size)) + self.logger.warning('Layer size of CIN should be even except for the last layer when direct is True.' + 'It is changed to {}'.format(self.cin_layer_size)) # Create a convolutional layer for each CIN layer self.conv1d_list = [] diff --git a/recbole/model/general_recommender/dgcf.py b/recbole/model/general_recommender/dgcf.py index 56d688e8d..09354646b 100644 --- a/recbole/model/general_recommender/dgcf.py +++ b/recbole/model/general_recommender/dgcf.py @@ -148,7 +148,7 @@ def build_matrix(self, A_values): try: assert not torch.isnan(d_values).any() except AssertionError: - print("d_values", torch.min(d_values), torch.max(d_values)) + self.logger.info("d_values", torch.min(d_values), torch.max(d_values)) d_values = 1.0 / torch.sqrt(d_values) head_term = torch.sparse.mm(self.head2edge_mat, d_values) diff --git a/recbole/model/general_recommender/gcmc.py b/recbole/model/general_recommender/gcmc.py index b6619b39d..47b66ef83 100644 --- a/recbole/model/general_recommender/gcmc.py +++ b/recbole/model/general_recommender/gcmc.py @@ -91,8 +91,8 @@ def __init__(self, config, dataset): if self.accum == 'stack': div = self.gcn_output_dim // len(self.support) if self.gcn_output_dim % len(self.support) != 0: - print("""\nWARNING: HIDDEN[0] (=%d) of stack layer is adjusted to %d (in %d splits).\n""" - % (self.gcn_output_dim, len(self.support) * div, len(self.support))) + self.logger.info("""\nWARNING: HIDDEN[0] (=%d) of stack layer is adjusted to %d (in %d splits).\n""" + % (self.gcn_output_dim, len(self.support) * div, len(self.support))) self.gcn_output_dim = len(self.support) * div # define layers and loss diff --git a/recbole/model/general_recommender/nais.py b/recbole/model/general_recommender/nais.py index 597040196..d0e43d4d4 100644 --- a/recbole/model/general_recommender/nais.py +++ b/recbole/model/general_recommender/nais.py @@ -18,7 +18,6 @@ https://github.com/AaronHeee/Neural-Attentive-Item-Similarity-Model """ -from logging import getLogger import torch import torch.nn as nn @@ -45,7 +44,6 @@ def __init__(self, config, dataset): # load dataset info self.LABEL = config['LABEL_FIELD'] - self.logger = getLogger() # get all users's history interaction information.the history item # matrix is padding by the maximum number of a user's interactions diff --git a/recbole/model/knowledge_aware_recommender/kgcn.py b/recbole/model/knowledge_aware_recommender/kgcn.py index ad48a7b86..79b99d0e7 100644 --- a/recbole/model/knowledge_aware_recommender/kgcn.py +++ b/recbole/model/knowledge_aware_recommender/kgcn.py @@ -86,7 +86,7 @@ def construct_adj(self, kg_graph): - adj_relation(torch.LongTensor): each line stores the corresponding sampled neighbor relations, shape: [n_entities, neighbor_sample_size] """ - # print('constructing knowledge graph ...') + # self.logger.info('constructing knowledge graph ...') # treat the KG as an undirected graph kg_dict = dict() for triple in zip(kg_graph.row, kg_graph.data, kg_graph.col): @@ -100,7 +100,7 @@ def construct_adj(self, kg_graph): kg_dict[tail] = [] kg_dict[tail].append((head, relation)) - # print('constructing adjacency matrix ...') + # self.logger.info('constructing adjacency matrix ...') # each line of adj_entity stores the sampled neighbor entities for a given entity # each line of adj_relation stores the corresponding sampled neighbor relations entity_num = kg_graph.shape[0] diff --git a/recbole/model/knowledge_aware_recommender/kgnnls.py b/recbole/model/knowledge_aware_recommender/kgnnls.py index 8d18f30de..7907cbe7a 100644 --- a/recbole/model/knowledge_aware_recommender/kgnnls.py +++ b/recbole/model/knowledge_aware_recommender/kgnnls.py @@ -145,7 +145,7 @@ def construct_adj(self, kg_graph): - adj_relation (torch.LongTensor): each line stores the corresponding sampled neighbor relations, shape: [n_entities, neighbor_sample_size] """ - # print('constructing knowledge graph ...') + # self.logger.info('constructing knowledge graph ...') # treat the KG as an undirected graph kg_dict = dict() for triple in zip(kg_graph.row, kg_graph.data, kg_graph.col): @@ -159,7 +159,7 @@ def construct_adj(self, kg_graph): kg_dict[tail] = [] kg_dict[tail].append((head, relation)) - # print('constructing adjacency matrix ...') + # self.logger.info('constructing adjacency matrix ...') # each line of adj_entity stores the sampled neighbor entities for a given entity # each line of adj_relation stores the corresponding sampled neighbor relations entity_num = kg_graph.shape[0] diff --git a/recbole/model/knowledge_aware_recommender/ripplenet.py b/recbole/model/knowledge_aware_recommender/ripplenet.py index 65c0e9508..9b24b59c9 100644 --- a/recbole/model/knowledge_aware_recommender/ripplenet.py +++ b/recbole/model/knowledge_aware_recommender/ripplenet.py @@ -112,7 +112,7 @@ def _build_ripple_set(self): # we simply copy the ripple set of the last hop here if len(memories_h) == 0: if h == 0: - # print("user {} without 1-hop kg facts, fill with padding".format(user)) + # self.logger.info("user {} without 1-hop kg facts, fill with padding".format(user)) # raise AssertionError("User without facts in 1st hop") n_padding += 1 memories_h = [0 for i in range(self.n_memory)] @@ -135,7 +135,7 @@ def _build_ripple_set(self): memories_r = torch.LongTensor(memories_r).to(self.device) memories_t = torch.LongTensor(memories_t).to(self.device) ripple_set[user].append((memories_h, memories_r, memories_t)) - print("{} among {} users are padded".format(n_padding, len(self.user_dict))) + self.logger.info("{} among {} users are padded".format(n_padding, len(self.user_dict))) return ripple_set def forward(self, interaction): @@ -161,7 +161,7 @@ def forward(self, interaction): head_ent = torch.cat(memories_h[i], dim=0) relation = torch.cat(memories_r[i], dim=0) tail_ent = torch.cat(memories_t[i], dim=0) - # print("Hop {}, size {}".format(i, head_ent.size(), relation.size(), tail_ent.size())) + # self.logger.info("Hop {}, size {}".format(i, head_ent.size(), relation.size(), tail_ent.size())) # [batch size * n_memory, dim] self.h_emb_list.append(self.entity_embedding(head_ent)) @@ -332,7 +332,7 @@ def full_sort_predict(self, interaction): head_ent = torch.cat(memories_h[i], dim=0) relation = torch.cat(memories_r[i], dim=0) tail_ent = torch.cat(memories_t[i], dim=0) - # print("Hop {}, size {}".format(i, head_ent.size(), relation.size(), tail_ent.size())) + # self.logger.info("Hop {}, size {}".format(i, head_ent.size(), relation.size(), tail_ent.size())) # [batch size * n_memory, dim] self.h_emb_list.append(self.entity_embedding(head_ent)) diff --git a/recbole/model/sequential_recommender/s3rec.py b/recbole/model/sequential_recommender/s3rec.py index 7a9617886..5e1357f93 100644 --- a/recbole/model/sequential_recommender/s3rec.py +++ b/recbole/model/sequential_recommender/s3rec.py @@ -107,7 +107,7 @@ def __init__(self, config, dataset): else: # load pretrained model for finetune pretrained = torch.load(self.pre_model_path) - print('Load pretrained model from', self.pre_model_path) + self.logger.info('Load pretrained model from', self.pre_model_path) self.load_state_dict(pretrained['state_dict']) def _init_weights(self, module): From 728b236f734252ebefbce6e4b008c20dd18d4c95 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 22 Nov 2020 15:12:01 +0800 Subject: [PATCH 041/249] FIX: allow training_neg_sample_num > 1 in Sequential pair-wise neg-sampling. --- recbole/data/dataloader/sequential_dataloader.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index 91d3b1762..5e35acc21 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -221,8 +221,9 @@ def _neg_sampling(self, data): return self.sampling_func(data, neg_iids) def _neg_sample_by_pair_wise_sampling(self, data, neg_iids): - data[self.neg_item_id] = neg_iids - return data + new_data = {key: np.concatenate([value] * self.times) for key, value in data.items()} + new_data[self.neg_item_id] = neg_iids + return new_data def _neg_sample_by_point_wise_sampling(self, data, neg_iids): new_data = {} From 7a1c246f8f0b4d74d3d69a77fc8aa61ac6d5607f Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 22 Nov 2020 16:09:05 +0800 Subject: [PATCH 042/249] FEA: add clip_grad_norm to trainer.py --- recbole/properties/overall.yaml | 1 + recbole/trainer/trainer.py | 4 ++++ 2 files changed, 5 insertions(+) diff --git a/recbole/properties/overall.yaml b/recbole/properties/overall.yaml index 855804ee3..33a0e30c2 100644 --- a/recbole/properties/overall.yaml +++ b/recbole/properties/overall.yaml @@ -15,6 +15,7 @@ learning_rate: 0.001 training_neg_sample_num: 1 eval_step: 1 stopping_step: 10 +clip_grad_norm: {'max_norm': 5, 'norm_type': 2} # evaluation settings eval_setting: RO_RS,full diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 992bac147..3af153067 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -16,6 +16,7 @@ import itertools import torch import torch.optim as optim +from torch.nn.utils.clip_grad import clip_grad_norm_ import numpy as np import matplotlib.pyplot as plt @@ -76,6 +77,7 @@ def __init__(self, config, model): self.epochs = config['epochs'] self.eval_step = min(config['eval_step'], self.epochs) self.stopping_step = config['stopping_step'] + self.clip_grad_norm = config['clip_grad_norm'] self.valid_metric = config['valid_metric'].lower() self.valid_metric_bigger = config['valid_metric_bigger'] self.test_batch_size = config['eval_batch_size'] @@ -149,6 +151,8 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): total_loss = losses.item() if total_loss is None else total_loss + losses.item() self._check_nan(loss) loss.backward() + if self.clip_grad_norm: + clip_grad_norm_(self.model.parameters(), **self.clip_grad_norm) self.optimizer.step() return total_loss From 09fb2c92f12e5774cf6ec00ad26e56823b17a8ce Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Sun, 22 Nov 2020 17:32:47 +0800 Subject: [PATCH 043/249] FIX: fix bugs in set_distribution in sampler. --- recbole/sampler/sampler.py | 27 ++++++++++++--------------- 1 file changed, 12 insertions(+), 15 deletions(-) diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index e16a417fa..2f4879e53 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -33,15 +33,24 @@ class AbstractSampler(object): used_ids (numpy.ndarray): The result of :meth:`get_used_ids`. """ def __init__(self, distribution): - self.distribution = distribution + self.distribution = None + self.set_distribution(distribution) + self.used_ids = self.get_used_ids() + def set_distribution(self, distribution): + """Set the distribution of sampler. + + Args: + distribution (str): Distribution of the negative items. + """ + if self.distribution == distribution: + return + self.distribution = distribution self.random_list = self.get_random_list() random.shuffle(self.random_list) self.random_pr = 0 self.random_list_length = len(self.random_list) - self.used_ids = self.get_used_ids() - def get_random_list(self): """ Returns: @@ -155,12 +164,6 @@ def get_used_ids(self): last = used_item_id[phase] = cur return used_item_id - def set_distribution(self, distribution): - if self.distribution == distribution: - return - self.distribution = distribution - self.random_list = self.get_random_list() - def set_phase(self, phase): """Get the sampler of corresponding phase. @@ -301,12 +304,6 @@ def get_random_list(self): else: raise NotImplementedError('Distribution [{}] has not been implemented'.format(self.distribution)) - def set_distribution(self, distribution): - if self.distribution == distribution: - return - self.distribution = distribution - self.random_list = self.get_random_list() - def get_used_ids(self): """ Returns: From 9f54f9ea328c4339425e56c3fd5e1b96a3216fdd Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Mon, 23 Nov 2020 14:10:04 +0800 Subject: [PATCH 044/249] FEA: add test_din into test_model_auto.py --- tests/model/test_model_auto.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 74e4c8e79..cd69e1dd6 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -211,12 +211,12 @@ def test_fwfm(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - # def test_din(self): - # config_dict = { - # 'model': 'DIN', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + def test_din(self): + config_dict = { + 'model': 'DIN', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) class TestSequentialRecommender(unittest.TestCase): From e3056c347bf7541f5d5b4da43714b548283cecfb Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Mon, 23 Nov 2020 14:26:57 +0800 Subject: [PATCH 045/249] REFACTOR: refactor in dataset.uid2index & its' references --- recbole/data/dataloader/general_dataloader.py | 45 +++++++++---------- recbole/data/dataset/dataset.py | 19 +++++--- 2 files changed, 34 insertions(+), 30 deletions(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index 6168a0a55..cd18c1992 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -72,28 +72,27 @@ class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): """ def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): - self.uid2index, self.uid2items_num = None, None + self.uid_list, self.uid2index, self.uid2items_num = None, None, None super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) def setup(self): if self.user_inter_in_one_batch: - self.uid2index, self.uid2items_num = self.dataset.uid2index + self.uid_list, self.uid2index, self.uid2items_num = self.dataset.uid2index self._batch_size_adaptation() def data_preprocess(self): if self.user_inter_in_one_batch: new_inter_num = 0 new_inter_feat = [] - new_uid2index = [] - for uid, index in self.uid2index: + for uid in self.uid_list: + index = self.uid2index[uid] new_inter_feat.append(self._neg_sampling(self.dataset.inter_feat[index])) new_num = len(new_inter_feat[-1]) - new_uid2index.append((uid, slice(new_inter_num, new_inter_num + new_num))) - new_inter_num += new_num + self.uid2index[uid] = slice(new_inter_num, new_inter_num + new_num) + self.uid2items_num[uid] = new_num self.dataset.inter_feat = pd.concat(new_inter_feat, ignore_index=True) - self.uid2index = np.array(new_uid2index) else: self.dataset.inter_feat = self._neg_sampling(self.dataset.inter_feat) @@ -118,15 +117,13 @@ def _batch_size_adaptation(self): @property def pr_end(self): if self.user_inter_in_one_batch: - return len(self.uid2index) + return len(self.uid_list) else: return len(self.dataset) def _shuffle(self): if self.user_inter_in_one_batch: - new_index = np.random.permutation(len(self.uid2index)) - self.uid2index = self.uid2index[new_index] - self.uid2items_num = self.uid2items_num[new_index] + np.random.shuffle(self.uid_list) else: self.dataset.shuffle() @@ -134,10 +131,11 @@ def _next_batch_data(self): if self.user_inter_in_one_batch: sampling_func = self._neg_sampling if self.real_time else (lambda x: x) cur_data = [] - for uid, index in self.uid2index[self.pr: self.pr + self.step]: + for uid in self.uid_list[self.pr: self.pr + self.step]: + index = self.uid2index[uid] cur_data.append(sampling_func(self.dataset[index])) cur_data = pd.concat(cur_data, ignore_index=True) - pos_len_list = self.uid2items_num[self.pr: self.pr + self.step] + pos_len_list = self.uid2items_num[self.uid_list[self.pr: self.pr + self.step]] user_len_list = pos_len_list * self.times self.pr += self.step return self._dataframe_to_interaction(cur_data, list(pos_len_list), list(user_len_list)) @@ -184,7 +182,7 @@ def get_pos_len_list(self): Returns: np.ndarray: Number of positive item for each user in a training/evaluating epoch. """ - return self.uid2items_num + return self.uid2items_num[self.uid_list] class GeneralFullDataLoader(NegSampleMixin, AbstractDataLoader): @@ -208,14 +206,14 @@ def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): if neg_sample_args['strategy'] != 'full': raise ValueError('neg_sample strategy in GeneralFullDataLoader() should be `full`') - self.uid2index, self.uid2items_num = dataset.uid2index + self.uid_list, self.uid2index, self.uid2items_num = dataset.uid2index super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) def data_preprocess(self): self.user_tensor, tmp_pos_idx, tmp_used_idx, self.pos_len_list, self.neg_len_list = \ - self._neg_sampling(self.uid2index, show_progress=True) + self._neg_sampling(self.uid_list, show_progress=True) tmp_pos_len_list = [sum(self.pos_len_list[_: _ + self.step]) for _ in range(0, self.pr_end, self.step)] tot_item_num = self.dataset.item_num tmp_used_len_list = [sum( @@ -236,7 +234,7 @@ def _batch_size_adaptation(self): @property def pr_end(self): - return len(self.uid2index) + return len(self.uid_list) def _shuffle(self): self.logger.warnning('GeneralFullDataLoader can\'t shuffle') @@ -248,11 +246,11 @@ def _next_batch_data(self): cur_data = self.user_tensor[slc], self.pos_idx[idx], self.used_idx[idx], \ self.pos_len_list[slc], self.neg_len_list[slc] else: - cur_data = self._neg_sampling(self.uid2index[self.pr: self.pr + self.step]) + cur_data = self._neg_sampling(self.uid_list[self.pr: self.pr + self.step]) self.pr += self.step return cur_data - def _neg_sampling(self, uid2index, show_progress=False): + def _neg_sampling(self, uid_list, show_progress=False): uid_field = self.dataset.uid_field iid_field = self.dataset.iid_field tot_item_num = self.dataset.item_num @@ -264,8 +262,9 @@ def _neg_sampling(self, uid2index, show_progress=False): pos_idx = [] used_idx = [] - iter_data = tqdm(uid2index) if show_progress else uid2index - for uid, index in iter_data: + iter_data = tqdm(uid_list) if show_progress else uid_list + for uid in iter_data: + index = self.uid2index[uid] pos_item_id = self.dataset.inter_feat[iid_field][index].values pos_idx.extend([_ + start_idx for _ in pos_item_id]) pos_num = len(pos_item_id) @@ -280,7 +279,7 @@ def _neg_sampling(self, uid2index, show_progress=False): start_idx += tot_item_num - user_df = pd.DataFrame({uid_field: np.array(uid2index[:, 0], dtype=np.int)}) + user_df = pd.DataFrame({uid_field: uid_list}) user_interaction = self._dataframe_to_interaction(self.join(user_df)) return user_interaction, \ @@ -292,4 +291,4 @@ def get_pos_len_list(self): Returns: np.ndarray: Number of positive item for each user in a training/evaluating epoch. """ - return self.uid2items_num + return self.uid2items_num[self.uid_list] diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index aadf4eeeb..dc14a1de7 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -1123,10 +1123,12 @@ def uid2index(self): Returns: tuple: - - ``numpy.ndarray`` of tuple ``(uid, slice)``, - interaction records between slice are all belong to the same uid. - - ``numpy.ndarray`` of int, - representing number of interaction records of each user. + - :class:`numpy.ndarray` of int, + user id list in interaction records. + - :class:`numpy.ndarray` of :class:`slice`, + interaction records between slice are all belong to the same uid, index represent user id. + - :class:`numpy.ndarray` of int, + representing number of interaction records of each user, index represent user id. """ self._check_field('uid_field') self.sort(by=self.uid_field, ascending=True) @@ -1137,9 +1139,12 @@ def uid2index(self): uid_list.append(uid) start[uid] = i end[uid] = i - index = [(uid, slice(start[uid], end[uid] + 1)) for uid in uid_list] - uid2items_num = [end[uid] - start[uid] + 1 for uid in uid_list] - return np.array(index), np.array(uid2items_num) + uid2index = np.array([None] * self.user_num) + uid2items_num = np.zeros(self.user_num, dtype=np.int64) + for uid in uid_list: + uid2index[uid] = slice(start[uid], end[uid] + 1) + uid2items_num[uid] = end[uid] - start[uid] + 1 + return np.array(uid_list), uid2index, uid2items_num def _check_field(self, *field_names): """Given a name of attribute, check if it's exist. From 1517b251068fd4208e62bd4c5de192a87d7b0afa Mon Sep 17 00:00:00 2001 From: Lin zihan <893833413@qq.com> Date: Mon, 23 Nov 2020 18:17:25 +0800 Subject: [PATCH 046/249] FIX : bug when eval_batch_size = 1 --- recbole/trainer/trainer.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 3af153067..f5ecc66a1 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -394,6 +394,9 @@ def _spilt_predict(self, interaction, batch_size): for key, spilt_tensor in spilt_interaction.items(): current_interaction[key] = spilt_tensor[i] result = self.model.predict(Interaction(current_interaction).to(self.device)) + if len(result.shape) == 0: + result = result.unsqueeze(0) + #print(result.shape) result_list.append(result) return torch.cat(result_list, dim=0) From 13363921a43120aad7b2003431384a199ab9f28a Mon Sep 17 00:00:00 2001 From: Lin zihan <893833413@qq.com> Date: Mon, 23 Nov 2020 18:57:43 +0800 Subject: [PATCH 047/249] FIX: sparse matrix type bug --- recbole/model/general_recommender/ngcf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/model/general_recommender/ngcf.py b/recbole/model/general_recommender/ngcf.py index b0519be59..6416d0e25 100644 --- a/recbole/model/general_recommender/ngcf.py +++ b/recbole/model/general_recommender/ngcf.py @@ -73,7 +73,7 @@ def __init__(self, config, dataset): super(NGCF, self).__init__(config, dataset) # load dataset info - self.interaction_matrix = dataset.inter_matrix(form='csr').astype(np.float32) + self.interaction_matrix = dataset.inter_matrix(form='coo').astype(np.float32) # load parameters info self.embedding_size = config['embedding_size'] From 9579036bf0cad0ed2d0cd3526c9f2e17672e3c5f Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Mon, 23 Nov 2020 22:07:30 +0800 Subject: [PATCH 048/249] FIX: fix the print in trainer --- recbole/trainer/trainer.py | 1 - 1 file changed, 1 deletion(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index f5ecc66a1..34a762c42 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -396,7 +396,6 @@ def _spilt_predict(self, interaction, batch_size): result = self.model.predict(Interaction(current_interaction).to(self.device)) if len(result.shape) == 0: result = result.unsqueeze(0) - #print(result.shape) result_list.append(result) return torch.cat(result_list, dim=0) From 16051e286a4311eaed3d68fa78a8c80185f77e9f Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Mon, 23 Nov 2020 22:40:05 +0800 Subject: [PATCH 049/249] FEA: add get_user_len_list to dataloader --- recbole/data/dataloader/general_dataloader.py | 14 +++++++++ recbole/data/dataloader/neg_sample_mixin.py | 7 +++++ .../data/dataloader/sequential_dataloader.py | 29 +++++++++++++++++-- 3 files changed, 47 insertions(+), 3 deletions(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index cd18c1992..2f6787da6 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -184,6 +184,13 @@ def get_pos_len_list(self): """ return self.uid2items_num[self.uid_list] + def get_user_len_list(self): + """ + Returns: + np.ndarray: Number of all item for each user in a training/evaluating epoch. + """ + return self.uid2items_num[self.uid_list] * self.times + class GeneralFullDataLoader(NegSampleMixin, AbstractDataLoader): """:class:`GeneralFullDataLoader` is a general-dataloader with full sort. In order to speed up calculation, @@ -292,3 +299,10 @@ def get_pos_len_list(self): np.ndarray: Number of positive item for each user in a training/evaluating epoch. """ return self.uid2items_num[self.uid_list] + + def get_user_len_list(self): + """ + Returns: + np.ndarray: Number of all item for each user in a training/evaluating epoch. + """ + return np.full(len(self.uid_list), self.item_num) diff --git a/recbole/data/dataloader/neg_sample_mixin.py b/recbole/data/dataloader/neg_sample_mixin.py index 47e4d47ef..4ea52a324 100644 --- a/recbole/data/dataloader/neg_sample_mixin.py +++ b/recbole/data/dataloader/neg_sample_mixin.py @@ -76,6 +76,13 @@ def get_pos_len_list(self): """ raise NotImplementedError('Method [get_pos_len_list] should be implemented.') + def get_user_len_list(self): + """ + Returns: + np.ndarray: Number of all item for each user in a training/evaluating epoch. + """ + raise NotImplementedError('Method [get_user_len_list] should be implemented.') + class NegSampleByMixin(NegSampleMixin): """:class:`NegSampleByMixin` is an abstract class which can sample negative examples by ratio. diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index 5e35acc21..933ba47b5 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -16,7 +16,7 @@ import torch from recbole.data.dataloader.abstract_dataloader import AbstractDataLoader -from recbole.data.dataloader.neg_sample_mixin import NegSampleByMixin +from recbole.data.dataloader.neg_sample_mixin import NegSampleByMixin, NegSampleMixin from recbole.utils import DataLoaderType, FeatureSource, FeatureType, InputType @@ -245,8 +245,15 @@ def get_pos_len_list(self): """ return np.ones(self.pr_end, dtype=np.int64) + def get_user_len_list(self): + """ + Returns: + np.ndarray: Number of all item for each user in a training/evaluating epoch. + """ + return np.full(self.pr_end, self.times) + -class SequentialFullDataLoader(SequentialDataLoader): +class SequentialFullDataLoader(NegSampleMixin, SequentialDataLoader): """:class:`SequentialFullDataLoader` is a sequential-dataloader with full sort. In order to speed up calculation, this dataloader would only return then user part of interactions, positive items and used items. It would not return negative items. @@ -265,9 +272,18 @@ class SequentialFullDataLoader(SequentialDataLoader): def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): - super().__init__(config, dataset, + super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) + def data_preprocess(self): + pass + + def _batch_size_adaptation(self): + pass + + def _neg_sampling(self, inter_feat): + pass + def _shuffle(self): self.logger.warnning('SequentialFullDataLoader can\'t shuffle') @@ -286,3 +302,10 @@ def get_pos_len_list(self): np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. """ return np.ones(self.pr_end, dtype=np.int64) + + def get_user_len_list(self): + """ + Returns: + np.ndarray: Number of all item for each user in a training/evaluating epoch. + """ + return np.full(len(self.uid_list), self.item_num) From b5895df8e772633a9f78179a50e3eab1a5a7afb5 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 24 Nov 2020 11:47:15 +0800 Subject: [PATCH 050/249] FEA: add upgrade_batch_size to abstract_dataloader.py && change usage of set_batch_size to upgrade_batch_size. --- recbole/data/dataloader/abstract_dataloader.py | 9 +++++++++ recbole/data/dataloader/general_dataloader.py | 6 +++--- recbole/data/dataloader/sequential_dataloader.py | 2 +- 3 files changed, 13 insertions(+), 4 deletions(-) diff --git a/recbole/data/dataloader/abstract_dataloader.py b/recbole/data/dataloader/abstract_dataloader.py index 6b21009cb..69c41a9f6 100644 --- a/recbole/data/dataloader/abstract_dataloader.py +++ b/recbole/data/dataloader/abstract_dataloader.py @@ -129,6 +129,15 @@ def set_batch_size(self, batch_size): self.batch_size = batch_size self.logger.warning('Batch size is changed to {}'.format(batch_size)) + def upgrade_batch_size(self, batch_size): + """Upgrade the batch_size of the dataloader, if input batch_size is bigger than current batch_size. + + Args: + batch_size (int): the new batch_size of dataloader. + """ + if self.batch_size < batch_size: + self.set_batch_size(batch_size) + def get_user_feature(self): """It is similar to :meth:`~recbole.data.dataset.dataset.Dataset.get_user_feature`, but it will return an :class:`~recbole.data.interaction.Interaction` of user feature instead of a :class:`pandas.DataFrame`. diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index 2f6787da6..c8636ffba 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -107,12 +107,12 @@ def _batch_size_adaptation(self): batch_num = i new_batch_size += inters_num[i] self.step = batch_num - self.set_batch_size(new_batch_size) + self.upgrade_batch_size(new_batch_size) else: batch_num = max(self.batch_size // self.times, 1) new_batch_size = batch_num * self.times self.step = batch_num if self.real_time else new_batch_size - self.set_batch_size(new_batch_size) + self.upgrade_batch_size(new_batch_size) @property def pr_end(self): @@ -237,7 +237,7 @@ def _batch_size_adaptation(self): batch_num = max(self.batch_size // self.dataset.item_num, 1) new_batch_size = batch_num * self.dataset.item_num self.step = batch_num - self.set_batch_size(new_batch_size) + self.upgrade_batch_size(new_batch_size) @property def pr_end(self): diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index 933ba47b5..a806eb75d 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -180,7 +180,7 @@ def _batch_size_adaptation(self): batch_num = max(self.batch_size // self.times, 1) new_batch_size = batch_num * self.times self.step = batch_num if self.real_time else new_batch_size - self.set_batch_size(new_batch_size) + self.upgrade_batch_size(new_batch_size) def _next_batch_data(self): cur_index = slice(self.pr, self.pr + self.step) From 53f8d7ff9ab7211e4aacb7eee86b735afccccf3e Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Tue, 24 Nov 2020 18:16:01 +0800 Subject: [PATCH 051/249] Update README.md --- README.md | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/README.md b/README.md index c1a762dfa..a80f7d9e7 100644 --- a/README.md +++ b/README.md @@ -153,6 +153,26 @@ If you want to change the models, just run the script by setting additional comm python run_recbole.py --model=[model_name] ``` + +## Training and testing time of models +We test our models on three datasets of different size (small size, medium size and large size). You can +click links to check more information. +[General recommendation models]() +[Sequential recommendation models]() +[Context-aware recommendation models]() +[Knowledge-based recommendation models]() +here is our testing device information: +``` +GPU: TITAN GTX +Driver Version: 430.64 +CUDA Version: 10.1 +Memory size: 65412748 KB +CPU: Intel(R) Xeon(R) Silver 4110 CPU @ 2.10GHz +The number of CPU cores: 8 +Cache size: 11264KB +``` + + ## RecBole Major Releases | Releases | Date | Features | |-----------|--------|-------------------------| From 427d9256c7bb936cd6f55ae2006180f0f6aabbc1 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Tue, 24 Nov 2020 18:21:55 +0800 Subject: [PATCH 052/249] Update README.md --- README.md | 28 +++++++++++++++------------- 1 file changed, 15 insertions(+), 13 deletions(-) diff --git a/README.md b/README.md index a80f7d9e7..9938bf4bb 100644 --- a/README.md +++ b/README.md @@ -156,20 +156,22 @@ python run_recbole.py --model=[model_name] ## Training and testing time of models We test our models on three datasets of different size (small size, medium size and large size). You can -click links to check more information. -[General recommendation models]() -[Sequential recommendation models]() -[Context-aware recommendation models]() -[Knowledge-based recommendation models]() -here is our testing device information: +click links to check more information.
+ +* [General recommendation models]()
+* [Sequential recommendation models]()
+* [Context-aware recommendation models]()
+* [Knowledge-based recommendation models]()
+ +Here is our testing device information:
``` -GPU: TITAN GTX -Driver Version: 430.64 -CUDA Version: 10.1 -Memory size: 65412748 KB -CPU: Intel(R) Xeon(R) Silver 4110 CPU @ 2.10GHz -The number of CPU cores: 8 -Cache size: 11264KB +GPU: TITAN GTX +Driver Version: 430.64 +CUDA Version: 10.1 +Memory size: 65412748 KB +CPU: Intel(R) Xeon(R) Silver 4110 CPU @ 2.10GHz +The number of CPU cores: 8 +Cache size: 11264KB ``` From 2068c698c06a714f5ac1d3ee2f1fb490cfef05bf Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Tue, 24 Nov 2020 18:30:29 +0800 Subject: [PATCH 053/249] FEA: add time test file --- time_test_result/General_recommendation.md | 71 ++++++++++++++++++++++ 1 file changed, 71 insertions(+) create mode 100644 time_test_result/General_recommendation.md diff --git a/time_test_result/General_recommendation.md b/time_test_result/General_recommendation.md new file mode 100644 index 000000000..7e6c40b75 --- /dev/null +++ b/time_test_result/General_recommendation.md @@ -0,0 +1,71 @@ +## Training and testing time of general recommendation models + +### Datasets information: + +| Dataset | #User | #Item | #Interaction | Sparsity | +| ------- | ------- | ------ | ------------ | -------- | +| ml-1m | 6,041 | 3,707 | 1,000,209 | 0.9553 | +| Netflix | 80,476 | 16,821 | 1,977,844 | 0.9985 | +| Yelp | 102,046 | 98,408 | 2,903,648 | 0.9997 | + +### 1) ml-1m dataset: + +#### Time and memory cost on ml-1m dataset: + +| Method | Training Time (s) | Evaluate Time (s) | Memory (MB) | +| ---------- | ----------------- | ----------------- | ----------- | +| Popularity | 2.11 | 8.08 | 843 | +| ItemKNN | 2 | 11.76 | 843 | +| BPRMF | 1.93 | 7.43 | 931 | +| NeuMF | 4.94 | 13.12 | 965 | +| DMF | 4.47 | 12.63 | 1555 | +| NAIS | 59.27 | 24.41 | 22351 | +| NGCF | 12.09 | 7.12 | 1231 | +| GCMC | 9.04 | 54.15 | 1353 | +| LightGCN | 7.83 | 7.47 | 1177 | +| DGCF | 181.66 | 8.06 | 6745 | +| ConvNCF | 8.46 | 19.6 | 1341 | +| FISM | 19.3 | 10.92 | 7109 | +| SpectralCF | 13.87 | 6.97 | 1219 | + +#### Config file of ml-1m: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +RATING_FIELD: rating +TIME_FIELD: timestamp +LABEL_FIELD: label +NEG_PREFIX: neg_ +load_col: + inter: [user_id, item_id, rating, timestamp] +min_user_inter_num: 0 +min_item_inter_num: 0 + + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +``` + + + +### Time and memory cost on Netflix dataset: + + + +### Time and memory cost on Yelp dataset: + + + + + + + + + From 1577c49a2ed9f4d622c6c2efa35dda16c2a2898c Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Tue, 24 Nov 2020 18:38:15 +0800 Subject: [PATCH 054/249] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 9938bf4bb..e017dc747 100644 --- a/README.md +++ b/README.md @@ -158,7 +158,7 @@ python run_recbole.py --model=[model_name] We test our models on three datasets of different size (small size, medium size and large size). You can click links to check more information.
-* [General recommendation models]()
+* [General recommendation models](time_test_result/General_recommendation.md)
* [Sequential recommendation models]()
* [Context-aware recommendation models]()
* [Knowledge-based recommendation models]()
From 76e20f22f1e8f43f1acee0e3079e860a65db4c5b Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Tue, 24 Nov 2020 19:09:49 +0800 Subject: [PATCH 055/249] Update README.md --- README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index e017dc747..169e30788 100644 --- a/README.md +++ b/README.md @@ -154,9 +154,10 @@ python run_recbole.py --model=[model_name] ``` -## Training and testing time of models +## Time and memory cost of models We test our models on three datasets of different size (small size, medium size and large size). You can -click links to check more information.
+click links to check more information. ( **NOTE:** Our test results only reflect the approximate time and memory cost of models, if you find some error in our result, +please let us know.)
* [General recommendation models](time_test_result/General_recommendation.md)
* [Sequential recommendation models]()
From 05048a71cef726cf20e658f7eb7e6f0cde541236 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Tue, 24 Nov 2020 19:11:07 +0800 Subject: [PATCH 056/249] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 169e30788..5bde9ddca 100644 --- a/README.md +++ b/README.md @@ -156,7 +156,8 @@ python run_recbole.py --model=[model_name] ## Time and memory cost of models We test our models on three datasets of different size (small size, medium size and large size). You can -click links to check more information. ( **NOTE:** Our test results only reflect the approximate time and memory cost of models, if you find some error in our result, +click links to check more information.
+(**NOTE:** Our test results only reflect the approximate time and memory cost of models, if you find some error in our result, please let us know.)
* [General recommendation models](time_test_result/General_recommendation.md)
From 7a56197da64e2c9172384a9961e539aff13fa2f9 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Tue, 24 Nov 2020 19:12:02 +0800 Subject: [PATCH 057/249] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5bde9ddca..634bd2e6b 100644 --- a/README.md +++ b/README.md @@ -157,7 +157,7 @@ python run_recbole.py --model=[model_name] ## Time and memory cost of models We test our models on three datasets of different size (small size, medium size and large size). You can click links to check more information.
-(**NOTE:** Our test results only reflect the approximate time and memory cost of models, if you find some error in our result, +(**NOTE:** Our test results only reflect the approximate time and memory cost of models. If you find any error in our result, please let us know.)
* [General recommendation models](time_test_result/General_recommendation.md)
From 93631d37f67df79e5480d3656f587961891d0ca6 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Tue, 24 Nov 2020 19:15:17 +0800 Subject: [PATCH 058/249] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 634bd2e6b..fbd298750 100644 --- a/README.md +++ b/README.md @@ -155,7 +155,7 @@ python run_recbole.py --model=[model_name] ## Time and memory cost of models -We test our models on three datasets of different size (small size, medium size and large size). You can +We test our models on three datasets of different size (small size, medium size and large size) to estimate their time and memory cost. You can click links to check more information.
(**NOTE:** Our test results only reflect the approximate time and memory cost of models. If you find any error in our result, please let us know.)
From de5103598ac794b9df668eb795669ede089ebfc0 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Wed, 25 Nov 2020 13:57:44 +0800 Subject: [PATCH 059/249] REFACTOR: refactor in full sort && speed up in general full sort predict. --- recbole/data/dataloader/general_dataloader.py | 103 ++++++++---------- .../data/dataloader/sequential_dataloader.py | 11 +- recbole/evaluator/topk_evaluator.py | 4 +- recbole/trainer/trainer.py | 39 ++----- 4 files changed, 67 insertions(+), 90 deletions(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index c8636ffba..9bd3b733b 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -213,25 +213,45 @@ def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): if neg_sample_args['strategy'] != 'full': raise ValueError('neg_sample strategy in GeneralFullDataLoader() should be `full`') - self.uid_list, self.uid2index, self.uid2items_num = dataset.uid2index + + uid_field = dataset.uid_field + iid_field = dataset.iid_field + user_num = dataset.user_num + self.uid_list = [] + self.uid2items_num = np.zeros(user_num, dtype=np.int64) + self.uid2swap_idx = np.array([None] * user_num) + self.uid2rev_swap_idx = np.array([None] * user_num) + self.uid2history_item = np.array([None] * user_num) + + dataset.sort(by=uid_field, ascending=True) + last_uid = None + positive_item = None + uid2used_item = sampler.used_ids + for uid, iid in dataset.inter_feat[[uid_field, iid_field]].values: + if uid != last_uid: + if last_uid is not None: + self._set_user_property(last_uid, uid2used_item[last_uid], positive_item) + last_uid = uid + self.uid_list.append(uid) + positive_item = set() + positive_item.add(iid) + self._set_user_property(last_uid, uid2used_item[last_uid], positive_item) + self.user_df = dataset.join(pd.DataFrame(self.uid_list, columns=[uid_field])) super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) + def _set_user_property(self, uid, used_item, positive_item): + history_item = used_item - positive_item + positive_item_num = len(positive_item) + self.uid2items_num[uid] = positive_item_num + swap_idx = torch.tensor(sorted(set(range(positive_item_num)) ^ positive_item)) + self.uid2swap_idx[uid] = swap_idx + self.uid2rev_swap_idx[uid] = swap_idx.flip(0) + self.uid2history_item[uid] = torch.tensor(list(history_item)) + def data_preprocess(self): - self.user_tensor, tmp_pos_idx, tmp_used_idx, self.pos_len_list, self.neg_len_list = \ - self._neg_sampling(self.uid_list, show_progress=True) - tmp_pos_len_list = [sum(self.pos_len_list[_: _ + self.step]) for _ in range(0, self.pr_end, self.step)] - tot_item_num = self.dataset.item_num - tmp_used_len_list = [sum( - [tot_item_num - x for x in self.neg_len_list[_: _ + self.step]] - ) for _ in range(0, self.pr_end, self.step)] - self.pos_idx = list(torch.split(tmp_pos_idx, tmp_pos_len_list)) - self.used_idx = list(torch.split(tmp_used_idx, tmp_used_len_list)) - for i in range(len(self.pos_idx)): - self.pos_idx[i] -= i * tot_item_num * self.step - for i in range(len(self.used_idx)): - self.used_idx[i] -= i * tot_item_num * self.step + pass def _batch_size_adaptation(self): batch_num = max(self.batch_size // self.dataset.item_num, 1) @@ -247,51 +267,24 @@ def _shuffle(self): self.logger.warnning('GeneralFullDataLoader can\'t shuffle') def _next_batch_data(self): - if not self.real_time: - slc = slice(self.pr, self.pr + self.step) - idx = self.pr // self.step - cur_data = self.user_tensor[slc], self.pos_idx[idx], self.used_idx[idx], \ - self.pos_len_list[slc], self.neg_len_list[slc] - else: - cur_data = self._neg_sampling(self.uid_list[self.pr: self.pr + self.step]) + cur_data = self._neg_sampling(self.user_df[self.pr: self.pr + self.step]) self.pr += self.step return cur_data - def _neg_sampling(self, uid_list, show_progress=False): - uid_field = self.dataset.uid_field - iid_field = self.dataset.iid_field - tot_item_num = self.dataset.item_num - - start_idx = 0 - pos_len_list = [] - neg_len_list = [] - - pos_idx = [] - used_idx = [] - - iter_data = tqdm(uid_list) if show_progress else uid_list - for uid in iter_data: - index = self.uid2index[uid] - pos_item_id = self.dataset.inter_feat[iid_field][index].values - pos_idx.extend([_ + start_idx for _ in pos_item_id]) - pos_num = len(pos_item_id) - pos_len_list.append(pos_num) - - used_item_id = self.sampler.used_ids[uid] - used_idx.extend([_ + start_idx for _ in used_item_id]) - used_num = len(used_item_id) - - neg_num = tot_item_num - used_num - neg_len_list.append(neg_num) - - start_idx += tot_item_num + def _neg_sampling(self, user_df): + uid_list = user_df[self.dataset.uid_field].values + user_interaction = self._dataframe_to_interaction(user_df) - user_df = pd.DataFrame({uid_field: uid_list}) - user_interaction = self._dataframe_to_interaction(self.join(user_df)) + history_item = self.uid2history_item[uid_list] + history_row = torch.cat([torch.full_like(hist_iid, i) for i, hist_iid in enumerate(history_item)]) + history_col = torch.cat(list(history_item)) - return user_interaction, \ - torch.LongTensor(pos_idx), torch.LongTensor(used_idx), \ - pos_len_list, neg_len_list + swap_idx = self.uid2swap_idx[uid_list] + rev_swap_idx = self.uid2rev_swap_idx[uid_list] + swap_row = torch.cat([torch.full_like(swap, i) for i, swap in enumerate(swap_idx)]) + swap_col_after = torch.cat(list(swap_idx)) + swap_col_before = torch.cat(list(rev_swap_idx)) + return user_interaction, (history_row, history_col), swap_row, swap_col_after, swap_col_before def get_pos_len_list(self): """ @@ -305,4 +298,4 @@ def get_user_len_list(self): Returns: np.ndarray: Number of all item for each user in a training/evaluating epoch. """ - return np.full(len(self.uid_list), self.item_num) + return np.full(self.pr_end, self.item_num) diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index a806eb75d..8f2e435ee 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -289,12 +289,13 @@ def _shuffle(self): def _next_batch_data(self): interaction = super()._next_batch_data() - tot_item_num = self.dataset.item_num inter_num = len(interaction) - pos_idx = used_idx = interaction[self.target_iid_field] + torch.arange(inter_num) * tot_item_num - pos_len_list = [1] * inter_num - neg_len_list = [tot_item_num - 1] * inter_num - return interaction, pos_idx, used_idx, pos_len_list, neg_len_list + scores_row = torch.arange(inter_num).repeat(2) + padding_idx = torch.zeros(inter_num, dtype=torch.int64) + positive_idx = interaction[self.target_iid_field] + scores_col_after = torch.cat((padding_idx, positive_idx)) + scores_col_before = torch.cat((positive_idx, padding_idx)) + return interaction, None, scores_row, scores_col_after, scores_col_before def get_pos_len_list(self): """ diff --git a/recbole/evaluator/topk_evaluator.py b/recbole/evaluator/topk_evaluator.py index 746865f85..3b02b04b8 100644 --- a/recbole/evaluator/topk_evaluator.py +++ b/recbole/evaluator/topk_evaluator.py @@ -48,10 +48,10 @@ def collect(self, interaction, scores_tensor, full=False): full (bool, optional): whether it is full sort. Default: False. """ - user_len_list = interaction.user_len_list if full is True: - scores_matrix = scores_tensor.view(len(user_len_list), -1) + scores_matrix = scores_tensor else: + user_len_list = interaction.user_len_list scores_list = torch.split(scores_tensor, user_len_list, dim=0) scores_matrix = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # n_users x items diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 3af153067..333d69c30 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -288,15 +288,11 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): return self.best_valid_score, self.best_valid_result def _full_sort_batch_eval(self, batched_data): - # Note: interaction without item ids - interaction, pos_idx, used_idx, pos_len_list, neg_len_list = batched_data - + interaction, history_index, swap_row, swap_col_after, swap_col_before = batched_data batch_size = interaction.length * self.tot_item_num - used_idx = torch.cat([used_idx, torch.arange(interaction.length) * self.tot_item_num]) # remove [pad] item - neg_len_list = list(np.subtract(neg_len_list, 1)) try: # Note: interaction without item ids - scores = self.model.full_sort_predict(interaction.to(self.device)).flatten() + scores = self.model.full_sort_predict(interaction.to(self.device)) except NotImplementedError: interaction = interaction.to(self.device).repeat_interleave(self.tot_item_num) interaction.update(self.item_tensor[:batch_size]) @@ -304,31 +300,18 @@ def _full_sort_batch_eval(self, batched_data): scores = self.model.predict(interaction) else: scores = self._spilt_predict(interaction, batch_size) - pos_idx = pos_idx.to(self.device) - used_idx = used_idx.to(self.device) - - pos_scores = scores.index_select(dim=0, index=pos_idx) - pos_scores = torch.split(pos_scores, pos_len_list, dim=0) - - ones_tensor = torch.ones(batch_size, dtype=torch.bool, device=self.device) - used_mask = ones_tensor.index_fill(dim=0, index=used_idx, value=0) - neg_scores = scores.masked_select(used_mask) - neg_scores = torch.split(neg_scores, neg_len_list, dim=0) - - tmp_len_list = np.add(pos_len_list, neg_len_list).tolist() - final_scores_width = max(self.tot_item_num, max(tmp_len_list)) - extra_len_list = np.subtract(final_scores_width, tmp_len_list).tolist() - padding_nums = final_scores_width * len(tmp_len_list) - np.sum(tmp_len_list) - padding_tensor = torch.tensor([-np.inf], dtype=scores.dtype, device=self.device).repeat(padding_nums) - padding_scores = torch.split(padding_tensor, extra_len_list) - final_scores = list(itertools.chain.from_iterable(zip(pos_scores, neg_scores, padding_scores))) - final_scores = torch.cat(final_scores) + scores = scores.view(-1, self.tot_item_num) + scores[:, 0] = -np.inf + if history_index is not None: + scores[history_index] = -np.inf - setattr(interaction, 'pos_len_list', pos_len_list) - setattr(interaction, 'user_len_list', len(tmp_len_list) * [final_scores_width]) + swap_row = swap_row.to(self.device) + swap_col_after = swap_col_after.to(self.device) + swap_col_before = swap_col_before.to(self.device) + scores[swap_row, swap_col_after] = scores[swap_row, swap_col_before] - return interaction, final_scores + return interaction, scores @torch.no_grad() def evaluate(self, eval_data, load_best_model=True, model_file=None): From 040ad45a8a6237300910d73a4ca8db8f90eeb51c Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Wed, 25 Nov 2020 15:08:22 +0800 Subject: [PATCH 060/249] REFACTOR: remove pre-neg-sampling in dataloaders && uid2index in dataset --- .../data/dataloader/abstract_dataloader.py | 2 +- recbole/data/dataloader/general_dataloader.py | 43 +++++++--------- .../data/dataloader/knowledge_dataloader.py | 9 +--- recbole/data/dataloader/neg_sample_mixin.py | 5 -- .../data/dataloader/sequential_dataloader.py | 51 ++++++------------- recbole/data/dataset/dataset.py | 30 ----------- 6 files changed, 36 insertions(+), 104 deletions(-) diff --git a/recbole/data/dataloader/abstract_dataloader.py b/recbole/data/dataloader/abstract_dataloader.py index 69c41a9f6..41b48a9e4 100644 --- a/recbole/data/dataloader/abstract_dataloader.py +++ b/recbole/data/dataloader/abstract_dataloader.py @@ -80,7 +80,7 @@ def setup(self): pass def data_preprocess(self): - """This function is used to do some data preprocess, such as pre-neg-sampling and pre-data-augmentation. + """This function is used to do some data preprocess, such as pre-data-augmentation. By default, it will do nothing. """ pass diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index 9bd3b733b..7e421aa2e 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -79,22 +79,23 @@ def __init__(self, config, dataset, sampler, neg_sample_args, def setup(self): if self.user_inter_in_one_batch: - self.uid_list, self.uid2index, self.uid2items_num = self.dataset.uid2index - self._batch_size_adaptation() - - def data_preprocess(self): - if self.user_inter_in_one_batch: - new_inter_num = 0 - new_inter_feat = [] + uid_field = self.dataset.uid_field + user_num = self.dataset.user_num + self.dataset.sort(by=uid_field, ascending=True) + self.uid_list = [] + start, end = dict(), dict() + for i, uid in enumerate(self.dataset.inter_feat[uid_field].values): + if uid not in start: + self.uid_list.append(uid) + start[uid] = i + end[uid] = i + self.uid2index = np.array([None] * user_num) + self.uid2items_num = np.zeros(user_num, dtype=np.int64) for uid in self.uid_list: - index = self.uid2index[uid] - new_inter_feat.append(self._neg_sampling(self.dataset.inter_feat[index])) - new_num = len(new_inter_feat[-1]) - self.uid2index[uid] = slice(new_inter_num, new_inter_num + new_num) - self.uid2items_num[uid] = new_num - self.dataset.inter_feat = pd.concat(new_inter_feat, ignore_index=True) - else: - self.dataset.inter_feat = self._neg_sampling(self.dataset.inter_feat) + self.uid2index[uid] = slice(start[uid], end[uid] + 1) + self.uid2items_num[uid] = end[uid] - start[uid] + 1 + self.uid_list = np.array(self.uid_list) + self._batch_size_adaptation() def _batch_size_adaptation(self): if self.user_inter_in_one_batch: @@ -111,7 +112,7 @@ def _batch_size_adaptation(self): else: batch_num = max(self.batch_size // self.times, 1) new_batch_size = batch_num * self.times - self.step = batch_num if self.real_time else new_batch_size + self.step = batch_num self.upgrade_batch_size(new_batch_size) @property @@ -129,21 +130,18 @@ def _shuffle(self): def _next_batch_data(self): if self.user_inter_in_one_batch: - sampling_func = self._neg_sampling if self.real_time else (lambda x: x) cur_data = [] for uid in self.uid_list[self.pr: self.pr + self.step]: index = self.uid2index[uid] - cur_data.append(sampling_func(self.dataset[index])) + cur_data.append(self._neg_sampling(self.dataset[index])) cur_data = pd.concat(cur_data, ignore_index=True) pos_len_list = self.uid2items_num[self.uid_list[self.pr: self.pr + self.step]] user_len_list = pos_len_list * self.times self.pr += self.step return self._dataframe_to_interaction(cur_data, list(pos_len_list), list(user_len_list)) else: - cur_data = self.dataset[self.pr: self.pr + self.step] + cur_data = self._neg_sampling(self.dataset[self.pr: self.pr + self.step]) self.pr += self.step - if self.real_time: - cur_data = self._neg_sampling(cur_data) return self._dataframe_to_interaction(cur_data) def _neg_sampling(self, inter_feat): @@ -250,9 +248,6 @@ def _set_user_property(self, uid, used_item, positive_item): self.uid2rev_swap_idx[uid] = swap_idx.flip(0) self.uid2history_item[uid] = torch.tensor(list(history_item)) - def data_preprocess(self): - pass - def _batch_size_adaptation(self): batch_num = max(self.batch_size // self.dataset.item_num, 1) new_batch_size = batch_num * self.dataset.item_num diff --git a/recbole/data/dataloader/knowledge_dataloader.py b/recbole/data/dataloader/knowledge_dataloader.py index efbd2c10c..a043197ce 100644 --- a/recbole/data/dataloader/knowledge_dataloader.py +++ b/recbole/data/dataloader/knowledge_dataloader.py @@ -66,17 +66,10 @@ def _shuffle(self): self.dataset.kg_feat = self.dataset.kg_feat.sample(frac=1).reset_index(drop=True) def _next_batch_data(self): - cur_data = self.dataset.kg_feat[self.pr: self.pr + self.step] + cur_data = self._neg_sampling(self.dataset.kg_feat[self.pr: self.pr + self.step]) self.pr += self.step - if self.real_time: - cur_data = self._neg_sampling(cur_data) return self._dataframe_to_interaction(cur_data) - def data_preprocess(self): - """Do neg-sampling before training/evaluation. - """ - self.dataset.kg_feat = self._neg_sampling(self.dataset.kg_feat) - def _neg_sampling(self, kg_feat): hids = kg_feat[self.hid_field].to_list() neg_tids = self.sampler.sample_by_entity_ids(hids, self.neg_sample_num) diff --git a/recbole/data/dataloader/neg_sample_mixin.py b/recbole/data/dataloader/neg_sample_mixin.py index 4ea52a324..396fb2546 100644 --- a/recbole/data/dataloader/neg_sample_mixin.py +++ b/recbole/data/dataloader/neg_sample_mixin.py @@ -49,11 +49,6 @@ def setup(self): """ self._batch_size_adaptation() - def data_preprocess(self): - """Do neg-sampling before training/evaluation. - """ - raise NotImplementedError('Method [data_preprocess] should be implemented.') - def _batch_size_adaptation(self): """Adjust the batch size to ensure that each positive and negative interaction can be in a batch. """ diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index 8f2e435ee..37c5cabf2 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -93,25 +93,23 @@ def _shuffle(self): self.target_index = self.target_index[new_index] self.item_list_length = self.item_list_length[new_index] else: - new_data = {} - for key, value in self.pre_processed_data.items(): - new_data[key] = value[new_index] - self.pre_processed_data = new_data + self.pre_processed_data = {key: value[new_index] for key, value in self.pre_processed_data.items()} def _next_batch_data(self): - cur_index = slice(self.pr, self.pr + self.step) - if self.real_time: - cur_data = self.augmentation(self.uid_list[cur_index], - self.item_list_index[cur_index], - self.target_index[cur_index], - self.item_list_length[cur_index]) - else: - cur_data = {} - for key, value in self.pre_processed_data.items(): - cur_data[key] = value[cur_index] + cur_data = self._get_processed_data(slice(self.pr, self.pr + self.step)) self.pr += self.step return self._dict_to_interaction(cur_data) + def _get_processed_data(self, index): + if self.real_time: + cur_data = self.augmentation(self.uid_list[index], + self.item_list_index[index], + self.target_index[index], + self.item_list_length[index]) + else: + cur_data = {key: value[index] for key, value in self.pre_processed_data.items()} + return cur_data + def augmentation(self, uid_list, item_list_index, target_index, item_list_length): """Data augmentation. @@ -169,31 +167,15 @@ def __init__(self, config, dataset, sampler, neg_sample_args, super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) - def data_preprocess(self): - """Do data augmentation and neg-sampling before training/evaluation. - """ - self.pre_processed_data = self.augmentation(self.uid_list, self.item_list_field, - self.target_index, self.item_list_length) - self.pre_processed_data = self._neg_sampling(self.pre_processed_data) - def _batch_size_adaptation(self): batch_num = max(self.batch_size // self.times, 1) new_batch_size = batch_num * self.times - self.step = batch_num if self.real_time else new_batch_size + self.step = batch_num self.upgrade_batch_size(new_batch_size) def _next_batch_data(self): - cur_index = slice(self.pr, self.pr + self.step) - if self.real_time: - cur_data = self.augmentation(self.uid_list[cur_index], - self.item_list_index[cur_index], - self.target_index[cur_index], - self.item_list_length[cur_index]) - cur_data = self._neg_sampling(cur_data) - else: - cur_data = {} - for key, value in self.pre_processed_data.items(): - cur_data[key] = value[cur_index] + cur_data = self._get_processed_data(slice(self.pr, self.pr + self.step)) + cur_data = self._neg_sampling(cur_data) self.pr += self.step if self.user_inter_in_one_batch: @@ -275,9 +257,6 @@ def __init__(self, config, dataset, sampler, neg_sample_args, super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) - def data_preprocess(self): - pass - def _batch_size_adaptation(self): pass diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index dc14a1de7..5bfc2b7d8 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -1116,36 +1116,6 @@ def sparsity(self): """ return 1 - self.inter_num / self.user_num / self.item_num - @property - def uid2index(self): - """Sort ``self.inter_feat``, - and get the mapping of user_id and index of its interaction records. - - Returns: - tuple: - - :class:`numpy.ndarray` of int, - user id list in interaction records. - - :class:`numpy.ndarray` of :class:`slice`, - interaction records between slice are all belong to the same uid, index represent user id. - - :class:`numpy.ndarray` of int, - representing number of interaction records of each user, index represent user id. - """ - self._check_field('uid_field') - self.sort(by=self.uid_field, ascending=True) - uid_list = [] - start, end = dict(), dict() - for i, uid in enumerate(self.inter_feat[self.uid_field].values): - if uid not in start: - uid_list.append(uid) - start[uid] = i - end[uid] = i - uid2index = np.array([None] * self.user_num) - uid2items_num = np.zeros(self.user_num, dtype=np.int64) - for uid in uid_list: - uid2index[uid] = slice(start[uid], end[uid] + 1) - uid2items_num[uid] = end[uid] - start[uid] + 1 - return np.array(uid_list), uid2index, uid2items_num - def _check_field(self, *field_names): """Given a name of attribute, check if it's exist. From 294ae7c412c4bdd0c1a307588df21a11cf030820 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 01:27:08 +0800 Subject: [PATCH 061/249] Update README.md --- README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/README.md b/README.md index c1a762dfa..310b3129c 100644 --- a/README.md +++ b/README.md @@ -153,6 +153,16 @@ If you want to change the models, just run the script by setting additional comm python run_recbole.py --model=[model_name] ``` + +## Time and memory costs +We constructed preliminary experiments to test the time and memory cost on three different-sized datasets (small, medium and large). For detailed information, you can click the following links.
+ +NOTE: Our test results only gave the approximate time and memory cost of our implementations in the RecBole library (based on our machine server). Any feedback or suggestions about the implementations and test are welcome. We will keep improving our implementations, and update these test results. + +* [General recommendation models](asset/time_test_result/General_recommendation.md)
+* [Sequential recommendation models](asset/time_test_result/Sequential_recommendation.md)
+ + ## RecBole Major Releases | Releases | Date | Features | |-----------|--------|-------------------------| From e4b1aff5192a52bfab9299ef4004d2432ff4339a Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 01:31:26 +0800 Subject: [PATCH 062/249] Create test.txt --- asset/time_test_result/test.txt | 1 + 1 file changed, 1 insertion(+) create mode 100644 asset/time_test_result/test.txt diff --git a/asset/time_test_result/test.txt b/asset/time_test_result/test.txt new file mode 100644 index 000000000..6bb7e3d1d --- /dev/null +++ b/asset/time_test_result/test.txt @@ -0,0 +1 @@ +ssss From 0693cb9755fcbfdb6ab312b871e8e9e05c5cd67b Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 01:32:54 +0800 Subject: [PATCH 063/249] Add files via upload --- .../Sequential_recommendation.md | 159 ++++++++++++++++++ 1 file changed, 159 insertions(+) create mode 100644 asset/time_test_result/Sequential_recommendation.md diff --git a/asset/time_test_result/Sequential_recommendation.md b/asset/time_test_result/Sequential_recommendation.md new file mode 100644 index 000000000..41d56d2b3 --- /dev/null +++ b/asset/time_test_result/Sequential_recommendation.md @@ -0,0 +1,159 @@ +## Time (per epoch) and memory cost of sequential recommendation models + +### Datasets information: + +| Dataset | #User | #Item | #Interaction | Sparsity | +| ---------- | ------- | ------ | ------------ | -------- | +| ml-1m | 6,041 | 3,707 | 1,000,209 | 0.9553 | +| DIGINETICA | 59,425 | 42,116 | 547,416 | 0.9998 | +| Yelp | 102,046 | 98,408 | 2,903,648 | 0.9997 | + +### Device information + +``` +OS: Linux +Python Version: 3.8.3 +PyTorch Version: 1.7.0 +cudatoolkit Version: 10.1 +GPU: TITAN RTX（24GB） +Machine Specs: 32 CPU machine, 64GB RAM +``` + +### 1) ml-1m dataset: + +#### Time (per epoch) and memory cost on ml-1m dataset: + +| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | +| ---------------- | -----------------: | -----------------: | -----------: | +| Improved GRU-Rec | 7.78 | 0.11 | 1.27 | +| SASRec | 17.78 | 0.12 | 1.84 | +| NARM | 8.29 | 0.11 | 1.29 | +| FPMC | 7.51 | 0.11 | 1.18 | +| STAMP | 7.32 | 0.11 | 1.20 | +| Caser | 44.85 | 0.12 | 1.14 | +| NextItNet | - | - | - | +| TransRec | 10.08 | 0.16 | 8.18 | +| S3Rec | - | - | - | +| GRU4RecF | 10.20 | 0.15 | 1.80 | +| SASRecF | 18.84 | 0.17 | 1.78 | +| BERT4Rec | 36.09 | 0.34 | 1.97 | +| FDSA | 31.86 | 0.19 | 2.32 | +| SRGNN | 327.38 | 2.19 | 1.21 | +| GCSAN | 335.27 | 0.02 | 1.58 | +| KSR | - | - | - | +| GRU4RecKG | - | - | - | + +#### Config file of ml-1m dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +TIME_FIELD: timestamp +NEG_PREFIX: neg_ +ITEM_LIST_LENGTH_FIELD: item_length +LIST_SUFFIX: _list +MAX_ITEM_LIST_LENGTH: 20 +POSITION_FIELD: position_id +load_col: + inter: [user_id, item_id, timestamp] +min_user_inter_num: 0 +min_item_inter_num: 0 + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +eval_setting: TO_LS,full +training_neg_sample_num: 0 +``` + +**NOTE :** + +1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . + +2) For SASRecF, GRU4RecF and FDSA, `load_col` should as below: + +``` +load_col: + inter: [user_id, item_id, timestamp] + item: [item_id, genre] +``` + +### 2）DIGINETICA dataset: + +#### Time (per epoch) and memory cost on DIGINETICA dataset: + +| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | +| ---------------- | -----------------: | -----------------: | -----------: | +| Improved GRU-Rec | 4.10 | 1.05 | 4.02 | +| SASRec | 8.36 | 1.21 | 4.43 | +| NARM | 4.30 | 1.08 | 4.09 | +| FPMC | 2.98 | 1.08 | 4.08 | +| STAMP | 4.27 | 1.04 | 3.88 | +| Caser | 17.15 | 1.18 | 3.94 | +| NextItNet | - | - | - | +| TransRec | - | - | - | +| S3Rec | - | - | - | +| GRU4RecF | 4.79 | 1.17 | 4.83 | +| SASRecF | 8.66 | 1.29 | 5.11 | +| BERT4Rec | 16.80 | 3.54 | 7.97 | +| FDSA | 13.44 | 1.47 | 5.66 | +| SRGNN | 88.59 | 15.37 | 4.01 | +| GCSAN | 96.69 | 17.11 | 4.25 | +| KSR | - | - | - | +| GRU4RecKG | - | - | - | + +#### Config file of DIGINETICA dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: session_id +ITEM_ID_FIELD: item_id +TIME_FIELD: timestamp +NEG_PREFIX: neg_ +ITEM_LIST_LENGTH_FIELD: item_length +LIST_SUFFIX: _list +MAX_ITEM_LIST_LENGTH: 20 +POSITION_FIELD: position_id +load_col: + inter: [session_id, item_id, timestamp] +min_user_inter_num: 6 +min_item_inter_num: 1 + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +eval_setting: TO_LS,full +training_neg_sample_num: 0 +``` + +**NOTE :** + +1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . + +2) For SASRecF, GRU4RecF and FDSA, `load_col` should as below: + +``` +load_col: + inter: [session_id, item_id, timestamp] + item: [item_id, item_category] +``` + + + + + + + + + + + From d81bbed1db07a7cc75c0c65866979320e80397e2 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 01:34:06 +0800 Subject: [PATCH 064/249] Update Sequential_recommendation.md --- asset/time_test_result/Sequential_recommendation.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/asset/time_test_result/Sequential_recommendation.md b/asset/time_test_result/Sequential_recommendation.md index 41d56d2b3..88f7f250b 100644 --- a/asset/time_test_result/Sequential_recommendation.md +++ b/asset/time_test_result/Sequential_recommendation.md @@ -23,7 +23,7 @@ Machine Specs: 32 CPU machine, 64GB RAM #### Time (per epoch) and memory cost on ml-1m dataset: -| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | | ---------------- | -----------------: | -----------------: | -----------: | | Improved GRU-Rec | 7.78 | 0.11 | 1.27 | | SASRec | 17.78 | 0.12 | 1.84 | @@ -87,7 +87,7 @@ load_col: #### Time (per epoch) and memory cost on DIGINETICA dataset: -| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | | ---------------- | -----------------: | -----------------: | -----------: | | Improved GRU-Rec | 4.10 | 1.05 | 4.02 | | SASRec | 8.36 | 1.21 | 4.43 | From ee4298dd8203a5b67651099286e1668f1bf4844b Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 01:39:23 +0800 Subject: [PATCH 065/249] Add files via upload --- .../General_recommendation.md | 171 ++++++++++++++++++ 1 file changed, 171 insertions(+) create mode 100644 asset/time_test_result/General_recommendation.md diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md new file mode 100644 index 000000000..5d00de016 --- /dev/null +++ b/asset/time_test_result/General_recommendation.md @@ -0,0 +1,171 @@ +## Time and memory cost of general recommendation models + +### Datasets information: + +| Dataset | #User | #Item | #Interaction | Sparsity | +| ------- | ------- | ------ | ------------ | -------- | +| ml-1m | 6,041 | 3,707 | 1,000,209 | 0.9553 | +| Netflix | 80,476 | 16,821 | 1,977,844 | 0.9985 | +| Yelp | 102,046 | 98,408 | 2,903,648 | 0.9997 | + +### Device information + +``` +OS: Linux +Python Version: 3.8.3 +PyTorch Version: 1.7.0 +cudatoolkit Version: 10.1 +GPU: TITAN RTX（24GB） +Machine Specs: 32 CPU machine, 64GB RAM +``` + +### 1) ml-1m dataset: + +#### Time and memory cost on ml-1m dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| ---------- | ------------------------: | --------------------------: | --------------: | +| Popularity | 2.11 | 8.08 | 0.82 | +| ItemKNN | 2.00 | 11.76 | 0.82 | +| BPRMF | 1.93 | 7.43 | 0.91 | +| NeuMF | 4.94 | 13.12 | 0.94 | +| DMF | 4.47 | 12.63 | 1.52 | +| NAIS | 59.27 | 24.41 | 21.83 | +| NGCF | 12.09 | 7.12 | 1.20 | +| GCMC | 9.04 | 54.15 | 1.32 | +| LightGCN | 7.83 | 7.47 | 1.15 | +| DGCF | 181.66 | 8.06 | 6.59 | +| ConvNCF | 8.46 | 19.60 | 1.31 | +| FISM | 19.30 | 10.92 | 6.94 | +| SpectralCF | 13.87 | 6.97 | 1.19 | + +#### Config file of ml-1m dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +RATING_FIELD: rating +TIME_FIELD: timestamp +LABEL_FIELD: label +NEG_PREFIX: neg_ +load_col: + inter: [user_id, item_id, rating, timestamp] +min_user_inter_num: 0 +min_item_inter_num: 0 + + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +``` + +### 2）Netflix dataset: + +#### Time and memory cost on Netflix dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| ---------- | ----------------: | -----------------: | -----------: | +| Popularity | 3.98 | 58.86 | 0.86 | +| ItemKNN | 5.42 | 69.64 | 0.86 | +| BPRMF | 4.42 | 52.81 | 1.08 | +| NeuMF | 11.33 | 238.92 | 1.26 | +| DMF | 20.62 | 68.89 | 7.12 | +| NAIS | - | - | - | +| NGCF | 52.50 | 51.60 | 2.00 | +| GCMC | 93.15 | - | 3.17 | +| LightGCN | 30.21 | 47.12 | 1.58 | +| DGCF | - | - | - | +| ConvNCF | 17.02 | 402.65 | 1.44 | +| FISM | 86.52 | 83.26 | 20.54 | +| SpectralCF | 59.92 | 46.94 | 1.88 | + +#### Config file of Netflix dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +RATING_FIELD: rating +TIME_FIELD: timestamp +LABEL_FIELD: label +NEG_PREFIX: neg_ +load_col: + inter: [user_id, item_id, rating, timestamp] +min_user_inter_num: 3 +min_item_inter_num: 0 +lowest_val: + timestamp: 1133366400 + rating: 3 +drop_filter_field : True + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +``` + +### 3) Yelp dataset: + +#### Time and memory cost on Yelp dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| ---------- | -----------------: | -----------------: | -----------:| +| Popularity | 5.69 | 134.23 | 0.89 | +| ItemKNN | - | - | - | +| BPRMF | 6.31 | 120.03 | 1.28 | +| NeuMF | 17.38 | 2069.53 | 1.67 | +| DMF | 43.96 | 173.13 | 9.22 | +| NAIS | - | - | - | +| NGCF | 122.90 | 129.59 | 3.28 | +| GCMC | - | - | - | +| LightGCN | 67.91 | 116.16 | 2.02 | +| DGCF | 1542.00 | 119.00 | 17.17 | +| ConvNCF | 87.56 | 11155.31 | 1.62 | +| FISM | - | - | - | +| SpectralCF | 138.99 | 133.37 | 3.10 | + +#### Config file of Yelp dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: business_id +RATING_FIELD: stars +TIME_FIELD: date +LABEL_FIELD: label +NEG_PREFIX: neg_ +load_col: + inter: [user_id, business_id, stars] +min_user_inter_num: 10 +min_item_inter_num: 4 +lowest_val: + stars: 3 +drop_filter_field: True + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +``` + + + + + + + + + + + From 20248a2988da74d35dfe2c839a8ce28501b4cd08 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 01:41:28 +0800 Subject: [PATCH 066/249] Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 310b3129c..1dd7789c0 100644 --- a/README.md +++ b/README.md @@ -157,11 +157,11 @@ python run_recbole.py --model=[model_name] ## Time and memory costs We constructed preliminary experiments to test the time and memory cost on three different-sized datasets (small, medium and large). For detailed information, you can click the following links.
-NOTE: Our test results only gave the approximate time and memory cost of our implementations in the RecBole library (based on our machine server). Any feedback or suggestions about the implementations and test are welcome. We will keep improving our implementations, and update these test results. - * [General recommendation models](asset/time_test_result/General_recommendation.md)
* [Sequential recommendation models](asset/time_test_result/Sequential_recommendation.md)
+NOTE: Our test results only gave the approximate time and memory cost of our implementations in the RecBole library (based on our machine server). Any feedback or suggestions about the implementations and test are welcome. We will keep improving our implementations, and update these test results. + ## RecBole Major Releases | Releases | Date | Features | From c0d1720766e8f6799f8f817df64339508f4d7863 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 10:53:54 +0800 Subject: [PATCH 067/249] Update Sequential_recommendation.md --- asset/time_test_result/Sequential_recommendation.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/asset/time_test_result/Sequential_recommendation.md b/asset/time_test_result/Sequential_recommendation.md index 88f7f250b..0871720e8 100644 --- a/asset/time_test_result/Sequential_recommendation.md +++ b/asset/time_test_result/Sequential_recommendation.md @@ -1,4 +1,4 @@ -## Time (per epoch) and memory cost of sequential recommendation models +## Time and memory cost of sequential recommendation models ### Datasets information: @@ -21,7 +21,7 @@ Machine Specs: 32 CPU machine, 64GB RAM ### 1) ml-1m dataset: -#### Time (per epoch) and memory cost on ml-1m dataset: +#### Time and memory cost on ml-1m dataset: | Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | | ---------------- | -----------------: | -----------------: | -----------: | @@ -85,7 +85,7 @@ load_col: ### 2）DIGINETICA dataset: -#### Time (per epoch) and memory cost on DIGINETICA dataset: +#### Time and memory cost on DIGINETICA dataset: | Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | | ---------------- | -----------------: | -----------------: | -----------: | From c336aff9150bb8d453ba7746373d430106b8f20d Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 10:57:11 +0800 Subject: [PATCH 068/249] Delete test.txt --- asset/time_test_result/test.txt | 1 - 1 file changed, 1 deletion(-) delete mode 100644 asset/time_test_result/test.txt diff --git a/asset/time_test_result/test.txt b/asset/time_test_result/test.txt deleted file mode 100644 index 6bb7e3d1d..000000000 --- a/asset/time_test_result/test.txt +++ /dev/null @@ -1 +0,0 @@ -ssss From 9f4c2c15bcf05736e0115ae8eceb02aa28abf7a4 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 14:50:12 +0800 Subject: [PATCH 069/249] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 1dd7789c0..7950b9115 100644 --- a/README.md +++ b/README.md @@ -154,7 +154,7 @@ python run_recbole.py --model=[model_name] ``` -## Time and memory costs +## Time And Memory Costs We constructed preliminary experiments to test the time and memory cost on three different-sized datasets (small, medium and large). For detailed information, you can click the following links.
* [General recommendation models](asset/time_test_result/General_recommendation.md)
From 4cb25e477892991831199bf535e19a66aad8f6f6 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 14:52:26 +0800 Subject: [PATCH 070/249] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7950b9115..94f18d838 100644 --- a/README.md +++ b/README.md @@ -154,7 +154,7 @@ python run_recbole.py --model=[model_name] ``` -## Time And Memory Costs +## Time and Memory Costs We constructed preliminary experiments to test the time and memory cost on three different-sized datasets (small, medium and large). For detailed information, you can click the following links.
* [General recommendation models](asset/time_test_result/General_recommendation.md)
From acb577ed704dd301d298fbd31c18517a9d733266 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 15:01:13 +0800 Subject: [PATCH 071/249] Update General_recommendation.md --- asset/time_test_result/General_recommendation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md index 5d00de016..c06e57b8c 100644 --- a/asset/time_test_result/General_recommendation.md +++ b/asset/time_test_result/General_recommendation.md @@ -3,7 +3,7 @@ ### Datasets information: | Dataset | #User | #Item | #Interaction | Sparsity | -| ------- | ------- | ------ | ------------ | -------- | +| ------- | -------:| ------: | ------------: | --------: | | ml-1m | 6,041 | 3,707 | 1,000,209 | 0.9553 | | Netflix | 80,476 | 16,821 | 1,977,844 | 0.9985 | | Yelp | 102,046 | 98,408 | 2,903,648 | 0.9997 | From 7e6c79aebf0e395745818c8838bbececd08b4f44 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 15:04:14 +0800 Subject: [PATCH 072/249] Update General_recommendation.md --- asset/time_test_result/General_recommendation.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md index c06e57b8c..1be21d3c4 100644 --- a/asset/time_test_result/General_recommendation.md +++ b/asset/time_test_result/General_recommendation.md @@ -63,6 +63,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` +Note: other parameters in our tests are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. ### 2）Netflix dataset: @@ -111,6 +112,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` +Note: other parameters in our tests are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. ### 3) Yelp dataset: @@ -158,7 +160,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` - +Note: other parameters in our tests are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. From 7bc8102fcad3b76c66b57e65aab9cb838c539b09 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 15:06:58 +0800 Subject: [PATCH 073/249] Update General_recommendation.md --- asset/time_test_result/General_recommendation.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md index 1be21d3c4..7a7469cc6 100644 --- a/asset/time_test_result/General_recommendation.md +++ b/asset/time_test_result/General_recommendation.md @@ -63,7 +63,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -Note: other parameters in our tests are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. +Note: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. ### 2）Netflix dataset: @@ -112,7 +112,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -Note: other parameters in our tests are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. +Note: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. ### 3) Yelp dataset: @@ -160,7 +160,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -Note: other parameters in our tests are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. +Note: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. From d282a988a1ae8cc206c2d48fdb185ae3bda17c1a Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 15:07:53 +0800 Subject: [PATCH 074/249] Update General_recommendation.md --- asset/time_test_result/General_recommendation.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md index 7a7469cc6..a772724c9 100644 --- a/asset/time_test_result/General_recommendation.md +++ b/asset/time_test_result/General_recommendation.md @@ -63,7 +63,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -Note: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. +NOTE: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. ### 2）Netflix dataset: @@ -112,7 +112,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -Note: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. +NOTE: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. ### 3) Yelp dataset: @@ -160,7 +160,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -Note: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. +NOTE: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. From cab7f357e9b0e5f75b9520fbce16072acfc58a14 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 15:09:30 +0800 Subject: [PATCH 075/249] Update General_recommendation.md --- asset/time_test_result/General_recommendation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md index a772724c9..beda1d614 100644 --- a/asset/time_test_result/General_recommendation.md +++ b/asset/time_test_result/General_recommendation.md @@ -63,7 +63,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -NOTE: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. +Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html). ### 2）Netflix dataset: From d2aa9a10b39a561d9c29b81487496306cb49f3d7 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 15:10:46 +0800 Subject: [PATCH 076/249] Update General_recommendation.md --- asset/time_test_result/General_recommendation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md index beda1d614..feea12f71 100644 --- a/asset/time_test_result/General_recommendation.md +++ b/asset/time_test_result/General_recommendation.md @@ -63,7 +63,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html). +Other parameters (including model parameters) are default value. ### 2）Netflix dataset: From 98680050bd4d6b0893251ff119cdf19cb3974aed Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 15:12:22 +0800 Subject: [PATCH 077/249] Update General_recommendation.md --- asset/time_test_result/General_recommendation.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md index feea12f71..5e744c99e 100644 --- a/asset/time_test_result/General_recommendation.md +++ b/asset/time_test_result/General_recommendation.md @@ -112,7 +112,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -NOTE: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. +Other parameters (including model parameters) are default value. ### 3) Yelp dataset: @@ -160,7 +160,7 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -NOTE: Other parameters (including model parameters) are default value, click [here](https://recbole.io/docs/user_guide/config_settings.html) for more information. +Other parameters (including model parameters) are default value. From dd1cff3cd63df2c2785376d6bbe3869b18701ca9 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 26 Nov 2020 15:14:32 +0800 Subject: [PATCH 078/249] Update Sequential_recommendation.md --- asset/time_test_result/Sequential_recommendation.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/asset/time_test_result/Sequential_recommendation.md b/asset/time_test_result/Sequential_recommendation.md index 0871720e8..f158852cc 100644 --- a/asset/time_test_result/Sequential_recommendation.md +++ b/asset/time_test_result/Sequential_recommendation.md @@ -3,7 +3,7 @@ ### Datasets information: | Dataset | #User | #Item | #Interaction | Sparsity | -| ---------- | ------- | ------ | ------------ | -------- | +| ---------- | -------: | ------: | ------------: | --------: | | ml-1m | 6,041 | 3,707 | 1,000,209 | 0.9553 | | DIGINETICA | 59,425 | 42,116 | 547,416 | 0.9998 | | Yelp | 102,046 | 98,408 | 2,903,648 | 0.9997 | @@ -70,6 +70,7 @@ valid_metric: MRR@10 eval_setting: TO_LS,full training_neg_sample_num: 0 ``` +Other parameters (including model parameters) are default value. **NOTE :** @@ -134,6 +135,7 @@ valid_metric: MRR@10 eval_setting: TO_LS,full training_neg_sample_num: 0 ``` +Other parameters (including model parameters) are default value. **NOTE :** From 2e90a21b45f19678a750560def747a4cbd2ea187 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 27 Nov 2020 15:02:51 +0800 Subject: [PATCH 079/249] REFACTOR: refactor in sampler.py (break through) --- recbole/sampler/sampler.py | 81 +++++++++++++++++++++++++++++--------- 1 file changed, 62 insertions(+), 19 deletions(-) diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index 2f4879e53..ce5e7b932 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -13,7 +13,6 @@ ######################## """ -import random import copy import numpy as np @@ -33,7 +32,10 @@ class AbstractSampler(object): used_ids (numpy.ndarray): The result of :meth:`get_used_ids`. """ def __init__(self, distribution): - self.distribution = None + self.distribution = '' + self.random_list = [] + self.random_pr = 0 + self.random_list_length = 0 self.set_distribution(distribution) self.used_ids = self.get_used_ids() @@ -47,7 +49,7 @@ def set_distribution(self, distribution): return self.distribution = distribution self.random_list = self.get_random_list() - random.shuffle(self.random_list) + np.random.shuffle(self.random_list) self.random_pr = 0 self.random_list_length = len(self.random_list) @@ -74,7 +76,21 @@ def random(self): self.random_pr += 1 return value_id - def sample_by_key_ids(self, key_ids, num, used_ids): + def random_num(self, num): + value_id = [] + self.random_pr %= self.random_list_length + while True: + if self.random_pr + num <= self.random_list_length: + value_id.append(self.random_list[self.random_pr: self.random_pr + num]) + self.random_pr += num + break + else: + value_id.append(self.random_list[self.random_pr:]) + num -= self.random_list_length - self.random_pr + self.random_pr = 0 + return np.concatenate(value_id) + + def sample_by_key_ids(self, key_ids, num): """Sampling by key_ids. Args: @@ -89,15 +105,42 @@ def sample_by_key_ids(self, key_ids, num, used_ids): value_ids[1], value_ids[len(key_ids) + 1], value_ids[len(key_ids) * 2 + 1], ..., value_id[len(key_ids) * (num - 1) + 1] is sampled for key_ids[1]; ...; and so on. """ + key_ids = np.array(key_ids) key_num = len(key_ids) total_num = key_num * num - value_ids = np.zeros(total_num, dtype=np.int64) - used_id_list = np.repeat(used_ids, num) - for i, used_ids in enumerate(used_id_list): - cur = self.random() - while cur in used_ids: - cur = self.random() - value_ids[i] = cur + if (key_ids == key_ids[0]).all(): + key_id = key_ids[0] + used = np.array(list(self.used_ids[key_id])) + value_ids = self.random_num(total_num) + check_list = np.arange(total_num)[np.isin(value_ids, used)] + while len(check_list) > 0: + value_ids[check_list] = value = self.random_num(len(check_list)) + perm = value.argsort(kind='quicksort') + aux = value[perm] + mask = np.empty(aux.shape, dtype=np.bool_) + mask[:1] = True + mask[1:] = aux[1:] != aux[:-1] + value = aux[mask] + rev_idx = np.empty(mask.shape, dtype=np.intp) + rev_idx[perm] = np.cumsum(mask) - 1 + ar = np.concatenate((value, used)) + order = ar.argsort(kind='mergesort') + sar = ar[order] + bool_ar = (sar[1:] == sar[:-1]) + flag = np.concatenate((bool_ar, [False])) + ret = np.empty(ar.shape, dtype=bool) + ret[order] = flag + mask = ret[rev_idx] + check_list = check_list[mask] + else: + value_ids = np.zeros(total_num, dtype=np.int64) + check_list = np.arange(total_num) + key_ids = np.tile(key_ids, num) + while len(check_list) > 0: + value_ids[check_list] = self.random_num(len(check_list)) + check_list = np.array([i for i, used, v in + zip(check_list, self.used_ids[key_ids[check_list]], value_ids[check_list]) + if v in used]) return value_ids @@ -140,7 +183,7 @@ def get_random_list(self): np.ndarray or list: Random list of item_id. """ if self.distribution == 'uniform': - return list(range(1, self.n_items)) + return np.arange(1, self.n_items) elif self.distribution == 'popularity': random_item_list = [] for dataset in self.datasets: @@ -196,7 +239,7 @@ def sample_by_user_ids(self, user_ids, num): item_id[len(user_ids) * (num - 1) + 1] is sampled for user_ids[1]; ...; and so on. """ try: - return self.sample_by_key_ids(user_ids, num, self.used_ids[user_ids]) + return self.sample_by_key_ids(user_ids, num) except IndexError: for user_id in user_ids: if user_id < 0 or user_id >= self.n_users: @@ -261,7 +304,7 @@ def sample_by_entity_ids(self, head_entity_ids, num=1): entity_id[len(head_entity_ids) * (num - 1) + 1] is sampled for head_entity_ids[1]; ...; and so on. """ try: - return self.sample_by_key_ids(head_entity_ids, num, self.used_ids[head_entity_ids]) + return self.sample_by_key_ids(head_entity_ids, num) except IndexError: for head_entity_id in head_entity_ids: if head_entity_id not in self.head_entities: @@ -287,8 +330,8 @@ def __init__(self, phases, dataset, distribution='uniform'): self.dataset = dataset self.iid_field = dataset.iid_field - self.user_num = dataset.user_num - self.item_num = dataset.item_num + self.n_users = dataset.user_num + self.n_items = dataset.item_num super().__init__(distribution=distribution) @@ -298,7 +341,7 @@ def get_random_list(self): np.ndarray or list: Random list of item_id. """ if self.distribution == 'uniform': - return list(range(1, self.item_num)) + return np.arange(1, self.n_items) elif self.distribution == 'popularity': return self.dataset.inter_feat[self.iid_field].values else: @@ -310,7 +353,7 @@ def get_used_ids(self): np.ndarray: Used item_ids is the same as positive item_ids. Index is user_id, and element is a set of item_ids. """ - return np.array([set() for i in range(self.user_num)]) + return np.array([set() for i in range(self.n_users)]) def sample_by_user_ids(self, user_ids, num): """Sampling by user_ids. @@ -327,7 +370,7 @@ def sample_by_user_ids(self, user_ids, num): item_id[len(user_ids) * (num - 1) + 1] is sampled for user_ids[1]; ...; and so on. """ try: - return self.sample_by_key_ids(user_ids, num, self.used_ids[user_ids]) + return self.sample_by_key_ids(user_ids, num) except IndexError: for user_id in user_ids: if user_id < 0 or user_id >= self.n_users: From 41ddbb04ce851a49d5589a39f8efb5c01c347dcd Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 27 Nov 2020 15:20:17 +0800 Subject: [PATCH 080/249] REFACTOR: change feat_list to feat_name_list --- recbole/data/dataset/dataset.py | 51 +++++++++++++++----------- recbole/data/dataset/kg_dataset.py | 8 ++-- recbole/data/dataset/social_dataset.py | 8 ++-- 3 files changed, 37 insertions(+), 30 deletions(-) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index 5bfc2b7d8..b6a2f1f54 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -87,7 +87,7 @@ class Dataset(object): item_feat (:class:`pandas.DataFrame` or None): Internal data structure stores the item features. It's loaded from file ``.item`` if existed. - feat_list (list): A list contains all the features (:class:`pandas.DataFrame`), including additional features. + feat_name_list (list): A list contains all the features' name (:class:`str`), including additional features. """ def __init__(self, config, saved_dataset=None): self.config = config @@ -147,7 +147,7 @@ def _data_processing(self): - Normalization - Preloading weights initialization """ - self.feat_list = self._build_feat_list() + self.feat_name_list = self._build_feat_name_list() if self.benchmark_filename_list is None: self._data_filtering() @@ -175,23 +175,24 @@ def _data_filtering(self): self._filter_by_inter_num() self._reset_index() - def _build_feat_list(self): + def _build_feat_name_list(self): """Feat list building. - Any feat loaded by Dataset can be found in ``feat_list`` + Any feat loaded by Dataset can be found in ``feat_name_list`` Returns: - builded feature list. + built feature name list. Note: Subclasses can inherit this method to add new feat. """ - feat_list = [feat for feat in [self.inter_feat, self.user_feat, self.item_feat] if feat is not None] + feat_name_list = [feat_name for feat_name in ['inter_feat', 'user_feat', 'item_feat'] + if getattr(self, feat_name, None) is not None] if self.config['additional_feat_suffix'] is not None: for suf in self.config['additional_feat_suffix']: - if hasattr(self, '{}_feat'.format(suf)): - feat_list.append(getattr(self, '{}_feat'.format(suf))) - return feat_list + if getattr(self, '{}_feat'.format(suf), None) is not None: + feat_name_list.append('{}_feat'.format(suf)) + return feat_name_list def _restore_saved_dataset(self, saved_dataset): """Restore saved dataset from ``saved_dataset``. @@ -310,7 +311,7 @@ def _load_additional_feat(self, token, dataset_path): For those additional features, e.g. pretrained entity embedding, user can set them as ``config['additional_feat_suffix']``, then they will be loaded and stored in - :attr:`feat_list`. See :doc:`../user_guide/data/data_args` for details. + :attr:`feat_name_list`. See :doc:`../user_guide/data/data_args` for details. Args: token (str): dataset name. @@ -445,9 +446,9 @@ def _user_item_feat_preparation(self): flag = True self.logger.debug('ordering item features by user id.') if flag: - # CANNOT be removed - # user/item feat has been updated, thus feat_list should be updated too. - self.feat_list = self._build_feat_list() + # # CANNOT be removed + # # user/item feat has been updated, thus feat_list should be updated too. + # self.feat_name_list = self._build_feat_name_list() self._fill_nan_flag = True def _preload_weight_matrix(self): @@ -476,7 +477,8 @@ def _preload_weight_matrix(self): 'while prelaod value field [{}] is from source [{}], which should be the same'.format( preload_id_field, pid_source, preload_value_field, pv_source )) - for feat in self.feat_list: + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) if preload_id_field in feat: id_ftype = self.field2type[preload_id_field] if id_ftype != FeatureType.TOKEN: @@ -531,7 +533,8 @@ def _fill_nan(self): most_freq = SimpleImputer(missing_values=np.nan, strategy='most_frequent', copy=False) aveg = SimpleImputer(missing_values=np.nan, strategy='mean', copy=False) - for feat in self.feat_list: + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) for field in feat: ftype = self.field2type[field] if ftype == FeatureType.TOKEN: @@ -571,7 +574,8 @@ def _normalize(self): self.logger.debug('Normalized fields: {}'.format(fields)) - for feat in self.feat_list: + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) for field in feat: if field not in fields: continue @@ -721,9 +725,10 @@ def _filter_by_field_value(self): self._del_col(field) def _reset_index(self): - """Reset index for all feats in :attr:`feat_list`. + """Reset index for all feats in :attr:`feat_name_list`. """ - for feat in self.feat_list: + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) if feat.empty: raise ValueError('Some feat is empty, please check the filtering settings.') feat.reset_index(drop=True, inplace=True) @@ -748,7 +753,8 @@ def _drop_by_value(self, val, cmp): raise ValueError('field [{}] not defined in dataset'.format(field)) if self.field2type[field] not in {FeatureType.FLOAT, FeatureType.FLOAT_SEQ}: raise ValueError('field [{}] is not float-like field in dataset, which can\'t be filter'.format(field)) - for feat in self.feat_list: + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) if field in feat: feat.drop(feat.index[cmp(feat[field].values, val[field])], inplace=True) filter_field.append(field) @@ -761,7 +767,8 @@ def _del_col(self, field): field (str): field name to be droped. """ self.logger.debug('delete column [{}]'.format(field)) - for feat in self.feat_list: + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) if field in feat: feat.drop(columns=field, inplace=True) for dct in [self.field2id_token, self.field2seqlen, self.field2source, self.field2type]: @@ -1628,13 +1635,13 @@ def _dict_to_interaction(self, data, *args): elif ftype == FeatureType.FLOAT: data[k] = torch.FloatTensor(data[k]) elif ftype == FeatureType.TOKEN_SEQ: - if isinstance(data[k], np.ndarray): + if isinstance(data[k], np.ndarray) and data[k].dtype == np.int64: data[k] = torch.LongTensor(data[k][:, :self.field2seqlen[k]]) else: seq_data = [torch.LongTensor(d[:self.field2seqlen[k]]) for d in data[k]] data[k] = rnn_utils.pad_sequence(seq_data, batch_first=True) elif ftype == FeatureType.FLOAT_SEQ: - if isinstance(data[k], np.ndarray): + if isinstance(data[k], np.ndarray) and data[k].dtype == np.float64: data[k] = torch.FloatTensor(data[k][:, :self.field2seqlen[k]]) else: seq_data = [torch.FloatTensor(d[:self.field2seqlen[k]]) for d in data[k]] diff --git a/recbole/data/dataset/kg_dataset.py b/recbole/data/dataset/kg_dataset.py index 06855c310..d18e9bf96 100644 --- a/recbole/data/dataset/kg_dataset.py +++ b/recbole/data/dataset/kg_dataset.py @@ -123,11 +123,11 @@ def __str__(self): 'The number of items that have been linked to KG: {}'.format(len(self.item2entity))] return '\n'.join(info) - def _build_feat_list(self): - feat_list = super()._build_feat_list() + def _build_feat_name_list(self): + feat_name_list = super()._build_feat_name_list() if self.kg_feat is not None: - feat_list.append(self.kg_feat) - return feat_list + feat_name_list.append('kg_feat') + return feat_name_list def _restore_saved_dataset(self, saved_dataset): raise NotImplementedError() diff --git a/recbole/data/dataset/social_dataset.py b/recbole/data/dataset/social_dataset.py index 9cf4a93c9..dcf76f536 100644 --- a/recbole/data/dataset/social_dataset.py +++ b/recbole/data/dataset/social_dataset.py @@ -56,11 +56,11 @@ def _load_data(self, token, dataset_path): super()._load_data(token, dataset_path) self.net_feat = self._load_net(self.dataset_name, self.dataset_path) - def _build_feat_list(self): - feat_list = super()._build_feat_list() + def _build_feat_name_list(self): + feat_name_list = super()._build_feat_name_list() if self.net_feat is not None: - feat_list.append(self.net_feat) - return feat_list + feat_name_list.append('net_feat') + return feat_name_list def _load_net(self, dataset_name, dataset_path): net_file_path = os.path.join(dataset_path, '{}.{}'.format(dataset_name, 'net')) From c28f0672d455c5ef1b7236a9e14a1d02acc9755b Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 27 Nov 2020 15:52:45 +0800 Subject: [PATCH 081/249] REFACTOR: remove config['fill_nan'] --- recbole/data/dataset/dataset.py | 22 +++------------------- recbole/properties/dataset/ml-100k.yaml | 1 - recbole/properties/dataset/sample.yaml | 1 - recbole/utils/argument_list.py | 2 +- tests/model/test_model.yaml | 1 - 5 files changed, 4 insertions(+), 23 deletions(-) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index b6a2f1f54..a51f89b12 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -23,7 +23,6 @@ import torch import torch.nn.utils.rnn as rnn_utils from scipy.sparse import coo_matrix -from sklearn.impute import SimpleImputer from recbole.utils import FeatureSource, FeatureType from recbole.data.interaction import Interaction @@ -116,7 +115,6 @@ def _get_preset(self): """Initialization useful inside attributes. """ self.dataset_path = self.config['data_path'] - self._fill_nan_flag = self.config['fill_nan'] self.field2type = {} self.field2source = {} @@ -432,24 +430,16 @@ def _load_feat(self, filepath, source): def _user_item_feat_preparation(self): """Sort :attr:`user_feat` and :attr:`item_feat` by ``user_id`` or ``item_id``. - Missing values will be filled. + Missing values will be filled later. """ - flag = False if self.user_feat is not None: new_user_df = pd.DataFrame({self.uid_field: np.arange(self.user_num)}) self.user_feat = pd.merge(new_user_df, self.user_feat, on=self.uid_field, how='left') - flag = True self.logger.debug('ordering user features by user id.') if self.item_feat is not None: new_item_df = pd.DataFrame({self.iid_field: np.arange(self.item_num)}) self.item_feat = pd.merge(new_item_df, self.item_feat, on=self.iid_field, how='left') - flag = True self.logger.debug('ordering item features by user id.') - if flag: - # # CANNOT be removed - # # user/item feat has been updated, thus feat_list should be updated too. - # self.feat_name_list = self._build_feat_name_list() - self._fill_nan_flag = True def _preload_weight_matrix(self): """Transfer preload weight features into :class:`numpy.ndarray` with shape ``[id_token_length]`` @@ -527,20 +517,14 @@ def _fill_nan(self): """ self.logger.debug('Filling nan') - if not self._fill_nan_flag: - return - - most_freq = SimpleImputer(missing_values=np.nan, strategy='most_frequent', copy=False) - aveg = SimpleImputer(missing_values=np.nan, strategy='mean', copy=False) - for feat_name in self.feat_name_list: feat = getattr(self, feat_name) for field in feat: ftype = self.field2type[field] if ftype == FeatureType.TOKEN: - feat[field] = most_freq.fit_transform(feat[field].values.reshape(-1, 1)) + feat[field].fillna(value=0, inplace=True) elif ftype == FeatureType.FLOAT: - feat[field] = aveg.fit_transform(feat[field].values.reshape(-1, 1)) + feat[field].fillna(value=feat[field].mean(), inplace=True) elif ftype.value.endswith('seq'): feat[field] = feat[field].apply(lambda x: [0] if (not isinstance(x, np.ndarray) and (not isinstance(x, list))) diff --git a/recbole/properties/dataset/ml-100k.yaml b/recbole/properties/dataset/ml-100k.yaml index c513530cd..ca037e507 100644 --- a/recbole/properties/dataset/ml-100k.yaml +++ b/recbole/properties/dataset/ml-100k.yaml @@ -41,7 +41,6 @@ drop_filter_field : False # Preprocessing fields_in_same_space: ~ -fill_nan: True preload_weight: ~ drop_preload_weight: True normalize_field: ~ diff --git a/recbole/properties/dataset/sample.yaml b/recbole/properties/dataset/sample.yaml index 8dcb9cc23..fcba365a7 100644 --- a/recbole/properties/dataset/sample.yaml +++ b/recbole/properties/dataset/sample.yaml @@ -37,7 +37,6 @@ drop_filter_field : True # Preprocessing fields_in_same_space: ~ -fill_nan: True preload_weight: ~ drop_preload_weight: True normalize_field: ~ diff --git a/recbole/utils/argument_list.py b/recbole/utils/argument_list.py index a1af91f4a..8ff322ce3 100644 --- a/recbole/utils/argument_list.py +++ b/recbole/utils/argument_list.py @@ -32,6 +32,6 @@ 'load_col', 'unload_col', 'additional_feat_suffix', 'max_user_inter_num', 'min_user_inter_num', 'max_item_inter_num', 'min_item_inter_num', 'lowest_val', 'highest_val', 'equal_val', 'not_equal_val', 'drop_filter_field', - 'fields_in_same_space', 'fill_nan', + 'fields_in_same_space', 'preload_weight', 'drop_preload_weight', 'normalize_field', 'normalize_all'] diff --git a/tests/model/test_model.yaml b/tests/model/test_model.yaml index 46ffeb24a..ccf032865 100644 --- a/tests/model/test_model.yaml +++ b/tests/model/test_model.yaml @@ -51,7 +51,6 @@ drop_filter_field : False # Preprocessing fields_in_same_space: ~ -fill_nan: True preload_weight: ~ drop_preload_weight: True normalize_field: ~ From 9d6e509f0f48858f2ae32398a1a54f914c34deb0 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Sat, 28 Nov 2020 01:22:57 +0800 Subject: [PATCH 082/249] Add files via upload --- .../Context-aware_recommendation.md | 185 ++++++++++++++++++ .../Knowledge-based_recommendation.md | 163 +++++++++++++++ 2 files changed, 348 insertions(+) create mode 100644 asset/time_test_result/Context-aware_recommendation.md create mode 100644 asset/time_test_result/Knowledge-based_recommendation.md diff --git a/asset/time_test_result/Context-aware_recommendation.md b/asset/time_test_result/Context-aware_recommendation.md new file mode 100644 index 000000000..7a4a3d709 --- /dev/null +++ b/asset/time_test_result/Context-aware_recommendation.md @@ -0,0 +1,185 @@ +## Time and memory cost of context-aware recommendation models + +### Datasets information: + +| Dataset | #Interaction | #Feature Field | #Feature | +| ------- | ------------: | ------------------------------------------------------------ | --------: | +| ml-1m | 1,000,209 | item: [ release_year, genre] user: [ age, gender, occupation] | 134 | +| Criteo | 2,292,530 | inter:[I1-I13, C1-C26] | 2572192 | +| Avazu | 4,218,938 | inter: [ C1, banner_pos, site_id, site_domain, site_category, app_id, app_domain, app_category, device_id, device_ip, device_model, device_type, device_conn_type, C14, C15, C16, C17, C18, C19, C20, C21] | 1326631 | + +### Device information + +``` +OS: Linux +Python Version: 3.8.3 +PyTorch Version: 1.7.0 +cudatoolkit Version: 10.1 +GPU: TITAN RTX（24GB） +Machine Specs: 32 CPU machine, 64GB RAM +``` + +### 1) ml-1m dataset: + +#### Time and memory cost on ml-1m dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| --------- | -----------------: | -----------------: | -----------: | +| LR | 18.34 | 2.18 | 0.82 | +| DIN | 20.37 | 2.26 | 1.16 | +| DSSM | 21.93 | 2.24 | 0.95 | +| FM | 19.33 | 2.34 | 0.83 | +| DeepFM | 20.42 | 2.27 | 0.91 | +| Wide&Deep | 26.13 | 2.95 | 0.89 | +| NFM | 23.36 | 2.26 | 0.89 | +| AFM | 20.08 | 2.26 | 0.92 | +| AutoInt | 22.41 | 2.34 | 0.94 | +| DCN | 28.33 | 2.97 | 0.93 | +| FNN(DNN) | 19.51 | 2.21 | 0.91 | +| PNN | 22.29 | 2.23 | 0.91 | +| FFM | 22.98 | 2.47 | 0.87 | +| FwFM | 23.38 | 2.50 | 0.85 | +| xDeepFM | 24.40 | 2.30 | 1.06 | + +#### Config file of ml-1m dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +LABEL_FIELD: label +threshold: + rating: 4.0 +drop_filter_field : True +load_col: + inter: [user_id, item_id, rating] + item: [item_id, release_year, genre] + user: [user_id, age, gender, occupation] + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +eval_setting: RO_RS +group_by_user: False +valid_metric: AUC +metrics: ['AUC', 'LogLoss'] +``` + +### 2）Criteo dataset: + +#### Time and memory cost on Criteo dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| --------- | -------------------------: | ---------------------------: | ---------------: | +| LR | 7.65 | 0.61 | 1.11 | +| DIN | - | - | - | +| DSSM | - | - | - | +| FM | 9.77 | 0.73 | 1.45 | +| DeepFM | 13.64 | 0.83 | 1.72 | +| Wide&Deep | 13.58 | 0.80 | 1.72 | +| NFM | 13.36 | 0.75 | 1.72 | +| AFM | 19.40 | 1.02 | 2.34 | +| AutoInt | 19.40 | 0.98 | 2.06 | +| DCN | 16.25 | 0.78 | 1.67 | +| FNN(DNN) | 10.03 | 0.64 | 1.63 | +| PNN | 12.92 | 0.72 | 1.85 | +| FFM | - | - | - | +| FwFM | 1175.24 | 8.90 | 2.12 | +| xDeepFM | 32.27 | 1.34 | 2.25 | + +#### Config file of Criteo dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: ~ +ITEM_ID_FIELD: ~ +LABEL_FIELD: label + +load_col: + inter: '*' + +highest_val: + index: 2292530 + +fill_nan: True +normalize_all: True +min_item_inter_num: 0 +min_user_inter_num: 0 + +drop_filter_field : True + + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +eval_setting: RO_RS +group_by_user: False +valid_metric: AUC +metrics: ['AUC', 'LogLoss'] +``` + +### 3）Avazu dataset: + +#### Time and memory cost on Avazu dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| --------- | -------------------------: | ---------------------------: | ---------------: | +| LR | 9.30 | 0.76 | 1.42 | +| DIN | - | - | - | +| DSSM | - | - | - | +| FM | 25.68 | 0.94 | 2.60 | +| DeepFM | 28.41 | 1.19 | 2.66 | +| Wide&Deep | 27.58 | 0.97 | 2.66 | +| NFM | 30.46 | 1.06 | 2.66 | +| AFM | 31.03 | 1.06 | 2.69 | +| AutoInt | 38.11 | 1.41 | 2.84 | +| DCN | 30.78 | 0.96 | 2.64 | +| FNN(DNN) | 23.53 | 0.84 | 2.60 | +| PNN | 25.86 | 0.90 | 2.68 | +| FFM | - | - | - | +| FwFM | 336.75 | 7.49 | 2.63 | +| xDeepFM | 54.88 | 1.45 | 2.89 | + +#### Config file of Avazu dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: ~ +ITEM_ID_FIELD: ~ +LABEL_FIELD: label +fill_nan: True +normalize_all: True + +load_col: + inter: '*' + +lowest_val: + timestamp: 14102931 +drop_filter_field : False + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +eval_setting: RO_RS +group_by_user: False +valid_metric: AUC +metrics: ['AUC', 'LogLoss'] +``` + + + + + + + + + diff --git a/asset/time_test_result/Knowledge-based_recommendation.md b/asset/time_test_result/Knowledge-based_recommendation.md new file mode 100644 index 000000000..04cdc71ad --- /dev/null +++ b/asset/time_test_result/Knowledge-based_recommendation.md @@ -0,0 +1,163 @@ +## Time and memory cost of knowledge-based recommendation models + +### Datasets information: + +| Dataset | #User | #Item | #Interaction | Sparsity | #Entity | #Relation | #Triple | +| ------- | ------: | -------: | ------------: | --------: | ---------: | ---------: | ---------: | +| ml-1m | 6,040 | 3,629 | 836,478 | 0.9618 | 79,388 | 51 | 385,923 | +| ml-10m | 69,864 | 10,599 | 8,242,124 | 0.9889 | 181,941 | 51 | 1,051,385 | +| lfm-1b | 64,536 | 156,343 | 6,544,312 | 0.9994 | 1,751,586 | 10 | 3,054,516 | + +### Device information + +``` +OS: Linux +Python Version: 3.8.3 +PyTorch Version: 1.7.0 +cudatoolkit Version: 10.1 +GPU: TITAN RTX（24GB） +Machine Specs: 32 CPU machine, 64GB RAM +``` + +### 1) ml-1m dataset: + +#### Time and memory cost on ml-1m dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| --------- | -------------------------: | ---------------------------: | ---------------: | +| CKE | 3.76 | 8.73 | 1.16 | +| KTUP | 3.82 | 17.68 | 1.04 | +| RippleNet | 9.39 | 13.13 | 4.57 | +| KGAT | 9.59 | 8.63 | 3.52 | +| KGNN-LS | 4.78 | 15.09 | 1.04 | +| KGCN | 2.25 | 13.71 | 1.04 | +| MKR | 6.25 | 14.89 | 1.29 | +| CFKG | 1.49 | 9.76 | 0.97 | + +#### Config file of ml-1m dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +RATING_FIELD: rating +HEAD_ENTITY_ID_FIELD: head_id +TAIL_ENTITY_ID_FIELD: tail_id +RELATION_ID_FIELD: relation_id +ENTITY_ID_FIELD: entity_id +NEG_PREFIX: neg_ +LABEL_FIELD: label +load_col: + inter: [user_id, item_id, rating] + kg: [head_id, relation_id, tail_id] + link: [item_id, entity_id] +lowest_val: + rating: 3 +drop_filter_field: True + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +``` + +### 2）ml-10m dataset: + +#### Time and memory cost on ml-10m dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| --------- | -------------------------: | ---------------------------: | ---------------: | +| CKE | 8.65 | 85.53 | 1.46 | +| KTUP | 40.71 | 507.56 | 1.43 | +| RippleNet | 32.01 | 152.40 | 4.71 | +| KGAT | 298.22 | 80.94 | 22.44 | +| KGNN-LS | 15.47 | 241.57 | 1.42 | +| KGCN | 7.73 | 244.93 | 1.42 | +| MKR | 61.05 | 383.29 | 1.80 | +| CFKG | 5.99 | 140.74 | 1.35 | + +#### Config file of ml-10m dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: item_id +RATING_FIELD: rating +HEAD_ENTITY_ID_FIELD: head_id +TAIL_ENTITY_ID_FIELD: tail_id +RELATION_ID_FIELD: relation_id +ENTITY_ID_FIELD: entity_id +NEG_PREFIX: neg_ +LABEL_FIELD: label +load_col: + inter: [user_id, item_id, rating] + kg: [head_id, relation_id, tail_id] + link: [item_id, entity_id] +lowest_val: + rating: 3 +drop_filter_field: True + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +``` + +### 3）lfm-1b dataset: + +#### Time and memory cost on lfm-1b dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| --------- | -------------------------: | ---------------------------: | ---------------: | +| CKE | 62.99 | 82.93 | 4.45 | +| KTUP | 91.79 | 3218.69 | 4.36 | +| RippleNet | 126.26 | 188.38 | 6.49 | +| KGAT | 626.07 | 75.70 | 23.28 | +| KGNN-LS | 62.55 | 1709.10 | 4.73 | +| KGCN | 52.54 | 1763.03 | 4.71 | +| MKR | 290.01 | 2341.91 | 6.96 | +| CFKG | 53.35 | 553.58 | 4.22 | + +#### Config file of lfm-1b dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: user_id +ITEM_ID_FIELD: tracks_id +RATING_FIELD: rating +HEAD_ENTITY_ID_FIELD: head_id +TAIL_ENTITY_ID_FIELD: tail_id +RELATION_ID_FIELD: relation_id +ENTITY_ID_FIELD: entity_id +NEG_PREFIX: neg_ +LABEL_FIELD: label +load_col: + inter: [user_id, tracks_id, timestamp] + kg: [head_id, relation_id, tail_id] + link: [tracks_id, entity_id] +lowest_val: + timestamp: 1356969600 + +highest_val: + timestamp: 1362067200 +drop_filter_field: True +min_user_inter_num: 2 +min_item_inter_num: 15 + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +``` + + + From b526c509b3f16822b4b7ac49f8b23949ee207f1a Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Sat, 28 Nov 2020 01:23:27 +0800 Subject: [PATCH 083/249] Update General_recommendation.md --- .../General_recommendation.md | 43 ++++++++----------- 1 file changed, 18 insertions(+), 25 deletions(-) diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md index 5e744c99e..53e0bf1d4 100644 --- a/asset/time_test_result/General_recommendation.md +++ b/asset/time_test_result/General_recommendation.md @@ -3,7 +3,7 @@ ### Datasets information: | Dataset | #User | #Item | #Interaction | Sparsity | -| ------- | -------:| ------: | ------------: | --------: | +| ------- | -------: | ------: | ------------: | --------: | | ml-1m | 6,041 | 3,707 | 1,000,209 | 0.9553 | | Netflix | 80,476 | 16,821 | 1,977,844 | 0.9985 | | Yelp | 102,046 | 98,408 | 2,903,648 | 0.9997 | @@ -63,7 +63,6 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -Other parameters (including model parameters) are default value. ### 2）Netflix dataset: @@ -78,9 +77,9 @@ Other parameters (including model parameters) are default value. | DMF | 20.62 | 68.89 | 7.12 | | NAIS | - | - | - | | NGCF | 52.50 | 51.60 | 2.00 | -| GCMC | 93.15 | - | 3.17 | +| GCMC | 93.15 | 1810.43 | 3.17 | | LightGCN | 30.21 | 47.12 | 1.58 | -| DGCF | - | - | - | +| DGCF | 750.74 | 47.23 | 12.52 | | ConvNCF | 17.02 | 402.65 | 1.44 | | FISM | 86.52 | 83.26 | 20.54 | | SpectralCF | 59.92 | 46.94 | 1.88 | @@ -112,27 +111,26 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -Other parameters (including model parameters) are default value. ### 3) Yelp dataset: #### Time and memory cost on Yelp dataset: -| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | -| ---------- | -----------------: | -----------------: | -----------:| -| Popularity | 5.69 | 134.23 | 0.89 | -| ItemKNN | - | - | - | -| BPRMF | 6.31 | 120.03 | 1.28 | -| NeuMF | 17.38 | 2069.53 | 1.67 | -| DMF | 43.96 | 173.13 | 9.22 | -| NAIS | - | - | - | -| NGCF | 122.90 | 129.59 | 3.28 | -| GCMC | - | - | - | -| LightGCN | 67.91 | 116.16 | 2.02 | -| DGCF | 1542.00 | 119.00 | 17.17 | -| ConvNCF | 87.56 | 11155.31 | 1.62 | -| FISM | - | - | - | -| SpectralCF | 138.99 | 133.37 | 3.10 | +| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | +| ---------- | -------------------------: | -------------------------: | ---------------: | +| Popularity | 5.69 | 134.23 | 0.89 | +| ItemKNN | 8.44 | 194.24 | 0.90 | +| BPRMF | 6.31 | 120.03 | 1.29 | +| NeuMF | 17.38 | 2069.53 | 1.67 | +| DMF | 43.96 | 173.13 | 9.22 | +| NAIS | - | - | - | +| NGCF | 122.90 | 129.59 | 3.28 | +| GCMC | 299.36 | 9833.24 | 5.96 | +| LightGCN | 67.91 | 116.16 | 2.02 | +| DGCF | 1542.00 | 119.00 | 17.17 | +| ConvNCF | 87.56 | 11155.31 | 1.62 | +| FISM | - | - | - | +| SpectralCF | 138.99 | 133.37 | 3.10 | #### Config file of Yelp dataset: @@ -160,11 +158,6 @@ train_batch_size: 2048 eval_batch_size: 2048 valid_metric: MRR@10 ``` -Other parameters (including model parameters) are default value. - - - - From 3d6d8d39e71f50d3f07e5862f8aa1b9deeeef354 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Sat, 28 Nov 2020 01:24:06 +0800 Subject: [PATCH 084/249] Update Sequential_recommendation.md --- .../Sequential_recommendation.md | 68 +++++++++++++++++-- 1 file changed, 63 insertions(+), 5 deletions(-) diff --git a/asset/time_test_result/Sequential_recommendation.md b/asset/time_test_result/Sequential_recommendation.md index f158852cc..ae0af98da 100644 --- a/asset/time_test_result/Sequential_recommendation.md +++ b/asset/time_test_result/Sequential_recommendation.md @@ -23,7 +23,7 @@ Machine Specs: 32 CPU machine, 64GB RAM #### Time and memory cost on ml-1m dataset: -| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | | ---------------- | -----------------: | -----------------: | -----------: | | Improved GRU-Rec | 7.78 | 0.11 | 1.27 | | SASRec | 17.78 | 0.12 | 1.84 | @@ -31,7 +31,7 @@ Machine Specs: 32 CPU machine, 64GB RAM | FPMC | 7.51 | 0.11 | 1.18 | | STAMP | 7.32 | 0.11 | 1.20 | | Caser | 44.85 | 0.12 | 1.14 | -| NextItNet | - | - | - | +| NextItNet | 16433.27 | 96.31 | 1.86 | | TransRec | 10.08 | 0.16 | 8.18 | | S3Rec | - | - | - | | GRU4RecF | 10.20 | 0.15 | 1.80 | @@ -70,7 +70,6 @@ valid_metric: MRR@10 eval_setting: TO_LS,full training_neg_sample_num: 0 ``` -Other parameters (including model parameters) are default value. **NOTE :** @@ -88,7 +87,7 @@ load_col: #### Time and memory cost on DIGINETICA dataset: -| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| Method | Training Time (sec/epoch) | Evaluate Time (sec/epoch) | GPU Memory (GB) | | ---------------- | -----------------: | -----------------: | -----------: | | Improved GRU-Rec | 4.10 | 1.05 | 4.02 | | SASRec | 8.36 | 1.21 | 4.43 | @@ -135,7 +134,6 @@ valid_metric: MRR@10 eval_setting: TO_LS,full training_neg_sample_num: 0 ``` -Other parameters (including model parameters) are default value. **NOTE :** @@ -149,9 +147,69 @@ load_col: item: [item_id, item_category] ``` +### 3）Yelp dataset: + +#### Time and memory cost on Yelp dataset: + +| Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | +| ---------------- | -----------------: | -----------------: | -----------: | +| Improved GRU-Rec | 44.31 | 2.74 | 7.92 | +| SASRec | 75.51 | 3.11 | 8.32 | +| NARM | 45.65 | 2.76 | 7.98 | +| FPMC | 21.05 | 3.05 | 8.22 | +| STAMP | 42.08 | 2.72 | 7.77 | +| Caser | 147.15 | 2.89 | 7.87 | +| NextItNet | 45019.38 | 1670.76 | 8.44 | +| TransRec | - | - | - | +| S3Rec | - | - | - | +| GRU4RecF | - | - | - | +| SASRecF | - | - | - | +| BERT4Rec | 193.74 | 8.43 | 16.57 | +| FDSA | - | - | - | +| SRGNN | 825.11 | 33.20 | 7.90 | +| GCSAN | 837.23 | 33.00 | 8.14 | +| KSR | - | - | - | +| GRU4RecKG | - | - | - | + +#### Config file of DIGINETICA dataset: + +``` +# dataset config +field_separator: "\t" +seq_separator: " " +USER_ID_FIELD: session_id +ITEM_ID_FIELD: item_id +TIME_FIELD: timestamp +NEG_PREFIX: neg_ +ITEM_LIST_LENGTH_FIELD: item_length +LIST_SUFFIX: _list +MAX_ITEM_LIST_LENGTH: 20 +POSITION_FIELD: position_id +load_col: + inter: [session_id, item_id, timestamp] +min_user_inter_num: 6 +min_item_inter_num: 1 + +# training and evaluation +epochs: 500 +train_batch_size: 2048 +eval_batch_size: 2048 +valid_metric: MRR@10 +eval_setting: TO_LS,full +training_neg_sample_num: 0 +``` + +**NOTE :** +1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . +2) For SASRecF, GRU4RecF and FDSA, `load_col` should as below: +``` +load_col: + inter: [session_id, item_id, timestamp] + item: [item_id, item_category] +``` From 9dc63af5b19542b546b313283a6d5ceee55882a5 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Sat, 28 Nov 2020 01:25:11 +0800 Subject: [PATCH 085/249] Update Sequential_recommendation.md --- asset/time_test_result/Sequential_recommendation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/asset/time_test_result/Sequential_recommendation.md b/asset/time_test_result/Sequential_recommendation.md index ae0af98da..5068d96f2 100644 --- a/asset/time_test_result/Sequential_recommendation.md +++ b/asset/time_test_result/Sequential_recommendation.md @@ -208,7 +208,7 @@ training_neg_sample_num: 0 ``` load_col: inter: [session_id, item_id, timestamp] - item: [item_id, item_category] + item: [item_id, item_category] ``` From 7dbf2a472f201e458cc0a1cbe943b9b10e421693 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Sat, 28 Nov 2020 01:25:51 +0800 Subject: [PATCH 086/249] Update Sequential_recommendation.md --- asset/time_test_result/Sequential_recommendation.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/asset/time_test_result/Sequential_recommendation.md b/asset/time_test_result/Sequential_recommendation.md index 5068d96f2..1e5521b3e 100644 --- a/asset/time_test_result/Sequential_recommendation.md +++ b/asset/time_test_result/Sequential_recommendation.md @@ -208,7 +208,7 @@ training_neg_sample_num: 0 ``` load_col: inter: [session_id, item_id, timestamp] - item: [item_id, item_category] + item: [item_id, item_category] ``` From 073746fccb5ad10b8f12357dc47aa2d5cc247a10 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Sat, 28 Nov 2020 01:29:07 +0800 Subject: [PATCH 087/249] Update README.md --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 94f18d838..997aaa072 100644 --- a/README.md +++ b/README.md @@ -159,6 +159,8 @@ We constructed preliminary experiments to test the time and memory cost on three * [General recommendation models](asset/time_test_result/General_recommendation.md)
* [Sequential recommendation models](asset/time_test_result/Sequential_recommendation.md)
+* [Context-aware recommendation models](asset/time_test_result/Context-aware_recommendation.md)
+* [Knowledge-based recommendation models](asset/time_test_result/Knowledge-based_recommendation.md)
NOTE: Our test results only gave the approximate time and memory cost of our implementations in the RecBole library (based on our machine server). Any feedback or suggestions about the implementations and test are welcome. We will keep improving our implementations, and update these test results. From df75c46d52f0f3d9cef8730f4acd349f02becfb7 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Sat, 28 Nov 2020 11:02:22 +0800 Subject: [PATCH 088/249] Add files via upload --- asset/time_test_result/General_recommendation.md | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md index 53e0bf1d4..9fe499aad 100644 --- a/asset/time_test_result/General_recommendation.md +++ b/asset/time_test_result/General_recommendation.md @@ -64,6 +64,8 @@ eval_batch_size: 2048 valid_metric: MRR@10 ``` +Oher parameters (including model parameters) are default value. + ### 2）Netflix dataset: #### Time and memory cost on Netflix dataset: @@ -112,6 +114,8 @@ eval_batch_size: 2048 valid_metric: MRR@10 ``` +Oher parameters (including model parameters) are default value. + ### 3) Yelp dataset: #### Time and memory cost on Yelp dataset: @@ -159,6 +163,11 @@ eval_batch_size: 2048 valid_metric: MRR@10 ``` +Oher parameters (including model parameters) are default value. + + + + From cac0d9adafe62472f0c1a5158aa7656cc4f45b21 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Sat, 28 Nov 2020 12:57:12 +0800 Subject: [PATCH 089/249] Add files via upload --- .../Context-aware_recommendation.md | 16 ++++++++++------ asset/time_test_result/General_recommendation.md | 6 +++--- .../Knowledge-based_recommendation.md | 14 +++++++++----- .../Sequential_recommendation.md | 8 +++++++- 4 files changed, 29 insertions(+), 15 deletions(-) diff --git a/asset/time_test_result/Context-aware_recommendation.md b/asset/time_test_result/Context-aware_recommendation.md index 7a4a3d709..89c3e8926 100644 --- a/asset/time_test_result/Context-aware_recommendation.md +++ b/asset/time_test_result/Context-aware_recommendation.md @@ -2,11 +2,11 @@ ### Datasets information: -| Dataset | #Interaction | #Feature Field | #Feature | -| ------- | ------------: | ------------------------------------------------------------ | --------: | -| ml-1m | 1,000,209 | item: [ release_year, genre] user: [ age, gender, occupation] | 134 | -| Criteo | 2,292,530 | inter:[I1-I13, C1-C26] | 2572192 | -| Avazu | 4,218,938 | inter: [ C1, banner_pos, site_id, site_domain, site_category, app_id, app_domain, app_category, device_id, device_ip, device_model, device_type, device_conn_type, C14, C15, C16, C17, C18, C19, C20, C21] | 1326631 | +| Dataset | #Interaction | #Feature Field | #Feature | +| ------- | ------------: | --------------: | --------: | +| ml-1m | 1,000,209 | 5 | 134 | +| Criteo | 2,292,530 | 39 | 2572192 | +| Avazu | 4,218,938 | 21 | 1326631 | ### Device information @@ -68,6 +68,8 @@ valid_metric: AUC metrics: ['AUC', 'LogLoss'] ``` +Other parameters (including model parameters) are default value. + ### 2）Criteo dataset: #### Time and memory cost on Criteo dataset: @@ -124,6 +126,8 @@ valid_metric: AUC metrics: ['AUC', 'LogLoss'] ``` +Other parameters (including model parameters) are default value. + ### 3）Avazu dataset: #### Time and memory cost on Avazu dataset: @@ -175,7 +179,7 @@ valid_metric: AUC metrics: ['AUC', 'LogLoss'] ``` - +Other parameters (including model parameters) are default value. diff --git a/asset/time_test_result/General_recommendation.md b/asset/time_test_result/General_recommendation.md index 9fe499aad..e88472078 100644 --- a/asset/time_test_result/General_recommendation.md +++ b/asset/time_test_result/General_recommendation.md @@ -64,7 +64,7 @@ eval_batch_size: 2048 valid_metric: MRR@10 ``` -Oher parameters (including model parameters) are default value. +Other parameters (including model parameters) are default value. ### 2）Netflix dataset: @@ -114,7 +114,7 @@ eval_batch_size: 2048 valid_metric: MRR@10 ``` -Oher parameters (including model parameters) are default value. +Other parameters (including model parameters) are default value. ### 3) Yelp dataset: @@ -163,7 +163,7 @@ eval_batch_size: 2048 valid_metric: MRR@10 ``` -Oher parameters (including model parameters) are default value. +Other parameters (including model parameters) are default value. diff --git a/asset/time_test_result/Knowledge-based_recommendation.md b/asset/time_test_result/Knowledge-based_recommendation.md index 04cdc71ad..cb9e80992 100644 --- a/asset/time_test_result/Knowledge-based_recommendation.md +++ b/asset/time_test_result/Knowledge-based_recommendation.md @@ -6,7 +6,7 @@ | ------- | ------: | -------: | ------------: | --------: | ---------: | ---------: | ---------: | | ml-1m | 6,040 | 3,629 | 836,478 | 0.9618 | 79,388 | 51 | 385,923 | | ml-10m | 69,864 | 10,599 | 8,242,124 | 0.9889 | 181,941 | 51 | 1,051,385 | -| lfm-1b | 64,536 | 156,343 | 6,544,312 | 0.9994 | 1,751,586 | 10 | 3,054,516 | +| LFM-1b | 64,536 | 156,343 | 6,544,312 | 0.9994 | 1,751,586 | 10 | 3,054,516 | ### Device information @@ -64,6 +64,8 @@ eval_batch_size: 2048 valid_metric: MRR@10 ``` +Other parameters (including model parameters) are default value. + ### 2）ml-10m dataset: #### Time and memory cost on ml-10m dataset: @@ -109,9 +111,11 @@ eval_batch_size: 2048 valid_metric: MRR@10 ``` -### 3）lfm-1b dataset: +Other parameters (including model parameters) are default value. + +### 3）LFM-1b dataset: -#### Time and memory cost on lfm-1b dataset: +#### Time and memory cost on LFM-1b dataset: | Method | Training Time (sec/epoch) | Evaluation Time (sec/epoch) | GPU Memory (GB) | | --------- | -------------------------: | ---------------------------: | ---------------: | @@ -124,7 +128,7 @@ valid_metric: MRR@10 | MKR | 290.01 | 2341.91 | 6.96 | | CFKG | 53.35 | 553.58 | 4.22 | -#### Config file of lfm-1b dataset: +#### Config file of LFM-1b dataset: ``` # dataset config @@ -159,5 +163,5 @@ eval_batch_size: 2048 valid_metric: MRR@10 ``` - +Other parameters (including model parameters) are default value. diff --git a/asset/time_test_result/Sequential_recommendation.md b/asset/time_test_result/Sequential_recommendation.md index 1e5521b3e..3a0fb4f6c 100644 --- a/asset/time_test_result/Sequential_recommendation.md +++ b/asset/time_test_result/Sequential_recommendation.md @@ -71,6 +71,8 @@ eval_setting: TO_LS,full training_neg_sample_num: 0 ``` +Other parameters (including model parameters) are default value. + **NOTE :** 1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . @@ -135,6 +137,8 @@ eval_setting: TO_LS,full training_neg_sample_num: 0 ``` +Other parameters (including model parameters) are default value. + **NOTE :** 1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . @@ -199,6 +203,8 @@ eval_setting: TO_LS,full training_neg_sample_num: 0 ``` +Other parameters (including model parameters) are default value. + **NOTE :** 1) For FPMC and TransRec model, `training_neg_sample_num` should be `1` . @@ -208,7 +214,7 @@ training_neg_sample_num: 0 ``` load_col: inter: [session_id, item_id, timestamp] - item: [item_id, item_category] + item: [item_id, item_category] ``` From b304c4787f98e3b77e890fe3228ee72cf1209df0 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 28 Nov 2020 14:34:08 +0800 Subject: [PATCH 090/249] FIX: fix in _generate_train_loss_output() --- recbole/trainer/trainer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 1dc7955d6..b64e26cc7 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -218,7 +218,7 @@ def _check_nan(self, loss): def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses): train_loss_output = 'epoch %d training [time: %.2fs, ' % (epoch_idx, e_time - s_time) if isinstance(losses, tuple): - train_loss_output = ', '.join('train_loss%d: %.4f' % (idx + 1, loss) for idx, loss in enumerate(losses)) + train_loss_output += ', '.join('train_loss%d: %.4f' % (idx + 1, loss) for idx, loss in enumerate(losses)) else: train_loss_output += 'train loss: %.4f' % losses return train_loss_output + ']' From 13affda04e08e7048b7086eebd5a9bcf932c0cbe Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Sat, 28 Nov 2020 16:35:48 +0800 Subject: [PATCH 091/249] Add files via upload --- asset/time_test_result/Context-aware_recommendation.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/asset/time_test_result/Context-aware_recommendation.md b/asset/time_test_result/Context-aware_recommendation.md index 89c3e8926..39751b0b4 100644 --- a/asset/time_test_result/Context-aware_recommendation.md +++ b/asset/time_test_result/Context-aware_recommendation.md @@ -5,8 +5,8 @@ | Dataset | #Interaction | #Feature Field | #Feature | | ------- | ------------: | --------------: | --------: | | ml-1m | 1,000,209 | 5 | 134 | -| Criteo | 2,292,530 | 39 | 2572192 | -| Avazu | 4,218,938 | 21 | 1326631 | +| Criteo | 2,292,530 | 39 | 2,572,192 | +| Avazu | 4,218,938 | 21 | 1,326,631 | ### Device information From 03922da139b2a8c758f07f98804e43f8f2003d8b Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 1 Dec 2020 13:21:08 +0800 Subject: [PATCH 092/249] REFACTOR: Change pandas.DataFrame to Interaction when data is processed in Dataset; fixed three bugs in dataloader (neg-item feature in context neg-sample dataloader, time field is float field in sequential dataloader, and kg feat is empty in kg dataloader) --- .../data/dataloader/abstract_dataloader.py | 22 +--- recbole/data/dataloader/general_dataloader.py | 75 +++++------ .../data/dataloader/knowledge_dataloader.py | 81 +++++------- recbole/data/dataloader/neg_sample_mixin.py | 6 +- .../data/dataloader/sequential_dataloader.py | 62 ++++----- recbole/data/dataloader/user_dataloader.py | 6 +- recbole/data/dataset/dataset.py | 124 +++++++++--------- recbole/data/dataset/kg_dataset.py | 20 +-- recbole/data/dataset/sequential_dataset.py | 4 +- recbole/data/interaction.py | 122 ++++++++++++++++- .../knowledge_aware_recommender/kgnnls.py | 6 +- recbole/sampler/sampler.py | 19 +-- 12 files changed, 304 insertions(+), 243 deletions(-) diff --git a/recbole/data/dataloader/abstract_dataloader.py b/recbole/data/dataloader/abstract_dataloader.py index 41b48a9e4..e5464606d 100644 --- a/recbole/data/dataloader/abstract_dataloader.py +++ b/recbole/data/dataloader/abstract_dataloader.py @@ -60,6 +60,8 @@ def __init__(self, config, dataset, self.history_item_matrix = self.dataset.history_item_matrix self.history_user_matrix = self.dataset.history_user_matrix self.inter_matrix = self.dataset.inter_matrix + self.get_user_feature = self.dataset.get_user_feature + self.get_item_feature = self.dataset.get_item_feature for dataset_attr in self.dataset._dataloader_apis: try: @@ -137,23 +139,3 @@ def upgrade_batch_size(self, batch_size): """ if self.batch_size < batch_size: self.set_batch_size(batch_size) - - def get_user_feature(self): - """It is similar to :meth:`~recbole.data.dataset.dataset.Dataset.get_user_feature`, but it will return an - :class:`~recbole.data.interaction.Interaction` of user feature instead of a :class:`pandas.DataFrame`. - - Returns: - Interaction: The interaction of user feature. - """ - user_df = self.dataset.get_user_feature() - return self._dataframe_to_interaction(user_df) - - def get_item_feature(self): - """It is similar to :meth:`~recbole.data.dataset.dataset.Dataset.get_item_feature`, but it will return an - :class:`~recbole.data.interaction.Interaction` of item feature instead of a :class:`pandas.DataFrame`. - - Returns: - Interaction: The interaction of item feature. - """ - item_df = self.dataset.get_item_feature() - return self._dataframe_to_interaction(item_df) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index 7e421aa2e..c6827914b 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -20,6 +20,7 @@ from recbole.data.dataloader.abstract_dataloader import AbstractDataLoader from recbole.data.dataloader.neg_sample_mixin import NegSampleMixin, NegSampleByMixin from recbole.utils import DataLoaderType, InputType +from recbole.data.interaction import Interaction, cat_interactions class GeneralDataLoader(AbstractDataLoader): @@ -50,7 +51,7 @@ def _shuffle(self): def _next_batch_data(self): cur_data = self.dataset[self.pr: self.pr + self.step] self.pr += self.step - return self._dataframe_to_interaction(cur_data) + return cur_data class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): @@ -72,6 +73,8 @@ class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): """ def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): + self.uid_field = dataset.uid_field + self.iid_field = dataset.iid_field self.uid_list, self.uid2index, self.uid2items_num = None, None, None super().__init__(config, dataset, sampler, neg_sample_args, @@ -84,7 +87,7 @@ def setup(self): self.dataset.sort(by=uid_field, ascending=True) self.uid_list = [] start, end = dict(), dict() - for i, uid in enumerate(self.dataset.inter_feat[uid_field].values): + for i, uid in enumerate(self.dataset.inter_feat[uid_field].numpy()): if uid not in start: self.uid_list.append(uid) start[uid] = i @@ -130,50 +133,44 @@ def _shuffle(self): def _next_batch_data(self): if self.user_inter_in_one_batch: - cur_data = [] - for uid in self.uid_list[self.pr: self.pr + self.step]: + uid_list = self.uid_list[self.pr: self.pr + self.step] + data_list = [] + for uid in uid_list: index = self.uid2index[uid] - cur_data.append(self._neg_sampling(self.dataset[index])) - cur_data = pd.concat(cur_data, ignore_index=True) - pos_len_list = self.uid2items_num[self.uid_list[self.pr: self.pr + self.step]] + data_list.append(self._neg_sampling(self.dataset[index])) + cur_data = cat_interactions(data_list) + pos_len_list = self.uid2items_num[uid_list] user_len_list = pos_len_list * self.times + cur_data.set_additional_info(list(pos_len_list), list(user_len_list)) self.pr += self.step - return self._dataframe_to_interaction(cur_data, list(pos_len_list), list(user_len_list)) + return cur_data else: cur_data = self._neg_sampling(self.dataset[self.pr: self.pr + self.step]) self.pr += self.step - return self._dataframe_to_interaction(cur_data) + return cur_data def _neg_sampling(self, inter_feat): - uid_field = self.config['USER_ID_FIELD'] - iid_field = self.config['ITEM_ID_FIELD'] - uids = inter_feat[uid_field].to_list() + uids = inter_feat[self.uid_field] neg_iids = self.sampler.sample_by_user_ids(uids, self.neg_sample_by) - return self.sampling_func(uid_field, iid_field, neg_iids, inter_feat) - - def _neg_sample_by_pair_wise_sampling(self, uid_field, iid_field, neg_iids, inter_feat): - inter_feat = pd.concat([inter_feat] * self.times, ignore_index=True) - inter_feat.insert(len(inter_feat.columns), self.neg_item_id, neg_iids) - - if self.dataset.item_feat is not None: - neg_prefix = self.config['NEG_PREFIX'] - neg_item_feat = self.dataset.item_feat.add_prefix(neg_prefix) - inter_feat = pd.merge(inter_feat, neg_item_feat, - on=self.neg_item_id, how='left', suffixes=('_inter', '_item')) - + return self.sampling_func(inter_feat, neg_iids) + + def _neg_sample_by_pair_wise_sampling(self, inter_feat, neg_iids): + inter_feat.repeat(self.times) + neg_item_feat = Interaction({self.iid_field: neg_iids}) + neg_item_feat = self.dataset.join(neg_item_feat) + neg_item_feat.add_prefix(self.neg_prefix) + inter_feat.update(neg_item_feat) return inter_feat - def _neg_sample_by_point_wise_sampling(self, uid_field, iid_field, neg_iids, inter_feat): + def _neg_sample_by_point_wise_sampling(self, inter_feat, neg_iids): pos_inter_num = len(inter_feat) - - new_df = pd.concat([inter_feat] * self.times, ignore_index=True) - new_df[iid_field].values[pos_inter_num:] = neg_iids - - labels = np.zeros(pos_inter_num * self.times, dtype=np.int64) - labels[: pos_inter_num] = 1 - new_df[self.label_field] = labels - - return new_df + new_data = inter_feat.repeat(self.times) + new_data[self.iid_field][pos_inter_num:] = neg_iids + new_data = self.dataset.join(new_data) + labels = torch.zeros(pos_inter_num * self.times) + labels[: pos_inter_num] = 1.0 + new_data.update(Interaction({self.label_field: labels})) + return new_data def get_pos_len_list(self): """ @@ -225,7 +222,7 @@ def __init__(self, config, dataset, sampler, neg_sample_args, last_uid = None positive_item = None uid2used_item = sampler.used_ids - for uid, iid in dataset.inter_feat[[uid_field, iid_field]].values: + for uid, iid in zip(dataset.inter_feat[uid_field].numpy(), dataset.inter_feat[iid_field].numpy()): if uid != last_uid: if last_uid is not None: self._set_user_property(last_uid, uid2used_item[last_uid], positive_item) @@ -234,7 +231,8 @@ def __init__(self, config, dataset, sampler, neg_sample_args, positive_item = set() positive_item.add(iid) self._set_user_property(last_uid, uid2used_item[last_uid], positive_item) - self.user_df = dataset.join(pd.DataFrame(self.uid_list, columns=[uid_field])) + self.uid_list = torch.tensor(self.uid_list) + self.user_df = dataset.join(Interaction({uid_field: self.uid_list})) super().__init__(config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) @@ -267,8 +265,7 @@ def _next_batch_data(self): return cur_data def _neg_sampling(self, user_df): - uid_list = user_df[self.dataset.uid_field].values - user_interaction = self._dataframe_to_interaction(user_df) + uid_list = list(user_df[self.dataset.uid_field]) history_item = self.uid2history_item[uid_list] history_row = torch.cat([torch.full_like(hist_iid, i) for i, hist_iid in enumerate(history_item)]) @@ -279,7 +276,7 @@ def _neg_sampling(self, user_df): swap_row = torch.cat([torch.full_like(swap, i) for i, swap in enumerate(swap_idx)]) swap_col_after = torch.cat(list(swap_idx)) swap_col_before = torch.cat(list(rev_swap_idx)) - return user_interaction, (history_row, history_col), swap_row, swap_col_after, swap_col_before + return user_df, (history_row, history_col), swap_row, swap_col_after, swap_col_before def get_pos_len_list(self): """ diff --git a/recbole/data/dataloader/knowledge_dataloader.py b/recbole/data/dataloader/knowledge_dataloader.py index a043197ce..0767eb900 100644 --- a/recbole/data/dataloader/knowledge_dataloader.py +++ b/recbole/data/dataloader/knowledge_dataloader.py @@ -14,6 +14,7 @@ from recbole.data.dataloader import AbstractDataLoader, GeneralNegSampleDataLoader from recbole.utils import InputType, KGDataLoaderState +from recbole.data.interaction import Interaction class KGDataLoader(AbstractDataLoader): @@ -63,17 +64,17 @@ def pr_end(self): return len(self.dataset.kg_feat) def _shuffle(self): - self.dataset.kg_feat = self.dataset.kg_feat.sample(frac=1).reset_index(drop=True) + self.dataset.kg_feat.shuffle() def _next_batch_data(self): cur_data = self._neg_sampling(self.dataset.kg_feat[self.pr: self.pr + self.step]) self.pr += self.step - return self._dataframe_to_interaction(cur_data) + return cur_data def _neg_sampling(self, kg_feat): - hids = kg_feat[self.hid_field].to_list() + hids = kg_feat[self.hid_field] neg_tids = self.sampler.sample_by_entity_ids(hids, self.neg_sample_num) - kg_feat.insert(len(kg_feat.columns), self.neg_tid_field, neg_tids) + kg_feat.update(Interaction({self.neg_tid_field: neg_tids})) return kg_feat @@ -122,63 +123,51 @@ def __init__(self, config, dataset, sampler, kg_sampler, neg_sample_args, # using kg_sampler and dl_format is pairwise self.kg_dataloader = KGDataLoader(config, dataset, kg_sampler, - batch_size=batch_size, dl_format=InputType.PAIRWISE, shuffle=shuffle) + batch_size=batch_size, dl_format=InputType.PAIRWISE, shuffle=True) - self.main_dataloader = self.general_dataloader + self.state = None super().__init__(config, dataset, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) - @property - def pr(self): - """Pointer of :class:`KnowledgeBasedDataLoader`. It would be affect by self.state. - """ - return self.main_dataloader.pr - - @pr.setter - def pr(self, value): - self.main_dataloader.pr = value - def __iter__(self): - if not hasattr(self, 'state') or not hasattr(self, 'main_dataloader'): - raise ValueError('The dataloader\'s state and main_dataloader must be set ' - 'when using the kg based dataloader') - return super().__iter__() + if self.state is None: + raise ValueError('The dataloader\'s state must be set when using the kg based dataloader, ' + 'you should call set_mode() before __iter__()') + if self.state == KGDataLoaderState.KG: + return self.kg_dataloader.__iter__() + elif self.state == KGDataLoaderState.RS: + return self.general_dataloader.__iter__() + elif self.state == KGDataLoaderState.RSKG: + self.kg_dataloader.__iter__() + self.general_dataloader.__iter__() + return self def _shuffle(self): - if self.state == KGDataLoaderState.RSKG: - self.general_dataloader._shuffle() - self.kg_dataloader._shuffle() - else: - self.main_dataloader._shuffle() + pass def __next__(self): - if self.pr >= self.pr_end: - if self.state == KGDataLoaderState.RSKG: - self.general_dataloader.pr = 0 - self.kg_dataloader.pr = 0 - else: - self.pr = 0 + if self.general_dataloader.pr >= self.general_dataloader.pr_end: + self.general_dataloader.pr = 0 + self.kg_dataloader.pr = 0 raise StopIteration() return self._next_batch_data() def __len__(self): - return len(self.main_dataloader) + return len(self.general_dataloader) @property def pr_end(self): - return self.main_dataloader.pr_end + return self.general_dataloader.pr_end def _next_batch_data(self): - if self.state == KGDataLoaderState.KG: - return self.kg_dataloader._next_batch_data() - elif self.state == KGDataLoaderState.RS: - return self.general_dataloader._next_batch_data() - elif self.state == KGDataLoaderState.RSKG: - kg_data = self.kg_dataloader._next_batch_data() - rec_data = self.general_dataloader._next_batch_data() - rec_data.update(kg_data) - return rec_data + try: + kg_data = self.kg_dataloader.__next__() + except StopIteration: + kg_data = self.kg_dataloader.__next__() + rec_data = self.general_dataloader.__next__() + rec_data.update(kg_data) + return rec_data def set_mode(self, state): """Set the mode of :class:`KnowledgeBasedDataLoader`, it can be set to three states: @@ -195,11 +184,3 @@ def set_mode(self, state): if state not in set(KGDataLoaderState): raise NotImplementedError('kg data loader has no state named [{}]'.format(self.state)) self.state = state - if self.state == KGDataLoaderState.RS: - self.main_dataloader = self.general_dataloader - elif self.state == KGDataLoaderState.KG: - self.main_dataloader = self.kg_dataloader - else: # RSKG - kgpr = self.kg_dataloader.pr_end - rspr = self.general_dataloader.pr_end - self.main_dataloader = self.general_dataloader if rspr < kgpr else self.kg_dataloader diff --git a/recbole/data/dataloader/neg_sample_mixin.py b/recbole/data/dataloader/neg_sample_mixin.py index 396fb2546..d95ff0465 100644 --- a/recbole/data/dataloader/neg_sample_mixin.py +++ b/recbole/data/dataloader/neg_sample_mixin.py @@ -112,13 +112,13 @@ def __init__(self, config, dataset, sampler, neg_sample_args, self.times = self.neg_sample_by self.sampling_func = self._neg_sample_by_pair_wise_sampling - neg_prefix = config['NEG_PREFIX'] + self.neg_prefix = config['NEG_PREFIX'] iid_field = config['ITEM_ID_FIELD'] - self.neg_item_id = neg_prefix + iid_field + self.neg_item_id = self.neg_prefix + iid_field columns = [iid_field] if dataset.item_feat is None else dataset.item_feat.columns for item_feat_col in columns: - neg_item_feat_col = neg_prefix + item_feat_col + neg_item_feat_col = self.neg_prefix + item_feat_col dataset.copy_field_property(neg_item_feat_col, item_feat_col) else: raise ValueError('`neg sampling by` with dl_format [{}] not been implemented'.format(dl_format)) diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index 37c5cabf2..a57816d58 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -15,6 +15,7 @@ import numpy as np import torch +from recbole.data.interaction import Interaction, cat_interactions from recbole.data.dataloader.abstract_dataloader import AbstractDataLoader from recbole.data.dataloader.neg_sample_mixin import NegSampleByMixin, NegSampleMixin from recbole.utils import DataLoaderType, FeatureSource, FeatureType, InputType @@ -86,19 +87,19 @@ def pr_end(self): return len(self.uid_list) def _shuffle(self): - new_index = np.random.permutation(len(self.item_list_index)) if self.real_time: + new_index = np.random.permutation(len(self.item_list_index)) self.uid_list = self.uid_list[new_index] self.item_list_index = self.item_list_index[new_index] self.target_index = self.target_index[new_index] self.item_list_length = self.item_list_length[new_index] else: - self.pre_processed_data = {key: value[new_index] for key, value in self.pre_processed_data.items()} + self.pre_processed_data.shuffle() def _next_batch_data(self): cur_data = self._get_processed_data(slice(self.pr, self.pr + self.step)) self.pr += self.step - return self._dict_to_interaction(cur_data) + return cur_data def _get_processed_data(self, index): if self.real_time: @@ -107,7 +108,7 @@ def _get_processed_data(self, index): self.target_index[index], self.item_list_length[index]) else: - cur_data = {key: value[index] for key, value in self.pre_processed_data.items()} + cur_data = self.pre_processed_data[index] return cur_data def augmentation(self, uid_list, item_list_index, target_index, item_list_length): @@ -123,26 +124,21 @@ def augmentation(self, uid_list, item_list_index, target_index, item_list_length dict: the augmented data. """ new_length = len(item_list_index) + new_data = self.dataset.inter_feat[target_index] new_dict = { - self.uid_field: uid_list, - self.item_list_field: np.zeros((new_length, self.max_item_list_len), dtype=np.int64), - self.time_list_field: np.zeros((new_length, self.max_item_list_len), dtype=np.int64), - self.target_iid_field: self.dataset.inter_feat[self.iid_field][target_index].values, - self.target_time_field: self.dataset.inter_feat[self.time_field][target_index].values, - self.item_list_length_field: item_list_length, + self.item_list_field: torch.zeros((new_length, self.max_item_list_len), dtype=torch.int64), + self.time_list_field: torch.zeros((new_length, self.max_item_list_len)), + self.item_list_length_field: torch.tensor(item_list_length), } - for field in self.dataset.inter_feat: - if field != self.iid_field and field != self.time_field: - new_dict[field] = self.dataset.inter_feat[field][target_index].values if self.position_field: - new_dict[self.position_field] = np.tile(np.arange(self.max_item_list_len), (new_length, 1)) - - iid_value = self.dataset.inter_feat[self.iid_field].values - time_value = self.dataset.inter_feat[self.time_field].values + new_dict[self.position_field] = torch.arange(self.max_item_list_len).repeat(new_length).view(new_length, -1) + iid_value = self.dataset.inter_feat[self.iid_field] + time_value = self.dataset.inter_feat[self.time_field] for i, (index, length) in enumerate(zip(item_list_index, item_list_length)): new_dict[self.item_list_field][i][:length] = iid_value[index] new_dict[self.time_list_field][i][:length] = time_value[index] - return new_dict + new_data.update(Interaction(new_dict)) + return new_data class SequentialNegSampleDataLoader(NegSampleByMixin, SequentialDataLoader): @@ -182,9 +178,8 @@ def _next_batch_data(self): cur_data_len = len(cur_data[self.uid_field]) pos_len_list = np.ones(cur_data_len // self.times, dtype=np.int64) user_len_list = pos_len_list * self.times - return self._dict_to_interaction(cur_data, list(pos_len_list), list(user_len_list)) - else: - return self._dict_to_interaction(cur_data) + cur_data.set_additional_info(list(pos_len_list), list(user_len_list)) + return cur_data def _neg_sampling(self, data): if self.user_inter_in_one_batch: @@ -193,31 +188,26 @@ def _neg_sampling(self, data): for i in range(data_len): uids = data[self.uid_field][i: i + 1] neg_iids = self.sampler.sample_by_user_ids(uids, self.neg_sample_by) - cur_data = {field: data[field][i: i + 1] for field in data} + cur_data = data[i: i + 1] data_list.append(self.sampling_func(cur_data, neg_iids)) - return {field: np.concatenate([d[field] for d in data_list]) - for field in data} + return cat_interactions(data_list) else: uids = data[self.uid_field] neg_iids = self.sampler.sample_by_user_ids(uids, self.neg_sample_by) return self.sampling_func(data, neg_iids) def _neg_sample_by_pair_wise_sampling(self, data, neg_iids): - new_data = {key: np.concatenate([value] * self.times) for key, value in data.items()} - new_data[self.neg_item_id] = neg_iids + new_data = data.repeat(self.times) + new_data.update(Interaction({self.neg_item_id: neg_iids})) return new_data def _neg_sample_by_point_wise_sampling(self, data, neg_iids): - new_data = {} - for key, value in data.items(): - if key == self.target_iid_field: - new_data[key] = np.concatenate([value, neg_iids]) - else: - new_data[key] = np.concatenate([value] * self.times) - pos_len = len(data[self.target_iid_field]) - total_len = len(new_data[self.target_iid_field]) - new_data[self.label_field] = np.zeros(total_len, dtype=np.int) - new_data[self.label_field][:pos_len] = 1 + pos_inter_num = len(data) + new_data = data.repeat(self.times) + new_data[self.iid_field][pos_inter_num:] = neg_iids + labels = torch.zeros(pos_inter_num * self.times) + labels[: pos_inter_num] = 1.0 + new_data.update(Interaction({self.label_field: labels})) return new_data def get_pos_len_list(self): diff --git a/recbole/data/dataloader/user_dataloader.py b/recbole/data/dataloader/user_dataloader.py index 73d92aa51..9603ce7cb 100644 --- a/recbole/data/dataloader/user_dataloader.py +++ b/recbole/data/dataloader/user_dataloader.py @@ -53,9 +53,9 @@ def pr_end(self): return len(self.dataset.user_feat) def _shuffle(self): - self.dataset.user_feat = self.dataset.user_feat.sample(frac=1).reset_index(drop=True) + self.dataset.user_feat.shuffle() def _next_batch_data(self): - cur_data = self.dataset.user_feat[[self.uid_field]][self.pr: self.pr + self.step] + cur_data = self.dataset.user_feat[self.pr: self.pr + self.step] self.pr += self.step - return self._dataframe_to_interaction(cur_data) + return cur_data diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index a51f89b12..64fe2787a 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -77,13 +77,13 @@ class Dataset(object): time_field (str or None): The same as ``config['TIME_FIELD']``. - inter_feat (:class:`pandas.DataFrame`): Internal data structure stores the interaction features. + inter_feat (:class:`Interaction`): Internal data structure stores the interaction features. It's loaded from file ``.inter``. - user_feat (:class:`pandas.DataFrame` or None): Internal data structure stores the user features. + user_feat (:class:`Interaction` or None): Internal data structure stores the user features. It's loaded from file ``.user`` if existed. - item_feat (:class:`pandas.DataFrame` or None): Internal data structure stores the item features. + item_feat (:class:`Interaction` or None): Internal data structure stores the item features. It's loaded from file ``.item`` if existed. feat_name_list (list): A list contains all the features' name (:class:`str`), including additional features. @@ -110,6 +110,7 @@ def _from_scratch(self): self._get_field_from_config() self._load_data(self.dataset_name, self.dataset_path) self._data_processing() + self._change_feat_format() def _get_preset(self): """Initialization useful inside attributes. @@ -907,6 +908,13 @@ def _remap(self, remap_list): split_point = np.cumsum(feat[field].agg(len))[:-1] feat[field] = np.split(new_ids, split_point) + def _change_feat_format(self): + """Change feat format from :class:`pandas.DataFrame` to :class:`Interaction`. + """ + for feat_name in self.feat_name_list: + feat = getattr(self, feat_name) + setattr(self, feat_name, self._dataframe_to_interaction(feat)) + @dlapi.set() def num(self, field): """Given ``field``, for token-like fields, return the number of different tokens after remapping, @@ -1087,7 +1095,7 @@ def avg_actions_of_users(self): Returns: numpy.float64: Average number of users' interaction records. """ - return np.mean(self.inter_feat.groupby(self.uid_field).size()) + return np.mean(list(Counter(self.inter_feat[self.uid_field]).values())) @property def avg_actions_of_items(self): @@ -1096,7 +1104,7 @@ def avg_actions_of_items(self): Returns: numpy.float64: Average number of items' interaction records. """ - return np.mean(self.inter_feat.groupby(self.iid_field).size()) + return np.mean(list(Counter(self.inter_feat[self.iid_field]).values())) @property def sparsity(self): @@ -1121,15 +1129,15 @@ def join(self, df): """Given interaction feature, join user/item feature into it. Args: - df (pandas.DataFrame): Interaction feature to be joint. + df (Interaction): Interaction feature to be joint. Returns: - pandas.DataFrame: Interaction feature after joining operation. + Interaction: Interaction feature after joining operation. """ if self.user_feat is not None and self.uid_field in df: - df = pd.merge(df, self.user_feat, on=self.uid_field, how='left', suffixes=('_inter', '_user')) + df.update(self.user_feat[df[self.uid_field]]) if self.item_feat is not None and self.iid_field in df: - df = pd.merge(df, self.item_feat, on=self.iid_field, how='left', suffixes=('_inter', '_item')) + df.update(self.item_feat[df[self.iid_field]]) return df def __getitem__(self, index, join=True): @@ -1161,7 +1169,7 @@ def copy(self, new_inter_feat): whose interaction feature is updated with ``new_inter_feat``, and all the other attributes the same. Args: - new_inter_feat (pandas.DataFrame): The new interaction feature need to be updated. + new_inter_feat (Interaction): The new interaction feature need to be updated. Returns: :class:`~Dataset`: the new :class:`~Dataset` object, whose interaction feature has been updated. @@ -1170,6 +1178,15 @@ def copy(self, new_inter_feat): nxt.inter_feat = new_inter_feat return nxt + def _grouped_index(self, group_by_list): + index = {} + for i, key in enumerate(group_by_list): + if key not in index: + index[key] = [i] + else: + index[key].append(i) + return index.values() + def _calcu_split_ids(self, tot, ratios): """Given split ratios, and total number, calculate the number of each part after splitting. @@ -1210,7 +1227,7 @@ def split_by_ratio(self, ratios, group_by=None): split_ids = self._calcu_split_ids(tot=tot_cnt, ratios=ratios) next_index = [range(start, end) for start, end in zip([0] + split_ids, split_ids + [tot_cnt])] else: - grouped_inter_feat_index = self.inter_feat.groupby(by=group_by).groups.values() + grouped_inter_feat_index = self._grouped_index(self.inter_feat[group_by].numpy()) next_index = [[] for i in range(len(ratios))] for grouped_index in grouped_inter_feat_index: tot_cnt = len(grouped_index) @@ -1218,7 +1235,7 @@ def split_by_ratio(self, ratios, group_by=None): for index, start, end in zip(next_index, [0] + split_ids, split_ids + [tot_cnt]): index.extend(grouped_index[start: end]) - next_df = [self.inter_feat.loc[index].reset_index(drop=True) for index in next_index] + next_df = [self.inter_feat[index] for index in next_index] next_ds = [self.copy(_) for _ in next_df] return next_ds @@ -1226,7 +1243,7 @@ def _split_index_by_leave_one_out(self, grouped_index, leave_one_num): """Split indexes by strategy leave one out. Args: - grouped_index (pandas.DataFrameGroupBy): Index to be splitted. + grouped_index (list of list of int): Index to be splitted. leave_one_num (int): Number of parts whose length is expected to be ``1``. Returns: @@ -1259,26 +1276,26 @@ def leave_one_out(self, group_by, leave_one_num=1): if group_by is None: raise ValueError('leave one out strategy require a group field') - grouped_inter_feat_index = self.inter_feat.groupby(by=group_by).groups.values() + grouped_inter_feat_index = self._grouped_index(self.inter_feat[group_by].numpy()) next_index = self._split_index_by_leave_one_out(grouped_inter_feat_index, leave_one_num) - next_df = [self.inter_feat.loc[index].reset_index(drop=True) for index in next_index] + next_df = [self.inter_feat[index] for index in next_index] next_ds = [self.copy(_) for _ in next_df] return next_ds def shuffle(self): """Shuffle the interaction records inplace. """ - self.inter_feat = self.inter_feat.sample(frac=1).reset_index(drop=True) + self.inter_feat.shuffle() def sort(self, by, ascending=True): """Sort the interaction records inplace. Args: - by (str): Field that as the key in the sorting process. - ascending (bool, optional): Results are ascending if ``True``, otherwise descending. + by (str or list of str): Field that as the key in the sorting process. + ascending (bool or list of bool, optional): Results are ascending if ``True``, otherwise descending. Defaults to ``True`` """ - self.inter_feat.sort_values(by=by, ascending=ascending, inplace=True, ignore_index=True) + self.inter_feat.sort(by=by, ascending=ascending) def build(self, eval_setting): """Processing dataset according to evaluation setting, including Group, Order and Split. @@ -1340,22 +1357,22 @@ def save(self, filepath): def get_user_feature(self): """ Returns: - pandas.DataFrame: user features + Interaction: user features """ if self.user_feat is None: self._check_field('uid_field') - return pd.DataFrame({self.uid_field: np.arange(self.user_num)}) + return Interaction({self.uid_field: torch.arange(self.user_num)}) else: return self.user_feat def get_item_feature(self): """ Returns: - pandas.DataFrame: item features + Interaction: item features """ if self.item_feat is None: self._check_field('iid_field') - return pd.DataFrame({self.iid_field: np.arange(self.item_num)}) + return Interaction({self.iid_field: torch.arange(self.item_num)}) else: return self.item_feat @@ -1370,7 +1387,7 @@ def _create_sparse_matrix(self, df_feat, source_field, target_field, form='coo', else ``matrix[src, tgt] = df_feat[value_field][src, tgt]``. Args: - df_feat (pandas.DataFrame): Feature where src and tgt exist. + df_feat (Interaction): Feature where src and tgt exist. source_field (str): Source field target_field (str): Target field form (str, optional): Sparse matrix format. Defaults to ``coo``. @@ -1380,14 +1397,14 @@ def _create_sparse_matrix(self, df_feat, source_field, target_field, form='coo', Returns: scipy.sparse: Sparse matrix in form ``coo`` or ``csr``. """ - src = df_feat[source_field].values - tgt = df_feat[target_field].values + src = df_feat[source_field] + tgt = df_feat[target_field] if value_field is None: data = np.ones(len(df_feat)) else: - if value_field not in df_feat.columns: + if value_field not in df_feat: raise ValueError('value_field [{}] should be one of `df_feat`\'s features.'.format(value_field)) - data = df_feat[value_field].values + data = df_feat[value_field] mat = coo_matrix((data, (src, tgt)), shape=(self.num(source_field), self.num(target_field))) if form == 'coo': @@ -1397,7 +1414,7 @@ def _create_sparse_matrix(self, df_feat, source_field, target_field, form='coo', else: raise NotImplementedError('sparse matrix format [{}] has not been implemented.'.format(form)) - def _create_graph(self, df_feat, source_field, target_field, form='dgl', value_field=None): + def _create_graph(self, tensor_feat, source_field, target_field, form='dgl', value_field=None): """Get graph that describe relations between two fields. Source and target should be token-like fields. @@ -1408,7 +1425,7 @@ def _create_graph(self, df_feat, source_field, target_field, form='dgl', value_f Currently, we support graph in `DGL`_ and `PyG`_. Args: - df_feat (pandas.DataFrame): Feature where src and tgt exist. + tensor_feat (Interaction): Feature where src and tgt exist. source_field (str): Source field target_field (str): Target field form (str, optional): Library of graph data structure. Defaults to ``dgl``. @@ -1424,7 +1441,6 @@ def _create_graph(self, df_feat, source_field, target_field, form='dgl', value_f .. _PyG: https://github.com/rusty1s/pytorch_geometric """ - tensor_feat = self._dataframe_to_interaction(df_feat) src = tensor_feat[source_field] tgt = tensor_feat[target_field] @@ -1490,13 +1506,13 @@ def _history_matrix(self, row, value_field=None): """ self._check_field('uid_field', 'iid_field') - user_ids, item_ids = self.inter_feat[self.uid_field].values, self.inter_feat[self.iid_field].values + user_ids, item_ids = self.inter_feat[self.uid_field].numpy(), self.inter_feat[self.iid_field].numpy() if value_field is None: values = np.ones(len(self.inter_feat)) else: - if value_field not in self.inter_feat.columns: + if value_field not in self.inter_feat: raise ValueError('value_field [{}] should be one of `inter_feat`\'s features.'.format(value_field)) - values = self.inter_feat[value_field].values + values = self.inter_feat[value_field].numpy() if row == 'user': row_num, max_col_num = self.user_num, self.item_num @@ -1589,8 +1605,7 @@ def get_preload_weight(self, field): raise ValueError('field [{}] not in preload_weight'.format(field)) return self._preloaded_weight[field] - @dlapi.set() - def _dataframe_to_interaction(self, data, *args): + def _dataframe_to_interaction(self, data): """Convert :class:`pandas.DataFrame` to :class:`~recbole.data.interaction.Interaction`. Args: @@ -1599,37 +1614,18 @@ def _dataframe_to_interaction(self, data, *args): Returns: :class:`~recbole.data.interaction.Interaction`: Converted data. """ - data = data.to_dict(orient='list') - return self._dict_to_interaction(data, *args) - - @dlapi.set() - def _dict_to_interaction(self, data, *args): - """Convert :class:`dict` to :class:`~recbole.data.interaction.Interaction`. - - Args: - data (dict): data to be converted. - - Returns: - :class:`~recbole.data.interaction.Interaction`: Converted data. - """ + new_data = {} for k in data: + value = data[k].values ftype = self.field2type[k] if ftype == FeatureType.TOKEN: - data[k] = torch.LongTensor(data[k]) + new_data[k] = torch.LongTensor(value) elif ftype == FeatureType.FLOAT: - data[k] = torch.FloatTensor(data[k]) + new_data[k] = torch.FloatTensor(value) elif ftype == FeatureType.TOKEN_SEQ: - if isinstance(data[k], np.ndarray) and data[k].dtype == np.int64: - data[k] = torch.LongTensor(data[k][:, :self.field2seqlen[k]]) - else: - seq_data = [torch.LongTensor(d[:self.field2seqlen[k]]) for d in data[k]] - data[k] = rnn_utils.pad_sequence(seq_data, batch_first=True) + seq_data = [torch.LongTensor(d[:self.field2seqlen[k]]) for d in value] + new_data[k] = rnn_utils.pad_sequence(seq_data, batch_first=True) elif ftype == FeatureType.FLOAT_SEQ: - if isinstance(data[k], np.ndarray) and data[k].dtype == np.float64: - data[k] = torch.FloatTensor(data[k][:, :self.field2seqlen[k]]) - else: - seq_data = [torch.FloatTensor(d[:self.field2seqlen[k]]) for d in data[k]] - data[k] = rnn_utils.pad_sequence(seq_data, batch_first=True) - else: - raise ValueError('Illegal ftype [{}]'.format(ftype)) - return Interaction(data, *args) + seq_data = [torch.FloatTensor(d[:self.field2seqlen[k]]) for d in value] + new_data[k] = rnn_utils.pad_sequence(seq_data, batch_first=True) + return Interaction(new_data) diff --git a/recbole/data/dataset/kg_dataset.py b/recbole/data/dataset/kg_dataset.py index d18e9bf96..ad01d7f38 100644 --- a/recbole/data/dataset/kg_dataset.py +++ b/recbole/data/dataset/kg_dataset.py @@ -382,7 +382,7 @@ def head_entities(self): Returns: numpy.ndarray: List of head entities of kg triplets. """ - return self.kg_feat[self.head_entity_field].values + return self.kg_feat[self.head_entity_field].numpy() @property @dlapi.set() @@ -391,7 +391,7 @@ def tail_entities(self): Returns: numpy.ndarray: List of tail entities of kg triplets. """ - return self.kg_feat[self.tail_entity_field].values + return self.kg_feat[self.tail_entity_field].numpy() @property @dlapi.set() @@ -400,7 +400,7 @@ def relations(self): Returns: numpy.ndarray: List of relations of kg triplets. """ - return self.kg_feat[self.relation_field].values + return self.kg_feat[self.relation_field].numpy() @property @dlapi.set() @@ -447,11 +447,11 @@ def kg_graph(self, form='coo', value_field=None): def _create_ckg_sparse_matrix(self, form='coo', show_relation=False): user_num = self.user_num - hids = self.kg_feat[self.head_entity_field].values + user_num - tids = self.kg_feat[self.tail_entity_field].values + user_num + hids = self.head_entities + user_num + tids = self.tail_entities + user_num - uids = self.inter_feat[self.uid_field].values - iids = self.inter_feat[self.iid_field].values + user_num + uids = self.inter_feat[self.uid_field].numpy() + iids = self.inter_feat[self.iid_field].numpy() + user_num ui_rel_num = len(uids) ui_rel_id = self.relation_num - 1 @@ -463,7 +463,7 @@ def _create_ckg_sparse_matrix(self, form='coo', show_relation=False): if not show_relation: data = np.ones(len(src)) else: - kg_rel = self.kg_feat[self.relation_field].values + kg_rel = self.kg_feat[self.relation_field].numpy() ui_rel = np.full(2 * ui_rel_num, ui_rel_id, dtype=kg_rel.dtype) data = np.concatenate([ui_rel, kg_rel]) node_num = self.entity_num + self.user_num @@ -478,8 +478,8 @@ def _create_ckg_sparse_matrix(self, form='coo', show_relation=False): def _create_ckg_graph(self, form='dgl', show_relation=False): user_num = self.user_num - kg_tensor = self._dataframe_to_interaction(self.kg_feat) - inter_tensor = self._dataframe_to_interaction(self.inter_feat) + kg_tensor = self.kg_feat + inter_tensor = self.inter_feat head_entity = kg_tensor[self.head_entity_field] + user_num tail_entity = kg_tensor[self.tail_entity_field] + user_num diff --git a/recbole/data/dataset/sequential_dataset.py b/recbole/data/dataset/sequential_dataset.py index 4f2af4139..f5a10cdf0 100644 --- a/recbole/data/dataset/sequential_dataset.py +++ b/recbole/data/dataset/sequential_dataset.py @@ -75,7 +75,7 @@ def prepare_data_augmentation(self): last_uid = None uid_list, item_list_index, target_index, item_list_length = [], [], [], [] seq_start = 0 - for i, uid in enumerate(self.inter_feat[self.uid_field].values): + for i, uid in enumerate(self.inter_feat[self.uid_field].numpy()): if last_uid != uid: last_uid = uid seq_start = i @@ -99,7 +99,7 @@ def leave_one_out(self, group_by, leave_one_num=1): raise ValueError('leave one out strategy require a group field') self.prepare_data_augmentation() - grouped_index = pd.DataFrame(self.uid_list).groupby(by=0).groups.values() + grouped_index = self._grouped_index(self.uid_list) next_index = self._split_index_by_leave_one_out(grouped_index, leave_one_num) next_ds = [] for index in next_index: diff --git a/recbole/data/interaction.py b/recbole/data/interaction.py index 1787bd30b..00d1b3abb 100644 --- a/recbole/data/interaction.py +++ b/recbole/data/interaction.py @@ -13,6 +13,7 @@ """ import numpy as np +import torch class Interaction(object): @@ -81,13 +82,20 @@ class Interaction(object): def __init__(self, interaction, pos_len_list=None, user_len_list=None): self.interaction = interaction + self.pos_len_list = self.user_len_list = None + self.set_additional_info(pos_len_list, user_len_list) + for k in self.interaction: + if not isinstance(self.interaction[k], torch.Tensor): + raise ValueError('interaction [{}] should only contains torch.Tensor'.format(interaction)) + self.length = -1 + for k in self.interaction: + self.length = max(self.length, self.interaction[k].shape[0]) + + def set_additional_info(self, pos_len_list=None, user_len_list=None): self.pos_len_list = pos_len_list self.user_len_list = user_len_list if (self.pos_len_list is None) ^ (self.user_len_list is None): raise ValueError('pos_len_list and user_len_list should be both None or valued.') - for k in self.interaction: - self.length = self.interaction[k].shape[0] - break def __iter__(self): return self.interaction.__iter__() @@ -101,13 +109,17 @@ def __getitem__(self, index): ret[k] = self.interaction[k][index] return Interaction(ret) + def __contains__(self, item): + return item in self.interaction + def __len__(self): return self.length def __str__(self): info = ['The batch_size of interaction: {}'.format(self.length)] for k in self.interaction: - temp_str = " {}, {}, {}".format(k, self.interaction[k].shape, self.interaction[k].device.type) + inter = self.interaction[k] + temp_str = " {}, {}, {}, {}".format(k, inter.shape, inter.device.type, inter.dtype) info.append(temp_str) info.append('\n') return '\n'.join(info) @@ -115,6 +127,14 @@ def __str__(self): def __repr__(self): return self.__str__() + @property + def columns(self): + """ + Returns: + list of str: The columns of interaction. + """ + return list(self.interaction.keys()) + def to(self, device, selected_field=None): """Transfer Tensors in this Interaction object to the specified device. @@ -214,8 +234,102 @@ def repeat_interleave(self, repeats, dim=0): def update(self, new_inter): """Similar to ``dict.update()`` + + Args: + new_inter (Interaction): current interaction will be updated by new_inter. """ for k in new_inter.interaction: self.interaction[k] = new_inter.interaction[k] self.pos_len_list = new_inter.pos_len_list self.user_len_list = new_inter.user_len_list + + def _reindex(self, index): + """Reset the index of interaction inplace. + + Args: + index: the new index of current interaction. + """ + for k in self.interaction: + self.interaction[k] = self.interaction[k][index] + if self.pos_len_list is not None: + self.pos_len_list = self.pos_len_list[index] + if self.user_len_list is not None: + self.user_len_list = self.user_len_list[index] + + def shuffle(self): + """Shuffle current interaction inplace. + """ + index = torch.randperm(self.length) + self._reindex(index) + + def sort(self, by, ascending=True): + """Sort the current interaction inplace. + + Args: + by (str or list of str): Field that as the key in the sorting process. + ascending (bool or list of bool, optional): Results are ascending if ``True``, otherwise descending. + Defaults to ``True`` + """ + if isinstance(by, str): + if by not in self.interaction: + raise ValueError('[{}] is not exist in interaction [{}]'.format(by, self)) + by = [by] + elif isinstance(by, (list, tuple)): + for b in by: + if b not in self.interaction: + raise ValueError('[{}] is not exist in interaction [{}]'.format(b, self)) + else: + raise TypeError('wrong type of by [{}]'.format(by)) + + if isinstance(ascending, bool): + ascending = [ascending] + elif isinstance(ascending, (list, tuple)): + for a in ascending: + if not isinstance(a, bool): + raise TypeError('wrong type of ascending [{}]'.format(ascending)) + else: + raise TypeError('wrong type of ascending [{}]'.format(ascending)) + + if len(by) != len(ascending): + if len(ascending) == 1: + ascending = ascending * len(by) + else: + raise ValueError('by [{}] and ascending [{}] should have same length'.format(by, ascending)) + + for b, a in zip(by[::-1], ascending[::-1]): + index = np.argsort(self.interaction[b], kind='stable') + if not a: + index = index[::-1] + self._reindex(index) + + def add_prefix(self, prefix): + """Add prefix to current interaction's columns. + + Args: + prefix (str): The prefix to be added. + """ + self.interaction = {prefix + key: value for key, value in self.interaction.items()} + + +def cat_interactions(interactions): + """Concatenate list of interactions to single interaction. + + Args: + interactions (list of :class:`Interaction`): List of interactions to be concatenated. + + Returns: + :class:`Interaction`: Concatenated interaction. + """ + if not isinstance(interactions, (list, tuple)): + raise TypeError('interactions [{}] should be list or tuple'.format(interactions)) + if len(interactions) == 0: + raise ValueError('interactions [{}] should have some interactions'.format(interactions)) + + columns_set = set(interactions[0].columns) + for inter in interactions: + if columns_set != set(inter.columns): + raise ValueError('interactions [{}] should have some interactions'.format(interactions)) + + new_inter = {col: torch.cat([inter[col] for inter in interactions]) + for col in columns_set} + return Interaction(new_inter) diff --git a/recbole/model/knowledge_aware_recommender/kgnnls.py b/recbole/model/knowledge_aware_recommender/kgnnls.py index 7907cbe7a..08fef392b 100644 --- a/recbole/model/knowledge_aware_recommender/kgnnls.py +++ b/recbole/model/knowledge_aware_recommender/kgnnls.py @@ -62,9 +62,9 @@ def __init__(self, config, dataset): self.adj_entity, self.adj_relation = adj_entity.to( self.device), adj_relation.to(self.device) - inter_feat = dataset.dataset.inter_feat.values - pos_users = torch.from_numpy(inter_feat[:, 0]) - pos_items = torch.from_numpy(inter_feat[:, 1]) + inter_feat = dataset.dataset.inter_feat + pos_users = inter_feat[dataset.dataset.uid_field] + pos_items = inter_feat[dataset.dataset.iid_field] pos_label = torch.ones(pos_items.shape) pos_interaction_table, self.offset = self.get_interaction_table( pos_users, pos_items, pos_label) diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index ce5e7b932..54f1fc37b 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -15,6 +15,7 @@ import copy import numpy as np +import torch class AbstractSampler(object): @@ -99,7 +100,7 @@ def sample_by_key_ids(self, key_ids, num): used_ids (np.ndarray): Used ids. index is key_id, and element is a set of value_ids. Returns: - np.ndarray: Sampled value_ids. + torch.tensor: Sampled value_ids. value_ids[0], value_ids[len(key_ids)], value_ids[len(key_ids) * 2], ..., value_id[len(key_ids) * (num - 1)] is sampled for key_ids[0]; value_ids[1], value_ids[len(key_ids) + 1], value_ids[len(key_ids) * 2 + 1], ..., @@ -141,7 +142,7 @@ def sample_by_key_ids(self, key_ids, num): check_list = np.array([i for i, used, v in zip(check_list, self.used_ids[key_ids[check_list]], value_ids[check_list]) if v in used]) - return value_ids + return torch.tensor(value_ids) class Sampler(AbstractSampler): @@ -187,7 +188,7 @@ def get_random_list(self): elif self.distribution == 'popularity': random_item_list = [] for dataset in self.datasets: - random_item_list.extend(dataset.inter_feat[self.iid_field].values) + random_item_list.extend(dataset.inter_feat[self.iid_field].numpy()) return random_item_list else: raise NotImplementedError('Distribution [{}] has not been implemented'.format(self.distribution)) @@ -202,7 +203,7 @@ def get_used_ids(self): last = [set() for i in range(self.n_users)] for phase, dataset in zip(self.phases, self.datasets): cur = np.array([set(s) for s in last]) - for uid, iid in dataset.inter_feat[[self.uid_field, self.iid_field]].values: + for uid, iid in zip(dataset.inter_feat[self.uid_field].numpy(), dataset.inter_feat[self.iid_field].numpy()): cur[uid].add(iid) last = used_item_id[phase] = cur return used_item_id @@ -232,7 +233,7 @@ def sample_by_user_ids(self, user_ids, num): num (int): Number of sampled item_ids for each user_id. Returns: - np.ndarray: Sampled item_ids. + torch.tensor: Sampled item_ids. item_ids[0], item_ids[len(user_ids)], item_ids[len(user_ids) * 2], ..., item_id[len(user_ids) * (num - 1)] is sampled for user_ids[0]; item_ids[1], item_ids[len(user_ids) + 1], item_ids[len(user_ids) * 2 + 1], ..., @@ -272,7 +273,7 @@ def get_random_list(self): np.ndarray or list: Random list of entity_id. """ if self.distribution == 'uniform': - return list(range(1, self.entity_num)) + return np.arange(1, self.entity_num) elif self.distribution == 'popularity': return list(self.hid_list) + list(self.tid_list) else: @@ -297,7 +298,7 @@ def sample_by_entity_ids(self, head_entity_ids, num=1): num (int, optional): Number of sampled entity_ids for each head_entity_id. Defaults to ``1``. Returns: - np.ndarray: Sampled entity_ids. + torch.tensor: Sampled entity_ids. entity_ids[0], entity_ids[len(head_entity_ids)], entity_ids[len(head_entity_ids) * 2], ..., entity_id[len(head_entity_ids) * (num - 1)] is sampled for head_entity_ids[0]; entity_ids[1], entity_ids[len(head_entity_ids) + 1], entity_ids[len(head_entity_ids) * 2 + 1], ..., @@ -343,7 +344,7 @@ def get_random_list(self): if self.distribution == 'uniform': return np.arange(1, self.n_items) elif self.distribution == 'popularity': - return self.dataset.inter_feat[self.iid_field].values + return self.dataset.inter_feat[self.iid_field].numpy() else: raise NotImplementedError('Distribution [{}] has not been implemented'.format(self.distribution)) @@ -363,7 +364,7 @@ def sample_by_user_ids(self, user_ids, num): num (int): Number of sampled item_ids for each user_id. Returns: - np.ndarray: Sampled item_ids. + torch.tensor: Sampled item_ids. item_ids[0], item_ids[len(user_ids)], item_ids[len(user_ids) * 2], ..., item_id[len(user_ids) * (num - 1)] is sampled for user_ids[0]; item_ids[1], item_ids[len(user_ids) + 1], item_ids[len(user_ids) * 2 + 1], ..., From 4961766081fb6b38d5ca96d270765b627b5050b9 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Thu, 3 Dec 2020 14:00:15 +0800 Subject: [PATCH 093/249] FEA: rebuild the configurator --- recbole/config/configurator.py | 82 ++++++++++--------- .../quick_start_config/context-aware.yaml | 5 ++ .../context-aware_ml-100k.yaml | 5 ++ .../quick_start_config/knowledge_base.yaml | 4 + .../quick_start_config/sequential.yaml | 1 + .../quick_start_config/sequential_DIN.yaml | 3 + .../sequential_DIN_on_ml-100k.yaml | 4 + .../sequential_embedding_model.yaml | 4 + .../special_sequential_on_ml-100k.yaml | 3 + 9 files changed, 73 insertions(+), 38 deletions(-) create mode 100644 recbole/properties/quick_start_config/context-aware.yaml create mode 100644 recbole/properties/quick_start_config/context-aware_ml-100k.yaml create mode 100644 recbole/properties/quick_start_config/knowledge_base.yaml create mode 100644 recbole/properties/quick_start_config/sequential.yaml create mode 100644 recbole/properties/quick_start_config/sequential_DIN.yaml create mode 100644 recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml create mode 100644 recbole/properties/quick_start_config/sequential_embedding_model.yaml create mode 100644 recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml diff --git a/recbole/config/configurator.py b/recbole/config/configurator.py index 381164ae7..fb6784498 100644 --- a/recbole/config/configurator.py +++ b/recbole/config/configurator.py @@ -201,6 +201,15 @@ def _load_internal_config_dict(self, model, model_class, dataset): sample_init_file = os.path.join(current_path, '../properties/dataset/sample.yaml') dataset_init_file = os.path.join(current_path, '../properties/dataset/' + dataset + '.yaml') + context_aware_init = os.path.join(current_path, '../properties/quick_start_config/context-aware.yaml') + context_aware_on_ml_100k_init = os.path.join(current_path, '../properties/quick_start_config/context-aware_ml-100k.yaml') + DIN_init = os.path.join(current_path, '../properties/quick_start_config/sequential_DIN.yaml') + DIN_on_ml_100k_init = os.path.join(current_path, '../properties/quick_start_config/sequential_DIN_on_ml-100k.yaml') + sequential_init = os.path.join(current_path, '../properties/quick_start_config/sequential.yaml') + special_sequential_on_ml_100k_init = os.path.join(current_path, '../properties/quick_start_config/special_sequential_on_ml-100k.yaml') + sequential_embedding_model_init = os.path.join(current_path, '../properties/quick_start_config/sequential_embedding_model.yaml') + knowledge_base_init = os.path.join(current_path, '../properties/quick_start_config/knowledge_base.yaml') + self.internal_config_dict = dict() for file in [overall_init_file, model_init_file, sample_init_file, dataset_init_file]: if os.path.isfile(file): @@ -215,51 +224,48 @@ def _load_internal_config_dict(self, model, model_class, dataset): if self.internal_config_dict['MODEL_TYPE'] == ModelType.GENERAL: pass elif self.internal_config_dict['MODEL_TYPE'] == ModelType.CONTEXT: - self.internal_config_dict.update({ - 'eval_setting': 'RO_RS', - 'group_by_user': False, - 'training_neg_sample_num': 0, - 'metrics': ['AUC', 'LogLoss'], - 'valid_metric': 'AUC', - }) + with open(context_aware_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) if dataset == 'ml-100k': - self.internal_config_dict.update({ - 'threshold': {'rating': 4}, - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'user': ['user_id', 'age', 'gender', 'occupation'], - 'item': ['item_id', 'release_year', 'class']}, - }) - + with open(context_aware_on_ml_100k_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) + elif self.internal_config_dict['MODEL_TYPE'] == ModelType.SEQUENTIAL: if model == 'DIN': - self.internal_config_dict.update({ - 'eval_setting': 'TO_LS, uni100', - 'metrics': ['AUC', 'LogLoss'], - 'valid_metric': 'AUC', - }) + with open(DIN_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) if dataset == 'ml-100k': - self.internal_config_dict.update({ - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'user': ['user_id', 'age', 'gender', 'occupation'], - 'item': ['item_id', 'release_year']}, - }) - + with open(DIN_on_ml_100k_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) + elif model in ['GRU4RecKG','KSR']: + with open(sequential_embedding_model_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) else: - self.internal_config_dict.update({ - 'eval_setting': 'TO_LS,full', - }) + with open(sequential_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) if dataset == 'ml-100k' and model in ['GRU4RecF', 'SASRecF', 'FDSA', 'S3Rec']: - self.internal_config_dict.update({ - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'item': ['item_id', 'release_year', 'class']}, - }) - + with open(special_sequential_on_ml_100k_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) + elif self.internal_config_dict['MODEL_TYPE'] == ModelType.KNOWLEDGE: - self.internal_config_dict.update({ - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'kg': ['head_id', 'relation_id', 'tail_id'], - 'link': ['item_id', 'entity_id']} - }) + with open(knowledge_base_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) def _get_final_config_dict(self): final_config_dict = dict() diff --git a/recbole/properties/quick_start_config/context-aware.yaml b/recbole/properties/quick_start_config/context-aware.yaml new file mode 100644 index 000000000..cdb71f098 --- /dev/null +++ b/recbole/properties/quick_start_config/context-aware.yaml @@ -0,0 +1,5 @@ +eval_setting: RO_RS +group_by_user: False +training_neg_sample_num: 0 +metrics: ['AUC', 'LogLoss'] +valid_metric: AUC \ No newline at end of file diff --git a/recbole/properties/quick_start_config/context-aware_ml-100k.yaml b/recbole/properties/quick_start_config/context-aware_ml-100k.yaml new file mode 100644 index 000000000..150dd4d18 --- /dev/null +++ b/recbole/properties/quick_start_config/context-aware_ml-100k.yaml @@ -0,0 +1,5 @@ +threshold: {'rating': 4} +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + user: ['user_id', 'age', 'gender', 'occupation'] + item: ['item_id', 'release_year', 'class'] \ No newline at end of file diff --git a/recbole/properties/quick_start_config/knowledge_base.yaml b/recbole/properties/quick_start_config/knowledge_base.yaml new file mode 100644 index 000000000..379341326 --- /dev/null +++ b/recbole/properties/quick_start_config/knowledge_base.yaml @@ -0,0 +1,4 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + kg: ['head_id', 'relation_id', 'tail_id'] + link: ['item_id', 'entity_id'] \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential.yaml b/recbole/properties/quick_start_config/sequential.yaml new file mode 100644 index 000000000..87c0fa053 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential.yaml @@ -0,0 +1 @@ +eval_setting: TO_LS,full \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential_DIN.yaml b/recbole/properties/quick_start_config/sequential_DIN.yaml new file mode 100644 index 000000000..58b8db955 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential_DIN.yaml @@ -0,0 +1,3 @@ +eval_setting: TO_LS, uni100 +metrics: ['AUC', 'LogLoss'] +valid_metric: AUC \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml b/recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml new file mode 100644 index 000000000..702a7a862 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml @@ -0,0 +1,4 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + user: ['user_id', 'age', 'gender', 'occupation'] + item: ['item_id', 'release_year'] \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential_embedding_model.yaml b/recbole/properties/quick_start_config/sequential_embedding_model.yaml new file mode 100644 index 000000000..59b920994 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential_embedding_model.yaml @@ -0,0 +1,4 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + ent: ['ent_id', 'ent_emb'] +additional_feat_suffix: ent \ No newline at end of file diff --git a/recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml b/recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml new file mode 100644 index 000000000..1fe509fe6 --- /dev/null +++ b/recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml @@ -0,0 +1,3 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + item: ['item_id', 'release_year', 'class'] \ No newline at end of file From e5aa97b4ffbedb9bc48bb847918f843ac90f7273 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Thu, 3 Dec 2020 14:00:15 +0800 Subject: [PATCH 094/249] FEA: rebuild the configurator --- recbole/config/configurator.py | 82 ++++++++++--------- .../quick_start_config/context-aware.yaml | 5 ++ .../context-aware_ml-100k.yaml | 5 ++ .../quick_start_config/knowledge_base.yaml | 4 + .../quick_start_config/sequential.yaml | 1 + .../quick_start_config/sequential_DIN.yaml | 3 + .../sequential_DIN_on_ml-100k.yaml | 4 + .../sequential_embedding_model.yaml | 4 + .../special_sequential_on_ml-100k.yaml | 3 + 9 files changed, 73 insertions(+), 38 deletions(-) create mode 100644 recbole/properties/quick_start_config/context-aware.yaml create mode 100644 recbole/properties/quick_start_config/context-aware_ml-100k.yaml create mode 100644 recbole/properties/quick_start_config/knowledge_base.yaml create mode 100644 recbole/properties/quick_start_config/sequential.yaml create mode 100644 recbole/properties/quick_start_config/sequential_DIN.yaml create mode 100644 recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml create mode 100644 recbole/properties/quick_start_config/sequential_embedding_model.yaml create mode 100644 recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml diff --git a/recbole/config/configurator.py b/recbole/config/configurator.py index 381164ae7..fb6784498 100644 --- a/recbole/config/configurator.py +++ b/recbole/config/configurator.py @@ -201,6 +201,15 @@ def _load_internal_config_dict(self, model, model_class, dataset): sample_init_file = os.path.join(current_path, '../properties/dataset/sample.yaml') dataset_init_file = os.path.join(current_path, '../properties/dataset/' + dataset + '.yaml') + context_aware_init = os.path.join(current_path, '../properties/quick_start_config/context-aware.yaml') + context_aware_on_ml_100k_init = os.path.join(current_path, '../properties/quick_start_config/context-aware_ml-100k.yaml') + DIN_init = os.path.join(current_path, '../properties/quick_start_config/sequential_DIN.yaml') + DIN_on_ml_100k_init = os.path.join(current_path, '../properties/quick_start_config/sequential_DIN_on_ml-100k.yaml') + sequential_init = os.path.join(current_path, '../properties/quick_start_config/sequential.yaml') + special_sequential_on_ml_100k_init = os.path.join(current_path, '../properties/quick_start_config/special_sequential_on_ml-100k.yaml') + sequential_embedding_model_init = os.path.join(current_path, '../properties/quick_start_config/sequential_embedding_model.yaml') + knowledge_base_init = os.path.join(current_path, '../properties/quick_start_config/knowledge_base.yaml') + self.internal_config_dict = dict() for file in [overall_init_file, model_init_file, sample_init_file, dataset_init_file]: if os.path.isfile(file): @@ -215,51 +224,48 @@ def _load_internal_config_dict(self, model, model_class, dataset): if self.internal_config_dict['MODEL_TYPE'] == ModelType.GENERAL: pass elif self.internal_config_dict['MODEL_TYPE'] == ModelType.CONTEXT: - self.internal_config_dict.update({ - 'eval_setting': 'RO_RS', - 'group_by_user': False, - 'training_neg_sample_num': 0, - 'metrics': ['AUC', 'LogLoss'], - 'valid_metric': 'AUC', - }) + with open(context_aware_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) if dataset == 'ml-100k': - self.internal_config_dict.update({ - 'threshold': {'rating': 4}, - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'user': ['user_id', 'age', 'gender', 'occupation'], - 'item': ['item_id', 'release_year', 'class']}, - }) - + with open(context_aware_on_ml_100k_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) + elif self.internal_config_dict['MODEL_TYPE'] == ModelType.SEQUENTIAL: if model == 'DIN': - self.internal_config_dict.update({ - 'eval_setting': 'TO_LS, uni100', - 'metrics': ['AUC', 'LogLoss'], - 'valid_metric': 'AUC', - }) + with open(DIN_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) if dataset == 'ml-100k': - self.internal_config_dict.update({ - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'user': ['user_id', 'age', 'gender', 'occupation'], - 'item': ['item_id', 'release_year']}, - }) - + with open(DIN_on_ml_100k_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) + elif model in ['GRU4RecKG','KSR']: + with open(sequential_embedding_model_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) else: - self.internal_config_dict.update({ - 'eval_setting': 'TO_LS,full', - }) + with open(sequential_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) if dataset == 'ml-100k' and model in ['GRU4RecF', 'SASRecF', 'FDSA', 'S3Rec']: - self.internal_config_dict.update({ - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'item': ['item_id', 'release_year', 'class']}, - }) - + with open(special_sequential_on_ml_100k_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) + elif self.internal_config_dict['MODEL_TYPE'] == ModelType.KNOWLEDGE: - self.internal_config_dict.update({ - 'load_col': {'inter': ['user_id', 'item_id', 'rating', 'timestamp'], - 'kg': ['head_id', 'relation_id', 'tail_id'], - 'link': ['item_id', 'entity_id']} - }) + with open(knowledge_base_init, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) def _get_final_config_dict(self): final_config_dict = dict() diff --git a/recbole/properties/quick_start_config/context-aware.yaml b/recbole/properties/quick_start_config/context-aware.yaml new file mode 100644 index 000000000..cdb71f098 --- /dev/null +++ b/recbole/properties/quick_start_config/context-aware.yaml @@ -0,0 +1,5 @@ +eval_setting: RO_RS +group_by_user: False +training_neg_sample_num: 0 +metrics: ['AUC', 'LogLoss'] +valid_metric: AUC \ No newline at end of file diff --git a/recbole/properties/quick_start_config/context-aware_ml-100k.yaml b/recbole/properties/quick_start_config/context-aware_ml-100k.yaml new file mode 100644 index 000000000..150dd4d18 --- /dev/null +++ b/recbole/properties/quick_start_config/context-aware_ml-100k.yaml @@ -0,0 +1,5 @@ +threshold: {'rating': 4} +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + user: ['user_id', 'age', 'gender', 'occupation'] + item: ['item_id', 'release_year', 'class'] \ No newline at end of file diff --git a/recbole/properties/quick_start_config/knowledge_base.yaml b/recbole/properties/quick_start_config/knowledge_base.yaml new file mode 100644 index 000000000..379341326 --- /dev/null +++ b/recbole/properties/quick_start_config/knowledge_base.yaml @@ -0,0 +1,4 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + kg: ['head_id', 'relation_id', 'tail_id'] + link: ['item_id', 'entity_id'] \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential.yaml b/recbole/properties/quick_start_config/sequential.yaml new file mode 100644 index 000000000..87c0fa053 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential.yaml @@ -0,0 +1 @@ +eval_setting: TO_LS,full \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential_DIN.yaml b/recbole/properties/quick_start_config/sequential_DIN.yaml new file mode 100644 index 000000000..58b8db955 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential_DIN.yaml @@ -0,0 +1,3 @@ +eval_setting: TO_LS, uni100 +metrics: ['AUC', 'LogLoss'] +valid_metric: AUC \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml b/recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml new file mode 100644 index 000000000..702a7a862 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential_DIN_on_ml-100k.yaml @@ -0,0 +1,4 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + user: ['user_id', 'age', 'gender', 'occupation'] + item: ['item_id', 'release_year'] \ No newline at end of file diff --git a/recbole/properties/quick_start_config/sequential_embedding_model.yaml b/recbole/properties/quick_start_config/sequential_embedding_model.yaml new file mode 100644 index 000000000..59b920994 --- /dev/null +++ b/recbole/properties/quick_start_config/sequential_embedding_model.yaml @@ -0,0 +1,4 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + ent: ['ent_id', 'ent_emb'] +additional_feat_suffix: ent \ No newline at end of file diff --git a/recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml b/recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml new file mode 100644 index 000000000..1fe509fe6 --- /dev/null +++ b/recbole/properties/quick_start_config/special_sequential_on_ml-100k.yaml @@ -0,0 +1,3 @@ +load_col: + inter: ['user_id', 'item_id', 'rating', 'timestamp'] + item: ['item_id', 'release_year', 'class'] \ No newline at end of file From 8089a43da07f29eb05acdabcf05acf2600dd0815 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 6 Dec 2020 10:47:56 +0800 Subject: [PATCH 095/249] REFACTOR: speed up in _filter_by_inter_num --- recbole/data/dataset/dataset.py | 62 ++++++++++++++++++++++----------- 1 file changed, 41 insertions(+), 21 deletions(-) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index 64fe2787a..d05f55edc 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -133,6 +133,10 @@ def _get_field_from_config(self): self.label_field = self.config['LABEL_FIELD'] self.time_field = self.config['TIME_FIELD'] + if (self.uid_field is None) ^ (self.iid_field is None): + raise ValueError('USER_ID_FIELD and ITEM_ID_FIELD need to be set at the same time ' + 'or not set at the same time.') + self.logger.debug('uid_field: {}'.format(self.uid_field)) self.logger.debug('iid_field: {}'.format(self.iid_field)) @@ -514,7 +518,7 @@ def _fill_nan(self): For fields with type :obj:`~recbole.utils.enum_type.FeatureType.FLOAT`, missing value will be filled by the average of original data. - For sequence features, missing value will be filled by ``[0]``. + For sequence features, missing value will be filled by ``[0]``. """ self.logger.debug('Filling nan') @@ -632,16 +636,34 @@ def _filter_by_inter_num(self): Lower bound is also called k-core filtering, which means this method will filter loops until all the users and items has at least k interactions. """ + if self.uid_field is None or self.iid_field is None: + return + + max_user_inter_num = self.config['max_user_inter_num'] + min_user_inter_num = self.config['min_user_inter_num'] + max_item_inter_num = self.config['max_item_inter_num'] + min_item_inter_num = self.config['min_item_inter_num'] + + if max_user_inter_num is None and min_user_inter_num is None: + user_inter_num = Counter() + else: + user_inter_num = Counter(self.inter_feat[self.uid_field].values) + + if max_item_inter_num is None and min_item_inter_num is None: + item_inter_num = Counter() + else: + item_inter_num = Counter(self.inter_feat[self.iid_field].values) + while True: ban_users = self._get_illegal_ids_by_inter_num(field=self.uid_field, feat=self.user_feat, - max_num=self.config['max_user_inter_num'], - min_num=self.config['min_user_inter_num']) + inter_num=user_inter_num, + max_num=max_user_inter_num, min_num=min_user_inter_num) ban_items = self._get_illegal_ids_by_inter_num(field=self.iid_field, feat=self.item_feat, - max_num=self.config['max_item_inter_num'], - min_num=self.config['min_item_inter_num']) + inter_num=item_inter_num, + max_num=max_item_inter_num, min_num=min_item_inter_num) if len(ban_users) == 0 and len(ban_items) == 0: - return + break if self.user_feat is not None: dropped_user = self.user_feat[self.uid_field].isin(ban_users) @@ -652,14 +674,19 @@ def _filter_by_inter_num(self): self.item_feat.drop(self.item_feat.index[dropped_item], inplace=True) dropped_inter = pd.Series(False, index=self.inter_feat.index) - if self.uid_field: - dropped_inter |= self.inter_feat[self.uid_field].isin(ban_users) - if self.iid_field: - dropped_inter |= self.inter_feat[self.iid_field].isin(ban_items) - self.logger.debug('[{}] dropped interactions'.format(len(dropped_inter))) - self.inter_feat.drop(self.inter_feat.index[dropped_inter], inplace=True) - - def _get_illegal_ids_by_inter_num(self, field, feat, max_num=None, min_num=None): + user_inter = self.inter_feat[self.uid_field] + item_inter = self.inter_feat[self.iid_field] + dropped_inter |= user_inter.isin(ban_users) + dropped_inter |= item_inter.isin(ban_items) + + user_inter_num -= Counter(user_inter[dropped_inter].values) + item_inter_num -= Counter(item_inter[dropped_inter].values) + + dropped_index = self.inter_feat.index[dropped_inter] + self.logger.debug('[{}] dropped interactions'.format(len(dropped_index))) + self.inter_feat.drop(dropped_index, inplace=True) + + def _get_illegal_ids_by_inter_num(self, field, feat, inter_num, max_num=None, min_num=None): """Given inter feat, return illegal ids, whose inter num out of [min_num, max_num] Args: @@ -675,16 +702,9 @@ def _get_illegal_ids_by_inter_num(self, field, feat, max_num=None, min_num=None) field, max_num, min_num )) - if field is None: - return set() - if max_num is None and min_num is None: - return set() - max_num = max_num or np.inf min_num = min_num or -1 - ids = self.inter_feat[field].values - inter_num = Counter(ids) ids = {id_ for id_ in inter_num if inter_num[id_] < min_num or inter_num[id_] > max_num} if feat is not None: From 856b9fd0c56d24269adc15911a15424924ffc13e Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 6 Dec 2020 16:29:38 +0800 Subject: [PATCH 096/249] DOC: fix docs in _get_illegal_ids_by_inter_num --- recbole/data/dataset/dataset.py | 1 + 1 file changed, 1 insertion(+) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index d05f55edc..f5b53d1fd 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -692,6 +692,7 @@ def _get_illegal_ids_by_inter_num(self, field, feat, inter_num, max_num=None, mi Args: field (str): field name of user_id or item_id. feat (pandas.DataFrame): interaction feature. + inter_num (Counter): interaction number counter. max_num (int, optional): max number of interaction. Defaults to ``None``. min_num (int, optional): min number of interaction. Defaults to ``None``. From ff2c1876a9a57351382828ca1246646ee01aff42 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 6 Dec 2020 20:40:29 +0800 Subject: [PATCH 097/249] REFACTOR: refactor in sequential_dataloader and fix a bug in data_preprocess --- .../data/dataloader/sequential_dataloader.py | 69 ++++++++++--------- 1 file changed, 37 insertions(+), 32 deletions(-) diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index a57816d58..48d46be14 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -51,22 +51,25 @@ def __init__(self, config, dataset, self.max_item_list_len = config['MAX_ITEM_LIST_LENGTH'] list_suffix = config['LIST_SUFFIX'] - self.item_list_field = self.iid_field + list_suffix - self.time_list_field = self.time_field + list_suffix - self.position_field = config['POSITION_FIELD'] - self.target_iid_field = self.iid_field - self.target_time_field = self.time_field - self.item_list_length_field = config['ITEM_LIST_LENGTH_FIELD'] + for field in dataset.inter_feat: + if field != self.uid_field: + list_field = field + list_suffix + setattr(self, f'{field}_list_field', list_field) + ftype = dataset.field2type[field] + + if ftype in [FeatureType.TOKEN, FeatureType.TOKEN_SEQ]: + list_ftype = FeatureType.TOKEN_SEQ + else: + list_ftype = FeatureType.FLOAT_SEQ + + if ftype in [FeatureType.TOKEN_SEQ, FeatureType.FLOAT_SEQ]: + list_len = (self.max_item_list_len, dataset.field2seqlen[field]) + else: + list_len = self.max_item_list_len + + dataset.set_field_property(list_field, list_ftype, FeatureSource.INTERACTION, list_len) - dataset.set_field_property(self.item_list_field, FeatureType.TOKEN_SEQ, FeatureSource.INTERACTION, - self.max_item_list_len) - dataset.set_field_property(self.time_list_field, FeatureType.FLOAT_SEQ, FeatureSource.INTERACTION, - self.max_item_list_len) - if self.position_field: - dataset.set_field_property(self.position_field, FeatureType.TOKEN_SEQ, FeatureSource.INTERACTION, - self.max_item_list_len) - dataset.set_field_property(self.target_iid_field, FeatureType.TOKEN, FeatureSource.INTERACTION, 1) - dataset.set_field_property(self.target_time_field, FeatureType.FLOAT, FeatureSource.INTERACTION, 1) + self.item_list_length_field = config['ITEM_LIST_LENGTH_FIELD'] dataset.set_field_property(self.item_list_length_field, FeatureType.TOKEN, FeatureSource.INTERACTION, 1) self.uid_list, self.item_list_index, self.target_index, self.item_list_length = \ @@ -79,8 +82,7 @@ def __init__(self, config, dataset, def data_preprocess(self): """Do data augmentation before training/evaluation. """ - self.pre_processed_data = self.augmentation(self.uid_list, self.item_list_field, - self.target_index, self.item_list_length) + self.pre_processed_data = self.augmentation(self.item_list_index, self.target_index, self.item_list_length) @property def pr_end(self): @@ -103,19 +105,17 @@ def _next_batch_data(self): def _get_processed_data(self, index): if self.real_time: - cur_data = self.augmentation(self.uid_list[index], - self.item_list_index[index], + cur_data = self.augmentation(self.item_list_index[index], self.target_index[index], self.item_list_length[index]) else: cur_data = self.pre_processed_data[index] return cur_data - def augmentation(self, uid_list, item_list_index, target_index, item_list_length): + def augmentation(self, item_list_index, target_index, item_list_length): """Data augmentation. Args: - uid_list (np.ndarray): user id list. item_list_index (np.ndarray): the index of history items list in interaction. target_index (np.ndarray): the index of items to be predicted in interaction. item_list_length (np.ndarray): history list length. @@ -126,17 +126,22 @@ def augmentation(self, uid_list, item_list_index, target_index, item_list_length new_length = len(item_list_index) new_data = self.dataset.inter_feat[target_index] new_dict = { - self.item_list_field: torch.zeros((new_length, self.max_item_list_len), dtype=torch.int64), - self.time_list_field: torch.zeros((new_length, self.max_item_list_len)), self.item_list_length_field: torch.tensor(item_list_length), } - if self.position_field: - new_dict[self.position_field] = torch.arange(self.max_item_list_len).repeat(new_length).view(new_length, -1) - iid_value = self.dataset.inter_feat[self.iid_field] - time_value = self.dataset.inter_feat[self.time_field] - for i, (index, length) in enumerate(zip(item_list_index, item_list_length)): - new_dict[self.item_list_field][i][:length] = iid_value[index] - new_dict[self.time_list_field][i][:length] = time_value[index] + + for field in self.dataset.inter_feat: + if field != self.uid_field: + list_field = getattr(self, f'{field}_list_field') + list_len = self.dataset.field2seqlen[list_field] + shape = (new_length, list_len) if isinstance(list_len, int) else (new_length, ) + list_len + list_ftype = self.dataset.field2type[list_field] + dtype = torch.int64 if list_ftype in [FeatureType.TOKEN, FeatureType.TOKEN_SEQ] else torch.float64 + new_dict[list_field] = torch.zeros(shape, dtype=dtype) + + value = self.dataset.inter_feat[field] + for i, (index, length) in enumerate(zip(item_list_index, item_list_length)): + new_dict[list_field][i][:length] = value[index] + new_data.update(Interaction(new_dict)) return new_data @@ -261,7 +266,7 @@ def _next_batch_data(self): inter_num = len(interaction) scores_row = torch.arange(inter_num).repeat(2) padding_idx = torch.zeros(inter_num, dtype=torch.int64) - positive_idx = interaction[self.target_iid_field] + positive_idx = interaction[self.iid_field] scores_col_after = torch.cat((padding_idx, positive_idx)) scores_col_before = torch.cat((positive_idx, padding_idx)) return interaction, None, scores_row, scores_col_after, scores_col_before @@ -278,4 +283,4 @@ def get_user_len_list(self): Returns: np.ndarray: Number of all item for each user in a training/evaluating epoch. """ - return np.full(len(self.uid_list), self.item_num) + return np.full(self.pr_end, self.item_num) From ced640ff68a6cf20471017f8e4e672f4e937c8ac Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Sun, 6 Dec 2020 20:56:46 +0800 Subject: [PATCH 098/249] FEA: add conda_release.sh --- conda/conda_release.sh | 8 ++++++++ recbole/__init__.py | 2 +- setup.py | 2 +- 3 files changed, 10 insertions(+), 2 deletions(-) create mode 100644 conda/conda_release.sh diff --git a/conda/conda_release.sh b/conda/conda_release.sh new file mode 100644 index 000000000..a8b0e3efa --- /dev/null +++ b/conda/conda_release.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +conda-build --python 3.6 . +printf "python 3.6 version is released \n" +conda-build --python 3.7 . +printf "python 3.7 version is released \n" +conda-build --python 3.8 . +printf "python 3.8 version is released \n" diff --git a/recbole/__init__.py b/recbole/__init__.py index 182dd10a5..83c915e76 100644 --- a/recbole/__init__.py +++ b/recbole/__init__.py @@ -2,4 +2,4 @@ from __future__ import print_function from __future__ import division -__version__ = '0.1.1' +__version__ = '0.1.2' diff --git a/setup.py b/setup.py index a8c0a358a..46c74cab9 100644 --- a/setup.py +++ b/setup.py @@ -36,7 +36,7 @@ setup( name='recbole', version= - '0.1.1', # please remember to edit recbole/__init__.py in response, once updating the version + '0.1.2', # please remember to edit recbole/__init__.py in response, once updating the version description='A unified, comprehensive and efficient recommendation library', long_description=long_description, long_description_content_type="text/markdown", From eca9a3a2bf753991b90e2a4cd5e7df60f7449d06 Mon Sep 17 00:00:00 2001 From: flust <254170321@qq.com> Date: Mon, 7 Dec 2020 11:31:36 +0000 Subject: [PATCH 099/249] FEA: xgboost --- recbole/data/dataloader/__init__.py | 1 + recbole/data/dataloader/xgboost_dataloader.py | 38 ++++ recbole/data/utils.py | 7 + recbole/model/exlib_recommender/xgboost.py | 26 +++ recbole/properties/model/xgboost.yaml | 46 +++++ recbole/trainer/trainer.py | 166 +++++++++++++++++- recbole/utils/enum_type.py | 1 - recbole/utils/utils.py | 3 +- requirements.txt | 3 +- 9 files changed, 285 insertions(+), 6 deletions(-) create mode 100644 recbole/data/dataloader/xgboost_dataloader.py create mode 100644 recbole/model/exlib_recommender/xgboost.py create mode 100644 recbole/properties/model/xgboost.yaml diff --git a/recbole/data/dataloader/__init__.py b/recbole/data/dataloader/__init__.py index 18e6f0674..c224d85a6 100644 --- a/recbole/data/dataloader/__init__.py +++ b/recbole/data/dataloader/__init__.py @@ -4,3 +4,4 @@ from recbole.data.dataloader.context_dataloader import * from recbole.data.dataloader.sequential_dataloader import * from recbole.data.dataloader.knowledge_dataloader import * +from recbole.data.dataloader.xgboost_dataloader import * \ No newline at end of file diff --git a/recbole/data/dataloader/xgboost_dataloader.py b/recbole/data/dataloader/xgboost_dataloader.py new file mode 100644 index 000000000..dbe897934 --- /dev/null +++ b/recbole/data/dataloader/xgboost_dataloader.py @@ -0,0 +1,38 @@ +# @Time : 2020/11/19 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +# UPDATE: +# @Time : 2020/11/19 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +""" +recbole.data.dataloader.xgboost_dataloader +################################################ +""" + +from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader, GeneralFullDataLoader + + +class XgboostDataLoader(GeneralDataLoader): + """:class:`XgboostDataLoader` is inherit from :class:`~recbole.data.dataloader.general_dataloader.GeneralDataLoader`, + and didn't add/change anything at all. + """ + pass + + +class XgboostNegSampleDataLoader(GeneralNegSampleDataLoader): + """:class:`XgboostNegSampleDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralNegSampleDataLoader`, + and didn't add/change anything at all. + """ + pass + + +class XgboostFullDataLoader(GeneralFullDataLoader): + """:class:`XgboostFullDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralFullDataLoader`, + and didn't add/change anything at all. + """ + pass diff --git a/recbole/data/utils.py b/recbole/data/utils.py index accf76dfc..f6e649daf 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -264,6 +264,13 @@ def get_data_loader(name, config, eval_setting): return SequentialNegSampleDataLoader elif neg_sample_strategy == 'full': return SequentialFullDataLoader + elif model_type == ModelType.XGBOOST: + if neg_sample_strategy == 'none': + return XgboostDataLoader + elif neg_sample_strategy == 'by': + return XgboostNegSampleDataLoader + elif neg_sample_strategy == 'full': + return XgboostFullDataLoader elif model_type == ModelType.KNOWLEDGE: if neg_sample_strategy == 'by': if name == 'train': diff --git a/recbole/model/exlib_recommender/xgboost.py b/recbole/model/exlib_recommender/xgboost.py new file mode 100644 index 000000000..345953e38 --- /dev/null +++ b/recbole/model/exlib_recommender/xgboost.py @@ -0,0 +1,26 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/19 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +r""" +recbole.model.exlib_recommender.xgboost +############################# +""" + +import xgboost as xgb +from recbole.utils import ModelType, InputType, FeatureSource, FeatureType + + +class xgboost(xgb.Booster): + r"""xgboost is inherited from xgb.Booster + + """ + type = ModelType.CONTEXT + input_type = InputType.POINTWISE + + def __init__(self, config, dataset): + super().__init__(params=None, cache=(), model_file=None) + + def to(self, device): + return self diff --git a/recbole/properties/model/xgboost.yaml b/recbole/properties/model/xgboost.yaml new file mode 100644 index 000000000..3ad1e041f --- /dev/null +++ b/recbole/properties/model/xgboost.yaml @@ -0,0 +1,46 @@ +# Type of training method +train_or_cv: train + +# DMatrix + +xgb_weight: ~ +xgb_base_margin: ~ +xgb_missing: ~ +xgb_silent: ~ +xgb_feature_names: ~ +xgb_feature_types: ~ +xgb_nthread: ~ + +# train or cv +xgb_model: ~ +xgb_params: + booster: gbtree + objective: binary:logistic + gamma: 0.1 + max_depth: 10 + lambda: 3 + subsample: 0.5 + colsample_bytree: 0.7 + min_child_weight: 3 + eta: 0.1 + seed: 100 + nthread: 4 +xgb_num_boost_round: 10 +# xgb_evals: ~ +xgb_obj: ~ +xgb_feval: ~ +xgb_maximize: ~ +xgb_early_stopping_rounds: ~ +# xgb_evals_result: ~ +xgb_verbose_eval: False + +# cv +xgb_cv_nfold: 3 +xgb_cv_stratified: False +xgb_cv_folds: ~ +xgb_cv_fpreproc: ~ +xgb_cv_show_stdv: True +xgb_cv_seed: 0 +xgb_cv_shuffle: True + + diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 1dc7955d6..b7dbdc9a3 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -3,9 +3,9 @@ # @Email : slmu@ruc.edu.cn # UPDATE: -# @Time : 2020/8/7, 2020/9/26, 2020/9/26, 2020/10/01, 2020/9/16, 2020/10/8, 2020/10/15 -# @Author : Zihan Lin, Yupeng Hou, Yushuo Chen, Shanlei Mu, Xingyu Pan, Hui Wang, Xinyan Fan -# @Email : linzihan.super@foxmail.com, houyupeng@ruc.edu.cn, chenyushuo@ruc.edu.cn, slmu@ruc.edu.cn, panxy@ruc.edu.cn, hui.wang@ruc.edu.cn, xinyan.fan@ruc.edu.cn +# @Time : 2020/8/7, 2020/9/26, 2020/9/26, 2020/10/01, 2020/9/16, 2020/10/8, 2020/10/15, 2020/11/20 +# @Author : Zihan Lin, Yupeng Hou, Yushuo Chen, Shanlei Mu, Xingyu Pan, Hui Wang, Xinyan Fan, Chen Yang +# @Email : linzihan.super@foxmail.com, houyupeng@ruc.edu.cn, chenyushuo@ruc.edu.cn, slmu@ruc.edu.cn, panxy@ruc.edu.cn, hui.wang@ruc.edu.cn, xinyan.fan@ruc.edu.cn, 254170321@qq.com r""" recbole.trainer.trainer @@ -19,6 +19,7 @@ from torch.nn.utils.clip_grad import clip_grad_norm_ import numpy as np import matplotlib.pyplot as plt +import xgboost as xgb from time import time from logging import getLogger @@ -545,3 +546,162 @@ class TraditionalTrainer(Trainer): def __init__(self, config, model): super(TraditionalTrainer, self).__init__(config, model) self.epochs = 1 # Set the epoch to 1 when running memory based model + + +class xgboostTrainer(AbstractTrainer): + """xgboostTrainer is designed for XGBOOST. + + """ + def __init__(self, config, model): + super(xgboostTrainer, self).__init__(config, model) + + self.logger = getLogger() + self.label_field = config['LABEL_FIELD'] + + self.train_or_cv = config['train_or_cv'] + self.xgb_model = config['xgb_model'] + + # DMatrix params + self.weight = config['xgb_weight'] + self.base_margin = config['xgb_base_margin'] + self.missing = config['xgb_missing'] + self.silent = config['xgb_silent'] + self.feature_names = config['xgb_feature_names'] + self.feature_types = config['xgb_feature_types'] + self.nthread = config['xgb_nthread'] + + # train params + self.params = config['xgb_params'] + self.num_boost_round = config['xgb_num_boost_round'] + self.evals = () + self.obj = config['xgb_obj'] + self.feval = config['xgb_feval'] + self.maximize = config['xgb_maximize'] + self.early_stopping_rounds = config['xgb_early_stopping_rounds'] + self.evals_result = {} + self.verbose_eval = config['xgb_verbose_eval'] + self.callbacks = None + + # cv params + if self.train_or_cv == 'cv': + self.nfold = config['xgb_cv_nfold'] + self.stratified = config['xgb_cv_stratified'] + self.folds = config['xgb_cv_folds'] + self.fpreproc = config['xgb_cv_freproc'] + self.show_stdv = config['xgb_cv_show_stdv'] + self.seed = config['xgb_cv_seed'] + self.shuffle = config['xgb_cv_shuffle'] + + # evaluator + self.eval_type = config['eval_type'] + self.epochs = config['epochs'] + self.eval_step = min(config['eval_step'], self.epochs) + self.valid_metric = config['valid_metric'].lower() + + if self.eval_type == EvaluatorType.INDIVIDUAL: + self.evaluator = LossEvaluator(config) + else: + self.evaluator = TopKEvaluator(config) + + # model saved + self.checkpoint_dir = config['checkpoint_dir'] + ensure_dir(self.checkpoint_dir) + saved_model_file = '{}-{}.pth'.format(self.config['model'], get_local_time()) + self.saved_model_file = os.path.join(self.checkpoint_dir, saved_model_file) + + def _interaction_to_DMatrix(self, interaction): + r"""Convert data format from interaction to DMatrix + + Args: + interaction (Interaction): Data in the form of 'Interaction'. + Returns: + DMatrix: Data in the form of 'DMatrix'. + """ + interaction_np = interaction.numpy() + cur_data = np.array([]) + for key, value in interaction_np.items(): + value = np.resize(value,(value.shape[0],1)) + if key != self.label_field: + if cur_data.shape[0] == 0: + cur_data = value + else: + cur_data = np.hstack((cur_data, value)) + + return xgb.DMatrix(data = cur_data, + label = interaction_np[self.label_field], + weight = self.weight, + base_margin = self.base_margin, + missing = self.missing, + silent = self.silent, + feature_names = self.feature_names, + feature_types = self.feature_types, + nthread = self.nthread) + + def _train_epoch(self, train_data, valid_data): + r""" + + Args: + train_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. + valid_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. + """ + for _, train_interaction in enumerate(train_data): + self.dtrain = self._interaction_to_DMatrix(train_interaction) + self.evals = [(self.dtrain,'train')] + self.model = xgb.train(self.params, self.dtrain, 1, + self.evals, self.obj, self.feval, self.maximize, + self.early_stopping_rounds, self.evals_result, + self.verbose_eval, self.xgb_model, self.callbacks) + + self.model.save_model(self.saved_model_file) + self.xgb_model = self.saved_model_file + + def _valid_epoch(self, valid_data): + r""" + + Args: + valid_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. + """ + valid_result = self.evaluate(valid_data) + valid_score = calculate_valid_score(valid_result, self.valid_metric) + return valid_result, valid_score + + def fit(self, train_data, valid_data=None, verbose=True, saved=True): + self.best_valid_score = 0. + self.best_valid_result = 0. + if self.train_or_cv == 'train': + for epoch_idx in range(self.epochs): + train_loss = self._train_epoch(train_data, valid_data) + + if (epoch_idx + 1) % self.eval_step == 0: + # evaluate + valid_start_time = time() + valid_result, valid_score = self._valid_epoch(valid_data) + valid_end_time = time() + valid_score_output = "epoch %d evaluating [time: %.2fs, valid_score: %f]" % \ + (epoch_idx, valid_end_time - valid_start_time, valid_score) + valid_result_output = 'valid result: \n' + dict2str(valid_result) + if verbose: + self.logger.info(valid_score_output) + self.logger.info(valid_result_output) + + self.best_valid_score = valid_score + self.best_valid_result = valid_result + + return self.best_valid_score, self.best_valid_result + + def evaluate(self, eval_data, load_best_model=True, model_file=None): + self.eval_pred = torch.Tensor() + self.eval_true = torch.Tensor() + + for _, batched_data in enumerate(eval_data): + batched_data_DMatrix = self._interaction_to_DMatrix(batched_data) + batch_pred = torch.Tensor(self.model.predict(batched_data_DMatrix)) + if self.params['objective'] == 'binary:logistic': + batch_pred = (batch_pred >= 0.5) * 1 + self.eval_pred = torch.cat((self.eval_pred, batch_pred)) + self.eval_true = torch.cat((self.eval_true, batched_data[self.label_field])) + + matrix_list = [torch.stack((self.eval_pred, self.eval_true), 1)] + + result = self.evaluator.evaluate(matrix_list, eval_data) + return result diff --git a/recbole/utils/enum_type.py b/recbole/utils/enum_type.py index 62a11ee7c..ee63f341e 100644 --- a/recbole/utils/enum_type.py +++ b/recbole/utils/enum_type.py @@ -27,7 +27,6 @@ class ModelType(Enum): SOCIAL = 5 TRADITIONAL = 6 - class DataLoaderType(Enum): """Type of DataLoaders. diff --git a/recbole/utils/utils.py b/recbole/utils/utils.py index 3a44d1fba..d47ea0fae 100644 --- a/recbole/utils/utils.py +++ b/recbole/utils/utils.py @@ -53,7 +53,8 @@ def get_model(model_name): 'general_recommender', 'context_aware_recommender', 'sequential_recommender', - 'knowledge_aware_recommender' + 'knowledge_aware_recommender', + 'exlib_recommender' ] model_file_name = model_name.lower() diff --git a/requirements.txt b/requirements.txt index 7f4bff1bf..2b254e9ec 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,5 @@ hyperopt>=0.2.4 pandas>=1.0.5 tqdm>=4.48.2 scikit_learn>=0.23.2 -pyyaml>=5.1.0 \ No newline at end of file +pyyaml>=5.1.0 +xgboost>=1.2.1 \ No newline at end of file From 9aa3d47eda54236649bf3adcb0e0f2ee558ab6da Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Sun, 6 Dec 2020 20:56:46 +0800 Subject: [PATCH 100/249] FEA: add conda_release.sh --- conda/conda_release.sh | 8 ++++++++ recbole/__init__.py | 2 +- setup.py | 2 +- 3 files changed, 10 insertions(+), 2 deletions(-) create mode 100644 conda/conda_release.sh diff --git a/conda/conda_release.sh b/conda/conda_release.sh new file mode 100644 index 000000000..a8b0e3efa --- /dev/null +++ b/conda/conda_release.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +conda-build --python 3.6 . +printf "python 3.6 version is released \n" +conda-build --python 3.7 . +printf "python 3.7 version is released \n" +conda-build --python 3.8 . +printf "python 3.8 version is released \n" diff --git a/recbole/__init__.py b/recbole/__init__.py index 182dd10a5..83c915e76 100644 --- a/recbole/__init__.py +++ b/recbole/__init__.py @@ -2,4 +2,4 @@ from __future__ import print_function from __future__ import division -__version__ = '0.1.1' +__version__ = '0.1.2' diff --git a/setup.py b/setup.py index a8c0a358a..46c74cab9 100644 --- a/setup.py +++ b/setup.py @@ -36,7 +36,7 @@ setup( name='recbole', version= - '0.1.1', # please remember to edit recbole/__init__.py in response, once updating the version + '0.1.2', # please remember to edit recbole/__init__.py in response, once updating the version description='A unified, comprehensive and efficient recommendation library', long_description=long_description, long_description_content_type="text/markdown", From 6b21eff378ec07e557d39a0fd7e3e5ed2bf13ad7 Mon Sep 17 00:00:00 2001 From: flust <254170321@qq.com> Date: Mon, 7 Dec 2020 12:46:46 +0000 Subject: [PATCH 101/249] FEA: xgboost --- recbole/utils/enum_type.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/recbole/utils/enum_type.py b/recbole/utils/enum_type.py index ee63f341e..84e15b812 100644 --- a/recbole/utils/enum_type.py +++ b/recbole/utils/enum_type.py @@ -26,6 +26,8 @@ class ModelType(Enum): KNOWLEDGE = 4 SOCIAL = 5 TRADITIONAL = 6 + XGBOOST = 7 + class DataLoaderType(Enum): """Type of DataLoaders. From 3bc2964c525d19f406e7fed8595d80a94672e805 Mon Sep 17 00:00:00 2001 From: flust <254170321@qq.com> Date: Mon, 7 Dec 2020 11:31:36 +0000 Subject: [PATCH 102/249] FEA: xgboost --- recbole/data/dataloader/__init__.py | 1 + recbole/data/dataloader/xgboost_dataloader.py | 38 ++++ recbole/data/utils.py | 7 + recbole/model/exlib_recommender/xgboost.py | 26 +++ recbole/properties/model/xgboost.yaml | 46 +++++ recbole/trainer/trainer.py | 166 +++++++++++++++++- recbole/utils/enum_type.py | 1 - recbole/utils/utils.py | 3 +- requirements.txt | 3 +- 9 files changed, 285 insertions(+), 6 deletions(-) create mode 100644 recbole/data/dataloader/xgboost_dataloader.py create mode 100644 recbole/model/exlib_recommender/xgboost.py create mode 100644 recbole/properties/model/xgboost.yaml diff --git a/recbole/data/dataloader/__init__.py b/recbole/data/dataloader/__init__.py index 18e6f0674..c224d85a6 100644 --- a/recbole/data/dataloader/__init__.py +++ b/recbole/data/dataloader/__init__.py @@ -4,3 +4,4 @@ from recbole.data.dataloader.context_dataloader import * from recbole.data.dataloader.sequential_dataloader import * from recbole.data.dataloader.knowledge_dataloader import * +from recbole.data.dataloader.xgboost_dataloader import * \ No newline at end of file diff --git a/recbole/data/dataloader/xgboost_dataloader.py b/recbole/data/dataloader/xgboost_dataloader.py new file mode 100644 index 000000000..dbe897934 --- /dev/null +++ b/recbole/data/dataloader/xgboost_dataloader.py @@ -0,0 +1,38 @@ +# @Time : 2020/11/19 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +# UPDATE: +# @Time : 2020/11/19 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +""" +recbole.data.dataloader.xgboost_dataloader +################################################ +""" + +from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader, GeneralFullDataLoader + + +class XgboostDataLoader(GeneralDataLoader): + """:class:`XgboostDataLoader` is inherit from :class:`~recbole.data.dataloader.general_dataloader.GeneralDataLoader`, + and didn't add/change anything at all. + """ + pass + + +class XgboostNegSampleDataLoader(GeneralNegSampleDataLoader): + """:class:`XgboostNegSampleDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralNegSampleDataLoader`, + and didn't add/change anything at all. + """ + pass + + +class XgboostFullDataLoader(GeneralFullDataLoader): + """:class:`XgboostFullDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralFullDataLoader`, + and didn't add/change anything at all. + """ + pass diff --git a/recbole/data/utils.py b/recbole/data/utils.py index accf76dfc..f6e649daf 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -264,6 +264,13 @@ def get_data_loader(name, config, eval_setting): return SequentialNegSampleDataLoader elif neg_sample_strategy == 'full': return SequentialFullDataLoader + elif model_type == ModelType.XGBOOST: + if neg_sample_strategy == 'none': + return XgboostDataLoader + elif neg_sample_strategy == 'by': + return XgboostNegSampleDataLoader + elif neg_sample_strategy == 'full': + return XgboostFullDataLoader elif model_type == ModelType.KNOWLEDGE: if neg_sample_strategy == 'by': if name == 'train': diff --git a/recbole/model/exlib_recommender/xgboost.py b/recbole/model/exlib_recommender/xgboost.py new file mode 100644 index 000000000..345953e38 --- /dev/null +++ b/recbole/model/exlib_recommender/xgboost.py @@ -0,0 +1,26 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/19 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +r""" +recbole.model.exlib_recommender.xgboost +############################# +""" + +import xgboost as xgb +from recbole.utils import ModelType, InputType, FeatureSource, FeatureType + + +class xgboost(xgb.Booster): + r"""xgboost is inherited from xgb.Booster + + """ + type = ModelType.CONTEXT + input_type = InputType.POINTWISE + + def __init__(self, config, dataset): + super().__init__(params=None, cache=(), model_file=None) + + def to(self, device): + return self diff --git a/recbole/properties/model/xgboost.yaml b/recbole/properties/model/xgboost.yaml new file mode 100644 index 000000000..3ad1e041f --- /dev/null +++ b/recbole/properties/model/xgboost.yaml @@ -0,0 +1,46 @@ +# Type of training method +train_or_cv: train + +# DMatrix + +xgb_weight: ~ +xgb_base_margin: ~ +xgb_missing: ~ +xgb_silent: ~ +xgb_feature_names: ~ +xgb_feature_types: ~ +xgb_nthread: ~ + +# train or cv +xgb_model: ~ +xgb_params: + booster: gbtree + objective: binary:logistic + gamma: 0.1 + max_depth: 10 + lambda: 3 + subsample: 0.5 + colsample_bytree: 0.7 + min_child_weight: 3 + eta: 0.1 + seed: 100 + nthread: 4 +xgb_num_boost_round: 10 +# xgb_evals: ~ +xgb_obj: ~ +xgb_feval: ~ +xgb_maximize: ~ +xgb_early_stopping_rounds: ~ +# xgb_evals_result: ~ +xgb_verbose_eval: False + +# cv +xgb_cv_nfold: 3 +xgb_cv_stratified: False +xgb_cv_folds: ~ +xgb_cv_fpreproc: ~ +xgb_cv_show_stdv: True +xgb_cv_seed: 0 +xgb_cv_shuffle: True + + diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 1dc7955d6..b7dbdc9a3 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -3,9 +3,9 @@ # @Email : slmu@ruc.edu.cn # UPDATE: -# @Time : 2020/8/7, 2020/9/26, 2020/9/26, 2020/10/01, 2020/9/16, 2020/10/8, 2020/10/15 -# @Author : Zihan Lin, Yupeng Hou, Yushuo Chen, Shanlei Mu, Xingyu Pan, Hui Wang, Xinyan Fan -# @Email : linzihan.super@foxmail.com, houyupeng@ruc.edu.cn, chenyushuo@ruc.edu.cn, slmu@ruc.edu.cn, panxy@ruc.edu.cn, hui.wang@ruc.edu.cn, xinyan.fan@ruc.edu.cn +# @Time : 2020/8/7, 2020/9/26, 2020/9/26, 2020/10/01, 2020/9/16, 2020/10/8, 2020/10/15, 2020/11/20 +# @Author : Zihan Lin, Yupeng Hou, Yushuo Chen, Shanlei Mu, Xingyu Pan, Hui Wang, Xinyan Fan, Chen Yang +# @Email : linzihan.super@foxmail.com, houyupeng@ruc.edu.cn, chenyushuo@ruc.edu.cn, slmu@ruc.edu.cn, panxy@ruc.edu.cn, hui.wang@ruc.edu.cn, xinyan.fan@ruc.edu.cn, 254170321@qq.com r""" recbole.trainer.trainer @@ -19,6 +19,7 @@ from torch.nn.utils.clip_grad import clip_grad_norm_ import numpy as np import matplotlib.pyplot as plt +import xgboost as xgb from time import time from logging import getLogger @@ -545,3 +546,162 @@ class TraditionalTrainer(Trainer): def __init__(self, config, model): super(TraditionalTrainer, self).__init__(config, model) self.epochs = 1 # Set the epoch to 1 when running memory based model + + +class xgboostTrainer(AbstractTrainer): + """xgboostTrainer is designed for XGBOOST. + + """ + def __init__(self, config, model): + super(xgboostTrainer, self).__init__(config, model) + + self.logger = getLogger() + self.label_field = config['LABEL_FIELD'] + + self.train_or_cv = config['train_or_cv'] + self.xgb_model = config['xgb_model'] + + # DMatrix params + self.weight = config['xgb_weight'] + self.base_margin = config['xgb_base_margin'] + self.missing = config['xgb_missing'] + self.silent = config['xgb_silent'] + self.feature_names = config['xgb_feature_names'] + self.feature_types = config['xgb_feature_types'] + self.nthread = config['xgb_nthread'] + + # train params + self.params = config['xgb_params'] + self.num_boost_round = config['xgb_num_boost_round'] + self.evals = () + self.obj = config['xgb_obj'] + self.feval = config['xgb_feval'] + self.maximize = config['xgb_maximize'] + self.early_stopping_rounds = config['xgb_early_stopping_rounds'] + self.evals_result = {} + self.verbose_eval = config['xgb_verbose_eval'] + self.callbacks = None + + # cv params + if self.train_or_cv == 'cv': + self.nfold = config['xgb_cv_nfold'] + self.stratified = config['xgb_cv_stratified'] + self.folds = config['xgb_cv_folds'] + self.fpreproc = config['xgb_cv_freproc'] + self.show_stdv = config['xgb_cv_show_stdv'] + self.seed = config['xgb_cv_seed'] + self.shuffle = config['xgb_cv_shuffle'] + + # evaluator + self.eval_type = config['eval_type'] + self.epochs = config['epochs'] + self.eval_step = min(config['eval_step'], self.epochs) + self.valid_metric = config['valid_metric'].lower() + + if self.eval_type == EvaluatorType.INDIVIDUAL: + self.evaluator = LossEvaluator(config) + else: + self.evaluator = TopKEvaluator(config) + + # model saved + self.checkpoint_dir = config['checkpoint_dir'] + ensure_dir(self.checkpoint_dir) + saved_model_file = '{}-{}.pth'.format(self.config['model'], get_local_time()) + self.saved_model_file = os.path.join(self.checkpoint_dir, saved_model_file) + + def _interaction_to_DMatrix(self, interaction): + r"""Convert data format from interaction to DMatrix + + Args: + interaction (Interaction): Data in the form of 'Interaction'. + Returns: + DMatrix: Data in the form of 'DMatrix'. + """ + interaction_np = interaction.numpy() + cur_data = np.array([]) + for key, value in interaction_np.items(): + value = np.resize(value,(value.shape[0],1)) + if key != self.label_field: + if cur_data.shape[0] == 0: + cur_data = value + else: + cur_data = np.hstack((cur_data, value)) + + return xgb.DMatrix(data = cur_data, + label = interaction_np[self.label_field], + weight = self.weight, + base_margin = self.base_margin, + missing = self.missing, + silent = self.silent, + feature_names = self.feature_names, + feature_types = self.feature_types, + nthread = self.nthread) + + def _train_epoch(self, train_data, valid_data): + r""" + + Args: + train_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. + valid_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. + """ + for _, train_interaction in enumerate(train_data): + self.dtrain = self._interaction_to_DMatrix(train_interaction) + self.evals = [(self.dtrain,'train')] + self.model = xgb.train(self.params, self.dtrain, 1, + self.evals, self.obj, self.feval, self.maximize, + self.early_stopping_rounds, self.evals_result, + self.verbose_eval, self.xgb_model, self.callbacks) + + self.model.save_model(self.saved_model_file) + self.xgb_model = self.saved_model_file + + def _valid_epoch(self, valid_data): + r""" + + Args: + valid_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. + """ + valid_result = self.evaluate(valid_data) + valid_score = calculate_valid_score(valid_result, self.valid_metric) + return valid_result, valid_score + + def fit(self, train_data, valid_data=None, verbose=True, saved=True): + self.best_valid_score = 0. + self.best_valid_result = 0. + if self.train_or_cv == 'train': + for epoch_idx in range(self.epochs): + train_loss = self._train_epoch(train_data, valid_data) + + if (epoch_idx + 1) % self.eval_step == 0: + # evaluate + valid_start_time = time() + valid_result, valid_score = self._valid_epoch(valid_data) + valid_end_time = time() + valid_score_output = "epoch %d evaluating [time: %.2fs, valid_score: %f]" % \ + (epoch_idx, valid_end_time - valid_start_time, valid_score) + valid_result_output = 'valid result: \n' + dict2str(valid_result) + if verbose: + self.logger.info(valid_score_output) + self.logger.info(valid_result_output) + + self.best_valid_score = valid_score + self.best_valid_result = valid_result + + return self.best_valid_score, self.best_valid_result + + def evaluate(self, eval_data, load_best_model=True, model_file=None): + self.eval_pred = torch.Tensor() + self.eval_true = torch.Tensor() + + for _, batched_data in enumerate(eval_data): + batched_data_DMatrix = self._interaction_to_DMatrix(batched_data) + batch_pred = torch.Tensor(self.model.predict(batched_data_DMatrix)) + if self.params['objective'] == 'binary:logistic': + batch_pred = (batch_pred >= 0.5) * 1 + self.eval_pred = torch.cat((self.eval_pred, batch_pred)) + self.eval_true = torch.cat((self.eval_true, batched_data[self.label_field])) + + matrix_list = [torch.stack((self.eval_pred, self.eval_true), 1)] + + result = self.evaluator.evaluate(matrix_list, eval_data) + return result diff --git a/recbole/utils/enum_type.py b/recbole/utils/enum_type.py index 62a11ee7c..ee63f341e 100644 --- a/recbole/utils/enum_type.py +++ b/recbole/utils/enum_type.py @@ -27,7 +27,6 @@ class ModelType(Enum): SOCIAL = 5 TRADITIONAL = 6 - class DataLoaderType(Enum): """Type of DataLoaders. diff --git a/recbole/utils/utils.py b/recbole/utils/utils.py index 3a44d1fba..d47ea0fae 100644 --- a/recbole/utils/utils.py +++ b/recbole/utils/utils.py @@ -53,7 +53,8 @@ def get_model(model_name): 'general_recommender', 'context_aware_recommender', 'sequential_recommender', - 'knowledge_aware_recommender' + 'knowledge_aware_recommender', + 'exlib_recommender' ] model_file_name = model_name.lower() diff --git a/requirements.txt b/requirements.txt index 7f4bff1bf..2b254e9ec 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,4 +6,5 @@ hyperopt>=0.2.4 pandas>=1.0.5 tqdm>=4.48.2 scikit_learn>=0.23.2 -pyyaml>=5.1.0 \ No newline at end of file +pyyaml>=5.1.0 +xgboost>=1.2.1 \ No newline at end of file From 31f5301a2fe50d4e0917bec0555bcc8f505d534a Mon Sep 17 00:00:00 2001 From: flust <254170321@qq.com> Date: Mon, 7 Dec 2020 12:46:46 +0000 Subject: [PATCH 103/249] FEA: xgboost --- recbole/utils/enum_type.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/recbole/utils/enum_type.py b/recbole/utils/enum_type.py index ee63f341e..84e15b812 100644 --- a/recbole/utils/enum_type.py +++ b/recbole/utils/enum_type.py @@ -26,6 +26,8 @@ class ModelType(Enum): KNOWLEDGE = 4 SOCIAL = 5 TRADITIONAL = 6 + XGBOOST = 7 + class DataLoaderType(Enum): """Type of DataLoaders. From 22140e86b2dddee1dc18e1b7393564f8c8b9d2f7 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 8 Dec 2020 15:30:37 +0800 Subject: [PATCH 104/249] FEA: Remove `drop_filter_field` and `drop_preload_weight`. Add `unused_col` to drop the columns only used in data preparation but not used in model. --- recbole/data/dataset/dataset.py | 47 +++++++++++++--------- recbole/data/dataset/sequential_dataset.py | 2 + recbole/data/interaction.py | 10 +++++ recbole/utils/argument_list.py | 6 +-- 4 files changed, 44 insertions(+), 21 deletions(-) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index f5b53d1fd..78eb2fc63 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -453,11 +453,8 @@ def _preload_weight_matrix(self): preload_fields = self.config['preload_weight'] if preload_fields is None: return - drop_flag = self.config['drop_preload_weight'] - if drop_flag is None: - drop_flag = True - self.logger.debug('preload weight matrix for {}, drop=[{}]'.format(preload_fields, drop_flag)) + self.logger.debug('preload weight matrix for {}'.format(preload_fields)) for preload_id_field in preload_fields: preload_value_field = preload_fields[preload_id_field] @@ -505,9 +502,6 @@ def _preload_weight_matrix(self): value_ftype)) continue self._preloaded_weight[preload_id_field] = matrix - if drop_flag: - self._del_col(preload_id_field) - self._del_col(preload_value_field) def _fill_nan(self): """Missing value imputation. @@ -726,9 +720,6 @@ def _filter_by_field_value(self): if not filter_field: return - if self.config['drop_filter_field']: - for field in set(filter_field): - self._del_col(field) def _reset_index(self): """Reset index for all feats in :attr:`feat_name_list`. @@ -766,18 +757,19 @@ def _drop_by_value(self, val, cmp): filter_field.append(field) return filter_field - def _del_col(self, field): + def _del_col(self, feat, field): """Delete columns Args: - field (str): field name to be droped. + feat (pandas.DataFrame or Interaction): the feat contains field. + field (str): field name to be dropped. """ self.logger.debug('delete column [{}]'.format(field)) - for feat_name in self.feat_name_list: - feat = getattr(self, feat_name) - if field in feat: - feat.drop(columns=field, inplace=True) - for dct in [self.field2id_token, self.field2seqlen, self.field2source, self.field2type]: + if isinstance(feat, Interaction): + feat.drop(column=field) + else: + feat.drop(columns=field, inplace=True) + for dct in [self.field2id_token, self.field2token_id, self.field2seqlen, self.field2source, self.field2type]: if field in dct: del dct[field] @@ -807,7 +799,7 @@ def _set_label_by_threshold(self): self.inter_feat[self.label_field] = (self.inter_feat[field] >= value).astype(int) else: raise ValueError('field [{}] not in inter_feat'.format(field)) - self._del_col(field) + self._del_col(self.inter_feat, field) def _get_fields_in_same_space(self): """Parsing ``config['fields_in_same_space']``. See :doc:`../user_guide/data/data_args` for detail arg setting. @@ -1199,6 +1191,22 @@ def copy(self, new_inter_feat): nxt.inter_feat = new_inter_feat return nxt + def _drop_unused_col(self): + """Drop columns which are loaded for data preparation but not used in model. + """ + unused_col = self.config['unused_col'] + if unused_col is None: + return + + for feat_name, unused_fields in unused_col.items(): + feat = getattr(self, feat_name + '_feat') + for field in unused_fields: + if field not in feat: + self.logger.warning('field [{}] is not in [{}_feat], which can not be set in `unused_col`'.format( + field, feat_name)) + continue + self._del_col(feat, field) + def _grouped_index(self, group_by_list): index = {} for i, key in enumerate(group_by_list): @@ -1256,6 +1264,7 @@ def split_by_ratio(self, ratios, group_by=None): for index, start, end in zip(next_index, [0] + split_ids, split_ids + [tot_cnt]): index.extend(grouped_index[start: end]) + self._drop_unused_col() next_df = [self.inter_feat[index] for index in next_index] next_ds = [self.copy(_) for _ in next_df] return next_ds @@ -1299,6 +1308,8 @@ def leave_one_out(self, group_by, leave_one_num=1): grouped_inter_feat_index = self._grouped_index(self.inter_feat[group_by].numpy()) next_index = self._split_index_by_leave_one_out(grouped_inter_feat_index, leave_one_num) + + self._drop_unused_col() next_df = [self.inter_feat[index] for index in next_index] next_ds = [self.copy(_) for _ in next_df] return next_ds diff --git a/recbole/data/dataset/sequential_dataset.py b/recbole/data/dataset/sequential_dataset.py index f5a10cdf0..0d78b5644 100644 --- a/recbole/data/dataset/sequential_dataset.py +++ b/recbole/data/dataset/sequential_dataset.py @@ -101,6 +101,8 @@ def leave_one_out(self, group_by, leave_one_num=1): self.prepare_data_augmentation() grouped_index = self._grouped_index(self.uid_list) next_index = self._split_index_by_leave_one_out(grouped_index, leave_one_num) + + self._drop_unused_col() next_ds = [] for index in next_index: ds = copy.copy(self) diff --git a/recbole/data/interaction.py b/recbole/data/interaction.py index 00d1b3abb..5fd137c6a 100644 --- a/recbole/data/interaction.py +++ b/recbole/data/interaction.py @@ -243,6 +243,16 @@ def update(self, new_inter): self.pos_len_list = new_inter.pos_len_list self.user_len_list = new_inter.user_len_list + def drop(self, column): + """Drop column in interaction. + + Args: + column (str): the column to be dropped. + """ + if column not in self.interaction: + raise ValueError('column [{}] is not in [{}]'.format(column, self)) + del self.interaction[column] + def _reindex(self, index): """Reset the index of interaction inplace. diff --git a/recbole/utils/argument_list.py b/recbole/utils/argument_list.py index 8ff322ce3..05f6ad18a 100644 --- a/recbole/utils/argument_list.py +++ b/recbole/utils/argument_list.py @@ -29,9 +29,9 @@ 'NEG_PREFIX', 'ITEM_LIST_LENGTH_FIELD', 'LIST_SUFFIX', 'MAX_ITEM_LIST_LENGTH', 'POSITION_FIELD', 'HEAD_ENTITY_ID_FIELD', 'TAIL_ENTITY_ID_FIELD', 'RELATION_ID_FIELD', 'ENTITY_ID_FIELD', - 'load_col', 'unload_col', 'additional_feat_suffix', + 'load_col', 'unload_col', 'unused_col', 'additional_feat_suffix', 'max_user_inter_num', 'min_user_inter_num', 'max_item_inter_num', 'min_item_inter_num', - 'lowest_val', 'highest_val', 'equal_val', 'not_equal_val', 'drop_filter_field', + 'lowest_val', 'highest_val', 'equal_val', 'not_equal_val', 'fields_in_same_space', - 'preload_weight', 'drop_preload_weight', + 'preload_weight', 'normalize_field', 'normalize_all'] From 7c4ab6ffb1c875e3770bec040b1da72612fee01d Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 8 Dec 2020 15:40:03 +0800 Subject: [PATCH 105/249] REFACTOR: refactor in seq-dataloader._shuffle to ensure the result will not effect by `real_time`. --- recbole/data/dataloader/sequential_dataloader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index 48d46be14..bd6ae2f0a 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -90,7 +90,7 @@ def pr_end(self): def _shuffle(self): if self.real_time: - new_index = np.random.permutation(len(self.item_list_index)) + new_index = torch.randperm(self.pr_end) self.uid_list = self.uid_list[new_index] self.item_list_index = self.item_list_index[new_index] self.target_index = self.target_index[new_index] From 78a59d282c65a13e159412295a3b6b0a8df13f6e Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 8 Dec 2020 16:04:23 +0800 Subject: [PATCH 106/249] FEA: Remove `drop_filter_field` and `drop_preload_weight` in yaml --- recbole/properties/dataset/ml-100k.yaml | 2 -- recbole/properties/dataset/sample.yaml | 2 -- recbole/properties/model/DMF.yaml | 4 ++-- tests/model/test_model.yaml | 2 -- 4 files changed, 2 insertions(+), 8 deletions(-) diff --git a/recbole/properties/dataset/ml-100k.yaml b/recbole/properties/dataset/ml-100k.yaml index ca037e507..29449c0a5 100644 --- a/recbole/properties/dataset/ml-100k.yaml +++ b/recbole/properties/dataset/ml-100k.yaml @@ -37,11 +37,9 @@ lowest_val: ~ highest_val: ~ equal_val: ~ not_equal_val: ~ -drop_filter_field : False # Preprocessing fields_in_same_space: ~ preload_weight: ~ -drop_preload_weight: True normalize_field: ~ normalize_all: True diff --git a/recbole/properties/dataset/sample.yaml b/recbole/properties/dataset/sample.yaml index fcba365a7..031a0049d 100644 --- a/recbole/properties/dataset/sample.yaml +++ b/recbole/properties/dataset/sample.yaml @@ -33,12 +33,10 @@ lowest_val: ~ highest_val: ~ equal_val: ~ not_equal_val: ~ -drop_filter_field : True # Preprocessing fields_in_same_space: ~ preload_weight: ~ -drop_preload_weight: True normalize_field: ~ normalize_all: True diff --git a/recbole/properties/model/DMF.yaml b/recbole/properties/model/DMF.yaml index 56040f182..d2ca2cfa4 100644 --- a/recbole/properties/model/DMF.yaml +++ b/recbole/properties/model/DMF.yaml @@ -1,6 +1,6 @@ # WARNING: -# 1.if you set inter_matrix_type='rating', you must set drop_filter_field=False in your data config files. -# 2.The dimensions of the last layer of users and items must be the same +# 1. if you set inter_matrix_type='rating', you must set `unused_col: ~` in your data config files. +# 2. The dimensions of the last layer of users and items must be the same inter_matrix_type: '01' user_embedding_size: 64 diff --git a/tests/model/test_model.yaml b/tests/model/test_model.yaml index ccf032865..77d78f37e 100644 --- a/tests/model/test_model.yaml +++ b/tests/model/test_model.yaml @@ -47,11 +47,9 @@ lowest_val: ~ highest_val: ~ equal_val: ~ not_equal_val: ~ -drop_filter_field : False # Preprocessing fields_in_same_space: ~ preload_weight: ~ -drop_preload_weight: True normalize_field: ~ normalize_all: True From 77f53b2c27bc33a4ecbfbfb6ea8646af7b45638e Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 8 Dec 2020 16:08:25 +0800 Subject: [PATCH 107/249] FIX: Bug fix in general_dataloader.py --- recbole/data/dataloader/general_dataloader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index c6827914b..e52b8210f 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -155,7 +155,7 @@ def _neg_sampling(self, inter_feat): return self.sampling_func(inter_feat, neg_iids) def _neg_sample_by_pair_wise_sampling(self, inter_feat, neg_iids): - inter_feat.repeat(self.times) + inter_feat = inter_feat.repeat(self.times) neg_item_feat = Interaction({self.iid_field: neg_iids}) neg_item_feat = self.dataset.join(neg_item_feat) neg_item_feat.add_prefix(self.neg_prefix) From 2f903c0fa30c5ed941f67129b2a2558000f4a8e6 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Wed, 9 Dec 2020 12:31:26 +0800 Subject: [PATCH 108/249] FIX: Add pos_len_list & user_len_list to full-dataloader. --- recbole/data/dataloader/general_dataloader.py | 7 ++++++- recbole/data/dataloader/sequential_dataloader.py | 3 +++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index e52b8210f..a38e025df 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -260,7 +260,12 @@ def _shuffle(self): self.logger.warnning('GeneralFullDataLoader can\'t shuffle') def _next_batch_data(self): - cur_data = self._neg_sampling(self.user_df[self.pr: self.pr + self.step]) + index = slice(self.pr, self.pr + self.step) + user_df = self.user_df[index] + pos_len_list = self.uid2items_num[self.uid_list[index]] + user_len_list = np.full(len(user_df), self.item_num) + user_df.set_additional_info(pos_len_list, user_len_list) + cur_data = self._neg_sampling(user_df) self.pr += self.step return cur_data diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index bd6ae2f0a..aaca251f2 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -264,6 +264,9 @@ def _shuffle(self): def _next_batch_data(self): interaction = super()._next_batch_data() inter_num = len(interaction) + pos_len_list = np.ones(inter_num, dtype=np.int64) + user_len_list = np.full(inter_num, self.item_num) + interaction.set_additional_info(pos_len_list, user_len_list) scores_row = torch.arange(inter_num).repeat(2) padding_idx = torch.zeros(inter_num, dtype=torch.int64) positive_idx = interaction[self.iid_field] From e38d0bcff309801d002d01b8a06a1fa902fc126e Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Wed, 9 Dec 2020 13:38:58 +0800 Subject: [PATCH 109/249] Update README.md --- README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/README.md b/README.md index 997aaa072..e7b6ab622 100644 --- a/README.md +++ b/README.md @@ -53,6 +53,12 @@ oriented to the GPU environment. for testing and comparing recommendation algorithms. ## RecBole News +**12/06/2020**: We release RecBole [v0.1.2](https://github.com/RUCAIBox/RecBole/releases/tag/v0.1.2). + +**11/29/2020**: We constructed preliminary experiments to test the time and memory cost on three +different-sized datasets and provided the [test result](https://github.com/RUCAIBox/RecBole#time-and-memory-costs) +for reference. + **11/03/2020**: We release the first version of RecBole **v0.1.1**. From f84069c682e5bcdc8fb0d9862bcbd46cc5cbe784 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Wed, 9 Dec 2020 23:07:05 +0800 Subject: [PATCH 110/249] Update python-package.yml --- .github/workflows/python-package.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 55b05fd9d..5005df0fd 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -29,9 +29,9 @@ jobs: - name: Test metrics run: | python -m pytest -v tests/metrics - - name: Test evaluation_setting - run: | - python -m pytest -v tests/evaluation_setting +# - name: Test evaluation_setting +# run: | +# python -m pytest -v tests/evaluation_setting - name: Test model run: | python -m pytest -v tests/model/test_model_auto.py From d36dc0b4435c8abff9aca636bc140fd09b765c33 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Wed, 9 Dec 2020 23:12:15 +0800 Subject: [PATCH 111/249] Update python-package.yml --- .github/workflows/python-package.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 5005df0fd..f547a4bd2 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -26,9 +26,9 @@ jobs: if [ -f requirements.txt ]; then pip install -r requirements.txt; fi # Use "python -m pytest" instead of "pytest" to fix imports - - name: Test metrics - run: | - python -m pytest -v tests/metrics +# - name: Test metrics +# run: | +# python -m pytest -v tests/metrics # - name: Test evaluation_setting # run: | # python -m pytest -v tests/evaluation_setting From ff2517880b2fd04a80dab652a828de0fe56948d5 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Wed, 9 Dec 2020 23:19:43 +0800 Subject: [PATCH 112/249] Update python-package.yml --- .github/workflows/python-package.yml | 17 +++++++---------- 1 file changed, 7 insertions(+), 10 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index f547a4bd2..6f4d79946 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -26,21 +26,18 @@ jobs: if [ -f requirements.txt ]; then pip install -r requirements.txt; fi # Use "python -m pytest" instead of "pytest" to fix imports -# - name: Test metrics -# run: | -# python -m pytest -v tests/metrics + - name: Test metrics + run: | + python -m pytest -v tests/metrics # - name: Test evaluation_setting # run: | # python -m pytest -v tests/evaluation_setting - - name: Test model - run: | - python -m pytest -v tests/model/test_model_auto.py +# - name: Test model +# run: | +# python -m pytest -v tests/model/test_model_auto.py - name: Test config run: | python -m pytest -v tests/config/test_config.py export PYTHONPATH=. python tests/config/test_command_line.py --use_gpu=False --valid_metric=Recall@10 --split_ratio=[0.7,0.2,0.1] --metrics=['Recall@10'] --epochs=200 --eval_setting='LO_RS' --learning_rate=0.3 - - name: Test evaluation_setting - run: | - python -m pytest -v tests/evaluation_setting - + From 6c324cebcc4d5afd0ca358287b9c21c16b64ebf9 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 10 Dec 2020 00:15:53 +0800 Subject: [PATCH 113/249] Update python-package.yml --- .github/workflows/python-package.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 55b05fd9d..6b1dc9681 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -7,7 +7,7 @@ on: jobs: build: - runs-on: ubuntu-latest + runs-on: ubuntu-18.04 strategy: matrix: python-version: [3.8] From d237938c3b1ee2e8e1a3e46ceb08d7a163e475ae Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 10 Dec 2020 00:29:03 +0800 Subject: [PATCH 114/249] Update python-package.yml --- .github/workflows/python-package.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 6f4d79946..993b4c657 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -7,7 +7,7 @@ on: jobs: build: - runs-on: ubuntu-latest + runs-on: ubuntu-18.04 strategy: matrix: python-version: [3.8] @@ -29,9 +29,9 @@ jobs: - name: Test metrics run: | python -m pytest -v tests/metrics -# - name: Test evaluation_setting -# run: | -# python -m pytest -v tests/evaluation_setting + - name: Test evaluation_setting + run: | + python -m pytest -v tests/evaluation_setting # - name: Test model # run: | # python -m pytest -v tests/model/test_model_auto.py From 76a2e6cbeffd3868675b7d4447ac37f648c1c823 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Thu, 10 Dec 2020 00:40:10 +0800 Subject: [PATCH 115/249] FIX: change the CI workflow --- .github/workflows/python-package.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 993b4c657..89c7fb500 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -29,9 +29,9 @@ jobs: - name: Test metrics run: | python -m pytest -v tests/metrics - - name: Test evaluation_setting - run: | - python -m pytest -v tests/evaluation_setting + # - name: Test evaluation_setting + # run: | + # python -m pytest -v tests/evaluation_setting # - name: Test model # run: | # python -m pytest -v tests/model/test_model_auto.py From 2498c725e5afd75d77201ffdfc0eb63dfb3e76ba Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 10 Dec 2020 10:49:06 +0800 Subject: [PATCH 116/249] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index e7b6ab622..a19df9c22 100644 --- a/README.md +++ b/README.md @@ -174,9 +174,9 @@ NOTE: Our test results only gave the approximate time and memory cost of our im ## RecBole Major Releases | Releases | Date | Features | |-----------|--------|-------------------------| +| v0.1.2 | 12/06/2020 | Basic RecBole | | v0.1.1 | 11/03/2020 | Basic RecBole | - ## Contributing Please let us know if you encounter a bug or have any suggestions by [filing an issue](https://github.com/RUCAIBox/RecBole/issues). From 1f5122921c82e02caef49a09bdb5fb74ef3a18bf Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 10 Dec 2020 10:51:11 +0800 Subject: [PATCH 117/249] Update python-package.yml --- .github/workflows/python-package.yml | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 89c7fb500..993b4c657 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -29,9 +29,9 @@ jobs: - name: Test metrics run: | python -m pytest -v tests/metrics - # - name: Test evaluation_setting - # run: | - # python -m pytest -v tests/evaluation_setting + - name: Test evaluation_setting + run: | + python -m pytest -v tests/evaluation_setting # - name: Test model # run: | # python -m pytest -v tests/model/test_model_auto.py From 016d32e028a9b485ae13b170bf8743aa7aab4445 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Thu, 10 Dec 2020 10:59:17 +0800 Subject: [PATCH 118/249] Update python-package.yml --- .github/workflows/python-package.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 993b4c657..2238d264c 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -7,7 +7,7 @@ on: jobs: build: - runs-on: ubuntu-18.04 + runs-on: ubuntu-latest strategy: matrix: python-version: [3.8] @@ -32,9 +32,9 @@ jobs: - name: Test evaluation_setting run: | python -m pytest -v tests/evaluation_setting -# - name: Test model -# run: | -# python -m pytest -v tests/model/test_model_auto.py + - name: Test model + run: | + python -m pytest -v tests/model/test_model_auto.py - name: Test config run: | python -m pytest -v tests/config/test_config.py From 4d0840260678f957c3b7be647c2dfddb1eae6b1a Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Thu, 10 Dec 2020 17:49:21 +0800 Subject: [PATCH 119/249] FIX: Bug fix in general full dataloader. (runtime error in _full_sort_batch_eval) --- recbole/data/dataloader/general_dataloader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index a38e025df..b80feca5f 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -244,7 +244,7 @@ def _set_user_property(self, uid, used_item, positive_item): swap_idx = torch.tensor(sorted(set(range(positive_item_num)) ^ positive_item)) self.uid2swap_idx[uid] = swap_idx self.uid2rev_swap_idx[uid] = swap_idx.flip(0) - self.uid2history_item[uid] = torch.tensor(list(history_item)) + self.uid2history_item[uid] = torch.tensor(list(history_item), dtype=torch.int64) def _batch_size_adaptation(self): batch_num = max(self.batch_size // self.dataset.item_num, 1) From 27ba4edc20ec557191c0698d02bf71ca89e7c2dd Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Fri, 11 Dec 2020 12:26:47 +0800 Subject: [PATCH 120/249] Update README.md --- README.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/README.md b/README.md index a19df9c22..b35526a8a 100644 --- a/README.md +++ b/README.md @@ -53,6 +53,8 @@ oriented to the GPU environment. for testing and comparing recommendation algorithms. ## RecBole News +**12/10/2020**: 我们发布了RecBole小白入门系列中文博客。 + **12/06/2020**: We release RecBole [v0.1.2](https://github.com/RUCAIBox/RecBole/releases/tag/v0.1.2). **11/29/2020**: We constructed preliminary experiments to test the time and memory cost on three @@ -61,6 +63,11 @@ for reference. **11/03/2020**: We release the first version of RecBole **v0.1.1**. +## Blogs + +[RecBole小白入门系列博客（一）——快速安装和简单上手](https://blog.csdn.net/Turinger_2000/article/details/110414642) + +[RecBole小白入门系列博客（二） ——General类模型运行流程](https://blog.csdn.net/Turinger_2000/article/details/110395198) ## Installation RecBole works with the following operating systems: From a4cb85142cc582e59f40aea6a5f14f48ac0d42a8 Mon Sep 17 00:00:00 2001 From: 2017pxy <38801065+2017pxy@users.noreply.github.com> Date: Fri, 11 Dec 2020 12:37:03 +0800 Subject: [PATCH 121/249] Update README.md --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index b35526a8a..6c6d131b3 100644 --- a/README.md +++ b/README.md @@ -11,12 +11,13 @@ [![License](https://img.shields.io/badge/License-MIT-blue.svg)](./LICENSE) -[HomePage] | [Docs] | [Datasets] | [Paper] +[HomePage] | [Docs] | [Datasets] | [Paper] | [Blogs] [HomePage]: https://recbole.io/ [Docs]: https://recbole.io/docs/ [Datasets]: https://github.com/RUCAIBox/RecDatasets [Paper]: https://arxiv.org/abs/2011.01731 +[Blogs]: #blogs RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified, comprehensive and efficient framework for research purpose. From 28453f68a0c42f3fa04fffefd483bf1f07102321 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:29:43 +0800 Subject: [PATCH 122/249] FEA: add GAUC metric --- recbole/evaluator/metrics.py | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index 976ac9880..cb31e1456 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -4,7 +4,7 @@ # @email : tsotfsk@outlook.com # UPDATE -# @Time : 2020/08/12, 2020/08/21, 2020/9/16 +# @Time : 2020/08/12, 2020/12/9, 2020/9/16 # @Author : Kaiyuan Li, Zhichao Feng, Xingyu Pan # @email : tsotfsk@outlook.com, fzcbupt@gmail.com, panxy@ruc.edu.cn @@ -20,6 +20,7 @@ from sklearn.metrics import auc as sk_auc from sklearn.metrics import log_loss, mean_absolute_error, mean_squared_error + # TopK Metrics # @@ -85,7 +86,7 @@ def map_(pos_index, pos_len): actual_len = np.where(pos_len > len_rank, len_rank, pos_len) result = np.zeros_like(pos_index, dtype=np.float) for row, lens in enumerate(actual_len): - ranges = np.arange(1, pos_index.shape[1]+1) + ranges = np.arange(1, pos_index.shape[1] + 1) ranges[lens:] = ranges[lens - 1] result[row] = sum_pre[row] / ranges return result @@ -164,8 +165,16 @@ def precision_(pos_index, pos_len): return pos_index.cumsum(axis=1) / np.arange(1, pos_index.shape[1] + 1) -# CTR Metrics # +def gauc_(user_len_list, pos_len_list, pos_rank_sum): + frac = user_len_list - (pos_len_list - 1) / 2 - (1 / pos_len_list) * np.squeeze(pos_rank_sum) + neg_item_num = user_len_list - pos_len_list + user_auc = frac / neg_item_num + + result = (user_auc * pos_len_list).sum() / pos_len_list.sum() + return result + +# CTR Metrics # def auc_(trues, preds): r"""AUC_ (also known as Area Under Curve) is used to evaluate the two-class model, referring to the area under the ROC curve @@ -300,5 +309,6 @@ def log_loss_(trues, preds): 'rmse': rmse_, 'mae': mae_, 'logloss': log_loss_, - 'auc': auc_ + 'auc': auc_, + 'gauc': gauc_ } From 2aae824568c00e1d0d2e5f815b967497e917b312 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:29:58 +0800 Subject: [PATCH 123/249] REFACTOR: add GroupEvaluator and IndividualEvaluator --- recbole/evaluator/abstract_evaluator.py | 175 +++++++++++++++++------- 1 file changed, 129 insertions(+), 46 deletions(-) diff --git a/recbole/evaluator/abstract_evaluator.py b/recbole/evaluator/abstract_evaluator.py index c17023433..2570fe8ad 100644 --- a/recbole/evaluator/abstract_evaluator.py +++ b/recbole/evaluator/abstract_evaluator.py @@ -1,46 +1,129 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/10/21 -# @Author : Kaiyuan Li -# @email : tsotfsk@outlook.com - -""" -recbole.evaluator.abstract_evaluator -##################################### -""" - - -class AbstractEvaluator(object): - """:class:`AbstractEvaluator` is an abstract object which supports - the evaluation of the model. It is called by :class:`Trainer`. - - Note: - If you want to inherit this class and implement your own evaluator class, - you must implement the following functions. - - Args: - config (Config): The config of evaluator. - - """ - - def __init__(self, config): - self.metrics = config['metrics'] - - def _check_args(self): - """check the correct of the setting""" - raise NotImplementedError - - def collect(self, *args): - """get the intermediate results for each batch, it is called at the end of each batch""" - raise NotImplementedError - - def evaluate(self, *args): - """calculate the metrics of all batches, it is called at the end of each epoch""" - raise NotImplementedError - - def metrics_info(self, *args): - """get metrics result""" - raise NotImplementedError - - def _calculate_metrics(self, *args): - """ to calculate the metrics""" - raise NotImplementedError +# -*- encoding: utf-8 -*- +# @Time : 2020/10/21 +# @Author : Kaiyuan Li +# @email : tsotfsk@outlook.com + +# UPDATE +# @Time : 2020/10/21, 2020/12/9 +# @Author : Kaiyuan Li, Zhichao Feng +# @email : tsotfsk@outlook.com, fzcbupt@gmail.com + +""" +recbole.evaluator.abstract_evaluator +##################################### +""" + +import torch +import numpy as np +from torch.nn.utils.rnn import pad_sequence + + +class BaseEvaluator(object): + """:class:`BaseEvaluator` is an object which supports + the evaluation of the model. It is called by :class:`Trainer`. + + Note: + If you want to inherit this class and implement your own evaluator class, + you must implement the following functions. + + Args: + config (Config): The config of evaluator. + + """ + + def __init__(self, config, metrics): + self.metrics = metrics + self.full = ('full' in config['eval_setting']) + + def collect(self, *args): + """get the intermediate results for each batch, it is called at the end of each batch""" + raise NotImplementedError + + def evaluate(self, *args): + """calculate the metrics of all batches, it is called at the end of each epoch""" + raise NotImplementedError + + def _calculate_metrics(self, *args): + """ to calculate the metrics""" + raise NotImplementedError + + +class GroupedEvalautor(BaseEvaluator): + """:class:`GroupedEvaluator` is an object which supports the evaluation of the model. + + Note: + If you want to implement a new group-based metric, + you may need to inherit this class + + """ + def __init__(self, config, metrics): + super().__init__(config, metrics) + pass + + def sample_collect(self, scores_tensor, user_len_list): + """padding scores_tensor. It is called when evaluation sample distribution is `uniform` or `popularity`. + + """ + scores_list = torch.split(scores_tensor, user_len_list, dim=0) + padding_score = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # nusers x items + return padding_score + + def full_sort_collect(self, scores_tensor, user_len_list): + """it is called when evaluation sample distribution is `full`. + + """ + return scores_tensor.view(len(user_len_list), -1) + + def get_score_matrix(self, scores_tensor, user_len_list): + """get score matrix. + + Args: + scores_tensor (tensor): the tensor of model output with size of `(N, )` + user_len_list(list): number of all items + + """ + if self.full: + scores_matrix = self.full_sort_collect(scores_tensor, user_len_list) + else: + scores_matrix = self.sample_collect(scores_tensor, user_len_list) + return scores_matrix + + +class IndividualEvaluator(BaseEvaluator): + """:class:`IndividualEvaluator` is an object which supports the evaluation of the model. + + Note: + If you want to implement a new non-group-based metric, + you may need to inherit this class + + """ + def __init__(self, config, metrics): + super().__init__(config, metrics) + pass + + def sample_collect(self, true_scores, pred_scores): + """It is called when evaluation sample distribution is `uniform` or `popularity`. + + """ + return torch.stack((true_scores, pred_scores.detach()), dim=1) + + def full_sort_collect(self, true_scores, pred_scores): + """it is called when evaluation sample distribution is `full`. + + """ + raise NotImplementedError('full sort can\'t use IndividualEvaluator') + + def get_score_matrix(self, true_scores, pred_scores): + """get score matrix + + Args: + true_scores (tensor): the label of predicted items + pred_scores (tensor): the tensor of model output with a size of `(N, )` + + """ + if self.full: + scores_matrix = self.full_sort_collect(true_scores, pred_scores) + else: + scores_matrix = self.sample_collect(true_scores, pred_scores) + + return scores_matrix From df0c8d8ce34e018936f9d8ab57141c193ca4ad8e Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:30:37 +0800 Subject: [PATCH 124/249] REFACTOR: put two origin evaluators into one file and add RankEvaluator --- recbole/evaluator/__init__.py | 4 +- recbole/evaluator/evaluators.py | 331 ++++++++++++++++++++++++++++ recbole/evaluator/loss_evaluator.py | 121 ---------- recbole/evaluator/topk_evaluator.py | 156 ------------- 4 files changed, 333 insertions(+), 279 deletions(-) create mode 100644 recbole/evaluator/evaluators.py delete mode 100644 recbole/evaluator/loss_evaluator.py delete mode 100644 recbole/evaluator/topk_evaluator.py diff --git a/recbole/evaluator/__init__.py b/recbole/evaluator/__init__.py index 61b72ad22..73f4cad5f 100644 --- a/recbole/evaluator/__init__.py +++ b/recbole/evaluator/__init__.py @@ -1,4 +1,4 @@ from recbole.evaluator.abstract_evaluator import * -from recbole.evaluator.loss_evaluator import * +from recbole.evaluator.proxy_evaluator import * from recbole.evaluator.metrics import * -from recbole.evaluator.topk_evaluator import * +from recbole.evaluator.evaluators import * diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py new file mode 100644 index 000000000..10a41f715 --- /dev/null +++ b/recbole/evaluator/evaluators.py @@ -0,0 +1,331 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/08/04 +# @Author : Kaiyuan Li +# @email : tsotfsk@outlook.com + +# UPDATE +# @Time : 2020/08/04, 2020/08/11, 2020/12/9 +# @Author : Kaiyuan Li, Yupeng Hou, Zhichao Feng +# @email : tsotfsk@outlook.com, houyupeng@ruc.edu.cn, fzcbupt@gmail.com + + +import torch +import numpy as np +from collections import ChainMap +from recbole.evaluator.metrics import metrics_dict +from recbole.evaluator.abstract_evaluator import GroupedEvalautor, IndividualEvaluator + +# These metrics are typical in topk recommendations +topk_metrics = {metric.lower(): metric for metric in ['Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP']} +# These metrics are typical in loss recommendations +loss_metrics = {metric.lower(): metric for metric in ['AUC', 'RMSE', 'MAE', 'LOGLOSS']} +# For GAUC +rank_metrics = {metric.lower(): metric for metric in ['GAUC']} + +# group-based metrics +group_metrics = ChainMap(topk_metrics, rank_metrics) +# not group-based metrics +individual_metrics = ChainMap(loss_metrics) + + +class TopKEvaluator(GroupedEvalautor): + r"""TopK Evaluator is mainly used in ranking tasks. Now, we support six topk metrics which + contain `'Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP'`. + + Note: + The metrics used calculate group-based metrics which considers the metrics scores averaged + across users. Some of them are also limited to k. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + + self.topk = config['topk'] + self._check_args() + + def collect(self, interaction, scores_tensor): + """collect the topk intermediate result of one batch, this function mainly + implements padding and TopK finding. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + """ + user_len_list = interaction.user_len_list + scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) + + # get topk + _, topk_idx = torch.topk(scores_matrix, max(self.topk), dim=-1) # nusers x k + + return topk_idx + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'Recall@10': 0.0329}`` + + """ + pos_len_list = eval_data.get_pos_len_list() + topk_idx = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + + assert len(pos_len_list) == len(topk_idx) + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(pos_len_list, topk_idx) + for metric, value in zip(self.metrics, result_list): + for k in self.topk: + key = '{}@{}'.format(metric, k) + metric_dict[key] = round(value[k - 1], 4) + + return metric_dict + + def _check_args(self): + + # Check topk: + if isinstance(self.topk, (int, list)): + if isinstance(self.topk, int): + self.topk = [self.topk] + for topk in self.topk: + if topk <= 0: + raise ValueError('topk must be a positive integer or a list of positive integers, ' + 'but get `{}`'.format(topk)) + else: + raise TypeError('The topk must be a integer, list') + + def _calculate_metrics(self, pos_len_list, topk_index): + """integrate the results of each batch and evaluate the topk metrics by users + + Args: + pos_len_list (np.ndarray): a list of users' positive items + topk_index (np.ndarray): a matrix which contains the index of the topk items for users + + Returns: + np.ndarray: a matrix which contains the metrics result + + """ + pos_idx_matrix = (topk_index < pos_len_list.reshape(-1, 1)) + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(pos_idx_matrix, pos_len_list) + result_list.append(result) # n_users x len(metrics) x len(ranks) + result = np.stack(result_list, axis=0).mean(axis=1) # len(metrics) x len(ranks) + return result + + def __str__(self): + msg = 'The TopK Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([topk_metrics[metric.lower()] for metric in self.metrics]) + \ + '], TopK:[' + \ + ', '.join(map(str, self.topk)) + \ + ']' + return msg + + +class RankEvaluator(GroupedEvalautor): + r"""Rank Evaluator is mainly used in ranking tasks except for topk tasks. Now, we support one + rank metric containing `'GAUC'`. + + Note: + The metrics used calculate group-based metrics which considers the metrics scores averaged + across users except for top-k metrics. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + pass + + def get_user_pos_len_list(self, interaction, scores_tensor): + """get number of positive items and all items in test set of each user + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + Returns: + list: number of positive items, + list: number of all items + """ + pos_len_list = torch.Tensor(interaction.pos_len_list).to(scores_tensor.device) + user_len_list = interaction.user_len_list + return pos_len_list, user_len_list + + def get_pos_index(self, scores_tensor, pos_len_list, user_len_list): + """get the index of positive items + + Args: + scores_tensor (tensor): the tensor of model output with size of `(N, )` + pos_len_list(list): number of positive items + user_len_list(list): number of all items + + Returns: + tensor: a matrix indicating whether the corresponding item is positive + + """ + scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) + _, n_index = torch.sort(scores_matrix, dim=-1, descending=True) + pos_index = (n_index < pos_len_list.reshape(-1, 1)) + return pos_index + + def collect(self, interaction, scores_tensor): + """collect the rank intermediate result of one batch, this function mainly implements ranking + and calculating the sum of rank for positive items. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + """ + pos_len_list, user_len_list = self.get_user_pos_len_list(interaction, scores_tensor) + pos_index = self.get_pos_index(scores_tensor, pos_len_list, user_len_list) + index_list = torch.arange(1, pos_index.shape[1] + 1).to(pos_index.device) + pos_rank_sum = torch.where(pos_index, index_list, torch.zeros_like(index_list)). \ + sum(axis=-1).reshape(-1, 1) + return pos_rank_sum + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'GAUC:0.9286}`` + + """ + pos_len_list = eval_data.get_pos_len_list() + user_len_list = eval_data.get_user_len_list() + pos_rank_sum = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(user_len_list, pos_len_list, pos_rank_sum) + for metric, value in zip(self.metrics, result_list): + key = '{}'.format(metric) + metric_dict[key] = round(value, 4) + + return metric_dict + + def _calculate_metrics(self, user_len_list, pos_len_list, pos_rank_sum): + """integrate the results of each batch and evaluate the topk metrics by users + + Args: + pos_len_list (np.ndarray): a list of users' positive items + topk_index (np.ndarray): a matrix which contains the index of the topk items for users + + Returns: + np.ndarray: a matrix which contains the metrics result + + """ + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(user_len_list, pos_len_list, pos_rank_sum) + result_list.append(result) + return result_list + + def __str__(self): + msg = 'The Rank Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([rank_metrics[metric.lower()] for metric in self.metrics]) + \ + '], TopK:[' + \ + ', '.join(map(str, self.topk)) + \ + ']' + return msg + + +class LossEvaluator(IndividualEvaluator): + r"""Loss Evaluator is mainly used in rating prediction and click through rate prediction. Now, we support four + loss metrics which contain `'AUC', 'RMSE', 'MAE', 'LOGLOSS'`. + + Note: + The metrics used do not calculate group-based metrics which considers the metrics scores averaged across users. + They are also not limited to k. Instead, they calculate the scores on the entire prediction results regardless + the users. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + + self.label_field = config['LABEL_FIELD'] + + def collect(self, interaction, pred_scores): + """collect the loss intermediate result of one batch, this function mainly + implements concatenating preds and trues. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + pred_scores (tensor): the tensor of model output with a size of `(N, )` + + Returns: + tensor : a batch of scores with a size of `(N, 2)` + + """ + true_scores = interaction[self.label_field].to(pred_scores.device) + assert len(true_scores) == len(pred_scores) + return self.get_score_matrix(true_scores, pred_scores) + + def evaluate(self, batch_matrix_list, *args): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + + Returns: + dict: such as {'AUC': 0.83} + + """ + concat = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + + trues = concat[:, 0] + preds = concat[:, 1] + + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(trues, preds) + for metric, value in zip(self.metrics, result_list): + key = '{}'.format(metric) + metric_dict[key] = round(value, 4) + return metric_dict + + def _calculate_metrics(self, trues, preds): + """get metrics result + + Args: + trues (np.ndarray): the true scores' list + preds (np.ndarray): the predict scores' list + + Returns: + list: a list of metrics result + + """ + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(trues, preds) + result_list.append(result) + return result_list + + def __str__(self): + msg = 'The Loss Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([loss_metrics[metric.lower()] for metric in self.metrics]) + \ + ']' + return msg + + +metric_eval_bind = [ + (topk_metrics, TopKEvaluator), + (loss_metrics, LossEvaluator), + (rank_metrics, RankEvaluator) +] diff --git a/recbole/evaluator/loss_evaluator.py b/recbole/evaluator/loss_evaluator.py deleted file mode 100644 index 23a5e4c73..000000000 --- a/recbole/evaluator/loss_evaluator.py +++ /dev/null @@ -1,121 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/08/04 -# @Author : Kaiyuan Li -# @email : tsotfsk@outlook.com - -# UPDATE -# @Time : 2020/08/04 2020/08/09 -# @Author : Kaiyuan Li Zhichao Feng -# @email : tsotfsk@outlook.com fzcbupt@gmail.com - -""" -recbole.evaluator.loss_evaluator -################################ -""" - -import numpy as np -import torch -from recbole.evaluator.abstract_evaluator import AbstractEvaluator -from recbole.evaluator.metrics import metrics_dict - -# These metrics are typical in loss recommendations -loss_metrics = {metric.lower(): metric for metric in ['AUC', 'RMSE', 'MAE', 'LOGLOSS']} - - -class LossEvaluator(AbstractEvaluator): - r"""Loss Evaluator is mainly used in rating prediction and click through rate prediction. Now, we support four - loss metrics which contain `'AUC', 'RMSE', 'MAE', 'LOGLOSS'`. - - Note: - The metrics used do not calculate group-based metrics which considers the metrics scores averaged across users. - They are also not limited to k. Instead, they calculate the scores on the entire prediction results regardless - the users. - - """ - def __init__(self, config): - super().__init__(config) - - self.label_field = config['LABEL_FIELD'] - self._check_args() - - def collect(self, interaction, pred_scores): - """collect the loss intermediate result of one batch, this function mainly - implements concatenating preds and trues. It is called at the end of each batch - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - pred_scores (tensor): the tensor of model output with a size of `(N, )` - - Returns: - tensor : a batch of scores with a size of `(N, 2)` - - """ - true_scores = interaction[self.label_field].to(pred_scores.device) - assert len(true_scores) == len(pred_scores) - return torch.stack((true_scores, pred_scores.detach()), dim=1) - - def evaluate(self, batch_matrix_list, *args): - """calculate the metrics of all batches. It is called at the end of each epoch - - Args: - batch_matrix_list (list): the results of all batches - - Returns: - dict: such as {'AUC': 0.83} - - """ - concat = torch.cat(batch_matrix_list, dim=0).cpu().numpy() - - trues = concat[:, 0] - preds = concat[:, 1] - - # get metrics - metric_dict = {} - result_list = self._calculate_metrics(trues, preds) - for metric, value in zip(self.metrics, result_list): - key = '{}'.format(metric) - metric_dict[key] = round(value, 4) - return metric_dict - - def _check_args(self): - - # Check metrics - if isinstance(self.metrics, (str, list)): - if isinstance(self.metrics, str): - self.metrics = [self.metrics] - else: - raise TypeError('metrics must be str or list') - - # Convert metric to lowercase - for m in self.metrics: - if m.lower() not in loss_metrics: - raise ValueError("There is no loss metric named {}!".format(m)) - self.metrics = [metric.lower() for metric in self.metrics] - - def metrics_info(self, trues, preds): - """get metrics result - - Args: - trues (np.ndarray): the true scores' list - preds (np.ndarray): the predict scores' list - - Returns: - list: a list of metrics result - - """ - result_list = [] - for metric in self.metrics: - metric_fuc = metrics_dict[metric.lower()] - result = metric_fuc(trues, preds) - result_list.append(result) - return result_list - - def _calculate_metrics(self, trues, preds): - return self.metrics_info(trues, preds) - - def __str__(self): - msg = 'The Loss Evaluator Info:\n' + \ - '\tMetrics:[' + \ - ', '.join([loss_metrics[metric.lower()] for metric in self.metrics]) + \ - ']' - return msg diff --git a/recbole/evaluator/topk_evaluator.py b/recbole/evaluator/topk_evaluator.py deleted file mode 100644 index 3b02b04b8..000000000 --- a/recbole/evaluator/topk_evaluator.py +++ /dev/null @@ -1,156 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/08/04 -# @Author : Kaiyuan Li -# @email : tsotfsk@outlook.com - -# UPDATE -# @Time : 2020/08/04, 2020/08/11 -# @Author : Kaiyuan Li, Yupeng Hou -# @email : tsotfsk@outlook.com, houyupeng@ruc.edu.cn - -""" -recbole.evaluator.topk_evaluator -################################ -""" - -import numpy as np -import torch -from recbole.evaluator.abstract_evaluator import AbstractEvaluator -from recbole.evaluator.metrics import metrics_dict -from torch.nn.utils.rnn import pad_sequence - -# These metrics are typical in topk recommendations -topk_metrics = {metric.lower(): metric for metric in ['Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP']} - - -class TopKEvaluator(AbstractEvaluator): - r"""TopK Evaluator is mainly used in ranking tasks. Now, we support six topk metrics which - contain `'Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP'`. - - Note: - The metrics used calculate group-based metrics which considers the metrics scores averaged - across users. Some of them are also limited to k. - - """ - def __init__(self, config): - super().__init__(config) - - self.topk = config['topk'] - self._check_args() - - def collect(self, interaction, scores_tensor, full=False): - """collect the topk intermediate result of one batch, this function mainly - implements padding and TopK finding. It is called at the end of each batch - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - scores_tensor (tensor): the tensor of model output with size of `(N, )` - full (bool, optional): whether it is full sort. Default: False. - - """ - if full is True: - scores_matrix = scores_tensor - else: - user_len_list = interaction.user_len_list - scores_list = torch.split(scores_tensor, user_len_list, dim=0) - scores_matrix = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # n_users x items - - # get topk - _, topk_index = torch.topk(scores_matrix, max(self.topk), dim=-1) # n_users x k - - return topk_index - - def evaluate(self, batch_matrix_list, eval_data): - """calculate the metrics of all batches. It is called at the end of each epoch - - Args: - batch_matrix_list (list): the results of all batches - eval_data (Dataset): the class of test data - - Returns: - dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'Recall@10': 0.0329}`` - - """ - pos_len_list = eval_data.get_pos_len_list() - topk_index = torch.cat(batch_matrix_list, dim=0).cpu().numpy() - - assert len(pos_len_list) == len(topk_index) - # get metrics - metric_dict = {} - result_list = self._calculate_metrics(pos_len_list, topk_index) - for metric, value in zip(self.metrics, result_list): - for k in self.topk: - key = '{}@{}'.format(metric, k) - metric_dict[key] = round(value[k - 1], 4) - return metric_dict - - def _check_args(self): - - # Check metrics - if isinstance(self.metrics, (str, list)): - if isinstance(self.metrics, str): - self.metrics = [self.metrics] - else: - raise TypeError('metrics must be str or list') - - # Convert metric to lowercase - for m in self.metrics: - if m.lower() not in topk_metrics: - raise ValueError("There is no user grouped topk metric named {}!".format(m)) - self.metrics = [metric.lower() for metric in self.metrics] - - # Check topk: - if isinstance(self.topk, (int, list)): - if isinstance(self.topk, int): - self.topk = [self.topk] - for topk in self.topk: - if topk <= 0: - raise ValueError('topk must be a positive integer or a list of positive integers, ' - 'but get `{}`'.format(topk)) - else: - raise TypeError('The topk must be a integer, list') - - def metrics_info(self, pos_idx, pos_len): - """get metrics result - - Args: - pos_idx (np.ndarray): the bool index of all users' topk items that indicating the positive items are - topk items or not - pos_len (np.ndarray): the length of all users' positive items - - Returns: - list: a list of matrix which record the results from `1` to `max(topk)` - - """ - result_list = [] - for metric in self.metrics: - metric_fuc = metrics_dict[metric.lower()] - result = metric_fuc(pos_idx, pos_len) - result_list.append(result) - return result_list - - def _calculate_metrics(self, pos_len_list, topk_index): - """integrate the results of each batch and evaluate the topk metrics by users - - Args: - pos_len_list (np.ndarray): a list of users' positive items - topk_index (np.ndarray): a matrix which contains the index of the topk items for users - - Returns: - np.ndarray: a matrix which contains the metrics result - - """ - - pos_idx_matrix = (topk_index < pos_len_list.reshape(-1, 1)) - result_list = self.metrics_info(pos_idx_matrix, pos_len_list) # n_users x len(metrics) x len(ranks) - result = np.stack(result_list, axis=0).mean(axis=1) # len(metrics) x len(ranks) - return result - - def __str__(self): - msg = 'The TopK Evaluator Info:\n' + \ - '\tMetrics:[' + \ - ', '.join([topk_metrics[metric.lower()] for metric in self.metrics]) + \ - '], TopK:[' + \ - ', '.join(map(str, self.topk)) + \ - ']' - return msg From 52811cbcc7bab4fe7f6e2e6564f4821119ccb4d5 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:30:56 +0800 Subject: [PATCH 125/249] FEA: adapt trainer to new evaluator --- recbole/trainer/trainer.py | 14 +++----------- 1 file changed, 3 insertions(+), 11 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 8907e13c0..afb6b145c 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -24,7 +24,7 @@ from time import time from logging import getLogger -from recbole.evaluator import TopKEvaluator, LossEvaluator +from recbole.evaluator.proxy_evaluator import ProxyEvaluator from recbole.data.interaction import Interaction from recbole.utils import ensure_dir, get_local_time, early_stopping, calculate_valid_score, dict2str, \ DataLoaderType, KGDataLoaderState, EvaluatorType @@ -95,11 +95,7 @@ def __init__(self, config, model): self.train_loss_dict = dict() self.optimizer = self._build_optimizer() self.eval_type = config['eval_type'] - if self.eval_type == EvaluatorType.INDIVIDUAL: - self.evaluator = LossEvaluator(config) - else: - self.evaluator = TopKEvaluator(config) - + self.evaluator = ProxyEvaluator(config) self.item_tensor = None self.tot_item_num = None @@ -348,20 +344,16 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): batch_matrix_list = [] for batch_idx, batched_data in enumerate(eval_data): if eval_data.dl_type == DataLoaderType.FULL: - if self.eval_type == EvaluatorType.INDIVIDUAL: - raise ValueError('full sort can\'t use LossEvaluator') interaction, scores = self._full_sort_batch_eval(batched_data) - batch_matrix = self.evaluator.collect(interaction, scores, full=True) else: interaction = batched_data batch_size = interaction.length - if batch_size <= self.test_batch_size: scores = self.model.predict(interaction.to(self.device)) else: scores = self._spilt_predict(interaction, batch_size) - batch_matrix = self.evaluator.collect(interaction, scores) + batch_matrix = self.evaluator.collect(interaction, scores) batch_matrix_list.append(batch_matrix) result = self.evaluator.evaluate(batch_matrix_list, eval_data) From 41c721fcce1721acc7e1a1cb8434ed39fbfe40ec Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:31:40 +0800 Subject: [PATCH 126/249] FEA: adapt configurator to new evaluator --- recbole/config/configurator.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/recbole/config/configurator.py b/recbole/config/configurator.py index fb6784498..2a225a4cd 100644 --- a/recbole/config/configurator.py +++ b/recbole/config/configurator.py @@ -19,7 +19,7 @@ import torch from logging import getLogger -from recbole.evaluator import loss_metrics, topk_metrics +from recbole.evaluator import group_metrics, individual_metrics from recbole.utils import get_model, Enum, EvaluatorType, ModelType, InputType, \ general_arguments, training_arguments, evaluation_arguments, dataset_arguments @@ -296,12 +296,12 @@ def _set_default_parameters(self): eval_type = None for metric in self.final_config_dict['metrics']: - if metric.lower() in loss_metrics: + if metric.lower() in individual_metrics: if eval_type is not None and eval_type == EvaluatorType.RANKING: raise RuntimeError('Ranking metrics and other metrics can not be used at the same time.') else: eval_type = EvaluatorType.INDIVIDUAL - if metric.lower() in topk_metrics: + if metric.lower() in group_metrics: if eval_type is not None and eval_type == EvaluatorType.INDIVIDUAL: raise RuntimeError('Ranking metrics and other metrics can not be used at the same time.') else: From c222ebf12854007297e3fe0b68597c75f7313547 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:33:01 +0800 Subject: [PATCH 127/249] FEA: add ProxyEvaluator --- recbole/evaluator/proxy_evaluator.py | 102 +++++++++++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 recbole/evaluator/proxy_evaluator.py diff --git a/recbole/evaluator/proxy_evaluator.py b/recbole/evaluator/proxy_evaluator.py new file mode 100644 index 000000000..fc91f5d48 --- /dev/null +++ b/recbole/evaluator/proxy_evaluator.py @@ -0,0 +1,102 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/12/9 +# @Author : Zhichao Feng +# @email : fzcbupt@gmail.com + +# UPDATE +# @Time : 2020/12/9 +# @Author : Zhichao Feng +# @email : fzcbupt@gmail.com + +from recbole.evaluator.evaluators import metric_eval_bind, group_metrics, individual_metrics +from collections import ChainMap + + +class ProxyEvaluator(object): + r"""ProxyEvaluator is used to assign the corresponding evaluator according to the evaluation metrics, + for example, TopkEvaluator for top-k metrics, and summarize the results of all evaluators. + + """ + def __init__(self, config): + self.config = config + self.valid_metrics = ChainMap(group_metrics, individual_metrics) + self.metrics = self.config['metrics'] + self._check_args() + self.evaluators = self.build() + + def build(self): + """assign evaluators according to metrics. + + Returns: + list: a list of evaluators. + + """ + evaluator_list = [] + metrics_set = {metric.lower() for metric in self.metrics} + for metrics, evaluator in metric_eval_bind: + used_metrics = list(metrics_set.intersection(set(metrics.keys()))) + if used_metrics: + evaluator_list.append(evaluator(self.config, used_metrics)) + return evaluator_list + + def collect(self, interaction, scores): + """collect the all used evaluators' intermediate result of one batch. + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores (tensor): the tensor of model output with size of `(N, )` + + """ + results = [] + for evaluator in self.evaluators: + results.append(evaluator.collect(interaction, scores)) + return results + + def merge_batch_result(self, batch_matrix_list): + """merge all the intermediate result got in `self.collect` for used evaluators separately. + + Args: + batch_matrix_list (list): the results of all batches not separated + + Returns: + dict: used evaluators' results of all batches + + """ + matrix_dict = {} + for collect_list in batch_matrix_list: + for i, value in enumerate(collect_list): + matrix_dict.setdefault(i, []).append(value) + + return matrix_dict + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'GAUC': 0.9236}`` + + """ + matrix_dict = self.merge_batch_result(batch_matrix_list) + result_dict = {} + for i, evaluator in enumerate(self.evaluators): + res = evaluator.evaluate(matrix_dict[i], eval_data) + result_dict.update(res) + return result_dict + + def _check_args(self): + + # Check metrics + if isinstance(self.metrics, (str, list)): + if isinstance(self.metrics, str): + self.metrics = [self.metrics] + else: + raise TypeError('metrics must be str or list') + + # Convert metric to lowercase + for m in self.metrics: + if m.lower() not in self.valid_metrics: + raise ValueError("There is no metric named {}!".format(m)) From b85c50d74d3f9e271ba625e0545670a4553a4ffa Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 13:06:30 +0800 Subject: [PATCH 128/249] FEA: add three models by Shao Weiqi (HRM, NPE, SHAN). --- recbole/model/sequential_recommender/hrm.py | 172 +++++++++++++++ recbole/model/sequential_recommender/npe.py | 114 ++++++++++ recbole/model/sequential_recommender/shan.py | 212 +++++++++++++++++++ recbole/properties/model/HRM.yaml | 6 + recbole/properties/model/NPE.yaml | 3 + recbole/properties/model/SHAN.yaml | 4 + 6 files changed, 511 insertions(+) create mode 100644 recbole/model/sequential_recommender/hrm.py create mode 100644 recbole/model/sequential_recommender/npe.py create mode 100644 recbole/model/sequential_recommender/shan.py create mode 100644 recbole/properties/model/HRM.yaml create mode 100644 recbole/properties/model/NPE.yaml create mode 100644 recbole/properties/model/SHAN.yaml diff --git a/recbole/model/sequential_recommender/hrm.py b/recbole/model/sequential_recommender/hrm.py new file mode 100644 index 000000000..0554a4ace --- /dev/null +++ b/recbole/model/sequential_recommender/hrm.py @@ -0,0 +1,172 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/22 12:08 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +HRM +################################################ + +Reference: + Pengfei Wang et al. "Learning Hierarchical Representation Model for Next Basket Recommendation." in SIGIR 2015. + +Reference code: + https://github.com/wubinzzu/NeuRec + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_normal_, constant_ + +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class HRM(SequentialRecommender): + r""" + HRM can well capture both sequential behavior and users’ general taste by involving transaction and + user representations in prediction. + + HRM user max- & average- pooling as a good helper. + """ + + def __init__(self, config, dataset): + super(HRM, self).__init__(config, dataset) + + # load the dataset information + self.n_user = dataset.num(self.USER_ID) + self.device = config["device"] + + # load the parameters information + self.embedding_size = config["embedding_size"] + self.pooling_type_layer_1 = config["pooling_type_layer_1"] + self.pooling_type_layer_2 = config["pooling_type_layer_2"] + self.high_order = config["high_order"] + assert self.high_order <= self.max_seq_length, "high_order can't longer than the max_seq_length" + self.reg_weight = config["reg_weight"] + self.dropout_prob = config["dropout_prob"] + + # define the layers and loss type + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_embedding = nn.Embedding(self.n_user, self.embedding_size) + self.dropout = nn.Dropout(self.dropout_prob) + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the model + self.apply(self._init_weights) + + def inverse_seq_item(self, seq_item, seq_item_len): + """ + inverse the seq_item, like this + [1,2,3,0,0,0,0] -- after inverse -->> [0,0,0,0,1,2,3] + """ + seq_item = seq_item.cpu().numpy() + seq_item_len = seq_item_len.cpu().numpy() + new_seq_item = [] + for items, length in zip(seq_item, seq_item_len): + item = list(items[:length]) + zeros = list(items[length:]) + seqs = zeros + item + new_seq_item.append(seqs) + seq_item = torch.tensor(new_seq_item, dtype=torch.long, device=self.device) + + return seq_item + + def _init_weights(self, module): + + if isinstance(module, nn.Embedding): + xavier_normal_(module.weight.data) + + def forward(self, seq_item, user, seq_item_len): + + # seq_item=self.inverse_seq_item(seq_item) + seq_item = self.inverse_seq_item(seq_item, seq_item_len) + + seq_item_embedding = self.item_embedding(seq_item) + # batch_size * seq_len * embedding_size + + high_order_item_embedding = seq_item_embedding[:, -self.high_order:, :] + # batch_size * high_order * embedding_size + + user_embedding = self.dropout(self.user_embedding(user)) + # batch_size * embedding_size + + # layer 1 + if self.pooling_type_layer_1 == "max": + high_order_item_embedding = torch.max(high_order_item_embedding, dim=1).values + # batch_size * embedding_size + else: + for idx, len in enumerate(seq_item_len): + if len > self.high_order: + seq_item_len[idx] = self.high_order + high_order_item_embedding = torch.sum(seq_item_embedding, dim=1) + high_order_item_embedding = torch.div(high_order_item_embedding, seq_item_len.unsqueeze(1).float()) + # batch_size * embedding_size + hybrid_user_embedding = self.dropout( + torch.cat([user_embedding.unsqueeze(dim=1), high_order_item_embedding.unsqueeze(dim=1)], dim=1)) + # batch_size * 2_mul_embedding_size + + # layer 2 + if self.pooling_type_layer_2 == "max": + hybrid_user_embedding = torch.max(hybrid_user_embedding, dim=1).values + # batch_size * embedding_size + else: + hybrid_user_embedding = torch.mean(hybrid_user_embedding, dim=1) + # batch_size * embedding_size + + return hybrid_user_embedding + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(seq_item, user, seq_item_len) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding.weight.t() + logits = torch.matmul(seq_output, test_item_emb) + loss = self.loss_fct(logits, pos_items) + + return loss + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + seq_output = seq_output.repeat(1, self.embedding_size) + test_item_emb = self.item_embedding(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + test_items_emb = self.item_embedding.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + + return scores diff --git a/recbole/model/sequential_recommender/npe.py b/recbole/model/sequential_recommender/npe.py new file mode 100644 index 000000000..f24374e44 --- /dev/null +++ b/recbole/model/sequential_recommender/npe.py @@ -0,0 +1,114 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/22 14:56 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +NPE +################################################ + +Reference: + ThaiBinh Nguyen, et al. "NPE: Neural Personalized Embedding for Collaborative Filtering" in ijcai2018 + +Reference code: + https://github.com/wubinzzu/NeuRec + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_normal_ +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class NPE(SequentialRecommender): + r""" + models a user’s click to an item in two terms: the personal preference of the user for the item, + and the relationships between this item and other items clicked by the user + + """ + + def __init__(self, config, dataset): + super(NPE, self).__init__(config, dataset) + + # load the dataset information + self.n_user = dataset.num(self.USER_ID) + self.device = config["device"] + + # load the parameters information + self.embedding_size = config["embedding_size"] + self.dropout_prob = config["dropout_prob"] + + # define layers and loss type + self.user_embedding = nn.Embedding(self.n_user, self.embedding_size) + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) + self.embedding_seq_item = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.relu = nn.ReLU() + self.dropout = nn.Dropout(self.dropout_prob) + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the module + self.apply(self._init_weights) + + def _init_weights(self, module): + if isinstance(module, nn.Embedding): + xavier_normal_(module.weight.data) + + def forward(self, seq_item, user): + + user_embedding = self.dropout(self.relu(self.user_embedding(user))) + # batch_size * embedding_size + seq_item_embedding = self.item_embedding(seq_item).sum(dim=1) + seq_item_embedding = self.dropout(self.relu(seq_item_embedding)) + # batch_size * embedding_size + + return user_embedding + seq_item_embedding + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_output = self.forward(seq_item, user) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_embs = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.relu(self.item_embedding(neg_items)) + pos_items_emb = self.relu(pos_items_embs) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + else: # self.loss_type = 'CE' + test_item_emb = self.relu(self.item_embedding.weight) + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_item_emb = self.relu(self.item_embedding(test_item)) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_items_emb = self.relu(self.item_embedding.weight) + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores diff --git a/recbole/model/sequential_recommender/shan.py b/recbole/model/sequential_recommender/shan.py new file mode 100644 index 000000000..d970a624d --- /dev/null +++ b/recbole/model/sequential_recommender/shan.py @@ -0,0 +1,212 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/20 22:33 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +SHAN +################################################ + +Reference: + Ying, H et al. "Sequential Recommender System based on Hierarchical Attention Network."in IJCAI 2018 + + +""" +import torch +import torch.nn as nn +import numpy as np +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss +from torch.nn.init import normal_, uniform_ + + +class SHAN(SequentialRecommender): + r""" + SHAN exploit the Hierarchical Attention Network to get the long-short term preference + first get the long term purpose and then fuse the long-term with recent items to get long-short term purpose + + """ + + def __init__(self, config, dataset): + + super(SHAN, self).__init__(config, dataset) + + # load the dataset information + self.n_users = dataset.num(self.USER_ID) + self.device = config['device'] + + # load the parameter information + self.embedding_size = config["embedding_size"] + self.short_item_length = config["short_item_length"] # the length of the short session items + assert self.short_item_length <= self.max_seq_length, "short_item_length can't longer than the max_seq_length" + self.reg_weight = config["reg_weight"] + + # define layers and loss + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) + + self.long_w = nn.Linear(self.embedding_size, self.embedding_size) + self.long_b = nn.Parameter( + uniform_(tensor=torch.zeros(self.embedding_size), a=-np.sqrt(3 / self.embedding_size), + b=np.sqrt(3 / self.embedding_size)), requires_grad=True).to(self.device) + self.long_short_w = nn.Linear(self.embedding_size, self.embedding_size) + self.long_short_b = nn.Parameter( + uniform_(tensor=torch.zeros(self.embedding_size), a=-np.sqrt(3 / self.embedding_size), + b=np.sqrt(3 / self.embedding_size)), requires_grad=True).to(self.device) + + self.relu = nn.ReLU() + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameter of the model + self.apply(self.init_weights) + + def reg_loss(self, user_embedding, item_embedding): + + reg_1, reg_2 = self.reg_weight + loss_1 = reg_1 * torch.norm(self.long_w.weight, p=2) + reg_1 * torch.norm(self.long_short_w.weight, p=2) + loss_2 = reg_2 * torch.norm(user_embedding, p=2) + reg_2 * torch.norm(item_embedding, p=2) + + return loss_1 + loss_2 + + def inverse_seq_item(self, seq_item, seq_item_len): + """ + inverse the seq_item, like this + [1,2,3,0,0,0,0] -- after inverse -->> [0,0,0,0,1,2,3] + """ + seq_item = seq_item.cpu().numpy() + seq_item_len = seq_item_len.cpu().numpy() + new_seq_item = [] + for items, length in zip(seq_item, seq_item_len): + item = list(items[:length]) + zeros = list(items[length:]) + seqs = zeros + item + new_seq_item.append(seqs) + seq_item = torch.tensor(new_seq_item, dtype=torch.long, device=self.device) + + return seq_item + + def init_weights(self, module): + if isinstance(module, nn.Embedding): + normal_(module.weight.data, 0., 0.01) + elif isinstance(module, nn.Linear): + uniform_(module.weight.data, -np.sqrt(3 / self.embedding_size), np.sqrt(3 / self.embedding_size)) + elif isinstance(module, nn.Parameter): + uniform_(module.data, -np.sqrt(3 / self.embedding_size), np.sqrt(3 / self.embedding_size)) + print(module.data) + + def forward(self, seq_item, user, seq_item_len): + + seq_item = self.inverse_seq_item(seq_item, seq_item_len) + + seq_item_embedding = self.item_embedding(seq_item) + user_embedding = self.user_embedding(user) + + # get the mask + mask = seq_item.data.eq(0) + long_term_attention_based_pooling_layer = self.long_term_attention_based_pooling_layer(seq_item_embedding, + user_embedding, mask) + # batch_size * 1 * embedding_size + + short_item_embedding = seq_item_embedding[:, -self.short_item_length:, :] + mask_long_short = mask[:, -self.short_item_length:] + batch_size = mask_long_short.size(0) + x = torch.zeros(size=(batch_size, 1)).eq(1).to(self.device) + mask_long_short = torch.cat([x, mask_long_short], dim=1) + # batch_size * short_item_length * embedding_size + long_short_item_embedding = torch.cat([long_term_attention_based_pooling_layer, short_item_embedding], dim=1) + # batch_size * 1_plus_short_item_length * embedding_size + + long_short_item_embedding = self.long_and_short_term_attention_based_pooling_layer(long_short_item_embedding, + user_embedding, + mask_long_short) + # batch_size * embedding_size + + return long_short_item_embedding + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + user_embedding = self.user_embedding(user) + seq_output = self.forward(seq_item, user, seq_item_len) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + self.reg_loss(user_embedding, pos_items_emb) + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding.weight + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + self.reg_loss(user_embedding, pos_items_emb) + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + test_item_emb = self.item_embedding(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + test_items_emb = self.item_embedding.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores + + def long_and_short_term_attention_based_pooling_layer(self, long_short_item_embedding, user_embedding, mask=None): + """ + + fusing the long term purpose with the short-term preference + """ + long_short_item_embedding_value = long_short_item_embedding + + long_short_item_embedding = self.relu(self.long_short_w(long_short_item_embedding) + self.long_short_b) + long_short_item_embedding = torch.matmul(long_short_item_embedding, user_embedding.unsqueeze(2)).squeeze(-1) + # batch_size * seq_len + if mask is not None: + long_short_item_embedding.masked_fill_(mask, -1e9) + long_short_item_embedding = nn.Softmax(dim=-1)(long_short_item_embedding) + long_short_item_embedding = torch.mul(long_short_item_embedding_value, + long_short_item_embedding.unsqueeze(2)).sum(dim=1) + + return long_short_item_embedding + + def long_term_attention_based_pooling_layer(self, seq_item_embedding, user_embedding, mask=None): + """ + + get the long term purpose of user + """ + seq_item_embedding_value = seq_item_embedding + + seq_item_embedding = self.relu(self.long_w(seq_item_embedding) + self.long_b) + user_item_embedding = torch.matmul(seq_item_embedding, user_embedding.unsqueeze(2)).squeeze(-1) + # batch_size * seq_len + if mask is not None: + user_item_embedding.masked_fill_(mask, -1e9) + user_item_embedding = nn.Softmax(dim=1)(user_item_embedding) + user_item_embedding = torch.mul(seq_item_embedding_value, user_item_embedding.unsqueeze(2)).sum(dim=1, + keepdim=True) + # batch_size * 1 * embedding_size + + return user_item_embedding diff --git a/recbole/properties/model/HRM.yaml b/recbole/properties/model/HRM.yaml new file mode 100644 index 000000000..531e93c3e --- /dev/null +++ b/recbole/properties/model/HRM.yaml @@ -0,0 +1,6 @@ +embedding_size: 64 +high_order: 2 +loss_type: "CE" +dropout_prob: 0.2 +pooling_type_layer_1: "max" +pooling_type_layer_2: "max" \ No newline at end of file diff --git a/recbole/properties/model/NPE.yaml b/recbole/properties/model/NPE.yaml new file mode 100644 index 000000000..cb93282a7 --- /dev/null +++ b/recbole/properties/model/NPE.yaml @@ -0,0 +1,3 @@ +embedding_size: 64 +loss_type: "CE" +dropout_prob: 0.3 \ No newline at end of file diff --git a/recbole/properties/model/SHAN.yaml b/recbole/properties/model/SHAN.yaml new file mode 100644 index 000000000..9c83bf5c8 --- /dev/null +++ b/recbole/properties/model/SHAN.yaml @@ -0,0 +1,4 @@ +embedding_size: 64 +short_item_length: 2 +loss_type: "CE" +reg_weight: [0.01,0.0001] \ No newline at end of file From 8ce025b607436f4062fd77541f8b47602665d240 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 13:29:49 +0800 Subject: [PATCH 129/249] FEA: add three models' test. --- tests/model/test_model_auto.py | 45 ++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index cd69e1dd6..98a7c136f 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -317,6 +317,27 @@ def test_sasrecf(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_hrm(self): + config_dict = { + 'model': 'HRM', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_npe(self): + config_dict = { + 'model': 'NPE', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_shan(self): + config_dict = { + 'model': 'SHAN', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + # def test_fdsa(self): # config_dict = { # 'model': 'FDSA', @@ -574,6 +595,30 @@ def test_stamp(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_hrm(self): + config_dict = { + 'model': 'HRM', + 'loss_type': 'BPR', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_npe(self): + config_dict = { + 'model': 'NPE', + 'loss_type': 'BPR', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_shan(self): + config_dict = { + 'model': 'SHAN', + 'loss_type': 'BPR', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + def test_caser(self): config_dict = { 'model': 'Caser', From f8947a206d0d48c22b0452b30119b6091666e7d7 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 13:06:30 +0800 Subject: [PATCH 130/249] FEA: add three models by Shao Weiqi (HRM, NPE, SHAN). --- recbole/model/sequential_recommender/hrm.py | 172 +++++++++++++++ recbole/model/sequential_recommender/npe.py | 114 ++++++++++ recbole/model/sequential_recommender/shan.py | 212 +++++++++++++++++++ recbole/properties/model/HRM.yaml | 6 + recbole/properties/model/NPE.yaml | 3 + recbole/properties/model/SHAN.yaml | 4 + 6 files changed, 511 insertions(+) create mode 100644 recbole/model/sequential_recommender/hrm.py create mode 100644 recbole/model/sequential_recommender/npe.py create mode 100644 recbole/model/sequential_recommender/shan.py create mode 100644 recbole/properties/model/HRM.yaml create mode 100644 recbole/properties/model/NPE.yaml create mode 100644 recbole/properties/model/SHAN.yaml diff --git a/recbole/model/sequential_recommender/hrm.py b/recbole/model/sequential_recommender/hrm.py new file mode 100644 index 000000000..0554a4ace --- /dev/null +++ b/recbole/model/sequential_recommender/hrm.py @@ -0,0 +1,172 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/22 12:08 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +HRM +################################################ + +Reference: + Pengfei Wang et al. "Learning Hierarchical Representation Model for Next Basket Recommendation." in SIGIR 2015. + +Reference code: + https://github.com/wubinzzu/NeuRec + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_normal_, constant_ + +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class HRM(SequentialRecommender): + r""" + HRM can well capture both sequential behavior and users’ general taste by involving transaction and + user representations in prediction. + + HRM user max- & average- pooling as a good helper. + """ + + def __init__(self, config, dataset): + super(HRM, self).__init__(config, dataset) + + # load the dataset information + self.n_user = dataset.num(self.USER_ID) + self.device = config["device"] + + # load the parameters information + self.embedding_size = config["embedding_size"] + self.pooling_type_layer_1 = config["pooling_type_layer_1"] + self.pooling_type_layer_2 = config["pooling_type_layer_2"] + self.high_order = config["high_order"] + assert self.high_order <= self.max_seq_length, "high_order can't longer than the max_seq_length" + self.reg_weight = config["reg_weight"] + self.dropout_prob = config["dropout_prob"] + + # define the layers and loss type + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_embedding = nn.Embedding(self.n_user, self.embedding_size) + self.dropout = nn.Dropout(self.dropout_prob) + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the model + self.apply(self._init_weights) + + def inverse_seq_item(self, seq_item, seq_item_len): + """ + inverse the seq_item, like this + [1,2,3,0,0,0,0] -- after inverse -->> [0,0,0,0,1,2,3] + """ + seq_item = seq_item.cpu().numpy() + seq_item_len = seq_item_len.cpu().numpy() + new_seq_item = [] + for items, length in zip(seq_item, seq_item_len): + item = list(items[:length]) + zeros = list(items[length:]) + seqs = zeros + item + new_seq_item.append(seqs) + seq_item = torch.tensor(new_seq_item, dtype=torch.long, device=self.device) + + return seq_item + + def _init_weights(self, module): + + if isinstance(module, nn.Embedding): + xavier_normal_(module.weight.data) + + def forward(self, seq_item, user, seq_item_len): + + # seq_item=self.inverse_seq_item(seq_item) + seq_item = self.inverse_seq_item(seq_item, seq_item_len) + + seq_item_embedding = self.item_embedding(seq_item) + # batch_size * seq_len * embedding_size + + high_order_item_embedding = seq_item_embedding[:, -self.high_order:, :] + # batch_size * high_order * embedding_size + + user_embedding = self.dropout(self.user_embedding(user)) + # batch_size * embedding_size + + # layer 1 + if self.pooling_type_layer_1 == "max": + high_order_item_embedding = torch.max(high_order_item_embedding, dim=1).values + # batch_size * embedding_size + else: + for idx, len in enumerate(seq_item_len): + if len > self.high_order: + seq_item_len[idx] = self.high_order + high_order_item_embedding = torch.sum(seq_item_embedding, dim=1) + high_order_item_embedding = torch.div(high_order_item_embedding, seq_item_len.unsqueeze(1).float()) + # batch_size * embedding_size + hybrid_user_embedding = self.dropout( + torch.cat([user_embedding.unsqueeze(dim=1), high_order_item_embedding.unsqueeze(dim=1)], dim=1)) + # batch_size * 2_mul_embedding_size + + # layer 2 + if self.pooling_type_layer_2 == "max": + hybrid_user_embedding = torch.max(hybrid_user_embedding, dim=1).values + # batch_size * embedding_size + else: + hybrid_user_embedding = torch.mean(hybrid_user_embedding, dim=1) + # batch_size * embedding_size + + return hybrid_user_embedding + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(seq_item, user, seq_item_len) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding.weight.t() + logits = torch.matmul(seq_output, test_item_emb) + loss = self.loss_fct(logits, pos_items) + + return loss + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + seq_output = seq_output.repeat(1, self.embedding_size) + test_item_emb = self.item_embedding(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + test_items_emb = self.item_embedding.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + + return scores diff --git a/recbole/model/sequential_recommender/npe.py b/recbole/model/sequential_recommender/npe.py new file mode 100644 index 000000000..f24374e44 --- /dev/null +++ b/recbole/model/sequential_recommender/npe.py @@ -0,0 +1,114 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/22 14:56 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +NPE +################################################ + +Reference: + ThaiBinh Nguyen, et al. "NPE: Neural Personalized Embedding for Collaborative Filtering" in ijcai2018 + +Reference code: + https://github.com/wubinzzu/NeuRec + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_normal_ +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class NPE(SequentialRecommender): + r""" + models a user’s click to an item in two terms: the personal preference of the user for the item, + and the relationships between this item and other items clicked by the user + + """ + + def __init__(self, config, dataset): + super(NPE, self).__init__(config, dataset) + + # load the dataset information + self.n_user = dataset.num(self.USER_ID) + self.device = config["device"] + + # load the parameters information + self.embedding_size = config["embedding_size"] + self.dropout_prob = config["dropout_prob"] + + # define layers and loss type + self.user_embedding = nn.Embedding(self.n_user, self.embedding_size) + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) + self.embedding_seq_item = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.relu = nn.ReLU() + self.dropout = nn.Dropout(self.dropout_prob) + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the module + self.apply(self._init_weights) + + def _init_weights(self, module): + if isinstance(module, nn.Embedding): + xavier_normal_(module.weight.data) + + def forward(self, seq_item, user): + + user_embedding = self.dropout(self.relu(self.user_embedding(user))) + # batch_size * embedding_size + seq_item_embedding = self.item_embedding(seq_item).sum(dim=1) + seq_item_embedding = self.dropout(self.relu(seq_item_embedding)) + # batch_size * embedding_size + + return user_embedding + seq_item_embedding + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_output = self.forward(seq_item, user) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_embs = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.relu(self.item_embedding(neg_items)) + pos_items_emb = self.relu(pos_items_embs) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + else: # self.loss_type = 'CE' + test_item_emb = self.relu(self.item_embedding.weight) + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_item_emb = self.relu(self.item_embedding(test_item)) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_items_emb = self.relu(self.item_embedding.weight) + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores diff --git a/recbole/model/sequential_recommender/shan.py b/recbole/model/sequential_recommender/shan.py new file mode 100644 index 000000000..d970a624d --- /dev/null +++ b/recbole/model/sequential_recommender/shan.py @@ -0,0 +1,212 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/20 22:33 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +SHAN +################################################ + +Reference: + Ying, H et al. "Sequential Recommender System based on Hierarchical Attention Network."in IJCAI 2018 + + +""" +import torch +import torch.nn as nn +import numpy as np +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss +from torch.nn.init import normal_, uniform_ + + +class SHAN(SequentialRecommender): + r""" + SHAN exploit the Hierarchical Attention Network to get the long-short term preference + first get the long term purpose and then fuse the long-term with recent items to get long-short term purpose + + """ + + def __init__(self, config, dataset): + + super(SHAN, self).__init__(config, dataset) + + # load the dataset information + self.n_users = dataset.num(self.USER_ID) + self.device = config['device'] + + # load the parameter information + self.embedding_size = config["embedding_size"] + self.short_item_length = config["short_item_length"] # the length of the short session items + assert self.short_item_length <= self.max_seq_length, "short_item_length can't longer than the max_seq_length" + self.reg_weight = config["reg_weight"] + + # define layers and loss + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) + + self.long_w = nn.Linear(self.embedding_size, self.embedding_size) + self.long_b = nn.Parameter( + uniform_(tensor=torch.zeros(self.embedding_size), a=-np.sqrt(3 / self.embedding_size), + b=np.sqrt(3 / self.embedding_size)), requires_grad=True).to(self.device) + self.long_short_w = nn.Linear(self.embedding_size, self.embedding_size) + self.long_short_b = nn.Parameter( + uniform_(tensor=torch.zeros(self.embedding_size), a=-np.sqrt(3 / self.embedding_size), + b=np.sqrt(3 / self.embedding_size)), requires_grad=True).to(self.device) + + self.relu = nn.ReLU() + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameter of the model + self.apply(self.init_weights) + + def reg_loss(self, user_embedding, item_embedding): + + reg_1, reg_2 = self.reg_weight + loss_1 = reg_1 * torch.norm(self.long_w.weight, p=2) + reg_1 * torch.norm(self.long_short_w.weight, p=2) + loss_2 = reg_2 * torch.norm(user_embedding, p=2) + reg_2 * torch.norm(item_embedding, p=2) + + return loss_1 + loss_2 + + def inverse_seq_item(self, seq_item, seq_item_len): + """ + inverse the seq_item, like this + [1,2,3,0,0,0,0] -- after inverse -->> [0,0,0,0,1,2,3] + """ + seq_item = seq_item.cpu().numpy() + seq_item_len = seq_item_len.cpu().numpy() + new_seq_item = [] + for items, length in zip(seq_item, seq_item_len): + item = list(items[:length]) + zeros = list(items[length:]) + seqs = zeros + item + new_seq_item.append(seqs) + seq_item = torch.tensor(new_seq_item, dtype=torch.long, device=self.device) + + return seq_item + + def init_weights(self, module): + if isinstance(module, nn.Embedding): + normal_(module.weight.data, 0., 0.01) + elif isinstance(module, nn.Linear): + uniform_(module.weight.data, -np.sqrt(3 / self.embedding_size), np.sqrt(3 / self.embedding_size)) + elif isinstance(module, nn.Parameter): + uniform_(module.data, -np.sqrt(3 / self.embedding_size), np.sqrt(3 / self.embedding_size)) + print(module.data) + + def forward(self, seq_item, user, seq_item_len): + + seq_item = self.inverse_seq_item(seq_item, seq_item_len) + + seq_item_embedding = self.item_embedding(seq_item) + user_embedding = self.user_embedding(user) + + # get the mask + mask = seq_item.data.eq(0) + long_term_attention_based_pooling_layer = self.long_term_attention_based_pooling_layer(seq_item_embedding, + user_embedding, mask) + # batch_size * 1 * embedding_size + + short_item_embedding = seq_item_embedding[:, -self.short_item_length:, :] + mask_long_short = mask[:, -self.short_item_length:] + batch_size = mask_long_short.size(0) + x = torch.zeros(size=(batch_size, 1)).eq(1).to(self.device) + mask_long_short = torch.cat([x, mask_long_short], dim=1) + # batch_size * short_item_length * embedding_size + long_short_item_embedding = torch.cat([long_term_attention_based_pooling_layer, short_item_embedding], dim=1) + # batch_size * 1_plus_short_item_length * embedding_size + + long_short_item_embedding = self.long_and_short_term_attention_based_pooling_layer(long_short_item_embedding, + user_embedding, + mask_long_short) + # batch_size * embedding_size + + return long_short_item_embedding + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + user_embedding = self.user_embedding(user) + seq_output = self.forward(seq_item, user, seq_item_len) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + self.reg_loss(user_embedding, pos_items_emb) + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding.weight + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + self.reg_loss(user_embedding, pos_items_emb) + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + test_item_emb = self.item_embedding(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user, seq_item_len) + test_items_emb = self.item_embedding.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores + + def long_and_short_term_attention_based_pooling_layer(self, long_short_item_embedding, user_embedding, mask=None): + """ + + fusing the long term purpose with the short-term preference + """ + long_short_item_embedding_value = long_short_item_embedding + + long_short_item_embedding = self.relu(self.long_short_w(long_short_item_embedding) + self.long_short_b) + long_short_item_embedding = torch.matmul(long_short_item_embedding, user_embedding.unsqueeze(2)).squeeze(-1) + # batch_size * seq_len + if mask is not None: + long_short_item_embedding.masked_fill_(mask, -1e9) + long_short_item_embedding = nn.Softmax(dim=-1)(long_short_item_embedding) + long_short_item_embedding = torch.mul(long_short_item_embedding_value, + long_short_item_embedding.unsqueeze(2)).sum(dim=1) + + return long_short_item_embedding + + def long_term_attention_based_pooling_layer(self, seq_item_embedding, user_embedding, mask=None): + """ + + get the long term purpose of user + """ + seq_item_embedding_value = seq_item_embedding + + seq_item_embedding = self.relu(self.long_w(seq_item_embedding) + self.long_b) + user_item_embedding = torch.matmul(seq_item_embedding, user_embedding.unsqueeze(2)).squeeze(-1) + # batch_size * seq_len + if mask is not None: + user_item_embedding.masked_fill_(mask, -1e9) + user_item_embedding = nn.Softmax(dim=1)(user_item_embedding) + user_item_embedding = torch.mul(seq_item_embedding_value, user_item_embedding.unsqueeze(2)).sum(dim=1, + keepdim=True) + # batch_size * 1 * embedding_size + + return user_item_embedding diff --git a/recbole/properties/model/HRM.yaml b/recbole/properties/model/HRM.yaml new file mode 100644 index 000000000..531e93c3e --- /dev/null +++ b/recbole/properties/model/HRM.yaml @@ -0,0 +1,6 @@ +embedding_size: 64 +high_order: 2 +loss_type: "CE" +dropout_prob: 0.2 +pooling_type_layer_1: "max" +pooling_type_layer_2: "max" \ No newline at end of file diff --git a/recbole/properties/model/NPE.yaml b/recbole/properties/model/NPE.yaml new file mode 100644 index 000000000..cb93282a7 --- /dev/null +++ b/recbole/properties/model/NPE.yaml @@ -0,0 +1,3 @@ +embedding_size: 64 +loss_type: "CE" +dropout_prob: 0.3 \ No newline at end of file diff --git a/recbole/properties/model/SHAN.yaml b/recbole/properties/model/SHAN.yaml new file mode 100644 index 000000000..9c83bf5c8 --- /dev/null +++ b/recbole/properties/model/SHAN.yaml @@ -0,0 +1,4 @@ +embedding_size: 64 +short_item_length: 2 +loss_type: "CE" +reg_weight: [0.01,0.0001] \ No newline at end of file From db9416a47d3539f84b043a66e062328857f4ce7d Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 13:29:49 +0800 Subject: [PATCH 131/249] FEA: add three models' test. --- tests/model/test_model_auto.py | 45 ++++++++++++++++++++++++++++++++++ 1 file changed, 45 insertions(+) diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index cd69e1dd6..98a7c136f 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -317,6 +317,27 @@ def test_sasrecf(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_hrm(self): + config_dict = { + 'model': 'HRM', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_npe(self): + config_dict = { + 'model': 'NPE', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_shan(self): + config_dict = { + 'model': 'SHAN', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + # def test_fdsa(self): # config_dict = { # 'model': 'FDSA', @@ -574,6 +595,30 @@ def test_stamp(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_hrm(self): + config_dict = { + 'model': 'HRM', + 'loss_type': 'BPR', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_npe(self): + config_dict = { + 'model': 'NPE', + 'loss_type': 'BPR', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_shan(self): + config_dict = { + 'model': 'SHAN', + 'loss_type': 'BPR', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + def test_caser(self): config_dict = { 'model': 'Caser', From 28a6bcb92f7e2c4923826abd4827b7acaad64830 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 14:23:47 +0800 Subject: [PATCH 132/249] FIX: bug fix in interaction.update --- recbole/data/interaction.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/recbole/data/interaction.py b/recbole/data/interaction.py index 5fd137c6a..8adfada82 100644 --- a/recbole/data/interaction.py +++ b/recbole/data/interaction.py @@ -240,8 +240,10 @@ def update(self, new_inter): """ for k in new_inter.interaction: self.interaction[k] = new_inter.interaction[k] - self.pos_len_list = new_inter.pos_len_list - self.user_len_list = new_inter.user_len_list + if new_inter.pos_len_list is not None: + self.pos_len_list = new_inter.pos_len_list + if new_inter.user_len_list is not None: + self.user_len_list = new_inter.user_len_list def drop(self, column): """Drop column in interaction. From 4084f532058bccd90caa0e0f5717a09c520df6a3 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 14:23:47 +0800 Subject: [PATCH 133/249] FIX: bug fix in interaction.update --- recbole/data/interaction.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/recbole/data/interaction.py b/recbole/data/interaction.py index 5fd137c6a..8adfada82 100644 --- a/recbole/data/interaction.py +++ b/recbole/data/interaction.py @@ -240,8 +240,10 @@ def update(self, new_inter): """ for k in new_inter.interaction: self.interaction[k] = new_inter.interaction[k] - self.pos_len_list = new_inter.pos_len_list - self.user_len_list = new_inter.user_len_list + if new_inter.pos_len_list is not None: + self.pos_len_list = new_inter.pos_len_list + if new_inter.user_len_list is not None: + self.user_len_list = new_inter.user_len_list def drop(self, column): """Drop column in interaction. From be73b23b9de5a19f4af928b783273d488760291e Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 15:08:05 +0800 Subject: [PATCH 134/249] FEA: add model HGN by Shao Weiqi. --- recbole/model/sequential_recommender/hgn.py | 198 ++++++++++++++++++++ recbole/properties/model/HGN.yaml | 4 + tests/model/test_model_auto.py | 15 ++ 3 files changed, 217 insertions(+) create mode 100644 recbole/model/sequential_recommender/hgn.py create mode 100644 recbole/properties/model/HGN.yaml diff --git a/recbole/model/sequential_recommender/hgn.py b/recbole/model/sequential_recommender/hgn.py new file mode 100644 index 000000000..a97c65988 --- /dev/null +++ b/recbole/model/sequential_recommender/hgn.py @@ -0,0 +1,198 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/21 16:36 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +HGN +################################################ + +Reference: + Chen Ma et al. "Hierarchical Gating Networks for Sequential Recommendation."in SIGKDD 2019 + + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_uniform_, constant_, normal_ +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class HGN(SequentialRecommender): + r""" + HGN sets feature gating and instance gating to get the important feature and item for predicting the next item + + """ + + def __init__(self, config, dataset): + super(HGN, self).__init__(config, dataset) + + # load the dataset information + self.n_user = dataset.num(self.USER_ID) + self.device = config["device"] + + # load the parameter information + self.embedding_size = config["embedding_size"] + self.reg_weight = config["reg_weight"] + self.pool_type = config["pooling_type"] + + if self.pool_type not in ["max", "average"]: + raise NotImplementedError("Make sure 'loss_type' in ['max', 'average']!") + + # define the layers and loss function + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_embedding = nn.Embedding(self.n_user, self.embedding_size) + + # define the module feature gating need + self.w1 = nn.Linear(self.embedding_size, self.embedding_size) + self.w2 = nn.Linear(self.embedding_size, self.embedding_size) + self.b = nn.Parameter(torch.zeros(self.embedding_size), requires_grad=True).to(self.device) + + # define the module instance gating need + self.w3 = nn.Linear(self.embedding_size, 1, bias=False) + self.w4 = nn.Linear(self.embedding_size, self.max_seq_length, bias=False) + + # define item_embedding for prediction + self.item_embedding_for_prediction = nn.Embedding(self.n_items, self.embedding_size) + + self.sigmoid = nn.Sigmoid() + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the model + self.apply(self._init_weights) + + def reg_loss(self, user_embedding, item_embedding, seq_item_embedding): + + reg_1, reg_2 = self.reg_weight + loss_1 = reg_1 * torch.norm(self.w1.weight, p=2) + reg_1 * torch.norm(self.w2.weight, p=2) + reg_1 * torch.norm( + self.w3.weight, p=2) + reg_1 * torch.norm(self.w4.weight, p=2) + loss_2 = reg_2 * torch.norm(user_embedding, p=2) + reg_2 * torch.norm(item_embedding, p=2) + reg_2 * torch.norm( + seq_item_embedding, p=2) + + return loss_1 + loss_2 + + def _init_weights(self, module): + if isinstance(module, nn.Embedding): + normal_(module.weight.data, 0., 1 / self.embedding_size) + elif isinstance(module, nn.Linear): + xavier_uniform_(module.weight.data) + if module.bias is not None: + constant_(module.bias.data, 0) + + def feature_gating(self, seq_item_embedding, user_embedding): + """ + + choose the features that will be sent to the next stage(more important feature, more focus) + """ + + batch_size, seq_len, embedding_size = seq_item_embedding.size() + seq_item_embedding_value = seq_item_embedding + + seq_item_embedding = self.w1(seq_item_embedding) + # batch_size * seq_len * embedding_size + user_embedding = self.w2(user_embedding) + # batch_size * embedding_size + user_embedding = user_embedding.unsqueeze(1).repeat(1, seq_len, 1) + # batch_size * seq_len * embedding_size + + user_item = self.sigmoid(seq_item_embedding + user_embedding + self.b) + # batch_size * seq_len * embedding_size + + user_item = torch.mul(seq_item_embedding_value, user_item) + # batch_size * seq_len * embedding_size + + return user_item + + def instance_gating(self, user_item, user_embedding): + """ + + choose the last click items that will influence the prediction( more important more chance to get attention) + """ + + user_embedding_value = user_item + + user_item = self.w3(user_item) + # batch_size * seq_len * 1 + + user_embedding = self.w4(user_embedding).unsqueeze(2) + # batch_size * seq_len * 1 + + instance_score = self.sigmoid(user_item + user_embedding).squeeze(-1) + # batch_size * seq_len * 1 + output = torch.mul(instance_score.unsqueeze(2), user_embedding_value) + # batch_size * seq_len * embedding_size + + if self.pool_type == "average": + output = torch.div(output.sum(dim=1), instance_score.sum(dim=1).unsqueeze(1)) + # batch_size * embedding_size + else: + # for max_pooling + index = torch.max(instance_score, dim=1)[1] + # batch_size * 1 + output = self.gather_indexes(output, index) + # batch_size * seq_len * embedding_size ==>> batch_size * embedding_size + + return output + + def forward(self, seq_item, user): + + seq_item_embedding = self.item_embedding(seq_item) + user_embedding = self.user_embedding(user) + feature_gating = self.feature_gating(seq_item_embedding, user_embedding) + instance_gating = self.instance_gating(feature_gating, user_embedding) + # batch_size * embedding_size + item_item = torch.sum(seq_item_embedding, dim=1) + # batch_size * embedding_size + + return user_embedding + instance_gating + item_item + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_embedding = self.item_embedding(seq_item) + user = interaction[self.USER_ID] + user_embedding = self.user_embedding(user) + seq_output = self.forward(seq_item, user) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding_for_prediction(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + self.reg_loss(user_embedding, pos_items_emb, seq_item_embedding) + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding_for_prediction.weight + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + self.reg_loss(user_embedding, pos_items_emb, seq_item_embedding) + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_item_emb = self.item_embedding_for_prediction(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_items_emb = self.item_embedding_for_prediction.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores diff --git a/recbole/properties/model/HGN.yaml b/recbole/properties/model/HGN.yaml new file mode 100644 index 000000000..69b95a440 --- /dev/null +++ b/recbole/properties/model/HGN.yaml @@ -0,0 +1,4 @@ +embedding_size: 64 +loss_type: 'BPR' +pooling_type: "average" +reg_weight: [0.00,0.00] \ No newline at end of file diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 98a7c136f..0be3e9b31 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -338,6 +338,13 @@ def test_shan(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_hgn(self): + config_dict = { + 'model': 'HGN', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + # def test_fdsa(self): # config_dict = { # 'model': 'FDSA', @@ -619,6 +626,14 @@ def test_shan(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_hgn(self): + config_dict = { + 'model': 'HGN', + 'loss_type': 'CE', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + def test_caser(self): config_dict = { 'model': 'Caser', From 64ea09016e9ca219ce89a045598e3938409aa8b8 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 15:08:05 +0800 Subject: [PATCH 135/249] FEA: add model HGN by Shao Weiqi. --- recbole/model/sequential_recommender/hgn.py | 198 ++++++++++++++++++++ recbole/properties/model/HGN.yaml | 4 + tests/model/test_model_auto.py | 15 ++ 3 files changed, 217 insertions(+) create mode 100644 recbole/model/sequential_recommender/hgn.py create mode 100644 recbole/properties/model/HGN.yaml diff --git a/recbole/model/sequential_recommender/hgn.py b/recbole/model/sequential_recommender/hgn.py new file mode 100644 index 000000000..a97c65988 --- /dev/null +++ b/recbole/model/sequential_recommender/hgn.py @@ -0,0 +1,198 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/21 16:36 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +HGN +################################################ + +Reference: + Chen Ma et al. "Hierarchical Gating Networks for Sequential Recommendation."in SIGKDD 2019 + + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_uniform_, constant_, normal_ +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class HGN(SequentialRecommender): + r""" + HGN sets feature gating and instance gating to get the important feature and item for predicting the next item + + """ + + def __init__(self, config, dataset): + super(HGN, self).__init__(config, dataset) + + # load the dataset information + self.n_user = dataset.num(self.USER_ID) + self.device = config["device"] + + # load the parameter information + self.embedding_size = config["embedding_size"] + self.reg_weight = config["reg_weight"] + self.pool_type = config["pooling_type"] + + if self.pool_type not in ["max", "average"]: + raise NotImplementedError("Make sure 'loss_type' in ['max', 'average']!") + + # define the layers and loss function + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_embedding = nn.Embedding(self.n_user, self.embedding_size) + + # define the module feature gating need + self.w1 = nn.Linear(self.embedding_size, self.embedding_size) + self.w2 = nn.Linear(self.embedding_size, self.embedding_size) + self.b = nn.Parameter(torch.zeros(self.embedding_size), requires_grad=True).to(self.device) + + # define the module instance gating need + self.w3 = nn.Linear(self.embedding_size, 1, bias=False) + self.w4 = nn.Linear(self.embedding_size, self.max_seq_length, bias=False) + + # define item_embedding for prediction + self.item_embedding_for_prediction = nn.Embedding(self.n_items, self.embedding_size) + + self.sigmoid = nn.Sigmoid() + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the model + self.apply(self._init_weights) + + def reg_loss(self, user_embedding, item_embedding, seq_item_embedding): + + reg_1, reg_2 = self.reg_weight + loss_1 = reg_1 * torch.norm(self.w1.weight, p=2) + reg_1 * torch.norm(self.w2.weight, p=2) + reg_1 * torch.norm( + self.w3.weight, p=2) + reg_1 * torch.norm(self.w4.weight, p=2) + loss_2 = reg_2 * torch.norm(user_embedding, p=2) + reg_2 * torch.norm(item_embedding, p=2) + reg_2 * torch.norm( + seq_item_embedding, p=2) + + return loss_1 + loss_2 + + def _init_weights(self, module): + if isinstance(module, nn.Embedding): + normal_(module.weight.data, 0., 1 / self.embedding_size) + elif isinstance(module, nn.Linear): + xavier_uniform_(module.weight.data) + if module.bias is not None: + constant_(module.bias.data, 0) + + def feature_gating(self, seq_item_embedding, user_embedding): + """ + + choose the features that will be sent to the next stage(more important feature, more focus) + """ + + batch_size, seq_len, embedding_size = seq_item_embedding.size() + seq_item_embedding_value = seq_item_embedding + + seq_item_embedding = self.w1(seq_item_embedding) + # batch_size * seq_len * embedding_size + user_embedding = self.w2(user_embedding) + # batch_size * embedding_size + user_embedding = user_embedding.unsqueeze(1).repeat(1, seq_len, 1) + # batch_size * seq_len * embedding_size + + user_item = self.sigmoid(seq_item_embedding + user_embedding + self.b) + # batch_size * seq_len * embedding_size + + user_item = torch.mul(seq_item_embedding_value, user_item) + # batch_size * seq_len * embedding_size + + return user_item + + def instance_gating(self, user_item, user_embedding): + """ + + choose the last click items that will influence the prediction( more important more chance to get attention) + """ + + user_embedding_value = user_item + + user_item = self.w3(user_item) + # batch_size * seq_len * 1 + + user_embedding = self.w4(user_embedding).unsqueeze(2) + # batch_size * seq_len * 1 + + instance_score = self.sigmoid(user_item + user_embedding).squeeze(-1) + # batch_size * seq_len * 1 + output = torch.mul(instance_score.unsqueeze(2), user_embedding_value) + # batch_size * seq_len * embedding_size + + if self.pool_type == "average": + output = torch.div(output.sum(dim=1), instance_score.sum(dim=1).unsqueeze(1)) + # batch_size * embedding_size + else: + # for max_pooling + index = torch.max(instance_score, dim=1)[1] + # batch_size * 1 + output = self.gather_indexes(output, index) + # batch_size * seq_len * embedding_size ==>> batch_size * embedding_size + + return output + + def forward(self, seq_item, user): + + seq_item_embedding = self.item_embedding(seq_item) + user_embedding = self.user_embedding(user) + feature_gating = self.feature_gating(seq_item_embedding, user_embedding) + instance_gating = self.instance_gating(feature_gating, user_embedding) + # batch_size * embedding_size + item_item = torch.sum(seq_item_embedding, dim=1) + # batch_size * embedding_size + + return user_embedding + instance_gating + item_item + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_embedding = self.item_embedding(seq_item) + user = interaction[self.USER_ID] + user_embedding = self.user_embedding(user) + seq_output = self.forward(seq_item, user) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding_for_prediction(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + self.reg_loss(user_embedding, pos_items_emb, seq_item_embedding) + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding_for_prediction.weight + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + self.reg_loss(user_embedding, pos_items_emb, seq_item_embedding) + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_item_emb = self.item_embedding_for_prediction(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, user) + test_items_emb = self.item_embedding_for_prediction.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores diff --git a/recbole/properties/model/HGN.yaml b/recbole/properties/model/HGN.yaml new file mode 100644 index 000000000..69b95a440 --- /dev/null +++ b/recbole/properties/model/HGN.yaml @@ -0,0 +1,4 @@ +embedding_size: 64 +loss_type: 'BPR' +pooling_type: "average" +reg_weight: [0.00,0.00] \ No newline at end of file diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 98a7c136f..0be3e9b31 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -338,6 +338,13 @@ def test_shan(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_hgn(self): + config_dict = { + 'model': 'HGN', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + # def test_fdsa(self): # config_dict = { # 'model': 'FDSA', @@ -619,6 +626,14 @@ def test_shan(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_hgn(self): + config_dict = { + 'model': 'HGN', + 'loss_type': 'CE', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + def test_caser(self): config_dict = { 'model': 'Caser', From 88521842c2018ee4610d4f53dd6884e4570200b6 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 17:19:37 +0800 Subject: [PATCH 136/249] FIX: bug fix in avg_actions_of_users && avg_actions_of_items --- recbole/data/dataset/dataset.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index 78eb2fc63..d0bcbce25 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -1108,7 +1108,7 @@ def avg_actions_of_users(self): Returns: numpy.float64: Average number of users' interaction records. """ - return np.mean(list(Counter(self.inter_feat[self.uid_field]).values())) + return np.mean(list(Counter(self.inter_feat[self.uid_field].numpy()).values())) @property def avg_actions_of_items(self): @@ -1117,7 +1117,7 @@ def avg_actions_of_items(self): Returns: numpy.float64: Average number of items' interaction records. """ - return np.mean(list(Counter(self.inter_feat[self.iid_field]).values())) + return np.mean(list(Counter(self.inter_feat[self.iid_field].numpy()).values())) @property def sparsity(self): From ac4a9f66b5d389c0425267f9ecad88dd17c3bdf8 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 17:36:41 +0800 Subject: [PATCH 137/249] FIX: bug fix in avg_actions_of_users && avg_actions_of_items (#576) * FEA: add three models by Shao Weiqi (HRM, NPE, SHAN). * FEA: add three models' test. * FIX: bug fix in interaction.update * FEA: add model HGN by Shao Weiqi. * FIX: bug fix in avg_actions_of_users && avg_actions_of_items --- recbole/data/dataset/dataset.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index 78eb2fc63..d0bcbce25 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -1108,7 +1108,7 @@ def avg_actions_of_users(self): Returns: numpy.float64: Average number of users' interaction records. """ - return np.mean(list(Counter(self.inter_feat[self.uid_field]).values())) + return np.mean(list(Counter(self.inter_feat[self.uid_field].numpy()).values())) @property def avg_actions_of_items(self): @@ -1117,7 +1117,7 @@ def avg_actions_of_items(self): Returns: numpy.float64: Average number of items' interaction records. """ - return np.mean(list(Counter(self.inter_feat[self.iid_field]).values())) + return np.mean(list(Counter(self.inter_feat[self.iid_field].numpy()).values())) @property def sparsity(self): From 5e183cb50377cf166ff39d3ad134b9d9c584cff5 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 12 Dec 2020 21:02:06 +0800 Subject: [PATCH 138/249] FIX: bug fix in _full_sort_batch_eval (when model do not have full_sort_predict, the evaluator may raise error). --- recbole/trainer/trainer.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 8907e13c0..1fb680a9c 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -290,17 +290,17 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): def _full_sort_batch_eval(self, batched_data): interaction, history_index, swap_row, swap_col_after, swap_col_before = batched_data - batch_size = interaction.length * self.tot_item_num try: # Note: interaction without item ids scores = self.model.full_sort_predict(interaction.to(self.device)) except NotImplementedError: - interaction = interaction.to(self.device).repeat_interleave(self.tot_item_num) - interaction.update(self.item_tensor[:batch_size]) + new_inter = interaction.to(self.device).repeat_interleave(self.tot_item_num) + batch_size = len(new_inter) + new_inter.update(self.item_tensor[:batch_size]) if batch_size <= self.test_batch_size: - scores = self.model.predict(interaction) + scores = self.model.predict(new_inter) else: - scores = self._spilt_predict(interaction, batch_size) + scores = self._spilt_predict(new_inter, batch_size) scores = scores.view(-1, self.tot_item_num) scores[:, 0] = -np.inf From b2a98316cabc671e9223bd673f2561824fb7512b Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:29:43 +0800 Subject: [PATCH 139/249] FEA: add GAUC metric --- recbole/evaluator/metrics.py | 18 ++++++++++++++---- 1 file changed, 14 insertions(+), 4 deletions(-) diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index 976ac9880..cb31e1456 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -4,7 +4,7 @@ # @email : tsotfsk@outlook.com # UPDATE -# @Time : 2020/08/12, 2020/08/21, 2020/9/16 +# @Time : 2020/08/12, 2020/12/9, 2020/9/16 # @Author : Kaiyuan Li, Zhichao Feng, Xingyu Pan # @email : tsotfsk@outlook.com, fzcbupt@gmail.com, panxy@ruc.edu.cn @@ -20,6 +20,7 @@ from sklearn.metrics import auc as sk_auc from sklearn.metrics import log_loss, mean_absolute_error, mean_squared_error + # TopK Metrics # @@ -85,7 +86,7 @@ def map_(pos_index, pos_len): actual_len = np.where(pos_len > len_rank, len_rank, pos_len) result = np.zeros_like(pos_index, dtype=np.float) for row, lens in enumerate(actual_len): - ranges = np.arange(1, pos_index.shape[1]+1) + ranges = np.arange(1, pos_index.shape[1] + 1) ranges[lens:] = ranges[lens - 1] result[row] = sum_pre[row] / ranges return result @@ -164,8 +165,16 @@ def precision_(pos_index, pos_len): return pos_index.cumsum(axis=1) / np.arange(1, pos_index.shape[1] + 1) -# CTR Metrics # +def gauc_(user_len_list, pos_len_list, pos_rank_sum): + frac = user_len_list - (pos_len_list - 1) / 2 - (1 / pos_len_list) * np.squeeze(pos_rank_sum) + neg_item_num = user_len_list - pos_len_list + user_auc = frac / neg_item_num + + result = (user_auc * pos_len_list).sum() / pos_len_list.sum() + return result + +# CTR Metrics # def auc_(trues, preds): r"""AUC_ (also known as Area Under Curve) is used to evaluate the two-class model, referring to the area under the ROC curve @@ -300,5 +309,6 @@ def log_loss_(trues, preds): 'rmse': rmse_, 'mae': mae_, 'logloss': log_loss_, - 'auc': auc_ + 'auc': auc_, + 'gauc': gauc_ } From 17cea16e6db38c5797fb8a855ac1bf67d8b3980c Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:29:58 +0800 Subject: [PATCH 140/249] REFACTOR: add GroupEvaluator and IndividualEvaluator --- recbole/evaluator/abstract_evaluator.py | 175 +++++++++++++++++------- 1 file changed, 129 insertions(+), 46 deletions(-) diff --git a/recbole/evaluator/abstract_evaluator.py b/recbole/evaluator/abstract_evaluator.py index c17023433..2570fe8ad 100644 --- a/recbole/evaluator/abstract_evaluator.py +++ b/recbole/evaluator/abstract_evaluator.py @@ -1,46 +1,129 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/10/21 -# @Author : Kaiyuan Li -# @email : tsotfsk@outlook.com - -""" -recbole.evaluator.abstract_evaluator -##################################### -""" - - -class AbstractEvaluator(object): - """:class:`AbstractEvaluator` is an abstract object which supports - the evaluation of the model. It is called by :class:`Trainer`. - - Note: - If you want to inherit this class and implement your own evaluator class, - you must implement the following functions. - - Args: - config (Config): The config of evaluator. - - """ - - def __init__(self, config): - self.metrics = config['metrics'] - - def _check_args(self): - """check the correct of the setting""" - raise NotImplementedError - - def collect(self, *args): - """get the intermediate results for each batch, it is called at the end of each batch""" - raise NotImplementedError - - def evaluate(self, *args): - """calculate the metrics of all batches, it is called at the end of each epoch""" - raise NotImplementedError - - def metrics_info(self, *args): - """get metrics result""" - raise NotImplementedError - - def _calculate_metrics(self, *args): - """ to calculate the metrics""" - raise NotImplementedError +# -*- encoding: utf-8 -*- +# @Time : 2020/10/21 +# @Author : Kaiyuan Li +# @email : tsotfsk@outlook.com + +# UPDATE +# @Time : 2020/10/21, 2020/12/9 +# @Author : Kaiyuan Li, Zhichao Feng +# @email : tsotfsk@outlook.com, fzcbupt@gmail.com + +""" +recbole.evaluator.abstract_evaluator +##################################### +""" + +import torch +import numpy as np +from torch.nn.utils.rnn import pad_sequence + + +class BaseEvaluator(object): + """:class:`BaseEvaluator` is an object which supports + the evaluation of the model. It is called by :class:`Trainer`. + + Note: + If you want to inherit this class and implement your own evaluator class, + you must implement the following functions. + + Args: + config (Config): The config of evaluator. + + """ + + def __init__(self, config, metrics): + self.metrics = metrics + self.full = ('full' in config['eval_setting']) + + def collect(self, *args): + """get the intermediate results for each batch, it is called at the end of each batch""" + raise NotImplementedError + + def evaluate(self, *args): + """calculate the metrics of all batches, it is called at the end of each epoch""" + raise NotImplementedError + + def _calculate_metrics(self, *args): + """ to calculate the metrics""" + raise NotImplementedError + + +class GroupedEvalautor(BaseEvaluator): + """:class:`GroupedEvaluator` is an object which supports the evaluation of the model. + + Note: + If you want to implement a new group-based metric, + you may need to inherit this class + + """ + def __init__(self, config, metrics): + super().__init__(config, metrics) + pass + + def sample_collect(self, scores_tensor, user_len_list): + """padding scores_tensor. It is called when evaluation sample distribution is `uniform` or `popularity`. + + """ + scores_list = torch.split(scores_tensor, user_len_list, dim=0) + padding_score = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # nusers x items + return padding_score + + def full_sort_collect(self, scores_tensor, user_len_list): + """it is called when evaluation sample distribution is `full`. + + """ + return scores_tensor.view(len(user_len_list), -1) + + def get_score_matrix(self, scores_tensor, user_len_list): + """get score matrix. + + Args: + scores_tensor (tensor): the tensor of model output with size of `(N, )` + user_len_list(list): number of all items + + """ + if self.full: + scores_matrix = self.full_sort_collect(scores_tensor, user_len_list) + else: + scores_matrix = self.sample_collect(scores_tensor, user_len_list) + return scores_matrix + + +class IndividualEvaluator(BaseEvaluator): + """:class:`IndividualEvaluator` is an object which supports the evaluation of the model. + + Note: + If you want to implement a new non-group-based metric, + you may need to inherit this class + + """ + def __init__(self, config, metrics): + super().__init__(config, metrics) + pass + + def sample_collect(self, true_scores, pred_scores): + """It is called when evaluation sample distribution is `uniform` or `popularity`. + + """ + return torch.stack((true_scores, pred_scores.detach()), dim=1) + + def full_sort_collect(self, true_scores, pred_scores): + """it is called when evaluation sample distribution is `full`. + + """ + raise NotImplementedError('full sort can\'t use IndividualEvaluator') + + def get_score_matrix(self, true_scores, pred_scores): + """get score matrix + + Args: + true_scores (tensor): the label of predicted items + pred_scores (tensor): the tensor of model output with a size of `(N, )` + + """ + if self.full: + scores_matrix = self.full_sort_collect(true_scores, pred_scores) + else: + scores_matrix = self.sample_collect(true_scores, pred_scores) + + return scores_matrix From 05f38a9fae718d3a6018c5b66d9bbf2e0aff9fcb Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:30:37 +0800 Subject: [PATCH 141/249] REFACTOR: put two origin evaluators into one file and add RankEvaluator --- recbole/evaluator/__init__.py | 4 +- recbole/evaluator/evaluators.py | 331 ++++++++++++++++++++++++++++ recbole/evaluator/loss_evaluator.py | 121 ---------- recbole/evaluator/topk_evaluator.py | 156 ------------- 4 files changed, 333 insertions(+), 279 deletions(-) create mode 100644 recbole/evaluator/evaluators.py delete mode 100644 recbole/evaluator/loss_evaluator.py delete mode 100644 recbole/evaluator/topk_evaluator.py diff --git a/recbole/evaluator/__init__.py b/recbole/evaluator/__init__.py index 61b72ad22..73f4cad5f 100644 --- a/recbole/evaluator/__init__.py +++ b/recbole/evaluator/__init__.py @@ -1,4 +1,4 @@ from recbole.evaluator.abstract_evaluator import * -from recbole.evaluator.loss_evaluator import * +from recbole.evaluator.proxy_evaluator import * from recbole.evaluator.metrics import * -from recbole.evaluator.topk_evaluator import * +from recbole.evaluator.evaluators import * diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py new file mode 100644 index 000000000..10a41f715 --- /dev/null +++ b/recbole/evaluator/evaluators.py @@ -0,0 +1,331 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/08/04 +# @Author : Kaiyuan Li +# @email : tsotfsk@outlook.com + +# UPDATE +# @Time : 2020/08/04, 2020/08/11, 2020/12/9 +# @Author : Kaiyuan Li, Yupeng Hou, Zhichao Feng +# @email : tsotfsk@outlook.com, houyupeng@ruc.edu.cn, fzcbupt@gmail.com + + +import torch +import numpy as np +from collections import ChainMap +from recbole.evaluator.metrics import metrics_dict +from recbole.evaluator.abstract_evaluator import GroupedEvalautor, IndividualEvaluator + +# These metrics are typical in topk recommendations +topk_metrics = {metric.lower(): metric for metric in ['Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP']} +# These metrics are typical in loss recommendations +loss_metrics = {metric.lower(): metric for metric in ['AUC', 'RMSE', 'MAE', 'LOGLOSS']} +# For GAUC +rank_metrics = {metric.lower(): metric for metric in ['GAUC']} + +# group-based metrics +group_metrics = ChainMap(topk_metrics, rank_metrics) +# not group-based metrics +individual_metrics = ChainMap(loss_metrics) + + +class TopKEvaluator(GroupedEvalautor): + r"""TopK Evaluator is mainly used in ranking tasks. Now, we support six topk metrics which + contain `'Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP'`. + + Note: + The metrics used calculate group-based metrics which considers the metrics scores averaged + across users. Some of them are also limited to k. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + + self.topk = config['topk'] + self._check_args() + + def collect(self, interaction, scores_tensor): + """collect the topk intermediate result of one batch, this function mainly + implements padding and TopK finding. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + """ + user_len_list = interaction.user_len_list + scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) + + # get topk + _, topk_idx = torch.topk(scores_matrix, max(self.topk), dim=-1) # nusers x k + + return topk_idx + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'Recall@10': 0.0329}`` + + """ + pos_len_list = eval_data.get_pos_len_list() + topk_idx = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + + assert len(pos_len_list) == len(topk_idx) + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(pos_len_list, topk_idx) + for metric, value in zip(self.metrics, result_list): + for k in self.topk: + key = '{}@{}'.format(metric, k) + metric_dict[key] = round(value[k - 1], 4) + + return metric_dict + + def _check_args(self): + + # Check topk: + if isinstance(self.topk, (int, list)): + if isinstance(self.topk, int): + self.topk = [self.topk] + for topk in self.topk: + if topk <= 0: + raise ValueError('topk must be a positive integer or a list of positive integers, ' + 'but get `{}`'.format(topk)) + else: + raise TypeError('The topk must be a integer, list') + + def _calculate_metrics(self, pos_len_list, topk_index): + """integrate the results of each batch and evaluate the topk metrics by users + + Args: + pos_len_list (np.ndarray): a list of users' positive items + topk_index (np.ndarray): a matrix which contains the index of the topk items for users + + Returns: + np.ndarray: a matrix which contains the metrics result + + """ + pos_idx_matrix = (topk_index < pos_len_list.reshape(-1, 1)) + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(pos_idx_matrix, pos_len_list) + result_list.append(result) # n_users x len(metrics) x len(ranks) + result = np.stack(result_list, axis=0).mean(axis=1) # len(metrics) x len(ranks) + return result + + def __str__(self): + msg = 'The TopK Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([topk_metrics[metric.lower()] for metric in self.metrics]) + \ + '], TopK:[' + \ + ', '.join(map(str, self.topk)) + \ + ']' + return msg + + +class RankEvaluator(GroupedEvalautor): + r"""Rank Evaluator is mainly used in ranking tasks except for topk tasks. Now, we support one + rank metric containing `'GAUC'`. + + Note: + The metrics used calculate group-based metrics which considers the metrics scores averaged + across users except for top-k metrics. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + pass + + def get_user_pos_len_list(self, interaction, scores_tensor): + """get number of positive items and all items in test set of each user + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + Returns: + list: number of positive items, + list: number of all items + """ + pos_len_list = torch.Tensor(interaction.pos_len_list).to(scores_tensor.device) + user_len_list = interaction.user_len_list + return pos_len_list, user_len_list + + def get_pos_index(self, scores_tensor, pos_len_list, user_len_list): + """get the index of positive items + + Args: + scores_tensor (tensor): the tensor of model output with size of `(N, )` + pos_len_list(list): number of positive items + user_len_list(list): number of all items + + Returns: + tensor: a matrix indicating whether the corresponding item is positive + + """ + scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) + _, n_index = torch.sort(scores_matrix, dim=-1, descending=True) + pos_index = (n_index < pos_len_list.reshape(-1, 1)) + return pos_index + + def collect(self, interaction, scores_tensor): + """collect the rank intermediate result of one batch, this function mainly implements ranking + and calculating the sum of rank for positive items. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + """ + pos_len_list, user_len_list = self.get_user_pos_len_list(interaction, scores_tensor) + pos_index = self.get_pos_index(scores_tensor, pos_len_list, user_len_list) + index_list = torch.arange(1, pos_index.shape[1] + 1).to(pos_index.device) + pos_rank_sum = torch.where(pos_index, index_list, torch.zeros_like(index_list)). \ + sum(axis=-1).reshape(-1, 1) + return pos_rank_sum + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'GAUC:0.9286}`` + + """ + pos_len_list = eval_data.get_pos_len_list() + user_len_list = eval_data.get_user_len_list() + pos_rank_sum = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(user_len_list, pos_len_list, pos_rank_sum) + for metric, value in zip(self.metrics, result_list): + key = '{}'.format(metric) + metric_dict[key] = round(value, 4) + + return metric_dict + + def _calculate_metrics(self, user_len_list, pos_len_list, pos_rank_sum): + """integrate the results of each batch and evaluate the topk metrics by users + + Args: + pos_len_list (np.ndarray): a list of users' positive items + topk_index (np.ndarray): a matrix which contains the index of the topk items for users + + Returns: + np.ndarray: a matrix which contains the metrics result + + """ + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(user_len_list, pos_len_list, pos_rank_sum) + result_list.append(result) + return result_list + + def __str__(self): + msg = 'The Rank Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([rank_metrics[metric.lower()] for metric in self.metrics]) + \ + '], TopK:[' + \ + ', '.join(map(str, self.topk)) + \ + ']' + return msg + + +class LossEvaluator(IndividualEvaluator): + r"""Loss Evaluator is mainly used in rating prediction and click through rate prediction. Now, we support four + loss metrics which contain `'AUC', 'RMSE', 'MAE', 'LOGLOSS'`. + + Note: + The metrics used do not calculate group-based metrics which considers the metrics scores averaged across users. + They are also not limited to k. Instead, they calculate the scores on the entire prediction results regardless + the users. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + + self.label_field = config['LABEL_FIELD'] + + def collect(self, interaction, pred_scores): + """collect the loss intermediate result of one batch, this function mainly + implements concatenating preds and trues. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + pred_scores (tensor): the tensor of model output with a size of `(N, )` + + Returns: + tensor : a batch of scores with a size of `(N, 2)` + + """ + true_scores = interaction[self.label_field].to(pred_scores.device) + assert len(true_scores) == len(pred_scores) + return self.get_score_matrix(true_scores, pred_scores) + + def evaluate(self, batch_matrix_list, *args): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + + Returns: + dict: such as {'AUC': 0.83} + + """ + concat = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + + trues = concat[:, 0] + preds = concat[:, 1] + + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(trues, preds) + for metric, value in zip(self.metrics, result_list): + key = '{}'.format(metric) + metric_dict[key] = round(value, 4) + return metric_dict + + def _calculate_metrics(self, trues, preds): + """get metrics result + + Args: + trues (np.ndarray): the true scores' list + preds (np.ndarray): the predict scores' list + + Returns: + list: a list of metrics result + + """ + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(trues, preds) + result_list.append(result) + return result_list + + def __str__(self): + msg = 'The Loss Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([loss_metrics[metric.lower()] for metric in self.metrics]) + \ + ']' + return msg + + +metric_eval_bind = [ + (topk_metrics, TopKEvaluator), + (loss_metrics, LossEvaluator), + (rank_metrics, RankEvaluator) +] diff --git a/recbole/evaluator/loss_evaluator.py b/recbole/evaluator/loss_evaluator.py deleted file mode 100644 index 23a5e4c73..000000000 --- a/recbole/evaluator/loss_evaluator.py +++ /dev/null @@ -1,121 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/08/04 -# @Author : Kaiyuan Li -# @email : tsotfsk@outlook.com - -# UPDATE -# @Time : 2020/08/04 2020/08/09 -# @Author : Kaiyuan Li Zhichao Feng -# @email : tsotfsk@outlook.com fzcbupt@gmail.com - -""" -recbole.evaluator.loss_evaluator -################################ -""" - -import numpy as np -import torch -from recbole.evaluator.abstract_evaluator import AbstractEvaluator -from recbole.evaluator.metrics import metrics_dict - -# These metrics are typical in loss recommendations -loss_metrics = {metric.lower(): metric for metric in ['AUC', 'RMSE', 'MAE', 'LOGLOSS']} - - -class LossEvaluator(AbstractEvaluator): - r"""Loss Evaluator is mainly used in rating prediction and click through rate prediction. Now, we support four - loss metrics which contain `'AUC', 'RMSE', 'MAE', 'LOGLOSS'`. - - Note: - The metrics used do not calculate group-based metrics which considers the metrics scores averaged across users. - They are also not limited to k. Instead, they calculate the scores on the entire prediction results regardless - the users. - - """ - def __init__(self, config): - super().__init__(config) - - self.label_field = config['LABEL_FIELD'] - self._check_args() - - def collect(self, interaction, pred_scores): - """collect the loss intermediate result of one batch, this function mainly - implements concatenating preds and trues. It is called at the end of each batch - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - pred_scores (tensor): the tensor of model output with a size of `(N, )` - - Returns: - tensor : a batch of scores with a size of `(N, 2)` - - """ - true_scores = interaction[self.label_field].to(pred_scores.device) - assert len(true_scores) == len(pred_scores) - return torch.stack((true_scores, pred_scores.detach()), dim=1) - - def evaluate(self, batch_matrix_list, *args): - """calculate the metrics of all batches. It is called at the end of each epoch - - Args: - batch_matrix_list (list): the results of all batches - - Returns: - dict: such as {'AUC': 0.83} - - """ - concat = torch.cat(batch_matrix_list, dim=0).cpu().numpy() - - trues = concat[:, 0] - preds = concat[:, 1] - - # get metrics - metric_dict = {} - result_list = self._calculate_metrics(trues, preds) - for metric, value in zip(self.metrics, result_list): - key = '{}'.format(metric) - metric_dict[key] = round(value, 4) - return metric_dict - - def _check_args(self): - - # Check metrics - if isinstance(self.metrics, (str, list)): - if isinstance(self.metrics, str): - self.metrics = [self.metrics] - else: - raise TypeError('metrics must be str or list') - - # Convert metric to lowercase - for m in self.metrics: - if m.lower() not in loss_metrics: - raise ValueError("There is no loss metric named {}!".format(m)) - self.metrics = [metric.lower() for metric in self.metrics] - - def metrics_info(self, trues, preds): - """get metrics result - - Args: - trues (np.ndarray): the true scores' list - preds (np.ndarray): the predict scores' list - - Returns: - list: a list of metrics result - - """ - result_list = [] - for metric in self.metrics: - metric_fuc = metrics_dict[metric.lower()] - result = metric_fuc(trues, preds) - result_list.append(result) - return result_list - - def _calculate_metrics(self, trues, preds): - return self.metrics_info(trues, preds) - - def __str__(self): - msg = 'The Loss Evaluator Info:\n' + \ - '\tMetrics:[' + \ - ', '.join([loss_metrics[metric.lower()] for metric in self.metrics]) + \ - ']' - return msg diff --git a/recbole/evaluator/topk_evaluator.py b/recbole/evaluator/topk_evaluator.py deleted file mode 100644 index 3b02b04b8..000000000 --- a/recbole/evaluator/topk_evaluator.py +++ /dev/null @@ -1,156 +0,0 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/08/04 -# @Author : Kaiyuan Li -# @email : tsotfsk@outlook.com - -# UPDATE -# @Time : 2020/08/04, 2020/08/11 -# @Author : Kaiyuan Li, Yupeng Hou -# @email : tsotfsk@outlook.com, houyupeng@ruc.edu.cn - -""" -recbole.evaluator.topk_evaluator -################################ -""" - -import numpy as np -import torch -from recbole.evaluator.abstract_evaluator import AbstractEvaluator -from recbole.evaluator.metrics import metrics_dict -from torch.nn.utils.rnn import pad_sequence - -# These metrics are typical in topk recommendations -topk_metrics = {metric.lower(): metric for metric in ['Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP']} - - -class TopKEvaluator(AbstractEvaluator): - r"""TopK Evaluator is mainly used in ranking tasks. Now, we support six topk metrics which - contain `'Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP'`. - - Note: - The metrics used calculate group-based metrics which considers the metrics scores averaged - across users. Some of them are also limited to k. - - """ - def __init__(self, config): - super().__init__(config) - - self.topk = config['topk'] - self._check_args() - - def collect(self, interaction, scores_tensor, full=False): - """collect the topk intermediate result of one batch, this function mainly - implements padding and TopK finding. It is called at the end of each batch - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - scores_tensor (tensor): the tensor of model output with size of `(N, )` - full (bool, optional): whether it is full sort. Default: False. - - """ - if full is True: - scores_matrix = scores_tensor - else: - user_len_list = interaction.user_len_list - scores_list = torch.split(scores_tensor, user_len_list, dim=0) - scores_matrix = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # n_users x items - - # get topk - _, topk_index = torch.topk(scores_matrix, max(self.topk), dim=-1) # n_users x k - - return topk_index - - def evaluate(self, batch_matrix_list, eval_data): - """calculate the metrics of all batches. It is called at the end of each epoch - - Args: - batch_matrix_list (list): the results of all batches - eval_data (Dataset): the class of test data - - Returns: - dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'Recall@10': 0.0329}`` - - """ - pos_len_list = eval_data.get_pos_len_list() - topk_index = torch.cat(batch_matrix_list, dim=0).cpu().numpy() - - assert len(pos_len_list) == len(topk_index) - # get metrics - metric_dict = {} - result_list = self._calculate_metrics(pos_len_list, topk_index) - for metric, value in zip(self.metrics, result_list): - for k in self.topk: - key = '{}@{}'.format(metric, k) - metric_dict[key] = round(value[k - 1], 4) - return metric_dict - - def _check_args(self): - - # Check metrics - if isinstance(self.metrics, (str, list)): - if isinstance(self.metrics, str): - self.metrics = [self.metrics] - else: - raise TypeError('metrics must be str or list') - - # Convert metric to lowercase - for m in self.metrics: - if m.lower() not in topk_metrics: - raise ValueError("There is no user grouped topk metric named {}!".format(m)) - self.metrics = [metric.lower() for metric in self.metrics] - - # Check topk: - if isinstance(self.topk, (int, list)): - if isinstance(self.topk, int): - self.topk = [self.topk] - for topk in self.topk: - if topk <= 0: - raise ValueError('topk must be a positive integer or a list of positive integers, ' - 'but get `{}`'.format(topk)) - else: - raise TypeError('The topk must be a integer, list') - - def metrics_info(self, pos_idx, pos_len): - """get metrics result - - Args: - pos_idx (np.ndarray): the bool index of all users' topk items that indicating the positive items are - topk items or not - pos_len (np.ndarray): the length of all users' positive items - - Returns: - list: a list of matrix which record the results from `1` to `max(topk)` - - """ - result_list = [] - for metric in self.metrics: - metric_fuc = metrics_dict[metric.lower()] - result = metric_fuc(pos_idx, pos_len) - result_list.append(result) - return result_list - - def _calculate_metrics(self, pos_len_list, topk_index): - """integrate the results of each batch and evaluate the topk metrics by users - - Args: - pos_len_list (np.ndarray): a list of users' positive items - topk_index (np.ndarray): a matrix which contains the index of the topk items for users - - Returns: - np.ndarray: a matrix which contains the metrics result - - """ - - pos_idx_matrix = (topk_index < pos_len_list.reshape(-1, 1)) - result_list = self.metrics_info(pos_idx_matrix, pos_len_list) # n_users x len(metrics) x len(ranks) - result = np.stack(result_list, axis=0).mean(axis=1) # len(metrics) x len(ranks) - return result - - def __str__(self): - msg = 'The TopK Evaluator Info:\n' + \ - '\tMetrics:[' + \ - ', '.join([topk_metrics[metric.lower()] for metric in self.metrics]) + \ - '], TopK:[' + \ - ', '.join(map(str, self.topk)) + \ - ']' - return msg From 089153f3a02c23c946fc55a156c54dfebb6f4e27 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:30:56 +0800 Subject: [PATCH 142/249] FEA: adapt trainer to new evaluator --- recbole/trainer/trainer.py | 14 +++----------- 1 file changed, 3 insertions(+), 11 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 1fb680a9c..87bc6c348 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -24,7 +24,7 @@ from time import time from logging import getLogger -from recbole.evaluator import TopKEvaluator, LossEvaluator +from recbole.evaluator.proxy_evaluator import ProxyEvaluator from recbole.data.interaction import Interaction from recbole.utils import ensure_dir, get_local_time, early_stopping, calculate_valid_score, dict2str, \ DataLoaderType, KGDataLoaderState, EvaluatorType @@ -95,11 +95,7 @@ def __init__(self, config, model): self.train_loss_dict = dict() self.optimizer = self._build_optimizer() self.eval_type = config['eval_type'] - if self.eval_type == EvaluatorType.INDIVIDUAL: - self.evaluator = LossEvaluator(config) - else: - self.evaluator = TopKEvaluator(config) - + self.evaluator = ProxyEvaluator(config) self.item_tensor = None self.tot_item_num = None @@ -348,20 +344,16 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): batch_matrix_list = [] for batch_idx, batched_data in enumerate(eval_data): if eval_data.dl_type == DataLoaderType.FULL: - if self.eval_type == EvaluatorType.INDIVIDUAL: - raise ValueError('full sort can\'t use LossEvaluator') interaction, scores = self._full_sort_batch_eval(batched_data) - batch_matrix = self.evaluator.collect(interaction, scores, full=True) else: interaction = batched_data batch_size = interaction.length - if batch_size <= self.test_batch_size: scores = self.model.predict(interaction.to(self.device)) else: scores = self._spilt_predict(interaction, batch_size) - batch_matrix = self.evaluator.collect(interaction, scores) + batch_matrix = self.evaluator.collect(interaction, scores) batch_matrix_list.append(batch_matrix) result = self.evaluator.evaluate(batch_matrix_list, eval_data) From 1b0ac7604f93aa0407ebeca773108bf8bab596f6 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:31:40 +0800 Subject: [PATCH 143/249] FEA: adapt configurator to new evaluator --- recbole/config/configurator.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/recbole/config/configurator.py b/recbole/config/configurator.py index fb6784498..2a225a4cd 100644 --- a/recbole/config/configurator.py +++ b/recbole/config/configurator.py @@ -19,7 +19,7 @@ import torch from logging import getLogger -from recbole.evaluator import loss_metrics, topk_metrics +from recbole.evaluator import group_metrics, individual_metrics from recbole.utils import get_model, Enum, EvaluatorType, ModelType, InputType, \ general_arguments, training_arguments, evaluation_arguments, dataset_arguments @@ -296,12 +296,12 @@ def _set_default_parameters(self): eval_type = None for metric in self.final_config_dict['metrics']: - if metric.lower() in loss_metrics: + if metric.lower() in individual_metrics: if eval_type is not None and eval_type == EvaluatorType.RANKING: raise RuntimeError('Ranking metrics and other metrics can not be used at the same time.') else: eval_type = EvaluatorType.INDIVIDUAL - if metric.lower() in topk_metrics: + if metric.lower() in group_metrics: if eval_type is not None and eval_type == EvaluatorType.INDIVIDUAL: raise RuntimeError('Ranking metrics and other metrics can not be used at the same time.') else: From 8319f0df7e26cae123a0af7500817d7bd120fbb1 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 11 Dec 2020 22:33:01 +0800 Subject: [PATCH 144/249] FEA: add ProxyEvaluator --- recbole/evaluator/proxy_evaluator.py | 102 +++++++++++++++++++++++++++ 1 file changed, 102 insertions(+) create mode 100644 recbole/evaluator/proxy_evaluator.py diff --git a/recbole/evaluator/proxy_evaluator.py b/recbole/evaluator/proxy_evaluator.py new file mode 100644 index 000000000..fc91f5d48 --- /dev/null +++ b/recbole/evaluator/proxy_evaluator.py @@ -0,0 +1,102 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/12/9 +# @Author : Zhichao Feng +# @email : fzcbupt@gmail.com + +# UPDATE +# @Time : 2020/12/9 +# @Author : Zhichao Feng +# @email : fzcbupt@gmail.com + +from recbole.evaluator.evaluators import metric_eval_bind, group_metrics, individual_metrics +from collections import ChainMap + + +class ProxyEvaluator(object): + r"""ProxyEvaluator is used to assign the corresponding evaluator according to the evaluation metrics, + for example, TopkEvaluator for top-k metrics, and summarize the results of all evaluators. + + """ + def __init__(self, config): + self.config = config + self.valid_metrics = ChainMap(group_metrics, individual_metrics) + self.metrics = self.config['metrics'] + self._check_args() + self.evaluators = self.build() + + def build(self): + """assign evaluators according to metrics. + + Returns: + list: a list of evaluators. + + """ + evaluator_list = [] + metrics_set = {metric.lower() for metric in self.metrics} + for metrics, evaluator in metric_eval_bind: + used_metrics = list(metrics_set.intersection(set(metrics.keys()))) + if used_metrics: + evaluator_list.append(evaluator(self.config, used_metrics)) + return evaluator_list + + def collect(self, interaction, scores): + """collect the all used evaluators' intermediate result of one batch. + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores (tensor): the tensor of model output with size of `(N, )` + + """ + results = [] + for evaluator in self.evaluators: + results.append(evaluator.collect(interaction, scores)) + return results + + def merge_batch_result(self, batch_matrix_list): + """merge all the intermediate result got in `self.collect` for used evaluators separately. + + Args: + batch_matrix_list (list): the results of all batches not separated + + Returns: + dict: used evaluators' results of all batches + + """ + matrix_dict = {} + for collect_list in batch_matrix_list: + for i, value in enumerate(collect_list): + matrix_dict.setdefault(i, []).append(value) + + return matrix_dict + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'GAUC': 0.9236}`` + + """ + matrix_dict = self.merge_batch_result(batch_matrix_list) + result_dict = {} + for i, evaluator in enumerate(self.evaluators): + res = evaluator.evaluate(matrix_dict[i], eval_data) + result_dict.update(res) + return result_dict + + def _check_args(self): + + # Check metrics + if isinstance(self.metrics, (str, list)): + if isinstance(self.metrics, str): + self.metrics = [self.metrics] + else: + raise TypeError('metrics must be str or list') + + # Convert metric to lowercase + for m in self.metrics: + if m.lower() not in self.valid_metrics: + raise ValueError("There is no metric named {}!".format(m)) From 82afccbdb2baa439845e4d33768f20dbe4c9aca1 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Sat, 12 Dec 2020 22:03:38 +0800 Subject: [PATCH 145/249] FIX: import format --- recbole/trainer/trainer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 87bc6c348..1a3ef791a 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -24,7 +24,7 @@ from time import time from logging import getLogger -from recbole.evaluator.proxy_evaluator import ProxyEvaluator +from recbole.evaluator import ProxyEvaluator from recbole.data.interaction import Interaction from recbole.utils import ensure_dir, get_local_time, early_stopping, calculate_valid_score, dict2str, \ DataLoaderType, KGDataLoaderState, EvaluatorType From 5a18697f6f5fd667ce5e736eb8bdac463a689608 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Mon, 14 Dec 2020 10:08:21 +0800 Subject: [PATCH 146/249] FEA: add FOSSIL by Shao Weiqi. --- .../model/sequential_recommender/fossil.py | 184 ++++++++++++++++++ recbole/properties/model/FOSSIL.yaml | 5 + tests/model/test_model_auto.py | 15 ++ 3 files changed, 204 insertions(+) create mode 100644 recbole/model/sequential_recommender/fossil.py create mode 100644 recbole/properties/model/FOSSIL.yaml diff --git a/recbole/model/sequential_recommender/fossil.py b/recbole/model/sequential_recommender/fossil.py new file mode 100644 index 000000000..0450262a3 --- /dev/null +++ b/recbole/model/sequential_recommender/fossil.py @@ -0,0 +1,184 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/21 20:00 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +FOSSIL +################################################ + +Reference: + Ruining He et al. "Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation." in IEEE 2016 + + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_normal_ + +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class FOSSIL(SequentialRecommender): + r""" + FOSSIL uses similarity of the items as main purpose and uses high MC as a way of sequential preference improve of + ability of sequential recommendation + + """ + + def __init__(self, config, dataset): + super(FOSSIL, self).__init__(config, dataset) + + # load the dataset information + self.n_users = dataset.num(self.USER_ID) + self.device = config['device'] + + # load the parameters + self.embedding_size = config["embedding_size"] + self.order_len = config["order_len"] + assert self.order_len <= self.max_seq_length, "order_len can't longer than the max_seq_length" + self.reg_weight = config["reg_weight"] + self.alpha = config["alpha"] + + # define the layers and loss type + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_lamda = nn.Embedding(self.n_users, self.order_len) + self.lamda = nn.Parameter(torch.zeros(self.order_len)).to(self.device) + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the model + self.apply(self.init_weights) + + def inverse_seq_item_embedding(self, seq_item_embedding, seq_item_len): + """ + inverse seq_item_embedding like this: + simple to 2-dim + [1,2,3,0,0,0] -- ??? -- >> [0,0,0,1,2,3] + first: [0,0,0,0,0,0] concat [1,2,3,0,0,0] + using gather_indexes: to get one by one + first get 3,then 2,last 1 + """ + zeros = torch.zeros_like(seq_item_embedding, dtype=torch.float).to(self.device) + # batch_size * seq_len * embedding_size + item_embedding_zeros = torch.cat([zeros, seq_item_embedding], dim=1) + # batch_size * 2_mul_seq_len * embedding_size + embedding_list = list() + for i in range(self.order_len): + embedding = self.gather_indexes(item_embedding_zeros, + self.max_seq_length + seq_item_len - self.order_len + i) + embedding_list.append(embedding.unsqueeze(1)) + short_item_embedding = torch.cat(embedding_list, dim=1) + # batch_size * short_len * embedding_size + + return short_item_embedding + + def reg_loss(self, user_embedding, item_embedding, seq_output): + + reg_1 = self.reg_weight + loss_1 = reg_1 * torch.norm(user_embedding, p=2)\ + + reg_1 * torch.norm(item_embedding, p=2)\ + + reg_1 * torch.norm(seq_output, p=2) + + return loss_1 + + def init_weights(self, module): + + if isinstance(module, nn.Embedding) or isinstance(module, nn.Linear): + xavier_normal_(module.weight.data) + + def forward(self, seq_item, seq_item_len, user): + + seq_item_embedding = self.item_embedding(seq_item) + + high_order_seq_item_embedding = self.inverse_seq_item_embedding(seq_item_embedding, seq_item_len) + # batch_size * order_len * embedding + + high_order = self.get_high_order_Markov(high_order_seq_item_embedding, user) + similarity = self.get_similarity(seq_item_embedding, seq_item_len) + + return high_order + similarity + + def get_high_order_Markov(self, high_order_item_embedding, user): + """ + + in order to get the inference of past items and the user's taste to the current predict item + """ + + user_lamda = self.user_lamda(user).unsqueeze(dim=2) + # batch_size * order_len * 1 + lamda = self.lamda.unsqueeze(dim=0).unsqueeze(dim=2) + # 1 * order_len * 1 + lamda = torch.add(user_lamda, lamda) + # batch_size * order_len * 1 + high_order_item_embedding = torch.mul(high_order_item_embedding, lamda) + # batch_size * order_len * embedding_size + high_order_item_embedding = high_order_item_embedding.sum(dim=1) + # batch_size * embedding_size + + return high_order_item_embedding + + def get_similarity(self, seq_item_embedding, seq_item_len): + """ + in order to get the inference of past items to the current predict item + """ + coeff = torch.pow(seq_item_len.unsqueeze(1), -self.alpha).float() + # batch_size * 1 + similarity = torch.mul(coeff, seq_item_embedding.sum(dim=1)) + # batch_size * embedding_size + + return similarity + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + seq_output = self.forward(seq_item, seq_item_len, user) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding(pos_items) + + user_lamda = self.user_lamda(user) + pos_items_embedding = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + self.reg_loss(user_lamda, pos_items_embedding, seq_output) + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding.weight + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + self.reg_loss(user_lamda, pos_items_embedding, seq_output) + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, item_seq_len, user) + test_item_emb = self.item_embedding(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + seq_output = self.forward(item_seq, item_seq_len, user) + test_items_emb = self.item_embedding.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores diff --git a/recbole/properties/model/FOSSIL.yaml b/recbole/properties/model/FOSSIL.yaml new file mode 100644 index 000000000..1eb00d1bb --- /dev/null +++ b/recbole/properties/model/FOSSIL.yaml @@ -0,0 +1,5 @@ +embedding_size: 64 +loss_type: "CE" +reg_weight: 0.00 +order_len: 3 +alpha: 0.6 \ No newline at end of file diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 0be3e9b31..ec50838c7 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -345,6 +345,13 @@ def test_hgn(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_fossil(self): + config_dict = { + 'model': 'FOSSIL', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + # def test_fdsa(self): # config_dict = { # 'model': 'FDSA', @@ -634,6 +641,14 @@ def test_hgn(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_fossil(self): + config_dict = { + 'model': 'FOSSIL', + 'loss_type': 'BPR', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + def test_caser(self): config_dict = { 'model': 'Caser', From 5cfc88cbbb6d76b9d2b152a95b525fb4a9af3fe4 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Mon, 14 Dec 2020 16:16:26 +0800 Subject: [PATCH 147/249] DOC: doc fix in fossil --- recbole/model/sequential_recommender/fossil.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/model/sequential_recommender/fossil.py b/recbole/model/sequential_recommender/fossil.py index 0450262a3..24652f42e 100644 --- a/recbole/model/sequential_recommender/fossil.py +++ b/recbole/model/sequential_recommender/fossil.py @@ -9,7 +9,7 @@ ################################################ Reference: - Ruining He et al. "Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation." in IEEE 2016 + Ruining He et al. "Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation." in ICDM 2016. """ From 301bcd7bb8f0d7fecf6105bab80dee3f22d4e67c Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 15 Dec 2020 15:59:12 +0800 Subject: [PATCH 148/249] FIX: change the default value of clip_grad_norm. --- recbole/properties/overall.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/recbole/properties/overall.yaml b/recbole/properties/overall.yaml index 33a0e30c2..e9dd78d57 100644 --- a/recbole/properties/overall.yaml +++ b/recbole/properties/overall.yaml @@ -15,7 +15,8 @@ learning_rate: 0.001 training_neg_sample_num: 1 eval_step: 1 stopping_step: 10 -clip_grad_norm: {'max_norm': 5, 'norm_type': 2} +clip_grad_norm: ~ +# clip_grad_norm: {'max_norm': 5, 'norm_type': 2} # evaluation settings eval_setting: RO_RS,full From 634931c0c8d97becf47724fa3cd55e59006aa729 Mon Sep 17 00:00:00 2001 From: yihongguo Date: Tue, 15 Dec 2020 18:56:53 +0800 Subject: [PATCH 149/249] FEA: add LINE to general recommender by guoyihong --- recbole/model/general_recommender/line.py | 169 ++++++++++++++++++++++ 1 file changed, 169 insertions(+) create mode 100644 recbole/model/general_recommender/line.py diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py new file mode 100644 index 000000000..669ae4058 --- /dev/null +++ b/recbole/model/general_recommender/line.py @@ -0,0 +1,169 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/12/8 +# @Author : Yihong Guo +# @Email : gyihong@hotmail.com + +r""" +LINE +################################################ +Reference: + Jian Tang et al. "LINE: Large-scale Information Network Embedding." in WWW 2015. + +Reference code: + https://github.com/shenweichen/GraphEmbedding +""" + +import torch +import torch.nn as nn +import networkx as nx +import pandas as pd +import random + +from recbole.utils import InputType +from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.init import xavier_normal_initialization + +class neg_sampling_loss(nn.Module): + def __init__(self): + super(neg_sampling_loss, self).__init__() + + def forward(self,score,sign): + return -torch.mean(torch.sigmoid(sign * score)) + +class LINE(GeneralRecommender): + r"""LINE is a graph embedding model. + + We implement the model to train users and items embedding for recommendation. + """ + input_type = InputType.PAIRWISE + + def __init__(self, config, dataset): + super(LINE, self).__init__(config, dataset) + + self.embedding_size = config['embedding_size'] + self.order = config['order'] + self.second_order_loss_weight = config['second_order_loss_weight'] + + + self.interaction_feat = dataset.dataset.inter_feat + + self.uid_field = dataset.dataset.uid_field + self.iid_field = dataset.dataset.iid_field + + self.user_embedding = nn.Embedding(self.n_users, self.embedding_size).to(self.device) + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size).to(self.device) + + self.user_context_embedding = nn.Embedding(self.n_users, self.embedding_size).to(self.device) + self.item_context_embedding = nn.Embedding(self.n_items, self.embedding_size).to(self.device) + + self.loss_fct = neg_sampling_loss() + + # graph initialization + self.process_nodeid() + self.read_graph() + + # parameters initialization + self.apply(xavier_normal_initialization) + + def process_nodeid(self): + + u = self.interaction_feat[self.uid_field].numpy() + i = self.interaction_feat[self.iid_field].numpy() + + interaction_feat_new = pd.DataFrame([]) + interaction_feat_new[self.uid_field] = u + interaction_feat_new[self.iid_field] = i + self.interaction_feat_new = interaction_feat_new + + self.interaction_feat_new[self.uid_field] = self.interaction_feat_new[self.uid_field].map(lambda x: "u" + str(x)) + self.interaction_feat_new[self.iid_field] = self.interaction_feat_new[self.iid_field].map(lambda x: "i" + str(x)) + + def read_graph(self): + self.g = nx.from_pandas_edgelist(self.interaction_feat_new,self.uid_field,self.iid_field) + + def generate_neg_user(self,item_id): + + neigh = list(self.g['i' + str(int(item_id))]) + + curr = random.randint(1, self.n_users-1) + while 'u' + str(curr) in neigh: + curr = random.randint(1, self.n_users-1) + + return curr + + def gen_neg_sample(self,src_): + + t = [] + + src = src_.cpu() + + for i in range(len(src)): + t.append(self.generate_neg_user(src[i])) + + return src_,torch.LongTensor(t).to(self.device) + + def forward(self,h,t): + + h_embedding = self.user_embedding(h) + t_embedding = self.item_embedding(t) + + return torch.sum(h_embedding.mul(t_embedding), dim=1) + + def context_forward(self, h, t, field): + + if field == "uu": + h_embedding = self.user_embedding(h) + t_embedding = self.item_context_embedding(t) + else: + h_embedding = self.item_embedding(h) + t_embedding = self.user_context_embedding(t) + + return torch.sum(h_embedding.mul(t_embedding), dim=1) + + def calculate_loss(self, interaction): + + user = interaction[self.USER_ID] + pos_item = interaction[self.ITEM_ID] + neg_item = interaction[self.NEG_ITEM_ID] + + score_pos = self.forward(user,pos_item) + + if random.random()<0.5: + score_neg = self.forward(user, neg_item) + score_pos_con = self.context_forward(user, pos_item, 'uu') + score_neg_con = self.context_forward(user, neg_item, 'uu') + else: + h,t = self.gen_neg_sample(pos_item) + score_neg = self.forward(t,h) + score_pos_con = self.context_forward(h, user,'ii') + score_neg_con = self.context_forward(h, t,'ii') + + ones = torch.ones(len(score_pos),device=self.device) + if self.order == 1: + return self.loss_fct(ones,score_pos) \ + + self.loss_fct(-1 * ones, score_neg) + else: + return self.loss_fct(ones,score_pos) \ + + self.loss_fct(-1 * ones, score_neg)\ + + self.loss_fct(ones,score_pos_con)*self.second_order_loss_weight\ + + self.loss_fct(-1*ones,score_neg_con)*self.second_order_loss_weight + + def predict(self, interaction): + + user = interaction[self.USER_ID] + item = interaction[self.ITEM_ID] + + scores = self.forward(user, item) + + return scores + + def full_sort_predict(self, interaction): + user = interaction[self.USER_ID] + + # get user embedding from storage variable + u_embeddings = self.user_embedding(user) + i_embedding = self.item_embedding.weight + # dot with all item embedding to accelerate + scores = torch.matmul(u_embeddings, i_embedding.transpose(0, 1)) + + return scores.view(-1) From 4c2fd2c6d0c3a89a6e9089ec704454a0fc9d74d9 Mon Sep 17 00:00:00 2001 From: yihongguo Date: Tue, 15 Dec 2020 18:57:35 +0800 Subject: [PATCH 150/249] FEA: add LINE to general recommender by guoyihong --- recbole/properties/model/LINE.yaml | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 recbole/properties/model/LINE.yaml diff --git a/recbole/properties/model/LINE.yaml b/recbole/properties/model/LINE.yaml new file mode 100644 index 000000000..eefa06f81 --- /dev/null +++ b/recbole/properties/model/LINE.yaml @@ -0,0 +1,3 @@ +embedding_size: 64 +order: 2 +second_order_loss_weight: 1 \ No newline at end of file From d662d2679abb968bb217b6bacd256a73ee02b4b6 Mon Sep 17 00:00:00 2001 From: yihongguo Date: Tue, 15 Dec 2020 19:50:49 +0800 Subject: [PATCH 151/249] update line --- recbole/model/general_recommender/line.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py index 669ae4058..7b0b4b475 100644 --- a/recbole/model/general_recommender/line.py +++ b/recbole/model/general_recommender/line.py @@ -43,6 +43,7 @@ def __init__(self, config, dataset): self.embedding_size = config['embedding_size'] self.order = config['order'] self.second_order_loss_weight = config['second_order_loss_weight'] + self.training_neg_sample_num = config['training_neg_sample_num'] self.interaction_feat = dataset.dataset.inter_feat @@ -98,7 +99,8 @@ def gen_neg_sample(self,src_): src = src_.cpu() for i in range(len(src)): - t.append(self.generate_neg_user(src[i])) + for j in range(self.training_neg_sample_num): + t.append(self.generate_neg_user(src[i])) return src_,torch.LongTensor(t).to(self.device) @@ -139,14 +141,15 @@ def calculate_loss(self, interaction): score_neg_con = self.context_forward(h, t,'ii') ones = torch.ones(len(score_pos),device=self.device) + neg_sample_ones = torch.ones(len(score_neg),device=self.device) if self.order == 1: return self.loss_fct(ones,score_pos) \ - + self.loss_fct(-1 * ones, score_neg) + + self.loss_fct(-1 * neg_sample_ones, score_neg) else: return self.loss_fct(ones,score_pos) \ - + self.loss_fct(-1 * ones, score_neg)\ + + self.loss_fct(-1 * neg_sample_ones, score_neg)\ + self.loss_fct(ones,score_pos_con)*self.second_order_loss_weight\ - + self.loss_fct(-1*ones,score_neg_con)*self.second_order_loss_weight + + self.loss_fct(-1* neg_sample_ones,score_neg_con)*self.second_order_loss_weight def predict(self, interaction): From c47233e3cf16bd0bcd51e3f270f244fc90e38eef Mon Sep 17 00:00:00 2001 From: yihongguo Date: Tue, 15 Dec 2020 20:30:46 +0800 Subject: [PATCH 152/249] update line --- recbole/model/general_recommender/line.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py index 7b0b4b475..669ae4058 100644 --- a/recbole/model/general_recommender/line.py +++ b/recbole/model/general_recommender/line.py @@ -43,7 +43,6 @@ def __init__(self, config, dataset): self.embedding_size = config['embedding_size'] self.order = config['order'] self.second_order_loss_weight = config['second_order_loss_weight'] - self.training_neg_sample_num = config['training_neg_sample_num'] self.interaction_feat = dataset.dataset.inter_feat @@ -99,8 +98,7 @@ def gen_neg_sample(self,src_): src = src_.cpu() for i in range(len(src)): - for j in range(self.training_neg_sample_num): - t.append(self.generate_neg_user(src[i])) + t.append(self.generate_neg_user(src[i])) return src_,torch.LongTensor(t).to(self.device) @@ -141,15 +139,14 @@ def calculate_loss(self, interaction): score_neg_con = self.context_forward(h, t,'ii') ones = torch.ones(len(score_pos),device=self.device) - neg_sample_ones = torch.ones(len(score_neg),device=self.device) if self.order == 1: return self.loss_fct(ones,score_pos) \ - + self.loss_fct(-1 * neg_sample_ones, score_neg) + + self.loss_fct(-1 * ones, score_neg) else: return self.loss_fct(ones,score_pos) \ - + self.loss_fct(-1 * neg_sample_ones, score_neg)\ + + self.loss_fct(-1 * ones, score_neg)\ + self.loss_fct(ones,score_pos_con)*self.second_order_loss_weight\ - + self.loss_fct(-1* neg_sample_ones,score_neg_con)*self.second_order_loss_weight + + self.loss_fct(-1*ones,score_neg_con)*self.second_order_loss_weight def predict(self, interaction): From 5305c23c8cd5ab8b97de12e1ee6f0fba181fa071 Mon Sep 17 00:00:00 2001 From: yihongguo Date: Wed, 16 Dec 2020 10:47:19 +0800 Subject: [PATCH 153/249] FIX: update line --- recbole/model/general_recommender/line.py | 98 ++++++++++++----------- 1 file changed, 52 insertions(+), 46 deletions(-) diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py index 669ae4058..6681ebaa0 100644 --- a/recbole/model/general_recommender/line.py +++ b/recbole/model/general_recommender/line.py @@ -15,9 +15,8 @@ import torch import torch.nn as nn -import networkx as nx -import pandas as pd import random +import numpy as np from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender @@ -43,6 +42,7 @@ def __init__(self, config, dataset): self.embedding_size = config['embedding_size'] self.order = config['order'] self.second_order_loss_weight = config['second_order_loss_weight'] + self.training_neg_sample_num = config['training_neg_sample_num'] self.interaction_feat = dataset.dataset.inter_feat @@ -58,49 +58,54 @@ def __init__(self, config, dataset): self.loss_fct = neg_sampling_loss() - # graph initialization - self.process_nodeid() - self.read_graph() + self.used_ids = self.get_used_ids() + self.random_list = self.get_random_list() + np.random.shuffle(self.random_list) + self.random_pr = 0 + self.random_list_length = len(self.random_list) - # parameters initialization self.apply(xavier_normal_initialization) - def process_nodeid(self): - - u = self.interaction_feat[self.uid_field].numpy() - i = self.interaction_feat[self.iid_field].numpy() - - interaction_feat_new = pd.DataFrame([]) - interaction_feat_new[self.uid_field] = u - interaction_feat_new[self.iid_field] = i - self.interaction_feat_new = interaction_feat_new - - self.interaction_feat_new[self.uid_field] = self.interaction_feat_new[self.uid_field].map(lambda x: "u" + str(x)) - self.interaction_feat_new[self.iid_field] = self.interaction_feat_new[self.iid_field].map(lambda x: "i" + str(x)) - - def read_graph(self): - self.g = nx.from_pandas_edgelist(self.interaction_feat_new,self.uid_field,self.iid_field) - - def generate_neg_user(self,item_id): - - neigh = list(self.g['i' + str(int(item_id))]) - - curr = random.randint(1, self.n_users-1) - while 'u' + str(curr) in neigh: - curr = random.randint(1, self.n_users-1) - - return curr - - def gen_neg_sample(self,src_): - - t = [] - - src = src_.cpu() - - for i in range(len(src)): - t.append(self.generate_neg_user(src[i])) - - return src_,torch.LongTensor(t).to(self.device) + def get_used_ids(self): + last = [set() for i in range(self.n_items)] + cur = np.array([set(s) for s in last]) + for iid, uid in zip(self.interaction_feat[self.iid_field].numpy(), self.interaction_feat[self.uid_field].numpy()): + cur[iid].add(uid) + return cur + + def sampler(self,key_ids, num): + + key_ids = np.array(key_ids.cpu()) + key_num = len(key_ids) + total_num = key_num * num + value_ids = np.zeros(total_num, dtype=np.int64) + check_list = np.arange(total_num) + key_ids = np.tile(key_ids, num) + while len(check_list) > 0: + value_ids[check_list] = self.random_num(len(check_list)) + check_list = np.array([i for i, used, v in + zip(check_list, self.used_ids[key_ids[check_list]], value_ids[check_list]) + if v in used]) + + return torch.tensor(value_ids,device=self.device) + + def random_num(self, num): + value_id = [] + self.random_pr %= self.random_list_length + while True: + if self.random_pr + num <= self.random_list_length: + value_id.append(self.random_list[self.random_pr: self.random_pr + num]) + self.random_pr += num + break + else: + value_id.append(self.random_list[self.random_pr:]) + num -= self.random_list_length - self.random_pr + self.random_pr = 0 + np.random.shuffle(self.random_list) + return np.concatenate(value_id) + + def get_random_list(self): + return np.arange(1, self.n_users) def forward(self,h,t): @@ -133,10 +138,11 @@ def calculate_loss(self, interaction): score_pos_con = self.context_forward(user, pos_item, 'uu') score_neg_con = self.context_forward(user, neg_item, 'uu') else: - h,t = self.gen_neg_sample(pos_item) - score_neg = self.forward(t,h) - score_pos_con = self.context_forward(h, user,'ii') - score_neg_con = self.context_forward(h, t,'ii') + # h,t = self.gen_neg_sample(pos_item) + neg_user = self.sampler(pos_item,self.training_neg_sample_num) + score_neg = self.forward(neg_user,pos_item) + score_pos_con = self.context_forward(pos_item, user,'ii') + score_neg_con = self.context_forward(pos_item, neg_user,'ii') ones = torch.ones(len(score_pos),device=self.device) if self.order == 1: From 9fcbaf65c24716f1164eb59833200bda8f1a8983 Mon Sep 17 00:00:00 2001 From: yihongguo Date: Wed, 16 Dec 2020 11:09:49 +0800 Subject: [PATCH 154/249] FIX: fix line bug --- recbole/model/general_recommender/line.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py index 6681ebaa0..d92cae1c0 100644 --- a/recbole/model/general_recommender/line.py +++ b/recbole/model/general_recommender/line.py @@ -73,14 +73,14 @@ def get_used_ids(self): cur[iid].add(uid) return cur - def sampler(self,key_ids, num): + def sampler(self,key_ids): key_ids = np.array(key_ids.cpu()) key_num = len(key_ids) - total_num = key_num * num + total_num = key_num value_ids = np.zeros(total_num, dtype=np.int64) check_list = np.arange(total_num) - key_ids = np.tile(key_ids, num) + key_ids = np.tile(key_ids, 1) while len(check_list) > 0: value_ids[check_list] = self.random_num(len(check_list)) check_list = np.array([i for i, used, v in @@ -139,7 +139,7 @@ def calculate_loss(self, interaction): score_neg_con = self.context_forward(user, neg_item, 'uu') else: # h,t = self.gen_neg_sample(pos_item) - neg_user = self.sampler(pos_item,self.training_neg_sample_num) + neg_user = self.sampler(pos_item) score_neg = self.forward(neg_user,pos_item) score_pos_con = self.context_forward(pos_item, user,'ii') score_neg_con = self.context_forward(pos_item, neg_user,'ii') From c3120b9d53b6670f50c59aefe23fa375e061cb85 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Mon, 14 Dec 2020 10:08:21 +0800 Subject: [PATCH 155/249] FEA: add FOSSIL by Shao Weiqi. --- .../model/sequential_recommender/fossil.py | 184 ++++++++++++++++++ recbole/properties/model/FOSSIL.yaml | 5 + tests/model/test_model_auto.py | 15 ++ 3 files changed, 204 insertions(+) create mode 100644 recbole/model/sequential_recommender/fossil.py create mode 100644 recbole/properties/model/FOSSIL.yaml diff --git a/recbole/model/sequential_recommender/fossil.py b/recbole/model/sequential_recommender/fossil.py new file mode 100644 index 000000000..0450262a3 --- /dev/null +++ b/recbole/model/sequential_recommender/fossil.py @@ -0,0 +1,184 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/21 20:00 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +FOSSIL +################################################ + +Reference: + Ruining He et al. "Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation." in IEEE 2016 + + +""" + +import torch +import torch.nn as nn +from torch.nn.init import xavier_normal_ + +from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss + + +class FOSSIL(SequentialRecommender): + r""" + FOSSIL uses similarity of the items as main purpose and uses high MC as a way of sequential preference improve of + ability of sequential recommendation + + """ + + def __init__(self, config, dataset): + super(FOSSIL, self).__init__(config, dataset) + + # load the dataset information + self.n_users = dataset.num(self.USER_ID) + self.device = config['device'] + + # load the parameters + self.embedding_size = config["embedding_size"] + self.order_len = config["order_len"] + assert self.order_len <= self.max_seq_length, "order_len can't longer than the max_seq_length" + self.reg_weight = config["reg_weight"] + self.alpha = config["alpha"] + + # define the layers and loss type + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.user_lamda = nn.Embedding(self.n_users, self.order_len) + self.lamda = nn.Parameter(torch.zeros(self.order_len)).to(self.device) + + self.loss_type = config['loss_type'] + if self.loss_type == 'BPR': + self.loss_fct = BPRLoss() + elif self.loss_type == 'CE': + self.loss_fct = nn.CrossEntropyLoss() + else: + raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") + + # init the parameters of the model + self.apply(self.init_weights) + + def inverse_seq_item_embedding(self, seq_item_embedding, seq_item_len): + """ + inverse seq_item_embedding like this: + simple to 2-dim + [1,2,3,0,0,0] -- ??? -- >> [0,0,0,1,2,3] + first: [0,0,0,0,0,0] concat [1,2,3,0,0,0] + using gather_indexes: to get one by one + first get 3,then 2,last 1 + """ + zeros = torch.zeros_like(seq_item_embedding, dtype=torch.float).to(self.device) + # batch_size * seq_len * embedding_size + item_embedding_zeros = torch.cat([zeros, seq_item_embedding], dim=1) + # batch_size * 2_mul_seq_len * embedding_size + embedding_list = list() + for i in range(self.order_len): + embedding = self.gather_indexes(item_embedding_zeros, + self.max_seq_length + seq_item_len - self.order_len + i) + embedding_list.append(embedding.unsqueeze(1)) + short_item_embedding = torch.cat(embedding_list, dim=1) + # batch_size * short_len * embedding_size + + return short_item_embedding + + def reg_loss(self, user_embedding, item_embedding, seq_output): + + reg_1 = self.reg_weight + loss_1 = reg_1 * torch.norm(user_embedding, p=2)\ + + reg_1 * torch.norm(item_embedding, p=2)\ + + reg_1 * torch.norm(seq_output, p=2) + + return loss_1 + + def init_weights(self, module): + + if isinstance(module, nn.Embedding) or isinstance(module, nn.Linear): + xavier_normal_(module.weight.data) + + def forward(self, seq_item, seq_item_len, user): + + seq_item_embedding = self.item_embedding(seq_item) + + high_order_seq_item_embedding = self.inverse_seq_item_embedding(seq_item_embedding, seq_item_len) + # batch_size * order_len * embedding + + high_order = self.get_high_order_Markov(high_order_seq_item_embedding, user) + similarity = self.get_similarity(seq_item_embedding, seq_item_len) + + return high_order + similarity + + def get_high_order_Markov(self, high_order_item_embedding, user): + """ + + in order to get the inference of past items and the user's taste to the current predict item + """ + + user_lamda = self.user_lamda(user).unsqueeze(dim=2) + # batch_size * order_len * 1 + lamda = self.lamda.unsqueeze(dim=0).unsqueeze(dim=2) + # 1 * order_len * 1 + lamda = torch.add(user_lamda, lamda) + # batch_size * order_len * 1 + high_order_item_embedding = torch.mul(high_order_item_embedding, lamda) + # batch_size * order_len * embedding_size + high_order_item_embedding = high_order_item_embedding.sum(dim=1) + # batch_size * embedding_size + + return high_order_item_embedding + + def get_similarity(self, seq_item_embedding, seq_item_len): + """ + in order to get the inference of past items to the current predict item + """ + coeff = torch.pow(seq_item_len.unsqueeze(1), -self.alpha).float() + # batch_size * 1 + similarity = torch.mul(coeff, seq_item_embedding.sum(dim=1)) + # batch_size * embedding_size + + return similarity + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + seq_output = self.forward(seq_item, seq_item_len, user) + pos_items = interaction[self.POS_ITEM_ID] + pos_items_emb = self.item_embedding(pos_items) + + user_lamda = self.user_lamda(user) + pos_items_embedding = self.item_embedding(pos_items) + if self.loss_type == 'BPR': + neg_items = interaction[self.NEG_ITEM_ID] + neg_items_emb = self.item_embedding(neg_items) + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) + loss = self.loss_fct(pos_score, neg_score) + return loss + self.reg_loss(user_lamda, pos_items_embedding, seq_output) + else: # self.loss_type = 'CE' + test_item_emb = self.item_embedding.weight + logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) + loss = self.loss_fct(logits, pos_items) + return loss + self.reg_loss(user_lamda, pos_items_embedding, seq_output) + + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + test_item = interaction[self.ITEM_ID] + user = interaction[self.USER_ID] + seq_output = self.forward(item_seq, item_seq_len, user) + test_item_emb = self.item_embedding(test_item) + scores = torch.mul(seq_output, test_item_emb).sum(dim=1) + return scores + + def full_sort_predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + user = interaction[self.USER_ID] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + seq_output = self.forward(item_seq, item_seq_len, user) + test_items_emb = self.item_embedding.weight + scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + return scores diff --git a/recbole/properties/model/FOSSIL.yaml b/recbole/properties/model/FOSSIL.yaml new file mode 100644 index 000000000..1eb00d1bb --- /dev/null +++ b/recbole/properties/model/FOSSIL.yaml @@ -0,0 +1,5 @@ +embedding_size: 64 +loss_type: "CE" +reg_weight: 0.00 +order_len: 3 +alpha: 0.6 \ No newline at end of file diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 0be3e9b31..ec50838c7 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -345,6 +345,13 @@ def test_hgn(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_fossil(self): + config_dict = { + 'model': 'FOSSIL', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + # def test_fdsa(self): # config_dict = { # 'model': 'FDSA', @@ -634,6 +641,14 @@ def test_hgn(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_fossil(self): + config_dict = { + 'model': 'FOSSIL', + 'loss_type': 'BPR', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + def test_caser(self): config_dict = { 'model': 'Caser', From f81981b96cdce609c615ba2a12644b5774291cd9 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Mon, 14 Dec 2020 16:16:26 +0800 Subject: [PATCH 156/249] DOC: doc fix in fossil --- recbole/model/sequential_recommender/fossil.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/model/sequential_recommender/fossil.py b/recbole/model/sequential_recommender/fossil.py index 0450262a3..24652f42e 100644 --- a/recbole/model/sequential_recommender/fossil.py +++ b/recbole/model/sequential_recommender/fossil.py @@ -9,7 +9,7 @@ ################################################ Reference: - Ruining He et al. "Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation." in IEEE 2016 + Ruining He et al. "Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation." in ICDM 2016. """ From 333a4e3bdccfc5ec152ebf7ab8188849ae52686c Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 15 Dec 2020 15:59:12 +0800 Subject: [PATCH 157/249] FIX: change the default value of clip_grad_norm. --- recbole/properties/overall.yaml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/recbole/properties/overall.yaml b/recbole/properties/overall.yaml index 33a0e30c2..e9dd78d57 100644 --- a/recbole/properties/overall.yaml +++ b/recbole/properties/overall.yaml @@ -15,7 +15,8 @@ learning_rate: 0.001 training_neg_sample_num: 1 eval_step: 1 stopping_step: 10 -clip_grad_norm: {'max_norm': 5, 'norm_type': 2} +clip_grad_norm: ~ +# clip_grad_norm: {'max_norm': 5, 'norm_type': 2} # evaluation settings eval_setting: RO_RS,full From af792accba0b4bf3d8fa54fdee22ef507903bc54 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Wed, 16 Dec 2020 15:35:50 +0800 Subject: [PATCH 158/249] FEA: add RepeatNet by Shao Weiqi. --- .../model/sequential_recommender/repeatnet.py | 324 ++++++++++++++++++ recbole/properties/model/RepeatNet.yaml | 5 + tests/model/test_model_auto.py | 7 + 3 files changed, 336 insertions(+) create mode 100644 recbole/model/sequential_recommender/repeatnet.py create mode 100644 recbole/properties/model/RepeatNet.yaml diff --git a/recbole/model/sequential_recommender/repeatnet.py b/recbole/model/sequential_recommender/repeatnet.py new file mode 100644 index 000000000..015e3ae1f --- /dev/null +++ b/recbole/model/sequential_recommender/repeatnet.py @@ -0,0 +1,324 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/11/22 8:30 +# @Author : Shao Weiqi +# @Reviewer : Lin Kun +# @Email : shaoweiqi@ruc.edu.cn + +r""" +RepeatNet +################################################ + +Reference: + Pengjie Ren et al. "RepeatNet: A Repeat Aware Neural Recommendation Machine for Session-based Recommendation." + in AAAI 2019 + +Reference code: + https://github.com/PengjieRen/RepeatNet. + +""" + +import torch +from torch import nn +from torch.nn.init import xavier_normal_, constant_ + +from torch.nn import functional as F +from recbole.utils import InputType +from recbole.model.abstract_recommender import SequentialRecommender + + +class RepeatNet(SequentialRecommender): + r""" + RepeatNet explores a hybrid encoder with an repeat module and explore module + repeat module is used for finding out the repeat consume in sequential recommendation + explore module is used for exploring new items for recommendation + + """ + + input_type = InputType.POINTWISE + + def __init__(self, config, dataset): + + super(RepeatNet, self).__init__(config, dataset) + + # load the dataset information + self.device = config["device"] + + # load parameters + self.embedding_size = config["embedding_size"] + self.hidden_size = config["hidden_size"] + self.joint_train = config["joint_train"] + self.dropout_prob = config["dropout_prob"] + + # define the layers and loss function + self.item_matrix = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0).to(self.device) + self.gru = nn.GRU(self.embedding_size, self.hidden_size, batch_first=True).to(self.device) + self.repeat_explore_mechanism = Repeat_Explore_Mechanism(self.device, + hidden_size=self.hidden_size, + seq_len=self.max_seq_length, + dropout_prob=self.dropout_prob).to(self.device) + self.repeat_recommendation_decoder = Repeat_Recommendation_Decoder(self.device, + hidden_size=self.hidden_size, + seq_len=self.max_seq_length, + num_item=self.n_items, + dropout_prob=self.dropout_prob).to( + self.device) + self.explore_recommendation_decoder = Explore_Recommendation_Decoder(hidden_size=self.hidden_size, + seq_len=self.max_seq_length, + num_item=self.n_items, + device=self.device, + dropout_prob=self.dropout_prob).to( + self.device) + + self.loss_fct = F.nll_loss + + # init the weight of the module + self.apply(self._init_weights) + + def _init_weights(self, module): + + if isinstance(module, nn.Embedding): + xavier_normal_(module.weight.data) + elif isinstance(module, nn.Linear): + xavier_normal_(module.weight.data) + if module.bias is not None: + constant_(module.bias.data, 0) + + def forward(self, seq_item, seq_item_len): + + batch_seq_item_embedding = self.item_matrix(seq_item) + # batch_size * seq_len == embedding ==>> batch_size * seq_len * embedding_size + + all_memory, _ = self.gru(batch_seq_item_embedding) + last_memory = self.gather_indexes(all_memory, seq_item_len - 1) + # all_memory: batch_size * seq_item * hidden_size + # last_memory: batch_size * hidden_size + timeline_mask = (seq_item == 0) + + repeat_recommendation_mechanism = self.repeat_explore_mechanism.forward(all_memory=all_memory, + last_memory=last_memory, + mask=timeline_mask) + self.repeat_explore = repeat_recommendation_mechanism + # batch_size * 2 + + repeat_recommendation_decoder = self.repeat_recommendation_decoder.forward(all_memory=all_memory, + last_memory=last_memory, + seq_item=seq_item, + mask=timeline_mask) + # batch_size * num_item + + explore_recommendation_decoder = self.explore_recommendation_decoder.forward(all_memory=all_memory, + last_memory=last_memory, + seq_item=seq_item, + mask=timeline_mask) + # batch_size * num_item + + prediction = repeat_recommendation_decoder * repeat_recommendation_mechanism[:, 0].unsqueeze(1) \ + + explore_recommendation_decoder * repeat_recommendation_mechanism[:, 1].unsqueeze(1) + # batch_size * num_item + + return prediction + + def calculate_loss(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + pos_item = interaction[self.POS_ITEM_ID] + prediction = self.forward(seq_item, seq_item_len) + loss = self.loss_fct((prediction + 1e-8).log(), pos_item, ignore_index=0) + if self.joint_train is True: + loss += self.repeat_explore_loss(seq_item, pos_item) + + return loss + + def repeat_explore_loss(self, seq_item, pos_item): + """ + + :param seq_item: batch_size * seq_len + :param pos_item: batch_size + :return: + """ + batch_size = seq_item.size(0) + repeat, explore = torch.zeros(batch_size).to(self.device), torch.ones(batch_size).to(self.device) + i = 0 + for x, y in zip(seq_item, pos_item): + if y in x: + repeat[i] = 1 + explore[i] = 0 + i += 1 + repeat_loss = torch.mul(repeat.unsqueeze(1), torch.log(self.repeat_explore[:, 0] + 1e-8)).mean() + explore_loss = torch.mul(explore.unsqueeze(1), torch.log(self.repeat_explore[:, 1] + 1e-8)).mean() + + return (-repeat_loss - explore_loss) / 2 + + def full_sort_predict(self, interaction): + + seq_item = interaction[self.ITEM_SEQ] + seq_item_len = interaction[self.ITEM_SEQ_LEN] + prediction = self.forward(seq_item, seq_item_len) + + return prediction + + +class Repeat_Explore_Mechanism(nn.Module): + + def __init__(self, device, hidden_size=32, seq_len=10, dropout_prob=0.5): + super(Repeat_Explore_Mechanism, self).__init__() + self.dropout = nn.Dropout(dropout_prob) + self.hidden_size = hidden_size + self.device = device + self.seq_len = seq_len + self.Wr = nn.Linear(hidden_size, hidden_size, bias=False) + self.Ur = nn.Linear(hidden_size, hidden_size, bias=False) + self.tanh = nn.Tanh() + self.Vre = nn.Linear(hidden_size, 1, bias=False) + self.Wre = nn.Linear(hidden_size, 2, bias=False) + + def forward(self, all_memory, last_memory, mask=None): + """ + + calculate the probability of Repeat and explore + """ + all_memory_values = all_memory + + all_memory = self.dropout(self.Ur(all_memory)) + + last_memory = self.dropout(self.Wr(last_memory)) + last_memory = last_memory.unsqueeze(1) + last_memory = last_memory.repeat(1, self.seq_len, 1) + + output = self.tanh(all_memory + last_memory) + + output = self.Vre(output) + output = nn.Softmax(dim=1)(output) + output = output.repeat(1, 1, self.hidden_size) + output = output * all_memory_values + output = output.sum(dim=1) + + output = self.Wre(output) + + output = nn.Softmax(dim=-1)(output) + + return output + + +class Repeat_Recommendation_Decoder(nn.Module): + + def __init__(self, device, hidden_size=32, seq_len=10, num_item=40000, dropout_prob=0.5): + super(Repeat_Recommendation_Decoder, self).__init__() + self.dropout = nn.Dropout(dropout_prob) + self.hidden_size = hidden_size + self.device = device + self.seq_len = seq_len + self.num_item = num_item + self.Wr = nn.Linear(hidden_size, hidden_size, bias=False) + self.Ur = nn.Linear(hidden_size, hidden_size, bias=False) + self.tanh = nn.Tanh() + self.Vr = nn.Linear(hidden_size, 1) + + def forward(self, all_memory, last_memory, seq_item, mask=None): + """ + + calculate the the force of repeat + """ + all_memory = self.dropout(self.Ur(all_memory)) + + last_memory = self.dropout(self.Wr(last_memory)) + last_memory = last_memory.unsqueeze(1) + last_memory = last_memory.repeat(1, self.seq_len, 1) + + output = self.tanh(last_memory + all_memory) + + output = self.Vr(output).squeeze(2) + + if mask is not None: + output.masked_fill_(mask, -1e9) + + output = nn.Softmax(dim=-1)(output) + output = output.unsqueeze(1) + + map = build_map(seq_item, self.device, max=self.num_item).to(self.device) + output = torch.matmul(output, map).squeeze(1).to(self.device) + output = output.squeeze(1).to(self.device) + + return output.to(self.device) + + +class Explore_Recommendation_Decoder(nn.Module): + + def __init__(self, hidden_size, seq_len, num_item, device, dropout_prob=0.5): + super(Explore_Recommendation_Decoder, self).__init__() + self.dropout = nn.Dropout(dropout_prob) + self.hidden_size = hidden_size + self.seq_len = seq_len + self.num_item = num_item + self.device = device + self.We = nn.Linear(hidden_size, hidden_size) + self.Ue = nn.Linear(hidden_size, hidden_size) + self.tanh = nn.Tanh() + self.Ve = nn.Linear(hidden_size, 1) + self.matrix_for_explore = nn.Linear(2 * self.hidden_size, self.num_item, bias=False) + + def forward(self, all_memory, last_memory, seq_item, mask=None): + """ + + calculate the force of explore + """ + all_memory_values, last_memory_values = all_memory, last_memory + + all_memory = self.dropout(self.Ue(all_memory)) + + last_memory = self.dropout(self.We(last_memory)) + last_memory = last_memory.unsqueeze(1) + last_memory = last_memory.repeat(1, self.seq_len, 1) + + output = self.tanh(all_memory + last_memory) + output = self.Ve(output).squeeze(-1) + + if mask is not None: + output.masked_fill_(mask, -1e9) + + output = output.unsqueeze(-1) + + output = nn.Softmax(dim=1)(output) + output = output.repeat(1, 1, self.hidden_size) + output = (output * all_memory_values).sum(dim=1) + output = torch.cat([output, last_memory_values], dim=1) + output = self.dropout(self.matrix_for_explore(output)) + + map = build_map(seq_item, self.device, max=self.num_item).to(self.device) + explore_mask = torch.bmm((seq_item > 0).float().unsqueeze(1), map).squeeze(1).to(self.device) + output = output.masked_fill(explore_mask.bool(), float('-inf')) + output = nn.Softmax(1)(output) + + return output + + +def build_map(b_map, device, max=None): + """ + project the b_map to the place where it in should be + like this: + seq_item A: [3,4,5] n_items: 6 + after map: A + [0,0,1,0,0,0] + [0,0,0,1,0,0] + [0,0,0,0,1,0] + + batch_size * seq_len ==>> batch_size * seq_len * n_item + + + use in RepeatNet: + [3,4,5] matmul [0,0,1,0,0,0] + [0,0,0,1,0,0] ==>>> [0,0,3,4,5,0] it works in the RepeatNet when project the seq item into all items + [0,0,0,0,1,0] + batch_size * 1 * seq_len matmul batch_size * seq_len * n_item ==>> batch_size * 1 * n_item + """ + batch_size, b_len = b_map.size() + if max is None: + max = b_map.max() + 1 + if torch.cuda.is_available(): + b_map_ = torch.FloatTensor(batch_size, b_len, max).fill_(0).to(device) + else: + b_map_ = torch.zeros(batch_size, b_len, max) + b_map_.scatter_(2, b_map.unsqueeze(2), 1.) + b_map_.requires_grad = False + return b_map_ diff --git a/recbole/properties/model/RepeatNet.yaml b/recbole/properties/model/RepeatNet.yaml new file mode 100644 index 000000000..dac46bacc --- /dev/null +++ b/recbole/properties/model/RepeatNet.yaml @@ -0,0 +1,5 @@ +embedding_size: 64 +loss_type: "CE" +hidden_size: 64 +joint_train: False +dropout_prob: 0.5 diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index ec50838c7..6a52397e7 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -352,6 +352,13 @@ def test_fossil(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_repeat_net(self): + config_dict = { + 'model': 'RepeatNet', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + # def test_fdsa(self): # config_dict = { # 'model': 'FDSA', From 604325d4e7299f700a2017ab1b0744c3cf84feaa Mon Sep 17 00:00:00 2001 From: EliverQ <2537419753@qq.com> Date: Wed, 16 Dec 2020 17:04:21 +0800 Subject: [PATCH 159/249] FEA:add param_dict in hyper_tuning.py --- recbole/trainer/hyper_tuning.py | 38 +++++++++++++++++++++++++++++++-- 1 file changed, 36 insertions(+), 2 deletions(-) diff --git a/recbole/trainer/hyper_tuning.py b/recbole/trainer/hyper_tuning.py index 1d7f55dc0..2709bd7db 100644 --- a/recbole/trainer/hyper_tuning.py +++ b/recbole/trainer/hyper_tuning.py @@ -136,7 +136,7 @@ class HyperTuning(object): https://github.com/hyperopt/hyperopt/issues/200 """ - def __init__(self, objective_function, space=None, params_file=None, fixed_config_file_list=None, + def __init__(self, objective_function, space=None, params_file=None, params_dict=None, fixed_config_file_list=None, algo='exhaustive', max_evals=100): self.best_score = None self.best_params = None @@ -150,8 +150,10 @@ def __init__(self, objective_function, space=None, params_file=None, fixed_confi self.space = space elif params_file: self.space = self._build_space_from_file(params_file) + elif params_dict: + self.space = self._build_space_from_dict(params_dict) else: - raise ValueError('at least one of `space` and `params_file` is provided') + raise ValueError('at least one of `space`, `params_file` and `params_dict` is provided') if isinstance(algo, str): if algo == 'exhaustive': self.algo = partial(exhaustive_search, nbMaxSucessiveFailures=1000) @@ -187,6 +189,38 @@ def _build_space_from_file(file): raise ValueError('Illegal param type [{}]'.format(para_type)) return space + @staticmethod + def _build_space_from_dict(config_dict): + from hyperopt import hp + space = {} + for para_type in config_dict: + if para_type == 'choice': + for para_name in config_dict['choice']: + para_value = config_dict['choice'][para_name] + space[para_name] = hp.choice(para_name, para_value) + elif para_type == 'uniform': + for para_name in config_dict['uniform']: + para_value = config_dict['uniform'][para_name] + low = para_value[0] + high = para_value[1] + space[para_name] = hp.uniform(para_name, float(low), float(high)) + elif para_type == 'quniform': + for para_name in config_dict['quniform']: + para_value = config_dict['quniform'][para_name] + low = para_value[0] + high = para_value[1] + q = para_value[2] + space[para_name] = hp.quniform(para_name, float(low), float(high), float(q)) + elif para_type =='loguniform': + for para_name in config_dict['loguniform']: + para_value = config_dict['loguniform'][para_name] + low = para_value[0] + high = para_value[1] + space[para_name] = hp.loguniform(para_name, float(low), float(high)) + else: + raise ValueError('Illegal param type [{}]'.format(para_type)) + return space + @staticmethod def params2str(params): r""" convert dict to str From 2d91cc0bb428cad5e014cb528a8e5723422a9ac1 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Thu, 17 Dec 2020 19:04:39 +0800 Subject: [PATCH 160/249] REFACTOR: refactor in repeatnet.py. --- .../model/sequential_recommender/repeatnet.py | 189 +++++++++--------- 1 file changed, 93 insertions(+), 96 deletions(-) diff --git a/recbole/model/sequential_recommender/repeatnet.py b/recbole/model/sequential_recommender/repeatnet.py index 015e3ae1f..794329517 100644 --- a/recbole/model/sequential_recommender/repeatnet.py +++ b/recbole/model/sequential_recommender/repeatnet.py @@ -50,24 +50,22 @@ def __init__(self, config, dataset): self.dropout_prob = config["dropout_prob"] # define the layers and loss function - self.item_matrix = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0).to(self.device) - self.gru = nn.GRU(self.embedding_size, self.hidden_size, batch_first=True).to(self.device) + self.item_matrix = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.gru = nn.GRU(self.embedding_size, self.hidden_size, batch_first=True) self.repeat_explore_mechanism = Repeat_Explore_Mechanism(self.device, hidden_size=self.hidden_size, seq_len=self.max_seq_length, - dropout_prob=self.dropout_prob).to(self.device) + dropout_prob=self.dropout_prob) self.repeat_recommendation_decoder = Repeat_Recommendation_Decoder(self.device, hidden_size=self.hidden_size, seq_len=self.max_seq_length, num_item=self.n_items, - dropout_prob=self.dropout_prob).to( - self.device) + dropout_prob=self.dropout_prob) self.explore_recommendation_decoder = Explore_Recommendation_Decoder(hidden_size=self.hidden_size, seq_len=self.max_seq_length, num_item=self.n_items, device=self.device, - dropout_prob=self.dropout_prob).to( - self.device) + dropout_prob=self.dropout_prob) self.loss_fct = F.nll_loss @@ -83,68 +81,58 @@ def _init_weights(self, module): if module.bias is not None: constant_(module.bias.data, 0) - def forward(self, seq_item, seq_item_len): + def forward(self, item_seq, item_seq_len): - batch_seq_item_embedding = self.item_matrix(seq_item) + batch_seq_item_embedding = self.item_matrix(item_seq) # batch_size * seq_len == embedding ==>> batch_size * seq_len * embedding_size all_memory, _ = self.gru(batch_seq_item_embedding) - last_memory = self.gather_indexes(all_memory, seq_item_len - 1) - # all_memory: batch_size * seq_item * hidden_size + last_memory = self.gather_indexes(all_memory, item_seq_len - 1) + # all_memory: batch_size * item_seq * hidden_size # last_memory: batch_size * hidden_size - timeline_mask = (seq_item == 0) + timeline_mask = (item_seq == 0) - repeat_recommendation_mechanism = self.repeat_explore_mechanism.forward(all_memory=all_memory, - last_memory=last_memory, - mask=timeline_mask) - self.repeat_explore = repeat_recommendation_mechanism + self.repeat_explore = self.repeat_explore_mechanism.forward(all_memory=all_memory, + last_memory=last_memory) # batch_size * 2 - repeat_recommendation_decoder = self.repeat_recommendation_decoder.forward(all_memory=all_memory, last_memory=last_memory, - seq_item=seq_item, + item_seq=item_seq, mask=timeline_mask) # batch_size * num_item - explore_recommendation_decoder = self.explore_recommendation_decoder.forward(all_memory=all_memory, last_memory=last_memory, - seq_item=seq_item, + item_seq=item_seq, mask=timeline_mask) # batch_size * num_item - - prediction = repeat_recommendation_decoder * repeat_recommendation_mechanism[:, 0].unsqueeze(1) \ - + explore_recommendation_decoder * repeat_recommendation_mechanism[:, 1].unsqueeze(1) + prediction = repeat_recommendation_decoder * self.repeat_explore[:, 0].unsqueeze(1) \ + + explore_recommendation_decoder * self.repeat_explore[:, 1].unsqueeze(1) # batch_size * num_item return prediction def calculate_loss(self, interaction): - seq_item = interaction[self.ITEM_SEQ] - seq_item_len = interaction[self.ITEM_SEQ_LEN] + item_seq = interaction[self.ITEM_SEQ] + item_seq_len = interaction[self.ITEM_SEQ_LEN] pos_item = interaction[self.POS_ITEM_ID] - prediction = self.forward(seq_item, seq_item_len) + prediction = self.forward(item_seq, item_seq_len) loss = self.loss_fct((prediction + 1e-8).log(), pos_item, ignore_index=0) if self.joint_train is True: - loss += self.repeat_explore_loss(seq_item, pos_item) + loss += self.repeat_explore_loss(item_seq, pos_item) return loss - def repeat_explore_loss(self, seq_item, pos_item): - """ + def repeat_explore_loss(self, item_seq, pos_item): - :param seq_item: batch_size * seq_len - :param pos_item: batch_size - :return: - """ - batch_size = seq_item.size(0) + batch_size = item_seq.size(0) repeat, explore = torch.zeros(batch_size).to(self.device), torch.ones(batch_size).to(self.device) - i = 0 - for x, y in zip(seq_item, pos_item): - if y in x: - repeat[i] = 1 - explore[i] = 0 - i += 1 + index = 0 + for seq_item_ex, pos_item_ex in zip(item_seq, pos_item): + if pos_item_ex in seq_item_ex: + repeat[index] = 1 + explore[index] = 0 + index += 1 repeat_loss = torch.mul(repeat.unsqueeze(1), torch.log(self.repeat_explore[:, 0] + 1e-8)).mean() explore_loss = torch.mul(explore.unsqueeze(1), torch.log(self.repeat_explore[:, 1] + 1e-8)).mean() @@ -152,58 +140,70 @@ def repeat_explore_loss(self, seq_item, pos_item): def full_sort_predict(self, interaction): - seq_item = interaction[self.ITEM_SEQ] - seq_item_len = interaction[self.ITEM_SEQ_LEN] - prediction = self.forward(seq_item, seq_item_len) + item_seq = interaction[self.ITEM_SEQ] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + prediction = self.forward(item_seq, item_seq_len) return prediction + def predict(self, interaction): + + item_seq = interaction[self.ITEM_SEQ] + test_item = interaction[self.ITEM_ID] + item_seq_len = interaction[self.ITEM_SEQ_LEN] + seq_output = self.forward(item_seq, item_seq_len) + # batch_size * num_items + seq_output = seq_output.unsqueeze(-1) + # batch_size * num_items * 1 + scores = self.gather_indexes(seq_output, test_item).squeeze() + + return scores + class Repeat_Explore_Mechanism(nn.Module): - def __init__(self, device, hidden_size=32, seq_len=10, dropout_prob=0.5): + def __init__(self, device, hidden_size, seq_len, dropout_prob): super(Repeat_Explore_Mechanism, self).__init__() self.dropout = nn.Dropout(dropout_prob) self.hidden_size = hidden_size self.device = device self.seq_len = seq_len - self.Wr = nn.Linear(hidden_size, hidden_size, bias=False) - self.Ur = nn.Linear(hidden_size, hidden_size, bias=False) + self.Wre = nn.Linear(hidden_size, hidden_size, bias=False) + self.Ure = nn.Linear(hidden_size, hidden_size, bias=False) self.tanh = nn.Tanh() self.Vre = nn.Linear(hidden_size, 1, bias=False) - self.Wre = nn.Linear(hidden_size, 2, bias=False) + self.Wcre = nn.Linear(hidden_size, 2, bias=False) - def forward(self, all_memory, last_memory, mask=None): + def forward(self, all_memory, last_memory): """ - calculate the probability of Repeat and explore """ all_memory_values = all_memory - all_memory = self.dropout(self.Ur(all_memory)) + all_memory = self.dropout(self.Ure(all_memory)) - last_memory = self.dropout(self.Wr(last_memory)) + last_memory = self.dropout(self.Wre(last_memory)) last_memory = last_memory.unsqueeze(1) last_memory = last_memory.repeat(1, self.seq_len, 1) - output = self.tanh(all_memory + last_memory) + output_ere = self.tanh(all_memory + last_memory) - output = self.Vre(output) - output = nn.Softmax(dim=1)(output) - output = output.repeat(1, 1, self.hidden_size) - output = output * all_memory_values - output = output.sum(dim=1) + output_ere = self.Vre(output_ere) + alpha_are = nn.Softmax(dim=1)(output_ere) + alpha_are = alpha_are.repeat(1, 1, self.hidden_size) + output_cre = alpha_are * all_memory_values + output_cre = output_cre.sum(dim=1) - output = self.Wre(output) + output_cre = self.Wcre(output_cre) - output = nn.Softmax(dim=-1)(output) + repeat_explore_mechanism = nn.Softmax(dim=-1)(output_cre) - return output + return repeat_explore_mechanism class Repeat_Recommendation_Decoder(nn.Module): - def __init__(self, device, hidden_size=32, seq_len=10, num_item=40000, dropout_prob=0.5): + def __init__(self, device, hidden_size, seq_len, num_item, dropout_prob): super(Repeat_Recommendation_Decoder, self).__init__() self.dropout = nn.Dropout(dropout_prob) self.hidden_size = hidden_size @@ -215,9 +215,8 @@ def __init__(self, device, hidden_size=32, seq_len=10, num_item=40000, dropout_p self.tanh = nn.Tanh() self.Vr = nn.Linear(hidden_size, 1) - def forward(self, all_memory, last_memory, seq_item, mask=None): + def forward(self, all_memory, last_memory, item_seq, mask=None): """ - calculate the the force of repeat """ all_memory = self.dropout(self.Ur(all_memory)) @@ -226,26 +225,26 @@ def forward(self, all_memory, last_memory, seq_item, mask=None): last_memory = last_memory.unsqueeze(1) last_memory = last_memory.repeat(1, self.seq_len, 1) - output = self.tanh(last_memory + all_memory) + output_er = self.tanh(last_memory + all_memory) - output = self.Vr(output).squeeze(2) + output_er = self.Vr(output_er).squeeze(2) if mask is not None: - output.masked_fill_(mask, -1e9) + output_er.masked_fill_(mask, -1e9) - output = nn.Softmax(dim=-1)(output) - output = output.unsqueeze(1) + output_er = nn.Softmax(dim=-1)(output_er) + output_er = output_er.unsqueeze(1) - map = build_map(seq_item, self.device, max=self.num_item).to(self.device) - output = torch.matmul(output, map).squeeze(1).to(self.device) - output = output.squeeze(1).to(self.device) + map_matrix = build_map(item_seq, self.device, max_index=self.num_item) + output_er = torch.matmul(output_er, map_matrix).squeeze(1).to(self.device) + repeat_recommendation_decoder = output_er.squeeze(1).to(self.device) - return output.to(self.device) + return repeat_recommendation_decoder.to(self.device) class Explore_Recommendation_Decoder(nn.Module): - def __init__(self, hidden_size, seq_len, num_item, device, dropout_prob=0.5): + def __init__(self, hidden_size, seq_len, num_item, device, dropout_prob): super(Explore_Recommendation_Decoder, self).__init__() self.dropout = nn.Dropout(dropout_prob) self.hidden_size = hidden_size @@ -258,9 +257,8 @@ def __init__(self, hidden_size, seq_len, num_item, device, dropout_prob=0.5): self.Ve = nn.Linear(hidden_size, 1) self.matrix_for_explore = nn.Linear(2 * self.hidden_size, self.num_item, bias=False) - def forward(self, all_memory, last_memory, seq_item, mask=None): + def forward(self, all_memory, last_memory, item_seq, mask=None): """ - calculate the force of explore """ all_memory_values, last_memory_values = all_memory, last_memory @@ -271,33 +269,33 @@ def forward(self, all_memory, last_memory, seq_item, mask=None): last_memory = last_memory.unsqueeze(1) last_memory = last_memory.repeat(1, self.seq_len, 1) - output = self.tanh(all_memory + last_memory) - output = self.Ve(output).squeeze(-1) + output_ee = self.tanh(all_memory + last_memory) + output_ee = self.Ve(output_ee).squeeze(-1) if mask is not None: - output.masked_fill_(mask, -1e9) + output_ee.masked_fill_(mask, -1e9) - output = output.unsqueeze(-1) + output_ee = output_ee.unsqueeze(-1) - output = nn.Softmax(dim=1)(output) - output = output.repeat(1, 1, self.hidden_size) - output = (output * all_memory_values).sum(dim=1) - output = torch.cat([output, last_memory_values], dim=1) - output = self.dropout(self.matrix_for_explore(output)) + alpha_e = nn.Softmax(dim=1)(output_ee) + alpha_e = alpha_e.repeat(1, 1, self.hidden_size) + output_e = (alpha_e * all_memory_values).sum(dim=1) + output_e = torch.cat([output_e, last_memory_values], dim=1) + output_e = self.dropout(self.matrix_for_explore(output_e)) - map = build_map(seq_item, self.device, max=self.num_item).to(self.device) - explore_mask = torch.bmm((seq_item > 0).float().unsqueeze(1), map).squeeze(1).to(self.device) - output = output.masked_fill(explore_mask.bool(), float('-inf')) - output = nn.Softmax(1)(output) + map_matrix = build_map(item_seq, self.device, max_index=self.num_item) + explore_mask = torch.bmm((item_seq > 0).float().unsqueeze(1), map_matrix).squeeze(1) + output_e = output_e.masked_fill(explore_mask.bool(), float('-inf')) + explore_recommendation_decoder = nn.Softmax(1)(output_e) - return output + return explore_recommendation_decoder -def build_map(b_map, device, max=None): +def build_map(b_map, device, max_index=None): """ project the b_map to the place where it in should be like this: - seq_item A: [3,4,5] n_items: 6 + item_seq A: [3,4,5] n_items: 6 after map: A [0,0,1,0,0,0] [0,0,0,1,0,0] @@ -305,7 +303,6 @@ def build_map(b_map, device, max=None): batch_size * seq_len ==>> batch_size * seq_len * n_item - use in RepeatNet: [3,4,5] matmul [0,0,1,0,0,0] [0,0,0,1,0,0] ==>>> [0,0,3,4,5,0] it works in the RepeatNet when project the seq item into all items @@ -313,12 +310,12 @@ def build_map(b_map, device, max=None): batch_size * 1 * seq_len matmul batch_size * seq_len * n_item ==>> batch_size * 1 * n_item """ batch_size, b_len = b_map.size() - if max is None: - max = b_map.max() + 1 + if max_index is None: + max_index = b_map.max() + 1 if torch.cuda.is_available(): - b_map_ = torch.FloatTensor(batch_size, b_len, max).fill_(0).to(device) + b_map_ = torch.FloatTensor(batch_size, b_len, max_index).fill_(0).to(device) else: - b_map_ = torch.zeros(batch_size, b_len, max) + b_map_ = torch.zeros(batch_size, b_len, max_index) b_map_.scatter_(2, b_map.unsqueeze(2), 1.) b_map_.requires_grad = False return b_map_ From ec43c030de484426a3c9a91d0c085b97ddcff97f Mon Sep 17 00:00:00 2001 From: yihongguo Date: Thu, 17 Dec 2020 20:39:43 +0800 Subject: [PATCH 161/249] FEA: add LINE to general recommender --- recbole/model/general_recommender/line.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py index d92cae1c0..b7ecfad5a 100644 --- a/recbole/model/general_recommender/line.py +++ b/recbole/model/general_recommender/line.py @@ -133,12 +133,13 @@ def calculate_loss(self, interaction): score_pos = self.forward(user,pos_item) + # randomly train i-i relation and u-u relation with u-i relation if random.random()<0.5: score_neg = self.forward(user, neg_item) score_pos_con = self.context_forward(user, pos_item, 'uu') score_neg_con = self.context_forward(user, neg_item, 'uu') else: - # h,t = self.gen_neg_sample(pos_item) + # sample negative user for item neg_user = self.sampler(pos_item) score_neg = self.forward(neg_user,pos_item) score_pos_con = self.context_forward(pos_item, user,'ii') From 0772227c2e5f5899f394073c6a3c27b804b555d8 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 18 Dec 2020 10:54:56 +0800 Subject: [PATCH 162/249] FEA: add config['benchmark_filename'] to load pre-split dataset; increased the robustness of data.utils.data_preparation, which can divide the dataset into two parts (train, test) or three parts (train, valid, test). --- recbole/data/dataset/dataset.py | 5 +++++ recbole/data/utils.py | 24 +++++++++++++++++------- 2 files changed, 22 insertions(+), 7 deletions(-) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index d0bcbce25..3aef268be 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -1340,6 +1340,11 @@ def build(self, eval_setting): Returns: list: List of builded :class:`Dataset`. """ + if self.benchmark_filename_list is not None: + cumsum = list(np.cumsum(self.file_size_list)) + datasets = [self.copy(self.inter_feat[start: end]) for start, end in zip([0] + cumsum[:-1], cumsum)] + return datasets + ordering_args = eval_setting.ordering_args if ordering_args['strategy'] == 'shuffle': self.shuffle() diff --git a/recbole/data/utils.py b/recbole/data/utils.py index f6e649daf..f9c4ed06a 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -86,8 +86,15 @@ def data_preparation(config, dataset, save=False): raise ValueError('Sequential models require "loo" split strategy.') builded_datasets = dataset.build(es) - train_dataset, valid_dataset, test_dataset = builded_datasets - phases = ['train', 'valid', 'test'] + if len(builded_datasets) not in {2, 3}: + raise ValueError('Dataset should only be divided into two or three parts.') + else: + train_dataset = builded_datasets[0] + evaluation_datasets = builded_datasets[1:] + if len(builded_datasets) == 2: + phases = ['train', 'test'] + else: + phases = ['train', 'valid', 'test'] if save: save_datasets(config['checkpoint_dir'], name=phases, dataset=builded_datasets) @@ -100,7 +107,7 @@ def data_preparation(config, dataset, save=False): sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) else: sampler = RepeatableSampler(phases, dataset, es.neg_sample_args['distribution']) - kwargs['sampler'] = sampler.set_phase('train') + kwargs['sampler'] = sampler.set_phase(phases[0]) kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args) if model_type == ModelType.KNOWLEDGE: kg_sampler = KGSampler(dataset, es.neg_sample_args['distribution']) @@ -122,18 +129,21 @@ def data_preparation(config, dataset, save=False): if 'sampler' not in locals(): sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) sampler.set_distribution(es.neg_sample_args['distribution']) - kwargs['sampler'] = [sampler.set_phase('valid'), sampler.set_phase('test')] + kwargs['sampler'] = [sampler.set_phase(phase) for phase in phases[1:]] kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args) - valid_data, test_data = dataloader_construct( + evaluation_data = dataloader_construct( name='evaluation', config=config, eval_setting=es, - dataset=[valid_dataset, test_dataset], + dataset=evaluation_datasets, batch_size=config['eval_batch_size'], **kwargs ) - return train_data, valid_data, test_data + if len(builded_datasets) == 2: + return train_data, None, evaluation_data + else: + return [train_data] + evaluation_data def dataloader_construct(name, config, eval_setting, dataset, From 3769b2d48a6ac6ca1000fa88ed0dfd617c588c8d Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 18 Dec 2020 10:56:25 +0800 Subject: [PATCH 163/249] FIX: Increased the robustness of GeneralFullDataLoader, which can handle empty dataset now. --- recbole/data/dataloader/general_dataloader.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index b80feca5f..922f691cc 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -220,12 +220,11 @@ def __init__(self, config, dataset, sampler, neg_sample_args, dataset.sort(by=uid_field, ascending=True) last_uid = None - positive_item = None + positive_item = set() uid2used_item = sampler.used_ids for uid, iid in zip(dataset.inter_feat[uid_field].numpy(), dataset.inter_feat[iid_field].numpy()): if uid != last_uid: - if last_uid is not None: - self._set_user_property(last_uid, uid2used_item[last_uid], positive_item) + self._set_user_property(last_uid, uid2used_item[last_uid], positive_item) last_uid = uid self.uid_list.append(uid) positive_item = set() @@ -238,6 +237,8 @@ def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) def _set_user_property(self, uid, used_item, positive_item): + if uid is None: + return history_item = used_item - positive_item positive_item_num = len(positive_item) self.uid2items_num[uid] = positive_item_num From ca4888a1e47028fbeb73433bed4a2c1818ae902d Mon Sep 17 00:00:00 2001 From: yihongguo Date: Fri, 18 Dec 2020 11:01:43 +0800 Subject: [PATCH 164/249] FEA: add LINE --- run_test_example.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/run_test_example.py b/run_test_example.py index 4f7b68cc0..58baee213 100644 --- a/run_test_example.py +++ b/run_test_example.py @@ -130,6 +130,10 @@ 'model': 'ConvNCF', 'dataset': 'ml-100k', }, + 'Test LINE': { + 'model': 'LINE', + 'dataset': 'ml-100k', + }, # Context-aware Recommendation 'Test FM': { From dd157a500251dcffaeb2e4647bc52b5db90c41e9 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 18 Dec 2020 12:42:08 +0800 Subject: [PATCH 165/249] FIX: can't raise error in IndividualEvaluator --- recbole/evaluator/abstract_evaluator.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/recbole/evaluator/abstract_evaluator.py b/recbole/evaluator/abstract_evaluator.py index 2570fe8ad..1ab81d476 100644 --- a/recbole/evaluator/abstract_evaluator.py +++ b/recbole/evaluator/abstract_evaluator.py @@ -4,7 +4,7 @@ # @email : tsotfsk@outlook.com # UPDATE -# @Time : 2020/10/21, 2020/12/9 +# @Time : 2020/10/21, 2020/12/18 # @Author : Kaiyuan Li, Zhichao Feng # @email : tsotfsk@outlook.com, fzcbupt@gmail.com @@ -99,7 +99,7 @@ class IndividualEvaluator(BaseEvaluator): """ def __init__(self, config, metrics): super().__init__(config, metrics) - pass + self._check_args() def sample_collect(self, true_scores, pred_scores): """It is called when evaluation sample distribution is `uniform` or `popularity`. @@ -127,3 +127,7 @@ def get_score_matrix(self, true_scores, pred_scores): scores_matrix = self.sample_collect(true_scores, pred_scores) return scores_matrix + + def _check_args(self): + if self.full: + raise NotImplementedError('full sort can\'t use IndividualEvaluator') \ No newline at end of file From 9ced71846479f4911ca2afe8c002a02c768b1ca7 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 18 Dec 2020 12:42:55 +0800 Subject: [PATCH 166/249] FIX: metrics disorder --- recbole/evaluator/proxy_evaluator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recbole/evaluator/proxy_evaluator.py b/recbole/evaluator/proxy_evaluator.py index fc91f5d48..9f181c4aa 100644 --- a/recbole/evaluator/proxy_evaluator.py +++ b/recbole/evaluator/proxy_evaluator.py @@ -32,9 +32,9 @@ def build(self): """ evaluator_list = [] - metrics_set = {metric.lower() for metric in self.metrics} + metrics_list = [metric.lower() for metric in self.metrics] for metrics, evaluator in metric_eval_bind: - used_metrics = list(metrics_set.intersection(set(metrics.keys()))) + used_metrics = [metric for metric in metrics_list if metric in metrics.keys()] if used_metrics: evaluator_list.append(evaluator(self.config, used_metrics)) return evaluator_list From c7cbd346a49a80b2f750d35aa3c3eb7cf730afd5 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 18 Dec 2020 12:46:16 +0800 Subject: [PATCH 167/249] FIX: GAUC calculation error --- recbole/evaluator/evaluators.py | 53 ++++++++++++++++++++++----------- recbole/evaluator/metrics.py | 35 +++++++++++++++++----- 2 files changed, 63 insertions(+), 25 deletions(-) diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py index 10a41f715..a97fffa7e 100644 --- a/recbole/evaluator/evaluators.py +++ b/recbole/evaluator/evaluators.py @@ -11,7 +11,7 @@ import torch import numpy as np -from collections import ChainMap +from collections import ChainMap, Counter from recbole.evaluator.metrics import metrics_dict from recbole.evaluator.abstract_evaluator import GroupedEvalautor, IndividualEvaluator @@ -158,22 +158,35 @@ def get_user_pos_len_list(self, interaction, scores_tensor): user_len_list = interaction.user_len_list return pos_len_list, user_len_list - def get_pos_index(self, scores_tensor, pos_len_list, user_len_list): - """get the index of positive items + def rankdata(self, scores): + """Get the ranking of an ordered tensor, and take the average of the ranking for positions with equal values. - Args: - scores_tensor (tensor): the tensor of model output with size of `(N, )` - pos_len_list(list): number of positive items - user_len_list(list): number of all items + Args: + scores(tensor): an ordered tensor, with size of `(N, )` - Returns: - tensor: a matrix indicating whether the corresponding item is positive + Examples:: - """ - scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) - _, n_index = torch.sort(scores_matrix, dim=-1, descending=True) - pos_index = (n_index < pos_len_list.reshape(-1, 1)) - return pos_index + >>> rankdata(tensor([[1,2,2,2,3,3,6],[2,2,2,2,4,4,5]])) + tensor([[1.0000, 3.0000, 3.0000, 3.0000, 5.5000, 5.5000, 7.0000], + [2.5000, 2.5000, 2.5000, 2.5000, 5.0000, 6.5000, 6.5000]]) + + """ + length, width = scores.shape + device = scores.device + true_tensor = torch.full((length, 1), True, dtype=np.bool, device=device) + + obs = torch.cat([true_tensor, scores[:, 1:] != scores[:, :-1]], dim=1) + # bias added to dense + bias = torch.arange(0, length, device=device).repeat(width).reshape(width, -1). \ + transpose(1, 0).reshape(-1) + dense = obs.view(-1).cumsum(0) + bias + + # cumulative counts of each unique value + count = torch.where(torch.cat([obs, true_tensor], dim=1))[1] + # get averange rank + avg_rank = .5 * (count[dense] + count[dense - 1] + 1).view(length, -1) + + return avg_rank def collect(self, interaction, scores_tensor): """collect the rank intermediate result of one batch, this function mainly implements ranking @@ -185,10 +198,16 @@ def collect(self, interaction, scores_tensor): """ pos_len_list, user_len_list = self.get_user_pos_len_list(interaction, scores_tensor) - pos_index = self.get_pos_index(scores_tensor, pos_len_list, user_len_list) - index_list = torch.arange(1, pos_index.shape[1] + 1).to(pos_index.device) - pos_rank_sum = torch.where(pos_index, index_list, torch.zeros_like(index_list)). \ + scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) + desc_scores, desc_index = torch.sort(scores_matrix, dim=-1, descending=True) + + # get the index of positive items in the ranking list + pos_index = (desc_index < pos_len_list.reshape(-1, 1)) + + avg_rank = self.rankdata(desc_scores) + pos_rank_sum = torch.where(pos_index, avg_rank, torch.zeros_like(avg_rank)). \ sum(axis=-1).reshape(-1, 1) + return pos_rank_sum def evaluate(self, batch_matrix_list, eval_data): diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index cb31e1456..df6854257 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -4,7 +4,7 @@ # @email : tsotfsk@outlook.com # UPDATE -# @Time : 2020/08/12, 2020/12/9, 2020/9/16 +# @Time : 2020/08/12, 2020/12/18, 2020/9/16 # @Author : Kaiyuan Li, Zhichao Feng, Xingyu Pan # @email : tsotfsk@outlook.com, fzcbupt@gmail.com, panxy@ruc.edu.cn @@ -23,7 +23,6 @@ # TopK Metrics # - def hit_(pos_index, pos_len): r"""Hit_ (also known as hit ratio at :math:`N`) is a way of calculating how many 'hits' you have in an n-sized list of ranked items. @@ -129,7 +128,6 @@ def ndcg_(pos_index, pos_len): :math:`U^{te}` is for all users in the test set. """ - len_rank = np.full_like(pos_len, pos_index.shape[1]) idcg_len = np.where(pos_len > len_rank, len_rank, pos_len) @@ -166,10 +164,31 @@ def precision_(pos_index, pos_len): def gauc_(user_len_list, pos_len_list, pos_rank_sum): - frac = user_len_list - (pos_len_list - 1) / 2 - (1 / pos_len_list) * np.squeeze(pos_rank_sum) - neg_item_num = user_len_list - pos_len_list - user_auc = frac / neg_item_num + r"""GAUC_ (also known as Group Area Under Curve) is used to evaluate the two-class model, referring to + the area under the ROC curve grouped by user. + + .. _GAUC: https://dl.acm.org/doi/10.1145/3219819.3219823 + + Note: + It calculates the AUC score of each user, and finally obtains GAUC by weighting the user AUC + . It is also not limited to k. + + .. math:: + \mathrm {GAUC} = \frac {{{M} \times {(M+N+1)} - \frac{M \times (M+1)}{2}} - + \sum\limits_{i=1}^M rank_{i}} {{M} \times {N}} + + :math:`M` is the number of positive samples. + :math:`N` is the number of negative samples. + :math:`rank_i` is the descending rank of the ith positive sample. + Note: Due to our padding for `scores_tensor` in `RankEvaluator` with `-np.inf`, the padding value will influence + the ranks of origin items. Therefore, we use descending sort here and make an identity transformation to the + formula of `AUC`, which is shown in `auc_` function. For readability, we didn't do simplification in the code. + + """ + pair_num = (user_len_list + 1) * pos_len_list - pos_len_list * (pos_len_list + 1) / 2 - np.squeeze(pos_rank_sum) + neg_item_num = user_len_list - pos_len_list + user_auc = pair_num / (neg_item_num * pos_len_list) result = (user_auc * pos_len_list).sum() / pos_len_list.sum() return result @@ -188,11 +207,11 @@ def auc_(trues, preds): .. math:: \mathrm {AUC} = \frac{\sum\limits_{i=1}^M rank_{i} - - {{M} \times {(M+1)}}} {{M} \times {N}} + - \frac {{M} \times {(M+1)}}{2}} {{{M} \times {N}}} :math:`M` is the number of positive samples. :math:`N` is the number of negative samples. - :math:`rank_i` is the rank of the ith positive sample. + :math:`rank_i` is the ascending rank of the ith positive sample. """ fps, tps = _binary_clf_curve(trues, preds) From 5c1c1473afe5a0751fd5e355d7d71ee79824e2b4 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 18 Dec 2020 13:45:48 +0800 Subject: [PATCH 168/249] FIX: rename & comment format --- recbole/evaluator/evaluators.py | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py index a97fffa7e..a0713188e 100644 --- a/recbole/evaluator/evaluators.py +++ b/recbole/evaluator/evaluators.py @@ -4,14 +4,14 @@ # @email : tsotfsk@outlook.com # UPDATE -# @Time : 2020/08/04, 2020/08/11, 2020/12/9 +# @Time : 2020/08/04, 2020/08/11, 2020/12/18 # @Author : Kaiyuan Li, Yupeng Hou, Zhichao Feng # @email : tsotfsk@outlook.com, houyupeng@ruc.edu.cn, fzcbupt@gmail.com import torch import numpy as np -from collections import ChainMap, Counter +from collections import ChainMap from recbole.evaluator.metrics import metrics_dict from recbole.evaluator.abstract_evaluator import GroupedEvalautor, IndividualEvaluator @@ -158,22 +158,27 @@ def get_user_pos_len_list(self, interaction, scores_tensor): user_len_list = interaction.user_len_list return pos_len_list, user_len_list - def rankdata(self, scores): + def average_rank(self, scores): """Get the ranking of an ordered tensor, and take the average of the ranking for positions with equal values. Args: scores(tensor): an ordered tensor, with size of `(N, )` - Examples:: + Returns: + torch.Tensor: average_rank + + Example: + >>> average_rank(tensor([[1,2,2,2,3,3,6],[2,2,2,2,4,4,5]])) + tensor([[1.0000, 3.0000, 3.0000, 3.0000, 5.5000, 5.5000, 7.0000], + [2.5000, 2.5000, 2.5000, 2.5000, 5.0000, 6.5000, 6.5000]]) - >>> rankdata(tensor([[1,2,2,2,3,3,6],[2,2,2,2,4,4,5]])) - tensor([[1.0000, 3.0000, 3.0000, 3.0000, 5.5000, 5.5000, 7.0000], - [2.5000, 2.5000, 2.5000, 2.5000, 5.0000, 6.5000, 6.5000]]) + Reference: + https://github.com/scipy/scipy/blob/v0.17.1/scipy/stats/stats.py#L5262-L5352 """ length, width = scores.shape device = scores.device - true_tensor = torch.full((length, 1), True, dtype=np.bool, device=device) + true_tensor = torch.full((length, 1), True, dtype=torch.bool, device=device) obs = torch.cat([true_tensor, scores[:, 1:] != scores[:, :-1]], dim=1) # bias added to dense @@ -183,7 +188,7 @@ def rankdata(self, scores): # cumulative counts of each unique value count = torch.where(torch.cat([obs, true_tensor], dim=1))[1] - # get averange rank + # get average rank avg_rank = .5 * (count[dense] + count[dense - 1] + 1).view(length, -1) return avg_rank @@ -204,7 +209,7 @@ def collect(self, interaction, scores_tensor): # get the index of positive items in the ranking list pos_index = (desc_index < pos_len_list.reshape(-1, 1)) - avg_rank = self.rankdata(desc_scores) + avg_rank = self.average_rank(desc_scores) pos_rank_sum = torch.where(pos_index, avg_rank, torch.zeros_like(avg_rank)). \ sum(axis=-1).reshape(-1, 1) From 2dcac2869a3fbf21bef41fe8b565fa725b85c32b Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 18 Dec 2020 21:07:46 +0800 Subject: [PATCH 169/249] REVERT: revert modify in data.utils --- recbole/data/utils.py | 26 ++++++++------------------ 1 file changed, 8 insertions(+), 18 deletions(-) diff --git a/recbole/data/utils.py b/recbole/data/utils.py index f9c4ed06a..2eda471e9 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -86,15 +86,8 @@ def data_preparation(config, dataset, save=False): raise ValueError('Sequential models require "loo" split strategy.') builded_datasets = dataset.build(es) - if len(builded_datasets) not in {2, 3}: - raise ValueError('Dataset should only be divided into two or three parts.') - else: - train_dataset = builded_datasets[0] - evaluation_datasets = builded_datasets[1:] - if len(builded_datasets) == 2: - phases = ['train', 'test'] - else: - phases = ['train', 'valid', 'test'] + train_dataset, valid_dataset, test_dataset = builded_datasets + phases = ['train', 'valid', 'test'] if save: save_datasets(config['checkpoint_dir'], name=phases, dataset=builded_datasets) @@ -107,7 +100,7 @@ def data_preparation(config, dataset, save=False): sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) else: sampler = RepeatableSampler(phases, dataset, es.neg_sample_args['distribution']) - kwargs['sampler'] = sampler.set_phase(phases[0]) + kwargs['sampler'] = sampler.set_phase('train') kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args) if model_type == ModelType.KNOWLEDGE: kg_sampler = KGSampler(dataset, es.neg_sample_args['distribution']) @@ -128,22 +121,19 @@ def data_preparation(config, dataset, save=False): getattr(es, es_str[1])() if 'sampler' not in locals(): sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) - sampler.set_distribution(es.neg_sample_args['distribution']) - kwargs['sampler'] = [sampler.set_phase(phase) for phase in phases[1:]] + sampler.set_distribution(es.neg_sample_args['distribution']) + kwargs['sampler'] = [sampler.set_phase('valid'), sampler.set_phase('test')] kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args) - evaluation_data = dataloader_construct( + valid_data, test_data = dataloader_construct( name='evaluation', config=config, eval_setting=es, - dataset=evaluation_datasets, + dataset=[valid_dataset, test_dataset], batch_size=config['eval_batch_size'], **kwargs ) - if len(builded_datasets) == 2: - return train_data, None, evaluation_data - else: - return [train_data] + evaluation_data + return train_data, valid_data, test_data def dataloader_construct(name, config, eval_setting, dataset, From fd86870a86a18b11efc02072263516a22e90d07e Mon Sep 17 00:00:00 2001 From: fzc <970955517@qq.com> Date: Sat, 19 Dec 2020 12:27:12 +0800 Subject: [PATCH 170/249] update notes --- recbole/evaluator/metrics.py | 11 +++++------ 1 file changed, 5 insertions(+), 6 deletions(-) diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index df6854257..b2bcaded1 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -170,8 +170,11 @@ def gauc_(user_len_list, pos_len_list, pos_rank_sum): .. _GAUC: https://dl.acm.org/doi/10.1145/3219819.3219823 Note: - It calculates the AUC score of each user, and finally obtains GAUC by weighting the user AUC - . It is also not limited to k. + It calculates the AUC score of each user, and finally obtains GAUC by weighting the user AUC. + It is also not limited to k.Due to our padding for `scores_tensor` in `RankEvaluator` with + `-np.inf`, the padding value will influence the ranks of origin items. Therefore, we use + descending sort here and make an identity transformation to the formula of `AUC`, which is + shown in `auc_` function. For readability, we didn't do simplification in the code. .. math:: \mathrm {GAUC} = \frac {{{M} \times {(M+N+1)} - \frac{M \times (M+1)}{2}} - @@ -181,10 +184,6 @@ def gauc_(user_len_list, pos_len_list, pos_rank_sum): :math:`N` is the number of negative samples. :math:`rank_i` is the descending rank of the ith positive sample. - Note: Due to our padding for `scores_tensor` in `RankEvaluator` with `-np.inf`, the padding value will influence - the ranks of origin items. Therefore, we use descending sort here and make an identity transformation to the - formula of `AUC`, which is shown in `auc_` function. For readability, we didn't do simplification in the code. - """ pair_num = (user_len_list + 1) * pos_len_list - pos_len_list * (pos_len_list + 1) / 2 - np.squeeze(pos_rank_sum) neg_item_num = user_len_list - pos_len_list From e84aeb79af3ab5be3147768cb4fefad22e27cbcb Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 20 Dec 2020 21:36:00 +0800 Subject: [PATCH 171/249] FEA: Increased the robustness of trainer.evaluate --- recbole/trainer/trainer.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 1a3ef791a..b033faa18 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -324,6 +324,9 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): Returns: dict: eval result, key is the eval metric and value in the corresponding metric value """ + if not eval_data: + return + if load_best_model: if model_file: checkpoint_file = model_file From 50bf9e808203b6b4c99c2ab87a956958667cd7f3 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sun, 20 Dec 2020 21:53:27 +0800 Subject: [PATCH 172/249] FIX: bug fix in GeneralFullDataLoader. --- recbole/data/dataloader/general_dataloader.py | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index 922f691cc..cf75341c3 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -261,17 +261,16 @@ def _shuffle(self): self.logger.warnning('GeneralFullDataLoader can\'t shuffle') def _next_batch_data(self): - index = slice(self.pr, self.pr + self.step) - user_df = self.user_df[index] - pos_len_list = self.uid2items_num[self.uid_list[index]] - user_len_list = np.full(len(user_df), self.item_num) - user_df.set_additional_info(pos_len_list, user_len_list) + user_df = self.user_df[self.pr: self.pr + self.step] cur_data = self._neg_sampling(user_df) self.pr += self.step return cur_data def _neg_sampling(self, user_df): uid_list = list(user_df[self.dataset.uid_field]) + pos_len_list = self.uid2items_num[uid_list] + user_len_list = np.full(len(uid_list), self.item_num) + user_df.set_additional_info(pos_len_list, user_len_list) history_item = self.uid2history_item[uid_list] history_row = torch.cat([torch.full_like(hist_iid, i) for i, hist_iid in enumerate(history_item)]) From 4b4b9a81b3866c9adae1699da80d39cc37dbb4bd Mon Sep 17 00:00:00 2001 From: shanlei <2015201909@ruc.edu.cn> Date: Mon, 21 Dec 2020 09:55:40 +0800 Subject: [PATCH 173/249] FIX: optimize update_attentive_A function in KGAT --- recbole/model/knowledge_aware_recommender/kgat.py | 2 +- recbole/trainer/trainer.py | 4 +++- 2 files changed, 4 insertions(+), 2 deletions(-) diff --git a/recbole/model/knowledge_aware_recommender/kgat.py b/recbole/model/knowledge_aware_recommender/kgat.py index 13dd1a01e..19fbe9879 100644 --- a/recbole/model/knowledge_aware_recommender/kgat.py +++ b/recbole/model/knowledge_aware_recommender/kgat.py @@ -268,7 +268,7 @@ def update_attentive_A(self): # Current PyTorch version does not support softmax on SparseCUDA, temporarily move to CPU to calculate softmax A_in = torch.sparse.FloatTensor(indices, kg_score, self.matrix_size).cpu() A_in = torch.sparse.softmax(A_in, dim=1).to(self.device) - self.A_in = copy.copy(A_in) + self.A_in = A_in def predict(self, interaction): user = interaction[self.USER_ID] diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index b033faa18..365503d2a 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -444,7 +444,9 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): kg_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) # update A - self.model.update_attentive_A() + self.model.eval() + with torch.no_grad(): + self.model.update_attentive_A() return rs_total_loss, kg_total_loss From 3549eb17ff98c0f5c1b097231664b65ecf168c03 Mon Sep 17 00:00:00 2001 From: flust <254170321@qq.com> Date: Mon, 21 Dec 2020 07:37:36 +0000 Subject: [PATCH 174/249] FEA: change xgboost train data to onehot and change train method --- recbole/config/configurator.py | 2 +- recbole/data/dataset/__init__.py | 1 + recbole/data/dataset/xgboost_dataset.py | 94 +++++++++++++++++++ recbole/data/utils.py | 3 + recbole/model/exlib_recommender/xgboost.py | 2 +- recbole/properties/model/xgboost.yaml | 36 +++----- recbole/trainer/trainer.py | 102 +++++++++------------ 7 files changed, 158 insertions(+), 82 deletions(-) create mode 100644 recbole/data/dataset/xgboost_dataset.py diff --git a/recbole/config/configurator.py b/recbole/config/configurator.py index 2a225a4cd..50d0c4aa9 100644 --- a/recbole/config/configurator.py +++ b/recbole/config/configurator.py @@ -223,7 +223,7 @@ def _load_internal_config_dict(self, model, model_class, dataset): self.internal_config_dict['MODEL_TYPE'] = model_class.type if self.internal_config_dict['MODEL_TYPE'] == ModelType.GENERAL: pass - elif self.internal_config_dict['MODEL_TYPE'] == ModelType.CONTEXT: + elif self.internal_config_dict['MODEL_TYPE'] == ModelType.CONTEXT or self.internal_config_dict['MODEL_TYPE'] == ModelType.XGBOOST: with open(context_aware_init, 'r', encoding='utf-8') as f: config_dict = yaml.load(f.read(), Loader=self.yaml_loader) if config_dict is not None: diff --git a/recbole/data/dataset/__init__.py b/recbole/data/dataset/__init__.py index 2bdb7f4ad..58026c332 100644 --- a/recbole/data/dataset/__init__.py +++ b/recbole/data/dataset/__init__.py @@ -3,4 +3,5 @@ from recbole.data.dataset.kg_dataset import KnowledgeBasedDataset from recbole.data.dataset.social_dataset import SocialDataset from recbole.data.dataset.kg_seq_dataset import Kg_Seq_Dataset +from recbole.data.dataset.xgboost_dataset import XgboostDataset from recbole.data.dataset.customized_dataset import * diff --git a/recbole/data/dataset/xgboost_dataset.py b/recbole/data/dataset/xgboost_dataset.py new file mode 100644 index 000000000..414efe551 --- /dev/null +++ b/recbole/data/dataset/xgboost_dataset.py @@ -0,0 +1,94 @@ +# @Time : 2020/12/17 +# @Author : Chen Yang +# @Email : 254170321@qq.com + +""" +recbole.data.xgboost_dataset +########################## +""" + +import numpy as np +import pandas as pd + +from recbole.data.dataset import Dataset +from recbole.utils import FeatureType +from recbole.data.interaction import Interaction +from recbole.data.utils import dlapi + + +class XgboostDataset(Dataset): + """:class:`XgboostDataset` is based on :class:`~recbole.data.dataset.dataset.Dataset`, + and + + Attributes: + + """ + def __init__(self, config, saved_dataset=None): + super().__init__(config, saved_dataset=saved_dataset) + + def _judge_token_and_convert(self, feat): + col_list = [] + for col_name in feat: + if col_name == self.uid_field or col_name == self.iid_field: + continue + if self.field2type[col_name] == FeatureType.TOKEN: + col_list.append(col_name) + elif self.field2type[col_name] == FeatureType.TOKEN_SEQ or self.field2type[col_name] == FeatureType.FLOAT_SEQ: + feat = feat.drop([col_name], axis=1, inplace=False) + feat = pd.get_dummies(feat, sparse = True, columns = col_list) + for col_name in feat.columns.values.tolist(): + if col_name not in self.field2type.keys(): + self.field2type[col_name] = FeatureType.TOKEN + return feat + + def _convert_token_to_onehot(self): + """Convert the data of token type to onehot form + + """ + if self.config['convert_token_to_onehot'] == True: + feat_list = [] + for feat in (self.inter_feat, self.user_feat, self.item_feat): + feat = self._judge_token_and_convert(feat) + feat_list.append(feat) + self.inter_feat_xgb = feat_list[0] + self.user_feat_xgb = feat_list[1] + self.item_feat_xgb = feat_list[2] + self.inter_feat = self.inter_feat_xgb + self.user_feat = self.user_feat_xgb + self.item_feat = self.item_feat + else: + self.inter_feat_xgb = self.inter_feat + self.user_feat_xgb = self.user_feat + self.item_feat_xgb = self.item_feat + + def _from_scratch(self): + """Load dataset from scratch. + Initialize attributes firstly, then load data from atomic files, pre-process the dataset lastly. + """ + self.logger.debug('Loading {} from scratch'.format(self.__class__)) + + self._get_preset() + self._get_field_from_config() + self._load_data(self.dataset_name, self.dataset_path) + self._data_processing() + self._convert_token_to_onehot() + self._change_feat_format() + + def join(self, df): + """Given interaction feature, join user/item feature into it. + + Args: + df (pandas.DataFrame): Interaction feature to be joint. + + Returns: + pandas.DataFrame: Interaction feature after joining operation. + """ + if self.user_feat is not None and self.uid_field in df: + df = pd.merge(df, self.user_feat_xgb, on=self.uid_field, how='left', suffixes=('_inter', '_user')) + if self.item_feat is not None and self.iid_field in df: + df = pd.merge(df, self.item_feat_xgb, on=self.iid_field, how='left', suffixes=('_inter', '_item')) + return df + + def __getitem__(self, index, join=True): + df = self.inter_feat_xgb[index] + return self.join(df) if join else df diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 2eda471e9..c86d395e2 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -44,6 +44,9 @@ def create_dataset(config): elif model_type == ModelType.SOCIAL: from .dataset import SocialDataset return SocialDataset(config) + elif model_type == ModelType.XGBOOST: + from .dataset import XgboostDataset + return XgboostDataset(config) else: from .dataset import Dataset return Dataset(config) diff --git a/recbole/model/exlib_recommender/xgboost.py b/recbole/model/exlib_recommender/xgboost.py index 345953e38..45f5e4954 100644 --- a/recbole/model/exlib_recommender/xgboost.py +++ b/recbole/model/exlib_recommender/xgboost.py @@ -16,7 +16,7 @@ class xgboost(xgb.Booster): r"""xgboost is inherited from xgb.Booster """ - type = ModelType.CONTEXT + type = ModelType.XGBOOST input_type = InputType.POINTWISE def __init__(self, config, dataset): diff --git a/recbole/properties/model/xgboost.yaml b/recbole/properties/model/xgboost.yaml index 3ad1e041f..c054cc879 100644 --- a/recbole/properties/model/xgboost.yaml +++ b/recbole/properties/model/xgboost.yaml @@ -1,5 +1,5 @@ # Type of training method -train_or_cv: train +convert_token_to_onehot: True # DMatrix @@ -11,36 +11,26 @@ xgb_feature_names: ~ xgb_feature_types: ~ xgb_nthread: ~ -# train or cv xgb_model: ~ xgb_params: booster: gbtree objective: binary:logistic - gamma: 0.1 - max_depth: 10 - lambda: 3 - subsample: 0.5 - colsample_bytree: 0.7 - min_child_weight: 3 - eta: 0.1 - seed: 100 - nthread: 4 -xgb_num_boost_round: 10 + eval_metric: ['auc','logloss'] + # gamma: 0.1 + max_depth: 3 + # lambda: 1 + # subsample: 0.7 + # colsample_bytree: 0.7 + # min_child_weight: 3 + eta: 1 + seed: 2020 + # nthread: -1 +xgb_num_boost_round: 200 # xgb_evals: ~ xgb_obj: ~ xgb_feval: ~ xgb_maximize: ~ xgb_early_stopping_rounds: ~ # xgb_evals_result: ~ -xgb_verbose_eval: False - -# cv -xgb_cv_nfold: 3 -xgb_cv_stratified: False -xgb_cv_folds: ~ -xgb_cv_fpreproc: ~ -xgb_cv_show_stdv: True -xgb_cv_seed: 0 -xgb_cv_shuffle: True - +xgb_verbose_eval: 100 diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 365503d2a..a06175463 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -324,9 +324,6 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): Returns: dict: eval result, key is the eval metric and value in the corresponding metric value """ - if not eval_data: - return - if load_best_model: if model_file: checkpoint_file = model_file @@ -444,9 +441,7 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): kg_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) # update A - self.model.eval() - with torch.no_grad(): - self.model.update_attentive_A() + self.model.update_attentive_A() return rs_total_loss, kg_total_loss @@ -555,7 +550,6 @@ def __init__(self, config, model): self.logger = getLogger() self.label_field = config['LABEL_FIELD'] - self.train_or_cv = config['train_or_cv'] self.xgb_model = config['xgb_model'] # DMatrix params @@ -579,26 +573,13 @@ def __init__(self, config, model): self.verbose_eval = config['xgb_verbose_eval'] self.callbacks = None - # cv params - if self.train_or_cv == 'cv': - self.nfold = config['xgb_cv_nfold'] - self.stratified = config['xgb_cv_stratified'] - self.folds = config['xgb_cv_folds'] - self.fpreproc = config['xgb_cv_freproc'] - self.show_stdv = config['xgb_cv_show_stdv'] - self.seed = config['xgb_cv_seed'] - self.shuffle = config['xgb_cv_shuffle'] - # evaluator self.eval_type = config['eval_type'] self.epochs = config['epochs'] self.eval_step = min(config['eval_step'], self.epochs) self.valid_metric = config['valid_metric'].lower() - if self.eval_type == EvaluatorType.INDIVIDUAL: - self.evaluator = LossEvaluator(config) - else: - self.evaluator = TopKEvaluator(config) + self.evaluator = ProxyEvaluator(config) # model saved self.checkpoint_dir = config['checkpoint_dir'] @@ -634,23 +615,28 @@ def _interaction_to_DMatrix(self, interaction): feature_types = self.feature_types, nthread = self.nthread) - def _train_epoch(self, train_data, valid_data): + def _train_at_once(self, train_data, valid_data): r""" Args: train_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. valid_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. """ - for _, train_interaction in enumerate(train_data): - self.dtrain = self._interaction_to_DMatrix(train_interaction) - self.evals = [(self.dtrain,'train')] - self.model = xgb.train(self.params, self.dtrain, 1, + train_x = train_data.dataset[:].drop([self.label_field], axis=1, inplace=False) + train_y = list(map(int, train_data.dataset[:][self.label_field].values.tolist())) + + valid_x = valid_data.dataset[:].drop([self.label_field], axis=1, inplace=False) + valid_y = list(map(int, valid_data.dataset[:][self.label_field].values.tolist())) + + self.dtrain = xgb.DMatrix(data = train_x, label = train_y) + self.dvalid = xgb.DMatrix(data = valid_x, label = valid_y) + self.evals = [(self.dtrain,'train'),(self.dvalid, 'valid')] + self.model = xgb.train(self.params, self.dtrain, self.num_boost_round, self.evals, self.obj, self.feval, self.maximize, self.early_stopping_rounds, self.evals_result, self.verbose_eval, self.xgb_model, self.callbacks) - - self.model.save_model(self.saved_model_file) - self.xgb_model = self.saved_model_file + self.model.save_model(self.saved_model_file) + self.xgb_model = self.saved_model_file def _valid_epoch(self, valid_data): r""" @@ -663,26 +649,30 @@ def _valid_epoch(self, valid_data): return valid_result, valid_score def fit(self, train_data, valid_data=None, verbose=True, saved=True): + # load model + if self.xgb_model != None: + self.model.load_model(self.xgb_model) + self.best_valid_score = 0. self.best_valid_result = 0. - if self.train_or_cv == 'train': - for epoch_idx in range(self.epochs): - train_loss = self._train_epoch(train_data, valid_data) - - if (epoch_idx + 1) % self.eval_step == 0: - # evaluate - valid_start_time = time() - valid_result, valid_score = self._valid_epoch(valid_data) - valid_end_time = time() - valid_score_output = "epoch %d evaluating [time: %.2fs, valid_score: %f]" % \ - (epoch_idx, valid_end_time - valid_start_time, valid_score) - valid_result_output = 'valid result: \n' + dict2str(valid_result) - if verbose: - self.logger.info(valid_score_output) - self.logger.info(valid_result_output) - self.best_valid_score = valid_score - self.best_valid_result = valid_result + for epoch_idx in range(self.epochs): + self._train_at_once(train_data, valid_data) + + if (epoch_idx + 1) % self.eval_step == 0: + # evaluate + valid_start_time = time() + valid_result, valid_score = self._valid_epoch(valid_data) + valid_end_time = time() + valid_score_output = "epoch %d evaluating [time: %.2fs, valid_score: %f]" % \ + (epoch_idx, valid_end_time - valid_start_time, valid_score) + valid_result_output = 'valid result: \n' + dict2str(valid_result) + if verbose: + self.logger.info(valid_score_output) + self.logger.info(valid_result_output) + + self.best_valid_score = valid_score + self.best_valid_result = valid_result return self.best_valid_score, self.best_valid_result @@ -690,15 +680,13 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): self.eval_pred = torch.Tensor() self.eval_true = torch.Tensor() - for _, batched_data in enumerate(eval_data): - batched_data_DMatrix = self._interaction_to_DMatrix(batched_data) - batch_pred = torch.Tensor(self.model.predict(batched_data_DMatrix)) - if self.params['objective'] == 'binary:logistic': - batch_pred = (batch_pred >= 0.5) * 1 - self.eval_pred = torch.cat((self.eval_pred, batch_pred)) - self.eval_true = torch.cat((self.eval_true, batched_data[self.label_field])) - - matrix_list = [torch.stack((self.eval_pred, self.eval_true), 1)] - - result = self.evaluator.evaluate(matrix_list, eval_data) + eval_x = eval_data.dataset[:].drop([self.label_field], axis=1, inplace=False) + eval_y = list(map(int,eval_data.dataset[:][self.label_field].values.tolist())) + + self.deval = xgb.DMatrix(data = eval_x, label = eval_y) + self.eval_true = torch.Tensor(self.deval.get_label()) + self.eval_pred = torch.Tensor(self.model.predict(self.deval)) + + batch_matrix_list = [[torch.stack((self.eval_true, self.eval_pred), 1)]] + result = self.evaluator.evaluate(batch_matrix_list, eval_data) return result From d4f68ccae8757aa83d28de06458874c892654446 Mon Sep 17 00:00:00 2001 From: Lin zihan <893833413@qq.com> Date: Mon, 21 Dec 2020 16:40:57 +0800 Subject: [PATCH 175/249] FIX: sparse dropout disable during eval --- recbole/model/general_recommender/gcmc.py | 5 +++-- recbole/model/general_recommender/ngcf.py | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/recbole/model/general_recommender/gcmc.py b/recbole/model/general_recommender/gcmc.py index 47b66ef83..263d9fedb 100644 --- a/recbole/model/general_recommender/gcmc.py +++ b/recbole/model/general_recommender/gcmc.py @@ -307,8 +307,9 @@ def _init_weights(self): def forward(self, user_X, item_X): # ----------------------------------------GCN layer---------------------------------------- - user_X = self.sparse_dropout(user_X) - item_X = self.sparse_dropout(item_X) + if self.training: + user_X = self.sparse_dropout(user_X) + item_X = self.sparse_dropout(item_X) embeddings = [] if self.accum == 'sum': diff --git a/recbole/model/general_recommender/ngcf.py b/recbole/model/general_recommender/ngcf.py index 6416d0e25..8a5a4751e 100644 --- a/recbole/model/general_recommender/ngcf.py +++ b/recbole/model/general_recommender/ngcf.py @@ -164,7 +164,7 @@ def get_ego_embeddings(self): def forward(self): # A_hat: spare tensor with shape of [n_items+n_users,n_items+n_users] A_hat = sparse_dropout(self.norm_adj_matrix, self.node_dropout, - self.norm_adj_matrix._nnz()) if self.node_dropout != 0 else self.norm_adj_matrix + self.norm_adj_matrix._nnz()) if self.node_dropout != 0 and self.training else self.norm_adj_matrix all_embeddings = self.get_ego_embeddings() embeddings_list = [all_embeddings] for gnn in self.GNNlayers: From 493c86bbfe857ec95a20842b44588961c17db7cc Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Mon, 21 Dec 2020 17:19:19 +0800 Subject: [PATCH 176/249] FEA: add SparseAdam trainer --- recbole/trainer/trainer.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index b033faa18..9905ffb0a 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -113,6 +113,8 @@ def _build_optimizer(self): optimizer = optim.Adagrad(self.model.parameters(), lr=self.learning_rate) elif self.learner.lower() == 'rmsprop': optimizer = optim.RMSprop(self.model.parameters(), lr=self.learning_rate) + elif self.learner.lower() == 'sparse_adam': + optimizer = optim.SparseAdam(self.model.parameters(), lr=self.learning_rate) else: self.logger.warning('Received unrecognized optimizer, set default Adam optimizer') optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate) From 83f514e2a58c2a817f5f27602d058cb80b7c8528 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 18 Dec 2020 12:42:08 +0800 Subject: [PATCH 177/249] FIX: can't raise error in IndividualEvaluator --- recbole/evaluator/abstract_evaluator.py | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/recbole/evaluator/abstract_evaluator.py b/recbole/evaluator/abstract_evaluator.py index 2570fe8ad..1ab81d476 100644 --- a/recbole/evaluator/abstract_evaluator.py +++ b/recbole/evaluator/abstract_evaluator.py @@ -4,7 +4,7 @@ # @email : tsotfsk@outlook.com # UPDATE -# @Time : 2020/10/21, 2020/12/9 +# @Time : 2020/10/21, 2020/12/18 # @Author : Kaiyuan Li, Zhichao Feng # @email : tsotfsk@outlook.com, fzcbupt@gmail.com @@ -99,7 +99,7 @@ class IndividualEvaluator(BaseEvaluator): """ def __init__(self, config, metrics): super().__init__(config, metrics) - pass + self._check_args() def sample_collect(self, true_scores, pred_scores): """It is called when evaluation sample distribution is `uniform` or `popularity`. @@ -127,3 +127,7 @@ def get_score_matrix(self, true_scores, pred_scores): scores_matrix = self.sample_collect(true_scores, pred_scores) return scores_matrix + + def _check_args(self): + if self.full: + raise NotImplementedError('full sort can\'t use IndividualEvaluator') \ No newline at end of file From 10243bba1749f054deb24475cb9bf6628f67f591 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 18 Dec 2020 12:42:55 +0800 Subject: [PATCH 178/249] FIX: metrics disorder --- recbole/evaluator/proxy_evaluator.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recbole/evaluator/proxy_evaluator.py b/recbole/evaluator/proxy_evaluator.py index fc91f5d48..9f181c4aa 100644 --- a/recbole/evaluator/proxy_evaluator.py +++ b/recbole/evaluator/proxy_evaluator.py @@ -32,9 +32,9 @@ def build(self): """ evaluator_list = [] - metrics_set = {metric.lower() for metric in self.metrics} + metrics_list = [metric.lower() for metric in self.metrics] for metrics, evaluator in metric_eval_bind: - used_metrics = list(metrics_set.intersection(set(metrics.keys()))) + used_metrics = [metric for metric in metrics_list if metric in metrics.keys()] if used_metrics: evaluator_list.append(evaluator(self.config, used_metrics)) return evaluator_list From ab95863f64500f206a9d110176c16032aeedd8f9 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 18 Dec 2020 12:46:16 +0800 Subject: [PATCH 179/249] FIX: GAUC calculation error --- recbole/evaluator/evaluators.py | 53 ++++++++++++++++++++++----------- recbole/evaluator/metrics.py | 35 +++++++++++++++++----- 2 files changed, 63 insertions(+), 25 deletions(-) diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py index 10a41f715..a97fffa7e 100644 --- a/recbole/evaluator/evaluators.py +++ b/recbole/evaluator/evaluators.py @@ -11,7 +11,7 @@ import torch import numpy as np -from collections import ChainMap +from collections import ChainMap, Counter from recbole.evaluator.metrics import metrics_dict from recbole.evaluator.abstract_evaluator import GroupedEvalautor, IndividualEvaluator @@ -158,22 +158,35 @@ def get_user_pos_len_list(self, interaction, scores_tensor): user_len_list = interaction.user_len_list return pos_len_list, user_len_list - def get_pos_index(self, scores_tensor, pos_len_list, user_len_list): - """get the index of positive items + def rankdata(self, scores): + """Get the ranking of an ordered tensor, and take the average of the ranking for positions with equal values. - Args: - scores_tensor (tensor): the tensor of model output with size of `(N, )` - pos_len_list(list): number of positive items - user_len_list(list): number of all items + Args: + scores(tensor): an ordered tensor, with size of `(N, )` - Returns: - tensor: a matrix indicating whether the corresponding item is positive + Examples:: - """ - scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) - _, n_index = torch.sort(scores_matrix, dim=-1, descending=True) - pos_index = (n_index < pos_len_list.reshape(-1, 1)) - return pos_index + >>> rankdata(tensor([[1,2,2,2,3,3,6],[2,2,2,2,4,4,5]])) + tensor([[1.0000, 3.0000, 3.0000, 3.0000, 5.5000, 5.5000, 7.0000], + [2.5000, 2.5000, 2.5000, 2.5000, 5.0000, 6.5000, 6.5000]]) + + """ + length, width = scores.shape + device = scores.device + true_tensor = torch.full((length, 1), True, dtype=np.bool, device=device) + + obs = torch.cat([true_tensor, scores[:, 1:] != scores[:, :-1]], dim=1) + # bias added to dense + bias = torch.arange(0, length, device=device).repeat(width).reshape(width, -1). \ + transpose(1, 0).reshape(-1) + dense = obs.view(-1).cumsum(0) + bias + + # cumulative counts of each unique value + count = torch.where(torch.cat([obs, true_tensor], dim=1))[1] + # get averange rank + avg_rank = .5 * (count[dense] + count[dense - 1] + 1).view(length, -1) + + return avg_rank def collect(self, interaction, scores_tensor): """collect the rank intermediate result of one batch, this function mainly implements ranking @@ -185,10 +198,16 @@ def collect(self, interaction, scores_tensor): """ pos_len_list, user_len_list = self.get_user_pos_len_list(interaction, scores_tensor) - pos_index = self.get_pos_index(scores_tensor, pos_len_list, user_len_list) - index_list = torch.arange(1, pos_index.shape[1] + 1).to(pos_index.device) - pos_rank_sum = torch.where(pos_index, index_list, torch.zeros_like(index_list)). \ + scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) + desc_scores, desc_index = torch.sort(scores_matrix, dim=-1, descending=True) + + # get the index of positive items in the ranking list + pos_index = (desc_index < pos_len_list.reshape(-1, 1)) + + avg_rank = self.rankdata(desc_scores) + pos_rank_sum = torch.where(pos_index, avg_rank, torch.zeros_like(avg_rank)). \ sum(axis=-1).reshape(-1, 1) + return pos_rank_sum def evaluate(self, batch_matrix_list, eval_data): diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index cb31e1456..df6854257 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -4,7 +4,7 @@ # @email : tsotfsk@outlook.com # UPDATE -# @Time : 2020/08/12, 2020/12/9, 2020/9/16 +# @Time : 2020/08/12, 2020/12/18, 2020/9/16 # @Author : Kaiyuan Li, Zhichao Feng, Xingyu Pan # @email : tsotfsk@outlook.com, fzcbupt@gmail.com, panxy@ruc.edu.cn @@ -23,7 +23,6 @@ # TopK Metrics # - def hit_(pos_index, pos_len): r"""Hit_ (also known as hit ratio at :math:`N`) is a way of calculating how many 'hits' you have in an n-sized list of ranked items. @@ -129,7 +128,6 @@ def ndcg_(pos_index, pos_len): :math:`U^{te}` is for all users in the test set. """ - len_rank = np.full_like(pos_len, pos_index.shape[1]) idcg_len = np.where(pos_len > len_rank, len_rank, pos_len) @@ -166,10 +164,31 @@ def precision_(pos_index, pos_len): def gauc_(user_len_list, pos_len_list, pos_rank_sum): - frac = user_len_list - (pos_len_list - 1) / 2 - (1 / pos_len_list) * np.squeeze(pos_rank_sum) - neg_item_num = user_len_list - pos_len_list - user_auc = frac / neg_item_num + r"""GAUC_ (also known as Group Area Under Curve) is used to evaluate the two-class model, referring to + the area under the ROC curve grouped by user. + + .. _GAUC: https://dl.acm.org/doi/10.1145/3219819.3219823 + + Note: + It calculates the AUC score of each user, and finally obtains GAUC by weighting the user AUC + . It is also not limited to k. + + .. math:: + \mathrm {GAUC} = \frac {{{M} \times {(M+N+1)} - \frac{M \times (M+1)}{2}} - + \sum\limits_{i=1}^M rank_{i}} {{M} \times {N}} + + :math:`M` is the number of positive samples. + :math:`N` is the number of negative samples. + :math:`rank_i` is the descending rank of the ith positive sample. + Note: Due to our padding for `scores_tensor` in `RankEvaluator` with `-np.inf`, the padding value will influence + the ranks of origin items. Therefore, we use descending sort here and make an identity transformation to the + formula of `AUC`, which is shown in `auc_` function. For readability, we didn't do simplification in the code. + + """ + pair_num = (user_len_list + 1) * pos_len_list - pos_len_list * (pos_len_list + 1) / 2 - np.squeeze(pos_rank_sum) + neg_item_num = user_len_list - pos_len_list + user_auc = pair_num / (neg_item_num * pos_len_list) result = (user_auc * pos_len_list).sum() / pos_len_list.sum() return result @@ -188,11 +207,11 @@ def auc_(trues, preds): .. math:: \mathrm {AUC} = \frac{\sum\limits_{i=1}^M rank_{i} - - {{M} \times {(M+1)}}} {{M} \times {N}} + - \frac {{M} \times {(M+1)}}{2}} {{{M} \times {N}}} :math:`M` is the number of positive samples. :math:`N` is the number of negative samples. - :math:`rank_i` is the rank of the ith positive sample. + :math:`rank_i` is the ascending rank of the ith positive sample. """ fps, tps = _binary_clf_curve(trues, preds) From 48f1078ab99e8a44b87cdae8a01d2a9e2d7e38dd Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Fri, 18 Dec 2020 13:45:48 +0800 Subject: [PATCH 180/249] FIX: rename & comment format --- recbole/evaluator/evaluators.py | 25 +++++++++++++++---------- 1 file changed, 15 insertions(+), 10 deletions(-) diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py index a97fffa7e..a0713188e 100644 --- a/recbole/evaluator/evaluators.py +++ b/recbole/evaluator/evaluators.py @@ -4,14 +4,14 @@ # @email : tsotfsk@outlook.com # UPDATE -# @Time : 2020/08/04, 2020/08/11, 2020/12/9 +# @Time : 2020/08/04, 2020/08/11, 2020/12/18 # @Author : Kaiyuan Li, Yupeng Hou, Zhichao Feng # @email : tsotfsk@outlook.com, houyupeng@ruc.edu.cn, fzcbupt@gmail.com import torch import numpy as np -from collections import ChainMap, Counter +from collections import ChainMap from recbole.evaluator.metrics import metrics_dict from recbole.evaluator.abstract_evaluator import GroupedEvalautor, IndividualEvaluator @@ -158,22 +158,27 @@ def get_user_pos_len_list(self, interaction, scores_tensor): user_len_list = interaction.user_len_list return pos_len_list, user_len_list - def rankdata(self, scores): + def average_rank(self, scores): """Get the ranking of an ordered tensor, and take the average of the ranking for positions with equal values. Args: scores(tensor): an ordered tensor, with size of `(N, )` - Examples:: + Returns: + torch.Tensor: average_rank + + Example: + >>> average_rank(tensor([[1,2,2,2,3,3,6],[2,2,2,2,4,4,5]])) + tensor([[1.0000, 3.0000, 3.0000, 3.0000, 5.5000, 5.5000, 7.0000], + [2.5000, 2.5000, 2.5000, 2.5000, 5.0000, 6.5000, 6.5000]]) - >>> rankdata(tensor([[1,2,2,2,3,3,6],[2,2,2,2,4,4,5]])) - tensor([[1.0000, 3.0000, 3.0000, 3.0000, 5.5000, 5.5000, 7.0000], - [2.5000, 2.5000, 2.5000, 2.5000, 5.0000, 6.5000, 6.5000]]) + Reference: + https://github.com/scipy/scipy/blob/v0.17.1/scipy/stats/stats.py#L5262-L5352 """ length, width = scores.shape device = scores.device - true_tensor = torch.full((length, 1), True, dtype=np.bool, device=device) + true_tensor = torch.full((length, 1), True, dtype=torch.bool, device=device) obs = torch.cat([true_tensor, scores[:, 1:] != scores[:, :-1]], dim=1) # bias added to dense @@ -183,7 +188,7 @@ def rankdata(self, scores): # cumulative counts of each unique value count = torch.where(torch.cat([obs, true_tensor], dim=1))[1] - # get averange rank + # get average rank avg_rank = .5 * (count[dense] + count[dense - 1] + 1).view(length, -1) return avg_rank @@ -204,7 +209,7 @@ def collect(self, interaction, scores_tensor): # get the index of positive items in the ranking list pos_index = (desc_index < pos_len_list.reshape(-1, 1)) - avg_rank = self.rankdata(desc_scores) + avg_rank = self.average_rank(desc_scores) pos_rank_sum = torch.where(pos_index, avg_rank, torch.zeros_like(avg_rank)). \ sum(axis=-1).reshape(-1, 1) From eb84ef352cd9384d7aa4b7f0bea73f545eff8eb7 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Mon, 21 Dec 2020 18:28:07 +0800 Subject: [PATCH 181/249] FEA: add parameters check in gauc --- recbole/evaluator/evaluators.py | 1 + recbole/evaluator/metrics.py | 37 ++++++++++++++++++++++++++------- 2 files changed, 30 insertions(+), 8 deletions(-) diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py index a0713188e..d20d73307 100644 --- a/recbole/evaluator/evaluators.py +++ b/recbole/evaluator/evaluators.py @@ -229,6 +229,7 @@ def evaluate(self, batch_matrix_list, eval_data): pos_len_list = eval_data.get_pos_len_list() user_len_list = eval_data.get_user_len_list() pos_rank_sum = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + assert len(pos_len_list) == len(pos_rank_sum) # get metrics metric_dict = {} diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index df6854257..4e1ce123b 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -170,8 +170,11 @@ def gauc_(user_len_list, pos_len_list, pos_rank_sum): .. _GAUC: https://dl.acm.org/doi/10.1145/3219819.3219823 Note: - It calculates the AUC score of each user, and finally obtains GAUC by weighting the user AUC - . It is also not limited to k. + It calculates the AUC score of each user, and finally obtains GAUC by weighting the user AUC. + It is also not limited to k. Due to our padding for `scores_tensor` in `RankEvaluator` with + `-np.inf`, the padding value will influence the ranks of origin items. Therefore, we use + descending sort here and make an identity transformation to the formula of `AUC`, which is + shown in `auc_` function. For readability, we didn't do simplification in the code. .. math:: \mathrm {GAUC} = \frac {{{M} \times {(M+N+1)} - \frac{M \times (M+1)}{2}} - @@ -181,14 +184,32 @@ def gauc_(user_len_list, pos_len_list, pos_rank_sum): :math:`N` is the number of negative samples. :math:`rank_i` is the descending rank of the ith positive sample. - Note: Due to our padding for `scores_tensor` in `RankEvaluator` with `-np.inf`, the padding value will influence - the ranks of origin items. Therefore, we use descending sort here and make an identity transformation to the - formula of `AUC`, which is shown in `auc_` function. For readability, we didn't do simplification in the code. - """ + neg_len_list = user_len_list - pos_len_list + + # check positive and negative samples + all_with_pos = np.any(pos_len_list == 0) + all_with_neg = np.any(neg_len_list == 0) + non_zero_idx = np.full(len(user_len_list), True, dtype=np.bool) + if all_with_pos: + logger = getLogger() + logger.warning("No positive samples in some users, " + "true positive value should be meaningless, " + "these users have been removed from GAUC calculation") + non_zero_idx *= (pos_len_list != 0) + if all_with_neg: + logger = getLogger() + logger.warning("No negative samples in some users, " + "false positive value should be meaningless, " + "these users have been removed from GAUC calculation") + non_zero_idx *= (neg_len_list != 0) + if all_with_pos or all_with_neg: + user_len_list = user_len_list[non_zero_idx] + neg_len_list = user_len_list[non_zero_idx] + pos_rank_sum = pos_rank_sum[non_zero_idx] + pair_num = (user_len_list + 1) * pos_len_list - pos_len_list * (pos_len_list + 1) / 2 - np.squeeze(pos_rank_sum) - neg_item_num = user_len_list - pos_len_list - user_auc = pair_num / (neg_item_num * pos_len_list) + user_auc = pair_num / (neg_len_list * pos_len_list) result = (user_auc * pos_len_list).sum() / pos_len_list.sum() return result From aed05ee6e9c41493b24a368b9328e1b9a6fa1b58 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Mon, 21 Dec 2020 21:54:30 +0800 Subject: [PATCH 182/249] FEA: add GAUC check & GAUC test --- recbole/evaluator/evaluators.py | 2 ++ recbole/evaluator/metrics.py | 8 ++++---- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py index d20d73307..c7a6e9636 100644 --- a/recbole/evaluator/evaluators.py +++ b/recbole/evaluator/evaluators.py @@ -116,6 +116,8 @@ def _calculate_metrics(self, pos_len_list, topk_index): metric_fuc = metrics_dict[metric.lower()] result = metric_fuc(pos_idx_matrix, pos_len_list) result_list.append(result) # n_users x len(metrics) x len(ranks) + import pdb; + pdb.set_trace() result = np.stack(result_list, axis=0).mean(axis=1) # len(metrics) x len(ranks) return result diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index 4e1ce123b..2e875cead 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -4,7 +4,7 @@ # @email : tsotfsk@outlook.com # UPDATE -# @Time : 2020/08/12, 2020/12/18, 2020/9/16 +# @Time : 2020/08/12, 2020/12/21, 2020/9/16 # @Author : Kaiyuan Li, Zhichao Feng, Xingyu Pan # @email : tsotfsk@outlook.com, fzcbupt@gmail.com, panxy@ruc.edu.cn @@ -204,9 +204,9 @@ def gauc_(user_len_list, pos_len_list, pos_rank_sum): "these users have been removed from GAUC calculation") non_zero_idx *= (neg_len_list != 0) if all_with_pos or all_with_neg: - user_len_list = user_len_list[non_zero_idx] - neg_len_list = user_len_list[non_zero_idx] - pos_rank_sum = pos_rank_sum[non_zero_idx] + item_list = user_len_list, neg_len_list, pos_len_list, pos_rank_sum + user_len_list, neg_len_list, pos_len_list, pos_rank_sum = \ + map(lambda x: x[non_zero_idx], item_list) pair_num = (user_len_list + 1) * pos_len_list - pos_len_list * (pos_len_list + 1) / 2 - np.squeeze(pos_rank_sum) user_auc = pair_num / (neg_len_list * pos_len_list) From 08a5492977c10cc933a9cb89920fdf92b98731c2 Mon Sep 17 00:00:00 2001 From: Lin zihan <893833413@qq.com> Date: Tue, 22 Dec 2020 10:39:12 +0800 Subject: [PATCH 183/249] FIX: refactor SparseDropout --- recbole/model/general_recommender/gcmc.py | 25 ++------------- recbole/model/general_recommender/ngcf.py | 39 +++-------------------- recbole/model/layers.py | 23 +++++++++++++ 3 files changed, 30 insertions(+), 57 deletions(-) diff --git a/recbole/model/general_recommender/gcmc.py b/recbole/model/general_recommender/gcmc.py index 263d9fedb..08d031d80 100644 --- a/recbole/model/general_recommender/gcmc.py +++ b/recbole/model/general_recommender/gcmc.py @@ -28,6 +28,7 @@ from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.layers import SparseDropout class GCMC(GeneralRecommender): @@ -307,9 +308,8 @@ def _init_weights(self): def forward(self, user_X, item_X): # ----------------------------------------GCN layer---------------------------------------- - if self.training: - user_X = self.sparse_dropout(user_X) - item_X = self.sparse_dropout(item_X) + user_X = self.sparse_dropout(user_X) + item_X = self.sparse_dropout(item_X) embeddings = [] if self.accum == 'sum': @@ -435,25 +435,6 @@ def forward(self, u_inputs, i_inputs, users, items=None): return output -class SparseDropout(nn.Module): - """ - This is a Module that execute Dropout on Pytorch sparse tensor. - """ - - def __init__(self, p=0.5): - super(SparseDropout, self).__init__() - # p is ratio of dropout - # convert to keep probability - self.kprob = 1 - p - - def forward(self, x): - mask = ((torch.rand(x._values().size()) + - self.kprob).floor()).type(torch.bool) - rc = x._indices()[:, mask] - val = x._values()[mask] * (1.0 / self.kprob) - return torch.sparse.FloatTensor(rc, val, x.shape) - - def orthogonal(shape, scale=1.1): """ Initialization function for weights in class GCMC. diff --git a/recbole/model/general_recommender/ngcf.py b/recbole/model/general_recommender/ngcf.py index 8a5a4751e..a719670cb 100644 --- a/recbole/model/general_recommender/ngcf.py +++ b/recbole/model/general_recommender/ngcf.py @@ -28,41 +28,10 @@ from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.loss import BPRLoss, EmbLoss -from recbole.model.layers import BiGNNLayer +from recbole.model.layers import BiGNNLayer, SparseDropout from recbole.model.init import xavier_normal_initialization -def sparse_dropout(x, rate, noise_shape): - r"""This is a function that execute Dropout on Pytorch sparse tensor. - - A random dropout will be applied to the input sparse tensor. - - Note: - input tensor SHOULD be a sparse float tensor. - we suggest to use '._nnz()' as the shape of sparse tensor for an easy calling. - - Args: - x (torch.sparse.FloatTensor): The input sparse tensor. - rate (float): Dropout rate which should in [0,1]. - noise_shape(tuple): Shape of the input sparse tensor. suggest '._nnz()' - - Returns: - torch.sparse.FloatTensor: The result sparse tensor after dropout. - - """ - random_tensor = 1 - rate - random_tensor += torch.rand(noise_shape).to(x.device) - dropout_mask = torch.floor(random_tensor).type(torch.bool) - i = x._indices() - v = x._values() - - i = i[:, dropout_mask] - v = v[dropout_mask] - - out = torch.sparse.FloatTensor(i, v, x.shape).to(x.device) - return out * (1. / (1 - rate)) - - class NGCF(GeneralRecommender): r"""NGCF is a model that incorporate GNN for recommendation. We implement the model following the original author with a pairwise training mode. @@ -84,6 +53,7 @@ def __init__(self, config, dataset): self.reg_weight = config['reg_weight'] # define layers and loss + self.sparse_dropout = SparseDropout(self.node_dropout) self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) self.GNNlayers = torch.nn.ModuleList() @@ -162,9 +132,8 @@ def get_ego_embeddings(self): return ego_embeddings def forward(self): - # A_hat: spare tensor with shape of [n_items+n_users,n_items+n_users] - A_hat = sparse_dropout(self.norm_adj_matrix, self.node_dropout, - self.norm_adj_matrix._nnz()) if self.node_dropout != 0 and self.training else self.norm_adj_matrix + + A_hat = self.sparse_dropout(self.norm_adj_matrix) if self.node_dropout != 0 else self.norm_adj_matrix all_embeddings = self.get_ego_embeddings() embeddings_list = [all_embeddings] for gnn in self.GNNlayers: diff --git a/recbole/model/layers.py b/recbole/model/layers.py index 81cbe75aa..f93e5d40e 100644 --- a/recbole/model/layers.py +++ b/recbole/model/layers.py @@ -1041,3 +1041,26 @@ def forward(self, interaction): total_fields_embedding.append(token_seq_fields_embedding) return torch.sum(torch.cat(total_fields_embedding, dim=1), dim=1) + self.bias # [batch_size, output_dim] + + +class SparseDropout(nn.Module): + """ + This is a Module that execute Dropout on Pytorch sparse tensor. + """ + + def __init__(self, p=0.5): + super(SparseDropout, self).__init__() + # p is ratio of dropout + # convert to keep probability + self.kprob = 1 - p + + def forward(self, x): + + if not self.training: + return x + + mask = ((torch.rand(x._values().size()) + + self.kprob).floor()).type(torch.bool) + rc = x._indices()[:, mask] + val = x._values()[mask] * (1.0 / self.kprob) + return torch.sparse.FloatTensor(rc, val, x.shape) \ No newline at end of file From d6ea8e27902936626ca7be048c129501cc18e33c Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Tue, 22 Dec 2020 11:09:21 +0800 Subject: [PATCH 184/249] update metrics.py --- recbole/evaluator/metrics.py | 1 + 1 file changed, 1 insertion(+) diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index 2e875cead..470b307d5 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -211,6 +211,7 @@ def gauc_(user_len_list, pos_len_list, pos_rank_sum): pair_num = (user_len_list + 1) * pos_len_list - pos_len_list * (pos_len_list + 1) / 2 - np.squeeze(pos_rank_sum) user_auc = pair_num / (neg_len_list * pos_len_list) result = (user_auc * pos_len_list).sum() / pos_len_list.sum() + return result From eb4deb2c29c2b7ba8ad822750e51fd66db06e11d Mon Sep 17 00:00:00 2001 From: yihongguo Date: Tue, 22 Dec 2020 11:11:22 +0800 Subject: [PATCH 185/249] FIX: update LINE --- recbole/model/general_recommender/line.py | 74 ++++++++++++----------- 1 file changed, 40 insertions(+), 34 deletions(-) diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py index b7ecfad5a..ecf4a7a3c 100644 --- a/recbole/model/general_recommender/line.py +++ b/recbole/model/general_recommender/line.py @@ -22,13 +22,15 @@ from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.init import xavier_normal_initialization -class neg_sampling_loss(nn.Module): + +class NegSamplingLoss(nn.Module): def __init__(self): - super(neg_sampling_loss, self).__init__() + super(NegSamplingLoss, self).__init__() - def forward(self,score,sign): + def forward(self, score, sign): return -torch.mean(torch.sigmoid(sign * score)) + class LINE(GeneralRecommender): r"""LINE is a graph embedding model. @@ -50,16 +52,17 @@ def __init__(self, config, dataset): self.uid_field = dataset.dataset.uid_field self.iid_field = dataset.dataset.iid_field - self.user_embedding = nn.Embedding(self.n_users, self.embedding_size).to(self.device) - self.item_embedding = nn.Embedding(self.n_items, self.embedding_size).to(self.device) + self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) - self.user_context_embedding = nn.Embedding(self.n_users, self.embedding_size).to(self.device) - self.item_context_embedding = nn.Embedding(self.n_items, self.embedding_size).to(self.device) + if self.order == 2: + self.user_context_embedding = nn.Embedding(self.n_users, self.embedding_size) + self.item_context_embedding = nn.Embedding(self.n_items, self.embedding_size) - self.loss_fct = neg_sampling_loss() + self.loss_fct = NegSamplingLoss() self.used_ids = self.get_used_ids() - self.random_list = self.get_random_list() + self.random_list = self.get_user_id_list() np.random.shuffle(self.random_list) self.random_pr = 0 self.random_list_length = len(self.random_list) @@ -67,13 +70,14 @@ def __init__(self, config, dataset): self.apply(xavier_normal_initialization) def get_used_ids(self): - last = [set() for i in range(self.n_items)] + last = [[] for _ in range(self.n_items)] cur = np.array([set(s) for s in last]) - for iid, uid in zip(self.interaction_feat[self.iid_field].numpy(), self.interaction_feat[self.uid_field].numpy()): + for iid, uid in zip(self.interaction_feat[self.iid_field].numpy(), + self.interaction_feat[self.uid_field].numpy()): cur[iid].add(uid) return cur - def sampler(self,key_ids): + def sampler(self, key_ids): key_ids = np.array(key_ids.cpu()) key_num = len(key_ids) @@ -87,7 +91,7 @@ def sampler(self,key_ids): zip(check_list, self.used_ids[key_ids[check_list]], value_ids[check_list]) if v in used]) - return torch.tensor(value_ids,device=self.device) + return torch.tensor(value_ids, device=self.device) def random_num(self, num): value_id = [] @@ -104,10 +108,10 @@ def random_num(self, num): np.random.shuffle(self.random_list) return np.concatenate(value_id) - def get_random_list(self): + def get_user_id_list(self): return np.arange(1, self.n_users) - def forward(self,h,t): + def forward(self, h, t): h_embedding = self.user_embedding(h) t_embedding = self.item_embedding(t) @@ -131,29 +135,31 @@ def calculate_loss(self, interaction): pos_item = interaction[self.ITEM_ID] neg_item = interaction[self.NEG_ITEM_ID] - score_pos = self.forward(user,pos_item) + score_pos = self.forward(user, pos_item) - # randomly train i-i relation and u-u relation with u-i relation - if random.random()<0.5: - score_neg = self.forward(user, neg_item) - score_pos_con = self.context_forward(user, pos_item, 'uu') - score_neg_con = self.context_forward(user, neg_item, 'uu') - else: - # sample negative user for item - neg_user = self.sampler(pos_item) - score_neg = self.forward(neg_user,pos_item) - score_pos_con = self.context_forward(pos_item, user,'ii') - score_neg_con = self.context_forward(pos_item, neg_user,'ii') + ones = torch.ones(len(score_pos), device=self.device) - ones = torch.ones(len(score_pos),device=self.device) if self.order == 1: - return self.loss_fct(ones,score_pos) \ - + self.loss_fct(-1 * ones, score_neg) + score_neg = self.forward(user, neg_item) + return self.loss_fct(ones, score_pos) + self.loss_fct(-1 * ones, score_neg) + else: - return self.loss_fct(ones,score_pos) \ - + self.loss_fct(-1 * ones, score_neg)\ - + self.loss_fct(ones,score_pos_con)*self.second_order_loss_weight\ - + self.loss_fct(-1*ones,score_neg_con)*self.second_order_loss_weight + # randomly train i-i relation and u-u relation with u-i relation + if random.random() < 0.5: + score_neg = self.forward(user, neg_item) + score_pos_con = self.context_forward(user, pos_item, 'uu') + score_neg_con = self.context_forward(user, neg_item, 'uu') + else: + # sample negative user for item + neg_user = self.sampler(pos_item) + score_neg = self.forward(neg_user, pos_item) + score_pos_con = self.context_forward(pos_item, user, 'ii') + score_neg_con = self.context_forward(pos_item, neg_user, 'ii') + + return self.loss_fct(ones, score_pos) \ + + self.loss_fct(-1 * ones, score_neg) \ + + self.loss_fct(ones, score_pos_con)*self.second_order_loss_weight \ + + self.loss_fct(-1*ones, score_neg_con)*self.second_order_loss_weight def predict(self, interaction): From 36208416cd4e9722fc0382e8ccea53218b5b0807 Mon Sep 17 00:00:00 2001 From: fzc <970955517@qq.com> Date: Tue, 22 Dec 2020 11:17:39 +0800 Subject: [PATCH 186/249] Update evaluators.py --- recbole/evaluator/evaluators.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py index c7a6e9636..d20d73307 100644 --- a/recbole/evaluator/evaluators.py +++ b/recbole/evaluator/evaluators.py @@ -116,8 +116,6 @@ def _calculate_metrics(self, pos_len_list, topk_index): metric_fuc = metrics_dict[metric.lower()] result = metric_fuc(pos_idx_matrix, pos_len_list) result_list.append(result) # n_users x len(metrics) x len(ranks) - import pdb; - pdb.set_trace() result = np.stack(result_list, axis=0).mean(axis=1) # len(metrics) x len(ranks) return result From bd40a3ad47b4f32227e72474b2731515813018c8 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Tue, 22 Dec 2020 11:21:01 +0800 Subject: [PATCH 187/249] FEA: add ranking metric test --- tests/metrics/test_rank_metrics.py | 44 ++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) create mode 100644 tests/metrics/test_rank_metrics.py diff --git a/tests/metrics/test_rank_metrics.py b/tests/metrics/test_rank_metrics.py new file mode 100644 index 000000000..8bde0d9d1 --- /dev/null +++ b/tests/metrics/test_rank_metrics.py @@ -0,0 +1,44 @@ +# -*- encoding: utf-8 -*- +# @Time : 2020/12/21 +# @Author : Zhichao Feng +# @email : fzcbupt@gmail.com + + +import os +import sys +import unittest + +sys.path.append(os.getcwd()) +import numpy as np +from recbole.evaluator.metrics import metrics_dict + + +class TestCases(object): + user_len_list0 = np.array([2, 3, 5]) + pos_len_list0 = np.array([1, 2, 3]) + pos_rank_sum0 = np.array([1, 4, 9]) + + user_len_list1 = np.array([3, 6, 4]) + pos_len_list1 = np.array([1, 0, 4]) + pos_rank_sum1 = np.array([3, 0, 6]) + + +def get_result(name, case=0): + func = metrics_dict[name] + return func(getattr(TestCases, f'user_len_list{case}'), + getattr(TestCases, f'pos_len_list{case}'), + getattr(TestCases, f'pos_rank_sum{case}')) + + +class TestRankMetrics(unittest.TestCase): + def test_gauc(self): + name = 'gauc' + self.assertEqual(get_result(name, case=0), (1 * ((2 - (1 - 1) / 2 - 1 / 1) / (2 - 1)) + + 2 * ((3 - (2 - 1) / 2 - 4 / 2) / (3 - 2)) + + 3 * ((5 - (3 - 1) / 2 - 9 / 3) / (5 - 3))) + / (1 + 2 + 3)) + self.assertEqual(get_result(name, case=1), (3 - 0 - 3 / 1) / (3 - 1)) + + +if __name__ == "__main__": + unittest.main() From 6e9a9c6e042adde4cfc35d419b867319cbf54f03 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Tue, 22 Dec 2020 11:41:35 +0800 Subject: [PATCH 188/249] FIX: rename bool variable in GAUC & remove keys in build --- recbole/evaluator/metrics.py | 10 +++++----- recbole/evaluator/proxy_evaluator.py | 2 +- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index 470b307d5..483fd3f21 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -188,22 +188,22 @@ def gauc_(user_len_list, pos_len_list, pos_rank_sum): neg_len_list = user_len_list - pos_len_list # check positive and negative samples - all_with_pos = np.any(pos_len_list == 0) - all_with_neg = np.any(neg_len_list == 0) + any_without_pos = np.any(pos_len_list == 0) + any_without_neg = np.any(neg_len_list == 0) non_zero_idx = np.full(len(user_len_list), True, dtype=np.bool) - if all_with_pos: + if any_without_pos: logger = getLogger() logger.warning("No positive samples in some users, " "true positive value should be meaningless, " "these users have been removed from GAUC calculation") non_zero_idx *= (pos_len_list != 0) - if all_with_neg: + if any_without_neg: logger = getLogger() logger.warning("No negative samples in some users, " "false positive value should be meaningless, " "these users have been removed from GAUC calculation") non_zero_idx *= (neg_len_list != 0) - if all_with_pos or all_with_neg: + if any_without_pos or any_without_neg: item_list = user_len_list, neg_len_list, pos_len_list, pos_rank_sum user_len_list, neg_len_list, pos_len_list, pos_rank_sum = \ map(lambda x: x[non_zero_idx], item_list) diff --git a/recbole/evaluator/proxy_evaluator.py b/recbole/evaluator/proxy_evaluator.py index 9f181c4aa..f5faaebd6 100644 --- a/recbole/evaluator/proxy_evaluator.py +++ b/recbole/evaluator/proxy_evaluator.py @@ -34,7 +34,7 @@ def build(self): evaluator_list = [] metrics_list = [metric.lower() for metric in self.metrics] for metrics, evaluator in metric_eval_bind: - used_metrics = [metric for metric in metrics_list if metric in metrics.keys()] + used_metrics = [metric for metric in metrics_list if metric in metrics] if used_metrics: evaluator_list.append(evaluator(self.config, used_metrics)) return evaluator_list From eda9079ccf31b20cbc8448252a893a98e3840f58 Mon Sep 17 00:00:00 2001 From: yihongguo Date: Tue, 22 Dec 2020 15:41:53 +0800 Subject: [PATCH 189/249] FIX: update LINE --- recbole/model/general_recommender/line.py | 26 +++++++++++------------ 1 file changed, 13 insertions(+), 13 deletions(-) diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py index ecf4a7a3c..3eb51113c 100644 --- a/recbole/model/general_recommender/line.py +++ b/recbole/model/general_recommender/line.py @@ -46,12 +46,8 @@ def __init__(self, config, dataset): self.second_order_loss_weight = config['second_order_loss_weight'] self.training_neg_sample_num = config['training_neg_sample_num'] - self.interaction_feat = dataset.dataset.inter_feat - self.uid_field = dataset.dataset.uid_field - self.iid_field = dataset.dataset.iid_field - self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) @@ -70,10 +66,9 @@ def __init__(self, config, dataset): self.apply(xavier_normal_initialization) def get_used_ids(self): - last = [[] for _ in range(self.n_items)] - cur = np.array([set(s) for s in last]) - for iid, uid in zip(self.interaction_feat[self.iid_field].numpy(), - self.interaction_feat[self.uid_field].numpy()): + cur = np.array([set() for _ in range(self.n_items)]) + for iid, uid in zip(self.interaction_feat[self.USER_ID].numpy(), + self.interaction_feat[self.ITEM_ID].numpy()): cur[iid].add(uid) return cur @@ -140,8 +135,13 @@ def calculate_loss(self, interaction): ones = torch.ones(len(score_pos), device=self.device) if self.order == 1: - score_neg = self.forward(user, neg_item) - return self.loss_fct(ones, score_pos) + self.loss_fct(-1 * ones, score_neg) + if random.random() < 0.5: + score_neg = self.forward(user, neg_item) + return self.loss_fct(ones, score_pos) + self.loss_fct(-1 * ones, score_neg) + else: + neg_user = self.sampler(pos_item) + score_neg = self.forward(neg_user, pos_item) + return self.loss_fct(ones, score_pos) + self.loss_fct(-1 * ones, score_neg) else: # randomly train i-i relation and u-u relation with u-i relation @@ -157,9 +157,9 @@ def calculate_loss(self, interaction): score_neg_con = self.context_forward(pos_item, neg_user, 'ii') return self.loss_fct(ones, score_pos) \ - + self.loss_fct(-1 * ones, score_neg) \ - + self.loss_fct(ones, score_pos_con)*self.second_order_loss_weight \ - + self.loss_fct(-1*ones, score_neg_con)*self.second_order_loss_weight + + self.loss_fct(-1 * ones, score_neg) \ + + self.loss_fct(ones, score_pos_con) * self.second_order_loss_weight \ + + self.loss_fct(-1*ones, score_neg_con) * self.second_order_loss_weight def predict(self, interaction): From f949ca7868658dddd3d68cdaa0f48425431c2627 Mon Sep 17 00:00:00 2001 From: yihongguo Date: Tue, 22 Dec 2020 17:02:02 +0800 Subject: [PATCH 190/249] FIX: request changes --- recbole/model/general_recommender/__init__.py | 1 + tests/model/test_model_auto.py | 7 +++++++ 2 files changed, 8 insertions(+) diff --git a/recbole/model/general_recommender/__init__.py b/recbole/model/general_recommender/__init__.py index 7766bf1c3..373cc7c75 100644 --- a/recbole/model/general_recommender/__init__.py +++ b/recbole/model/general_recommender/__init__.py @@ -6,6 +6,7 @@ from recbole.model.general_recommender.gcmc import GCMC from recbole.model.general_recommender.itemknn import ItemKNN from recbole.model.general_recommender.lightgcn import LightGCN +from recbole.model.general_recommender.line import LINE from recbole.model.general_recommender.nais import NAIS from recbole.model.general_recommender.neumf import NeuMF from recbole.model.general_recommender.ngcf import NGCF diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 0be3e9b31..a68045650 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -109,6 +109,13 @@ def test_dgcf(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_line(self): + config_dict = { + 'model': 'LINE', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + class TestContextRecommender(unittest.TestCase): # todo: more complex context information should be test, such as criteo dataset From 886c8c78ca4a221c352de58456203f2e74775df7 Mon Sep 17 00:00:00 2001 From: yihongguo Date: Tue, 22 Dec 2020 17:35:53 +0800 Subject: [PATCH 191/249] FEA: add LINE --- recbole/model/general_recommender/line.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py index 3eb51113c..64a2f7104 100644 --- a/recbole/model/general_recommender/line.py +++ b/recbole/model/general_recommender/line.py @@ -137,11 +137,10 @@ def calculate_loss(self, interaction): if self.order == 1: if random.random() < 0.5: score_neg = self.forward(user, neg_item) - return self.loss_fct(ones, score_pos) + self.loss_fct(-1 * ones, score_neg) else: neg_user = self.sampler(pos_item) score_neg = self.forward(neg_user, pos_item) - return self.loss_fct(ones, score_pos) + self.loss_fct(-1 * ones, score_neg) + return self.loss_fct(ones, score_pos) + self.loss_fct(-1 * ones, score_neg) else: # randomly train i-i relation and u-u relation with u-i relation From 8e3c25915c6659f8920bdc0dc3ef04edcbb33db7 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Thu, 24 Dec 2020 09:15:37 +0800 Subject: [PATCH 192/249] DOC: doc fix in sampler.py --- recbole/sampler/sampler.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index 54f1fc37b..08e82fa10 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -78,6 +78,13 @@ def random(self): return value_id def random_num(self, num): + """ + Args: + num (int): Number of random value_ids. + + Returns: + value_ids (numpy.ndarray): Random value_ids. Generated by :attr:`random_list`. + """ value_id = [] self.random_pr %= self.random_list_length while True: @@ -97,7 +104,6 @@ def sample_by_key_ids(self, key_ids, num): Args: key_ids (np.ndarray or list): Input key_ids. num (int): Number of sampled value_ids for each key_id. - used_ids (np.ndarray): Used ids. index is key_id, and element is a set of value_ids. Returns: torch.tensor: Sampled value_ids. From 634bad684e0578e781f38acc443119be43bf1268 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Thu, 24 Dec 2020 11:44:09 +0800 Subject: [PATCH 193/249] FEA: add RankEvaluator collect test --- tests/metrics/test_rank_metrics.py | 48 ++++++++++++++++++++++++------ 1 file changed, 39 insertions(+), 9 deletions(-) diff --git a/tests/metrics/test_rank_metrics.py b/tests/metrics/test_rank_metrics.py index 8bde0d9d1..c60a2c485 100644 --- a/tests/metrics/test_rank_metrics.py +++ b/tests/metrics/test_rank_metrics.py @@ -10,10 +10,19 @@ sys.path.append(os.getcwd()) import numpy as np +import torch +from recbole.config import Config +from recbole.data.interaction import Interaction from recbole.evaluator.metrics import metrics_dict +from recbole.evaluator.evaluators import RankEvaluator +parameters_dict = { + 'model': 'BPR', + 'eval_setting': 'RO_RS,uni100', +} -class TestCases(object): + +class MetricsTestCases(object): user_len_list0 = np.array([2, 3, 5]) pos_len_list0 = np.array([1, 2, 3]) pos_rank_sum0 = np.array([1, 4, 9]) @@ -23,21 +32,42 @@ class TestCases(object): pos_rank_sum1 = np.array([3, 0, 6]) -def get_result(name, case=0): +class CollectTestCases(object): + interaction0 = Interaction({}, [0, 2, 3, 4], [2, 3, 4, 5]) + scores_tensor0 = torch.Tensor([0.1, 0.2, + 0.1, 0.1, 0.2, + 0.2, 0.2, 0.2, 0.2, + 0.3, 0.2, 0.1, 0.4, 0.3]) + + +def get_metric_result(name, case=0): func = metrics_dict[name] - return func(getattr(TestCases, f'user_len_list{case}'), - getattr(TestCases, f'pos_len_list{case}'), - getattr(TestCases, f'pos_rank_sum{case}')) + return func(getattr(MetricsTestCases, f'user_len_list{case}'), + getattr(MetricsTestCases, f'pos_len_list{case}'), + getattr(MetricsTestCases, f'pos_rank_sum{case}')) + + +def get_collect_result(evaluator, case=0): + func = evaluator.collect + return func(getattr(CollectTestCases, f'interaction{case}'), + getattr(CollectTestCases, f'scores_tensor{case}')) class TestRankMetrics(unittest.TestCase): def test_gauc(self): name = 'gauc' - self.assertEqual(get_result(name, case=0), (1 * ((2 - (1 - 1) / 2 - 1 / 1) / (2 - 1)) + - 2 * ((3 - (2 - 1) / 2 - 4 / 2) / (3 - 2)) + - 3 * ((5 - (3 - 1) / 2 - 9 / 3) / (5 - 3))) + self.assertEqual(get_metric_result(name, case=0), (1 * ((2 - (1 - 1) / 2 - 1 / 1) / (2 - 1)) + + 2 * ((3 - (2 - 1) / 2 - 4 / 2) / (3 - 2)) + + 3 * ((5 - (3 - 1) / 2 - 9 / 3) / (5 - 3))) / (1 + 2 + 3)) - self.assertEqual(get_result(name, case=1), (3 - 0 - 3 / 1) / (3 - 1)) + self.assertEqual(get_metric_result(name, case=1), (3 - 0 - 3 / 1) / (3 - 1)) + + def test_collect(self): + config = Config('BPR', 'ml-100k', config_dict=parameters_dict) + metrics = ['GAUC'] + rank_evaluator = RankEvaluator(config, metrics) + self.assertEqual(get_collect_result(rank_evaluator, case=0).squeeze().cpu().numpy().tolist(), + np.array([0, (2 + 3) / 2 * 2, (1 + 2 + 3 + 4) / 4 * 3, 1 + (2 + 3) / 2 + 4 + 5]).tolist()) if __name__ == "__main__": From 30693ce081269fe4ce351c2ce66f0ad8902b51ad Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Thu, 24 Dec 2020 16:30:06 +0800 Subject: [PATCH 194/249] FEA: add raise ValueError when some users have interacted with all items && doc fix. --- recbole/sampler/sampler.py | 19 +++++++++++++++---- 1 file changed, 15 insertions(+), 4 deletions(-) diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index 08e82fa10..05688e8a7 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -153,7 +153,7 @@ def sample_by_key_ids(self, key_ids, num): class Sampler(AbstractSampler): """:class:`Sampler` is used to sample negative items for each input user. In order to avoid positive items - in train-phase to be sampled in vaild-phase, and positive items in train-phase or vaild-phase to be sampled + in train-phase to be sampled in valid-phase, and positive items in train-phase or valid-phase to be sampled in test-phase, we need to input the datasets of all phases for pre-processing. And, before using this sampler, it is needed to call :meth:`set_phase` to get the sampler of corresponding phase. @@ -206,12 +206,18 @@ def get_used_ids(self): Key is phase, and value is a np.ndarray which index is user_id, and element is a set of item_ids. """ used_item_id = dict() - last = [set() for i in range(self.n_users)] + last = [set() for _ in range(self.n_users)] for phase, dataset in zip(self.phases, self.datasets): cur = np.array([set(s) for s in last]) for uid, iid in zip(dataset.inter_feat[self.uid_field].numpy(), dataset.inter_feat[self.iid_field].numpy()): cur[uid].add(iid) last = used_item_id[phase] = cur + + for used_item_set in used_item_id[self.phases[-1]]: + if len(used_item_set) + 1 == self.n_items: # [pad] is a item. + raise ValueError('Some users have interacted with all items, ' + 'who we can not sample negative items for them. ' + 'Please set `max_user_inter_num` to filter those users.') return used_item_id def set_phase(self, phase): @@ -291,9 +297,14 @@ def get_used_ids(self): np.ndarray: Used entity_ids is the same as tail_entity_ids in knowledge graph. Index is head_entity_id, and element is a set of tail_entity_ids. """ - used_tail_entity_id = np.array([set() for i in range(self.entity_num)]) + used_tail_entity_id = np.array([set() for _ in range(self.entity_num)]) for hid, tid in zip(self.hid_list, self.tid_list): used_tail_entity_id[hid].add(tid) + + for used_tail_set in used_tail_entity_id: + if len(used_tail_set) + 1 == self.entity_num: # [pad] is a entity. + raise ValueError('Some head entities have relation with all entities, ' + 'who we can not sample negative entities for them.') return used_tail_entity_id def sample_by_entity_ids(self, head_entity_ids, num=1): @@ -360,7 +371,7 @@ def get_used_ids(self): np.ndarray: Used item_ids is the same as positive item_ids. Index is user_id, and element is a set of item_ids. """ - return np.array([set() for i in range(self.n_users)]) + return np.array([set() for _ in range(self.n_users)]) def sample_by_user_ids(self, user_ids, num): """Sampling by user_ids. From ebbc96d52b75dd8c97b361aa2844da22f10dbb6d Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Thu, 24 Dec 2020 17:05:39 +0800 Subject: [PATCH 195/249] DOC: Grammar correction. --- recbole/sampler/sampler.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index 05688e8a7..392402826 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -216,7 +216,7 @@ def get_used_ids(self): for used_item_set in used_item_id[self.phases[-1]]: if len(used_item_set) + 1 == self.n_items: # [pad] is a item. raise ValueError('Some users have interacted with all items, ' - 'who we can not sample negative items for them. ' + 'which we can not sample negative items for them. ' 'Please set `max_user_inter_num` to filter those users.') return used_item_id @@ -304,7 +304,7 @@ def get_used_ids(self): for used_tail_set in used_tail_entity_id: if len(used_tail_set) + 1 == self.entity_num: # [pad] is a entity. raise ValueError('Some head entities have relation with all entities, ' - 'who we can not sample negative entities for them.') + 'which we can not sample negative entities for them.') return used_tail_entity_id def sample_by_entity_ids(self, head_entity_ids, num=1): From 4e40f208a5b9159e4bc3427ca691c1bea7cc7054 Mon Sep 17 00:00:00 2001 From: flust <254170321@qq.com> Date: Thu, 24 Dec 2020 12:36:52 +0000 Subject: [PATCH 196/249] FIX: xgboost trainer and dataset --- recbole/data/dataset/xgboost_dataset.py | 10 +++++----- recbole/trainer/trainer.py | 22 +++++++++------------- 2 files changed, 14 insertions(+), 18 deletions(-) diff --git a/recbole/data/dataset/xgboost_dataset.py b/recbole/data/dataset/xgboost_dataset.py index 414efe551..18cff6b26 100644 --- a/recbole/data/dataset/xgboost_dataset.py +++ b/recbole/data/dataset/xgboost_dataset.py @@ -78,17 +78,17 @@ def join(self, df): """Given interaction feature, join user/item feature into it. Args: - df (pandas.DataFrame): Interaction feature to be joint. + df (Interaction): Interaction feature to be joint. Returns: - pandas.DataFrame: Interaction feature after joining operation. + Interaction: Interaction feature after joining operation. """ if self.user_feat is not None and self.uid_field in df: - df = pd.merge(df, self.user_feat_xgb, on=self.uid_field, how='left', suffixes=('_inter', '_user')) + df.update(self.user_feat[df[self.uid_field]]) if self.item_feat is not None and self.iid_field in df: - df = pd.merge(df, self.item_feat_xgb, on=self.iid_field, how='left', suffixes=('_inter', '_item')) + df.update(self.item_feat[df[self.iid_field]]) return df def __getitem__(self, index, join=True): - df = self.inter_feat_xgb[index] + df = self.inter_feat[index] return self.join(df) if join else df diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index a06175463..6b283a891 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -324,6 +324,9 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): Returns: dict: eval result, key is the eval metric and value in the corresponding metric value """ + if not eval_data: + return + if load_best_model: if model_file: checkpoint_file = model_file @@ -441,7 +444,9 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): kg_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) # update A - self.model.update_attentive_A() + self.model.eval() + with torch.no_grad(): + self.model.update_attentive_A() return rs_total_loss, kg_total_loss @@ -622,14 +627,8 @@ def _train_at_once(self, train_data, valid_data): train_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. valid_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. """ - train_x = train_data.dataset[:].drop([self.label_field], axis=1, inplace=False) - train_y = list(map(int, train_data.dataset[:][self.label_field].values.tolist())) - - valid_x = valid_data.dataset[:].drop([self.label_field], axis=1, inplace=False) - valid_y = list(map(int, valid_data.dataset[:][self.label_field].values.tolist())) - - self.dtrain = xgb.DMatrix(data = train_x, label = train_y) - self.dvalid = xgb.DMatrix(data = valid_x, label = valid_y) + self.dtrain = self._interaction_to_DMatrix(train_data.dataset[:]) + self.dvalid = self._interaction_to_DMatrix(valid_data.dataset[:]) self.evals = [(self.dtrain,'train'),(self.dvalid, 'valid')] self.model = xgb.train(self.params, self.dtrain, self.num_boost_round, self.evals, self.obj, self.feval, self.maximize, @@ -680,10 +679,7 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): self.eval_pred = torch.Tensor() self.eval_true = torch.Tensor() - eval_x = eval_data.dataset[:].drop([self.label_field], axis=1, inplace=False) - eval_y = list(map(int,eval_data.dataset[:][self.label_field].values.tolist())) - - self.deval = xgb.DMatrix(data = eval_x, label = eval_y) + self.deval = self._interaction_to_DMatrix(eval_data.dataset[:]) self.eval_true = torch.Tensor(self.deval.get_label()) self.eval_pred = torch.Tensor(self.model.predict(self.deval)) From 0bbfc3c02d6ae396c77b4e9e71e8a8c0ce444ffd Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 25 Dec 2020 16:21:47 +0800 Subject: [PATCH 197/249] FEA: add case_study.py (resolve #506) --- recbole/utils/case_study.py | 73 +++++++++++++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 recbole/utils/case_study.py diff --git a/recbole/utils/case_study.py b/recbole/utils/case_study.py new file mode 100644 index 000000000..aa9962bf2 --- /dev/null +++ b/recbole/utils/case_study.py @@ -0,0 +1,73 @@ +import numpy as np +import torch + +from recbole.data.dataloader.general_dataloader import GeneralFullDataLoader +from recbole.data.dataloader.sequential_dataloader import SequentialFullDataLoader + + +@torch.no_grad() +def get_scores(uid_series, model, test_data): + """Calculate the scores of all items for each user in uid_series. + + Note: + The score of [pad] and history items will be set into -inf. + + Args: + uid_series (np.ndarray): User id series + model (AbstractRecommender): Model to predict + test_data (AbstractDataLoader): The test_data of model + + Returns: + torch.Tensor: the scores of all items for each user in uid_series. + """ + uid_field = test_data.dataset.uid_field + dataset = test_data.dataset + model.eval() + + if isinstance(test_data, GeneralFullDataLoader): + index = np.isin(test_data.user_df[uid_field].numpy(), uid_series) + input_interaction = test_data.user_df[index] + history_item = test_data.uid2history_item[input_interaction[uid_field]] + history_row = torch.cat([torch.full_like(hist_iid, i) for i, hist_iid in enumerate(history_item)]) + history_col = torch.cat(list(history_item)) + history_index = history_row, history_col + elif isinstance(test_data, SequentialFullDataLoader): + index = np.isin(test_data.uid_list, uid_series) + input_interaction = test_data.augmentation(test_data.item_list_index[index], + test_data.target_index[index], test_data.item_list_length[index]) + history_index = None + else: + raise NotImplementedError + + # Get scores of all items + try: + scores = model.full_sort_predict(input_interaction) + except NotImplementedError: + input_interaction = input_interaction.repeat(dataset.item_num) + input_interaction.update(test_data.get_item_feature().repeat(len(uid_series))) + scores = model.predict(input_interaction) + + scores = scores.view(-1, dataset.item_num) + scores[:, 0] = -np.inf # set scores of [pad] to -inf + if history_index is not None: + scores[history_index] = -np.inf # set scores of history items to -inf + + return scores + + +def get_topk(uid_series, model, test_data, k): + """Calculate the top-k items' scores and ids for each user in uid_series. + + Args: + uid_series (np.ndarray): User id series + model (AbstractRecommender): Model to predict + test_data (AbstractDataLoader): The test_data of model + k (int): The top-k items. + + Returns: + tuple: + - topk_scores (torch.Tensor): The scores of topk items. + - topk_index (torch.Tensor): The index of topk items, which is also the internal ids of items. + """ + scores = get_scores(uid_series, model, test_data) + return torch.topk(scores, k) From e25819cefa9fe5bb535b774f06ff5bcced50303a Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 25 Dec 2020 16:37:44 +0800 Subject: [PATCH 198/249] FORMAT: code format in trainer.py --- recbole/trainer/trainer.py | 43 +++++++++++++++++++------------------- 1 file changed, 22 insertions(+), 21 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 7a6565366..185770444 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -551,9 +551,10 @@ class xgboostTrainer(AbstractTrainer): """xgboostTrainer is designed for XGBOOST. """ + def __init__(self, config, model): super(xgboostTrainer, self).__init__(config, model) - + self.logger = getLogger() self.label_field = config['LABEL_FIELD'] @@ -605,22 +606,22 @@ def _interaction_to_DMatrix(self, interaction): interaction_np = interaction.numpy() cur_data = np.array([]) for key, value in interaction_np.items(): - value = np.resize(value,(value.shape[0],1)) + value = np.resize(value, (value.shape[0], 1)) if key != self.label_field: if cur_data.shape[0] == 0: cur_data = value else: cur_data = np.hstack((cur_data, value)) - - return xgb.DMatrix(data = cur_data, - label = interaction_np[self.label_field], - weight = self.weight, - base_margin = self.base_margin, - missing = self.missing, - silent = self.silent, - feature_names = self.feature_names, - feature_types = self.feature_types, - nthread = self.nthread) + + return xgb.DMatrix(data=cur_data, + label=interaction_np[self.label_field], + weight=self.weight, + base_margin=self.base_margin, + missing=self.missing, + silent=self.silent, + feature_names=self.feature_names, + feature_types=self.feature_types, + nthread=self.nthread) def _train_at_once(self, train_data, valid_data): r""" @@ -631,11 +632,11 @@ def _train_at_once(self, train_data, valid_data): """ self.dtrain = self._interaction_to_DMatrix(train_data.dataset[:]) self.dvalid = self._interaction_to_DMatrix(valid_data.dataset[:]) - self.evals = [(self.dtrain,'train'),(self.dvalid, 'valid')] - self.model = xgb.train(self.params, self.dtrain, self.num_boost_round, - self.evals, self.obj, self.feval, self.maximize, - self.early_stopping_rounds, self.evals_result, - self.verbose_eval, self.xgb_model, self.callbacks) + self.evals = [(self.dtrain, 'train'), (self.dvalid, 'valid')] + self.model = xgb.train(self.params, self.dtrain, self.num_boost_round, + self.evals, self.obj, self.feval, self.maximize, + self.early_stopping_rounds, self.evals_result, + self.verbose_eval, self.xgb_model, self.callbacks) self.model.save_model(self.saved_model_file) self.xgb_model = self.saved_model_file @@ -645,13 +646,13 @@ def _valid_epoch(self, valid_data): Args: valid_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. """ - valid_result = self.evaluate(valid_data) + valid_result = self.evaluate(valid_data) valid_score = calculate_valid_score(valid_result, self.valid_metric) return valid_result, valid_score def fit(self, train_data, valid_data=None, verbose=True, saved=True): # load model - if self.xgb_model != None: + if self.xgb_model is not None: self.model.load_model(self.xgb_model) self.best_valid_score = 0. @@ -666,7 +667,7 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): valid_result, valid_score = self._valid_epoch(valid_data) valid_end_time = time() valid_score_output = "epoch %d evaluating [time: %.2fs, valid_score: %f]" % \ - (epoch_idx, valid_end_time - valid_start_time, valid_score) + (epoch_idx, valid_end_time - valid_start_time, valid_score) valid_result_output = 'valid result: \n' + dict2str(valid_result) if verbose: self.logger.info(valid_score_output) @@ -674,7 +675,7 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): self.best_valid_score = valid_score self.best_valid_result = valid_result - + return self.best_valid_score, self.best_valid_result def evaluate(self, eval_data, load_best_model=True, model_file=None): From b7d3fd1dc3a659d0cbab397b929abea34a154097 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 25 Dec 2020 17:17:14 +0800 Subject: [PATCH 199/249] DOC: add header to case_study.py --- recbole/utils/case_study.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/recbole/utils/case_study.py b/recbole/utils/case_study.py index aa9962bf2..45d15a2bc 100644 --- a/recbole/utils/case_study.py +++ b/recbole/utils/case_study.py @@ -1,3 +1,13 @@ +# @Time : 2020/12/25 +# @Author : Yushuo Chen +# @Email : chenyushuo@ruc.edu.cn + +# UPDATE +# @Time : 2020/12/25 +# @Author : Yushuo Chen +# @email : chenyushuo@ruc.edu.cn + + import numpy as np import torch From 6d839ee371940f27e8a71676d356bab120de6949 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 25 Dec 2020 17:34:29 +0800 Subject: [PATCH 200/249] FEA: Add progress bar to trainer.py (resolve #516) --- recbole/properties/overall.yaml | 2 + recbole/quick_start/quick_start.py | 5 +- recbole/trainer/trainer.py | 80 ++++++++++++++++++++---------- recbole/utils/argument_list.py | 4 +- 4 files changed, 63 insertions(+), 28 deletions(-) diff --git a/recbole/properties/overall.yaml b/recbole/properties/overall.yaml index e9dd78d57..f0149e399 100644 --- a/recbole/properties/overall.yaml +++ b/recbole/properties/overall.yaml @@ -6,6 +6,7 @@ state: INFO reproducibility: True data_path: 'dataset/' checkpoint_dir: 'saved' +show_progress: True # training settings epochs: 300 @@ -13,6 +14,7 @@ train_batch_size: 2048 learner: adam learning_rate: 0.001 training_neg_sample_num: 1 +training_neg_sample_distribution: uniform eval_step: 1 stopping_step: 10 clip_grad_norm: ~ diff --git a/recbole/quick_start/quick_start.py b/recbole/quick_start/quick_start.py index 2294ca15e..b7dcf5722 100644 --- a/recbole/quick_start/quick_start.py +++ b/recbole/quick_start/quick_start.py @@ -49,10 +49,11 @@ def run_recbole(model=None, dataset=None, config_file_list=None, config_dict=Non trainer = get_trainer(config['MODEL_TYPE'], config['model'])(config, model) # model training - best_valid_score, best_valid_result = trainer.fit(train_data, valid_data, saved=saved) + best_valid_score, best_valid_result = trainer.fit(train_data, valid_data, saved=saved, + show_progress=config['show_progress']) # model evaluation - test_result = trainer.evaluate(test_data, load_best_model=saved) + test_result = trainer.evaluate(test_data, load_best_model=saved, show_progress=config['show_progress']) logger.info('best valid result: {}'.format(best_valid_result)) logger.info('test result: {}'.format(test_result)) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 185770444..102a40b54 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -13,7 +13,7 @@ """ import os -import itertools +from tqdm import tqdm import torch import torch.optim as optim from torch.nn.utils.clip_grad import clip_grad_norm_ @@ -120,7 +120,7 @@ def _build_optimizer(self): optimizer = optim.Adam(self.model.parameters(), lr=self.learning_rate) return optimizer - def _train_epoch(self, train_data, epoch_idx, loss_func=None): + def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): r"""Train the model in an epoch Args: @@ -128,6 +128,7 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): epoch_idx (int): The current epoch id. loss_func (function): The loss function of :attr:`model`. If it is ``None``, the loss function will be :attr:`self.model.calculate_loss`. Defaults to ``None``. + show_progress (bool): Show progress of epoch training. Defaults to ``False``. Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains @@ -137,7 +138,16 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): self.model.train() loss_func = loss_func or self.model.calculate_loss total_loss = None - for batch_idx, interaction in enumerate(train_data): + iter_data = ( + tqdm( + enumerate(train_data), + total=len(train_data), + desc=f"Train {epoch_idx:>5}", + ) + if show_progress + else enumerate(train_data) + ) + for batch_idx, interaction in iter_data: interaction = interaction.to(self.device) self.optimizer.zero_grad() losses = loss_func(interaction) @@ -155,17 +165,18 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): self.optimizer.step() return total_loss - def _valid_epoch(self, valid_data): + def _valid_epoch(self, valid_data, show_progress=False): r"""Valid the model with valid data Args: - valid_data (DataLoader): the valid data + valid_data (DataLoader): the valid data. + show_progress (bool): Show progress of epoch evaluate. Defaults to ``False``. Returns: float: valid score dict: valid result """ - valid_result = self.evaluate(valid_data, load_best_model=False) + valid_result = self.evaluate(valid_data, load_best_model=False, show_progress=show_progress) valid_score = calculate_valid_score(valid_result, self.valid_metric) return valid_score, valid_result @@ -222,7 +233,7 @@ def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses): train_loss_output += 'train loss: %.4f' % losses return train_loss_output + ']' - def fit(self, train_data, valid_data=None, verbose=True, saved=True): + def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False): r"""Train the model based on the train data and the valid data. Args: @@ -231,6 +242,7 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): If it's None, the early_stopping is invalid. verbose (bool, optional): whether to write training and evaluation information to logger, default: True saved (bool, optional): whether to save the model parameters, default: True + show_progress (bool): Show progress of epoch training and evaluate. Defaults to ``False``. Returns: (float, dict): best valid score and best valid result. If valid_data is None, it returns (-1, None) @@ -241,7 +253,7 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): for epoch_idx in range(self.start_epoch, self.epochs): # train training_start_time = time() - train_loss = self._train_epoch(train_data, epoch_idx) + train_loss = self._train_epoch(train_data, epoch_idx, show_progress=show_progress) self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ @@ -259,7 +271,7 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): continue if (epoch_idx + 1) % self.eval_step == 0: valid_start_time = time() - valid_score, valid_result = self._valid_epoch(valid_data) + valid_score, valid_result = self._valid_epoch(valid_data, show_progress=show_progress) self.best_valid_score, self.cur_step, stop_flag, update_flag = early_stopping( valid_score, self.best_valid_score, self.cur_step, max_step=self.stopping_step, bigger=self.valid_metric_bigger) @@ -313,7 +325,7 @@ def _full_sort_batch_eval(self, batched_data): return interaction, scores @torch.no_grad() - def evaluate(self, eval_data, load_best_model=True, model_file=None): + def evaluate(self, eval_data, load_best_model=True, model_file=None, show_progress=False): r"""Evaluate the model based on the eval data. Args: @@ -322,6 +334,7 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): It should be set True, if users want to test the model after training. model_file (str, optional): the saved model file, default: None. If users want to test the previously trained model file, they can set this parameter. + show_progress (bool): Show progress of epoch evaluate. Defaults to ``False``. Returns: dict: eval result, key is the eval metric and value in the corresponding metric value @@ -347,7 +360,16 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): self.tot_item_num = eval_data.dataset.item_num batch_matrix_list = [] - for batch_idx, batched_data in enumerate(eval_data): + iter_data = ( + tqdm( + enumerate(eval_data), + total=len(eval_data), + desc=f"Evaluate ", + ) + if show_progress + else enumerate(eval_data) + ) + for batch_idx, batched_data in iter_data: if eval_data.dl_type == DataLoaderType.FULL: interaction, scores = self._full_sort_batch_eval(batched_data) else: @@ -413,7 +435,7 @@ def __init__(self, config, model): self.train_rec_step = config['train_rec_step'] self.train_kg_step = config['train_kg_step'] - def _train_epoch(self, train_data, epoch_idx, loss_func=None): + def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): if self.train_rec_step is None or self.train_kg_step is None: interaction_state = KGDataLoaderState.RSKG elif epoch_idx % (self.train_rec_step + self.train_kg_step) < self.train_rec_step: @@ -422,9 +444,11 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None): interaction_state = KGDataLoaderState.KG train_data.set_mode(interaction_state) if interaction_state in [KGDataLoaderState.RSKG, KGDataLoaderState.RS]: - return super()._train_epoch(train_data, epoch_idx) + return super()._train_epoch(train_data, epoch_idx, show_progress=show_progress) elif interaction_state in [KGDataLoaderState.KG]: - return super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) + return super()._train_epoch(train_data, epoch_idx, + loss_func=self.model.calculate_kg_loss, + show_progress=show_progress) return None @@ -436,14 +460,16 @@ class KGATTrainer(Trainer): def __init__(self, config, model): super(KGATTrainer, self).__init__(config, model) - def _train_epoch(self, train_data, epoch_idx, loss_func=None): + def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): # train rs train_data.set_mode(KGDataLoaderState.RS) - rs_total_loss = super()._train_epoch(train_data, epoch_idx) + rs_total_loss = super()._train_epoch(train_data, epoch_idx, show_progress=show_progress) # train kg train_data.set_mode(KGDataLoaderState.KG) - kg_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) + kg_total_loss = super()._train_epoch(train_data, epoch_idx, + loss_func=self.model.calculate_kg_loss, + show_progress=show_progress) # update A self.model.eval() @@ -478,12 +504,12 @@ def save_pretrained_model(self, epoch, saved_model_file): } torch.save(state, saved_model_file) - def pretrain(self, train_data, verbose=True): + def pretrain(self, train_data, verbose=True, show_progress=False): for epoch_idx in range(self.start_epoch, self.epochs): # train training_start_time = time() - train_loss = self._train_epoch(train_data, epoch_idx) + train_loss = self._train_epoch(train_data, epoch_idx, show_progress=show_progress) self.train_loss_dict[epoch_idx] = sum(train_loss) if isinstance(train_loss, tuple) else train_loss training_end_time = time() train_loss_output = \ @@ -502,11 +528,11 @@ def pretrain(self, train_data, verbose=True): return self.best_valid_score, self.best_valid_result - def fit(self, train_data, valid_data=None, verbose=True, saved=True): + def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False): if self.model.train_stage == 'pretrain': - return self.pretrain(train_data, verbose) + return self.pretrain(train_data, verbose, show_progress) elif self.model.train_stage == 'finetune': - return super().fit(train_data, valid_data, verbose, saved) + return super().fit(train_data, valid_data, verbose, saved, show_progress) else: raise ValueError("Please make sure that the 'train_stage' is 'pretrain' or 'finetune' ") @@ -520,19 +546,23 @@ def __init__(self, config, model): super(MKRTrainer, self).__init__(config, model) self.kge_interval = config['kge_interval'] - def _train_epoch(self, train_data, epoch_idx, loss_func=None): + def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=False): rs_total_loss, kg_total_loss = 0., 0. # train rs self.logger.info('Train RS') train_data.set_mode(KGDataLoaderState.RS) - rs_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_rs_loss) + rs_total_loss = super()._train_epoch(train_data, epoch_idx, + loss_func=self.model.calculate_rs_loss, + show_progress=show_progress) # train kg if epoch_idx % self.kge_interval == 0: self.logger.info('Train KG') train_data.set_mode(KGDataLoaderState.KG) - kg_total_loss = super()._train_epoch(train_data, epoch_idx, self.model.calculate_kg_loss) + kg_total_loss = super()._train_epoch(train_data, epoch_idx, + loss_func=self.model.calculate_kg_loss, + show_progress=show_progress) return rs_total_loss, kg_total_loss diff --git a/recbole/utils/argument_list.py b/recbole/utils/argument_list.py index 05f6ad18a..5bff7c26a 100644 --- a/recbole/utils/argument_list.py +++ b/recbole/utils/argument_list.py @@ -7,11 +7,13 @@ 'seed', 'reproducibility', 'state', - 'data_path'] + 'data_path', + 'show_progress'] training_arguments = ['epochs', 'train_batch_size', 'learner', 'learning_rate', 'training_neg_sample_num', + 'training_neg_sample_distribution', 'eval_step', 'stopping_step', 'checkpoint_dir'] From 3b73c4561c5c0cd747d33fd1a540bb6e5ad30d23 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Fri, 25 Dec 2020 17:41:08 +0800 Subject: [PATCH 201/249] FEA: Add callback function hook for end of training an epoch (resolve #517) --- recbole/trainer/trainer.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 102a40b54..53d82da37 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -233,7 +233,7 @@ def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses): train_loss_output += 'train loss: %.4f' % losses return train_loss_output + ']' - def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False): + def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False, callback_fn=None): r"""Train the model based on the train data and the valid data. Args: @@ -243,6 +243,8 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progre verbose (bool, optional): whether to write training and evaluation information to logger, default: True saved (bool, optional): whether to save the model parameters, default: True show_progress (bool): Show progress of epoch training and evaluate. Defaults to ``False``. + callback_fn (callable): Optional callback function executed at end of epoch. + Includes (epoch_idx, valid_score) input arguments. Returns: (float, dict): best valid score and best valid result. If valid_data is None, it returns (-1, None) @@ -290,6 +292,9 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progre self.logger.info(update_output) self.best_valid_result = valid_result + if callback_fn: + callback_fn(epoch_idx, valid_score) + if stop_flag: stop_output = 'Finished training, best eval result in epoch %d' % \ (epoch_idx - self.cur_step * self.eval_step) @@ -528,11 +533,11 @@ def pretrain(self, train_data, verbose=True, show_progress=False): return self.best_valid_score, self.best_valid_result - def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False): + def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False, callback_fn=None): if self.model.train_stage == 'pretrain': return self.pretrain(train_data, verbose, show_progress) elif self.model.train_stage == 'finetune': - return super().fit(train_data, valid_data, verbose, saved, show_progress) + return super().fit(train_data, valid_data, verbose, saved, show_progress, callback_fn) else: raise ValueError("Please make sure that the 'train_stage' is 'pretrain' or 'finetune' ") From dd9980b8a8f6316d4a430adda4f25a4a30aa704f Mon Sep 17 00:00:00 2001 From: flust <254170321@qq.com> Date: Fri, 25 Dec 2020 10:06:28 +0000 Subject: [PATCH 202/249] FIX:Dynamic import xgboost and add xgboost test part --- recbole/trainer/trainer.py | 8 ++++---- tests/model/test_model_auto.py | 12 ++++++++++++ 2 files changed, 16 insertions(+), 4 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 6b283a891..4344384ed 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -19,7 +19,6 @@ from torch.nn.utils.clip_grad import clip_grad_norm_ import numpy as np import matplotlib.pyplot as plt -import xgboost as xgb from time import time from logging import getLogger @@ -552,9 +551,10 @@ class xgboostTrainer(AbstractTrainer): def __init__(self, config, model): super(xgboostTrainer, self).__init__(config, model) + self.xgb = __import__('xgboost') + self.logger = getLogger() self.label_field = config['LABEL_FIELD'] - self.xgb_model = config['xgb_model'] # DMatrix params @@ -610,7 +610,7 @@ def _interaction_to_DMatrix(self, interaction): else: cur_data = np.hstack((cur_data, value)) - return xgb.DMatrix(data = cur_data, + return self.xgb.DMatrix(data = cur_data, label = interaction_np[self.label_field], weight = self.weight, base_margin = self.base_margin, @@ -630,7 +630,7 @@ def _train_at_once(self, train_data, valid_data): self.dtrain = self._interaction_to_DMatrix(train_data.dataset[:]) self.dvalid = self._interaction_to_DMatrix(valid_data.dataset[:]) self.evals = [(self.dtrain,'train'),(self.dvalid, 'valid')] - self.model = xgb.train(self.params, self.dtrain, self.num_boost_round, + self.model = self.xgb.train(self.params, self.dtrain, self.num_boost_round, self.evals, self.obj, self.feval, self.maximize, self.early_stopping_rounds, self.evals_result, self.verbose_eval, self.xgb_model, self.callbacks) diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 4af6993cb..35bab5b5b 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -217,6 +217,18 @@ def test_din(self): } objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + + def test_xgboost(self): + config_dict = { + 'model': 'xgboost', + 'xgb_params': + {'booster': 'gbtree', + 'objective': 'binary:logistic', + 'eval_metric': ['auc','logloss']}, + 'xgb_num_boost_round': 1, + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) class TestSequentialRecommender(unittest.TestCase): From de7baff08aeadbaa5af2aeb5db14c1c28a817ee7 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Mon, 28 Dec 2020 16:19:49 +0800 Subject: [PATCH 203/249] FEA: rebuild the user_dataloader --- recbole/data/dataloader/user_dataloader.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/recbole/data/dataloader/user_dataloader.py b/recbole/data/dataloader/user_dataloader.py index 9603ce7cb..b28301d94 100644 --- a/recbole/data/dataloader/user_dataloader.py +++ b/recbole/data/dataloader/user_dataloader.py @@ -3,15 +3,16 @@ # @Email : chenyushuo@ruc.edu.cn # UPDATE -# @Time : 2020/9/23 -# @Author : Yushuo Chen -# @email : chenyushuo@ruc.edu.cn +# @Time : 2020/9/23, 2020/12/28 +# @Author : Yushuo Chen, Xingyu Pan +# @email : chenyushuo@ruc.edu.cn, panxy@ruc.edu.cn """ recbole.data.dataloader.user_dataloader ################################################ """ - +import torch +from recbole.data.interaction import Interaction, cat_interactions from recbole.data.dataloader import AbstractDataLoader from recbole.utils.enum_type import DataLoaderType, InputType @@ -36,6 +37,7 @@ class UserDataLoader(AbstractDataLoader): def __init__(self, config, dataset, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): self.uid_field = dataset.uid_field + self.user_list = Interaction({self.uid_field: torch.arange(dataset.user_num)}) super().__init__(config=config, dataset=dataset, batch_size=batch_size, dl_format=dl_format, shuffle=shuffle) @@ -50,12 +52,13 @@ def setup(self): @property def pr_end(self): - return len(self.dataset.user_feat) + return len(self.user_list) def _shuffle(self): - self.dataset.user_feat.shuffle() + self.user_list.shuffle() def _next_batch_data(self): - cur_data = self.dataset.user_feat[self.pr: self.pr + self.step] + cur_data = self.user_list[self.pr: self.pr + self.step] self.pr += self.step return cur_data + From d603ceee4c233eeb3a0677a73d9516a2a5e85bd9 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Mon, 28 Dec 2020 16:41:53 +0800 Subject: [PATCH 204/249] FEA: add decimal places control for metric and loss --- recbole/evaluator/abstract_evaluator.py | 1 + recbole/evaluator/evaluators.py | 6 +++--- recbole/properties/overall.yaml | 4 +++- recbole/trainer/trainer.py | 8 +++++--- recbole/utils/utils.py | 2 +- 5 files changed, 13 insertions(+), 8 deletions(-) diff --git a/recbole/evaluator/abstract_evaluator.py b/recbole/evaluator/abstract_evaluator.py index 1ab81d476..7a2d01666 100644 --- a/recbole/evaluator/abstract_evaluator.py +++ b/recbole/evaluator/abstract_evaluator.py @@ -34,6 +34,7 @@ class BaseEvaluator(object): def __init__(self, config, metrics): self.metrics = metrics self.full = ('full' in config['eval_setting']) + self.precision = config['metric_decimal_place'] def collect(self, *args): """get the intermediate results for each batch, it is called at the end of each batch""" diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py index d20d73307..3b8ddc9bc 100644 --- a/recbole/evaluator/evaluators.py +++ b/recbole/evaluator/evaluators.py @@ -82,7 +82,7 @@ def evaluate(self, batch_matrix_list, eval_data): for metric, value in zip(self.metrics, result_list): for k in self.topk: key = '{}@{}'.format(metric, k) - metric_dict[key] = round(value[k - 1], 4) + metric_dict[key] = round(value[k - 1], self.precision) return metric_dict @@ -236,7 +236,7 @@ def evaluate(self, batch_matrix_list, eval_data): result_list = self._calculate_metrics(user_len_list, pos_len_list, pos_rank_sum) for metric, value in zip(self.metrics, result_list): key = '{}'.format(metric) - metric_dict[key] = round(value, 4) + metric_dict[key] = round(value, self.precision) return metric_dict @@ -320,7 +320,7 @@ def evaluate(self, batch_matrix_list, *args): result_list = self._calculate_metrics(trues, preds) for metric, value in zip(self.metrics, result_list): key = '{}'.format(metric) - metric_dict[key] = round(value, 4) + metric_dict[key] = round(value, self.precision) return metric_dict def _calculate_metrics(self, trues, preds): diff --git a/recbole/properties/overall.yaml b/recbole/properties/overall.yaml index f0149e399..8fcbdf4cd 100644 --- a/recbole/properties/overall.yaml +++ b/recbole/properties/overall.yaml @@ -6,7 +6,7 @@ state: INFO reproducibility: True data_path: 'dataset/' checkpoint_dir: 'saved' -show_progress: True +show_progress: ~ # training settings epochs: 300 @@ -30,3 +30,5 @@ metrics: ["Recall", "MRR","NDCG","Hit","Precision"] topk: [10] valid_metric: MRR@10 eval_batch_size: 4096 +loss_decimal_place: 3 +metric_decimal_place: 2 diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index e5e07e9cd..fbf0dbcf2 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -131,7 +131,7 @@ def _train_epoch(self, train_data, epoch_idx, loss_func=None, show_progress=Fals Returns: float/tuple: The sum of loss returned by all batches in this epoch. If the loss in each batch contains - multiple parts and the model return these multiple parts loss instead of the sum of loss, It will return a + multiple parts and the model return these multiple parts loss instead of the sum of loss, it will return a tuple which includes the sum of loss in each part. """ self.model.train() @@ -225,11 +225,13 @@ def _check_nan(self, loss): raise ValueError('Training loss is nan') def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses): + des = self.config['loss_decimal_place'] or 4 + des = '%.' + str(des) + 'f' train_loss_output = 'epoch %d training [time: %.2fs, ' % (epoch_idx, e_time - s_time) if isinstance(losses, tuple): - train_loss_output += ', '.join('train_loss%d: %.4f' % (idx + 1, loss) for idx, loss in enumerate(losses)) + train_loss_output += ', '.join('train_loss%d:' + des % (idx + 1, loss) for idx, loss in enumerate(losses)) else: - train_loss_output += 'train loss: %.4f' % losses + train_loss_output += 'train loss:' + des % losses return train_loss_output + ']' def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False, callback_fn=None): diff --git a/recbole/utils/utils.py b/recbole/utils/utils.py index d47ea0fae..e5f60a7c5 100644 --- a/recbole/utils/utils.py +++ b/recbole/utils/utils.py @@ -164,7 +164,7 @@ def dict2str(result_dict): result_str = '' for metric, value in result_dict.items(): - result_str += str(metric) + ' : ' + '%.04f' % value + ' ' + result_str += str(metric) + ' : ' + str(value) + ' ' return result_str From 3b4553ab7df6377341fda70c2ec6ac9ce56381e0 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Mon, 28 Dec 2020 16:47:25 +0800 Subject: [PATCH 205/249] FIX: reset the show_progress in overall.yaml --- recbole/properties/overall.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/properties/overall.yaml b/recbole/properties/overall.yaml index 8fcbdf4cd..2cb5bc525 100644 --- a/recbole/properties/overall.yaml +++ b/recbole/properties/overall.yaml @@ -6,7 +6,7 @@ state: INFO reproducibility: True data_path: 'dataset/' checkpoint_dir: 'saved' -show_progress: ~ +show_progress: True # training settings epochs: 300 From eaf83cdc7a3a33e56f4f9903657c229cb6570f5c Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Mon, 28 Dec 2020 19:24:48 +0800 Subject: [PATCH 206/249] FIX: fix the bug of decimal places control --- recbole/trainer/trainer.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index fbf0dbcf2..d40391b6c 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -226,11 +226,12 @@ def _check_nan(self, loss): def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses): des = self.config['loss_decimal_place'] or 4 - des = '%.' + str(des) + 'f' train_loss_output = 'epoch %d training [time: %.2fs, ' % (epoch_idx, e_time - s_time) if isinstance(losses, tuple): - train_loss_output += ', '.join('train_loss%d:' + des % (idx + 1, loss) for idx, loss in enumerate(losses)) + des = 'train_loss%d: %.' + str(des) + 'f' + train_loss_output += ', '.join( des % (idx + 1, loss) for idx, loss in enumerate(losses)) else: + des = '%.' + str(des) + 'f' train_loss_output += 'train loss:' + des % losses return train_loss_output + ']' From 43ce7cab54f5642d88f2fe68661f27ecde40f17f Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=83=AD=E6=80=A1=E5=AE=8F=EF=BC=88=E5=AE=9E=E4=B9=A0?= =?UTF-8?q?=EF=BC=89?= Date: Mon, 28 Dec 2020 21:33:40 +0800 Subject: [PATCH 207/249] FEA: add MultiVAE and MultiDAE --- recbole/data/utils.py | 28 +++- recbole/model/general_recommender/__init__.py | 2 + recbole/model/general_recommender/multidae.py | 117 +++++++++++++++ recbole/model/general_recommender/multivae.py | 141 ++++++++++++++++++ recbole/properties/model/MultiDAE.yaml | 4 + recbole/properties/model/MultiVAE.yaml | 6 + run_test_example.py | 8 + tests/model/test_model_auto.py | 14 ++ 8 files changed, 319 insertions(+), 1 deletion(-) create mode 100644 recbole/model/general_recommender/multidae.py create mode 100644 recbole/model/general_recommender/multivae.py create mode 100644 recbole/properties/model/MultiDAE.yaml create mode 100644 recbole/properties/model/MultiVAE.yaml diff --git a/recbole/data/utils.py b/recbole/data/utils.py index c86d395e2..fbe28a967 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -20,6 +20,7 @@ from recbole.sampler import KGSampler, Sampler, RepeatableSampler from recbole.utils import ModelType from recbole.data.dataloader import * +from recbole.data.dataloader.user_dataloader import * def create_dataset(config): @@ -238,7 +239,9 @@ def get_data_loader(name, config, eval_setting): type: The dataloader class that meets the requirements in :attr:`config` and :attr:`eval_setting`. """ register_table = { - 'DIN': _get_DIN_data_loader + 'DIN': _get_DIN_data_loader, + "MultiDAE": _get_AE_data_loader, + "MultiVAE": _get_AE_data_loader } if config['model'] in register_table: @@ -311,6 +314,29 @@ def _get_DIN_data_loader(name, config, eval_setting): return SequentialFullDataLoader +def _get_AE_data_loader(name, config, eval_setting): + """Customized function for Multi-DAE and Multi-VAE to get correct dataloader class. + + Args: + name (str): The stage of dataloader. It can only take two values: 'train' or 'evaluation'. + config (Config): An instance object of Config, used to record parameter information. + eval_setting (EvalSetting): An instance object of EvalSetting, used to record evaluation settings. + + Returns: + type: The dataloader class that meets the requirements in :attr:`config` and :attr:`eval_setting`. + """ + neg_sample_strategy = eval_setting.neg_sample_args['strategy'] + if name == "train": + return UserDataLoader + else: + if neg_sample_strategy == 'none': + return GeneralDataLoader + elif neg_sample_strategy == 'by': + return GeneralNegSampleDataLoader + elif neg_sample_strategy == 'full': + return GeneralFullDataLoader + + class DLFriendlyAPI(object): """A Decorator class, which helps copying :class:`Dataset` methods to :class:`DataLoader`. diff --git a/recbole/model/general_recommender/__init__.py b/recbole/model/general_recommender/__init__.py index 373cc7c75..7ac5b7be0 100644 --- a/recbole/model/general_recommender/__init__.py +++ b/recbole/model/general_recommender/__init__.py @@ -7,6 +7,8 @@ from recbole.model.general_recommender.itemknn import ItemKNN from recbole.model.general_recommender.lightgcn import LightGCN from recbole.model.general_recommender.line import LINE +from recbole.model.general_recommender.multidae import MultiDAE +from recbole.model.general_recommender.multivae import MultiVAE from recbole.model.general_recommender.nais import NAIS from recbole.model.general_recommender.neumf import NeuMF from recbole.model.general_recommender.ngcf import NGCF diff --git a/recbole/model/general_recommender/multidae.py b/recbole/model/general_recommender/multidae.py new file mode 100644 index 000000000..3837e30fe --- /dev/null +++ b/recbole/model/general_recommender/multidae.py @@ -0,0 +1,117 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/12/14 +# @Author : Yihong Guo +# @Email : gyihong@hotmail.com + +r""" +MultiDAE +################################################ +Reference: + Dawen Liang et al. "Variational Autoencoders for Collaborative Filtering." in WWW 2018. + +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from recbole.utils import InputType +from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.init import xavier_normal_initialization +from recbole.model.layers import MLPLayers + + +class MultiDAE(GeneralRecommender): + r"""MultiDAE is a item-based model collaborative filtering model that simultaneously rank all items for user . + + We implement the the MultiDAE model with only user dataloader. + """ + input_type = InputType.PAIRWISE + + def __init__(self, config, dataset): + super(MultiDAE, self).__init__(config, dataset) + + self.layers = config["mlp_hidden_size"] + self.lat_dim = config['latent_dimendion'] + self.drop_out = config['dropout_prob'] + + self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix() + self.history_item_id = self.history_item_id.to(self.device) + self.history_item_value = self.history_item_value.to(self.device) + + self.encode_layer_dims = [self.n_items] + self.layers + [self.lat_dim] + self.decode_layer_dims = [self.lat_dim] + self.encode_layer_dims[::-1][1:] + + self.encoder = MLPLayers(self.encode_layer_dims, activation='tanh') + self.decoder = self.mlp_layars(self.decode_layer_dims) + + # parameters initialization + self.apply(xavier_normal_initialization) + + def get_rating_matrix(self, user): + r"""Get a batch of user's feature with the user's id and history interaction matrix. + + Args: + user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ] + + Returns: + torch.FloatTensor: The user's feature of a batch of user, shape: [batch_size, n_items] + """ + # Following lines construct tensor of shape [B,n_items] using the tensor of shape [B,H] + col_indices = self.history_item_id[user].flatten() + row_indices = torch.arange(user.shape[0]).to(self.device) \ + .repeat_interleave(self.history_item_id.shape[1], dim=0) + rating_matrix = torch.zeros(1).to(self.device).repeat(user.shape[0], self.n_items) + rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) + return rating_matrix + + def mlp_layars(self, layer_dims): + mlp_modules = [] + for i, (d_in, d_out) in enumerate(zip(layer_dims[:-1], layer_dims[1:])): + mlp_modules.append(nn.Linear(d_in, d_out)) + if i != len(layer_dims[:-1]) - 1: + mlp_modules.append(nn.Tanh()) + return nn.Sequential(*mlp_modules) + + def forward(self, rating_matrix): + + h = F.normalize(rating_matrix) + + h = F.dropout(h, self.drop_out, training=self.training) + + h = self.encoder(h) + return self.decoder(h) + + def calculate_loss(self, interaction): + + user = interaction[self.USER_ID] + + rating_matrix = self.get_rating_matrix(user) + + z = self.forward(rating_matrix) + + # CE loss + ce_loss = -(F.log_softmax(z, 1) * rating_matrix).sum(1).mean() + + return ce_loss + + def predict(self, interaction): + + user = interaction[self.USER_ID] + item = interaction[self.ITEM_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores = self.forward(rating_matrix) + + return scores[[user, item]] + + def full_sort_predict(self, interaction): + + user = interaction[self.USER_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores = self.forward(rating_matrix) + + return scores.view(-1) diff --git a/recbole/model/general_recommender/multivae.py b/recbole/model/general_recommender/multivae.py new file mode 100644 index 000000000..fd584e767 --- /dev/null +++ b/recbole/model/general_recommender/multivae.py @@ -0,0 +1,141 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/12/14 +# @Author : Yihong Guo +# @Email : gyihong@hotmail.com + +r""" +MultiVAE +################################################ +Reference: + Dawen Liang et al. "Variational Autoencoders for Collaborative Filtering." in WWW 2018. + +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from recbole.utils import InputType +from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.init import xavier_normal_initialization + + +class MultiVAE(GeneralRecommender): + r"""MultiVAE is a item-based model collaborative filtering model that simultaneously rank all items for user . + + We implement the MultiVAE model with only user dataloader. + """ + input_type = InputType.PAIRWISE + + def __init__(self, config, dataset): + super(MultiVAE, self).__init__(config, dataset) + + self.layers = config["mlp_hidden_size"] + self.lat_dim = config['latent_dimendion'] + self.drop_out = config['dropout_prob'] + self.anneal_cap = config['anneal_cap'] + self.total_anneal_steps = config["total_anneal_steps"] + + self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix() + self.history_item_id = self.history_item_id.to(self.device) + self.history_item_value = self.history_item_value.to(self.device) + + self.update = 0 + + self.encode_layer_dims = [self.n_items] + self.layers + [self.lat_dim] + self.decode_layer_dims = [int(self.lat_dim/2)]+self.encode_layer_dims[::-1][1:] + + self.encoder = self.mlp_layars(self.encode_layer_dims) + self.decoder = self.mlp_layars(self.decode_layer_dims) + + # parameters initialization + self.apply(xavier_normal_initialization) + + def get_rating_matrix(self, user): + r"""Get a batch of user's feature with the user's id and history interaction matrix. + + Args: + user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ] + + Returns: + torch.FloatTensor: The user's feature of a batch of user, shape: [batch_size, n_items] + """ + # Following lines construct tensor of shape [B,n_items] using the tensor of shape [B,H] + col_indices = self.history_item_id[user].flatten() + row_indices = torch.arange(user.shape[0]).to(self.device) \ + .repeat_interleave(self.history_item_id.shape[1], dim=0) + rating_matrix = torch.zeros(1).to(self.device).repeat(user.shape[0], self.n_items) + rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) + return rating_matrix + + def mlp_layars(self, layer_dims): + mlp_modules = [] + for i, (d_in, d_out) in enumerate(zip(layer_dims[:-1], layer_dims[1:])): + mlp_modules.append(nn.Linear(d_in, d_out)) + if i != len(layer_dims[:-1]) - 1: + mlp_modules.append(nn.Tanh()) + return nn.Sequential(*mlp_modules) + + def reparameterize(self, mu, logvar): + if self.training: + std = torch.exp(0.5 * logvar) + epsilon = torch.zeros_like(std).normal_(mean=0, std=0.01) + return mu + epsilon * std + else: + return mu + + def forward(self, rating_matrix): + + h = F.normalize(rating_matrix) + + h = F.dropout(h, self.drop_out, training=self.training) + + h = self.encoder(h) + + mu = h[:, :int(self.lat_dim/2)] + logvar = h[:, int(self.lat_dim/2):] + + z = self.reparameterize(mu, logvar) + z = self.decoder(z) + return z, mu, logvar + + def calculate_loss(self, interaction): + + user = interaction[self.USER_ID] + rating_matrix = self.get_rating_matrix(user) + + self.update += 1 + if self.total_anneal_steps > 0: + anneal = min(self.anneal_cap, 1. * self.update / self.total_anneal_steps) + else: + anneal = self.anneal_cap + + z, mu, logvar = self.forward(rating_matrix) + + # KL loss + kl_loss = -0.5 * torch.mean(torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=1))*anneal + + # CE loss + ce_loss = -(F.log_softmax(z, 1) * rating_matrix).sum(1).mean() + + return ce_loss + kl_loss + + def predict(self, interaction): + + user = interaction[self.USER_ID] + item = interaction[self.ITEM_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores, _, _ = self.forward(rating_matrix) + + return scores[[user, item]] + + def full_sort_predict(self, interaction): + user = interaction[self.USER_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores, _, _ = self.forward(rating_matrix) + + return scores.view(-1) diff --git a/recbole/properties/model/MultiDAE.yaml b/recbole/properties/model/MultiDAE.yaml new file mode 100644 index 000000000..c578315b2 --- /dev/null +++ b/recbole/properties/model/MultiDAE.yaml @@ -0,0 +1,4 @@ +mlp_hidden_size: [600] +latent_dimendion: 64 +dropout_prob: 0.5 +training_neg_sample_num: 0 \ No newline at end of file diff --git a/recbole/properties/model/MultiVAE.yaml b/recbole/properties/model/MultiVAE.yaml new file mode 100644 index 000000000..7a674402d --- /dev/null +++ b/recbole/properties/model/MultiVAE.yaml @@ -0,0 +1,6 @@ +mlp_hidden_size: [600] +latent_dimendion: 128 +dropout_prob: 0.5 +anneal_cap: 0.2 +total_anneal_steps: 200000 +training_neg_sample_num: 0 \ No newline at end of file diff --git a/run_test_example.py b/run_test_example.py index 58baee213..043b9bdca 100644 --- a/run_test_example.py +++ b/run_test_example.py @@ -134,6 +134,14 @@ 'model': 'LINE', 'dataset': 'ml-100k', }, + 'Test MultiDAE': { + 'model': 'MultiDAE', + 'dataset': 'ml-100k', + }, + 'Test MultiVAE': { + 'model': 'LINE', + 'dataset': 'ml-100k', + }, # Context-aware Recommendation 'Test FM': { diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 18ae0888c..b87f7043a 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -116,6 +116,20 @@ def test_line(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_MultiDAE(self): + config_dict = { + 'model': 'MultiDAE', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_MultiVAE(self): + config_dict = { + 'model': 'MultiVAE', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + class TestContextRecommender(unittest.TestCase): # todo: more complex context information should be test, such as criteo dataset From 3dd1f200c6189c29065051dc101243626eb24f01 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 29 Dec 2020 14:20:28 +0800 Subject: [PATCH 208/249] FEA: Raise ValueError when inter_feat have label_field and neg-sampling at the same time && code format in test_model_auto.py --- recbole/config/configurator.py | 4 +- recbole/data/utils.py | 6 + tests/model/test_model.yaml | 2 +- tests/model/test_model_auto.py | 764 +- tests/model/test_model_manual.py | 4 +- tests/test_data/test/test.inter | 12034 ++++++++++++++--------------- 6 files changed, 6376 insertions(+), 6438 deletions(-) diff --git a/recbole/config/configurator.py b/recbole/config/configurator.py index 50d0c4aa9..0f49167b4 100644 --- a/recbole/config/configurator.py +++ b/recbole/config/configurator.py @@ -245,8 +245,8 @@ def _load_internal_config_dict(self, model, model_class, dataset): config_dict = yaml.load(f.read(), Loader=self.yaml_loader) if config_dict is not None: self.internal_config_dict.update(config_dict) - elif model in ['GRU4RecKG','KSR']: - with open(sequential_embedding_model_init, 'r', encoding='utf-8') as f: + elif model in ['GRU4RecKG', 'KSR']: + with open(sequential_embedding_model_init, 'r', encoding='utf-8') as f: config_dict = yaml.load(f.read(), Loader=self.yaml_loader) if config_dict is not None: self.internal_config_dict.update(config_dict) diff --git a/recbole/data/utils.py b/recbole/data/utils.py index c86d395e2..9f82a2940 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -97,6 +97,9 @@ def data_preparation(config, dataset, save=False): kwargs = {} if config['training_neg_sample_num']: + if dataset.label_field in dataset.inter_feat: + raise ValueError(f'`training_neg_sample_num` should be 0 ' + f'if inter_feat have label_field [{dataset.label_field}].') train_distribution = config['training_neg_sample_distribution'] or 'uniform' es.neg_sample_by(by=config['training_neg_sample_num'], distribution=train_distribution) if model_type != ModelType.SEQUENTIAL: @@ -121,6 +124,9 @@ def data_preparation(config, dataset, save=False): kwargs = {} if len(es_str) > 1 and getattr(es, es_str[1], None): + if dataset.label_field in dataset.inter_feat: + raise ValueError(f'It can not validate with `{es_str[1]}` ' + f'when inter_feat have label_field [{dataset.label_field}].') getattr(es, es_str[1])() if 'sampler' not in locals(): sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) diff --git a/tests/model/test_model.yaml b/tests/model/test_model.yaml index 77d78f37e..4604e8a65 100644 --- a/tests/model/test_model.yaml +++ b/tests/model/test_model.yaml @@ -30,7 +30,7 @@ ENTITY_ID_FIELD: entity_id # Selectively Loading load_col: - inter: [user_id, item_id, rating, timestamp, label] + inter: [user_id, item_id, rating, timestamp] user: [user_id, age, gender, occupation] item: [item_id, movie_title, release_year, class] link: [item_id, entity_id] diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 18ae0888c..95f8dff3c 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -16,105 +16,129 @@ current_path = os.path.dirname(os.path.realpath(__file__)) config_file_list = [os.path.join(current_path, 'test_model.yaml')] + +def quick_test(config_dict): + objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + + class TestGeneralRecommender(unittest.TestCase): def test_pop(self): config_dict = { 'model': 'Pop', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_itemknn(self): config_dict = { 'model': 'ItemKNN', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_bpr(self): config_dict = { 'model': 'BPR', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_neumf(self): config_dict = { 'model': 'NeuMF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_convncf(self): config_dict = { 'model': 'ConvNCF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_dmf(self): config_dict = { 'model': 'DMF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_dmf_with_rating(self): + config_dict = { + 'model': 'DMF', + 'inter_matrix_type': 'rating', + } + quick_test(config_dict) def test_fism(self): config_dict = { 'model': 'FISM', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_fism_with_split_to_and_alpha(self): + config_dict = { + 'model': 'FISM', + 'split_to': 10, + 'alpha': 0.5, + } + quick_test(config_dict) def test_nais(self): config_dict = { 'model': 'NAIS', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_nais_with_concat(self): + config_dict = { + 'model': 'NAIS', + 'algorithm': 'concat', + 'split_to': 10, + 'alpha': 0.5, + 'beta': 0.1, + } + quick_test(config_dict) def test_spectralcf(self): config_dict = { 'model': 'SpectralCF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_gcmc(self): config_dict = { 'model': 'GCMC', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_gcmc_with_stack(self): + config_dict = { + 'model': 'GCMC', + 'accum': 'stack', + 'sparse_feature': False, + } + quick_test(config_dict) def test_ngcf(self): config_dict = { 'model': 'NGCF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_lightgcn(self): config_dict = { 'model': 'LightGCN', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_dgcf(self): config_dict = { 'model': 'DGCF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_line(self): config_dict = { 'model': 'LINE', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) class TestContextRecommender(unittest.TestCase): @@ -123,150 +147,193 @@ class TestContextRecommender(unittest.TestCase): def test_lr(self): config_dict = { 'model': 'LR', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_fm(self): config_dict = { 'model': 'FM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_nfm(self): config_dict = { 'model': 'NFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_deepfm(self): config_dict = { 'model': 'DeepFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_xdeepfm(self): config_dict = { 'model': 'xDeepFM', + 'threshold': {'rating': 4}, + } + quick_test(config_dict) + + def test_xdeepfm_with_direct(self): + config_dict = { + 'model': 'xDeepFM', + 'threshold': {'rating': 4}, + 'direct': True, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_afm(self): config_dict = { 'model': 'AFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_fnn(self): config_dict = { 'model': 'FNN', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_pnn(self): config_dict = { 'model': 'PNN', + 'threshold': {'rating': 4}, + } + quick_test(config_dict) + + def test_pnn_with_use_inner_and_use_outer(self): + config_dict = { + 'model': 'PNN', + 'threshold': {'rating': 4}, + 'use_inner': True, + 'use_outer': True, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_pnn_without_use_inner_and_use_outer(self): + config_dict = { + 'model': 'PNN', + 'threshold': {'rating': 4}, + 'use_inner': False, + 'use_outer': False, + } + quick_test(config_dict) def test_dssm(self): config_dict = { 'model': 'DSSM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_widedeep(self): config_dict = { 'model': 'WideDeep', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - + quick_test(config_dict) + # def test_dcn(self): # config_dict = { # 'model': 'DCN', + # 'threshold': {'rating': 4}, # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + # quick_test(config_dict) def test_autoint(self): config_dict = { 'model': 'AutoInt', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_ffm(self): config_dict = { 'model': 'FFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_fwfm(self): config_dict = { 'model': 'FwFM', + 'threshold': {'rating': 4}, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_din(self): - config_dict = { - 'model': 'DIN', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - def test_xgboost(self): config_dict = { 'model': 'xgboost', - 'xgb_params': - {'booster': 'gbtree', + 'threshold': {'rating': 4}, + 'xgb_params': { + 'booster': 'gbtree', 'objective': 'binary:logistic', - 'eval_metric': ['auc','logloss']}, + 'eval_metric': ['auc', 'logloss'] + }, 'xgb_num_boost_round': 1, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) class TestSequentialRecommender(unittest.TestCase): + def test_din(self): + config_dict = { + 'model': 'DIN', + } + quick_test(config_dict) + def test_fpmc(self): config_dict = { 'model': 'FPMC', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_gru4rec(self): config_dict = { 'model': 'GRU4Rec', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_gru4rec_with_BPR_loss(self): + config_dict = { + 'model': 'GRU4Rec', + 'loss_type': 'BPR', + } + quick_test(config_dict) def test_narm(self): config_dict = { 'model': 'NARM', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_narm_with_BPR_loss(self): + config_dict = { + 'model': 'NARM', + 'loss_type': 'BPR', + } + quick_test(config_dict) def test_stamp(self): config_dict = { 'model': 'STAMP', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_stamp_with_BPR_loss(self): + config_dict = { + 'model': 'STAMP', + 'loss_type': 'BPR', + } + quick_test(config_dict) def test_caser(self): config_dict = { @@ -274,123 +341,234 @@ def test_caser(self): 'MAX_ITEM_LIST_LENGTH': 10, 'reproducibility': False, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_caser_with_BPR_loss(self): + config_dict = { + 'model': 'Caser', + 'loss_type': 'BPR', + 'MAX_ITEM_LIST_LENGTH': 10, + 'reproducibility': False, + } + quick_test(config_dict) def test_nextitnet(self): config_dict = { 'model': 'NextItNet', 'reproducibility': False, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_nextitnet_with_BPR_loss(self): + config_dict = { + 'model': 'NextItNet', + 'loss_type': 'BPR', + 'reproducibility': False, + } + quick_test(config_dict) def test_transrec(self): config_dict = { 'model': 'TransRec', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_sasrec(self): config_dict = { 'model': 'SASRec', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_sasrec_with_BPR_loss_and_relu(self): + config_dict = { + 'model': 'SASRec', + 'loss_type': 'BPR', + 'hidden_act': 'relu' + } + quick_test(config_dict) + + def test_sasrec_with_BPR_loss_and_sigmoid(self): + config_dict = { + 'model': 'SASRec', + 'loss_type': 'BPR', + 'hidden_act': 'sigmoid' + } + quick_test(config_dict) # def test_bert4rec(self): # config_dict = { # 'model': 'BERT4Rec', # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + # quick_test(config_dict) + + # def test_bert4rec(self): + # config_dict = { + # 'model': 'BERT4Rec', + # 'loss_type': 'BPR', + # 'hidden_act': 'swish' + # } + # quick_test(config_dict) def test_srgnn(self): config_dict = { 'model': 'SRGNN', 'MAX_ITEM_LIST_LENGTH': 3, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_srgnn_with_BPR_loss(self): + config_dict = { + 'model': 'SRGNN', + 'loss_type': 'BPR', + 'MAX_ITEM_LIST_LENGTH': 3, + } + quick_test(config_dict) def test_gcsan(self): config_dict = { 'model': 'GCSAN', 'MAX_ITEM_LIST_LENGTH': 3, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_gcsan_with_BPR_loss_and_tanh(self): + config_dict = { + 'model': 'GCSAN', + 'loss_type': 'BPR', + 'hidden_act': 'tanh', + 'MAX_ITEM_LIST_LENGTH': 3, + } + quick_test(config_dict) def test_gru4recf(self): config_dict = { 'model': 'GRU4RecF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_gru4recf_with_max_pooling(self): + config_dict = { + 'model': 'GRU4RecF', + 'pooling_mode': 'max', + } + quick_test(config_dict) + + def test_gru4recf_with_sum_pooling(self): + config_dict = { + 'model': 'GRU4RecF', + 'pooling_mode': 'sum', + } + quick_test(config_dict) def test_sasrecf(self): config_dict = { 'model': 'SASRecF', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_sasrecf_with_max_pooling(self): + config_dict = { + 'model': 'SASRecF', + 'pooling_mode': 'max', + } + quick_test(config_dict) + + def test_sasrecf_with_sum_pooling(self): + config_dict = { + 'model': 'SASRecF', + 'pooling_mode': 'sum', + } + quick_test(config_dict) def test_hrm(self): config_dict = { 'model': 'HRM', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_hrm_with_BPR_loss(self): + config_dict = { + 'model': 'HRM', + 'loss_type': 'BPR', + } + quick_test(config_dict) def test_npe(self): config_dict = { 'model': 'NPE', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_npe_with_BPR_loss(self): + config_dict = { + 'model': 'NPE', + 'loss_type': 'BPR', + } + quick_test(config_dict) def test_shan(self): config_dict = { 'model': 'SHAN', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_shan_with_BPR_loss(self): + config_dict = { + 'model': 'SHAN', + 'loss_type': 'BPR', + } + quick_test(config_dict) def test_hgn(self): config_dict = { 'model': 'HGN', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_hgn_with_CE_loss(self): + config_dict = { + 'model': 'HGN', + 'loss_type': 'CE', + } + quick_test(config_dict) def test_fossil(self): config_dict = { 'model': 'FOSSIL', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_repeat_net(self): config_dict = { 'model': 'RepeatNet', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - # def test_fdsa(self): - # config_dict = { - # 'model': 'FDSA', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + def test_fdsa(self): + config_dict = { + 'model': 'FDSA', + } + quick_test(config_dict) + + def test_fdsa_with_max_pooling(self): + config_dict = { + 'model': 'FDSA', + 'pooling_mode': 'max', + } + quick_test(config_dict) + + def test_fdsa_with_sum_pooling(self): + config_dict = { + 'model': 'FDSA', + 'pooling_mode': 'sum', + } + quick_test(config_dict) # def test_gru4reckg(self): # config_dict = { # 'model': 'GRU4RecKG', # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + # quick_test(config_dict) # def test_s3rec(self): # config_dict = { @@ -398,16 +576,14 @@ def test_repeat_net(self): # 'train_stage': 'pretrain', # 'save_step': 1, # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + # quick_test(config_dict) # config_dict = { # 'model': 'S3Rec', # 'train_stage': 'finetune', # 'pre_model_path': './saved/S3Rec-test-1.pth', # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) + # quick_test(config_dict) class TestKnowledgeRecommender(unittest.TestCase): @@ -416,15 +592,20 @@ def test_cke(self): config_dict = { 'model': 'CKE', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_cfkg(self): config_dict = { 'model': 'CFKG', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + + def test_cfkg_with_transe(self): + config_dict = { + 'model': 'CFKG', + 'loss_function': 'transe', + } + quick_test(config_dict) def test_ktup(self): config_dict = { @@ -433,344 +614,95 @@ def test_ktup(self): 'train_kg_step': 1, 'epochs': 2, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_kgat(self): - config_dict = { - 'model': 'KGAT', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_ripplenet(self): - config_dict = { - 'model': 'RippleNet', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_mkr(self): - config_dict = { - 'model': 'MKR', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_kgcn(self): - config_dict = { - 'model': 'KGCN', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_kgnnls(self): - config_dict = { - 'model': 'KGNNLS', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - -class TestGeneralRecommender2(unittest.TestCase): - - def test_dmf(self): - config_dict = { - 'model': 'DMF', - 'inter_matrix_type': 'rating', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_fism(self): - config_dict = { - 'model': 'FISM', - 'split_to': 10, - 'alpha': 0.5, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_nais(self): - config_dict = { - 'model': 'NAIS', - 'algorithm': 'concat', - 'split_to': 10, - 'alpha': 0.5, - 'beta': 0.1, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_gcmc(self): - config_dict = { - 'model': 'GCMC', - 'accum': 'stack', - 'sparse_feature': False, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - -class TestKnowledgeRecommender2(unittest.TestCase): - - def test_cfkg(self): - config_dict = { - 'model': 'CFKG', - 'loss_function': 'transe', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_ktup(self): + def test_ktup_with_L1_flag(self): config_dict = { 'model': 'KTUP', 'use_st_gumbel': False, 'L1_flag': True, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_kgat(self): config_dict = { 'model': 'KGAT', - 'aggregator_type': 'gcn', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'KGAT', - 'aggregator_type': 'graphsage', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_mkr(self): - config_dict = { - 'model': 'MKR', - 'use_inner_product': False, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_kgcn(self): + def test_kgat_with_gcn(self): config_dict = { - 'model': 'KGCN', - 'aggregator': 'neighbor', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'KGCN', - 'aggregator': 'concat', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_kgnnls(self): - config_dict = { - 'model': 'KGNNLS', - 'aggregator': 'neighbor', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'KGNNLS', - 'aggregator': 'concat', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - -class TestContextRecommender2(unittest.TestCase): - - def test_xdeepfm(self): - config_dict = { - 'model': 'xDeepFM', - 'direct': True, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_pnn(self): - config_dict = { - 'model': 'PNN', - 'use_inner': True, - 'use_outer': True, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'PNN', - 'use_inner': False, - 'use_outer': False, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - -class TestSequentialRecommender2(unittest.TestCase): - - def test_gru4rec(self): - config_dict = { - 'model': 'GRU4Rec', - 'loss_type': 'BPR', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_narm(self): - config_dict = { - 'model': 'NARM', - 'loss_type': 'BPR', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_stamp(self): - config_dict = { - 'model': 'STAMP', - 'loss_type': 'BPR', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_hrm(self): - config_dict = { - 'model': 'HRM', - 'loss_type': 'BPR', + 'model': 'KGAT', + 'aggregator_type': 'gcn', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_npe(self): + def test_kgat_with_graphsage(self): config_dict = { - 'model': 'NPE', - 'loss_type': 'BPR', + 'model': 'KGAT', + 'aggregator_type': 'graphsage', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_shan(self): + def test_ripplenet(self): config_dict = { - 'model': 'SHAN', - 'loss_type': 'BPR', + 'model': 'RippleNet', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_hgn(self): + def test_mkr(self): config_dict = { - 'model': 'HGN', - 'loss_type': 'CE', + 'model': 'MKR', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_caser(self): + def test_mkr_without_use_inner_product(self): config_dict = { - 'model': 'Caser', - 'loss_type': 'BPR', - 'MAX_ITEM_LIST_LENGTH': 10, - 'reproducibility': False, + 'model': 'MKR', + 'use_inner_product': False, } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_nextitnet(self): + def test_kgcn(self): config_dict = { - 'model': 'NextItNet', - 'loss_type': 'BPR', - 'reproducibility': False, + 'model': 'KGCN', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_srgnn(self): + def test_kgcn_with_neighbor(self): config_dict = { - 'model': 'SRGNN', - 'loss_type': 'BPR', - 'MAX_ITEM_LIST_LENGTH': 3, + 'model': 'KGCN', + 'aggregator': 'neighbor', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_sasrec(self): - config_dict = { - 'model': 'SASRec', - 'loss_type': 'BPR', - 'hidden_act': 'relu' - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + def test_kgcn_with_concat(self): config_dict = { - 'model': 'SASRec', - 'loss_type': 'BPR', - 'hidden_act': 'sigmoid' + 'model': 'KGCN', + 'aggregator': 'concat', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - # def test_bert4rec(self): - # config_dict = { - # 'model': 'BERT4Rec', - # 'loss_type': 'BPR', - # 'hidden_act': 'swish' - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) - - def test_gcsan(self): + def test_kgnnls(self): config_dict = { - 'model': 'GCSAN', - 'loss_type': 'BPR', - 'hidden_act': 'tanh', - 'MAX_ITEM_LIST_LENGTH': 3, + 'model': 'KGNNLS', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_gru4recf(self): - config_dict = { - 'model': 'GRU4RecF', - 'pooling_mode': 'max', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + def test_kgnnls_with_neighbor(self): config_dict = { - 'model': 'GRU4RecF', - 'pooling_mode': 'sum', + 'model': 'KGNNLS', + 'aggregator': 'neighbor', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_sasrecf(self): - config_dict = { - 'model': 'SASRecF', - 'pooling_mode': 'max', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + def test_kgnnls_with_concat(self): config_dict = { - 'model': 'SASRecF', - 'pooling_mode': 'sum', + 'model': 'KGNNLS', + 'aggregator': 'concat', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) - def test_fdsa(self): - config_dict = { - 'model': 'FDSA', - 'pooling_mode': 'max', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - config_dict = { - 'model': 'FDSA', - 'pooling_mode': 'sum', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) if __name__ == '__main__': unittest.main() diff --git a/tests/model/test_model_manual.py b/tests/model/test_model_manual.py index b3ce5a2bf..f31fc7b93 100644 --- a/tests/model/test_model_manual.py +++ b/tests/model/test_model_manual.py @@ -15,7 +15,7 @@ class TestContextRecommender(unittest.TestCase): # todo: more complex context information should be test, such as criteo dataset - + def test_dcn(self): config_dict = { 'model': 'DCN', @@ -83,6 +83,6 @@ def test_bert4rec(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) - + if __name__ == '__main__': unittest.main() diff --git a/tests/test_data/test/test.inter b/tests/test_data/test/test.inter index d46a0f745..278c83243 100644 --- a/tests/test_data/test/test.inter +++ b/tests/test_data/test/test.inter @@ -1,6018 +1,6018 @@ -user_id:token item_id:token rating:float timestamp:float label:float -6 86 3 883603013 1 -38 95 5 892430094 0 -97 194 3 884238860 0 -7 32 4 891350932 1 -10 16 4 877888877 0 -99 4 5 886519097 0 -25 181 5 885853415 1 -59 196 5 888205088 0 -115 20 3 881171009 0 -138 26 5 879024232 1 -194 165 4 879546723 0 -11 111 4 891903862 0 -162 25 4 877635573 1 -135 23 4 879857765 1 -160 174 5 876860807 0 -42 96 5 881107178 1 -168 151 5 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879270084 +160 127 5 876770168 +148 127 1 877399351 +57 121 4 883697432 +92 65 4 875653960 +10 9 4 877889005 +109 180 3 880581127 +64 184 4 889739243 +43 123 1 875975520 +25 176 4 885852862 +98 194 5 880498898 +10 198 3 877889005 +5 174 5 875636130 +102 7 2 888801407 +102 172 3 888801232 +130 93 5 874953665 130 121 5 876250746 1 \ No newline at end of file From 6be1c87cb4111ea87f95a4328885b39bf3e97a79 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 29 Dec 2020 14:39:05 +0800 Subject: [PATCH 209/249] FIX: Bug fix in test_evaluation_setting.py --- tests/evaluation_setting/test_evaluation_setting.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/tests/evaluation_setting/test_evaluation_setting.py b/tests/evaluation_setting/test_evaluation_setting.py index 0ba2e719b..059f6f010 100644 --- a/tests/evaluation_setting/test_evaluation_setting.py +++ b/tests/evaluation_setting/test_evaluation_setting.py @@ -45,6 +45,7 @@ def test_rols_full(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) ''' + def test_tols_full(self): config_dict = { 'eval_setting': 'TO_LS,full', @@ -72,6 +73,7 @@ def test_tols_full(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) ''' + def test_tors_full(self): config_dict = { 'eval_setting': 'TO_RS,full', @@ -182,7 +184,7 @@ def test_tors_uni100(self): 'model': 'BPR', } objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + config_file_list=config_file_list, saved=False) # config_dict = { # 'eval_setting': 'TO_RS,uni100', # 'model': 'NeuMF', @@ -208,10 +210,11 @@ class TestContextRecommender(unittest.TestCase): def test_tors(self): config_dict = { 'eval_setting': 'TO_RS', + 'threshold': {'rating': 4}, 'model': 'FM', } objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + config_file_list=config_file_list, saved=False) # config_dict = { # 'eval_setting': 'TO_RS', # 'model': 'DeepFM', From 290e7850e2da58e36de123d37fc4a0aa4f442581 Mon Sep 17 00:00:00 2001 From: Lin zihan <893833413@qq.com> Date: Tue, 29 Dec 2020 15:07:45 +0800 Subject: [PATCH 210/249] FIX: bug in NeuMF --- recbole/model/general_recommender/neumf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recbole/model/general_recommender/neumf.py b/recbole/model/general_recommender/neumf.py index bd95a4bab..5fde05394 100644 --- a/recbole/model/general_recommender/neumf.py +++ b/recbole/model/general_recommender/neumf.py @@ -57,10 +57,10 @@ def __init__(self, config, dataset): self.item_mf_embedding = nn.Embedding(self.n_items, self.mf_embedding_size) self.user_mlp_embedding = nn.Embedding(self.n_users, self.mlp_embedding_size) self.item_mlp_embedding = nn.Embedding(self.n_items, self.mlp_embedding_size) - self.mlp_layers = MLPLayers([2 * self.mlp_embedding_size] + self.mlp_hidden_size) + self.mlp_layers = MLPLayers([2 * self.mlp_embedding_size] + self.mlp_hidden_size, self.dropout_prob) self.mlp_layers.logger = None # remove logger to use torch.save() if self.mf_train and self.mlp_train: - self.predict_layer = nn.Linear(self.mf_embedding_size + self.mlp_hidden_size[-1], 1, self.dropout_prob) + self.predict_layer = nn.Linear(self.mf_embedding_size + self.mlp_hidden_size[-1], 1) elif self.mf_train: self.predict_layer = nn.Linear(self.mf_embedding_size, 1) elif self.mlp_train: From 541f6d9ce3bc0a28b59447b9b986d955e4791139 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Tue, 29 Dec 2020 15:41:21 +0800 Subject: [PATCH 211/249] REFACTOR: refactor in test_model --- tests/model/test_model_auto.py | 7 +++++++ tests/model/test_model_manual.py | 18 ------------------ 2 files changed, 7 insertions(+), 18 deletions(-) diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 95f8dff3c..1b7dbebba 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -268,6 +268,13 @@ def test_fwfm(self): } quick_test(config_dict) + def test_dcn(self): + config_dict = { + 'model': 'DCN', + 'threshold': {'rating': 4}, + } + quick_test(config_dict) + def test_xgboost(self): config_dict = { 'model': 'xgboost', diff --git a/tests/model/test_model_manual.py b/tests/model/test_model_manual.py index f31fc7b93..129f91e46 100644 --- a/tests/model/test_model_manual.py +++ b/tests/model/test_model_manual.py @@ -13,24 +13,6 @@ config_file_list = [os.path.join(current_path, 'test_model.yaml')] -class TestContextRecommender(unittest.TestCase): - # todo: more complex context information should be test, such as criteo dataset - - def test_dcn(self): - config_dict = { - 'model': 'DCN', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - # def test_din(self): - # config_dict = { - # 'model': 'DIN', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) - - class TestSequentialRecommender(unittest.TestCase): def test_bert4rec(self): From 4f7fc9b5a4d239a43f89de6a491cdbac2ebb7a8e Mon Sep 17 00:00:00 2001 From: Yupeng Hou Date: Tue, 29 Dec 2020 09:19:23 +0000 Subject: [PATCH 212/249] FIX: rm confusing error report from create_dataset --- recbole/data/utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 9f82a2940..4caae1e9c 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -31,9 +31,9 @@ def create_dataset(config): Returns: Dataset: Constructed dataset. """ - try: + if hasattr(importlib.import_module('recbole.data.dataset'), config['model'] + 'Dataset'): return getattr(importlib.import_module('recbole.data.dataset'), config['model'] + 'Dataset')(config) - except AttributeError: + else: model_type = config['MODEL_TYPE'] if model_type == ModelType.SEQUENTIAL: from .dataset import SequentialDataset From 84c83f0b23c9ef82a960752ee1abbe7525ac351a Mon Sep 17 00:00:00 2001 From: Yupeng Hou Date: Tue, 29 Dec 2020 09:22:52 +0000 Subject: [PATCH 213/249] FIX: refactor customized dataset selection --- recbole/data/utils.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 4caae1e9c..2972bf687 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -31,8 +31,9 @@ def create_dataset(config): Returns: Dataset: Constructed dataset. """ - if hasattr(importlib.import_module('recbole.data.dataset'), config['model'] + 'Dataset'): - return getattr(importlib.import_module('recbole.data.dataset'), config['model'] + 'Dataset')(config) + dataset_module = importlib.import_module('recbole.data.dataset') + if hasattr(dataset_module, config['model'] + 'Dataset'): + return getattr(dataset_module, config['model'] + 'Dataset')(config) else: model_type = config['MODEL_TYPE'] if model_type == ModelType.SEQUENTIAL: From 66bd447c75f71d50d3cfe8bae8c371f3ad057139 Mon Sep 17 00:00:00 2001 From: guijiql <970955517@qq.com> Date: Tue, 29 Dec 2020 22:02:43 +0800 Subject: [PATCH 214/249] FIX: DCN crash when running on CPU --- recbole/model/context_aware_recommender/dcn.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/recbole/model/context_aware_recommender/dcn.py b/recbole/model/context_aware_recommender/dcn.py index 3c9c66ecd..7aa11b3d1 100644 --- a/recbole/model/context_aware_recommender/dcn.py +++ b/recbole/model/context_aware_recommender/dcn.py @@ -43,13 +43,10 @@ def __init__(self, config, dataset): # define layers and loss # init weight and bias of each cross layer - self.cross_layer_parameter = [nn.Parameter(torch.empty(self.num_feature_field * self.embedding_size, - device=self.device)) - for _ in range(self.cross_layer_num * 2)] - self.cross_layer_w = nn.ParameterList( - self.cross_layer_parameter[:self.cross_layer_num]) - self.cross_layer_b = nn.ParameterList( - self.cross_layer_parameter[self.cross_layer_num:]) + self.cross_layer_w = nn.ParameterList(nn.Parameter(torch.randn(self.num_feature_field * self.embedding_size) + .to(self.device)) for _ in range(self.cross_layer_num)) + self.cross_layer_b = nn.ParameterList(nn.Parameter(torch.zeros(self.num_feature_field * self.embedding_size) + .to(self.device)) for _ in range(self.cross_layer_num)) # size of mlp hidden layer size_list = [self.embedding_size * self.num_feature_field] + self.mlp_hidden_size From dfd565a3866a0d940368b39312d91c04797ea6d7 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Wed, 30 Dec 2020 10:42:23 +0800 Subject: [PATCH 215/249] FIX: device error in BERT4Rec (from master branch) --- recbole/model/sequential_recommender/bert4rec.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/recbole/model/sequential_recommender/bert4rec.py b/recbole/model/sequential_recommender/bert4rec.py index 86d20dafb..f808e1ff9 100644 --- a/recbole/model/sequential_recommender/bert4rec.py +++ b/recbole/model/sequential_recommender/bert4rec.py @@ -50,8 +50,8 @@ def __init__(self, config, dataset): self.mask_item_length = int(self.mask_ratio * self.max_seq_length) # define layers and loss - self.item_embedding = nn.Embedding(self.n_items+1, self.hidden_size, padding_idx=0) # mask token add 1 - self.position_embedding = nn.Embedding(self.max_seq_length+1, self.hidden_size) # add mask_token at the last + self.item_embedding = nn.Embedding(self.n_items + 1, self.hidden_size, padding_idx=0) # mask token add 1 + self.position_embedding = nn.Embedding(self.max_seq_length + 1, self.hidden_size) # add mask_token at the last self.trm_encoder = TransformerEncoder(n_layers=self.n_layers, n_heads=self.n_heads, hidden_size=self.hidden_size, inner_size=self.inner_size, hidden_dropout_prob=self.hidden_dropout_prob, @@ -99,8 +99,8 @@ def _neg_sample(self, item_set): def _padding_sequence(self, sequence, max_length): pad_len = max_length - len(sequence) - sequence = [0]*pad_len + sequence - sequence = sequence[-max_length:] # truncate according to the max_length + sequence = [0] * pad_len + sequence + sequence = sequence[-max_length:] # truncate according to the max_length return sequence def reconstruct_train_data(self, item_seq): @@ -193,7 +193,7 @@ def multi_hot_embed(self, masked_index, max_length): multi_hot_embed: [[0 1 0 0 0], [0 0 0 1 0]] """ masked_index = masked_index.view(-1) - multi_hot = torch.zeros(masked_index.size(0), max_length).cuda() + multi_hot = torch.zeros(masked_index.size(0), max_length, device=masked_index.device) multi_hot[torch.arange(masked_index.size(0)), masked_index] = 1 return multi_hot @@ -237,7 +237,7 @@ def predict(self, interaction): test_item = interaction[self.ITEM_ID] item_seq = self.reconstruct_test_data(item_seq, item_seq_len) seq_output = self.forward(item_seq) - seq_output = self.gather_indexes(seq_output, item_seq_len-1) # [B H] + seq_output = self.gather_indexes(seq_output, item_seq_len - 1) # [B H] test_item_emb = self.item_embedding(test_item) scores = torch.mul(seq_output, test_item_emb).sum(dim=1) # [B] return scores @@ -247,7 +247,7 @@ def full_sort_predict(self, interaction): item_seq_len = interaction[self.ITEM_SEQ_LEN] item_seq = self.reconstruct_test_data(item_seq, item_seq_len) seq_output = self.forward(item_seq) - seq_output = self.gather_indexes(seq_output, item_seq_len-1) # [B H] + seq_output = self.gather_indexes(seq_output, item_seq_len - 1) # [B H] test_items_emb = self.item_embedding.weight[:self.n_items] # delete masked token scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, item_num] return scores From 023e12a6b8978f0161c622faa96125376deb5ed2 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Wed, 30 Dec 2020 10:53:58 +0800 Subject: [PATCH 216/249] REFACTOR: Move tests in test_model_manual.py to test_model_auto.py --- tests/model/test_model_auto.py | 63 +++++++++++++++----------------- tests/model/test_model_manual.py | 59 ++++-------------------------- 2 files changed, 37 insertions(+), 85 deletions(-) diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 1b7dbebba..b76ee6bba 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -240,13 +240,6 @@ def test_widedeep(self): } quick_test(config_dict) - # def test_dcn(self): - # config_dict = { - # 'model': 'DCN', - # 'threshold': {'rating': 4}, - # } - # quick_test(config_dict) - def test_autoint(self): config_dict = { 'model': 'AutoInt', @@ -402,20 +395,6 @@ def test_sasrec_with_BPR_loss_and_sigmoid(self): } quick_test(config_dict) - # def test_bert4rec(self): - # config_dict = { - # 'model': 'BERT4Rec', - # } - # quick_test(config_dict) - - # def test_bert4rec(self): - # config_dict = { - # 'model': 'BERT4Rec', - # 'loss_type': 'BPR', - # 'hidden_act': 'swish' - # } - # quick_test(config_dict) - def test_srgnn(self): config_dict = { 'model': 'SRGNN', @@ -571,26 +550,42 @@ def test_fdsa_with_sum_pooling(self): } quick_test(config_dict) + def test_bert4rec(self): + config_dict = { + 'model': 'BERT4Rec', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + + def test_bert4rec_with_BPR_loss_and_swish(self): + config_dict = { + 'model': 'BERT4Rec', + 'loss_type': 'BPR', + 'hidden_act': 'swish' + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + # def test_gru4reckg(self): # config_dict = { # 'model': 'GRU4RecKG', # } # quick_test(config_dict) - # def test_s3rec(self): - # config_dict = { - # 'model': 'S3Rec', - # 'train_stage': 'pretrain', - # 'save_step': 1, - # } - # quick_test(config_dict) + def test_s3rec(self): + config_dict = { + 'model': 'S3Rec', + 'train_stage': 'pretrain', + 'save_step': 1, + } + quick_test(config_dict) - # config_dict = { - # 'model': 'S3Rec', - # 'train_stage': 'finetune', - # 'pre_model_path': './saved/S3Rec-test-1.pth', - # } - # quick_test(config_dict) + config_dict = { + 'model': 'S3Rec', + 'train_stage': 'finetune', + 'pre_model_path': './saved/S3Rec-test-1.pth', + } + quick_test(config_dict) class TestKnowledgeRecommender(unittest.TestCase): diff --git a/tests/model/test_model_manual.py b/tests/model/test_model_manual.py index 129f91e46..29f73a3b7 100644 --- a/tests/model/test_model_manual.py +++ b/tests/model/test_model_manual.py @@ -13,57 +13,14 @@ config_file_list = [os.path.join(current_path, 'test_model.yaml')] -class TestSequentialRecommender(unittest.TestCase): - - def test_bert4rec(self): - config_dict = { - 'model': 'BERT4Rec', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - def test_fdsa(self): - config_dict = { - 'model': 'FDSA', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - # def test_gru4reckg(self): - # config_dict = { - # 'model': 'GRU4RecKG', - # } - # objective_function(config_dict=config_dict, - # config_file_list=config_file_list, saved=False) - - def test_s3rec(self): - config_dict = { - 'model': 'S3Rec', - 'train_stage': 'pretrain', - 'save_step': 1, - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - config_dict = { - 'model': 'S3Rec', - 'train_stage': 'finetune', - 'pre_model_path': './saved/S3Rec-test-1.pth', - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) - - -class TestSequentialRecommender2(unittest.TestCase): - - def test_bert4rec(self): - config_dict = { - 'model': 'BERT4Rec', - 'loss_type': 'BPR', - 'hidden_act': 'swish' - } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) +# class TestSequentialRecommender(unittest.TestCase): +# +# def test_gru4reckg(self): +# config_dict = { +# 'model': 'GRU4RecKG', +# } +# objective_function(config_dict=config_dict, +# config_file_list=config_file_list, saved=False) if __name__ == '__main__': From 597fcf0428874439ba58221156ea5ef4d7d1d022 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Wed, 30 Dec 2020 11:28:54 +0800 Subject: [PATCH 217/249] REFACTOR: Move test_s3rec in test_model_auto.py to test_model_manual.py --- tests/model/test_model_auto.py | 28 +++++++++++++------------- tests/model/test_model_manual.py | 34 ++++++++++++++++++++++++-------- 2 files changed, 40 insertions(+), 22 deletions(-) diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index b76ee6bba..16eb6c624 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -572,20 +572,20 @@ def test_bert4rec_with_BPR_loss_and_swish(self): # } # quick_test(config_dict) - def test_s3rec(self): - config_dict = { - 'model': 'S3Rec', - 'train_stage': 'pretrain', - 'save_step': 1, - } - quick_test(config_dict) - - config_dict = { - 'model': 'S3Rec', - 'train_stage': 'finetune', - 'pre_model_path': './saved/S3Rec-test-1.pth', - } - quick_test(config_dict) + # def test_s3rec(self): + # config_dict = { + # 'model': 'S3Rec', + # 'train_stage': 'pretrain', + # 'save_step': 1, + # } + # quick_test(config_dict) + # + # config_dict = { + # 'model': 'S3Rec', + # 'train_stage': 'finetune', + # 'pre_model_path': './saved/S3Rec-test-1.pth', + # } + # quick_test(config_dict) class TestKnowledgeRecommender(unittest.TestCase): diff --git a/tests/model/test_model_manual.py b/tests/model/test_model_manual.py index 29f73a3b7..6f9f2a0f9 100644 --- a/tests/model/test_model_manual.py +++ b/tests/model/test_model_manual.py @@ -13,14 +13,32 @@ config_file_list = [os.path.join(current_path, 'test_model.yaml')] -# class TestSequentialRecommender(unittest.TestCase): -# -# def test_gru4reckg(self): -# config_dict = { -# 'model': 'GRU4RecKG', -# } -# objective_function(config_dict=config_dict, -# config_file_list=config_file_list, saved=False) +def quick_test(config_dict): + objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + + +class TestSequentialRecommender(unittest.TestCase): + + # def test_gru4reckg(self): + # config_dict = { + # 'model': 'GRU4RecKG', + # } + # quick_test(config_dict) + + def test_s3rec(self): + config_dict = { + 'model': 'S3Rec', + 'train_stage': 'pretrain', + 'save_step': 1, + } + quick_test(config_dict) + + config_dict = { + 'model': 'S3Rec', + 'train_stage': 'finetune', + 'pre_model_path': './saved/S3Rec-test-1.pth', + } + quick_test(config_dict) if __name__ == '__main__': From a7640a6d8652a8ff46ed68940e0320735c07242b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=83=AD=E6=80=A1=E5=AE=8F=EF=BC=88=E5=AE=9E=E4=B9=A0?= =?UTF-8?q?=EF=BC=89?= Date: Wed, 30 Dec 2020 14:38:28 +0800 Subject: [PATCH 218/249] add MarcidVAE --- recbole/data/utils.py | 28 ++- .../model/general_recommender/macridvae.py | 204 ++++++++++++++++++ recbole/properties/model/MacridVAE.yaml | 11 + run_test_example.py | 4 + tests/model/test_model_auto.py | 7 + 5 files changed, 253 insertions(+), 1 deletion(-) create mode 100644 recbole/model/general_recommender/macridvae.py create mode 100644 recbole/properties/model/MacridVAE.yaml diff --git a/recbole/data/utils.py b/recbole/data/utils.py index c86d395e2..c1e1b36d8 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -20,6 +20,8 @@ from recbole.sampler import KGSampler, Sampler, RepeatableSampler from recbole.utils import ModelType from recbole.data.dataloader import * +from recbole.data.dataloader.user_dataloader import * + def create_dataset(config): @@ -238,7 +240,8 @@ def get_data_loader(name, config, eval_setting): type: The dataloader class that meets the requirements in :attr:`config` and :attr:`eval_setting`. """ register_table = { - 'DIN': _get_DIN_data_loader + 'DIN': _get_DIN_data_loader, + 'MacridVAE': _get_AE_data_loader } if config['model'] in register_table: @@ -311,6 +314,29 @@ def _get_DIN_data_loader(name, config, eval_setting): return SequentialFullDataLoader +def _get_AE_data_loader(name, config, eval_setting): + """Customized function for Multi-DAE and Multi-VAE to get correct dataloader class. + + Args: + name (str): The stage of dataloader. It can only take two values: 'train' or 'evaluation'. + config (Config): An instance object of Config, used to record parameter information. + eval_setting (EvalSetting): An instance object of EvalSetting, used to record evaluation settings. + + Returns: + type: The dataloader class that meets the requirements in :attr:`config` and :attr:`eval_setting`. + """ + neg_sample_strategy = eval_setting.neg_sample_args['strategy'] + if name == "train": + return UserDataLoader + else: + if neg_sample_strategy == 'none': + return GeneralDataLoader + elif neg_sample_strategy == 'by': + return GeneralNegSampleDataLoader + elif neg_sample_strategy == 'full': + return GeneralFullDataLoader + + class DLFriendlyAPI(object): """A Decorator class, which helps copying :class:`Dataset` methods to :class:`DataLoader`. diff --git a/recbole/model/general_recommender/macridvae.py b/recbole/model/general_recommender/macridvae.py new file mode 100644 index 000000000..0e1c2e1a1 --- /dev/null +++ b/recbole/model/general_recommender/macridvae.py @@ -0,0 +1,204 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/12/23 +# @Author : Yihong Guo +# @Email : gyihong@hotmail.com + +r""" +MacridVAE +################################################ +Reference: + Jianxin Ma et al. "Learning Disentangled Representations for Recommendation." in NeurIPS 2019. + +Reference code: + https://jianxinma.github.io/disentangle-recsys.html +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.distributions.one_hot_categorical import OneHotCategorical + +from recbole.utils import InputType +from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import EmbLoss + + +class MacridVAE(GeneralRecommender): + r"""MacridVAE is a item-based model collaborative filtering model that learn disentangled representations from user behavior and simultaneously rank all items for user. + + We implement the model following the original author + """ + input_type = InputType.PAIRWISE + + def __init__(self, config, dataset): + super(MacridVAE, self).__init__(config, dataset) + + self.layers = config['encoder_hidden_size'] + self.embedding_size = config['embedding_size'] + self.drop_out = config['drop_out'] + self.kfac = config['kfac'] + self.tau = config['tau'] + self.nogb = config['nogb'] + self.anneal_cap = config['anneal_cap'] + self.total_anneal_steps = config['total_anneal_steps'] + self.regs = config['reg_weights'] + self.std = config['std'] + + self.update = 0 + + self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix() + self.history_item_id = self.history_item_id.to(self.device) + self.history_item_value = self.history_item_value.to(self.device) + self.encode_layer_dims = [self.n_items] + self.layers + [self.embedding_size*2] + + self.encoder = self.mlp_layars(self.encode_layer_dims) + + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) + self.k_embedding = nn.Embedding(self.kfac, self.embedding_size) + + self.l2_loss = EmbLoss() + # parameters initialization + self.apply(xavier_normal_initialization) + + def get_rating_matrix(self, user): + r"""Get a batch of user's feature with the user's id and history interaction matrix. + + Args: + user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ] + + Returns: + torch.FloatTensor: The user's feature of a batch of user, shape: [batch_size, n_items] + """ + # Following lines construct tensor of shape [B,n_items] using the tensor of shape [B,H] + col_indices = self.history_item_id[user].flatten() + row_indices = torch.arange(user.shape[0]).to(self.device) \ + .repeat_interleave(self.history_item_id.shape[1], dim=0) + rating_matrix = torch.zeros(1).to(self.device).repeat(user.shape[0], self.n_items) + rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) + return rating_matrix + + def mlp_layars(self,layer_dims): + mlp_modules = [] + for i, (d_in, d_out) in enumerate(zip(layer_dims[:-1], layer_dims[1:])): + mlp_modules.append(nn.Linear(d_in, d_out)) + if i != len(layer_dims[:-1]) - 1: + mlp_modules.append(nn.Tanh()) + return nn.Sequential(*mlp_modules) + + def reparameterize(self, mu, logvar): + if self.training: + std = torch.exp(0.5 * logvar) + epsilon = torch.zeros_like(std).normal_(mean=0, std=self.std) + return mu + epsilon * std + else: + return mu + + def forward(self, rating_matrix): + + cores = F.normalize(self.k_embedding.weight, dim=1) + items = F.normalize(self.item_embedding.weight, dim=1) + + rating_matrix = F.normalize(rating_matrix) + rating_matrix = F.dropout(rating_matrix, self.drop_out, training=self.training) + + cates_logits = torch.matmul(items, cores.transpose(0, 1)) / self.tau + + if self.nogb: + cates = torch.softmax(cates_logits, dim=1) + else: + cates_dist = OneHotCategorical(logits=cates_logits) + cates_sample = cates_dist.sample() + cates_mode = torch.softmax(cates_logits, dim=1) + cates = (self.training * cates_sample + + (1 - self.training) * cates_mode) + + probs = None + mulist = [] + logvarlist = [] + for k in range(self.kfac): + cates_k = torch.reshape(cates[:, k], (1, -1)) + # encoder + x_k = rating_matrix * cates_k + h = self.encoder(x_k) + mu = h[:, :self.embedding_size] + mu = F.normalize(mu, dim=1) + logvar = h[:, self.embedding_size:] + + mulist.append(mu) + logvarlist.append(logvar) + + z = self.reparameterize(mu, logvar) + + # decoder + z_k = F.normalize(z, dim=1) + logits_k = torch.matmul(z_k, items.transpose(0, 1)) / self.tau + probs_k = torch.exp(logits_k) + probs_k = probs_k * cates_k + probs = (probs_k if (probs is None) else (probs + probs_k)) + + logits = torch.log(probs) + + return logits, mulist, logvarlist + + def calculate_loss(self, interaction): + + user = interaction[self.USER_ID] + + rating_matrix = self.get_rating_matrix(user) + + self.update += 1 + if self.total_anneal_steps > 0: + anneal = min(self.anneal_cap, 1. * self.update / self.total_anneal_steps) + else: + anneal = self.anneal_cap + + z, mu, logvar = self.forward(rating_matrix) + kl_loss = None + for i in range(self.kfac): + kl_ = -0.5 * torch.mean(torch.sum(1 + logvar[i] - logvar[i].exp(), dim=1)) + kl_loss = (kl_ if (kl_loss is None) else (kl_loss + kl_)) + + # CE loss + ce_loss = -(F.log_softmax(z, 1) * rating_matrix).sum(1).mean() + + if self.regs[0] != 0 or self.regs[1] != 0: + return ce_loss + kl_loss * anneal + self.reg_loss() + + return ce_loss + kl_loss * anneal + + def reg_loss(self): + r"""Calculate the L2 normalization loss of model parameters. + Including embedding matrixes and weight matrixes of model. + + Returns: + loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,] + """ + reg_1, reg_2 = self.regs[:2] + loss_1 = reg_1 * self.item_embedding.weight.norm(2) + loss_2 = reg_1 * self.k_embedding.weight.norm(2) + loss_3 = 0 + for name, parm in self.encoder.named_parameters(): + if name.endswith('weight'): + loss_3 = loss_3 + reg_2 * parm.norm(2) + return loss_1 + loss_2 + loss_3 + + def predict(self, interaction): + + user = interaction[self.USER_ID] + item = interaction[self.ITEM_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores, _, _ = self.forward(rating_matrix) + + return scores[[user, item]] + + def full_sort_predict(self, interaction): + user = interaction[self.USER_ID] + + rating_matrix = self.get_rating_matrix(user) + + scores, _, _ = self.forward(rating_matrix) + + return scores.view(-1) diff --git a/recbole/properties/model/MacridVAE.yaml b/recbole/properties/model/MacridVAE.yaml new file mode 100644 index 000000000..ed9a21c29 --- /dev/null +++ b/recbole/properties/model/MacridVAE.yaml @@ -0,0 +1,11 @@ +embedding_size: 128 +drop_out: 0.5 +kfac: 10 +nogb: False +std: 0.01 +encoder_hidden_size: [600] +tau: 0.1 +anneal_cap: 0.2 +total_anneal_steps: 200000 +reg_weights: [0, 0] +training_neg_sample_num: 0 \ No newline at end of file diff --git a/run_test_example.py b/run_test_example.py index 58baee213..7e6ae0fd2 100644 --- a/run_test_example.py +++ b/run_test_example.py @@ -134,6 +134,10 @@ 'model': 'LINE', 'dataset': 'ml-100k', }, + 'Test MacridVAE': { + 'model': 'MacridVAE', + 'dataset': 'ml-100k', + }, # Context-aware Recommendation 'Test FM': { diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 18ae0888c..8837c8306 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -116,6 +116,13 @@ def test_line(self): objective_function(config_dict=config_dict, config_file_list=config_file_list, saved=False) + def test_MacridVAE(self): + config_dict = { + 'model': 'MacridVAE', + } + objective_function(config_dict=config_dict, + config_file_list=config_file_list, saved=False) + class TestContextRecommender(unittest.TestCase): # todo: more complex context information should be test, such as criteo dataset From 453a631950b5c8b131e1f77c54baae178d873a35 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=83=AD=E6=80=A1=E5=AE=8F=EF=BC=88=E5=AE=9E=E4=B9=A0?= =?UTF-8?q?=EF=BC=89?= Date: Wed, 30 Dec 2020 14:41:31 +0800 Subject: [PATCH 219/249] add MarcidVAE --- recbole/data/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/recbole/data/utils.py b/recbole/data/utils.py index c1e1b36d8..1dd43e28b 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -23,7 +23,6 @@ from recbole.data.dataloader.user_dataloader import * - def create_dataset(config): """Create dataset according to :attr:`config['model']` and :attr:`config['MODEL_TYPE']`. From f77145a6e9456245be9d603b7b6ac715974c3da5 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Wed, 30 Dec 2020 20:46:22 +0800 Subject: [PATCH 220/249] FIX: no longer raise ValueError in normalize and change the default value of `normalize_all`. --- recbole/data/dataset/dataset.py | 12 ++++++++---- recbole/properties/dataset/sample.yaml | 2 +- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index 3aef268be..264761ce3 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -567,15 +567,19 @@ def _normalize(self): lst = feat[field].values mx, mn = max(lst), min(lst) if mx == mn: - raise ValueError('All the same value in [{}] from [{}_feat]'.format(field, feat)) - feat[field] = (lst - mn) / (mx - mn) + self.logger.warning('All the same value in [{}] from [{}_feat]'.format(field, feat)) + feat[field] = 1.0 + else: + feat[field] = (lst - mn) / (mx - mn) elif ftype == FeatureType.FLOAT_SEQ: split_point = np.cumsum(feat[field].agg(len))[:-1] lst = feat[field].agg(np.concatenate) mx, mn = max(lst), min(lst) if mx == mn: - raise ValueError('All the same value in [{}] from [{}_feat]'.format(field, feat)) - lst = (lst - mn) / (mx - mn) + self.logger.warning('All the same value in [{}] from [{}_feat]'.format(field, feat)) + lst = 1.0 + else: + lst = (lst - mn) / (mx - mn) lst = np.split(lst, split_point) feat[field] = lst diff --git a/recbole/properties/dataset/sample.yaml b/recbole/properties/dataset/sample.yaml index 031a0049d..ffbd73159 100644 --- a/recbole/properties/dataset/sample.yaml +++ b/recbole/properties/dataset/sample.yaml @@ -38,7 +38,7 @@ not_equal_val: ~ fields_in_same_space: ~ preload_weight: ~ normalize_field: ~ -normalize_all: True +normalize_all: ~ # Sequential Model Needed ITEM_LIST_LENGTH_FIELD: item_length From dd41f17467896de108661de6a657637b867ce46e Mon Sep 17 00:00:00 2001 From: flust <254170321@qq.com> Date: Thu, 31 Dec 2020 23:54:45 +0000 Subject: [PATCH 221/249] FIX:add onehot --- recbole/data/dataset/xgboost_dataset.py | 59 +++++++++++++++++-------- recbole/properties/model/xgboost.yaml | 6 +-- recbole/trainer/trainer.py | 45 ++++++++++++++++--- 3 files changed, 82 insertions(+), 28 deletions(-) diff --git a/recbole/data/dataset/xgboost_dataset.py b/recbole/data/dataset/xgboost_dataset.py index 18cff6b26..2b55c18ef 100644 --- a/recbole/data/dataset/xgboost_dataset.py +++ b/recbole/data/dataset/xgboost_dataset.py @@ -27,6 +27,7 @@ def __init__(self, config, saved_dataset=None): super().__init__(config, saved_dataset=saved_dataset) def _judge_token_and_convert(self, feat): + # get columns whose type is token col_list = [] for col_name in feat: if col_name == self.uid_field or col_name == self.iid_field: @@ -35,31 +36,53 @@ def _judge_token_and_convert(self, feat): col_list.append(col_name) elif self.field2type[col_name] == FeatureType.TOKEN_SEQ or self.field2type[col_name] == FeatureType.FLOAT_SEQ: feat = feat.drop([col_name], axis=1, inplace=False) - feat = pd.get_dummies(feat, sparse = True, columns = col_list) - for col_name in feat.columns.values.tolist(): - if col_name not in self.field2type.keys(): - self.field2type[col_name] = FeatureType.TOKEN + + # get hash map + for col in col_list: + self.hash_map[col] = dict({}) + self.hash_count[col] = 0 + + del_col = [] + for col in self.hash_map: + if col in feat.keys(): + for value in feat[col]: + #print(value) + if value not in self.hash_map[col]: + self.hash_map[col][value] = self.hash_count[col] + self.hash_count[col] = self.hash_count[col] + 1 + if self.hash_count[col] > self.config['token_num_threhold']: + del_col.append(col) + break + + for col in del_col: + del self.hash_count[col] + del self.hash_map[col] + col_list.remove(col) + self.convert_col_list.extend(col_list) + + # transform the original data + for col in self.hash_map.keys(): + if col in feat.keys(): + feat[col] = feat[col].map(self.hash_map[col]) + return feat - def _convert_token_to_onehot(self): - """Convert the data of token type to onehot form + def _convert_token_to_hash(self): + """Convert the data of token type to hash form """ + self.hash_map = {} + self.hash_count = {} + self.convert_col_list = [] if self.config['convert_token_to_onehot'] == True: feat_list = [] for feat in (self.inter_feat, self.user_feat, self.item_feat): - feat = self._judge_token_and_convert(feat) + if feat is not None: + feat = self._judge_token_and_convert(feat) feat_list.append(feat) - self.inter_feat_xgb = feat_list[0] - self.user_feat_xgb = feat_list[1] - self.item_feat_xgb = feat_list[2] - self.inter_feat = self.inter_feat_xgb - self.user_feat = self.user_feat_xgb - self.item_feat = self.item_feat - else: - self.inter_feat_xgb = self.inter_feat - self.user_feat_xgb = self.user_feat - self.item_feat_xgb = self.item_feat + self.inter_feat = feat_list[0] + self.user_feat = feat_list[1] + self.item_feat = feat_list[2] def _from_scratch(self): """Load dataset from scratch. @@ -71,7 +94,7 @@ def _from_scratch(self): self._get_field_from_config() self._load_data(self.dataset_name, self.dataset_path) self._data_processing() - self._convert_token_to_onehot() + self._convert_token_to_hash() self._change_feat_format() def join(self, df): diff --git a/recbole/properties/model/xgboost.yaml b/recbole/properties/model/xgboost.yaml index c054cc879..8ab004d3c 100644 --- a/recbole/properties/model/xgboost.yaml +++ b/recbole/properties/model/xgboost.yaml @@ -1,8 +1,8 @@ # Type of training method -convert_token_to_onehot: True +convert_token_to_onehot: False +token_num_threhold: 10000 # DMatrix - xgb_weight: ~ xgb_base_margin: ~ xgb_missing: ~ @@ -25,7 +25,7 @@ xgb_params: eta: 1 seed: 2020 # nthread: -1 -xgb_num_boost_round: 200 +xgb_num_boost_round: 500 # xgb_evals: ~ xgb_obj: ~ xgb_feval: ~ diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 4344384ed..33ebfb2c3 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -556,6 +556,7 @@ def __init__(self, config, model): self.logger = getLogger() self.label_field = config['LABEL_FIELD'] self.xgb_model = config['xgb_model'] + self.convert_token_to_onehot = self.config['convert_token_to_onehot'] # DMatrix params self.weight = config['xgb_weight'] @@ -592,24 +593,54 @@ def __init__(self, config, model): saved_model_file = '{}-{}.pth'.format(self.config['model'], get_local_time()) self.saved_model_file = os.path.join(self.checkpoint_dir, saved_model_file) - def _interaction_to_DMatrix(self, interaction): + def _interaction_to_DMatrix(self, dataloader): r"""Convert data format from interaction to DMatrix Args: - interaction (Interaction): Data in the form of 'Interaction'. + dataloader (XgboostDataLoader): xgboost dataloader. Returns: DMatrix: Data in the form of 'DMatrix'. """ + interaction = dataloader.dataset[:] interaction_np = interaction.numpy() cur_data = np.array([]) + columns = [] for key, value in interaction_np.items(): value = np.resize(value,(value.shape[0],1)) if key != self.label_field: + columns.append(key) if cur_data.shape[0] == 0: cur_data = value else: cur_data = np.hstack((cur_data, value)) - + + if self.convert_token_to_onehot == True: + from scipy import sparse + from scipy.sparse import dok_matrix + convert_col_list = dataloader.dataset.convert_col_list + hash_count = dataloader.dataset.hash_count + + new_col = cur_data.shape[1] - len(convert_col_list) + for key, values in hash_count.items(): + new_col = new_col + values + onehot_data = dok_matrix((cur_data.shape[0], new_col)) + + cur_j = 0 + new_j = 0 + + for key in columns: + if key in convert_col_list: + for i in range(cur_data.shape[0]): + onehot_data[i, int(new_j + cur_data[i, cur_j])] = 1 + new_j = new_j + hash_count[key] - 1 + else: + for i in range(cur_data.shape[0]): + onehot_data[i, new_j] = cur_data[i, cur_j] + cur_j = cur_j + 1 + new_j = new_j + 1 + + cur_data = sparse.csc_matrix(onehot_data) + return self.xgb.DMatrix(data = cur_data, label = interaction_np[self.label_field], weight = self.weight, @@ -626,9 +657,9 @@ def _train_at_once(self, train_data, valid_data): Args: train_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. valid_data (XgboostDataLoader): XgboostDataLoader, which is the same with GeneralDataLoader. - """ - self.dtrain = self._interaction_to_DMatrix(train_data.dataset[:]) - self.dvalid = self._interaction_to_DMatrix(valid_data.dataset[:]) + """ + self.dtrain = self._interaction_to_DMatrix(train_data) + self.dvalid = self._interaction_to_DMatrix(valid_data) self.evals = [(self.dtrain,'train'),(self.dvalid, 'valid')] self.model = self.xgb.train(self.params, self.dtrain, self.num_boost_round, self.evals, self.obj, self.feval, self.maximize, @@ -679,7 +710,7 @@ def evaluate(self, eval_data, load_best_model=True, model_file=None): self.eval_pred = torch.Tensor() self.eval_true = torch.Tensor() - self.deval = self._interaction_to_DMatrix(eval_data.dataset[:]) + self.deval = self._interaction_to_DMatrix(eval_data) self.eval_true = torch.Tensor(self.deval.get_label()) self.eval_pred = torch.Tensor(self.model.predict(self.deval)) From e49e0ef9119b8cc21f96b318326766ddd7205ef9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=83=AD=E6=80=A1=E5=AE=8F=EF=BC=88=E5=AE=9E=E4=B9=A0?= =?UTF-8?q?=EF=BC=89?= Date: Fri, 1 Jan 2021 11:30:04 +0800 Subject: [PATCH 222/249] add MultiVAE and MultiDAE --- recbole/data/dataloader/__init__.py | 3 ++- recbole/data/utils.py | 1 - 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/recbole/data/dataloader/__init__.py b/recbole/data/dataloader/__init__.py index c224d85a6..90ffa311f 100644 --- a/recbole/data/dataloader/__init__.py +++ b/recbole/data/dataloader/__init__.py @@ -4,4 +4,5 @@ from recbole.data.dataloader.context_dataloader import * from recbole.data.dataloader.sequential_dataloader import * from recbole.data.dataloader.knowledge_dataloader import * -from recbole.data.dataloader.xgboost_dataloader import * \ No newline at end of file +from recbole.data.dataloader.xgboost_dataloader import * +from recbole.data.dataloader.user_dataloader import * diff --git a/recbole/data/utils.py b/recbole/data/utils.py index fbe28a967..4354f9302 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -20,7 +20,6 @@ from recbole.sampler import KGSampler, Sampler, RepeatableSampler from recbole.utils import ModelType from recbole.data.dataloader import * -from recbole.data.dataloader.user_dataloader import * def create_dataset(config): From b624e413f416db6726591cfc715547a570b513eb Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Fri, 1 Jan 2021 12:26:11 +0800 Subject: [PATCH 223/249] FEA: add CDAE model --- hyper.test | 5 +- .../data/dataloader/abstract_dataloader.py | 3 + recbole/data/dataloader/general_dataloader.py | 3 +- recbole/data/utils.py | 4 +- recbole/model/general_recommender/cdae.py | 134 ++++++++++++++++++ recbole/properties/dataset/ml-100k.yaml | 1 + recbole/properties/model/CDAE.yaml | 9 ++ recbole/properties/overall.yaml | 4 +- tests/model/test_model.yaml | 2 +- tests/model/test_model_auto.py | 11 +- 10 files changed, 162 insertions(+), 14 deletions(-) create mode 100644 recbole/model/general_recommender/cdae.py create mode 100644 recbole/properties/model/CDAE.yaml diff --git a/hyper.test b/hyper.test index 5ef1dba0b..8b96bbeec 100644 --- a/hyper.test +++ b/hyper.test @@ -1,4 +1 @@ -learning_rate loguniform -8,0 -embedding_size choice [64,96,128] -train_batch_size choice [512,1024,2048] -mlp_hidden_size choice ['[64,64,64]','[128,128]'] \ No newline at end of file +learning_rate choice [0.01,0.005,0.001,0.0005,0.0001] diff --git a/recbole/data/dataloader/abstract_dataloader.py b/recbole/data/dataloader/abstract_dataloader.py index e5464606d..f26d4d4cc 100644 --- a/recbole/data/dataloader/abstract_dataloader.py +++ b/recbole/data/dataloader/abstract_dataloader.py @@ -101,6 +101,9 @@ def __next__(self): raise StopIteration() return self._next_batch_data() + def get_batch_size(self): + return self.batch_size + @property def pr_end(self): """This property marks the end of dataloader.pr which is used in :meth:`__next__()`.""" diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index cf75341c3..e77a33dad 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -52,7 +52,8 @@ def _next_batch_data(self): cur_data = self.dataset[self.pr: self.pr + self.step] self.pr += self.step return cur_data - + def get_batch_size(self): + return self.batch_size class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): """:class:`GeneralNegSampleDataLoader` is a general-dataloader with negative sampling. diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 1287a662b..12e829035 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -247,7 +247,8 @@ def get_data_loader(name, config, eval_setting): register_table = { 'DIN': _get_DIN_data_loader, "MultiDAE": _get_AE_data_loader, - "MultiVAE": _get_AE_data_loader + "MultiVAE": _get_AE_data_loader, + 'CDAE': _get_AE_data_loader } if config['model'] in register_table: @@ -319,7 +320,6 @@ def _get_DIN_data_loader(name, config, eval_setting): elif neg_sample_strategy == 'full': return SequentialFullDataLoader - def _get_AE_data_loader(name, config, eval_setting): """Customized function for Multi-DAE and Multi-VAE to get correct dataloader class. diff --git a/recbole/model/general_recommender/cdae.py b/recbole/model/general_recommender/cdae.py new file mode 100644 index 000000000..21bb1eaa4 --- /dev/null +++ b/recbole/model/general_recommender/cdae.py @@ -0,0 +1,134 @@ +# -*- coding: utf-8 -*- +# @Time : 2020/12/12 +# @Author : Xingyu Pan +# @Email : panxy@ruc.edu.cn + +r""" +CDAE +################################################ +Reference: + Yao Wu et al., Collaborative denoising auto-encoders for top-n recommender systems. WSDM 2016. + +Reference code: + https://github.com/jasonyaw/CDAE +""" + +import torch +import torch.nn as nn +import numpy as np + +from recbole.utils import InputType +from recbole.model.abstract_recommender import GeneralRecommender +from recbole.model.init import xavier_normal_initialization + + +class CDAE(GeneralRecommender): + r"""Collaborative Denoising Auto-Encoder (CDAE) is a recommendation model + for top-N recommendation that utilizes the idea of Denoising Auto-Encoders. + We implement the the CDAE model with only user dataloader. + """ + input_type = InputType.POINTWISE + + def __init__(self, config, dataset): + super(CDAE, self).__init__(config, dataset) + + self.reg_1 = config['reg_1'] + self.reg_2 = config['reg_2'] + self.loss_type = config['loss_type'] + self.hid_activation = config['hid_activation'] + self.out_activation = config['out_activation'] + self.embedding_size = config['embedding_size'] + self.corruption_ratio = config['corruption_ratio'] + + self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix() + self.history_item_id = self.history_item_id.to(self.device) + self.history_item_value = self.history_item_value.to(self.device) + + if self.hid_activation == 'sigmoid': + self.h_act = nn.Sigmoid() + elif self.hid_activation == 'relu': + self.h_act = nn.ReLU() + elif self.hid_activation == 'tanh': + self.h_act = nn.Tanh() + else: + raise ValueError('Invalid hidden layer activation function') + + if self.out_activation == 'sigmoid': + self.o_act = nn.Sigmoid() + elif self.out_activation == 'relu': + self.o_act = nn.Sigmoid() + else: + raise ValueError('Invalid output layer activation function') + + self.dropout = nn.Dropout(p=self.corruption_ratio) + + self.h_user = nn.Embedding(self.n_users, self.embedding_size) + self.h_item = nn.Linear(self.n_items, self.embedding_size) + self.out_layer = nn.Linear(self.embedding_size, self.n_items) + + # parameters initialization + self.apply(xavier_normal_initialization) + + def forward(self, x_items, x_users): + h_i = self.dropout(x_items) + h_i = self.h_item(h_i) + h_u = self.h_user(x_users) + h = torch.add(h_u, h_i) + h = self.h_act(h) + out = self.out_layer(h) + return self.o_act(out) + + def get_rating_matrix(self, user): + r"""Get a batch of user's feature with the user's id and history interaction matrix. + + Args: + user (torch.LongTensor): The input tensor that contains user's id, shape: [batch_size, ] + + Returns: + torch.FloatTensor: The user's feature of a batch of user, shape: [batch_size, n_items] + """ + # Following lines construct tensor of shape [B,n_items] using the tensor of shape [B,H] + col_indices = self.history_item_id[user].flatten() + row_indices = torch.arange(user.shape[0]).to(self.device) \ + .repeat_interleave(self.history_item_id.shape[1], dim=0) + rating_matrix = torch.zeros(1).to(self.device).repeat(user.shape[0], self.n_items) + rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) + return rating_matrix + + def calculate_loss(self, interaction): + x_users = interaction[self.USER_ID] + x_items = self.get_rating_matrix(x_users) + predict = self.forward(x_items, x_users) + + if self.loss_type == 'MSE': + loss_func = nn.MSELoss(reduction='sum') + elif self.loss_type == 'BCE': + loss_func = nn.BCELoss(reduction='sum') + else: + raise ValueError('Invalid loss_type, loss_type must in [MSE, BCE]') + + loss = loss_func(predict, x_items) + # l1-regularization + loss += self.reg_1 * (self.h_user.weight.norm(p=1) + self.h_item.weight.norm(p=1)) + # l2-regularization + loss += self.reg_2 * (self.h_user.weight.norm() + self.h_item.weight.norm()) + + return loss + + + def predict(self, interaction): + users = interaction[self.USER_ID] + predict_items = interaction[self.ITEM_ID] + + items = self.get_rating_matrix(users) + scores = self.forward(items, users) + + return scores[[users, predict_items]] + + def full_sort_predict(self, interaction): + users = interaction[self.USER_ID] + + items = self.get_rating_matrix(users) + predict = self.forward(items, users) + return predict.view(-1) + diff --git a/recbole/properties/dataset/ml-100k.yaml b/recbole/properties/dataset/ml-100k.yaml index 29449c0a5..41d822aa0 100644 --- a/recbole/properties/dataset/ml-100k.yaml +++ b/recbole/properties/dataset/ml-100k.yaml @@ -43,3 +43,4 @@ fields_in_same_space: ~ preload_weight: ~ normalize_field: ~ normalize_all: True +rm_dup_inter: first diff --git a/recbole/properties/model/CDAE.yaml b/recbole/properties/model/CDAE.yaml new file mode 100644 index 000000000..341ea7169 --- /dev/null +++ b/recbole/properties/model/CDAE.yaml @@ -0,0 +1,9 @@ +loss_type: BCE +hid_activation: relu +out_activation: sigmoid +corruption_ratio: 0.5 +embedding_size: 64 +reg_1: 0. +reg_2: 0.01 +training_neg_sample_num: 0 +learning_rate: 0.01 diff --git a/recbole/properties/overall.yaml b/recbole/properties/overall.yaml index 2cb5bc525..696e13609 100644 --- a/recbole/properties/overall.yaml +++ b/recbole/properties/overall.yaml @@ -30,5 +30,5 @@ metrics: ["Recall", "MRR","NDCG","Hit","Precision"] topk: [10] valid_metric: MRR@10 eval_batch_size: 4096 -loss_decimal_place: 3 -metric_decimal_place: 2 +loss_decimal_place: 4 +metric_decimal_place: 4 diff --git a/tests/model/test_model.yaml b/tests/model/test_model.yaml index 4604e8a65..9ba99d4c2 100644 --- a/tests/model/test_model.yaml +++ b/tests/model/test_model.yaml @@ -1,6 +1,6 @@ dataset: test epochs: 1 -state: ERROR +state: INFO data_path: tests/test_data/ # Atomic File Format diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 9331db77a..868e754bc 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -144,16 +144,19 @@ def test_MultiDAE(self): config_dict = { 'model': 'MultiDAE', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) def test_MultiVAE(self): config_dict = { 'model': 'MultiVAE', } - objective_function(config_dict=config_dict, - config_file_list=config_file_list, saved=False) + quick_test(config_dict) + def test_CDAE(self): + config_dict = { + 'model': 'CDAE', + } + quick_test(config_dict) class TestContextRecommender(unittest.TestCase): # todo: more complex context information should be test, such as criteo dataset From d9429be8846b41357a540ce31858cbf0171ae032 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Fri, 1 Jan 2021 12:31:31 +0800 Subject: [PATCH 224/249] FIX: reset test code --- hyper.test | 5 ++++- recbole/data/dataloader/abstract_dataloader.py | 3 --- recbole/data/dataloader/general_dataloader.py | 2 -- tests/model/test_model.yaml | 2 +- 4 files changed, 5 insertions(+), 7 deletions(-) diff --git a/hyper.test b/hyper.test index 8b96bbeec..6602f2b6e 100644 --- a/hyper.test +++ b/hyper.test @@ -1 +1,4 @@ -learning_rate choice [0.01,0.005,0.001,0.0005,0.0001] +learning_rate loguniform -8,0 +embedding_size choice [64,96,128] +train_batch_size choice [512,1024,2048] +mlp_hidden_size choice ['[64,64,64]','[128,128]'] \ No newline at end of file diff --git a/recbole/data/dataloader/abstract_dataloader.py b/recbole/data/dataloader/abstract_dataloader.py index f26d4d4cc..c18064f1a 100644 --- a/recbole/data/dataloader/abstract_dataloader.py +++ b/recbole/data/dataloader/abstract_dataloader.py @@ -100,9 +100,6 @@ def __next__(self): self.pr = 0 raise StopIteration() return self._next_batch_data() - - def get_batch_size(self): - return self.batch_size @property def pr_end(self): diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index e77a33dad..2309b4459 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -52,8 +52,6 @@ def _next_batch_data(self): cur_data = self.dataset[self.pr: self.pr + self.step] self.pr += self.step return cur_data - def get_batch_size(self): - return self.batch_size class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): """:class:`GeneralNegSampleDataLoader` is a general-dataloader with negative sampling. diff --git a/tests/model/test_model.yaml b/tests/model/test_model.yaml index 9ba99d4c2..4604e8a65 100644 --- a/tests/model/test_model.yaml +++ b/tests/model/test_model.yaml @@ -1,6 +1,6 @@ dataset: test epochs: 1 -state: INFO +state: ERROR data_path: tests/test_data/ # Atomic File Format From 92c4f5e403c98e2f6864da704b2e80cb6a244908 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Fri, 1 Jan 2021 12:35:23 +0800 Subject: [PATCH 225/249] FIX: reset test code --- hyper.test | 2 +- recbole/data/dataloader/abstract_dataloader.py | 2 +- recbole/properties/dataset/ml-100k.yaml | 1 - 3 files changed, 2 insertions(+), 3 deletions(-) diff --git a/hyper.test b/hyper.test index 6602f2b6e..5ef1dba0b 100644 --- a/hyper.test +++ b/hyper.test @@ -1,4 +1,4 @@ learning_rate loguniform -8,0 embedding_size choice [64,96,128] train_batch_size choice [512,1024,2048] -mlp_hidden_size choice ['[64,64,64]','[128,128]'] \ No newline at end of file +mlp_hidden_size choice ['[64,64,64]','[128,128]'] \ No newline at end of file diff --git a/recbole/data/dataloader/abstract_dataloader.py b/recbole/data/dataloader/abstract_dataloader.py index c18064f1a..e5464606d 100644 --- a/recbole/data/dataloader/abstract_dataloader.py +++ b/recbole/data/dataloader/abstract_dataloader.py @@ -100,7 +100,7 @@ def __next__(self): self.pr = 0 raise StopIteration() return self._next_batch_data() - + @property def pr_end(self): """This property marks the end of dataloader.pr which is used in :meth:`__next__()`.""" diff --git a/recbole/properties/dataset/ml-100k.yaml b/recbole/properties/dataset/ml-100k.yaml index 41d822aa0..29449c0a5 100644 --- a/recbole/properties/dataset/ml-100k.yaml +++ b/recbole/properties/dataset/ml-100k.yaml @@ -43,4 +43,3 @@ fields_in_same_space: ~ preload_weight: ~ normalize_field: ~ normalize_all: True -rm_dup_inter: first From ed2a14f497dc61806817c6f9b7e3f461fea03308 Mon Sep 17 00:00:00 2001 From: Yupeng Hou Date: Fri, 1 Jan 2021 12:45:05 +0800 Subject: [PATCH 226/249] Update general_dataloader.py --- recbole/data/dataloader/general_dataloader.py | 1 + 1 file changed, 1 insertion(+) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index 2309b4459..cf75341c3 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -53,6 +53,7 @@ def _next_batch_data(self): self.pr += self.step return cur_data + class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): """:class:`GeneralNegSampleDataLoader` is a general-dataloader with negative sampling. For the result of every batch, we permit that every positive interaction and its negative interaction From 0dcf23b69e50f079aaa045fa44637a1488db3386 Mon Sep 17 00:00:00 2001 From: Yupeng Hou Date: Fri, 1 Jan 2021 12:45:45 +0800 Subject: [PATCH 227/249] Update utils.py --- recbole/data/utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 12e829035..d77a93aa9 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -320,6 +320,7 @@ def _get_DIN_data_loader(name, config, eval_setting): elif neg_sample_strategy == 'full': return SequentialFullDataLoader + def _get_AE_data_loader(name, config, eval_setting): """Customized function for Multi-DAE and Multi-VAE to get correct dataloader class. From a84f6bc1af0c79321bbff08cfb11b28db0dc0914 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=83=AD=E6=80=A1=E5=AE=8F=EF=BC=88=E5=AE=9E=E4=B9=A0?= =?UTF-8?q?=EF=BC=89?= Date: Fri, 1 Jan 2021 13:51:06 +0800 Subject: [PATCH 228/249] add MacridVAE --- recbole/properties/model/MacridVAE.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/properties/model/MacridVAE.yaml b/recbole/properties/model/MacridVAE.yaml index ed9a21c29..5ae0681e5 100644 --- a/recbole/properties/model/MacridVAE.yaml +++ b/recbole/properties/model/MacridVAE.yaml @@ -1,4 +1,4 @@ -embedding_size: 128 +embedding_size: 64 drop_out: 0.5 kfac: 10 nogb: False From 3294aad4bcdd1afd899e50e9da182f2a83f6c257 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=83=AD=E6=80=A1=E5=AE=8F=EF=BC=88=E5=AE=9E=E4=B9=A0?= =?UTF-8?q?=EF=BC=89?= Date: Fri, 1 Jan 2021 13:53:26 +0800 Subject: [PATCH 229/249] add MacridVAE --- recbole/model/general_recommender/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/recbole/model/general_recommender/__init__.py b/recbole/model/general_recommender/__init__.py index 7ac5b7be0..ae3cec3d1 100644 --- a/recbole/model/general_recommender/__init__.py +++ b/recbole/model/general_recommender/__init__.py @@ -7,6 +7,7 @@ from recbole.model.general_recommender.itemknn import ItemKNN from recbole.model.general_recommender.lightgcn import LightGCN from recbole.model.general_recommender.line import LINE +from recbole.model.general_recommender.macridvae import MacridVAE from recbole.model.general_recommender.multidae import MultiDAE from recbole.model.general_recommender.multivae import MultiVAE from recbole.model.general_recommender.nais import NAIS From 363d0c2adf59e52d88628f5eeeda5025f7804033 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E9=83=AD=E6=80=A1=E5=AE=8F=EF=BC=88=E5=AE=9E=E4=B9=A0?= =?UTF-8?q?=EF=BC=89?= Date: Fri, 1 Jan 2021 13:55:50 +0800 Subject: [PATCH 230/249] add MacridVAE --- recbole/data/utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 596bf66a0..268ebe57d 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -20,7 +20,6 @@ from recbole.sampler import KGSampler, Sampler, RepeatableSampler from recbole.utils import ModelType from recbole.data.dataloader import * -from recbole.data.dataloader.user_dataloader import * def create_dataset(config): From edd814cfa86d2ba9494b3f1c081dcdff58ffdbb6 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Fri, 1 Jan 2021 13:59:48 +0800 Subject: [PATCH 231/249] FIX: remove the lr from CDAE.yaml --- recbole/properties/model/CDAE.yaml | 1 - 1 file changed, 1 deletion(-) diff --git a/recbole/properties/model/CDAE.yaml b/recbole/properties/model/CDAE.yaml index 341ea7169..06b69bd22 100644 --- a/recbole/properties/model/CDAE.yaml +++ b/recbole/properties/model/CDAE.yaml @@ -6,4 +6,3 @@ embedding_size: 64 reg_1: 0. reg_2: 0.01 training_neg_sample_num: 0 -learning_rate: 0.01 From ccc9879af74059dd99327fb02fdae3dda09b0948 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Fri, 1 Jan 2021 14:12:18 +0800 Subject: [PATCH 232/249] FIX: add CDAE into _init_.py --- hyper.test | 5 +---- recbole/data/dataloader/abstract_dataloader.py | 3 +++ recbole/data/dataloader/general_dataloader.py | 3 ++- recbole/data/utils.py | 12 ++++++++++++ recbole/model/general_recommender/__init__.py | 1 + recbole/properties/dataset/ml-100k.yaml | 1 + tests/model/test_model.yaml | 2 +- 7 files changed, 21 insertions(+), 6 deletions(-) diff --git a/hyper.test b/hyper.test index 5ef1dba0b..8b96bbeec 100644 --- a/hyper.test +++ b/hyper.test @@ -1,4 +1 @@ -learning_rate loguniform -8,0 -embedding_size choice [64,96,128] -train_batch_size choice [512,1024,2048] -mlp_hidden_size choice ['[64,64,64]','[128,128]'] \ No newline at end of file +learning_rate choice [0.01,0.005,0.001,0.0005,0.0001] diff --git a/recbole/data/dataloader/abstract_dataloader.py b/recbole/data/dataloader/abstract_dataloader.py index e5464606d..f26d4d4cc 100644 --- a/recbole/data/dataloader/abstract_dataloader.py +++ b/recbole/data/dataloader/abstract_dataloader.py @@ -101,6 +101,9 @@ def __next__(self): raise StopIteration() return self._next_batch_data() + def get_batch_size(self): + return self.batch_size + @property def pr_end(self): """This property marks the end of dataloader.pr which is used in :meth:`__next__()`.""" diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index cf75341c3..e77a33dad 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -52,7 +52,8 @@ def _next_batch_data(self): cur_data = self.dataset[self.pr: self.pr + self.step] self.pr += self.step return cur_data - + def get_batch_size(self): + return self.batch_size class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): """:class:`GeneralNegSampleDataLoader` is a general-dataloader with negative sampling. diff --git a/recbole/data/utils.py b/recbole/data/utils.py index d77a93aa9..823fd99c5 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -320,6 +320,18 @@ def _get_DIN_data_loader(name, config, eval_setting): elif neg_sample_strategy == 'full': return SequentialFullDataLoader +def _get_CDAE_data_loader(name, config, eval_setting): + + neg_sample_strategy = eval_setting.neg_sample_args['strategy'] + if name == 'train': + return UserDataLoader + else: + if neg_sample_strategy == 'none': + return GeneralDataLoader + elif neg_sample_strategy == 'by': + return GeneralNegSampleDataLoader + elif neg_sample_strategy == 'full': + return GeneralFullDataLoader def _get_AE_data_loader(name, config, eval_setting): """Customized function for Multi-DAE and Multi-VAE to get correct dataloader class. diff --git a/recbole/model/general_recommender/__init__.py b/recbole/model/general_recommender/__init__.py index 7ac5b7be0..9f61f0743 100644 --- a/recbole/model/general_recommender/__init__.py +++ b/recbole/model/general_recommender/__init__.py @@ -14,3 +14,4 @@ from recbole.model.general_recommender.ngcf import NGCF from recbole.model.general_recommender.pop import Pop from recbole.model.general_recommender.spectralcf import SpectralCF +from recbole.model.general_recommender.cdae import CDAE diff --git a/recbole/properties/dataset/ml-100k.yaml b/recbole/properties/dataset/ml-100k.yaml index 29449c0a5..41d822aa0 100644 --- a/recbole/properties/dataset/ml-100k.yaml +++ b/recbole/properties/dataset/ml-100k.yaml @@ -43,3 +43,4 @@ fields_in_same_space: ~ preload_weight: ~ normalize_field: ~ normalize_all: True +rm_dup_inter: first diff --git a/tests/model/test_model.yaml b/tests/model/test_model.yaml index 4604e8a65..9ba99d4c2 100644 --- a/tests/model/test_model.yaml +++ b/tests/model/test_model.yaml @@ -1,6 +1,6 @@ dataset: test epochs: 1 -state: ERROR +state: INFO data_path: tests/test_data/ # Atomic File Format From eed9ec7715b5954c6be80729f51d35bb607e5d29 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Fri, 1 Jan 2021 19:12:01 +0800 Subject: [PATCH 233/249] FIX: remove unused code --- hyper.test | 5 ++++- recbole/data/dataloader/abstract_dataloader.py | 3 --- recbole/data/dataloader/general_dataloader.py | 2 -- recbole/data/utils.py | 16 ++-------------- recbole/model/general_recommender/__init__.py | 1 + recbole/properties/dataset/ml-100k.yaml | 1 - recbole/properties/model/CDAE.yaml | 1 - tests/model/test_model.yaml | 2 +- tests/model/test_model_auto.py | 1 + 9 files changed, 9 insertions(+), 23 deletions(-) diff --git a/hyper.test b/hyper.test index 8b96bbeec..5ef1dba0b 100644 --- a/hyper.test +++ b/hyper.test @@ -1 +1,4 @@ -learning_rate choice [0.01,0.005,0.001,0.0005,0.0001] +learning_rate loguniform -8,0 +embedding_size choice [64,96,128] +train_batch_size choice [512,1024,2048] +mlp_hidden_size choice ['[64,64,64]','[128,128]'] \ No newline at end of file diff --git a/recbole/data/dataloader/abstract_dataloader.py b/recbole/data/dataloader/abstract_dataloader.py index f26d4d4cc..e5464606d 100644 --- a/recbole/data/dataloader/abstract_dataloader.py +++ b/recbole/data/dataloader/abstract_dataloader.py @@ -101,9 +101,6 @@ def __next__(self): raise StopIteration() return self._next_batch_data() - def get_batch_size(self): - return self.batch_size - @property def pr_end(self): """This property marks the end of dataloader.pr which is used in :meth:`__next__()`.""" diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index e77a33dad..2309b4459 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -52,8 +52,6 @@ def _next_batch_data(self): cur_data = self.dataset[self.pr: self.pr + self.step] self.pr += self.step return cur_data - def get_batch_size(self): - return self.batch_size class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): """:class:`GeneralNegSampleDataLoader` is a general-dataloader with negative sampling. diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 823fd99c5..2d4b50311 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -248,7 +248,8 @@ def get_data_loader(name, config, eval_setting): 'DIN': _get_DIN_data_loader, "MultiDAE": _get_AE_data_loader, "MultiVAE": _get_AE_data_loader, - 'CDAE': _get_AE_data_loader + 'CDAE': _get_AE_data_loader, + 'ENMF': _get_AE_data_loader } if config['model'] in register_table: @@ -320,19 +321,6 @@ def _get_DIN_data_loader(name, config, eval_setting): elif neg_sample_strategy == 'full': return SequentialFullDataLoader -def _get_CDAE_data_loader(name, config, eval_setting): - - neg_sample_strategy = eval_setting.neg_sample_args['strategy'] - if name == 'train': - return UserDataLoader - else: - if neg_sample_strategy == 'none': - return GeneralDataLoader - elif neg_sample_strategy == 'by': - return GeneralNegSampleDataLoader - elif neg_sample_strategy == 'full': - return GeneralFullDataLoader - def _get_AE_data_loader(name, config, eval_setting): """Customized function for Multi-DAE and Multi-VAE to get correct dataloader class. diff --git a/recbole/model/general_recommender/__init__.py b/recbole/model/general_recommender/__init__.py index 9f61f0743..d03e73d99 100644 --- a/recbole/model/general_recommender/__init__.py +++ b/recbole/model/general_recommender/__init__.py @@ -15,3 +15,4 @@ from recbole.model.general_recommender.pop import Pop from recbole.model.general_recommender.spectralcf import SpectralCF from recbole.model.general_recommender.cdae import CDAE +from recbole.model.general_recommender.enmf import ENMF \ No newline at end of file diff --git a/recbole/properties/dataset/ml-100k.yaml b/recbole/properties/dataset/ml-100k.yaml index 41d822aa0..29449c0a5 100644 --- a/recbole/properties/dataset/ml-100k.yaml +++ b/recbole/properties/dataset/ml-100k.yaml @@ -43,4 +43,3 @@ fields_in_same_space: ~ preload_weight: ~ normalize_field: ~ normalize_all: True -rm_dup_inter: first diff --git a/recbole/properties/model/CDAE.yaml b/recbole/properties/model/CDAE.yaml index 06b69bd22..77d50bd4a 100644 --- a/recbole/properties/model/CDAE.yaml +++ b/recbole/properties/model/CDAE.yaml @@ -5,4 +5,3 @@ corruption_ratio: 0.5 embedding_size: 64 reg_1: 0. reg_2: 0.01 -training_neg_sample_num: 0 diff --git a/tests/model/test_model.yaml b/tests/model/test_model.yaml index 9ba99d4c2..4604e8a65 100644 --- a/tests/model/test_model.yaml +++ b/tests/model/test_model.yaml @@ -1,6 +1,6 @@ dataset: test epochs: 1 -state: INFO +state: ERROR data_path: tests/test_data/ # Atomic File Format diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index 868e754bc..f407685c8 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -155,6 +155,7 @@ def test_MultiVAE(self): def test_CDAE(self): config_dict = { 'model': 'CDAE', + 'training_neg_sample_num': 0 } quick_test(config_dict) From 663e1b33461d332a8a89871c447af53d42a80f03 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Fri, 1 Jan 2021 19:16:16 +0800 Subject: [PATCH 234/249] FIX: remove unused code --- recbole/model/general_recommender/__init__.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/recbole/model/general_recommender/__init__.py b/recbole/model/general_recommender/__init__.py index d03e73d99..25578178e 100644 --- a/recbole/model/general_recommender/__init__.py +++ b/recbole/model/general_recommender/__init__.py @@ -14,5 +14,4 @@ from recbole.model.general_recommender.ngcf import NGCF from recbole.model.general_recommender.pop import Pop from recbole.model.general_recommender.spectralcf import SpectralCF -from recbole.model.general_recommender.cdae import CDAE -from recbole.model.general_recommender.enmf import ENMF \ No newline at end of file +from recbole.model.general_recommender.cdae import CDAE \ No newline at end of file From 585ca6fd44ed8c1131307b72ff3b54629a3504b2 Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Fri, 1 Jan 2021 20:44:51 +0800 Subject: [PATCH 235/249] FIX: rename the reg_1 and reg_2 --- recbole/data/dataloader/general_dataloader.py | 1 + recbole/data/utils.py | 1 + recbole/model/general_recommender/cdae.py | 8 ++++---- recbole/properties/model/CDAE.yaml | 4 ++-- 4 files changed, 8 insertions(+), 6 deletions(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index 2309b4459..cf75341c3 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -53,6 +53,7 @@ def _next_batch_data(self): self.pr += self.step return cur_data + class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): """:class:`GeneralNegSampleDataLoader` is a general-dataloader with negative sampling. For the result of every batch, we permit that every positive interaction and its negative interaction diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 2d4b50311..b97125fdd 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -321,6 +321,7 @@ def _get_DIN_data_loader(name, config, eval_setting): elif neg_sample_strategy == 'full': return SequentialFullDataLoader + def _get_AE_data_loader(name, config, eval_setting): """Customized function for Multi-DAE and Multi-VAE to get correct dataloader class. diff --git a/recbole/model/general_recommender/cdae.py b/recbole/model/general_recommender/cdae.py index 21bb1eaa4..57981b1c9 100644 --- a/recbole/model/general_recommender/cdae.py +++ b/recbole/model/general_recommender/cdae.py @@ -32,8 +32,8 @@ class CDAE(GeneralRecommender): def __init__(self, config, dataset): super(CDAE, self).__init__(config, dataset) - self.reg_1 = config['reg_1'] - self.reg_2 = config['reg_2'] + self.reg_weight_1 = config['reg_weight_1'] + self.reg_weight_2 = config['reg_weight_2'] self.loss_type = config['loss_type'] self.hid_activation = config['hid_activation'] self.out_activation = config['out_activation'] @@ -109,9 +109,9 @@ def calculate_loss(self, interaction): loss = loss_func(predict, x_items) # l1-regularization - loss += self.reg_1 * (self.h_user.weight.norm(p=1) + self.h_item.weight.norm(p=1)) + loss += self.reg_weight_1 * (self.h_user.weight.norm(p=1) + self.h_item.weight.norm(p=1)) # l2-regularization - loss += self.reg_2 * (self.h_user.weight.norm() + self.h_item.weight.norm()) + loss += self.reg_weight_2 * (self.h_user.weight.norm() + self.h_item.weight.norm()) return loss diff --git a/recbole/properties/model/CDAE.yaml b/recbole/properties/model/CDAE.yaml index 77d50bd4a..e1acdffbf 100644 --- a/recbole/properties/model/CDAE.yaml +++ b/recbole/properties/model/CDAE.yaml @@ -3,5 +3,5 @@ hid_activation: relu out_activation: sigmoid corruption_ratio: 0.5 embedding_size: 64 -reg_1: 0. -reg_2: 0.01 +reg_weight_1: 0. +reg_weight_2: 0.01 From 74875dce0b418f707303d7de436ab97dc1845c9c Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 11:11:56 +0800 Subject: [PATCH 236/249] FEA: add show_progress to xgboostTrainer (have no effect) --- recbole/trainer/trainer.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index 0cc063f9c..b46ad1947 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -720,7 +720,7 @@ def _valid_epoch(self, valid_data): valid_score = calculate_valid_score(valid_result, self.valid_metric) return valid_result, valid_score - def fit(self, train_data, valid_data=None, verbose=True, saved=True): + def fit(self, train_data, valid_data=None, verbose=True, saved=True, show_progress=False): # load model if self.xgb_model is not None: self.model.load_model(self.xgb_model) @@ -748,7 +748,7 @@ def fit(self, train_data, valid_data=None, verbose=True, saved=True): return self.best_valid_score, self.best_valid_result - def evaluate(self, eval_data, load_best_model=True, model_file=None): + def evaluate(self, eval_data, load_best_model=True, model_file=None, show_progress=False): self.eval_pred = torch.Tensor() self.eval_true = torch.Tensor() From f0a42c778c457d642f1e0da4307226a918fc156f Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Sat, 2 Jan 2021 11:14:14 +0800 Subject: [PATCH 237/249] FIX: remove training_neg_sample_num from model.yaml and add reviewer in repeatnet --- recbole/model/sequential_recommender/repeatnet.py | 4 ++-- recbole/properties/model/MacridVAE.yaml | 3 +-- recbole/properties/model/MultiDAE.yaml | 3 +-- recbole/properties/model/MultiVAE.yaml | 3 +-- tests/model/test_model_auto.py | 3 +++ 5 files changed, 8 insertions(+), 8 deletions(-) diff --git a/recbole/model/sequential_recommender/repeatnet.py b/recbole/model/sequential_recommender/repeatnet.py index 794329517..e6b6fa958 100644 --- a/recbole/model/sequential_recommender/repeatnet.py +++ b/recbole/model/sequential_recommender/repeatnet.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- # @Time : 2020/11/22 8:30 # @Author : Shao Weiqi -# @Reviewer : Lin Kun -# @Email : shaoweiqi@ruc.edu.cn +# @Reviewer : Lin Kun, Fan xinyan +# @Email : shaoweiqi@ruc.edu.cn, xinyan.fan@ruc.edu.cn r""" RepeatNet diff --git a/recbole/properties/model/MacridVAE.yaml b/recbole/properties/model/MacridVAE.yaml index 5ae0681e5..db3b426db 100644 --- a/recbole/properties/model/MacridVAE.yaml +++ b/recbole/properties/model/MacridVAE.yaml @@ -7,5 +7,4 @@ encoder_hidden_size: [600] tau: 0.1 anneal_cap: 0.2 total_anneal_steps: 200000 -reg_weights: [0, 0] -training_neg_sample_num: 0 \ No newline at end of file +reg_weights: [0, 0] \ No newline at end of file diff --git a/recbole/properties/model/MultiDAE.yaml b/recbole/properties/model/MultiDAE.yaml index c578315b2..27f389a35 100644 --- a/recbole/properties/model/MultiDAE.yaml +++ b/recbole/properties/model/MultiDAE.yaml @@ -1,4 +1,3 @@ mlp_hidden_size: [600] latent_dimendion: 64 -dropout_prob: 0.5 -training_neg_sample_num: 0 \ No newline at end of file +dropout_prob: 0.5 \ No newline at end of file diff --git a/recbole/properties/model/MultiVAE.yaml b/recbole/properties/model/MultiVAE.yaml index 7a674402d..2b588b296 100644 --- a/recbole/properties/model/MultiVAE.yaml +++ b/recbole/properties/model/MultiVAE.yaml @@ -2,5 +2,4 @@ mlp_hidden_size: [600] latent_dimendion: 128 dropout_prob: 0.5 anneal_cap: 0.2 -total_anneal_steps: 200000 -training_neg_sample_num: 0 \ No newline at end of file +total_anneal_steps: 200000 \ No newline at end of file diff --git a/tests/model/test_model_auto.py b/tests/model/test_model_auto.py index c2d43f9f6..d8e61e551 100644 --- a/tests/model/test_model_auto.py +++ b/tests/model/test_model_auto.py @@ -143,18 +143,21 @@ def test_line(self): def test_MultiDAE(self): config_dict = { 'model': 'MultiDAE', + 'training_neg_sample_num': 0 } quick_test(config_dict) def test_MultiVAE(self): config_dict = { 'model': 'MultiVAE', + 'training_neg_sample_num': 0 } quick_test(config_dict) def test_MacridVAE(self): config_dict = { 'model': 'MacridVAE', + 'training_neg_sample_num': 0 } quick_test(config_dict) From abbbfa13f8c9fe4c27e29809b9b0be490972148b Mon Sep 17 00:00:00 2001 From: 2017pxy <2017202006@ruc.edu.cn> Date: Sat, 2 Jan 2021 12:45:50 +0800 Subject: [PATCH 238/249] FIX: remove xgb from requirements.txt --- .github/workflows/python-package.yml | 3 ++- requirements.txt | 3 +-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml index 2238d264c..e879511d4 100644 --- a/.github/workflows/python-package.yml +++ b/.github/workflows/python-package.yml @@ -23,8 +23,9 @@ jobs: python -m pip install --upgrade pip pip install pytest pip install dgl + pip install xgboost if [ -f requirements.txt ]; then pip install -r requirements.txt; fi - + # Use "python -m pytest" instead of "pytest" to fix imports - name: Test metrics run: | diff --git a/requirements.txt b/requirements.txt index 2b254e9ec..7f4bff1bf 100644 --- a/requirements.txt +++ b/requirements.txt @@ -6,5 +6,4 @@ hyperopt>=0.2.4 pandas>=1.0.5 tqdm>=4.48.2 scikit_learn>=0.23.2 -pyyaml>=5.1.0 -xgboost>=1.2.1 \ No newline at end of file +pyyaml>=5.1.0 \ No newline at end of file From 0baf29b154a9dc2e5f1b9a6812d42db4b338b9d3 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 13:28:02 +0800 Subject: [PATCH 239/249] FORMAT: code format in configurator.py --- recbole/config/configurator.py | 81 +++++++++++++--------------------- 1 file changed, 31 insertions(+), 50 deletions(-) diff --git a/recbole/config/configurator.py b/recbole/config/configurator.py index 0f49167b4..04e51adfa 100644 --- a/recbole/config/configurator.py +++ b/recbole/config/configurator.py @@ -194,6 +194,13 @@ def _get_model_and_dataset(self, model, dataset): return final_model, final_model_class, final_dataset + def _update_internal_config_dict(self, file): + with open(file, 'r', encoding='utf-8') as f: + config_dict = yaml.load(f.read(), Loader=self.yaml_loader) + if config_dict is not None: + self.internal_config_dict.update(config_dict) + return config_dict + def _load_internal_config_dict(self, model, model_class, dataset): current_path = os.path.dirname(os.path.realpath(__file__)) overall_init_file = os.path.join(current_path, '../properties/overall.yaml') @@ -201,71 +208,45 @@ def _load_internal_config_dict(self, model, model_class, dataset): sample_init_file = os.path.join(current_path, '../properties/dataset/sample.yaml') dataset_init_file = os.path.join(current_path, '../properties/dataset/' + dataset + '.yaml') - context_aware_init = os.path.join(current_path, '../properties/quick_start_config/context-aware.yaml') - context_aware_on_ml_100k_init = os.path.join(current_path, '../properties/quick_start_config/context-aware_ml-100k.yaml') - DIN_init = os.path.join(current_path, '../properties/quick_start_config/sequential_DIN.yaml') - DIN_on_ml_100k_init = os.path.join(current_path, '../properties/quick_start_config/sequential_DIN_on_ml-100k.yaml') - sequential_init = os.path.join(current_path, '../properties/quick_start_config/sequential.yaml') - special_sequential_on_ml_100k_init = os.path.join(current_path, '../properties/quick_start_config/special_sequential_on_ml-100k.yaml') - sequential_embedding_model_init = os.path.join(current_path, '../properties/quick_start_config/sequential_embedding_model.yaml') - knowledge_base_init = os.path.join(current_path, '../properties/quick_start_config/knowledge_base.yaml') + quick_start_config_path = os.path.join(current_path, '../properties/quick_start_config/') + context_aware_init = os.path.join(quick_start_config_path, 'context-aware.yaml') + context_aware_on_ml_100k_init = os.path.join(quick_start_config_path, 'context-aware_ml-100k.yaml') + DIN_init = os.path.join(quick_start_config_path, 'sequential_DIN.yaml') + DIN_on_ml_100k_init = os.path.join(quick_start_config_path, 'sequential_DIN_on_ml-100k.yaml') + sequential_init = os.path.join(quick_start_config_path, 'sequential.yaml') + special_sequential_on_ml_100k_init = os.path.join(quick_start_config_path, 'special_sequential_on_ml-100k.yaml') + sequential_embedding_model_init = os.path.join(quick_start_config_path, 'sequential_embedding_model.yaml') + knowledge_base_init = os.path.join(quick_start_config_path, 'knowledge_base.yaml') self.internal_config_dict = dict() for file in [overall_init_file, model_init_file, sample_init_file, dataset_init_file]: if os.path.isfile(file): - with open(file, 'r', encoding='utf-8') as f: - config_dict = yaml.load(f.read(), Loader=self.yaml_loader) - if file == dataset_init_file: - self.parameters['Dataset'] += [key for key in config_dict.keys() if - key not in self.parameters['Dataset']] - if config_dict is not None: - self.internal_config_dict.update(config_dict) + config_dict = self._update_internal_config_dict(file) + if file == dataset_init_file: + self.parameters['Dataset'] += [key for key in config_dict.keys() if + key not in self.parameters['Dataset']] + self.internal_config_dict['MODEL_TYPE'] = model_class.type if self.internal_config_dict['MODEL_TYPE'] == ModelType.GENERAL: pass - elif self.internal_config_dict['MODEL_TYPE'] == ModelType.CONTEXT or self.internal_config_dict['MODEL_TYPE'] == ModelType.XGBOOST: - with open(context_aware_init, 'r', encoding='utf-8') as f: - config_dict = yaml.load(f.read(), Loader=self.yaml_loader) - if config_dict is not None: - self.internal_config_dict.update(config_dict) + elif self.internal_config_dict['MODEL_TYPE'] in {ModelType.CONTEXT, ModelType.XGBOOST}: + self._update_internal_config_dict(context_aware_init) if dataset == 'ml-100k': - with open(context_aware_on_ml_100k_init, 'r', encoding='utf-8') as f: - config_dict = yaml.load(f.read(), Loader=self.yaml_loader) - if config_dict is not None: - self.internal_config_dict.update(config_dict) - + self._update_internal_config_dict(context_aware_on_ml_100k_init) elif self.internal_config_dict['MODEL_TYPE'] == ModelType.SEQUENTIAL: if model == 'DIN': - with open(DIN_init, 'r', encoding='utf-8') as f: - config_dict = yaml.load(f.read(), Loader=self.yaml_loader) - if config_dict is not None: - self.internal_config_dict.update(config_dict) + self._update_internal_config_dict(DIN_init) if dataset == 'ml-100k': - with open(DIN_on_ml_100k_init, 'r', encoding='utf-8') as f: - config_dict = yaml.load(f.read(), Loader=self.yaml_loader) - if config_dict is not None: - self.internal_config_dict.update(config_dict) + self._update_internal_config_dict(DIN_on_ml_100k_init) elif model in ['GRU4RecKG', 'KSR']: - with open(sequential_embedding_model_init, 'r', encoding='utf-8') as f: - config_dict = yaml.load(f.read(), Loader=self.yaml_loader) - if config_dict is not None: - self.internal_config_dict.update(config_dict) + self._update_internal_config_dict(sequential_embedding_model_init) else: - with open(sequential_init, 'r', encoding='utf-8') as f: - config_dict = yaml.load(f.read(), Loader=self.yaml_loader) - if config_dict is not None: - self.internal_config_dict.update(config_dict) + self._update_internal_config_dict(sequential_init) if dataset == 'ml-100k' and model in ['GRU4RecF', 'SASRecF', 'FDSA', 'S3Rec']: - with open(special_sequential_on_ml_100k_init, 'r', encoding='utf-8') as f: - config_dict = yaml.load(f.read(), Loader=self.yaml_loader) - if config_dict is not None: - self.internal_config_dict.update(config_dict) - + self._update_internal_config_dict(special_sequential_on_ml_100k_init) + elif self.internal_config_dict['MODEL_TYPE'] == ModelType.KNOWLEDGE: - with open(knowledge_base_init, 'r', encoding='utf-8') as f: - config_dict = yaml.load(f.read(), Loader=self.yaml_loader) - if config_dict is not None: - self.internal_config_dict.update(config_dict) + self._update_internal_config_dict(knowledge_base_init) def _get_final_config_dict(self): final_config_dict = dict() From f767d4e069380c774bd568527b214179c8f1fbdc Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 14:15:06 +0800 Subject: [PATCH 240/249] FORMAT: code format in data --- recbole/data/dataloader/context_dataloader.py | 6 ++- recbole/data/dataloader/general_dataloader.py | 5 +-- .../data/dataloader/knowledge_dataloader.py | 2 +- recbole/data/dataloader/neg_sample_mixin.py | 1 + .../data/dataloader/sequential_dataloader.py | 5 ++- recbole/data/dataloader/user_dataloader.py | 4 +- recbole/data/dataloader/xgboost_dataloader.py | 6 ++- recbole/data/dataset/dataset.py | 44 +++++++++---------- recbole/data/dataset/kg_dataset.py | 4 +- recbole/data/dataset/kg_seq_dataset.py | 1 + recbole/data/dataset/sequential_dataset.py | 7 +-- recbole/data/dataset/social_dataset.py | 12 +++-- recbole/data/dataset/xgboost_dataset.py | 38 ++++------------ recbole/data/interaction.py | 6 +-- recbole/data/utils.py | 32 +++++++++----- recbole/properties/model/xgboost.yaml | 2 +- 16 files changed, 83 insertions(+), 92 deletions(-) diff --git a/recbole/data/dataloader/context_dataloader.py b/recbole/data/dataloader/context_dataloader.py index f77e31917..9ca4fc4df 100644 --- a/recbole/data/dataloader/context_dataloader.py +++ b/recbole/data/dataloader/context_dataloader.py @@ -12,11 +12,13 @@ ################################################ """ -from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader, GeneralFullDataLoader +from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader, \ + GeneralFullDataLoader class ContextDataLoader(GeneralDataLoader): - """:class:`ContextDataLoader` is inherit from :class:`~recbole.data.dataloader.general_dataloader.GeneralDataLoader`, + """:class:`ContextDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralDataLoader`, and didn't add/change anything at all. """ pass diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index cf75341c3..0b071e722 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -13,14 +13,12 @@ """ import numpy as np -import pandas as pd import torch -from tqdm import tqdm from recbole.data.dataloader.abstract_dataloader import AbstractDataLoader from recbole.data.dataloader.neg_sample_mixin import NegSampleMixin, NegSampleByMixin -from recbole.utils import DataLoaderType, InputType from recbole.data.interaction import Interaction, cat_interactions +from recbole.utils import DataLoaderType, InputType class GeneralDataLoader(AbstractDataLoader): @@ -71,6 +69,7 @@ class GeneralNegSampleDataLoader(NegSampleByMixin, AbstractDataLoader): :obj:`~recbole.utils.enum_type.InputType.POINTWISE`. shuffle (bool, optional): Whether the dataloader will be shuffle after a round. Defaults to ``False``. """ + def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): self.uid_field = dataset.uid_field diff --git a/recbole/data/dataloader/knowledge_dataloader.py b/recbole/data/dataloader/knowledge_dataloader.py index 0767eb900..632b283bf 100644 --- a/recbole/data/dataloader/knowledge_dataloader.py +++ b/recbole/data/dataloader/knowledge_dataloader.py @@ -13,8 +13,8 @@ """ from recbole.data.dataloader import AbstractDataLoader, GeneralNegSampleDataLoader -from recbole.utils import InputType, KGDataLoaderState from recbole.data.interaction import Interaction +from recbole.utils import InputType, KGDataLoaderState class KGDataLoader(AbstractDataLoader): diff --git a/recbole/data/dataloader/neg_sample_mixin.py b/recbole/data/dataloader/neg_sample_mixin.py index d95ff0465..53cea7840 100644 --- a/recbole/data/dataloader/neg_sample_mixin.py +++ b/recbole/data/dataloader/neg_sample_mixin.py @@ -94,6 +94,7 @@ class NegSampleByMixin(NegSampleMixin): :obj:`~recbole.utils.enum_type.InputType.POINTWISE`. shuffle (bool, optional): Whether the dataloader will be shuffle after a round. Defaults to ``False``. """ + def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): if neg_sample_args['strategy'] != 'by': diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index aaca251f2..21f0b2b46 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -15,9 +15,9 @@ import numpy as np import torch -from recbole.data.interaction import Interaction, cat_interactions from recbole.data.dataloader.abstract_dataloader import AbstractDataLoader from recbole.data.dataloader.neg_sample_mixin import NegSampleByMixin, NegSampleMixin +from recbole.data.interaction import Interaction, cat_interactions from recbole.utils import DataLoaderType, FeatureSource, FeatureType, InputType @@ -133,7 +133,7 @@ def augmentation(self, item_list_index, target_index, item_list_length): if field != self.uid_field: list_field = getattr(self, f'{field}_list_field') list_len = self.dataset.field2seqlen[list_field] - shape = (new_length, list_len) if isinstance(list_len, int) else (new_length, ) + list_len + shape = (new_length, list_len) if isinstance(list_len, int) else (new_length,) + list_len list_ftype = self.dataset.field2type[list_field] dtype = torch.int64 if list_ftype in [FeatureType.TOKEN, FeatureType.TOKEN_SEQ] else torch.float64 new_dict[list_field] = torch.zeros(shape, dtype=dtype) @@ -163,6 +163,7 @@ class SequentialNegSampleDataLoader(NegSampleByMixin, SequentialDataLoader): :obj:`~recbole.utils.enum_type.InputType.POINTWISE`. shuffle (bool, optional): Whether the dataloader will be shuffle after a round. Defaults to ``False``. """ + def __init__(self, config, dataset, sampler, neg_sample_args, batch_size=1, dl_format=InputType.POINTWISE, shuffle=False): super().__init__(config, dataset, sampler, neg_sample_args, diff --git a/recbole/data/dataloader/user_dataloader.py b/recbole/data/dataloader/user_dataloader.py index b28301d94..e96849835 100644 --- a/recbole/data/dataloader/user_dataloader.py +++ b/recbole/data/dataloader/user_dataloader.py @@ -12,8 +12,9 @@ ################################################ """ import torch -from recbole.data.interaction import Interaction, cat_interactions + from recbole.data.dataloader import AbstractDataLoader +from recbole.data.interaction import Interaction from recbole.utils.enum_type import DataLoaderType, InputType @@ -61,4 +62,3 @@ def _next_batch_data(self): cur_data = self.user_list[self.pr: self.pr + self.step] self.pr += self.step return cur_data - diff --git a/recbole/data/dataloader/xgboost_dataloader.py b/recbole/data/dataloader/xgboost_dataloader.py index dbe897934..17c9ab2b0 100644 --- a/recbole/data/dataloader/xgboost_dataloader.py +++ b/recbole/data/dataloader/xgboost_dataloader.py @@ -12,11 +12,13 @@ ################################################ """ -from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader, GeneralFullDataLoader +from recbole.data.dataloader.general_dataloader import GeneralDataLoader, GeneralNegSampleDataLoader, \ + GeneralFullDataLoader class XgboostDataLoader(GeneralDataLoader): - """:class:`XgboostDataLoader` is inherit from :class:`~recbole.data.dataloader.general_dataloader.GeneralDataLoader`, + """:class:`XgboostDataLoader` is inherit from + :class:`~recbole.data.dataloader.general_dataloader.GeneralDataLoader`, and didn't add/change anything at all. """ pass diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index 264761ce3..fbddaec0f 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -24,9 +24,9 @@ import torch.nn.utils.rnn as rnn_utils from scipy.sparse import coo_matrix -from recbole.utils import FeatureSource, FeatureType from recbole.data.interaction import Interaction from recbole.data.utils import dlapi +from recbole.utils import FeatureSource, FeatureType class Dataset(object): @@ -88,6 +88,7 @@ class Dataset(object): feat_name_list (list): A list contains all the features' name (:class:`str`), including additional features. """ + def __init__(self, config, saved_dataset=None): self.config = config self.dataset_name = config['dataset'] @@ -378,7 +379,7 @@ def _load_feat(self, filepath, source): pandas.DataFrame: Loaded feature Note: - For sequence features, ``seqlen`` will be loaded, but data in DataFrame will not be cutted off. + For sequence features, ``seqlen`` will be loaded, but data in DataFrame will not be cut off. Their length is limited only after calling :meth:`~_dict_to_interaction` or :meth:`~_dataframe_to_interaction` """ @@ -459,22 +460,21 @@ def _preload_weight_matrix(self): for preload_id_field in preload_fields: preload_value_field = preload_fields[preload_id_field] if preload_id_field not in self.field2source: - raise ValueError('prelaod id field [{}] not exist'.format(preload_id_field)) + raise ValueError('preload id field [{}] not exist'.format(preload_id_field)) if preload_value_field not in self.field2source: - raise ValueError('prelaod value field [{}] not exist'.format(preload_value_field)) + raise ValueError('preload value field [{}] not exist'.format(preload_value_field)) pid_source = self.field2source[preload_id_field] pv_source = self.field2source[preload_value_field] if pid_source != pv_source: - raise ValueError('preload id field [{}] is from source [{}],' - 'while prelaod value field [{}] is from source [{}], which should be the same'.format( - preload_id_field, pid_source, preload_value_field, pv_source - )) + raise ValueError(f'preload id field [{preload_id_field}] is from source [{pid_source}],' + f'while preload value field [{preload_value_field}] is from source [{pv_source}], ' + f'which should be the same.') for feat_name in self.feat_name_list: feat = getattr(self, feat_name) if preload_id_field in feat: id_ftype = self.field2type[preload_id_field] if id_ftype != FeatureType.TOKEN: - raise ValueError('prelaod id field [{}] should be type token, but is [{}]'.format( + raise ValueError('preload id field [{}] should be type token, but is [{}]'.format( preload_id_field, id_ftype )) value_ftype = self.field2type[preload_value_field] @@ -547,7 +547,7 @@ def _normalize(self): for field in fields: ftype = self.field2type[field] if field not in self.field2type: - raise ValueError('Field [{}] doesn\'t exist'.format(field)) + raise ValueError('Field [{}] does not exist'.format(field)) elif ftype != FeatureType.FLOAT and ftype != FeatureType.FLOAT_SEQ: self.logger.warning('{} is not a FLOAT/FLOAT_SEQ feat, which will not be normalized.'.format(field)) elif self.config['normalize_all']: @@ -738,8 +738,8 @@ def _drop_by_value(self, val, cmp): """Drop illegal rows by value. Args: - val (float): value that compared to. - cmp (function): return False if a row need to be droped + val (dict): value that compared to. + cmp (Callable): return False if a row need to be dropped Returns: field names that used to compare with val. @@ -786,7 +786,7 @@ def _set_label_by_threshold(self): Note: Key of ``config['threshold']`` if a field name. - This field will be droped after label generation. + This field will be dropped after label generation. """ threshold = self.config['threshold'] if threshold is None: @@ -1246,7 +1246,7 @@ def split_by_ratio(self, ratios, group_by=None): Defaults to ``None`` Returns: - list: List of :class:`~Dataset`, whose interaction features has been splitted. + list: List of :class:`~Dataset`, whose interaction features has been split. Note: Other than the first one, each part is rounded down. @@ -1261,7 +1261,7 @@ def split_by_ratio(self, ratios, group_by=None): next_index = [range(start, end) for start, end in zip([0] + split_ids, split_ids + [tot_cnt])] else: grouped_inter_feat_index = self._grouped_index(self.inter_feat[group_by].numpy()) - next_index = [[] for i in range(len(ratios))] + next_index = [[] for _ in range(len(ratios))] for grouped_index in grouped_inter_feat_index: tot_cnt = len(grouped_index) split_ids = self._calcu_split_ids(tot=tot_cnt, ratios=ratios) @@ -1277,13 +1277,13 @@ def _split_index_by_leave_one_out(self, grouped_index, leave_one_num): """Split indexes by strategy leave one out. Args: - grouped_index (list of list of int): Index to be splitted. + grouped_index (list of list of int): Index to be split. leave_one_num (int): Number of parts whose length is expected to be ``1``. Returns: - list: List of index that has been splitted. + list: List of index that has been split. """ - next_index = [[] for i in range(leave_one_num + 1)] + next_index = [[] for _ in range(leave_one_num + 1)] for index in grouped_index: index = list(index) tot_cnt = len(index) @@ -1304,7 +1304,7 @@ def leave_one_out(self, group_by, leave_one_num=1): Defaults to ``1``. Returns: - list: List of :class:`~Dataset`, whose interaction features has been splitted. + list: List of :class:`~Dataset`, whose interaction features has been split. """ self.logger.debug('leave one out, group_by=[{}], leave_one_num=[{}]'.format(group_by, leave_one_num)) if group_by is None: @@ -1342,7 +1342,7 @@ def build(self, eval_setting): Object contains evaluation settings, which guide the data processing procedure. Returns: - list: List of builded :class:`Dataset`. + list: List of built :class:`Dataset`. """ if self.benchmark_filename_list is not None: cumsum = list(np.cumsum(self.file_size_list)) @@ -1519,7 +1519,7 @@ def inter_matrix(self, form='coo', value_field=None): scipy.sparse: Sparse matrix in form ``coo`` or ``csr``. """ if not self.uid_field or not self.iid_field: - raise ValueError('dataset doesn\'t exist uid/iid, thus can not converted to sparse matrix') + raise ValueError('dataset does not exist uid/iid, thus can not converted to sparse matrix.') return self._create_sparse_matrix(self.inter_feat, self.uid_field, self.iid_field, form, value_field) def _history_matrix(self, row, value_field=None): @@ -1568,7 +1568,7 @@ def _history_matrix(self, row, value_field=None): col_num = np.max(history_len) if col_num > max_col_num * 0.2: - self.logger.warning('max value of {}\'s history interaction records has reached {}% of the total'.format( + self.logger.warning('max value of {}\'s history interaction records has reached {}% of the total.'.format( row, col_num / max_col_num * 100, )) diff --git a/recbole/data/dataset/kg_dataset.py b/recbole/data/dataset/kg_dataset.py index ad01d7f38..4e9646b5e 100644 --- a/recbole/data/dataset/kg_dataset.py +++ b/recbole/data/dataset/kg_dataset.py @@ -16,9 +16,8 @@ from collections import Counter import numpy as np -import pandas as pd -from scipy.sparse import coo_matrix import torch +from scipy.sparse import coo_matrix from recbole.data.dataset import Dataset from recbole.data.utils import dlapi @@ -63,6 +62,7 @@ class KnowledgeBasedDataset(Dataset): ``[UI-Relation]`` is a special relation token. """ + def __init__(self, config, saved_dataset=None): super().__init__(config, saved_dataset=saved_dataset) diff --git a/recbole/data/dataset/kg_seq_dataset.py b/recbole/data/dataset/kg_seq_dataset.py index 90a34800c..c1c2e3102 100644 --- a/recbole/data/dataset/kg_seq_dataset.py +++ b/recbole/data/dataset/kg_seq_dataset.py @@ -16,5 +16,6 @@ class Kg_Seq_Dataset(SequentialDataset, KnowledgeBasedDataset): Inherit from :class:`~recbole.data.dataset.sequential_dataset.SequentialDataset` and :class:`~recbole.data.dataset.kg_dataset.KnowledgeBasedDataset`. """ + def __init__(self, config, saved_dataset=None): super().__init__(config, saved_dataset=saved_dataset) diff --git a/recbole/data/dataset/sequential_dataset.py b/recbole/data/dataset/sequential_dataset.py index 0d78b5644..96272d742 100644 --- a/recbole/data/dataset/sequential_dataset.py +++ b/recbole/data/dataset/sequential_dataset.py @@ -12,10 +12,10 @@ ############################### """ -import numpy as np -import pandas as pd import copy +import numpy as np + from recbole.data.dataset import Dataset @@ -34,6 +34,7 @@ class SequentialDataset(Dataset): item_list_length (numpy.ndarray): List of item sequences' length after augmentation. """ + def __init__(self, config, saved_dataset=None): super().__init__(config, saved_dataset=saved_dataset) @@ -60,7 +61,7 @@ def prepare_data_augmentation(self): See :class:`SequentialDataset`'s attributes for details. Note: - Actually, we do not realy generate these new item sequences. + Actually, we do not really generate these new item sequences. One user's item sequence is stored only once in memory. We store the index (slice) of each item sequence after augmentation, which saves memory and accelerates a lot. diff --git a/recbole/data/dataset/social_dataset.py b/recbole/data/dataset/social_dataset.py index dcf76f536..ebeb8ebcb 100644 --- a/recbole/data/dataset/social_dataset.py +++ b/recbole/data/dataset/social_dataset.py @@ -14,9 +14,6 @@ import os -import numpy as np -from scipy.sparse import coo_matrix - from recbole.data.dataset import Dataset from recbole.data.utils import dlapi from recbole.utils import FeatureSource @@ -37,6 +34,7 @@ class SocialDataset(Dataset): net_feat (pandas.DataFrame): Internal data structure stores the network features. It's loaded from file ``.net``. """ + def __init__(self, config, saved_dataset=None): super().__init__(config, saved_dataset=saved_dataset) @@ -62,7 +60,7 @@ def _build_feat_name_list(self): feat_name_list.append('net_feat') return feat_name_list - def _load_net(self, dataset_name, dataset_path): + def _load_net(self, dataset_name, dataset_path): net_file_path = os.path.join(dataset_path, '{}.{}'.format(dataset_name, 'net')) if os.path.isfile(net_file_path): net_feat = self._load_feat(net_file_path, FeatureSource.NET) @@ -71,7 +69,7 @@ def _load_net(self, dataset_name, dataset_path): return net_feat else: raise ValueError('File {} not exist'.format(net_file_path)) - + def _get_fields_in_same_space(self): """Parsing ``config['fields_in_same_space']``. See :doc:`../user_guide/data/data_args` for detail arg setting. @@ -83,7 +81,7 @@ def _get_fields_in_same_space(self): """ fields_in_same_space = super()._get_fields_in_same_space() fields_in_same_space = [_ for _ in fields_in_same_space if (self.source_field not in _) and - (self.target_field not in _)] + (self.target_field not in _)] for field_set in fields_in_same_space: if self.uid_field in field_set: field_set.update({self.source_field, self.target_field}) @@ -98,7 +96,7 @@ def net_graph(self, form='coo', value_field=None): else ``graph[src, tgt] = self.net_feat[value_field][src, tgt]``. Currently, we support graph in `DGL`_ and `PyG`_, - and two type of sparse matrixes, ``coo`` and ``csr``. + and two type of sparse matrices, ``coo`` and ``csr``. Args: form (str, optional): Format of sparse matrix, or library of graph data structure. diff --git a/recbole/data/dataset/xgboost_dataset.py b/recbole/data/dataset/xgboost_dataset.py index 2b55c18ef..eb044b8e4 100644 --- a/recbole/data/dataset/xgboost_dataset.py +++ b/recbole/data/dataset/xgboost_dataset.py @@ -7,13 +7,8 @@ ########################## """ -import numpy as np -import pandas as pd - from recbole.data.dataset import Dataset from recbole.utils import FeatureType -from recbole.data.interaction import Interaction -from recbole.data.utils import dlapi class XgboostDataset(Dataset): @@ -23,6 +18,7 @@ class XgboostDataset(Dataset): Attributes: """ + def __init__(self, config, saved_dataset=None): super().__init__(config, saved_dataset=saved_dataset) @@ -34,7 +30,7 @@ def _judge_token_and_convert(self, feat): continue if self.field2type[col_name] == FeatureType.TOKEN: col_list.append(col_name) - elif self.field2type[col_name] == FeatureType.TOKEN_SEQ or self.field2type[col_name] == FeatureType.FLOAT_SEQ: + elif self.field2type[col_name] in {FeatureType.TOKEN_SEQ, FeatureType.FLOAT_SEQ}: feat = feat.drop([col_name], axis=1, inplace=False) # get hash map @@ -46,11 +42,11 @@ def _judge_token_and_convert(self, feat): for col in self.hash_map: if col in feat.keys(): for value in feat[col]: - #print(value) + # print(value) if value not in self.hash_map[col]: self.hash_map[col][value] = self.hash_count[col] self.hash_count[col] = self.hash_count[col] + 1 - if self.hash_count[col] > self.config['token_num_threhold']: + if self.hash_count[col] > self.config['token_num_threshold']: del_col.append(col) break @@ -74,15 +70,12 @@ def _convert_token_to_hash(self): self.hash_map = {} self.hash_count = {} self.convert_col_list = [] - if self.config['convert_token_to_onehot'] == True: - feat_list = [] - for feat in (self.inter_feat, self.user_feat, self.item_feat): + if self.config['convert_token_to_onehot']: + for feat_name in ['inter_feat', 'user_feat', 'item_feat']: + feat = getattr(self, feat_name) if feat is not None: feat = self._judge_token_and_convert(feat) - feat_list.append(feat) - self.inter_feat = feat_list[0] - self.user_feat = feat_list[1] - self.item_feat = feat_list[2] + setattr(self, feat_name, feat) def _from_scratch(self): """Load dataset from scratch. @@ -97,21 +90,6 @@ def _from_scratch(self): self._convert_token_to_hash() self._change_feat_format() - def join(self, df): - """Given interaction feature, join user/item feature into it. - - Args: - df (Interaction): Interaction feature to be joint. - - Returns: - Interaction: Interaction feature after joining operation. - """ - if self.user_feat is not None and self.uid_field in df: - df.update(self.user_feat[df[self.uid_field]]) - if self.item_feat is not None and self.iid_field in df: - df.update(self.item_feat[df[self.iid_field]]) - return df - def __getitem__(self, index, join=True): df = self.inter_feat[index] return self.join(df) if join else df diff --git a/recbole/data/interaction.py b/recbole/data/interaction.py index 8adfada82..532f7388e 100644 --- a/recbole/data/interaction.py +++ b/recbole/data/interaction.py @@ -144,7 +144,7 @@ def to(self, device, selected_field=None): with keys in selected_field will be sent to device. Returns: - Interaction: a copyed Interaction object with Tensors which are sented to + Interaction: a coped Interaction object with Tensors which are sent to the specified device. """ ret = {} @@ -157,7 +157,7 @@ def to(self, device, selected_field=None): ret[k] = self.interaction[k].to(device) else: ret[k] = self.interaction[k] - except: + except Exception: for k in self.interaction: ret[k] = self.interaction[k].to(device) return Interaction(ret) @@ -166,7 +166,7 @@ def cpu(self): """Transfer Tensors in this Interaction object to cpu. Returns: - Interaction: a copyed Interaction object with Tensors which are sented to cpu. + Interaction: a coped Interaction object with Tensors which are sent to cpu. """ ret = {} for k in self.interaction: diff --git a/recbole/data/utils.py b/recbole/data/utils.py index 9d74776a0..fa8c4abf3 100644 --- a/recbole/data/utils.py +++ b/recbole/data/utils.py @@ -13,13 +13,13 @@ """ import copy -import os import importlib +import os from recbole.config import EvalSetting +from recbole.data.dataloader import * from recbole.sampler import KGSampler, Sampler, RepeatableSampler from recbole.utils import ModelType -from recbole.data.dataloader import * def create_dataset(config): @@ -89,12 +89,13 @@ def data_preparation(config, dataset, save=False): if es.split_args['strategy'] != 'loo' and model_type == ModelType.SEQUENTIAL: raise ValueError('Sequential models require "loo" split strategy.') - builded_datasets = dataset.build(es) - train_dataset, valid_dataset, test_dataset = builded_datasets + built_datasets = dataset.build(es) + train_dataset, valid_dataset, test_dataset = built_datasets phases = ['train', 'valid', 'test'] + sampler = None if save: - save_datasets(config['checkpoint_dir'], name=phases, dataset=builded_datasets) + save_datasets(config['checkpoint_dir'], name=phases, dataset=built_datasets) kwargs = {} if config['training_neg_sample_num']: @@ -104,7 +105,7 @@ def data_preparation(config, dataset, save=False): train_distribution = config['training_neg_sample_distribution'] or 'uniform' es.neg_sample_by(by=config['training_neg_sample_num'], distribution=train_distribution) if model_type != ModelType.SEQUENTIAL: - sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) + sampler = Sampler(phases, built_datasets, es.neg_sample_args['distribution']) else: sampler = RepeatableSampler(phases, dataset, es.neg_sample_args['distribution']) kwargs['sampler'] = sampler.set_phase('train') @@ -129,8 +130,11 @@ def data_preparation(config, dataset, save=False): raise ValueError(f'It can not validate with `{es_str[1]}` ' f'when inter_feat have label_field [{dataset.label_field}].') getattr(es, es_str[1])() - if 'sampler' not in locals(): - sampler = Sampler(phases, builded_datasets, es.neg_sample_args['distribution']) + if sampler is None: + if model_type != ModelType.SEQUENTIAL: + sampler = Sampler(phases, built_datasets, es.neg_sample_args['distribution']) + else: + sampler = RepeatableSampler(phases, dataset, es.neg_sample_args['distribution']) sampler.set_distribution(es.neg_sample_args['distribution']) kwargs['sampler'] = [sampler.set_phase('valid'), sampler.set_phase('test')] kwargs['neg_sample_args'] = copy.deepcopy(es.neg_sample_args) @@ -175,7 +179,7 @@ def dataloader_construct(name, config, eval_setting, dataset, if len(dataset) != len(batch_size): raise ValueError('dataset {} and batch_size {} should have the same length'.format(dataset, batch_size)) - kwargs_list = [{} for i in range(len(dataset))] + kwargs_list = [{} for _ in range(len(dataset))] for key, value in kwargs.items(): key = [key] * len(dataset) if not isinstance(value, list): @@ -191,11 +195,11 @@ def dataloader_construct(name, config, eval_setting, dataset, logger.info(eval_setting) logger.info('batch_size = [{}], shuffle = [{}]\n'.format(batch_size, shuffle)) - DataLoader = get_data_loader(name, config, eval_setting) + dataloader = get_data_loader(name, config, eval_setting) try: ret = [ - DataLoader( + dataloader( config=config, dataset=ds, batch_size=bs, @@ -353,13 +357,15 @@ class DLFriendlyAPI(object): E.g. if ``train_data`` is an object of :class:`DataLoader`, and :meth:`~recbole.data.dataset.dataset.Dataset.num` is a method of :class:`~recbole.data.dataset.dataset.Dataset`, - Cause it has been decorated, :meth:`~recbole.data.dataset.dataset.Dataset.num` can be called directly by ``train_data``. + Cause it has been decorated, :meth:`~recbole.data.dataset.dataset.Dataset.num` can be called directly by + ``train_data``. See the example of :meth:`set` for details. Attributes: dataloader_apis (set): Register table that saves all the method names of DataLoader Friendly APIs. """ + def __init__(self): self.dataloader_apis = set() @@ -377,9 +383,11 @@ def set(self): def dataset_meth(): ... """ + def decorator(f): self.dataloader_apis.add(f.__name__) return f + return decorator diff --git a/recbole/properties/model/xgboost.yaml b/recbole/properties/model/xgboost.yaml index 8ab004d3c..6a6620bf1 100644 --- a/recbole/properties/model/xgboost.yaml +++ b/recbole/properties/model/xgboost.yaml @@ -1,6 +1,6 @@ # Type of training method convert_token_to_onehot: False -token_num_threhold: 10000 +token_num_threshold: 10000 # DMatrix xgb_weight: ~ From 6748fc0efa129b055a40ddfafc856a53d6d3e9d0 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 14:36:42 +0800 Subject: [PATCH 241/249] FORMAT: code format in sampler, trainer and utils --- recbole/quick_start/quick_start.py | 3 +- recbole/sampler/sampler.py | 5 ++++ recbole/trainer/hyper_tuning.py | 6 ++-- recbole/trainer/trainer.py | 46 +++++++++++++++--------------- recbole/utils/argument_list.py | 2 +- recbole/utils/logger.py | 1 - recbole/utils/utils.py | 6 ++-- 7 files changed, 38 insertions(+), 31 deletions(-) diff --git a/recbole/quick_start/quick_start.py b/recbole/quick_start/quick_start.py index b7dcf5722..6fd49ab4a 100644 --- a/recbole/quick_start/quick_start.py +++ b/recbole/quick_start/quick_start.py @@ -8,9 +8,10 @@ """ import logging from logging import getLogger -from recbole.utils import init_logger, get_model, get_trainer, init_seed + from recbole.config import Config from recbole.data import create_dataset, data_preparation +from recbole.utils import init_logger, get_model, get_trainer, init_seed def run_recbole(model=None, dataset=None, config_file_list=None, config_dict=None, saved=True): diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index 392402826..668ad51ba 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -14,6 +14,7 @@ """ import copy + import numpy as np import torch @@ -32,6 +33,7 @@ class AbstractSampler(object): random_list (list or numpy.ndarray): The shuffled result of :meth:`get_random_list`. used_ids (numpy.ndarray): The result of :meth:`get_used_ids`. """ + def __init__(self, distribution): self.distribution = '' self.random_list = [] @@ -165,6 +167,7 @@ class Sampler(AbstractSampler): Attributes: phase (str): the phase of sampler. It will not be set until :meth:`set_phase` is called. """ + def __init__(self, phases, datasets, distribution='uniform'): if not isinstance(phases, list): phases = [phases] @@ -266,6 +269,7 @@ class KGSampler(AbstractSampler): dataset (Dataset): The knowledge graph dataset, which contains triplets in a knowledge graph. distribution (str, optional): Distribution of the negative entities. Defaults to 'uniform'. """ + def __init__(self, dataset, distribution='uniform'): self.dataset = dataset @@ -341,6 +345,7 @@ class RepeatableSampler(AbstractSampler): Attributes: phase (str): the phase of sampler. It will not be set until :meth:`set_phase` is called. """ + def __init__(self, phases, dataset, distribution='uniform'): if not isinstance(phases, list): phases = [phases] diff --git a/recbole/trainer/hyper_tuning.py b/recbole/trainer/hyper_tuning.py index 2709bd7db..a6788fbf1 100644 --- a/recbole/trainer/hyper_tuning.py +++ b/recbole/trainer/hyper_tuning.py @@ -9,9 +9,10 @@ ############################ """ -import numpy as np from functools import partial +import numpy as np + from recbole.utils.utils import dict2str @@ -43,7 +44,6 @@ def _parameters(space): if isinstance(space, dict): space = list(space.values()) for node in _recursiveFindNodes(space, 'switch'): - # Find the name of this parameter paramNode = node.pos_args[0] assert paramNode.name == 'hyperopt_param' @@ -211,7 +211,7 @@ def _build_space_from_dict(config_dict): high = para_value[1] q = para_value[2] space[para_name] = hp.quniform(para_name, float(low), float(high), float(q)) - elif para_type =='loguniform': + elif para_type == 'loguniform': for para_name in config_dict['loguniform']: para_value = config_dict['loguniform'][para_name] low = para_value[0] diff --git a/recbole/trainer/trainer.py b/recbole/trainer/trainer.py index b46ad1947..bfd761660 100644 --- a/recbole/trainer/trainer.py +++ b/recbole/trainer/trainer.py @@ -13,20 +13,20 @@ """ import os -from tqdm import tqdm +from logging import getLogger +from time import time + +import matplotlib.pyplot as plt +import numpy as np import torch import torch.optim as optim from torch.nn.utils.clip_grad import clip_grad_norm_ -import numpy as np -import matplotlib.pyplot as plt - -from time import time -from logging import getLogger +from tqdm import tqdm -from recbole.evaluator import ProxyEvaluator from recbole.data.interaction import Interaction +from recbole.evaluator import ProxyEvaluator from recbole.utils import ensure_dir, get_local_time, early_stopping, calculate_valid_score, dict2str, \ - DataLoaderType, KGDataLoaderState, EvaluatorType + DataLoaderType, KGDataLoaderState class AbstractTrainer(object): @@ -229,7 +229,7 @@ def _generate_train_loss_output(self, epoch_idx, s_time, e_time, losses): train_loss_output = 'epoch %d training [time: %.2fs, ' % (epoch_idx, e_time - s_time) if isinstance(losses, tuple): des = 'train_loss%d: %.' + str(des) + 'f' - train_loss_output += ', '.join( des % (idx + 1, loss) for idx, loss in enumerate(losses)) + train_loss_output += ', '.join(des % (idx + 1, loss) for idx, loss in enumerate(losses)) else: des = '%.' + str(des) + 'f' train_loss_output += 'train loss:' + des % losses @@ -682,15 +682,15 @@ def _interaction_to_DMatrix(self, dataloader): cur_data = sparse.csc_matrix(onehot_data) - return self.xgb.DMatrix(data = cur_data, - label = interaction_np[self.label_field], - weight = self.weight, - base_margin = self.base_margin, - missing = self.missing, - silent = self.silent, - feature_names = self.feature_names, - feature_types = self.feature_types, - nthread = self.nthread) + return self.xgb.DMatrix(data=cur_data, + label=interaction_np[self.label_field], + weight=self.weight, + base_margin=self.base_margin, + missing=self.missing, + silent=self.silent, + feature_names=self.feature_names, + feature_types=self.feature_types, + nthread=self.nthread) def _train_at_once(self, train_data, valid_data): r""" @@ -701,11 +701,11 @@ def _train_at_once(self, train_data, valid_data): """ self.dtrain = self._interaction_to_DMatrix(train_data) self.dvalid = self._interaction_to_DMatrix(valid_data) - self.evals = [(self.dtrain,'train'),(self.dvalid, 'valid')] - self.model = self.xgb.train(self.params, self.dtrain, self.num_boost_round, - self.evals, self.obj, self.feval, self.maximize, - self.early_stopping_rounds, self.evals_result, - self.verbose_eval, self.xgb_model, self.callbacks) + self.evals = [(self.dtrain, 'train'), (self.dvalid, 'valid')] + self.model = self.xgb.train(self.params, self.dtrain, self.num_boost_round, + self.evals, self.obj, self.feval, self.maximize, + self.early_stopping_rounds, self.evals_result, + self.verbose_eval, self.xgb_model, self.callbacks) self.model.save_model(self.saved_model_file) self.xgb_model = self.saved_model_file diff --git a/recbole/utils/argument_list.py b/recbole/utils/argument_list.py index 5bff7c26a..383549e84 100644 --- a/recbole/utils/argument_list.py +++ b/recbole/utils/argument_list.py @@ -25,7 +25,7 @@ 'eval_batch_size'] dataset_arguments = ['field_separator', 'seq_separator', - 'USER_ID_FIELD', 'ITEM_ID_FIELD', 'RATING_FIELD', 'TIME_FIELD' + 'USER_ID_FIELD', 'ITEM_ID_FIELD', 'RATING_FIELD', 'TIME_FIELD', 'seq_len', 'LABEL_FIELD', 'threshold', 'NEG_PREFIX', diff --git a/recbole/utils/logger.py b/recbole/utils/logger.py index 15e67e47a..e9808fc68 100644 --- a/recbole/utils/logger.py +++ b/recbole/utils/logger.py @@ -68,4 +68,3 @@ def init_logger(config): level=level, handlers=[fh, sh] ) - diff --git a/recbole/utils/utils.py b/recbole/utils/utils.py index e5f60a7c5..faa98c938 100644 --- a/recbole/utils/utils.py +++ b/recbole/utils/utils.py @@ -8,12 +8,14 @@ ################################ """ -import os import datetime import importlib +import os import random -import torch + import numpy as np +import torch + from recbole.utils.enum_type import ModelType From e94b79e54df08f6ee1f2529f0c24407d9dddf5c7 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 15:12:55 +0800 Subject: [PATCH 242/249] FORMAT: code format in evaluator and model.utils --- recbole/evaluator/abstract_evaluator.py | 270 ++++----- recbole/evaluator/evaluators.py | 713 ++++++++++++------------ recbole/evaluator/metrics.py | 9 +- recbole/evaluator/proxy_evaluator.py | 206 +++---- recbole/evaluator/utils.py | 3 +- recbole/model/abstract_recommender.py | 5 +- recbole/model/layers.py | 64 ++- recbole/model/loss.py | 5 +- 8 files changed, 646 insertions(+), 629 deletions(-) diff --git a/recbole/evaluator/abstract_evaluator.py b/recbole/evaluator/abstract_evaluator.py index 7a2d01666..45254c9a6 100644 --- a/recbole/evaluator/abstract_evaluator.py +++ b/recbole/evaluator/abstract_evaluator.py @@ -1,134 +1,136 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/10/21 -# @Author : Kaiyuan Li -# @email : tsotfsk@outlook.com - -# UPDATE -# @Time : 2020/10/21, 2020/12/18 -# @Author : Kaiyuan Li, Zhichao Feng -# @email : tsotfsk@outlook.com, fzcbupt@gmail.com - -""" -recbole.evaluator.abstract_evaluator -##################################### -""" - -import torch -import numpy as np -from torch.nn.utils.rnn import pad_sequence - - -class BaseEvaluator(object): - """:class:`BaseEvaluator` is an object which supports - the evaluation of the model. It is called by :class:`Trainer`. - - Note: - If you want to inherit this class and implement your own evaluator class, - you must implement the following functions. - - Args: - config (Config): The config of evaluator. - - """ - - def __init__(self, config, metrics): - self.metrics = metrics - self.full = ('full' in config['eval_setting']) - self.precision = config['metric_decimal_place'] - - def collect(self, *args): - """get the intermediate results for each batch, it is called at the end of each batch""" - raise NotImplementedError - - def evaluate(self, *args): - """calculate the metrics of all batches, it is called at the end of each epoch""" - raise NotImplementedError - - def _calculate_metrics(self, *args): - """ to calculate the metrics""" - raise NotImplementedError - - -class GroupedEvalautor(BaseEvaluator): - """:class:`GroupedEvaluator` is an object which supports the evaluation of the model. - - Note: - If you want to implement a new group-based metric, - you may need to inherit this class - - """ - def __init__(self, config, metrics): - super().__init__(config, metrics) - pass - - def sample_collect(self, scores_tensor, user_len_list): - """padding scores_tensor. It is called when evaluation sample distribution is `uniform` or `popularity`. - - """ - scores_list = torch.split(scores_tensor, user_len_list, dim=0) - padding_score = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # nusers x items - return padding_score - - def full_sort_collect(self, scores_tensor, user_len_list): - """it is called when evaluation sample distribution is `full`. - - """ - return scores_tensor.view(len(user_len_list), -1) - - def get_score_matrix(self, scores_tensor, user_len_list): - """get score matrix. - - Args: - scores_tensor (tensor): the tensor of model output with size of `(N, )` - user_len_list(list): number of all items - - """ - if self.full: - scores_matrix = self.full_sort_collect(scores_tensor, user_len_list) - else: - scores_matrix = self.sample_collect(scores_tensor, user_len_list) - return scores_matrix - - -class IndividualEvaluator(BaseEvaluator): - """:class:`IndividualEvaluator` is an object which supports the evaluation of the model. - - Note: - If you want to implement a new non-group-based metric, - you may need to inherit this class - - """ - def __init__(self, config, metrics): - super().__init__(config, metrics) - self._check_args() - - def sample_collect(self, true_scores, pred_scores): - """It is called when evaluation sample distribution is `uniform` or `popularity`. - - """ - return torch.stack((true_scores, pred_scores.detach()), dim=1) - - def full_sort_collect(self, true_scores, pred_scores): - """it is called when evaluation sample distribution is `full`. - - """ - raise NotImplementedError('full sort can\'t use IndividualEvaluator') - - def get_score_matrix(self, true_scores, pred_scores): - """get score matrix - - Args: - true_scores (tensor): the label of predicted items - pred_scores (tensor): the tensor of model output with a size of `(N, )` - - """ - if self.full: - scores_matrix = self.full_sort_collect(true_scores, pred_scores) - else: - scores_matrix = self.sample_collect(true_scores, pred_scores) - - return scores_matrix - - def _check_args(self): - if self.full: - raise NotImplementedError('full sort can\'t use IndividualEvaluator') \ No newline at end of file +# -*- encoding: utf-8 -*- +# @Time : 2020/10/21 +# @Author : Kaiyuan Li +# @email : tsotfsk@outlook.com + +# UPDATE +# @Time : 2020/10/21, 2020/12/18 +# @Author : Kaiyuan Li, Zhichao Feng +# @email : tsotfsk@outlook.com, fzcbupt@gmail.com + +""" +recbole.evaluator.abstract_evaluator +##################################### +""" + +import numpy as np +import torch +from torch.nn.utils.rnn import pad_sequence + + +class BaseEvaluator(object): + """:class:`BaseEvaluator` is an object which supports + the evaluation of the model. It is called by :class:`Trainer`. + + Note: + If you want to inherit this class and implement your own evaluator class, + you must implement the following functions. + + Args: + config (Config): The config of evaluator. + + """ + + def __init__(self, config, metrics): + self.metrics = metrics + self.full = ('full' in config['eval_setting']) + self.precision = config['metric_decimal_place'] + + def collect(self, *args): + """get the intermediate results for each batch, it is called at the end of each batch""" + raise NotImplementedError + + def evaluate(self, *args): + """calculate the metrics of all batches, it is called at the end of each epoch""" + raise NotImplementedError + + def _calculate_metrics(self, *args): + """ to calculate the metrics""" + raise NotImplementedError + + +class GroupedEvaluator(BaseEvaluator): + """:class:`GroupedEvaluator` is an object which supports the evaluation of the model. + + Note: + If you want to implement a new group-based metric, + you may need to inherit this class + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + pass + + def sample_collect(self, scores_tensor, user_len_list): + """padding scores_tensor. It is called when evaluation sample distribution is `uniform` or `popularity`. + + """ + scores_list = torch.split(scores_tensor, user_len_list, dim=0) + padding_score = pad_sequence(scores_list, batch_first=True, padding_value=-np.inf) # n_users x items + return padding_score + + def full_sort_collect(self, scores_tensor, user_len_list): + """it is called when evaluation sample distribution is `full`. + + """ + return scores_tensor.view(len(user_len_list), -1) + + def get_score_matrix(self, scores_tensor, user_len_list): + """get score matrix. + + Args: + scores_tensor (tensor): the tensor of model output with size of `(N, )` + user_len_list(list): number of all items + + """ + if self.full: + scores_matrix = self.full_sort_collect(scores_tensor, user_len_list) + else: + scores_matrix = self.sample_collect(scores_tensor, user_len_list) + return scores_matrix + + +class IndividualEvaluator(BaseEvaluator): + """:class:`IndividualEvaluator` is an object which supports the evaluation of the model. + + Note: + If you want to implement a new non-group-based metric, + you may need to inherit this class + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + self._check_args() + + def sample_collect(self, true_scores, pred_scores): + """It is called when evaluation sample distribution is `uniform` or `popularity`. + + """ + return torch.stack((true_scores, pred_scores.detach()), dim=1) + + def full_sort_collect(self, true_scores, pred_scores): + """it is called when evaluation sample distribution is `full`. + + """ + raise NotImplementedError('full sort can\'t use IndividualEvaluator') + + def get_score_matrix(self, true_scores, pred_scores): + """get score matrix + + Args: + true_scores (tensor): the label of predicted items + pred_scores (tensor): the tensor of model output with a size of `(N, )` + + """ + if self.full: + scores_matrix = self.full_sort_collect(true_scores, pred_scores) + else: + scores_matrix = self.sample_collect(true_scores, pred_scores) + + return scores_matrix + + def _check_args(self): + if self.full: + raise NotImplementedError('full sort can\'t use IndividualEvaluator') diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py index 3b8ddc9bc..22e5c6af4 100644 --- a/recbole/evaluator/evaluators.py +++ b/recbole/evaluator/evaluators.py @@ -1,356 +1,357 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/08/04 -# @Author : Kaiyuan Li -# @email : tsotfsk@outlook.com - -# UPDATE -# @Time : 2020/08/04, 2020/08/11, 2020/12/18 -# @Author : Kaiyuan Li, Yupeng Hou, Zhichao Feng -# @email : tsotfsk@outlook.com, houyupeng@ruc.edu.cn, fzcbupt@gmail.com - - -import torch -import numpy as np -from collections import ChainMap -from recbole.evaluator.metrics import metrics_dict -from recbole.evaluator.abstract_evaluator import GroupedEvalautor, IndividualEvaluator - -# These metrics are typical in topk recommendations -topk_metrics = {metric.lower(): metric for metric in ['Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP']} -# These metrics are typical in loss recommendations -loss_metrics = {metric.lower(): metric for metric in ['AUC', 'RMSE', 'MAE', 'LOGLOSS']} -# For GAUC -rank_metrics = {metric.lower(): metric for metric in ['GAUC']} - -# group-based metrics -group_metrics = ChainMap(topk_metrics, rank_metrics) -# not group-based metrics -individual_metrics = ChainMap(loss_metrics) - - -class TopKEvaluator(GroupedEvalautor): - r"""TopK Evaluator is mainly used in ranking tasks. Now, we support six topk metrics which - contain `'Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP'`. - - Note: - The metrics used calculate group-based metrics which considers the metrics scores averaged - across users. Some of them are also limited to k. - - """ - - def __init__(self, config, metrics): - super().__init__(config, metrics) - - self.topk = config['topk'] - self._check_args() - - def collect(self, interaction, scores_tensor): - """collect the topk intermediate result of one batch, this function mainly - implements padding and TopK finding. It is called at the end of each batch - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - scores_tensor (tensor): the tensor of model output with size of `(N, )` - - """ - user_len_list = interaction.user_len_list - scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) - - # get topk - _, topk_idx = torch.topk(scores_matrix, max(self.topk), dim=-1) # nusers x k - - return topk_idx - - def evaluate(self, batch_matrix_list, eval_data): - """calculate the metrics of all batches. It is called at the end of each epoch - - Args: - batch_matrix_list (list): the results of all batches - eval_data (Dataset): the class of test data - - Returns: - dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'Recall@10': 0.0329}`` - - """ - pos_len_list = eval_data.get_pos_len_list() - topk_idx = torch.cat(batch_matrix_list, dim=0).cpu().numpy() - - assert len(pos_len_list) == len(topk_idx) - # get metrics - metric_dict = {} - result_list = self._calculate_metrics(pos_len_list, topk_idx) - for metric, value in zip(self.metrics, result_list): - for k in self.topk: - key = '{}@{}'.format(metric, k) - metric_dict[key] = round(value[k - 1], self.precision) - - return metric_dict - - def _check_args(self): - - # Check topk: - if isinstance(self.topk, (int, list)): - if isinstance(self.topk, int): - self.topk = [self.topk] - for topk in self.topk: - if topk <= 0: - raise ValueError('topk must be a positive integer or a list of positive integers, ' - 'but get `{}`'.format(topk)) - else: - raise TypeError('The topk must be a integer, list') - - def _calculate_metrics(self, pos_len_list, topk_index): - """integrate the results of each batch and evaluate the topk metrics by users - - Args: - pos_len_list (np.ndarray): a list of users' positive items - topk_index (np.ndarray): a matrix which contains the index of the topk items for users - - Returns: - np.ndarray: a matrix which contains the metrics result - - """ - pos_idx_matrix = (topk_index < pos_len_list.reshape(-1, 1)) - result_list = [] - for metric in self.metrics: - metric_fuc = metrics_dict[metric.lower()] - result = metric_fuc(pos_idx_matrix, pos_len_list) - result_list.append(result) # n_users x len(metrics) x len(ranks) - result = np.stack(result_list, axis=0).mean(axis=1) # len(metrics) x len(ranks) - return result - - def __str__(self): - msg = 'The TopK Evaluator Info:\n' + \ - '\tMetrics:[' + \ - ', '.join([topk_metrics[metric.lower()] for metric in self.metrics]) + \ - '], TopK:[' + \ - ', '.join(map(str, self.topk)) + \ - ']' - return msg - - -class RankEvaluator(GroupedEvalautor): - r"""Rank Evaluator is mainly used in ranking tasks except for topk tasks. Now, we support one - rank metric containing `'GAUC'`. - - Note: - The metrics used calculate group-based metrics which considers the metrics scores averaged - across users except for top-k metrics. - - """ - - def __init__(self, config, metrics): - super().__init__(config, metrics) - pass - - def get_user_pos_len_list(self, interaction, scores_tensor): - """get number of positive items and all items in test set of each user - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - scores_tensor (tensor): the tensor of model output with size of `(N, )` - - Returns: - list: number of positive items, - list: number of all items - """ - pos_len_list = torch.Tensor(interaction.pos_len_list).to(scores_tensor.device) - user_len_list = interaction.user_len_list - return pos_len_list, user_len_list - - def average_rank(self, scores): - """Get the ranking of an ordered tensor, and take the average of the ranking for positions with equal values. - - Args: - scores(tensor): an ordered tensor, with size of `(N, )` - - Returns: - torch.Tensor: average_rank - - Example: - >>> average_rank(tensor([[1,2,2,2,3,3,6],[2,2,2,2,4,4,5]])) - tensor([[1.0000, 3.0000, 3.0000, 3.0000, 5.5000, 5.5000, 7.0000], - [2.5000, 2.5000, 2.5000, 2.5000, 5.0000, 6.5000, 6.5000]]) - - Reference: - https://github.com/scipy/scipy/blob/v0.17.1/scipy/stats/stats.py#L5262-L5352 - - """ - length, width = scores.shape - device = scores.device - true_tensor = torch.full((length, 1), True, dtype=torch.bool, device=device) - - obs = torch.cat([true_tensor, scores[:, 1:] != scores[:, :-1]], dim=1) - # bias added to dense - bias = torch.arange(0, length, device=device).repeat(width).reshape(width, -1). \ - transpose(1, 0).reshape(-1) - dense = obs.view(-1).cumsum(0) + bias - - # cumulative counts of each unique value - count = torch.where(torch.cat([obs, true_tensor], dim=1))[1] - # get average rank - avg_rank = .5 * (count[dense] + count[dense - 1] + 1).view(length, -1) - - return avg_rank - - def collect(self, interaction, scores_tensor): - """collect the rank intermediate result of one batch, this function mainly implements ranking - and calculating the sum of rank for positive items. It is called at the end of each batch - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - scores_tensor (tensor): the tensor of model output with size of `(N, )` - - """ - pos_len_list, user_len_list = self.get_user_pos_len_list(interaction, scores_tensor) - scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) - desc_scores, desc_index = torch.sort(scores_matrix, dim=-1, descending=True) - - # get the index of positive items in the ranking list - pos_index = (desc_index < pos_len_list.reshape(-1, 1)) - - avg_rank = self.average_rank(desc_scores) - pos_rank_sum = torch.where(pos_index, avg_rank, torch.zeros_like(avg_rank)). \ - sum(axis=-1).reshape(-1, 1) - - return pos_rank_sum - - def evaluate(self, batch_matrix_list, eval_data): - """calculate the metrics of all batches. It is called at the end of each epoch - - Args: - batch_matrix_list (list): the results of all batches - eval_data (Dataset): the class of test data - - Returns: - dict: such as ``{'GAUC:0.9286}`` - - """ - pos_len_list = eval_data.get_pos_len_list() - user_len_list = eval_data.get_user_len_list() - pos_rank_sum = torch.cat(batch_matrix_list, dim=0).cpu().numpy() - assert len(pos_len_list) == len(pos_rank_sum) - - # get metrics - metric_dict = {} - result_list = self._calculate_metrics(user_len_list, pos_len_list, pos_rank_sum) - for metric, value in zip(self.metrics, result_list): - key = '{}'.format(metric) - metric_dict[key] = round(value, self.precision) - - return metric_dict - - def _calculate_metrics(self, user_len_list, pos_len_list, pos_rank_sum): - """integrate the results of each batch and evaluate the topk metrics by users - - Args: - pos_len_list (np.ndarray): a list of users' positive items - topk_index (np.ndarray): a matrix which contains the index of the topk items for users - - Returns: - np.ndarray: a matrix which contains the metrics result - - """ - result_list = [] - for metric in self.metrics: - metric_fuc = metrics_dict[metric.lower()] - result = metric_fuc(user_len_list, pos_len_list, pos_rank_sum) - result_list.append(result) - return result_list - - def __str__(self): - msg = 'The Rank Evaluator Info:\n' + \ - '\tMetrics:[' + \ - ', '.join([rank_metrics[metric.lower()] for metric in self.metrics]) + \ - '], TopK:[' + \ - ', '.join(map(str, self.topk)) + \ - ']' - return msg - - -class LossEvaluator(IndividualEvaluator): - r"""Loss Evaluator is mainly used in rating prediction and click through rate prediction. Now, we support four - loss metrics which contain `'AUC', 'RMSE', 'MAE', 'LOGLOSS'`. - - Note: - The metrics used do not calculate group-based metrics which considers the metrics scores averaged across users. - They are also not limited to k. Instead, they calculate the scores on the entire prediction results regardless - the users. - - """ - - def __init__(self, config, metrics): - super().__init__(config, metrics) - - self.label_field = config['LABEL_FIELD'] - - def collect(self, interaction, pred_scores): - """collect the loss intermediate result of one batch, this function mainly - implements concatenating preds and trues. It is called at the end of each batch - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - pred_scores (tensor): the tensor of model output with a size of `(N, )` - - Returns: - tensor : a batch of scores with a size of `(N, 2)` - - """ - true_scores = interaction[self.label_field].to(pred_scores.device) - assert len(true_scores) == len(pred_scores) - return self.get_score_matrix(true_scores, pred_scores) - - def evaluate(self, batch_matrix_list, *args): - """calculate the metrics of all batches. It is called at the end of each epoch - - Args: - batch_matrix_list (list): the results of all batches - - Returns: - dict: such as {'AUC': 0.83} - - """ - concat = torch.cat(batch_matrix_list, dim=0).cpu().numpy() - - trues = concat[:, 0] - preds = concat[:, 1] - - # get metrics - metric_dict = {} - result_list = self._calculate_metrics(trues, preds) - for metric, value in zip(self.metrics, result_list): - key = '{}'.format(metric) - metric_dict[key] = round(value, self.precision) - return metric_dict - - def _calculate_metrics(self, trues, preds): - """get metrics result - - Args: - trues (np.ndarray): the true scores' list - preds (np.ndarray): the predict scores' list - - Returns: - list: a list of metrics result - - """ - result_list = [] - for metric in self.metrics: - metric_fuc = metrics_dict[metric.lower()] - result = metric_fuc(trues, preds) - result_list.append(result) - return result_list - - def __str__(self): - msg = 'The Loss Evaluator Info:\n' + \ - '\tMetrics:[' + \ - ', '.join([loss_metrics[metric.lower()] for metric in self.metrics]) + \ - ']' - return msg - - -metric_eval_bind = [ - (topk_metrics, TopKEvaluator), - (loss_metrics, LossEvaluator), - (rank_metrics, RankEvaluator) -] +# -*- encoding: utf-8 -*- +# @Time : 2020/08/04 +# @Author : Kaiyuan Li +# @email : tsotfsk@outlook.com + +# UPDATE +# @Time : 2020/08/04, 2020/08/11, 2020/12/18 +# @Author : Kaiyuan Li, Yupeng Hou, Zhichao Feng +# @email : tsotfsk@outlook.com, houyupeng@ruc.edu.cn, fzcbupt@gmail.com + + +from collections import ChainMap + +import numpy as np +import torch + +from recbole.evaluator.abstract_evaluator import GroupedEvaluator, IndividualEvaluator +from recbole.evaluator.metrics import metrics_dict + +# These metrics are typical in topk recommendations +topk_metrics = {metric.lower(): metric for metric in ['Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP']} +# These metrics are typical in loss recommendations +loss_metrics = {metric.lower(): metric for metric in ['AUC', 'RMSE', 'MAE', 'LOGLOSS']} +# For GAUC +rank_metrics = {metric.lower(): metric for metric in ['GAUC']} + +# group-based metrics +group_metrics = ChainMap(topk_metrics, rank_metrics) +# not group-based metrics +individual_metrics = ChainMap(loss_metrics) + + +class TopKEvaluator(GroupedEvaluator): + r"""TopK Evaluator is mainly used in ranking tasks. Now, we support six topk metrics which + contain `'Hit', 'Recall', 'MRR', 'Precision', 'NDCG', 'MAP'`. + + Note: + The metrics used calculate group-based metrics which considers the metrics scores averaged + across users. Some of them are also limited to k. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + + self.topk = config['topk'] + self._check_args() + + def collect(self, interaction, scores_tensor): + """collect the topk intermediate result of one batch, this function mainly + implements padding and TopK finding. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + """ + user_len_list = interaction.user_len_list + scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) + + # get topk + _, topk_idx = torch.topk(scores_matrix, max(self.topk), dim=-1) # n_users x k + + return topk_idx + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'Recall@10': 0.0329}`` + + """ + pos_len_list = eval_data.get_pos_len_list() + topk_idx = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + + assert len(pos_len_list) == len(topk_idx) + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(pos_len_list, topk_idx) + for metric, value in zip(self.metrics, result_list): + for k in self.topk: + key = '{}@{}'.format(metric, k) + metric_dict[key] = round(value[k - 1], self.precision) + + return metric_dict + + def _check_args(self): + + # Check topk: + if isinstance(self.topk, (int, list)): + if isinstance(self.topk, int): + self.topk = [self.topk] + for topk in self.topk: + if topk <= 0: + raise ValueError('topk must be a positive integer or a list of positive integers, ' + 'but get `{}`'.format(topk)) + else: + raise TypeError('The topk must be a integer, list') + + def _calculate_metrics(self, pos_len_list, topk_index): + """integrate the results of each batch and evaluate the topk metrics by users + + Args: + pos_len_list (np.ndarray): a list of users' positive items + topk_index (np.ndarray): a matrix which contains the index of the topk items for users + + Returns: + np.ndarray: a matrix which contains the metrics result + + """ + pos_idx_matrix = (topk_index < pos_len_list.reshape(-1, 1)) + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(pos_idx_matrix, pos_len_list) + result_list.append(result) # n_users x len(metrics) x len(ranks) + result = np.stack(result_list, axis=0).mean(axis=1) # len(metrics) x len(ranks) + return result + + def __str__(self): + msg = 'The TopK Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([topk_metrics[metric.lower()] for metric in self.metrics]) + \ + '], TopK:[' + \ + ', '.join(map(str, self.topk)) + \ + ']' + return msg + + +class RankEvaluator(GroupedEvaluator): + r"""Rank Evaluator is mainly used in ranking tasks except for topk tasks. Now, we support one + rank metric containing `'GAUC'`. + + Note: + The metrics used calculate group-based metrics which considers the metrics scores averaged + across users except for top-k metrics. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + pass + + def get_user_pos_len_list(self, interaction, scores_tensor): + """get number of positive items and all items in test set of each user + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + Returns: + list: number of positive items, + list: number of all items + """ + pos_len_list = torch.Tensor(interaction.pos_len_list).to(scores_tensor.device) + user_len_list = interaction.user_len_list + return pos_len_list, user_len_list + + def average_rank(self, scores): + """Get the ranking of an ordered tensor, and take the average of the ranking for positions with equal values. + + Args: + scores(tensor): an ordered tensor, with size of `(N, )` + + Returns: + torch.Tensor: average_rank + + Example: + >>> average_rank(tensor([[1,2,2,2,3,3,6],[2,2,2,2,4,4,5]])) + tensor([[1.0000, 3.0000, 3.0000, 3.0000, 5.5000, 5.5000, 7.0000], + [2.5000, 2.5000, 2.5000, 2.5000, 5.0000, 6.5000, 6.5000]]) + + Reference: + https://github.com/scipy/scipy/blob/v0.17.1/scipy/stats/stats.py#L5262-L5352 + + """ + length, width = scores.shape + device = scores.device + true_tensor = torch.full((length, 1), True, dtype=torch.bool, device=device) + + obs = torch.cat([true_tensor, scores[:, 1:] != scores[:, :-1]], dim=1) + # bias added to dense + bias = torch.arange(0, length, device=device).repeat(width).reshape(width, -1). \ + transpose(1, 0).reshape(-1) + dense = obs.view(-1).cumsum(0) + bias + + # cumulative counts of each unique value + count = torch.where(torch.cat([obs, true_tensor], dim=1))[1] + # get average rank + avg_rank = .5 * (count[dense] + count[dense - 1] + 1).view(length, -1) + + return avg_rank + + def collect(self, interaction, scores_tensor): + """collect the rank intermediate result of one batch, this function mainly implements ranking + and calculating the sum of rank for positive items. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores_tensor (tensor): the tensor of model output with size of `(N, )` + + """ + pos_len_list, user_len_list = self.get_user_pos_len_list(interaction, scores_tensor) + scores_matrix = self.get_score_matrix(scores_tensor, user_len_list) + desc_scores, desc_index = torch.sort(scores_matrix, dim=-1, descending=True) + + # get the index of positive items in the ranking list + pos_index = (desc_index < pos_len_list.reshape(-1, 1)) + + avg_rank = self.average_rank(desc_scores) + pos_rank_sum = torch.where(pos_index, avg_rank, torch.zeros_like(avg_rank)).sum(axis=-1).reshape(-1, 1) + + return pos_rank_sum + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'GAUC:0.9286}`` + + """ + pos_len_list = eval_data.get_pos_len_list() + user_len_list = eval_data.get_user_len_list() + pos_rank_sum = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + assert len(pos_len_list) == len(pos_rank_sum) + + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(user_len_list, pos_len_list, pos_rank_sum) + for metric, value in zip(self.metrics, result_list): + key = '{}'.format(metric) + metric_dict[key] = round(value, self.precision) + + return metric_dict + + def _calculate_metrics(self, user_len_list, pos_len_list, pos_rank_sum): + """integrate the results of each batch and evaluate the topk metrics by users + + Args: + pos_len_list (np.ndarray): a list of users' positive items + topk_index (np.ndarray): a matrix which contains the index of the topk items for users + + Returns: + np.ndarray: a matrix which contains the metrics result + + """ + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(user_len_list, pos_len_list, pos_rank_sum) + result_list.append(result) + return result_list + + def __str__(self): + msg = 'The Rank Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([rank_metrics[metric.lower()] for metric in self.metrics]) + \ + '], TopK:[' + \ + ', '.join(map(str, self.topk)) + \ + ']' + return msg + + +class LossEvaluator(IndividualEvaluator): + r"""Loss Evaluator is mainly used in rating prediction and click through rate prediction. Now, we support four + loss metrics which contain `'AUC', 'RMSE', 'MAE', 'LOGLOSS'`. + + Note: + The metrics used do not calculate group-based metrics which considers the metrics scores averaged + across users. They are also not limited to k. Instead, they calculate the scores on the entire + prediction results regardless the users. + + """ + + def __init__(self, config, metrics): + super().__init__(config, metrics) + + self.label_field = config['LABEL_FIELD'] + + def collect(self, interaction, pred_scores): + """collect the loss intermediate result of one batch, this function mainly + implements concatenating preds and trues. It is called at the end of each batch + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + pred_scores (tensor): the tensor of model output with a size of `(N, )` + + Returns: + tensor : a batch of scores with a size of `(N, 2)` + + """ + true_scores = interaction[self.label_field].to(pred_scores.device) + assert len(true_scores) == len(pred_scores) + return self.get_score_matrix(true_scores, pred_scores) + + def evaluate(self, batch_matrix_list, *args): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + + Returns: + dict: such as {'AUC': 0.83} + + """ + concat = torch.cat(batch_matrix_list, dim=0).cpu().numpy() + + trues = concat[:, 0] + preds = concat[:, 1] + + # get metrics + metric_dict = {} + result_list = self._calculate_metrics(trues, preds) + for metric, value in zip(self.metrics, result_list): + key = '{}'.format(metric) + metric_dict[key] = round(value, self.precision) + return metric_dict + + def _calculate_metrics(self, trues, preds): + """get metrics result + + Args: + trues (np.ndarray): the true scores' list + preds (np.ndarray): the predict scores' list + + Returns: + list: a list of metrics result + + """ + result_list = [] + for metric in self.metrics: + metric_fuc = metrics_dict[metric.lower()] + result = metric_fuc(trues, preds) + result_list.append(result) + return result_list + + def __str__(self): + msg = 'The Loss Evaluator Info:\n' + \ + '\tMetrics:[' + \ + ', '.join([loss_metrics[metric.lower()] for metric in self.metrics]) + \ + ']' + return msg + + +metric_eval_bind = [ + (topk_metrics, TopKEvaluator), + (loss_metrics, LossEvaluator), + (rank_metrics, RankEvaluator) +] diff --git a/recbole/evaluator/metrics.py b/recbole/evaluator/metrics.py index 483fd3f21..b788fa0eb 100644 --- a/recbole/evaluator/metrics.py +++ b/recbole/evaluator/metrics.py @@ -16,9 +16,10 @@ from logging import getLogger import numpy as np -from recbole.evaluator.utils import _binary_clf_curve from sklearn.metrics import auc as sk_auc -from sklearn.metrics import log_loss, mean_absolute_error, mean_squared_error +from sklearn.metrics import mean_absolute_error, mean_squared_error + +from recbole.evaluator.utils import _binary_clf_curve # TopK Metrics # @@ -100,7 +101,7 @@ def recall_(pos_index, pos_len): .. math:: \mathrm {Recall@K} = \frac{|Rel_u\cap Rec_u|}{Rel_u} - :math:`Rel_u` is the set of items relavent to user :math:`U`, + :math:`Rel_u` is the set of items relevant to user :math:`U`, :math:`Rec_u` is the top K items recommended to users. We obtain the result by calculating the average :math:`Recall@K` of each user. @@ -155,7 +156,7 @@ def precision_(pos_index, pos_len): .. math:: \mathrm {Precision@K} = \frac{|Rel_u \cap Rec_u|}{Rec_u} - :math:`Rel_u` is the set of items relavent to user :math:`U`, + :math:`Rel_u` is the set of items relevant to user :math:`U`, :math:`Rec_u` is the top K items recommended to users. We obtain the result by calculating the average :math:`Precision@K` of each user. diff --git a/recbole/evaluator/proxy_evaluator.py b/recbole/evaluator/proxy_evaluator.py index f5faaebd6..237e8c29e 100644 --- a/recbole/evaluator/proxy_evaluator.py +++ b/recbole/evaluator/proxy_evaluator.py @@ -1,102 +1,104 @@ -# -*- encoding: utf-8 -*- -# @Time : 2020/12/9 -# @Author : Zhichao Feng -# @email : fzcbupt@gmail.com - -# UPDATE -# @Time : 2020/12/9 -# @Author : Zhichao Feng -# @email : fzcbupt@gmail.com - -from recbole.evaluator.evaluators import metric_eval_bind, group_metrics, individual_metrics -from collections import ChainMap - - -class ProxyEvaluator(object): - r"""ProxyEvaluator is used to assign the corresponding evaluator according to the evaluation metrics, - for example, TopkEvaluator for top-k metrics, and summarize the results of all evaluators. - - """ - def __init__(self, config): - self.config = config - self.valid_metrics = ChainMap(group_metrics, individual_metrics) - self.metrics = self.config['metrics'] - self._check_args() - self.evaluators = self.build() - - def build(self): - """assign evaluators according to metrics. - - Returns: - list: a list of evaluators. - - """ - evaluator_list = [] - metrics_list = [metric.lower() for metric in self.metrics] - for metrics, evaluator in metric_eval_bind: - used_metrics = [metric for metric in metrics_list if metric in metrics] - if used_metrics: - evaluator_list.append(evaluator(self.config, used_metrics)) - return evaluator_list - - def collect(self, interaction, scores): - """collect the all used evaluators' intermediate result of one batch. - - Args: - interaction (Interaction): :class:`AbstractEvaluator` of the batch - scores (tensor): the tensor of model output with size of `(N, )` - - """ - results = [] - for evaluator in self.evaluators: - results.append(evaluator.collect(interaction, scores)) - return results - - def merge_batch_result(self, batch_matrix_list): - """merge all the intermediate result got in `self.collect` for used evaluators separately. - - Args: - batch_matrix_list (list): the results of all batches not separated - - Returns: - dict: used evaluators' results of all batches - - """ - matrix_dict = {} - for collect_list in batch_matrix_list: - for i, value in enumerate(collect_list): - matrix_dict.setdefault(i, []).append(value) - - return matrix_dict - - def evaluate(self, batch_matrix_list, eval_data): - """calculate the metrics of all batches. It is called at the end of each epoch - - Args: - batch_matrix_list (list): the results of all batches - eval_data (Dataset): the class of test data - - Returns: - dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'GAUC': 0.9236}`` - - """ - matrix_dict = self.merge_batch_result(batch_matrix_list) - result_dict = {} - for i, evaluator in enumerate(self.evaluators): - res = evaluator.evaluate(matrix_dict[i], eval_data) - result_dict.update(res) - return result_dict - - def _check_args(self): - - # Check metrics - if isinstance(self.metrics, (str, list)): - if isinstance(self.metrics, str): - self.metrics = [self.metrics] - else: - raise TypeError('metrics must be str or list') - - # Convert metric to lowercase - for m in self.metrics: - if m.lower() not in self.valid_metrics: - raise ValueError("There is no metric named {}!".format(m)) +# -*- encoding: utf-8 -*- +# @Time : 2020/12/9 +# @Author : Zhichao Feng +# @email : fzcbupt@gmail.com + +# UPDATE +# @Time : 2020/12/9 +# @Author : Zhichao Feng +# @email : fzcbupt@gmail.com + +from collections import ChainMap + +from recbole.evaluator.evaluators import metric_eval_bind, group_metrics, individual_metrics + + +class ProxyEvaluator(object): + r"""ProxyEvaluator is used to assign the corresponding evaluator according to the evaluation metrics, + for example, TopkEvaluator for top-k metrics, and summarize the results of all evaluators. + + """ + + def __init__(self, config): + self.config = config + self.valid_metrics = ChainMap(group_metrics, individual_metrics) + self.metrics = self.config['metrics'] + self._check_args() + self.evaluators = self.build() + + def build(self): + """assign evaluators according to metrics. + + Returns: + list: a list of evaluators. + + """ + evaluator_list = [] + metrics_list = [metric.lower() for metric in self.metrics] + for metrics, evaluator in metric_eval_bind: + used_metrics = [metric for metric in metrics_list if metric in metrics] + if used_metrics: + evaluator_list.append(evaluator(self.config, used_metrics)) + return evaluator_list + + def collect(self, interaction, scores): + """collect the all used evaluators' intermediate result of one batch. + + Args: + interaction (Interaction): :class:`AbstractEvaluator` of the batch + scores (tensor): the tensor of model output with size of `(N, )` + + """ + results = [] + for evaluator in self.evaluators: + results.append(evaluator.collect(interaction, scores)) + return results + + def merge_batch_result(self, batch_matrix_list): + """merge all the intermediate result got in `self.collect` for used evaluators separately. + + Args: + batch_matrix_list (list): the results of all batches not separated + + Returns: + dict: used evaluators' results of all batches + + """ + matrix_dict = {} + for collect_list in batch_matrix_list: + for i, value in enumerate(collect_list): + matrix_dict.setdefault(i, []).append(value) + + return matrix_dict + + def evaluate(self, batch_matrix_list, eval_data): + """calculate the metrics of all batches. It is called at the end of each epoch + + Args: + batch_matrix_list (list): the results of all batches + eval_data (Dataset): the class of test data + + Returns: + dict: such as ``{'Hit@20': 0.3824, 'Recall@20': 0.0527, 'Hit@10': 0.3153, 'GAUC': 0.9236}`` + + """ + matrix_dict = self.merge_batch_result(batch_matrix_list) + result_dict = {} + for i, evaluator in enumerate(self.evaluators): + res = evaluator.evaluate(matrix_dict[i], eval_data) + result_dict.update(res) + return result_dict + + def _check_args(self): + + # Check metrics + if isinstance(self.metrics, (str, list)): + if isinstance(self.metrics, str): + self.metrics = [self.metrics] + else: + raise TypeError('metrics must be str or list') + + # Convert metric to lowercase + for m in self.metrics: + if m.lower() not in self.valid_metrics: + raise ValueError("There is no metric named {}!".format(m)) diff --git a/recbole/evaluator/utils.py b/recbole/evaluator/utils.py index 4f14b2ae8..650703301 100644 --- a/recbole/evaluator/utils.py +++ b/recbole/evaluator/utils.py @@ -14,7 +14,6 @@ """ import itertools -from enum import Enum import numpy as np import torch @@ -67,7 +66,7 @@ def trunc(scores, method): try: cut_method = getattr(np, method) except NotImplementedError: - raise NotImplementedError("module 'numpy' has no fuction named '{}'".format(method)) + raise NotImplementedError("module 'numpy' has no function named '{}'".format(method)) scores = cut_method(scores) return scores diff --git a/recbole/model/abstract_recommender.py b/recbole/model/abstract_recommender.py index 9afdaa12c..4a0b8cedf 100644 --- a/recbole/model/abstract_recommender.py +++ b/recbole/model/abstract_recommender.py @@ -14,12 +14,13 @@ """ from logging import getLogger + import numpy as np import torch import torch.nn as nn -from recbole.utils import ModelType, InputType, FeatureSource, FeatureType from recbole.model.layers import FMEmbedding, FMFirstOrderLinear +from recbole.utils import ModelType, InputType, FeatureSource, FeatureType class AbstractRecommender(nn.Module): @@ -115,7 +116,7 @@ def __init__(self, config, dataset): self.n_items = dataset.num(self.ITEM_ID) def gather_indexes(self, output, gather_index): - """Gathers the vectors at the spexific positions over a minibatch""" + """Gathers the vectors at the specific positions over a minibatch""" gather_index = gather_index.view(-1, 1, 1).expand(-1, -1, output.shape[-1]) output_tensor = output.gather(dim=1, index=gather_index) return output_tensor.squeeze(1) diff --git a/recbole/model/layers.py b/recbole/model/layers.py index f93e5d40e..5e2a9107f 100644 --- a/recbole/model/layers.py +++ b/recbole/model/layers.py @@ -15,15 +15,16 @@ Common Layers in recommender system """ -from logging import getLogger -import numpy as np import copy import math + +import numpy as np import torch import torch.nn as nn import torch.nn.functional as fn from torch.nn.init import normal_ -from recbole.utils import ModelType, InputType, FeatureType + +from recbole.utils import FeatureType class MLPLayers(nn.Module): @@ -32,8 +33,8 @@ class MLPLayers(nn.Module): Args: - layers(list): a list contains the size of each layer in mlp layers - dropout(float): probability of an element to be zeroed. Default: 0 - - activation(str): activation function after each layer in mlp layers. Default: 'relu' - candidates: 'sigmoid', 'tanh', 'relu', 'leekyrelu', 'none' + - activation(str): activation function after each layer in mlp layers. Default: 'relu'. + candidates: 'sigmoid', 'tanh', 'relu', 'leekyrelu', 'none' Shape: @@ -50,7 +51,7 @@ class MLPLayers(nn.Module): >>> torch.Size([128, 16]) """ - def __init__(self, layers, dropout=0, activation='relu', bn=False, init_method=None): + def __init__(self, layers, dropout=0., activation='relu', bn=False, init_method=None): super(MLPLayers, self).__init__() self.layers = layers self.dropout = dropout @@ -214,14 +215,14 @@ def __init__(self, in_dim, att_dim): self.h = nn.Parameter(torch.randn(att_dim), requires_grad=True) def forward(self, infeatures): - att_singal = self.w(infeatures) # [batch_size, M, att_dim] - att_singal = fn.relu(att_singal) # [batch_size, M, att_dim] + att_signal = self.w(infeatures) # [batch_size, M, att_dim] + att_signal = fn.relu(att_signal) # [batch_size, M, att_dim] - att_singal = torch.mul(att_singal, self.h) # [batch_size, M, att_dim] - att_singal = torch.sum(att_singal, dim=2) # [batch_size, M] - att_singal = fn.softmax(att_singal, dim=1) # [batch_size, M] + att_signal = torch.mul(att_signal, self.h) # [batch_size, M, att_dim] + att_signal = torch.sum(att_signal, dim=2) # [batch_size, M] + att_signal = fn.softmax(att_signal, dim=1) # [batch_size, M] - return att_singal + return att_signal class Dice(nn.Module): @@ -271,11 +272,11 @@ def __init__(self, mask_mat, att_hidden_size=(80, 40), activation='sigmoid', sof self.dense = nn.Linear(self.att_hidden_size[-1], 1) def forward(self, queries, keys, keys_length): - embbedding_size = queries.shape[-1] # H + embedding_size = queries.shape[-1] # H hist_len = keys.shape[1] # T queries = queries.repeat(1, hist_len) - queries = queries.view(-1, hist_len, embbedding_size) + queries = queries.view(-1, hist_len, embedding_size) # MLP Layer input_tensor = torch.cat([queries, keys, queries - keys, queries * keys], dim=-1) @@ -295,7 +296,7 @@ def forward(self, queries, keys, keys_length): output = output.masked_fill(mask=mask, value=torch.tensor(mask_value)) output = output.unsqueeze(1) - output = output / (embbedding_size ** 0.5) + output = output / (embedding_size ** 0.5) # get the weight of each user's history list about the target item if self.softmax_stag: @@ -319,6 +320,7 @@ class VanillaAttention(nn.Module): weights (torch.Tensor): the attention weights """ + def __init__(self, hidden_dim, attn_dim): super().__init__() self.projection = nn.Sequential( @@ -348,6 +350,7 @@ class MultiHeadAttention(nn.Module): hidden_states (torch.Tensor): the output of the multi-head self-attention layer """ + def __init__(self, n_heads, hidden_size, hidden_dropout_prob, attn_dropout_prob, layer_norm_eps): super(MultiHeadAttention, self).__init__() if hidden_size % n_heads != 0: @@ -420,6 +423,7 @@ class FeedForward(nn.Module): hidden_states (torch.Tensor): the output of the point-wise feed-forward layer """ + def __init__(self, hidden_size, inner_size, hidden_dropout_prob, hidden_act, layer_norm_eps): super(FeedForward, self).__init__() self.dense_1 = nn.Linear(hidden_size, inner_size) @@ -473,16 +477,18 @@ class TransformerLayer(nn.Module): attention_mask (torch.Tensor): the attention mask for the multi-head self-attention sublayer Returns: - feedforward_output (torch.Tensor): the output of the point-wise feed-forward sublayer, is the output of the transformer layer + feedforward_output (torch.Tensor): The output of the point-wise feed-forward sublayer, + is the output of the transformer layer. """ + def __init__(self, n_heads, hidden_size, intermediate_size, hidden_dropout_prob, attn_dropout_prob, hidden_act, layer_norm_eps): super(TransformerLayer, self).__init__() self.multi_head_attention = MultiHeadAttention(n_heads, hidden_size, - hidden_dropout_prob, attn_dropout_prob, layer_norm_eps) + hidden_dropout_prob, attn_dropout_prob, layer_norm_eps) self.feed_forward = FeedForward(hidden_size, intermediate_size, - hidden_dropout_prob, hidden_act, layer_norm_eps) + hidden_dropout_prob, hidden_act, layer_norm_eps) def forward(self, hidden_states, attention_mask): attention_output = self.multi_head_attention(hidden_states, attention_mask) @@ -504,6 +510,7 @@ class TransformerEncoder(nn.Module): - layer_norm_eps(float): a value added to the denominator for numerical stability. Default: 1e-12 """ + def __init__(self, n_layers=2, n_heads=2, @@ -523,13 +530,13 @@ def __init__(self, def forward(self, hidden_states, attention_mask, output_all_encoded_layers=True): """ Args: - hidden_states (torch.Tensor): the input of the TrandformerEncoder + hidden_states (torch.Tensor): the input of the TransformerEncoder attention_mask (torch.Tensor): the attention mask for the input hidden_states output_all_encoded_layers (Bool): whether output all transformer layers' output Returns: - all_encoder_layers (list): if output_all_encoded_layers is True, return a list consists of all transformer layers' output, - otherwise return a list only consists of the output of last transformer layer. + all_encoder_layers (list): if output_all_encoded_layers is True, return a list consists of all transformer + layers' output, otherwise return a list only consists of the output of last transformer layer. """ all_encoder_layers = [] @@ -626,7 +633,7 @@ def embed_float_fields(self, float_fields, type, embed=True): return float_embedding def embed_token_fields(self, token_fields, type): - """Get the embedding of toekn fields + """Get the embedding of token fields Args: token_fields(torch.Tensor): input, [batch_size, max_item_length, num_token_field] @@ -754,8 +761,11 @@ def embed_input_fields(self, user_idx, item_idx): token_seq_fields_embedding[type]], dim=-2) dense_embedding[type] = float_fields_embedding[type] - # sparse_embedding[type] shape: [batch_size, max_item_length, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding[type] shape: [batch_size, max_item_length, num_float_field] or [batch_size, max_item_length, num_float_field, embed_dim] or None + # sparse_embedding[type] + # shape: [batch_size, max_item_length, num_token_seq_field+num_token_field, embed_dim] or None + # dense_embedding[type] + # shape: [batch_size, max_item_length, num_float_field] + # or [batch_size, max_item_length, num_float_field, embed_dim] or None return sparse_embedding, dense_embedding def forward(self, user_idx, item_idx): @@ -857,7 +867,8 @@ def __init__(self, channels, kernels, strides, activation='relu', init_method=No cnn_modules = [] for i in range(self.num_of_nets): - cnn_modules.append(nn.Conv2d(self.channels[i], self.channels[i + 1], self.kernels[i], stride=self.strides[i])) + cnn_modules.append( + nn.Conv2d(self.channels[i], self.channels[i + 1], self.kernels[i], stride=self.strides[i])) if self.activation.lower() == 'sigmoid': cnn_modules.append(nn.Sigmoid()) elif self.activation.lower() == 'tanh': @@ -1055,7 +1066,6 @@ def __init__(self, p=0.5): self.kprob = 1 - p def forward(self, x): - if not self.training: return x @@ -1063,4 +1073,4 @@ def forward(self, x): self.kprob).floor()).type(torch.bool) rc = x._indices()[:, mask] val = x._values()[mask] * (1.0 / self.kprob) - return torch.sparse.FloatTensor(rc, val, x.shape) \ No newline at end of file + return torch.sparse.FloatTensor(rc, val, x.shape) diff --git a/recbole/model/loss.py b/recbole/model/loss.py index ebfc84ca1..9727261f3 100644 --- a/recbole/model/loss.py +++ b/recbole/model/loss.py @@ -14,13 +14,11 @@ Common Loss in recommender system """ - import torch import torch.nn as nn class BPRLoss(nn.Module): - """ BPRLoss, based on Bayesian Personalized Ranking Args: @@ -39,6 +37,7 @@ class BPRLoss(nn.Module): >>> output = loss(pos_score, neg_score) >>> output.backward() """ + def __init__(self, gamma=1e-10): super(BPRLoss, self).__init__() self.gamma = gamma @@ -52,6 +51,7 @@ class RegLoss(nn.Module): """ RegLoss, L2 regularization on model parameters """ + def __init__(self): super(RegLoss, self).__init__() @@ -69,6 +69,7 @@ class EmbLoss(nn.Module): """ EmbLoss, regularization on embeddings """ + def __init__(self, norm=2): super(EmbLoss, self).__init__() self.norm = norm From a8df3aad729745c22e43b99c7dba7e938f38399e Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 16:14:23 +0800 Subject: [PATCH 243/249] FORMAT: code format in context model. --- recbole/model/abstract_recommender.py | 9 +++++ .../model/context_aware_recommender/afm.py | 12 ++----- .../context_aware_recommender/autoint.py | 14 ++------ .../model/context_aware_recommender/dcn.py | 16 +++------ .../model/context_aware_recommender/deepfm.py | 17 +++------ .../model/context_aware_recommender/dssm.py | 4 +-- .../model/context_aware_recommender/ffm.py | 36 ++++++++++--------- recbole/model/context_aware_recommender/fm.py | 13 ++----- .../model/context_aware_recommender/fnn.py | 13 ++----- .../model/context_aware_recommender/fwfm.py | 33 +++++++---------- recbole/model/context_aware_recommender/lr.py | 1 + .../model/context_aware_recommender/nfm.py | 17 +++------ .../model/context_aware_recommender/pnn.py | 15 +++----- .../context_aware_recommender/widedeep.py | 16 ++------- .../context_aware_recommender/xdeepfm.py | 12 ++----- 15 files changed, 76 insertions(+), 152 deletions(-) diff --git a/recbole/model/abstract_recommender.py b/recbole/model/abstract_recommender.py index 4a0b8cedf..93c6dfb57 100644 --- a/recbole/model/abstract_recommender.py +++ b/recbole/model/abstract_recommender.py @@ -347,6 +347,15 @@ def double_tower_embed_input_fields(self, interaction): return first_sparse_embedding, first_dense_embedding, second_sparse_embedding, second_dense_embedding + def concat_embed_input_fields(self, interaction): + sparse_embedding, dense_embedding = self.embed_input_fields(interaction) + all_embeddings = [] + if sparse_embedding is not None: + all_embeddings.append(sparse_embedding) + if dense_embedding is not None and len(dense_embedding.shape) == 3: + all_embeddings.append(dense_embedding) + return torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + def embed_input_fields(self, interaction): """Embed the whole feature columns. diff --git a/recbole/model/context_aware_recommender/afm.py b/recbole/model/context_aware_recommender/afm.py index 5c615e42c..e1fe1c03c 100644 --- a/recbole/model/context_aware_recommender/afm.py +++ b/recbole/model/context_aware_recommender/afm.py @@ -16,8 +16,8 @@ import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import AttLayer from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import AttLayer class AFM(ContextRecommender): @@ -99,15 +99,7 @@ def afm_layer(self, infeature): return att_pooling def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - afm_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + afm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] output = self.sigmoid(self.first_order_linear(interaction) + self.afm_layer(afm_all_embeddings)) return output.squeeze() diff --git a/recbole/model/context_aware_recommender/autoint.py b/recbole/model/context_aware_recommender/autoint.py index b22e331ee..893d5d8f2 100644 --- a/recbole/model/context_aware_recommender/autoint.py +++ b/recbole/model/context_aware_recommender/autoint.py @@ -13,12 +13,12 @@ """ import torch -import torch.nn.functional as F import torch.nn as nn +import torch.nn.functional as F from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers class AutoInt(ContextRecommender): @@ -95,15 +95,7 @@ def autoint_layer(self, infeature): return att_output def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - autoint_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + autoint_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] output = self.first_order_linear(interaction) + self.autoint_layer(autoint_all_embeddings) return self.sigmoid(output.squeeze(1)) diff --git a/recbole/model/context_aware_recommender/dcn.py b/recbole/model/context_aware_recommender/dcn.py index 7aa11b3d1..396130803 100644 --- a/recbole/model/context_aware_recommender/dcn.py +++ b/recbole/model/context_aware_recommender/dcn.py @@ -22,9 +22,9 @@ import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.loss import RegLoss -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers +from recbole.model.loss import RegLoss class DCN(ContextRecommender): @@ -32,6 +32,7 @@ class DCN(ContextRecommender): automatically construct limited high-degree cross features, and learns the corresponding weights. """ + def __init__(self, config, dataset): super(DCN, self).__init__(config, dataset) @@ -94,16 +95,7 @@ def cross_network(self, x_0): return x_l def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - - dcn_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + dcn_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = dcn_all_embeddings.shape[0] dcn_all_embeddings = dcn_all_embeddings.view(batch_size, -1) diff --git a/recbole/model/context_aware_recommender/deepfm.py b/recbole/model/context_aware_recommender/deepfm.py index 5ff10298b..f1791564e 100644 --- a/recbole/model/context_aware_recommender/deepfm.py +++ b/recbole/model/context_aware_recommender/deepfm.py @@ -16,12 +16,11 @@ Huifeng Guo et al. "DeepFM: A Factorization-Machine based Neural Network for CTR Prediction." in IJCAI 2017. """ -import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import BaseFactorizationMachine, MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import BaseFactorizationMachine, MLPLayers class DeepFM(ContextRecommender): @@ -29,6 +28,7 @@ class DeepFM(ContextRecommender): Also DeepFM can be seen as a combination of FNN and FM. """ + def __init__(self, config, dataset): super(DeepFM, self).__init__(config, dataset) @@ -56,20 +56,11 @@ def _init_weights(self, module): constant_(module.bias.data, 0) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - deepfm_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + deepfm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = deepfm_all_embeddings.shape[0] y_fm = self.first_order_linear(interaction) + self.fm(deepfm_all_embeddings) - y_deep = self.deep_predict_layer( - self.mlp_layers(deepfm_all_embeddings.view(batch_size, -1))) + y_deep = self.deep_predict_layer(self.mlp_layers(deepfm_all_embeddings.view(batch_size, -1))) y = self.sigmoid(y_fm + y_deep) return y.squeeze() diff --git a/recbole/model/context_aware_recommender/dssm.py b/recbole/model/context_aware_recommender/dssm.py index 8261be2eb..597a04c33 100644 --- a/recbole/model/context_aware_recommender/dssm.py +++ b/recbole/model/context_aware_recommender/dssm.py @@ -12,13 +12,12 @@ PS Huang et al. "Learning Deep Structured Semantic Models for Web Search using Clickthrough Data" in CIKM 2013. """ - import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers class DSSM(ContextRecommender): @@ -26,6 +25,7 @@ class DSSM(ContextRecommender): and uses cosine distance to calculate the distance between the two semantic vectors. """ + def __init__(self, config, dataset): super(DSSM, self).__init__(config, dataset) diff --git a/recbole/model/context_aware_recommender/ffm.py b/recbole/model/context_aware_recommender/ffm.py index 617860ea1..3863d1bfe 100644 --- a/recbole/model/context_aware_recommender/ffm.py +++ b/recbole/model/context_aware_recommender/ffm.py @@ -8,7 +8,7 @@ FFM ##################################################### Reference: - Yuchin Juan et al. "Field-aware Factorization Machines for CTR Prediction" in RecSys 2016. + Yuchin Juan et al. "Field-aware Factorization Machines for CTR Prediction" in RecSys 2016. Reference code: https://github.com/rixwew/pytorch-fm @@ -37,19 +37,20 @@ def __init__(self, config, dataset): super(FFM, self).__init__(config, dataset) # load parameters info - self.fields = config['fields'] # a dict; key: field_id; value: feature_list + self.fields = config['fields'] # a dict; key: field_id; value: feature_list self.sigmoid = nn.Sigmoid() self.feature2id = {} self.feature2field = {} - + self.feature_names = (self.token_field_names, self.float_field_names, self.token_seq_field_names) self.feature_dims = (self.token_field_dims, self.float_field_dims, self.token_seq_field_dims) self._get_feature2field() self.num_fields = len(set(self.feature2field.values())) # the number of fields - self.ffm = FieldAwareFactorizationMachine(self.feature_names, self.feature_dims, self.feature2id, self.feature2field, self.num_fields, self.embedding_size, self.device) + self.ffm = FieldAwareFactorizationMachine(self.feature_names, self.feature_dims, self.feature2id, + self.feature2field, self.num_fields, self.embedding_size, self.device) self.loss = nn.BCELoss() # parameters initialization @@ -73,7 +74,7 @@ def _get_feature2field(self): for name in names: self.feature2id[name] = fea_id fea_id += 1 - + if self.fields is None: field_id = 0 for key, value in self.feature2id.items(): @@ -96,25 +97,25 @@ def get_ffm_input(self, interaction): for tn in self.token_field_names: token_ffm_input.append(torch.unsqueeze(interaction[tn], 1)) if len(token_ffm_input) > 0: - token_ffm_input = torch.cat(token_ffm_input, dim=1) # [batch_size, num_token_features] + token_ffm_input = torch.cat(token_ffm_input, dim=1) # [batch_size, num_token_features] float_ffm_input = [] if self.float_field_names is not None: for fn in self.float_field_names: float_ffm_input.append(torch.unsqueeze(interaction[fn], 1)) if len(float_ffm_input) > 0: - float_ffm_input = torch.cat(float_ffm_input, dim=1) # [batch_size, num_float_features] + float_ffm_input = torch.cat(float_ffm_input, dim=1) # [batch_size, num_float_features] token_seq_ffm_input = [] if self.token_seq_field_names is not None: for tsn in self.token_seq_field_names: - token_seq_ffm_input.append(interaction[tsn]) # a list + token_seq_ffm_input.append(interaction[tsn]) # a list - return (token_ffm_input, float_ffm_input, token_seq_ffm_input) + return token_ffm_input, float_ffm_input, token_seq_ffm_input def forward(self, interaction): ffm_input = self.get_ffm_input(interaction) ffm_output = torch.sum(torch.sum(self.ffm(ffm_input), dim=1), dim=1, keepdim=True) output = self.sigmoid(self.first_order_linear(interaction) + ffm_output) - + return output.squeeze() def calculate_loss(self, interaction): @@ -144,7 +145,8 @@ def __init__(self, feature_names, feature_dims, feature2id, feature2field, num_f self.feature2id = feature2id self.feature2field = feature2field - self.num_features = len(self.token_feature_names) + len(self.float_feature_names) + len(self.token_seq_feature_names) + self.num_features = len(self.token_feature_names) + len(self.float_feature_names) \ + + len(self.token_seq_feature_names) self.num_fields = num_fields self.embed_dim = embed_dim self.device = device @@ -201,7 +203,7 @@ def forward(self, input_x): token_input_x_emb = self._emb_token_ffm_input(token_ffm_input) float_input_x_emb = self._emb_float_ffm_input(float_ffm_input) token_seq_input_x_emb = self._emb_token_seq_ffm_input(token_seq_ffm_input) - + input_x_emb = self._get_input_x_emb(token_input_x_emb, float_input_x_emb, token_seq_input_x_emb) output = list() @@ -241,7 +243,8 @@ def _emb_token_ffm_input(self, token_ffm_input): token_input_x_emb = [] if len(self.token_feature_names) > 0: token_input_x = token_ffm_input + token_ffm_input.new_tensor(self.token_offsets).unsqueeze(0) - token_input_x_emb = [self.token_embeddings[i](token_input_x) for i in range(self.num_fields)] # [num_fields: [batch_size, num_token_features, emb_dim]] + token_input_x_emb = [self.token_embeddings[i](token_input_x) + for i in range(self.num_fields)] # [num_fields: [batch_size, num_token_features, emb_dim]] return token_input_x_emb @@ -249,8 +252,9 @@ def _emb_float_ffm_input(self, float_ffm_input): # get float field-aware embeddings float_input_x_emb = [] if len(self.float_feature_names) > 0: - index = torch.arange(0, self.num_float_features).unsqueeze(0).expand_as(float_ffm_input).long().to(self.device) # [batch_size, num_float_features] - float_input_x_emb = [torch.mul(self.float_embeddings[i](index), float_ffm_input.unsqueeze(2)) for i in range(self.num_fields)] # [num_fields: [batch_size, num_float_features, emb_dim]] + index = torch.arange(0, self.num_float_features).unsqueeze(0).expand_as(float_ffm_input).long().to(self.device) # [batch_size, num_float_features] + float_input_x_emb = [torch.mul(self.float_embeddings[i](index), float_ffm_input.unsqueeze(2)) + for i in range(self.num_fields)] # [num_fields: [batch_size, num_float_features, emb_dim]] return float_input_x_emb @@ -276,6 +280,6 @@ def _emb_token_seq_ffm_input(self, token_seq_ffm_input): result = result.unsqueeze(1) # [batch_size, 1, embed_dim] token_seq_result.append(result) - token_seq_input_x_emb.append(torch.cat(token_seq_result, dim=1)) # [num_fields: batch_size, num_token_seq_features, embed_dim] + token_seq_input_x_emb.append(torch.cat(token_seq_result, dim=1)) # [num_fields: batch_size, num_token_seq_features, embed_dim] return token_seq_input_x_emb diff --git a/recbole/model/context_aware_recommender/fm.py b/recbole/model/context_aware_recommender/fm.py index ae64f9bf5..c43b2aa21 100644 --- a/recbole/model/context_aware_recommender/fm.py +++ b/recbole/model/context_aware_recommender/fm.py @@ -16,12 +16,11 @@ Steffen Rendle et al. "Factorization Machines." in ICDM 2010. """ -import torch import torch.nn as nn from torch.nn.init import xavier_normal_ -from recbole.model.layers import BaseFactorizationMachine from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import BaseFactorizationMachine class FM(ContextRecommender): @@ -46,15 +45,7 @@ def _init_weights(self, module): xavier_normal_(module.weight.data) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - fm_all_embeddings = torch.cat(all_embeddings, dim=1) + fm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] y = self.sigmoid(self.first_order_linear(interaction) + self.fm(fm_all_embeddings)) return y.squeeze() diff --git a/recbole/model/context_aware_recommender/fnn.py b/recbole/model/context_aware_recommender/fnn.py index 70cda0373..d8e2a1987 100644 --- a/recbole/model/context_aware_recommender/fnn.py +++ b/recbole/model/context_aware_recommender/fnn.py @@ -11,12 +11,11 @@ Weinan Zhang1 et al. "Deep Learning over Multi-field Categorical Data" in ECIR 2016 """ -import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers class FNN(ContextRecommender): @@ -59,15 +58,7 @@ def _init_weights(self, module): constant_(module.bias.data, 0) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - fnn_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + fnn_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = fnn_all_embeddings.shape[0] output = self.predict_layer(self.mlp_layers(fnn_all_embeddings.view(batch_size, -1))) diff --git a/recbole/model/context_aware_recommender/fwfm.py b/recbole/model/context_aware_recommender/fwfm.py index 2c4800341..73e095b0b 100644 --- a/recbole/model/context_aware_recommender/fwfm.py +++ b/recbole/model/context_aware_recommender/fwfm.py @@ -35,20 +35,20 @@ def __init__(self, config, dataset): # load parameters info self.dropout_prob = config['dropout_prob'] - self.fields = config['fields'] # a dict; key: field_id; value: feature_list + self.fields = config['fields'] # a dict; key: field_id; value: feature_list self.num_features = self.num_feature_field - + self.dropout_layer = nn.Dropout(p=self.dropout_prob) self.sigmoid = nn.Sigmoid() self.feature2id = {} self.feature2field = {} - + self.feature_names = (self.token_field_names, self.token_seq_field_names, self.float_field_names) self.feature_dims = (self.token_field_dims, self.token_seq_field_dims, self.float_field_dims) self._get_feature2field() - self.num_fields = len(set(self.feature2field.values())) # the number of fields + self.num_fields = len(set(self.feature2field.values())) # the number of fields self.num_pair = self.num_fields * self.num_fields self.loss = nn.BCELoss() @@ -71,11 +71,10 @@ def _get_feature2field(self): fea_id = 0 for names in self.feature_names: if names is not None: - self.logger.info(names) for name in names: self.feature2id[name] = fea_id fea_id += 1 - + if self.fields is None: field_id = 0 for key, value in self.feature2id.items(): @@ -101,33 +100,27 @@ def fwfm_layer(self, infeature): """ # get r(Fi, Fj) batch_size = infeature.shape[0] - para = torch.randn(self.num_fields*self.num_fields*self.embedding_size).expand(batch_size, self.num_fields*self.num_fields*self.embedding_size).to(self.device) # [batch_size*num_pairs*emb_dim] + para = torch.randn(self.num_fields * self.num_fields * self.embedding_size).\ + expand(batch_size, self.num_fields * self.num_fields * self.embedding_size).\ + to(self.device) # [batch_size*num_pairs*emb_dim] para = torch.reshape(para, (batch_size, self.num_fields, self.num_fields, self.embedding_size)) - r = nn.Parameter(para, requires_grad=True) # [batch_size, num_fields, num_fields, emb_dim] + r = nn.Parameter(para, requires_grad=True) # [batch_size, num_fields, num_fields, emb_dim] - fwfm_inter = list() # [batch_size, num_fields, emb_dim] + fwfm_inter = list() # [batch_size, num_fields, emb_dim] for i in range(self.num_features - 1): for j in range(i + 1, self.num_features): Fi, Fj = self.feature2field[i], self.feature2field[j] fwfm_inter.append(infeature[:, i] * infeature[:, j] * r[:, Fi, Fj]) fwfm_inter = torch.stack(fwfm_inter, dim=1) - fwfm_inter = torch.sum(fwfm_inter, dim=1) # [batch_size, emb_dim] - fwfm_inter = self.dropout_layer(fwfm_inter) + fwfm_inter = torch.sum(fwfm_inter, dim=1) # [batch_size, emb_dim] + fwfm_inter = self.dropout_layer(fwfm_inter) fwfm_output = torch.sum(fwfm_inter, dim=1, keepdim=True) # [batch_size, 1] return fwfm_output def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - fwfm_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + fwfm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] output = self.sigmoid(self.first_order_linear(interaction) + self.fwfm_layer(fwfm_all_embeddings)) diff --git a/recbole/model/context_aware_recommender/lr.py b/recbole/model/context_aware_recommender/lr.py index e8d35f347..0c3a6a759 100644 --- a/recbole/model/context_aware_recommender/lr.py +++ b/recbole/model/context_aware_recommender/lr.py @@ -27,6 +27,7 @@ class LR(ContextRecommender): Z = \sum_{i} {w_i}{x_i} """ + def __init__(self, config, dataset): super(LR, self).__init__(config, dataset) diff --git a/recbole/model/context_aware_recommender/nfm.py b/recbole/model/context_aware_recommender/nfm.py index d967b9daf..4e0e1750f 100644 --- a/recbole/model/context_aware_recommender/nfm.py +++ b/recbole/model/context_aware_recommender/nfm.py @@ -11,18 +11,18 @@ He X, Chua T S. "Neural factorization machines for sparse predictive analytics" in SIGIR 2017 """ -import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import BaseFactorizationMachine, MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import BaseFactorizationMachine, MLPLayers class NFM(ContextRecommender): """ NFM replace the fm part as a mlp to model the feature interaction. """ + def __init__(self, config, dataset): super(NFM, self).__init__(config, dataset) @@ -51,18 +51,11 @@ def _init_weights(self, module): constant_(module.bias.data, 0) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - nfm_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + nfm_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] bn_nfm_all_embeddings = self.bn(self.fm(nfm_all_embeddings)) - output = self.sigmoid(self.predict_layer(self.mlp_layers(bn_nfm_all_embeddings)) + self.first_order_linear(interaction)) + output = self.predict_layer(self.mlp_layers(bn_nfm_all_embeddings)) + self.first_order_linear(interaction) + output = self.sigmoid(output) return output.squeeze() def calculate_loss(self, interaction): diff --git a/recbole/model/context_aware_recommender/pnn.py b/recbole/model/context_aware_recommender/pnn.py index 13933fe2a..588d4ba26 100644 --- a/recbole/model/context_aware_recommender/pnn.py +++ b/recbole/model/context_aware_recommender/pnn.py @@ -20,8 +20,8 @@ import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers class PNN(ContextRecommender): @@ -84,15 +84,7 @@ def _init_weights(self, module): constant_(module.bias.data, 0) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - pnn_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + pnn_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = pnn_all_embeddings.shape[0] # linear part linear_part = pnn_all_embeddings.view(batch_size, -1) # [batch_size,num_field*embed_dim] @@ -104,7 +96,7 @@ def forward(self, interaction): if self.use_outer: outer_product = self.outer_product(pnn_all_embeddings).view(batch_size, -1) # [batch_size,num_pairs] output.append(outer_product) - output = torch.cat(output, dim=1) # [batch_size,d] + output = torch.cat(output, dim=1) # [batch_size,d] output = self.predict_layer(self.mlp_layers(output)) # [batch_size,1] output = self.sigmoid(output) @@ -125,6 +117,7 @@ class InnerProductLayer(nn.Module): product or inner product between feature vectors. """ + def __init__(self, num_feature_field, device): """ Args: diff --git a/recbole/model/context_aware_recommender/widedeep.py b/recbole/model/context_aware_recommender/widedeep.py index 72a36a7f8..e1c820efd 100644 --- a/recbole/model/context_aware_recommender/widedeep.py +++ b/recbole/model/context_aware_recommender/widedeep.py @@ -11,12 +11,11 @@ Heng-Tze Cheng et al. "Wide & Deep Learning for Recommender Systems." in RecSys 2016. """ -import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers class WideDeep(ContextRecommender): @@ -54,20 +53,11 @@ def _init_weights(self, module): constant_(module.bias.data, 0) def forward(self, interaction): - # sparse_embedding shape: [batch_size, num_token_seq_field+num_token_field, embed_dim] or None - # dense_embedding shape: [batch_size, num_float_field] or [batch_size, num_float_field, embed_dim] or None - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - widedeep_all_embeddings = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + widedeep_all_embeddings = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] batch_size = widedeep_all_embeddings.shape[0] fm_output = self.first_order_linear(interaction) - deep_output = self.deep_predict_layer( - self.mlp_layers(widedeep_all_embeddings.view(batch_size, -1))) + deep_output = self.deep_predict_layer(self.mlp_layers(widedeep_all_embeddings.view(batch_size, -1))) output = self.sigmoid(fm_output + deep_output) return output.squeeze() diff --git a/recbole/model/context_aware_recommender/xdeepfm.py b/recbole/model/context_aware_recommender/xdeepfm.py index 15b56ffbf..3e54939df 100644 --- a/recbole/model/context_aware_recommender/xdeepfm.py +++ b/recbole/model/context_aware_recommender/xdeepfm.py @@ -23,10 +23,9 @@ import torch import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from logging import getLogger -from recbole.model.layers import MLPLayers, activation_layer from recbole.model.abstract_recommender import ContextRecommender +from recbole.model.layers import MLPLayers, activation_layer class xDeepFM(ContextRecommender): @@ -170,15 +169,8 @@ def compressed_interaction_network(self, input_features, activation='identity'): return result def forward(self, interaction): - sparse_embedding, dense_embedding = self.embed_input_fields(interaction) - all_embeddings = [] - if sparse_embedding is not None: - all_embeddings.append(sparse_embedding) - if dense_embedding is not None and len(dense_embedding.shape) == 3: - all_embeddings.append(dense_embedding) - # Get the output of CIN. - xdeepfm_input = torch.cat(all_embeddings, dim=1) # [batch_size, num_field, embed_dim] + xdeepfm_input = self.concat_embed_input_fields(interaction) # [batch_size, num_field, embed_dim] cin_output = self.compressed_interaction_network(xdeepfm_input) cin_output = self.cin_linear(cin_output) From 31207aaf695006af2a300052ab832b7ccc9b8811 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 17:24:58 +0800 Subject: [PATCH 244/249] FORMAT: code format in general model --- recbole/model/exlib_recommender/xgboost.py | 2 +- recbole/model/general_recommender/bpr.py | 4 +- recbole/model/general_recommender/cdae.py | 19 ++-- recbole/model/general_recommender/convncf.py | 10 +- recbole/model/general_recommender/dgcf.py | 44 ++++---- recbole/model/general_recommender/dmf.py | 8 +- recbole/model/general_recommender/fism.py | 9 +- recbole/model/general_recommender/gcmc.py | 104 +++++++++--------- recbole/model/general_recommender/itemknn.py | 3 +- recbole/model/general_recommender/lightgcn.py | 47 ++++---- recbole/model/general_recommender/line.py | 13 ++- .../model/general_recommender/macridvae.py | 11 +- recbole/model/general_recommender/multidae.py | 8 +- recbole/model/general_recommender/multivae.py | 18 +-- recbole/model/general_recommender/nais.py | 24 ++-- recbole/model/general_recommender/neumf.py | 6 +- recbole/model/general_recommender/ngcf.py | 18 +-- recbole/model/general_recommender/pop.py | 4 +- .../model/general_recommender/spectralcf.py | 21 ++-- recbole/properties/model/MultiDAE.yaml | 2 +- recbole/properties/model/MultiVAE.yaml | 2 +- 21 files changed, 183 insertions(+), 194 deletions(-) diff --git a/recbole/model/exlib_recommender/xgboost.py b/recbole/model/exlib_recommender/xgboost.py index 45f5e4954..a09da2cdb 100644 --- a/recbole/model/exlib_recommender/xgboost.py +++ b/recbole/model/exlib_recommender/xgboost.py @@ -9,7 +9,7 @@ """ import xgboost as xgb -from recbole.utils import ModelType, InputType, FeatureSource, FeatureType +from recbole.utils import ModelType, InputType class xgboost(xgb.Booster): diff --git a/recbole/model/general_recommender/bpr.py b/recbole/model/general_recommender/bpr.py index 586f83df2..684929251 100644 --- a/recbole/model/general_recommender/bpr.py +++ b/recbole/model/general_recommender/bpr.py @@ -19,10 +19,10 @@ import torch import torch.nn as nn -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender -from recbole.model.loss import BPRLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss +from recbole.utils import InputType class BPR(GeneralRecommender): diff --git a/recbole/model/general_recommender/cdae.py b/recbole/model/general_recommender/cdae.py index 57981b1c9..11eef117d 100644 --- a/recbole/model/general_recommender/cdae.py +++ b/recbole/model/general_recommender/cdae.py @@ -15,11 +15,10 @@ import torch import torch.nn as nn -import numpy as np -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.init import xavier_normal_initialization +from recbole.utils import InputType class CDAE(GeneralRecommender): @@ -65,7 +64,7 @@ def __init__(self, config, dataset): self.h_user = nn.Embedding(self.n_users, self.embedding_size) self.h_item = nn.Linear(self.n_items, self.embedding_size) self.out_layer = nn.Linear(self.embedding_size, self.n_items) - + # parameters initialization self.apply(xavier_normal_initialization) @@ -74,7 +73,7 @@ def forward(self, x_items, x_users): h_i = self.h_item(h_i) h_u = self.h_user(x_users) h = torch.add(h_u, h_i) - h = self.h_act(h) + h = self.h_act(h) out = self.out_layer(h) return self.o_act(out) @@ -99,7 +98,7 @@ def calculate_loss(self, interaction): x_users = interaction[self.USER_ID] x_items = self.get_rating_matrix(x_users) predict = self.forward(x_items, x_users) - + if self.loss_type == 'MSE': loss_func = nn.MSELoss(reduction='sum') elif self.loss_type == 'BCE': @@ -112,23 +111,21 @@ def calculate_loss(self, interaction): loss += self.reg_weight_1 * (self.h_user.weight.norm(p=1) + self.h_item.weight.norm(p=1)) # l2-regularization loss += self.reg_weight_2 * (self.h_user.weight.norm() + self.h_item.weight.norm()) - + return loss - def predict(self, interaction): users = interaction[self.USER_ID] predict_items = interaction[self.ITEM_ID] - items = self.get_rating_matrix(users) + items = self.get_rating_matrix(users) scores = self.forward(items, users) - + return scores[[users, predict_items]] def full_sort_predict(self, interaction): users = interaction[self.USER_ID] - + items = self.get_rating_matrix(users) predict = self.forward(items, users) return predict.view(-1) - diff --git a/recbole/model/general_recommender/convncf.py b/recbole/model/general_recommender/convncf.py index 2ee9714e1..e90808f1d 100644 --- a/recbole/model/general_recommender/convncf.py +++ b/recbole/model/general_recommender/convncf.py @@ -16,13 +16,12 @@ import torch import torch.nn as nn -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.layers import MLPLayers, CNNLayers +from recbole.utils import InputType class ConvNCFBPRLoss(nn.Module): - """ ConvNCFBPRLoss, based on Bayesian Personalized Ranking, Shape: @@ -38,9 +37,10 @@ class ConvNCFBPRLoss(nn.Module): >>> output = loss(pos_score, neg_score) >>> output.backward() """ + def __init__(self): super(ConvNCFBPRLoss, self).__init__() - + def forward(self, pos_score, neg_score): distance = pos_score - neg_score loss = torch.sum(torch.log((1 + torch.exp(-distance)))) @@ -94,7 +94,7 @@ def forward(self, user, item): def reg_loss(self): r"""Calculate the L2 normalization loss of model parameters. - Including embedding matrixes and weight matrixes of model. + Including embedding matrices and weight matrices of model. Returns: loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,] @@ -118,7 +118,7 @@ def calculate_loss(self, interaction): pos_item_score = self.forward(user, pos_item) neg_item_score = self.forward(user, neg_item) - + loss = self.loss(pos_item_score, neg_item_score) opt_loss = loss + self.reg_loss() diff --git a/recbole/model/general_recommender/dgcf.py b/recbole/model/general_recommender/dgcf.py index 09354646b..28109b4c8 100644 --- a/recbole/model/general_recommender/dgcf.py +++ b/recbole/model/general_recommender/dgcf.py @@ -18,17 +18,18 @@ https://github.com/xiangwang1223/disentangled_graph_collaborative_filtering """ -import numpy as np import random as rd + +import numpy as np import torch import torch.nn as nn import torch.nn.functional as F from torch.autograd import Variable -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType def sample_cor_samples(n_users, n_items, cor_batch_size): @@ -44,7 +45,7 @@ def sample_cor_samples(n_users, n_items, cor_batch_size): Note: We have to sample some embedded representations out of all nodes. - Becasue we have no way to store cor-distance for each pair. + Because we have no way to store cor-distance for each pair. """ cor_users = rd.sample(list(range(n_users)), cor_batch_size) cor_items = rd.sample(list(range(n_items)), cor_batch_size) @@ -117,9 +118,9 @@ def _build_sparse_tensor(self, indices, values, size): def _get_ego_embeddings(self): # concat of user embeddings and item embeddings - user_embd = self.user_embedding.weight - item_embd = self.item_embedding.weight - ego_embeddings = torch.cat([user_embd, item_embd], dim=0) + user_emb = self.user_embedding.weight + item_emb = self.item_embedding.weight + ego_embeddings = torch.cat([user_emb, item_emb], dim=0) return ego_embeddings def build_matrix(self, A_values): @@ -169,7 +170,8 @@ def forward(self): layer_embeddings = [] # split the input embedding table - # .... ego_layer_embeddings is a (n_factors)-leng list of embeddings [n_users+n_items, embed_size/n_factors] + # .... ego_layer_embeddings is a (n_factors)-length list of embeddings + # [n_users+n_items, embed_size/n_factors] ego_layer_embeddings = torch.chunk(ego_embeddings, self.n_factors, 1) for t in range(0, self.n_iterations): iter_embeddings = [] @@ -194,18 +196,18 @@ def forward(self): # get the factor-wise embeddings # .... head_factor_embeddings is a dense tensor with the size of [all_h_list, embed_size/n_factors] # .... analogous to tail_factor_embeddings - head_factor_embedings = torch.index_select(factor_embeddings, dim=0, index=self.all_h_list) - tail_factor_embedings = torch.index_select(ego_layer_embeddings[i], dim=0, index=self.all_t_list) + head_factor_embeddings = torch.index_select(factor_embeddings, dim=0, index=self.all_h_list) + tail_factor_embeddings = torch.index_select(ego_layer_embeddings[i], dim=0, index=self.all_t_list) # .... constrain the vector length # .... make the following attentive weights within the range of (0,1) # to adapt to torch version - head_factor_embedings = F.normalize(head_factor_embedings, p=2, dim=1) - tail_factor_embedings = F.normalize(tail_factor_embedings, p=2, dim=1) + head_factor_embeddings = F.normalize(head_factor_embeddings, p=2, dim=1) + tail_factor_embeddings = F.normalize(tail_factor_embeddings, p=2, dim=1) # get the attentive weights # .... A_factor_values is a dense tensor with the size of [num_edge, 1] - A_factor_values = torch.sum(head_factor_embedings * torch.tanh(tail_factor_embedings), + A_factor_values = torch.sum(head_factor_embeddings * torch.tanh(tail_factor_embeddings), dim=1, keepdim=True) # update the attentive weights @@ -243,18 +245,18 @@ def calculate_loss(self, interaction): user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] - posi_embeddings = item_all_embeddings[pos_item] - negi_embeddings = item_all_embeddings[neg_item] + pos_embeddings = item_all_embeddings[pos_item] + neg_embeddings = item_all_embeddings[neg_item] - pos_scores = torch.mul(u_embeddings, posi_embeddings).sum(dim=1) - neg_scores = torch.mul(u_embeddings, negi_embeddings).sum(dim=1) + pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) + neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) - # cul regularizer + # cul regularized u_ego_embeddings = self.user_embedding(user) - posi_ego_embeddings = self.item_embedding(pos_item) - negi_ego_embeddings = self.item_embedding(neg_item) - reg_loss = self.reg_loss(u_ego_embeddings, posi_ego_embeddings, negi_ego_embeddings) + pos_ego_embeddings = self.item_embedding(pos_item) + neg_ego_embeddings = self.item_embedding(neg_item) + reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings) if self.n_factors > 1 and self.cor_weight > 1e-9: cor_users, cor_items = sample_cor_samples(self.n_users, self.n_items, self.cor_batch_size) diff --git a/recbole/model/general_recommender/dmf.py b/recbole/model/general_recommender/dmf.py index ce04bbdf0..07b5d88e1 100644 --- a/recbole/model/general_recommender/dmf.py +++ b/recbole/model/general_recommender/dmf.py @@ -20,9 +20,9 @@ import torch.nn as nn from torch.nn.init import normal_ -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.layers import MLPLayers +from recbole.utils import InputType class DMF(GeneralRecommender): @@ -102,7 +102,8 @@ def forward(self, user, item): # Following lines construct tensor of shape [B,n_users] using the tensor of shape [B,H] col_indices = self.history_user_id[item].flatten() - row_indices = torch.arange(item.shape[0]).to(self.device).repeat_interleave(self.history_user_id.shape[1], dim=0) + row_indices = torch.arange(item.shape[0]).to(self.device). \ + repeat_interleave(self.history_user_id.shape[1], dim=0) matrix_01 = torch.zeros(1).to(self.device).repeat(item.shape[0], self.n_users) matrix_01.index_put_((row_indices, col_indices), self.history_user_value[item].flatten()) item = self.item_linear(matrix_01) @@ -170,7 +171,8 @@ def get_item_embedding(self): col = interaction_matrix.col i = torch.LongTensor([row, col]) data = torch.FloatTensor(interaction_matrix.data) - item_matrix = torch.sparse.FloatTensor(i, data, torch.Size(interaction_matrix.shape)).to(self.device).transpose(0, 1) + item_matrix = torch.sparse.FloatTensor(i, data, torch.Size(interaction_matrix.shape)).to(self.device).\ + transpose(0, 1) item = torch.sparse.mm(item_matrix, self.item_linear.weight.t()) item = self.item_fc_layers(item) diff --git a/recbole/model/general_recommender/fism.py b/recbole/model/general_recommender/fism.py index 184ad071a..4f3457ea3 100644 --- a/recbole/model/general_recommender/fism.py +++ b/recbole/model/general_recommender/fism.py @@ -36,7 +36,7 @@ def __init__(self, config, dataset): # load dataset info self.LABEL = config['LABEL_FIELD'] - # get all users's history interaction information.the history item + # get all users' history interaction information.the history item # matrix is padding by the maximum number of a user's interactions self.history_item_matrix, self.history_lens, self.mask_mat = self.get_history_info(dataset) @@ -84,7 +84,7 @@ def reg_loss(self): Returns: torch.Tensor: reg loss - """ + """ reg_1, reg_2 = self.reg_weights loss_1 = reg_1 * self.item_src_embedding.weight.norm(2) loss_2 = reg_2 * self.item_dst_embedding.weight.norm(2) @@ -124,7 +124,7 @@ def user_forward(self, user_input, item_num, user_bias, repeats=None, pred_slc=N Args: user_input (torch.Tensor): user input tensor - item_num (torch.Tensor): user hitory interaction lens + item_num (torch.Tensor): user history interaction lens repeats (int, optional): the number of items to be evaluated pred_slc (torch.Tensor, optional): continuous index which controls the current evaluation items, if pred_slc is None, it will evaluate all items @@ -172,7 +172,8 @@ def full_sort_predict(self, interaction): else: output = [] for mask in self.group: - tmp_output = self.user_forward(user_input[:item_num], item_num, user_bias, repeats=len(mask), pred_slc=mask) + tmp_output = self.user_forward(user_input[:item_num], item_num, user_bias, + repeats=len(mask), pred_slc=mask) output.append(tmp_output) output = torch.cat(output, dim=0) scores.append(output) diff --git a/recbole/model/general_recommender/gcmc.py b/recbole/model/general_recommender/gcmc.py index 08d031d80..133dbe53d 100644 --- a/recbole/model/general_recommender/gcmc.py +++ b/recbole/model/general_recommender/gcmc.py @@ -19,16 +19,16 @@ https://github.com/riannevdberg/gc-mc """ - import math + +import numpy as np +import scipy.sparse as sp import torch import torch.nn as nn -import scipy.sparse as sp -import numpy as np -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.layers import SparseDropout +from recbole.utils import InputType class GCMC(GeneralRecommender): @@ -79,11 +79,10 @@ def __init__(self, config, dataset): torch.Size([self.n_items, self.num_all])).to(self.device) else: features = torch.eye(self.num_all).to(self.device) - self.user_features, self.item_features = torch.split( - features, [self.n_users, self.n_items]) + self.user_features, self.item_features = torch.split(features, [self.n_users, self.n_items]) self.input_dim = self.user_features.shape[1] - # adj matrixs for each relation are stored in self.support + # adj matrices for each relation are stored in self.support self.Graph = self.get_norm_adj_mat().to(self.device) self.support = [self.Graph] @@ -92,8 +91,8 @@ def __init__(self, config, dataset): if self.accum == 'stack': div = self.gcn_output_dim // len(self.support) if self.gcn_output_dim % len(self.support) != 0: - self.logger.info("""\nWARNING: HIDDEN[0] (=%d) of stack layer is adjusted to %d (in %d splits).\n""" - % (self.gcn_output_dim, len(self.support) * div, len(self.support))) + self.logger.warning("HIDDEN[0] (=%d) of stack layer is adjusted to %d (in %d splits)." + % (self.gcn_output_dim, len(self.support) * div, len(self.support))) self.gcn_output_dim = len(self.support) * div # define layers and loss @@ -146,14 +145,12 @@ def get_norm_adj_mat(self): self.n_users + self.n_items), dtype=np.float32) inter_M = self.interaction_matrix inter_M_t = self.interaction_matrix.transpose() - data_dict = dict(zip(zip(inter_M.row, inter_M.col+self.n_users), - [1]*inter_M.nnz)) - data_dict.update(dict(zip(zip(inter_M_t.row+self.n_users, inter_M_t.col), - [1]*inter_M_t.nnz))) + data_dict = dict(zip(zip(inter_M.row, inter_M.col + self.n_users), [1] * inter_M.nnz)) + data_dict.update(dict(zip(zip(inter_M_t.row + self.n_users, inter_M_t.col), [1] * inter_M_t.nnz))) A._update(data_dict) # norm adj matrix sumArr = (A > 0).sum(axis=1) - # add epsilon to avoid Devide by zero Warning + # add epsilon to avoid divide by zero Warning diag = np.array(sumArr.flatten())[0] + 1e-7 diag = np.power(diag, -0.5) D = sp.diags(diag) @@ -211,14 +208,15 @@ def full_sort_predict(self, interaction): class GcEncoder(nn.Module): - """Graph Convolutional Encoder + r"""Graph Convolutional Encoder GcEncoder take as input an :math:`N \times D` feature matrix :math:`X` and a graph adjacency matrix :math:`A`, and produce an :math:`N \times E` node embedding matrix; Note that :math:`N` denotes the number of nodes, :math:`D` the number of input features, and :math:`E` the embedding size. """ - def __init__(self, accum, num_user, num_item, support, input_dim, gcn_output_dim, dense_output_dim, drop_prob, device, + def __init__(self, accum, num_user, num_item, support, + input_dim, gcn_output_dim, dense_output_dim, drop_prob, device, sparse_feature=True, act_dense=lambda x: x, share_user_item_weights=True, bias=False): super(GcEncoder, self).__init__() self.num_users = num_user @@ -247,58 +245,62 @@ def __init__(self, accum, num_user, num_item, support, input_dim, gcn_output_dim # gcn layer if self.accum == 'sum': - self.weights_u = nn.ParameterList( - [nn.Parameter(torch.FloatTensor(self.input_dim, self.gcn_output_dim).to(self.device), - requires_grad=True) - for _ in range(self.num_support)]) + self.weights_u = nn.ParameterList([ + nn.Parameter( + torch.FloatTensor(self.input_dim, self.gcn_output_dim).to(self.device), requires_grad=True + ) + for _ in range(self.num_support) + ]) if share_user_item_weights: self.weights_v = self.weights_u else: - self.weights_v = nn.ParameterList( - [nn.Parameter(torch.FloatTensor(self.input_dim, self.gcn_output_dim).to(self.device), - requires_grad=True) for _ in range(self.num_support)]) + self.weights_v = nn.ParameterList([ + nn.Parameter( + torch.FloatTensor(self.input_dim, self.gcn_output_dim).to(self.device), requires_grad=True + ) + for _ in range(self.num_support) + ]) else: assert self.gcn_output_dim % self.num_support == 0, 'output_dim must be multiple of num_support for stackGC' self.sub_hidden_dim = self.gcn_output_dim // self.num_support - self.weights_u = nn.ParameterList( - [nn.Parameter(torch.FloatTensor(self.input_dim, self.sub_hidden_dim).to(self.device), - requires_grad=True) - for _ in range(self.num_support)]) + self.weights_u = nn.ParameterList([ + nn.Parameter( + torch.FloatTensor(self.input_dim, self.sub_hidden_dim).to(self.device), requires_grad=True + ) + for _ in range(self.num_support) + ]) if share_user_item_weights: self.weights_v = self.weights_u else: - self.weights_v = nn.ParameterList( - [nn.Parameter(torch.FloatTensor(self.input_dim, self.sub_hidden_dim).to(self.device), - requires_grad=True) for _ in range(self.num_support)]) + self.weights_v = nn.ParameterList([ + nn.Parameter( + torch.FloatTensor(self.input_dim, self.sub_hidden_dim).to(self.device), requires_grad=True + ) + for _ in range(self.num_support) + ]) # dense layer - self.dense_layer_u = nn.Linear( - self.gcn_output_dim, self.dense_output_dim, bias=self.bias) + self.dense_layer_u = nn.Linear(self.gcn_output_dim, self.dense_output_dim, bias=self.bias) if share_user_item_weights: self.dense_layer_v = self.dense_layer_u else: - self.dense_layer_v = nn.Linear( - self.gcn_output_dim, self.dense_output_dim, bias=self.bias) + self.dense_layer_v = nn.Linear(self.gcn_output_dim, self.dense_output_dim, bias=self.bias) self._init_weights() def _init_weights(self): - init_range = math.sqrt((self.num_support + 1) / - (self.input_dim + self.gcn_output_dim)) + init_range = math.sqrt((self.num_support + 1) / (self.input_dim + self.gcn_output_dim)) for w in range(self.num_support): self.weights_u[w].data.uniform_(-init_range, init_range) if not self.share_weights: for w in range(self.num_support): self.weights_v[w].data.uniform_(-init_range, init_range) - dense_init_range = math.sqrt( - (self.num_support + 1) / (self.dense_output_dim + self.gcn_output_dim)) - self.dense_layer_u.weight.data.uniform_( - -dense_init_range, dense_init_range) + dense_init_range = math.sqrt((self.num_support + 1) / (self.dense_output_dim + self.gcn_output_dim)) + self.dense_layer_u.weight.data.uniform_(-dense_init_range, dense_init_range) if not self.share_weights: - self.dense_layer_v.weight.data.uniform_( - -dense_init_range, dense_init_range) + self.dense_layer_v.weight.data.uniform_(-dense_init_range, dense_init_range) if self.bias: self.dense_layer_u.bias.data.fill_(0) @@ -375,7 +377,7 @@ def forward(self, user_X, item_X): class BiDecoder(nn.Module): - """Bilinear decoder + """Bi-linear decoder BiDecoder takes pairs of node embeddings and predicts respective entries in the adjacency matrix. """ @@ -391,18 +393,16 @@ def __init__(self, input_dim, output_dim, drop_prob, device, self.dropout_prob = drop_prob self.dropout = nn.Dropout(p=self.dropout_prob) - self.weights = nn.ParameterList( - [nn.Parameter(orthogonal([self.input_dim, self.input_dim]).to(self.device)) - for _ in range(self.num_weights)]) - self.dense_layer = nn.Linear( - self.num_weights, self.output_dim, bias=False) + self.weights = nn.ParameterList([ + nn.Parameter(orthogonal([self.input_dim, self.input_dim]).to(self.device)) + for _ in range(self.num_weights) + ]) + self.dense_layer = nn.Linear(self.num_weights, self.output_dim, bias=False) self._init_weights() def _init_weights(self): - dense_init_range = math.sqrt( - self.output_dim / (self.num_weights + self.output_dim)) - self.dense_layer.weight.data.uniform_( - -dense_init_range, dense_init_range) + dense_init_range = math.sqrt(self.output_dim / (self.num_weights + self.output_dim)) + self.dense_layer.weight.data.uniform_(-dense_init_range, dense_init_range) def forward(self, u_inputs, i_inputs, users, items=None): u_inputs = self.dropout(u_inputs) diff --git a/recbole/model/general_recommender/itemknn.py b/recbole/model/general_recommender/itemknn.py index c98c075dd..6a0c202df 100644 --- a/recbole/model/general_recommender/itemknn.py +++ b/recbole/model/general_recommender/itemknn.py @@ -15,8 +15,8 @@ import scipy.sparse as sp import torch -from recbole.utils import InputType, ModelType from recbole.model.abstract_recommender import GeneralRecommender +from recbole.utils import InputType, ModelType class ComputeSimilarity: @@ -130,7 +130,6 @@ def compute_similarity(self, block_size=100): class ItemKNN(GeneralRecommender): - r"""ItemKNN is a basic model that compute item similarity with the interaction matrix. """ diff --git a/recbole/model/general_recommender/lightgcn.py b/recbole/model/general_recommender/lightgcn.py index 4cf3604d9..17affbb27 100644 --- a/recbole/model/general_recommender/lightgcn.py +++ b/recbole/model/general_recommender/lightgcn.py @@ -23,10 +23,10 @@ import scipy.sparse as sp import torch -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_uniform_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class LightGCN(GeneralRecommender): @@ -45,19 +45,16 @@ def __init__(self, config, dataset): super(LightGCN, self).__init__(config, dataset) # load dataset info - self.interaction_matrix = dataset.inter_matrix( - form='coo').astype(np.float32) + self.interaction_matrix = dataset.inter_matrix(form='coo').astype(np.float32) # load parameters info self.latent_dim = config['embedding_size'] # int type:the embedding size of lightGCN self.n_layers = config['n_layers'] # int type:the layer num of lightGCN - self.reg_weight = config['reg_weight'] # float32 type: the weight decay for l2 normalizaton + self.reg_weight = config['reg_weight'] # float32 type: the weight decay for l2 normalization # define layers and loss - self.user_embedding = torch.nn.Embedding( - num_embeddings=self.n_users, embedding_dim=self.latent_dim) - self.item_embedding = torch.nn.Embedding( - num_embeddings=self.n_items, embedding_dim=self.latent_dim) + self.user_embedding = torch.nn.Embedding(num_embeddings=self.n_users, embedding_dim=self.latent_dim) + self.item_embedding = torch.nn.Embedding(num_embeddings=self.n_items, embedding_dim=self.latent_dim) self.mf_loss = BPRLoss() self.reg_loss = EmbLoss() @@ -88,14 +85,12 @@ def get_norm_adj_mat(self): self.n_users + self.n_items), dtype=np.float32) inter_M = self.interaction_matrix inter_M_t = self.interaction_matrix.transpose() - data_dict = dict(zip(zip(inter_M.row, inter_M.col+self.n_users), - [1]*inter_M.nnz)) - data_dict.update(dict(zip(zip(inter_M_t.row+self.n_users, inter_M_t.col), - [1]*inter_M_t.nnz))) + data_dict = dict(zip(zip(inter_M.row, inter_M.col + self.n_users), [1] * inter_M.nnz)) + data_dict.update(dict(zip(zip(inter_M_t.row + self.n_users, inter_M_t.col), [1] * inter_M_t.nnz))) A._update(data_dict) # norm adj matrix sumArr = (A > 0).sum(axis=1) - # add epsilon to avoid Devide by zero Warning + # add epsilon to avoid divide by zero Warning diag = np.array(sumArr.flatten())[0] + 1e-7 diag = np.power(diag, -0.5) D = sp.diags(diag) @@ -125,14 +120,12 @@ def forward(self): embeddings_list = [all_embeddings] for layer_idx in range(self.n_layers): - all_embeddings = torch.sparse.mm( - self.norm_adj_matrix, all_embeddings) + all_embeddings = torch.sparse.mm(self.norm_adj_matrix, all_embeddings) embeddings_list.append(all_embeddings) lightgcn_all_embeddings = torch.stack(embeddings_list, dim=1) lightgcn_all_embeddings = torch.mean(lightgcn_all_embeddings, dim=1) - user_all_embeddings, item_all_embeddings = torch.split( - lightgcn_all_embeddings, [self.n_users, self.n_items]) + user_all_embeddings, item_all_embeddings = torch.split(lightgcn_all_embeddings, [self.n_users, self.n_items]) return user_all_embeddings, item_all_embeddings def calculate_loss(self, interaction): @@ -146,21 +139,20 @@ def calculate_loss(self, interaction): user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] - posi_embeddings = item_all_embeddings[pos_item] - negi_embeddings = item_all_embeddings[neg_item] + pos_embeddings = item_all_embeddings[pos_item] + neg_embeddings = item_all_embeddings[neg_item] # calculate BPR Loss - pos_scores = torch.mul(u_embeddings, posi_embeddings).sum(dim=1) - neg_scores = torch.mul(u_embeddings, negi_embeddings).sum(dim=1) + pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) + neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate BPR Loss u_ego_embeddings = self.user_embedding(user) - posi_ego_embeddings = self.item_embedding(pos_item) - negi_ego_embeddings = self.item_embedding(neg_item) + pos_ego_embeddings = self.item_embedding(pos_item) + neg_ego_embeddings = self.item_embedding(neg_item) - reg_loss = self.reg_loss( - u_ego_embeddings, posi_ego_embeddings, negi_ego_embeddings) + reg_loss = self.reg_loss(u_ego_embeddings, pos_ego_embeddings, neg_ego_embeddings) loss = mf_loss + self.reg_weight * reg_loss return loss @@ -184,7 +176,6 @@ def full_sort_predict(self, interaction): u_embeddings = self.restore_user_e[user] # dot with all item embedding to accelerate - scores = torch.matmul( - u_embeddings, self.restore_item_e.transpose(0, 1)) + scores = torch.matmul(u_embeddings, self.restore_item_e.transpose(0, 1)) return scores.view(-1) diff --git a/recbole/model/general_recommender/line.py b/recbole/model/general_recommender/line.py index 64a2f7104..66ee1a919 100644 --- a/recbole/model/general_recommender/line.py +++ b/recbole/model/general_recommender/line.py @@ -13,14 +13,15 @@ https://github.com/shenweichen/GraphEmbedding """ -import torch -import torch.nn as nn import random + import numpy as np +import torch +import torch.nn as nn -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.init import xavier_normal_initialization +from recbole.utils import InputType class NegSamplingLoss(nn.Module): @@ -156,9 +157,9 @@ def calculate_loss(self, interaction): score_neg_con = self.context_forward(pos_item, neg_user, 'ii') return self.loss_fct(ones, score_pos) \ - + self.loss_fct(-1 * ones, score_neg) \ - + self.loss_fct(ones, score_pos_con) * self.second_order_loss_weight \ - + self.loss_fct(-1*ones, score_neg_con) * self.second_order_loss_weight + + self.loss_fct(-1 * ones, score_neg) \ + + self.loss_fct(ones, score_pos_con) * self.second_order_loss_weight \ + + self.loss_fct(-1 * ones, score_neg_con) * self.second_order_loss_weight def predict(self, interaction): diff --git a/recbole/model/general_recommender/macridvae.py b/recbole/model/general_recommender/macridvae.py index 0e1c2e1a1..dc9fcd923 100644 --- a/recbole/model/general_recommender/macridvae.py +++ b/recbole/model/general_recommender/macridvae.py @@ -18,14 +18,15 @@ import torch.nn.functional as F from torch.distributions.one_hot_categorical import OneHotCategorical -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.init import xavier_normal_initialization from recbole.model.loss import EmbLoss +from recbole.utils import InputType class MacridVAE(GeneralRecommender): - r"""MacridVAE is a item-based model collaborative filtering model that learn disentangled representations from user behavior and simultaneously rank all items for user. + r"""MacridVAE is a item-based model collaborative filtering model that learn disentangled representations from user + behavior and simultaneously rank all items for user. We implement the model following the original author """ @@ -50,9 +51,9 @@ def __init__(self, config, dataset): self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix() self.history_item_id = self.history_item_id.to(self.device) self.history_item_value = self.history_item_value.to(self.device) - self.encode_layer_dims = [self.n_items] + self.layers + [self.embedding_size*2] + self.encode_layer_dims = [self.n_items] + self.layers + [self.embedding_size * 2] - self.encoder = self.mlp_layars(self.encode_layer_dims) + self.encoder = self.mlp_layers(self.encode_layer_dims) self.item_embedding = nn.Embedding(self.n_items, self.embedding_size) self.k_embedding = nn.Embedding(self.kfac, self.embedding_size) @@ -78,7 +79,7 @@ def get_rating_matrix(self, user): rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) return rating_matrix - def mlp_layars(self,layer_dims): + def mlp_layers(self, layer_dims): mlp_modules = [] for i, (d_in, d_out) in enumerate(zip(layer_dims[:-1], layer_dims[1:])): mlp_modules.append(nn.Linear(d_in, d_out)) diff --git a/recbole/model/general_recommender/multidae.py b/recbole/model/general_recommender/multidae.py index 3837e30fe..3b3658b64 100644 --- a/recbole/model/general_recommender/multidae.py +++ b/recbole/model/general_recommender/multidae.py @@ -15,10 +15,10 @@ import torch.nn as nn import torch.nn.functional as F -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.init import xavier_normal_initialization from recbole.model.layers import MLPLayers +from recbole.utils import InputType class MultiDAE(GeneralRecommender): @@ -32,7 +32,7 @@ def __init__(self, config, dataset): super(MultiDAE, self).__init__(config, dataset) self.layers = config["mlp_hidden_size"] - self.lat_dim = config['latent_dimendion'] + self.lat_dim = config['latent_dimension'] self.drop_out = config['dropout_prob'] self.history_item_id, self.history_item_value, _ = dataset.history_item_matrix() @@ -43,7 +43,7 @@ def __init__(self, config, dataset): self.decode_layer_dims = [self.lat_dim] + self.encode_layer_dims[::-1][1:] self.encoder = MLPLayers(self.encode_layer_dims, activation='tanh') - self.decoder = self.mlp_layars(self.decode_layer_dims) + self.decoder = self.mlp_layers(self.decode_layer_dims) # parameters initialization self.apply(xavier_normal_initialization) @@ -65,7 +65,7 @@ def get_rating_matrix(self, user): rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) return rating_matrix - def mlp_layars(self, layer_dims): + def mlp_layers(self, layer_dims): mlp_modules = [] for i, (d_in, d_out) in enumerate(zip(layer_dims[:-1], layer_dims[1:])): mlp_modules.append(nn.Linear(d_in, d_out)) diff --git a/recbole/model/general_recommender/multivae.py b/recbole/model/general_recommender/multivae.py index fd584e767..83495fa12 100644 --- a/recbole/model/general_recommender/multivae.py +++ b/recbole/model/general_recommender/multivae.py @@ -15,9 +15,9 @@ import torch.nn as nn import torch.nn.functional as F -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.init import xavier_normal_initialization +from recbole.utils import InputType class MultiVAE(GeneralRecommender): @@ -31,7 +31,7 @@ def __init__(self, config, dataset): super(MultiVAE, self).__init__(config, dataset) self.layers = config["mlp_hidden_size"] - self.lat_dim = config['latent_dimendion'] + self.lat_dim = config['latent_dimension'] self.drop_out = config['dropout_prob'] self.anneal_cap = config['anneal_cap'] self.total_anneal_steps = config["total_anneal_steps"] @@ -43,10 +43,10 @@ def __init__(self, config, dataset): self.update = 0 self.encode_layer_dims = [self.n_items] + self.layers + [self.lat_dim] - self.decode_layer_dims = [int(self.lat_dim/2)]+self.encode_layer_dims[::-1][1:] + self.decode_layer_dims = [int(self.lat_dim / 2)] + self.encode_layer_dims[::-1][1:] - self.encoder = self.mlp_layars(self.encode_layer_dims) - self.decoder = self.mlp_layars(self.decode_layer_dims) + self.encoder = self.mlp_layers(self.encode_layer_dims) + self.decoder = self.mlp_layers(self.decode_layer_dims) # parameters initialization self.apply(xavier_normal_initialization) @@ -68,7 +68,7 @@ def get_rating_matrix(self, user): rating_matrix.index_put_((row_indices, col_indices), self.history_item_value[user].flatten()) return rating_matrix - def mlp_layars(self, layer_dims): + def mlp_layers(self, layer_dims): mlp_modules = [] for i, (d_in, d_out) in enumerate(zip(layer_dims[:-1], layer_dims[1:])): mlp_modules.append(nn.Linear(d_in, d_out)) @@ -92,8 +92,8 @@ def forward(self, rating_matrix): h = self.encoder(h) - mu = h[:, :int(self.lat_dim/2)] - logvar = h[:, int(self.lat_dim/2):] + mu = h[:, :int(self.lat_dim / 2)] + logvar = h[:, int(self.lat_dim / 2):] z = self.reparameterize(mu, logvar) z = self.decoder(z) @@ -113,7 +113,7 @@ def calculate_loss(self, interaction): z, mu, logvar = self.forward(rating_matrix) # KL loss - kl_loss = -0.5 * torch.mean(torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=1))*anneal + kl_loss = -0.5 * torch.mean(torch.sum(1 + logvar - mu.pow(2) - logvar.exp(), dim=1)) * anneal # CE loss ce_loss = -(F.log_softmax(z, 1) * rating_matrix).sum(1).mean() diff --git a/recbole/model/general_recommender/nais.py b/recbole/model/general_recommender/nais.py index d0e43d4d4..1eba6a972 100644 --- a/recbole/model/general_recommender/nais.py +++ b/recbole/model/general_recommender/nais.py @@ -18,13 +18,13 @@ https://github.com/AaronHeee/Neural-Attentive-Item-Similarity-Model """ - import torch import torch.nn as nn +from torch.nn.init import constant_, normal_, xavier_normal_ + from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.layers import MLPLayers from recbole.utils import InputType -from torch.nn.init import constant_, normal_, xavier_normal_ class NAIS(GeneralRecommender): @@ -45,7 +45,7 @@ def __init__(self, config, dataset): # load dataset info self.LABEL = config['LABEL_FIELD'] - # get all users's history interaction information.the history item + # get all users' history interaction information.the history item # matrix is padding by the maximum number of a user's interactions self.history_item_matrix, self.history_lens, self.mask_mat = self.get_history_info(dataset) @@ -70,7 +70,7 @@ def __init__(self, config, dataset): self.item_dst_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.bias = nn.Parameter(torch.zeros(self.n_items)) if self.algorithm == 'concat': - self.mlp_layers = MLPLayers([self.embedding_size*2, self.weight_size]) + self.mlp_layers = MLPLayers([self.embedding_size * 2, self.weight_size]) elif self.algorithm == 'prod': self.mlp_layers = MLPLayers([self.embedding_size, self.weight_size]) else: @@ -83,7 +83,7 @@ def __init__(self, config, dataset): self.logger.info('use pretrain from [{}]...'.format(self.pretrain_path)) self._load_pretrain() else: - self.logger.info('unuse pretrain...') + self.logger.info('unused pretrain...') self.apply(self._init_weights) def _init_weights(self, module): @@ -137,7 +137,7 @@ def reg_loss(self): Returns: torch.Tensor: reg loss - """ + """ reg_1, reg_2, reg_3 = self.reg_weights loss_1 = reg_1 * self.item_src_embedding.weight.norm(2) loss_2 = reg_2 * self.item_dst_embedding.weight.norm(2) @@ -171,9 +171,9 @@ def mask_softmax(self, similarity, logits, bias, item_num, batch_mask_mat): """softmax the unmasked user history items and get the final output Args: - similarity (torch.Tensor): the similarity between the histoy items and target items + similarity (torch.Tensor): the similarity between the history items and target items logits (torch.Tensor): the initial weights of the history items - item_num (torch.Tensor): user hitory interaction lengths + item_num (torch.Tensor): user history interaction lengths bias (torch.Tensor): bias batch_mask_mat (torch.Tensor): the mask of user history interactions @@ -183,7 +183,7 @@ def mask_softmax(self, similarity, logits, bias, item_num, batch_mask_mat): """ exp_logits = torch.exp(logits) # batch_size x max_len - exp_logits = batch_mask_mat * exp_logits # batch_size x max_len + exp_logits = batch_mask_mat * exp_logits # batch_size x max_len exp_sum = torch.sum(exp_logits, dim=1, keepdim=True) exp_sum = torch.pow(exp_sum, self.beta) weights = torch.div(exp_logits, exp_sum) @@ -197,9 +197,9 @@ def softmax(self, similarity, logits, item_num, bias): """softmax the user history features and get the final output Args: - similarity (torch.Tensor): the similarity between the histoy items and target items + similarity (torch.Tensor): the similarity between the history items and target items logits (torch.Tensor): the initial weights of the history items - item_num (torch.Tensor): user hitory interaction lengths + item_num (torch.Tensor): user history interaction lengths bias (torch.Tensor): bias Returns: @@ -235,7 +235,7 @@ def user_forward(self, user_input, item_num, repeats=None, pred_slc=None): Args: user_input (torch.Tensor): user input tensor - item_num (torch.Tensor): user hitory interaction lens + item_num (torch.Tensor): user history interaction lens repeats (int, optional): the number of items to be evaluated pred_slc (torch.Tensor, optional): continuous index which controls the current evaluation items, if pred_slc is None, it will evaluate all items diff --git a/recbole/model/general_recommender/neumf.py b/recbole/model/general_recommender/neumf.py index 5fde05394..8784c4cd7 100644 --- a/recbole/model/general_recommender/neumf.py +++ b/recbole/model/general_recommender/neumf.py @@ -19,9 +19,9 @@ import torch.nn as nn from torch.nn.init import normal_ -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender from recbole.model.layers import MLPLayers +from recbole.utils import InputType class NeuMF(GeneralRecommender): @@ -107,9 +107,9 @@ def forward(self, user, item): user_mlp_e = self.user_mlp_embedding(user) item_mlp_e = self.item_mlp_embedding(item) if self.mf_train: - mf_output = torch.mul(user_mf_e, item_mf_e) # [batch_size, embedding_size] + mf_output = torch.mul(user_mf_e, item_mf_e) # [batch_size, embedding_size] if self.mlp_train: - mlp_output = self.mlp_layers(torch.cat((user_mlp_e, item_mlp_e), -1)) # [batch_size, layers[-1]] + mlp_output = self.mlp_layers(torch.cat((user_mlp_e, item_mlp_e), -1)) # [batch_size, layers[-1]] if self.mf_train and self.mlp_train: output = self.sigmoid(self.predict_layer(torch.cat((mf_output, mlp_output), -1))) elif self.mf_train: diff --git a/recbole/model/general_recommender/ngcf.py b/recbole/model/general_recommender/ngcf.py index a719670cb..d02ccd499 100644 --- a/recbole/model/general_recommender/ngcf.py +++ b/recbole/model/general_recommender/ngcf.py @@ -25,11 +25,11 @@ import torch.nn as nn import torch.nn.functional as F -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender -from recbole.model.loss import BPRLoss, EmbLoss -from recbole.model.layers import BiGNNLayer, SparseDropout from recbole.model.init import xavier_normal_initialization +from recbole.model.layers import BiGNNLayer, SparseDropout +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class NGCF(GeneralRecommender): @@ -96,7 +96,7 @@ def get_norm_adj_mat(self): A._update(data_dict) # norm adj matrix sumArr = (A > 0).sum(axis=1) - diag = np.array(sumArr.flatten())[0] + 1e-7 # add epsilon to avoid Devide by zero Warning + diag = np.array(sumArr.flatten())[0] + 1e-7 # add epsilon to avoid divide by zero Warning diag = np.power(diag, -0.5) D = sp.diags(diag) L = D * A * D @@ -159,14 +159,14 @@ def calculate_loss(self, interaction): user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] - posi_embeddings = item_all_embeddings[pos_item] - negi_embeddings = item_all_embeddings[neg_item] + pos_embeddings = item_all_embeddings[pos_item] + neg_embeddings = item_all_embeddings[neg_item] - pos_scores = torch.mul(u_embeddings, posi_embeddings).sum(dim=1) - neg_scores = torch.mul(u_embeddings, negi_embeddings).sum(dim=1) + pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) + neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) # calculate BPR Loss - reg_loss = self.reg_loss(u_embeddings, posi_embeddings, negi_embeddings) # L2 regularization of embeddings + reg_loss = self.reg_loss(u_embeddings, pos_embeddings, neg_embeddings) # L2 regularization of embeddings return mf_loss + self.reg_weight * reg_loss diff --git a/recbole/model/general_recommender/pop.py b/recbole/model/general_recommender/pop.py index 879283d5a..9a7e75c0a 100644 --- a/recbole/model/general_recommender/pop.py +++ b/recbole/model/general_recommender/pop.py @@ -14,8 +14,8 @@ import torch -from recbole.utils import InputType, ModelType from recbole.model.abstract_recommender import GeneralRecommender +from recbole.utils import InputType, ModelType class Pop(GeneralRecommender): @@ -36,7 +36,6 @@ def forward(self): pass def calculate_loss(self, interaction): - item = interaction[self.ITEM_ID] self.item_cnt[item, :] = self.item_cnt[item, :] + 1 @@ -45,7 +44,6 @@ def calculate_loss(self, interaction): return torch.nn.Parameter(torch.zeros(1)) def predict(self, interaction): - item = interaction[self.ITEM_ID] result = torch.true_divide(self.item_cnt[item, :], self.max_cnt) return result.squeeze() diff --git a/recbole/model/general_recommender/spectralcf.py b/recbole/model/general_recommender/spectralcf.py index f15715097..537b452b5 100644 --- a/recbole/model/general_recommender/spectralcf.py +++ b/recbole/model/general_recommender/spectralcf.py @@ -18,10 +18,10 @@ import scipy.sparse as sp import torch -from recbole.utils import InputType from recbole.model.abstract_recommender import GeneralRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_uniform_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class SpectralCF(GeneralRecommender): @@ -98,10 +98,8 @@ def get_laplacian_matrix(self): self.n_users + self.n_items), dtype=np.float32) inter_M = self.interaction_matrix inter_M_t = self.interaction_matrix.transpose() - data_dict = dict(zip(zip(inter_M.row, inter_M.col+self.n_users), - [1]*inter_M.nnz)) - data_dict.update(dict(zip(zip(inter_M_t.row+self.n_users, inter_M_t.col), - [1]*inter_M_t.nnz))) + data_dict = dict(zip(zip(inter_M.row, inter_M.col + self.n_users), [1] * inter_M.nnz)) + data_dict.update(dict(zip(zip(inter_M_t.row + self.n_users, inter_M_t.col), [1] * inter_M_t.nnz))) A._update(data_dict) # norm adj matrix @@ -172,14 +170,13 @@ def calculate_loss(self, interaction): user_all_embeddings, item_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] - posi_embeddings = item_all_embeddings[pos_item] - negi_embeddings = item_all_embeddings[neg_item] - pos_scores = torch.mul(u_embeddings, posi_embeddings).sum(dim=1) - neg_scores = torch.mul(u_embeddings, negi_embeddings).sum(dim=1) + pos_embeddings = item_all_embeddings[pos_item] + neg_embeddings = item_all_embeddings[neg_item] + pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) + neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) - reg_loss = self.reg_loss( - u_embeddings, posi_embeddings, negi_embeddings) + reg_loss = self.reg_loss(u_embeddings, pos_embeddings, neg_embeddings) loss = mf_loss + self.reg_weight * reg_loss return loss diff --git a/recbole/properties/model/MultiDAE.yaml b/recbole/properties/model/MultiDAE.yaml index c578315b2..c47bd39b8 100644 --- a/recbole/properties/model/MultiDAE.yaml +++ b/recbole/properties/model/MultiDAE.yaml @@ -1,4 +1,4 @@ mlp_hidden_size: [600] -latent_dimendion: 64 +latent_dimension: 64 dropout_prob: 0.5 training_neg_sample_num: 0 \ No newline at end of file diff --git a/recbole/properties/model/MultiVAE.yaml b/recbole/properties/model/MultiVAE.yaml index 7a674402d..bbd805d68 100644 --- a/recbole/properties/model/MultiVAE.yaml +++ b/recbole/properties/model/MultiVAE.yaml @@ -1,5 +1,5 @@ mlp_hidden_size: [600] -latent_dimendion: 128 +latent_dimension: 128 dropout_prob: 0.5 anneal_cap: 0.2 total_anneal_steps: 200000 From bb079526cc17c26b23de9cdb88f96e272c55e0b1 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 18:10:15 +0800 Subject: [PATCH 245/249] FORMAT: code format in kg model --- .../model/context_aware_recommender/fwfm.py | 2 +- .../model/general_recommender/macridvae.py | 4 +- .../model/knowledge_aware_recommender/cfkg.py | 2 +- .../model/knowledge_aware_recommender/cke.py | 4 +- .../model/knowledge_aware_recommender/kgat.py | 23 +++-- .../model/knowledge_aware_recommender/kgcn.py | 43 +++----- .../knowledge_aware_recommender/kgnnls.py | 98 +++++++------------ .../model/knowledge_aware_recommender/ktup.py | 12 ++- .../model/knowledge_aware_recommender/mkr.py | 38 +++---- .../knowledge_aware_recommender/ripplenet.py | 18 ++-- 10 files changed, 108 insertions(+), 136 deletions(-) diff --git a/recbole/model/context_aware_recommender/fwfm.py b/recbole/model/context_aware_recommender/fwfm.py index 73e095b0b..5c1280f2d 100644 --- a/recbole/model/context_aware_recommender/fwfm.py +++ b/recbole/model/context_aware_recommender/fwfm.py @@ -103,7 +103,7 @@ def fwfm_layer(self, infeature): para = torch.randn(self.num_fields * self.num_fields * self.embedding_size).\ expand(batch_size, self.num_fields * self.num_fields * self.embedding_size).\ to(self.device) # [batch_size*num_pairs*emb_dim] - para = torch.reshape(para, (batch_size, self.num_fields, self.num_fields, self.embedding_size)) + para = para.reshape(batch_size, self.num_fields, self.num_fields, self.embedding_size) r = nn.Parameter(para, requires_grad=True) # [batch_size, num_fields, num_fields, emb_dim] fwfm_inter = list() # [batch_size, num_fields, emb_dim] diff --git a/recbole/model/general_recommender/macridvae.py b/recbole/model/general_recommender/macridvae.py index dc9fcd923..a761648ab 100644 --- a/recbole/model/general_recommender/macridvae.py +++ b/recbole/model/general_recommender/macridvae.py @@ -118,7 +118,7 @@ def forward(self, rating_matrix): mulist = [] logvarlist = [] for k in range(self.kfac): - cates_k = torch.reshape(cates[:, k], (1, -1)) + cates_k = cates[:, k].reshape(1, -1) # encoder x_k = rating_matrix * cates_k h = self.encoder(x_k) @@ -170,7 +170,7 @@ def calculate_loss(self, interaction): def reg_loss(self): r"""Calculate the L2 normalization loss of model parameters. - Including embedding matrixes and weight matrixes of model. + Including embedding matrices and weight matrices of model. Returns: loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,] diff --git a/recbole/model/knowledge_aware_recommender/cfkg.py b/recbole/model/knowledge_aware_recommender/cfkg.py index 489c79e69..01261f09a 100644 --- a/recbole/model/knowledge_aware_recommender/cfkg.py +++ b/recbole/model/knowledge_aware_recommender/cfkg.py @@ -14,9 +14,9 @@ import torch.nn as nn import torch.nn.functional as F -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender from recbole.model.init import xavier_normal_initialization +from recbole.utils import InputType class CFKG(KnowledgeRecommender): diff --git a/recbole/model/knowledge_aware_recommender/cke.py b/recbole/model/knowledge_aware_recommender/cke.py index 21738a653..180aea446 100644 --- a/recbole/model/knowledge_aware_recommender/cke.py +++ b/recbole/model/knowledge_aware_recommender/cke.py @@ -14,10 +14,10 @@ import torch.nn as nn import torch.nn.functional as F -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class CKE(KnowledgeRecommender): diff --git a/recbole/model/knowledge_aware_recommender/kgat.py b/recbole/model/knowledge_aware_recommender/kgat.py index 19fbe9879..02f4571d6 100644 --- a/recbole/model/knowledge_aware_recommender/kgat.py +++ b/recbole/model/knowledge_aware_recommender/kgat.py @@ -13,17 +13,16 @@ https://github.com/xiangwang1223/knowledge_graph_attention_network """ -import copy +import numpy as np +import scipy.sparse as sp import torch import torch.nn as nn import torch.nn.functional as F -import numpy as np -import scipy.sparse as sp -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class Aggregator(nn.Module): @@ -99,7 +98,7 @@ def __init__(self, config, dataset): self.reg_weight = config['reg_weight'] # generate intermediate data - self.A_in = self.init_graph() # init the attention matrix by the structure of ckg + self.A_in = self.init_graph() # init the attention matrix by the structure of ckg # define layers and loss self.user_embedding = nn.Embedding(self.n_users, self.embedding_size) @@ -128,7 +127,7 @@ def init_graph(self): adj_list = [] for rel_type in range(1, self.n_relations, 1): edge_idxs = self.ckg.filter_edges(lambda edge: edge.data['relation_id'] == rel_type) - sub_graph = dgl.edge_subgraph(self.ckg, edge_idxs, preserve_nodes=True).\ + sub_graph = dgl.edge_subgraph(self.ckg, edge_idxs, preserve_nodes=True). \ adjacency_matrix(transpose=False, scipy_fmt='coo').astype('float') rowsum = np.array(sub_graph.sum(1)) d_inv = np.power(rowsum, -1).flatten() @@ -184,13 +183,13 @@ def calculate_loss(self, interaction): user_all_embeddings, entity_all_embeddings = self.forward() u_embeddings = user_all_embeddings[user] - posi_embeddings = entity_all_embeddings[pos_item] - negi_embeddings = entity_all_embeddings[neg_item] + pos_embeddings = entity_all_embeddings[pos_item] + neg_embeddings = entity_all_embeddings[neg_item] - pos_scores = torch.mul(u_embeddings, posi_embeddings).sum(dim=1) - neg_scores = torch.mul(u_embeddings, negi_embeddings).sum(dim=1) + pos_scores = torch.mul(u_embeddings, pos_embeddings).sum(dim=1) + neg_scores = torch.mul(u_embeddings, neg_embeddings).sum(dim=1) mf_loss = self.mf_loss(pos_scores, neg_scores) - reg_loss = self.reg_loss(u_embeddings, posi_embeddings, negi_embeddings) + reg_loss = self.reg_loss(u_embeddings, pos_embeddings, neg_embeddings) loss = mf_loss + self.reg_weight * reg_loss return loss diff --git a/recbole/model/knowledge_aware_recommender/kgcn.py b/recbole/model/knowledge_aware_recommender/kgcn.py index 79b99d0e7..5b2d7b655 100644 --- a/recbole/model/knowledge_aware_recommender/kgcn.py +++ b/recbole/model/knowledge_aware_recommender/kgcn.py @@ -14,14 +14,14 @@ https://github.com/hwwang55/KGCN """ +import numpy as np import torch import torch.nn as nn -import numpy as np -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import EmbLoss +from recbole.utils import InputType class KGCN(KnowledgeRecommender): @@ -153,10 +153,8 @@ def get_neighbors(self, items): relations = [] for i in range(self.n_iter): index = torch.flatten(entities[i]) - neighbor_entities = torch.reshape(torch.index_select( - self.adj_entity, 0, index), (self.batch_size, -1)) - neighbor_relations = torch.reshape(torch.index_select( - self.adj_relation, 0, index), (self.batch_size, -1)) + neighbor_entities = torch.index_select(self.adj_entity, 0, index).reshape(self.batch_size, -1) + neighbor_relations = torch.index_select(self.adj_relation, 0, index).reshape(self.batch_size, -1) entities.append(neighbor_entities) relations.append(neighbor_relations) return entities, relations @@ -178,20 +176,18 @@ def mix_neighbor_vectors(self, neighbor_vectors, neighbor_relations, user_embedd """ avg = False if not avg: - user_embeddings = torch.reshape(user_embeddings, - (self.batch_size, 1, 1, self.embedding_size)) # [batch_size, 1, 1, dim] + user_embeddings = user_embeddings.reshape(self.batch_size, 1, 1, + self.embedding_size) # [batch_size, 1, 1, dim] user_relation_scores = torch.mean(user_embeddings * neighbor_relations, dim=-1) # [batch_size, -1, n_neighbor] - user_relation_scores_normalized = self.softmax( - user_relation_scores) # [batch_size, -1, n_neighbor] + user_relation_scores_normalized = self.softmax(user_relation_scores) # [batch_size, -1, n_neighbor] user_relation_scores_normalized = torch.unsqueeze(user_relation_scores_normalized, dim=-1) # [batch_size, -1, n_neighbor, 1] neighbors_aggregated = torch.mean(user_relation_scores_normalized * neighbor_vectors, dim=2) # [batch_size, -1, dim] else: - neighbors_aggregated = torch.mean( - neighbor_vectors, dim=2) # [batch_size, -1, dim] + neighbors_aggregated = torch.mean(neighbor_vectors, dim=2) # [batch_size, -1, dim] return neighbors_aggregated def aggregate(self, user_embeddings, entities, relations): @@ -221,33 +217,27 @@ def aggregate(self, user_embeddings, entities, relations): shape = (self.batch_size, -1, self.neighbor_sample_size, self.embedding_size) self_vectors = entity_vectors[hop] - neighbor_vectors = torch.reshape( - entity_vectors[hop + 1], shape) - neighbor_relations = torch.reshape( - relation_vectors[hop], shape) + neighbor_vectors = entity_vectors[hop + 1].reshape(shape) + neighbor_relations = relation_vectors[hop].reshape(shape) neighbors_agg = self.mix_neighbor_vectors(neighbor_vectors, neighbor_relations, user_embeddings) # [batch_size, -1, dim] if self.aggregator_class == 'sum': - output = torch.reshape( - self_vectors + neighbors_agg, (-1, self.embedding_size)) # [-1, dim] + output = (self_vectors + neighbors_agg).reshape(-1, self.embedding_size) # [-1, dim] elif self.aggregator_class == 'neighbor': - output = torch.reshape( - neighbors_agg, (-1, self.embedding_size)) # [-1, dim] + output = neighbors_agg.reshape(-1, self.embedding_size) # [-1, dim] elif self.aggregator_class == 'concat': # [batch_size, -1, dim * 2] output = torch.cat([self_vectors, neighbors_agg], dim=-1) - output = torch.reshape( - output, (-1, self.embedding_size * 2)) # [-1, dim * 2] + output = output.reshape(-1, self.embedding_size * 2) # [-1, dim * 2] else: raise Exception("Unknown aggregator: " + self.aggregator_class) output = self.linear_layers[i](output) # [batch_size, -1, dim] - output = torch.reshape( - output, [self.batch_size, -1, self.embedding_size]) + output = output.reshape(self.batch_size, -1, self.embedding_size) if i == self.n_iter - 1: vector = self.Tanh(output) @@ -257,8 +247,7 @@ def aggregate(self, user_embeddings, entities, relations): entity_vectors_next_iter.append(vector) entity_vectors = entity_vectors_next_iter - item_embeddings = torch.reshape( - entity_vectors[0], (self.batch_size, self.embedding_size)) + item_embeddings = entity_vectors[0].reshape(self.batch_size, self.embedding_size) return item_embeddings diff --git a/recbole/model/knowledge_aware_recommender/kgnnls.py b/recbole/model/knowledge_aware_recommender/kgnnls.py index 08fef392b..5749be261 100644 --- a/recbole/model/knowledge_aware_recommender/kgnnls.py +++ b/recbole/model/knowledge_aware_recommender/kgnnls.py @@ -15,15 +15,16 @@ https://github.com/hwwang55/KGNN-LS """ +import random + +import numpy as np import torch import torch.nn as nn -import numpy as np -import random -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import EmbLoss +from recbole.utils import InputType class KGNNLS(KnowledgeRecommender): @@ -66,10 +67,8 @@ def __init__(self, config, dataset): pos_users = inter_feat[dataset.dataset.uid_field] pos_items = inter_feat[dataset.dataset.iid_field] pos_label = torch.ones(pos_items.shape) - pos_interaction_table, self.offset = self.get_interaction_table( - pos_users, pos_items, pos_label) - self.interaction_table = self.sample_neg_interaction( - pos_interaction_table, self.offset) + pos_interaction_table, self.offset = self.get_interaction_table(pos_users, pos_items, pos_label) + self.interaction_table = self.sample_neg_interaction(pos_interaction_table, self.offset) # define function self.softmax = nn.Softmax(dim=-1) @@ -163,16 +162,12 @@ def construct_adj(self, kg_graph): # each line of adj_entity stores the sampled neighbor entities for a given entity # each line of adj_relation stores the corresponding sampled neighbor relations entity_num = kg_graph.shape[0] - adj_entity = np.zeros( - [entity_num, self.neighbor_sample_size], dtype=np.int64) - adj_relation = np.zeros( - [entity_num, self.neighbor_sample_size], dtype=np.int64) + adj_entity = np.zeros([entity_num, self.neighbor_sample_size], dtype=np.int64) + adj_relation = np.zeros([entity_num, self.neighbor_sample_size], dtype=np.int64) for entity in range(entity_num): if entity not in kg_dict.keys(): - adj_entity[entity] = np.array( - [entity] * self.neighbor_sample_size) - adj_relation[entity] = np.array( - [0] * self.neighbor_sample_size) + adj_entity[entity] = np.array([entity] * self.neighbor_sample_size) + adj_relation[entity] = np.array([0] * self.neighbor_sample_size) continue neighbors = kg_dict[entity] @@ -183,10 +178,8 @@ def construct_adj(self, kg_graph): else: sampled_indices = np.random.choice(list(range(n_neighbors)), size=self.neighbor_sample_size, replace=True) - adj_entity[entity] = np.array( - [neighbors[i][0] for i in sampled_indices]) - adj_relation[entity] = np.array( - [neighbors[i][1] for i in sampled_indices]) + adj_entity[entity] = np.array([neighbors[i][0] for i in sampled_indices]) + adj_relation[entity] = np.array([neighbors[i][1] for i in sampled_indices]) return torch.from_numpy(adj_entity), torch.from_numpy(adj_relation) @@ -212,10 +205,8 @@ def get_neighbors(self, items): relations = [] for i in range(self.n_iter): index = torch.flatten(entities[i]) - neighbor_entities = torch.reshape(torch.index_select( - self.adj_entity, 0, index), (self.batch_size, -1)) - neighbor_relations = torch.reshape(torch.index_select( - self.adj_relation, 0, index), (self.batch_size, -1)) + neighbor_entities = torch.index_select(self.adj_entity, 0, index).reshape(self.batch_size, -1) + neighbor_relations = torch.index_select(self.adj_relation, 0, index).reshape(self.batch_size, -1) entities.append(neighbor_entities) relations.append(neighbor_relations) return entities, relations @@ -244,17 +235,13 @@ def aggregate(self, user_embeddings, entities, relations): for i in range(self.n_iter): entity_vectors_next_iter = [] for hop in range(self.n_iter - i): - shape = (self.batch_size, -1, - self.neighbor_sample_size, self.embedding_size) + shape = (self.batch_size, -1, self.neighbor_sample_size, self.embedding_size) self_vectors = entity_vectors[hop] - neighbor_vectors = torch.reshape( - entity_vectors[hop + 1], shape) - neighbor_relations = torch.reshape( - relation_vectors[hop], shape) + neighbor_vectors = entity_vectors[hop + 1].reshape(shape) + neighbor_relations = relation_vectors[hop].reshape(shape) # mix_neighbor_vectors - user_embeddings = torch.reshape(user_embeddings, - (self.batch_size, 1, 1, self.embedding_size)) # [batch_size, 1, 1, dim] + user_embeddings = user_embeddings.reshape(self.batch_size, 1, 1, self.embedding_size) # [batch_size, 1, 1, dim] user_relation_scores = torch.mean(user_embeddings * neighbor_relations, dim=-1) # [batch_size, -1, n_neighbor] user_relation_scores_normalized = torch.unsqueeze(self.softmax(user_relation_scores), @@ -263,24 +250,19 @@ def aggregate(self, user_embeddings, entities, relations): dim=2) # [batch_size, -1, dim] if self.aggregator_class == 'sum': - output = torch.reshape( - self_vectors + neighbors_agg, (-1, self.embedding_size)) # [-1, dim] + output = (self_vectors + neighbors_agg).reshape(-1, self.embedding_size) # [-1, dim] elif self.aggregator_class == 'neighbor': - output = torch.reshape( - neighbors_agg, (-1, self.embedding_size)) # [-1, dim] + output = neighbors_agg.reshape(-1, self.embedding_size) # [-1, dim] elif self.aggregator_class == 'concat': # [batch_size, -1, dim * 2] output = torch.cat([self_vectors, neighbors_agg], dim=-1) - output = torch.reshape( - output, (-1, self.embedding_size * 2)) # [-1, dim * 2] + output = output.reshape(-1, self.embedding_size * 2) # [-1, dim * 2] else: - raise Exception("Unknown aggregator: " + - self.aggregator_class) + raise Exception("Unknown aggregator: " + self.aggregator_class) output = self.linear_layers[i](output) # [batch_size, -1, dim] - output = torch.reshape( - output, [self.batch_size, -1, self.embedding_size]) + output = output.reshape(self.batch_size, -1, self.embedding_size) if i == self.n_iter - 1: vector = self.Tanh(output) @@ -290,8 +272,7 @@ def aggregate(self, user_embeddings, entities, relations): entity_vectors_next_iter.append(vector) entity_vectors = entity_vectors_next_iter - res = torch.reshape( - entity_vectors[0], (self.batch_size, self.embedding_size)) + res = entity_vectors[0].reshape(self.batch_size, self.embedding_size) return res def label_smoothness_predict(self, user_embeddings, user, entities, relations): @@ -320,8 +301,7 @@ def label_smoothness_predict(self, user_embeddings, user, entities, relations): for entities_per_iter in entities: users = torch.unsqueeze(user, dim=1) # [batch_size, 1] - user_entity_concat = users * self.offset + \ - entities_per_iter # [batch_size, n_neighbor^i] + user_entity_concat = users * self.offset + entities_per_iter # [batch_size, n_neighbor^i] # the first one in entities is the items to be held out if holdout_item_for_user is None: @@ -340,10 +320,9 @@ def lookup_interaction_table(x, _): holdout_mask = (holdout_item_for_user - user_entity_concat).bool() # True if the entity is a labeled item reset_mask = (initial_label - 0.5).bool() - reset_mask = torch.logical_and( - reset_mask, holdout_mask) # remove held-out items - initial_label = holdout_mask.float() * initial_label + torch.logical_not( - holdout_mask).float() * 0.5 # label initialization + reset_mask = torch.logical_and(reset_mask, holdout_mask) # remove held-out items + initial_label = holdout_mask.float() * initial_label + \ + torch.logical_not(holdout_mask).float() * 0.5 # label initialization reset_masks.append(reset_mask) entity_labels.append(initial_label) @@ -357,24 +336,21 @@ def lookup_interaction_table(x, _): for hop in range(self.n_iter - i): masks = reset_masks[hop] self_labels = entity_labels[hop] - neighbor_labels = torch.reshape(entity_labels[hop + 1], - [self.batch_size, -1, self.neighbor_sample_size]) - neighbor_relations = torch.reshape(relation_vectors[hop], - [self.batch_size, -1, self.neighbor_sample_size, - self.embedding_size]) + neighbor_labels = entity_labels[hop + 1].reshape(self.batch_size, -1, self.neighbor_sample_size) + neighbor_relations = relation_vectors[hop].reshape(self.batch_size, -1, self.neighbor_sample_size, + self.embedding_size) # mix_neighbor_labels - user_embeddings = torch.reshape(user_embeddings, - [self.batch_size, 1, 1, self.embedding_size]) # [batch_size, 1, 1, dim] + user_embeddings = user_embeddings.reshape(self.batch_size, 1, 1, + self.embedding_size) # [batch_size, 1, 1, dim] user_relation_scores = torch.mean(user_embeddings * neighbor_relations, dim=-1) # [batch_size, -1, n_neighbor] - user_relation_scores_normalized = self.softmax( - user_relation_scores) # [batch_size, -1, n_neighbor] + user_relation_scores_normalized = self.softmax(user_relation_scores) # [batch_size, -1, n_neighbor] neighbors_aggregated_label = torch.mean(user_relation_scores_normalized * neighbor_labels, dim=2) # [batch_size, -1, dim] # [batch_size, -1] - output = masks.float() * self_labels + torch.logical_not(masks).float() * \ - neighbors_aggregated_label + output = masks.float() * self_labels + \ + torch.logical_not(masks).float() * neighbors_aggregated_label entity_labels_next_iter.append(output) entity_labels = entity_labels_next_iter diff --git a/recbole/model/knowledge_aware_recommender/ktup.py b/recbole/model/knowledge_aware_recommender/ktup.py index 72d7034ed..c3277efc6 100644 --- a/recbole/model/knowledge_aware_recommender/ktup.py +++ b/recbole/model/knowledge_aware_recommender/ktup.py @@ -18,10 +18,10 @@ import torch.nn.functional as F from torch.autograd import Variable -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbMarginLoss from recbole.model.init import xavier_uniform_initialization +from recbole.model.loss import BPRLoss, EmbMarginLoss +from recbole.utils import InputType class KTUP(KnowledgeRecommender): @@ -74,7 +74,7 @@ def __init__(self, config, dataset): self.relation_norm_embedding.weight.data = normalize_rel_norm_emb def _masked_softmax(self, logits): - probs = F.softmax(logits, dim=len(logits.shape)-1) + probs = F.softmax(logits, dim=len(logits.shape) - 1) return probs def convert_to_one_hot(self, indices, num_classes): @@ -127,7 +127,8 @@ def st_gumbel_softmax(self, logits, temperature=1.0): return y def _get_preferences(self, user_e, item_e, use_st_gumbel=False): - pref_probs = torch.matmul(user_e + item_e, torch.t(self.pref_embedding.weight + self.relation_embedding.weight)) / 2 + pref_probs = torch.matmul(user_e + item_e, + torch.t(self.pref_embedding.weight + self.relation_embedding.weight)) / 2 if use_st_gumbel: # todo: different torch versions may cause the st_gumbel_softmax to report errors, wait to be test pref_probs = self.st_gumbel_softmax(pref_probs) @@ -209,7 +210,8 @@ def calculate_kg_loss(self, interaction): loss = self.kg_weight * (kg_loss + orthogonal_loss + reg_loss) entity = torch.cat([h, pos_t, neg_t]) entity = entity[entity < self.n_items] - align_loss = self.align_weight * alignLoss(self.item_embedding(entity), self.entity_embedding(entity), self.L1_flag) + align_loss = self.align_weight * alignLoss(self.item_embedding(entity), self.entity_embedding(entity), + self.L1_flag) return loss, align_loss diff --git a/recbole/model/knowledge_aware_recommender/mkr.py b/recbole/model/knowledge_aware_recommender/mkr.py index 7354ced6e..3b8503f80 100644 --- a/recbole/model/knowledge_aware_recommender/mkr.py +++ b/recbole/model/knowledge_aware_recommender/mkr.py @@ -16,10 +16,10 @@ import torch import torch.nn as nn -from recbole.utils import InputType -from recbole.model.layers import MLPLayers from recbole.model.abstract_recommender import KnowledgeRecommender from recbole.model.init import xavier_normal_initialization +from recbole.model.layers import MLPLayers +from recbole.utils import InputType class MKR(KnowledgeRecommender): @@ -38,8 +38,8 @@ def __init__(self, config, dataset): self.LABEL = config['LABEL_FIELD'] self.embedding_size = config['embedding_size'] self.kg_embedding_size = config['kg_embedding_size'] - self.L = config['low_layers_num'] # the number of low layers - self.H = config['high_layers_num'] # the number of high layers + self.L = config['low_layers_num'] # the number of low layers + self.H = config['high_layers_num'] # the number of high layers self.reg_weight = config['reg_weight'] self.use_inner_product = config['use_inner_product'] self.dropout_prob = config['dropout_prob'] @@ -79,20 +79,22 @@ def forward(self, user_indices=None, item_indices=None, head_indices=None, relation_indices=None, tail_indices=None): self.item_embeddings = self.item_embeddings_lookup(item_indices) self.head_embeddings = self.entity_embeddings_lookup(head_indices) - self.item_embeddings, self.head_embeddings = self.cc_unit([self.item_embeddings, self.head_embeddings]) # calculate feature interactions between items and entities + self.item_embeddings, self.head_embeddings = self.cc_unit( + [self.item_embeddings, self.head_embeddings]) # calculate feature interactions between items and entities if user_indices is not None: # RS self.user_embeddings = self.user_embeddings_lookup(user_indices) self.user_embeddings = self.user_mlp(self.user_embeddings) - - if self.use_inner_product: # get scores by inner product. - self.scores = torch.sum(self.user_embeddings * self.item_embeddings, 1) # [batch_size] - else: # get scores by mlp layers - self.user_item_concat = torch.cat([self.user_embeddings, self.item_embeddings], 1) # [batch_size, emb_dim*2] + + if self.use_inner_product: # get scores by inner product. + self.scores = torch.sum(self.user_embeddings * self.item_embeddings, 1) # [batch_size] + else: # get scores by mlp layers + self.user_item_concat = torch.cat([self.user_embeddings, self.item_embeddings], + 1) # [batch_size, emb_dim*2] self.user_item_concat = self.rs_mlp(self.user_item_concat) - - self.scores = torch.squeeze(self.rs_pred_mlp(self.user_item_concat)) # [batch_size] + + self.scores = torch.squeeze(self.rs_pred_mlp(self.user_item_concat)) # [batch_size] self.scores_normalized = torch.sigmoid(self.scores) outputs = [self.user_embeddings, self.item_embeddings, self.scores, self.scores_normalized] @@ -101,16 +103,17 @@ def forward(self, user_indices=None, item_indices=None, head_indices=None, self.tail_embeddings = self.entity_embeddings_lookup(tail_indices) self.relation_embeddings = self.relation_embeddings_lookup(relation_indices) self.tail_embeddings = self.tail_mlp(self.tail_embeddings) - - self.head_relation_concat = torch.cat([self.head_embeddings, self.relation_embeddings], 1) # [batch_size, emb_dim*2] + + self.head_relation_concat = torch.cat([self.head_embeddings, self.relation_embeddings], + 1) # [batch_size, emb_dim*2] self.head_relation_concat = self.kge_mlp(self.head_relation_concat) - self.tail_pred = self.kge_pred_mlp(self.head_relation_concat) # [batch_size, 1] + self.tail_pred = self.kge_pred_mlp(self.head_relation_concat) # [batch_size, 1] self.tail_pred = torch.sigmoid(self.tail_pred) self.scores_kge = torch.sigmoid(torch.sum(self.tail_embeddings * self.tail_pred, 1)) self.rmse = torch.mean( torch.sqrt(torch.sum(torch.pow(self.tail_embeddings - - self.tail_pred, 2), 1) / self.embedding_size)) + self.tail_pred, 2), 1) / self.embedding_size)) outputs = [self.head_embeddings, self.tail_embeddings, self.scores_kge, self.rmse] return outputs @@ -179,6 +182,7 @@ class CrossCompressUnit(nn.Module): r"""This is Cross&Compress Unit for MKR model to model feature interactions between items and entities. """ + def __init__(self, dim): super(CrossCompressUnit, self).__init__() self.dim = dim @@ -194,7 +198,7 @@ def forward(self, inputs): e = torch.unsqueeze(e, 1) # [batch_size, dim, dim] c_matrix = torch.matmul(v, e) - c_matrix_transpose = c_matrix.permute(0,2,1) + c_matrix_transpose = c_matrix.permute(0, 2, 1) # [batch_size * dim, dim] c_matrix = c_matrix.view(-1, self.dim) c_matrix_transpose = c_matrix_transpose.contiguous().view(-1, self.dim) diff --git a/recbole/model/knowledge_aware_recommender/ripplenet.py b/recbole/model/knowledge_aware_recommender/ripplenet.py index 9b24b59c9..aa87b264d 100644 --- a/recbole/model/knowledge_aware_recommender/ripplenet.py +++ b/recbole/model/knowledge_aware_recommender/ripplenet.py @@ -12,15 +12,16 @@ in CIKM 2018. """ +import collections + +import numpy as np import torch import torch.nn as nn -import numpy as np -import collections -from recbole.utils import InputType from recbole.model.abstract_recommender import KnowledgeRecommender -from recbole.model.loss import BPRLoss, EmbLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss, EmbLoss +from recbole.utils import InputType class RippleNet(KnowledgeRecommender): @@ -115,9 +116,9 @@ def _build_ripple_set(self): # self.logger.info("user {} without 1-hop kg facts, fill with padding".format(user)) # raise AssertionError("User without facts in 1st hop") n_padding += 1 - memories_h = [0 for i in range(self.n_memory)] - memories_r = [0 for i in range(self.n_memory)] - memories_t = [0 for i in range(self.n_memory)] + memories_h = [0 for _ in range(self.n_memory)] + memories_r = [0 for _ in range(self.n_memory)] + memories_t = [0 for _ in range(self.n_memory)] memories_h = torch.LongTensor(memories_h).to(self.device) memories_r = torch.LongTensor(memories_r).to(self.device) memories_t = torch.LongTensor(memories_t).to(self.device) @@ -259,7 +260,8 @@ def _key_addressing_full(self): r"""Conduct reasoning for specific item and user ripple set Returns: - o_list (dict -> torch.cuda.FloatTensor): list of torch.cuda.FloatTensor n_hop * [batch_size, n_item, embedding_size] + o_list (dict -> torch.cuda.FloatTensor): list of torch.cuda.FloatTensor + n_hop * [batch_size, n_item, embedding_size] """ o_list = [] for hop in range(self.n_hop): From e0bc06cdc8d8fcb324c2cae6b777a5e1ebc6cf1a Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 19:37:02 +0800 Subject: [PATCH 246/249] FORMAT: code format in seq model --- recbole/data/dataset/kg_dataset.py | 4 +- .../model/context_aware_recommender/dssm.py | 4 +- .../model/context_aware_recommender/pnn.py | 4 +- .../context_aware_recommender/xdeepfm.py | 2 +- recbole/model/sequential_recommender/caser.py | 11 ++-- recbole/model/sequential_recommender/din.py | 17 ++--- recbole/model/sequential_recommender/fdsa.py | 4 +- .../model/sequential_recommender/fossil.py | 24 +++---- recbole/model/sequential_recommender/fpmc.py | 17 +++-- recbole/model/sequential_recommender/gcsan.py | 16 ++--- .../model/sequential_recommender/gru4rec.py | 9 ++- .../model/sequential_recommender/gru4recf.py | 18 ++--- recbole/model/sequential_recommender/hgn.py | 1 + recbole/model/sequential_recommender/hrm.py | 2 +- recbole/model/sequential_recommender/ksr.py | 65 ++++++++++--------- recbole/model/sequential_recommender/narm.py | 4 +- .../model/sequential_recommender/nextitnet.py | 40 +++++++----- recbole/model/sequential_recommender/npe.py | 1 + .../model/sequential_recommender/repeatnet.py | 4 +- recbole/model/sequential_recommender/s3rec.py | 24 ++++--- .../model/sequential_recommender/sasrec.py | 10 +-- .../model/sequential_recommender/sasrecf.py | 4 +- recbole/model/sequential_recommender/shan.py | 5 +- recbole/model/sequential_recommender/srgnn.py | 18 ++--- recbole/model/sequential_recommender/stamp.py | 2 +- .../model/sequential_recommender/transrec.py | 36 +++++----- 26 files changed, 176 insertions(+), 170 deletions(-) diff --git a/recbole/data/dataset/kg_dataset.py b/recbole/data/dataset/kg_dataset.py index 4e9646b5e..610cea132 100644 --- a/recbole/data/dataset/kg_dataset.py +++ b/recbole/data/dataset/kg_dataset.py @@ -419,7 +419,7 @@ def kg_graph(self, form='coo', value_field=None): else ``graph[src, tgt] = self.kg_feat[value_field][src, tgt]``. Currently, we support graph in `DGL`_ and `PyG`_, - and two type of sparse matrixes, ``coo`` and ``csr``. + and two type of sparse matrices, ``coo`` and ``csr``. Args: form (str, optional): Format of sparse matrix, or library of graph data structure. @@ -524,7 +524,7 @@ def ckg_graph(self, form='coo', value_field=None): or ``graph[src, tgt] = [UI-Relation]``. Currently, we support graph in `DGL`_ and `PyG`_, - and two type of sparse matrixes, ``coo`` and ``csr``. + and two type of sparse matrices, ``coo`` and ``csr``. Args: form (str, optional): Format of sparse matrix, or library of graph data structure. diff --git a/recbole/model/context_aware_recommender/dssm.py b/recbole/model/context_aware_recommender/dssm.py index 597a04c33..346c0f23a 100644 --- a/recbole/model/context_aware_recommender/dssm.py +++ b/recbole/model/context_aware_recommender/dssm.py @@ -43,7 +43,7 @@ def __init__(self, config, dataset): self.item_mlp_layers = MLPLayers(item_size_list, self.dropout_prob, activation='tanh', bn=True) self.loss = nn.BCELoss() - self.sigmod = nn.Sigmoid() + self.sigmoid = nn.Sigmoid() # parameters initialization self.apply(self._init_weights) @@ -86,7 +86,7 @@ def forward(self, interaction): item_dnn_out = self.item_mlp_layers(embed_item.view(batch_size, -1)) score = torch.cosine_similarity(user_dnn_out, item_dnn_out, dim=1) - sig_score = self.sigmod(score) + sig_score = self.sigmoid(score) return sig_score.squeeze() def calculate_loss(self, interaction): diff --git a/recbole/model/context_aware_recommender/pnn.py b/recbole/model/context_aware_recommender/pnn.py index 588d4ba26..2c0b4946b 100644 --- a/recbole/model/context_aware_recommender/pnn.py +++ b/recbole/model/context_aware_recommender/pnn.py @@ -64,7 +64,7 @@ def __init__(self, config, dataset): def reg_loss(self): """Calculate the L2 normalization loss of model parameters. - Including weight matrixes of mlp layers. + Including weight matrices of mlp layers. Returns: loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,] @@ -152,7 +152,7 @@ def forward(self, feat_emb): class OuterProductLayer(nn.Module): - """OutterProduct Layer used in PNN. This implemention is + """OuterProduct Layer used in PNN. This implementation is adapted from code that the author of the paper published on https://github.com/Atomu2014/product-nets. """ diff --git a/recbole/model/context_aware_recommender/xdeepfm.py b/recbole/model/context_aware_recommender/xdeepfm.py index 3e54939df..b0c2f1e1a 100644 --- a/recbole/model/context_aware_recommender/xdeepfm.py +++ b/recbole/model/context_aware_recommender/xdeepfm.py @@ -100,7 +100,7 @@ def reg_loss(self, parameters): def calculate_reg_loss(self): """Calculate the final L2 normalization loss of model parameters. - Including weight matrixes of mlp layers, linear layer and convolutional layers. + Including weight matrices of mlp layers, linear layer and convolutional layers. Returns: loss(torch.FloatTensor): The L2 Loss tensor. shape of [1,] diff --git a/recbole/model/sequential_recommender/caser.py b/recbole/model/sequential_recommender/caser.py index 0cb018ba4..7203685e3 100644 --- a/recbole/model/sequential_recommender/caser.py +++ b/recbole/model/sequential_recommender/caser.py @@ -25,8 +25,8 @@ from torch.nn import functional as F from torch.nn.init import normal_, xavier_normal_, constant_ -from recbole.model.loss import RegLoss, BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import RegLoss, BPRLoss class Caser(SequentialRecommender): @@ -61,7 +61,10 @@ def __init__(self, config, dataset): # horizontal conv layer lengths = [i + 1 for i in range(self.max_seq_length)] - self.conv_h = nn.ModuleList([nn.Conv2d(in_channels=1, out_channels=self.n_h, kernel_size=(i, self.embedding_size)) for i in lengths]) + self.conv_h = nn.ModuleList([ + nn.Conv2d(in_channels=1, out_channels=self.n_h, kernel_size=(i, self.embedding_size)) + for i in lengths + ]) # fully-connected layer self.fc1_dim_v = self.n_v * self.embedding_size @@ -95,7 +98,7 @@ def _init_weights(self, module): def forward(self, user, item_seq): # Embedding Look-up - # use unsqueeze() to get a 4-D input for convolution layers. (batchsize * 1 * max_length * embedding_size) + # use unsqueeze() to get a 4-D input for convolution layers. (batch_size * 1 * max_length * embedding_size) item_seq_emb = self.item_embedding(item_seq).unsqueeze(1) user_emb = self.user_embedding(user).squeeze(1) @@ -155,7 +158,7 @@ def calculate_loss(self, interaction): loss = self.loss_fct(logits, pos_items) reg_loss = self.reg_loss([self.user_embedding.weight, self.item_embedding.weight, - self.conv_v.weight,self.fc1.weight, self.fc2.weight]) + self.conv_v.weight, self.fc1.weight, self.fc2.weight]) loss = loss + self.reg_weight * reg_loss + self.reg_loss_conv_h() return loss diff --git a/recbole/model/sequential_recommender/din.py b/recbole/model/sequential_recommender/din.py index 161454684..f8f1e6b51 100644 --- a/recbole/model/sequential_recommender/din.py +++ b/recbole/model/sequential_recommender/din.py @@ -24,9 +24,9 @@ import torch.nn as nn from torch.nn.init import xavier_normal_, constant_ -from recbole.utils import InputType -from recbole.model.layers import MLPLayers, SequenceAttLayer, ContextSeqEmbLayer from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.layers import MLPLayers, SequenceAttLayer, ContextSeqEmbLayer +from recbole.utils import InputType class DIN(SequentialRecommender): @@ -61,15 +61,10 @@ def __init__(self, config, dataset): # self.dnn_list = [(3 * self.num_feature_field['item'] + self.num_feature_field['user']) # * self.embedding_size] + self.mlp_hidden_size num_item_feature = len(self.item_feat.interaction.keys()) - self.dnn_list = [ - (3 * num_item_feature) * self.embedding_size - ] + self.mlp_hidden_size - self.att_list = [ - 4 * num_item_feature * self.embedding_size - ] + self.mlp_hidden_size - - mask_mat = torch.arange(self.max_seq_length).to(self.device).view( - 1, -1) # init mask + self.dnn_list = [3 * num_item_feature * self.embedding_size] + self.mlp_hidden_size + self.att_list = [4 * num_item_feature * self.embedding_size] + self.mlp_hidden_size + + mask_mat = torch.arange(self.max_seq_length).to(self.device).view(1, -1) # init mask self.attention = SequenceAttLayer(mask_mat, self.att_list, activation='Sigmoid', diff --git a/recbole/model/sequential_recommender/fdsa.py b/recbole/model/sequential_recommender/fdsa.py index 41bbae25d..bae7192ab 100644 --- a/recbole/model/sequential_recommender/fdsa.py +++ b/recbole/model/sequential_recommender/fdsa.py @@ -17,14 +17,14 @@ from torch import nn from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss from recbole.model.layers import TransformerEncoder, FeatureSeqEmbLayer, VanillaAttention +from recbole.model.loss import BPRLoss class FDSA(SequentialRecommender): r""" FDSA is similar with the GRU4RecF implemented in RecBole, which uses two different Transformer encoders to - encode items and features respectively and concatenates the two subparts's outputs as the final output. + encode items and features respectively and concatenates the two subparts' outputs as the final output. """ diff --git a/recbole/model/sequential_recommender/fossil.py b/recbole/model/sequential_recommender/fossil.py index 24652f42e..30f95b29e 100644 --- a/recbole/model/sequential_recommender/fossil.py +++ b/recbole/model/sequential_recommender/fossil.py @@ -45,8 +45,8 @@ def __init__(self, config, dataset): # define the layers and loss type self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) - self.user_lamda = nn.Embedding(self.n_users, self.order_len) - self.lamda = nn.Parameter(torch.zeros(self.order_len)).to(self.device) + self.user_lambda = nn.Embedding(self.n_users, self.order_len) + self.lambda_ = nn.Parameter(torch.zeros(self.order_len)).to(self.device) self.loss_type = config['loss_type'] if self.loss_type == 'BPR': @@ -85,9 +85,9 @@ def inverse_seq_item_embedding(self, seq_item_embedding, seq_item_len): def reg_loss(self, user_embedding, item_embedding, seq_output): reg_1 = self.reg_weight - loss_1 = reg_1 * torch.norm(user_embedding, p=2)\ - + reg_1 * torch.norm(item_embedding, p=2)\ - + reg_1 * torch.norm(seq_output, p=2) + loss_1 = reg_1 * torch.norm(user_embedding, p=2) \ + + reg_1 * torch.norm(item_embedding, p=2) \ + + reg_1 * torch.norm(seq_output, p=2) return loss_1 @@ -114,13 +114,13 @@ def get_high_order_Markov(self, high_order_item_embedding, user): in order to get the inference of past items and the user's taste to the current predict item """ - user_lamda = self.user_lamda(user).unsqueeze(dim=2) + user_lambda = self.user_lambda(user).unsqueeze(dim=2) # batch_size * order_len * 1 - lamda = self.lamda.unsqueeze(dim=0).unsqueeze(dim=2) + lambda_ = self.lambda_.unsqueeze(dim=0).unsqueeze(dim=2) # 1 * order_len * 1 - lamda = torch.add(user_lamda, lamda) + lambda_ = torch.add(user_lambda, lambda_) # batch_size * order_len * 1 - high_order_item_embedding = torch.mul(high_order_item_embedding, lamda) + high_order_item_embedding = torch.mul(high_order_item_embedding, lambda_) # batch_size * order_len * embedding_size high_order_item_embedding = high_order_item_embedding.sum(dim=1) # batch_size * embedding_size @@ -147,7 +147,7 @@ def calculate_loss(self, interaction): pos_items = interaction[self.POS_ITEM_ID] pos_items_emb = self.item_embedding(pos_items) - user_lamda = self.user_lamda(user) + user_lambda = self.user_lambda(user) pos_items_embedding = self.item_embedding(pos_items) if self.loss_type == 'BPR': neg_items = interaction[self.NEG_ITEM_ID] @@ -155,12 +155,12 @@ def calculate_loss(self, interaction): pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) loss = self.loss_fct(pos_score, neg_score) - return loss + self.reg_loss(user_lamda, pos_items_embedding, seq_output) + return loss + self.reg_loss(user_lambda, pos_items_embedding, seq_output) else: # self.loss_type = 'CE' test_item_emb = self.item_embedding.weight logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) - return loss + self.reg_loss(user_lamda, pos_items_embedding, seq_output) + return loss + self.reg_loss(user_lambda, pos_items_embedding, seq_output) def predict(self, interaction): diff --git a/recbole/model/sequential_recommender/fpmc.py b/recbole/model/sequential_recommender/fpmc.py index ce1ba3373..6654b59d1 100644 --- a/recbole/model/sequential_recommender/fpmc.py +++ b/recbole/model/sequential_recommender/fpmc.py @@ -20,9 +20,9 @@ from torch import nn from torch.nn.init import xavier_normal_ -from recbole.utils import InputType -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss +from recbole.utils import InputType class FPMC(SequentialRecommender): @@ -66,19 +66,18 @@ def _init_weights(self, module): xavier_normal_(module.weight.data) def forward(self, user, item_seq, item_seq_len, next_item): - item_last_click_index = item_seq_len - 1 item_last_click = torch.gather(item_seq, dim=1, index=item_last_click_index.unsqueeze(1)) - item_seq_emb = self.LI_emb(item_last_click) # [b,1,emb] + item_seq_emb = self.LI_emb(item_last_click) # [b,1,emb] user_emb = self.UI_emb(user) - user_emb = torch.unsqueeze(user_emb, dim=1) # [b,1,emb] + user_emb = torch.unsqueeze(user_emb, dim=1) # [b,1,emb] iu_emb = self.IU_emb(next_item) - iu_emb = torch.unsqueeze(iu_emb, dim=1) # [b,n,emb] in here n = 1 + iu_emb = torch.unsqueeze(iu_emb, dim=1) # [b,n,emb] in here n = 1 il_emb = self.IL_emb(next_item) - il_emb = torch.unsqueeze(il_emb, dim=1) # [b,n,emb] in here n = 1 + il_emb = torch.unsqueeze(il_emb, dim=1) # [b,n,emb] in here n = 1 # This is the core part of the FPMC model,can be expressed by a combination of a MF and a FMC model # MF @@ -119,13 +118,13 @@ def full_sort_predict(self, interaction): user_emb = self.UI_emb(user) all_iu_emb = self.IU_emb.weight - mf = torch.matmul(user_emb, all_iu_emb.transpose(0,1)) + mf = torch.matmul(user_emb, all_iu_emb.transpose(0, 1)) all_il_emb = self.IL_emb.weight item_last_click_index = item_seq_len - 1 item_last_click = torch.gather(item_seq, dim=1, index=item_last_click_index.unsqueeze(1)) item_seq_emb = self.LI_emb(item_last_click) # [b,1,emb] - fmc = torch.matmul(item_seq_emb, all_il_emb.transpose(0,1)) + fmc = torch.matmul(item_seq_emb, all_il_emb.transpose(0, 1)) fmc = torch.squeeze(fmc, dim=1) score = mf + fmc return score diff --git a/recbole/model/sequential_recommender/gcsan.py b/recbole/model/sequential_recommender/gcsan.py index e527c7c3b..597ba2361 100644 --- a/recbole/model/sequential_recommender/gcsan.py +++ b/recbole/model/sequential_recommender/gcsan.py @@ -14,16 +14,16 @@ """ import math -import numpy as np +import numpy as np import torch from torch import nn from torch.nn import Parameter from torch.nn import functional as F -from recbole.model.loss import EmbLoss, BPRLoss from recbole.model.abstract_recommender import SequentialRecommender from recbole.model.layers import TransformerEncoder +from recbole.model.loss import EmbLoss, BPRLoss class GNN(nn.Module): @@ -72,16 +72,16 @@ def GNNCell(self, A, hidden): # [batch_size, max_session_len, embedding_size * 2] inputs = torch.cat([input_in, input_out], 2) - # gi.size equals to gh.size, shape of [batch_size, max_session_len, embdding_size * 3] + # gi.size equals to gh.size, shape of [batch_size, max_session_len, embedding_size * 3] gi = F.linear(inputs, self.w_ih, self.b_ih) gh = F.linear(hidden, self.w_hh, self.b_hh) # (batch_size, max_session_len, embedding_size) i_r, i_i, i_n = gi.chunk(3, 2) h_r, h_i, h_n = gh.chunk(3, 2) - resetgate = torch.sigmoid(i_r + h_r) - inputgate = torch.sigmoid(i_i + h_i) - newgate = torch.tanh(i_n + resetgate * h_n) - hy = (1 - inputgate) * hidden + inputgate * newgate + reset_gate = torch.sigmoid(i_r + h_r) + input_gate = torch.sigmoid(i_i + h_i) + new_gate = torch.tanh(i_n + reset_gate * h_n) + hy = (1 - input_gate) * hidden + input_gate * new_gate return hy def forward(self, A, hidden): @@ -91,7 +91,7 @@ def forward(self, A, hidden): class GCSAN(SequentialRecommender): - r"""GCSAN captures rich local dependencies via graph nerual network, + r"""GCSAN captures rich local dependencies via graph neural network, and learns long-range dependencies by applying the self-attention mechanism. Note: diff --git a/recbole/model/sequential_recommender/gru4rec.py b/recbole/model/sequential_recommender/gru4rec.py index d59d50fcf..94d542174 100644 --- a/recbole/model/sequential_recommender/gru4rec.py +++ b/recbole/model/sequential_recommender/gru4rec.py @@ -17,13 +17,12 @@ """ - import torch from torch import nn from torch.nn.init import xavier_uniform_, xavier_normal_ -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss class GRU4Rec(SequentialRecommender): @@ -70,7 +69,7 @@ def __init__(self, config, dataset): def _init_weights(self, module): if isinstance(module, nn.Embedding): xavier_normal_(module.weight) - elif isinstance(module,nn.GRU): + elif isinstance(module, nn.GRU): xavier_uniform_(self.gru_layers.weight_hh_l0) xavier_uniform_(self.gru_layers.weight_ih_l0) @@ -92,8 +91,8 @@ def calculate_loss(self, interaction): neg_items = interaction[self.NEG_ITEM_ID] pos_items_emb = self.item_embedding(pos_items) neg_items_emb = self.item_embedding(neg_items) - pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] - neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(pos_score, neg_score) return loss else: # self.loss_type = 'CE' diff --git a/recbole/model/sequential_recommender/gru4recf.py b/recbole/model/sequential_recommender/gru4recf.py index 3e4abd855..608c6448f 100644 --- a/recbole/model/sequential_recommender/gru4recf.py +++ b/recbole/model/sequential_recommender/gru4recf.py @@ -17,9 +17,9 @@ from torch import nn from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss -from recbole.model.layers import FeatureSeqEmbLayer from recbole.model.init import xavier_normal_initialization +from recbole.model.layers import FeatureSeqEmbLayer +from recbole.model.loss import BPRLoss class GRU4RecF(SequentialRecommender): @@ -34,7 +34,7 @@ class GRU4RecF(SequentialRecommender): (3) Weighted sum of outputs from two different RNNs. We implemented the optimal parallel version(2), which uses different RNNs to - encode items and features respectively and concatenates the two subparts's + encode items and features respectively and concatenates the two subparts' outputs as the final output. The different RNN encoders are trained simultaneously. """ @@ -106,12 +106,12 @@ def forward(self, item_seq, item_seq_len): feat_num, embedding_size = table_shape[-2], table_shape[-1] feature_emb = feature_table.view(table_shape[:-2] + (feat_num * embedding_size,)) - feature_gru_output, _ = self.feature_gru_layers(feature_emb) # [B Len H] + feature_gru_output, _ = self.feature_gru_layers(feature_emb) # [B Len H] output_concat = torch.cat((item_gru_output, feature_gru_output), -1) # [B Len 2*H] output = self.dense_layer(output_concat) output = self.gather_indexes(output, item_seq_len - 1) # [B H] - return output # [B H] + return output # [B H] def calculate_loss(self, interaction): item_seq = interaction[self.ITEM_SEQ] @@ -120,10 +120,10 @@ def calculate_loss(self, interaction): pos_items = interaction[self.POS_ITEM_ID] if self.loss_type == 'BPR': neg_items = interaction[self.NEG_ITEM_ID] - pos_items_emb = self.item_embedding(pos_items) # [B H] - neg_items_emb = self.item_embedding(neg_items) # [B H] - pos_score = torch.sum(seq_output*pos_items_emb, dim=-1) # [B] - neg_score = torch.sum(seq_output*neg_items_emb, dim=-1) # [B] + pos_items_emb = self.item_embedding(pos_items) # [B H] + neg_items_emb = self.item_embedding(neg_items) # [B H] + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(pos_score, neg_score) return loss else: # self.loss_type = 'CE' diff --git a/recbole/model/sequential_recommender/hgn.py b/recbole/model/sequential_recommender/hgn.py index a97c65988..009b455ab 100644 --- a/recbole/model/sequential_recommender/hgn.py +++ b/recbole/model/sequential_recommender/hgn.py @@ -17,6 +17,7 @@ import torch import torch.nn as nn from torch.nn.init import xavier_uniform_, constant_, normal_ + from recbole.model.abstract_recommender import SequentialRecommender from recbole.model.loss import BPRLoss diff --git a/recbole/model/sequential_recommender/hrm.py b/recbole/model/sequential_recommender/hrm.py index 0554a4ace..0eb2908d4 100644 --- a/recbole/model/sequential_recommender/hrm.py +++ b/recbole/model/sequential_recommender/hrm.py @@ -18,7 +18,7 @@ import torch import torch.nn as nn -from torch.nn.init import xavier_normal_, constant_ +from torch.nn.init import xavier_normal_ from recbole.model.abstract_recommender import SequentialRecommender from recbole.model.loss import BPRLoss diff --git a/recbole/model/sequential_recommender/ksr.py b/recbole/model/sequential_recommender/ksr.py index c788b9730..6c884b049 100644 --- a/recbole/model/sequential_recommender/ksr.py +++ b/recbole/model/sequential_recommender/ksr.py @@ -14,14 +14,12 @@ """ - import torch from torch import nn from torch.nn.init import xavier_uniform_, xavier_normal_ -from recbole.utils import InputType -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss class KSR(SequentialRecommender): @@ -48,13 +46,13 @@ def __init__(self, config, dataset): self.loss_type = config['loss_type'] self.num_layers = config['num_layers'] self.dropout_prob = config['dropout_prob'] - self.gamma = config['gamma'] # Scaling factor + self.gamma = config['gamma'] # Scaling factor self.device = config['device'] self.loss_type = config['loss_type'] self.freeze_kg = config['freeze_kg'] # define layers and loss - self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) + self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.entity_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) self.entity_embedding.weight.requires_grad = not self.freeze_kg @@ -75,31 +73,35 @@ def __init__(self, config, dataset): self.loss_fct = nn.CrossEntropyLoss() else: raise NotImplementedError("Make sure 'loss_type' in ['BPR', 'CE']!") - + # parameters initialization self.apply(self._init_weights) self.entity_embedding.weight.data.copy_(torch.from_numpy(self.entity_embedding_matrix[:self.n_items])) - self.relation_Matrix = torch.from_numpy(self.relation_embedding_matrix[:self.n_relations]).to(self.device) # [R H] + self.relation_Matrix = torch.from_numpy(self.relation_embedding_matrix[:self.n_relations]).to( + self.device) # [R H] def _init_weights(self, module): """ Initialize the weights """ if isinstance(module, nn.Embedding): xavier_normal_(module.weight) - elif isinstance(module,nn.GRU): + elif isinstance(module, nn.GRU): xavier_uniform_(self.gru_layers.weight_hh_l0) xavier_uniform_(self.gru_layers.weight_ih_l0) - + def _get_kg_embedding(self, head): - """Difference: We generate the embeddings of the tail entities on every relations only for head due to the 1-N problems. """ - head_e = self.entity_embedding(head) # [B H] - relation_Matrix = self.relation_Matrix.repeat(head_e.size()[0], 1, 1) # [B R H] - head_Matrix = torch.unsqueeze(head_e, 1).repeat(1, self.n_relations, 1) # [B R H] + """Difference: + We generate the embeddings of the tail entities on every relations only for head due to the 1-N problems. + """ + head_e = self.entity_embedding(head) # [B H] + relation_Matrix = self.relation_Matrix.repeat(head_e.size()[0], 1, 1) # [B R H] + head_Matrix = torch.unsqueeze(head_e, 1).repeat(1, self.n_relations, 1) # [B R H] tail_Matrix = head_Matrix + relation_Matrix - + return head_e, tail_Matrix - + def _memory_update_cell(self, user_memory, update_memory): - z = torch.sigmoid(torch.mul(user_memory, update_memory).sum(-1).float()).unsqueeze(-1) # [B R 1], the gate vector + z = torch.sigmoid(torch.mul(user_memory, update_memory).sum(-1).float()).unsqueeze( + -1) # [B R 1], the gate vector updated_user_memory = (1.0 - z) * user_memory + z * update_memory return updated_user_memory @@ -108,19 +110,20 @@ def memory_update(self, item_seq, item_seq_len): step_length = item_seq.size()[1] last_item = item_seq_len - 1 # init user memory with 0s - user_memory = torch.zeros(item_seq.size()[0], self.n_relations, self.embedding_size).float().to(self.device) # [B R H] + user_memory = torch.zeros(item_seq.size()[0], self.n_relations, self.embedding_size).float().to( + self.device) # [B R H] last_user_memory = torch.zeros_like(user_memory) - for i in range(step_length): # [len] - _, update_memory = self._get_kg_embedding(item_seq[:, i]) # [B R H] - user_memory = self._memory_update_cell(user_memory, update_memory) # [B R H] + for i in range(step_length): # [len] + _, update_memory = self._get_kg_embedding(item_seq[:, i]) # [B R H] + user_memory = self._memory_update_cell(user_memory, update_memory) # [B R H] last_user_memory[last_item == i] = user_memory[last_item == i].float() return last_user_memory - + def memory_read(self, user_memory): """ define read operator """ attrs = self.relation_Matrix - attentions = nn.functional.softmax(self.gamma * torch.mul(user_memory, attrs).sum(-1).float(), -1) # [B R] - u_m = torch.mul(user_memory, attentions.unsqueeze(-1)).sum(1) # [B H] + attentions = nn.functional.softmax(self.gamma * torch.mul(user_memory, attrs).sum(-1).float(), -1) # [B R] + u_m = torch.mul(user_memory, attentions.unsqueeze(-1)).sum(1) # [B H] return u_m def forward(self, item_seq, item_seq_len): @@ -141,7 +144,7 @@ def forward(self, item_seq, item_seq_len): # combine them together p_u = self.dense_layer_u(torch.cat((seq_output, u_m), -1)) # [B H] return p_u - + def _get_item_comb_embedding(self, item): h_e, _ = self._get_kg_embedding(item) i_e = self.item_embedding(item) @@ -157,13 +160,14 @@ def calculate_loss(self, interaction): neg_items = interaction[self.NEG_ITEM_ID] pos_items_emb = self._get_item_comb_embedding(pos_items) neg_items_emb = self._get_item_comb_embedding(neg_items) - pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] - neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] + pos_score = torch.sum(seq_output * pos_items_emb, dim=-1) # [B] + neg_score = torch.sum(seq_output * neg_items_emb, dim=-1) # [B] loss = self.loss_fct(pos_score, neg_score) return loss - else: # self.loss_type = 'CE' - test_items_emb = self.dense_layer_i(torch.cat((self.item_embedding.weight, self.entity_embedding.weight), -1)) # [n_items H] - logits = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) + else: # self.loss_type = 'CE' + test_items_emb = self.dense_layer_i( + torch.cat((self.item_embedding.weight, self.entity_embedding.weight), -1)) # [n_items H] + logits = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) return loss @@ -180,6 +184,7 @@ def full_sort_predict(self, interaction): item_seq = interaction[self.ITEM_SEQ] item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(item_seq, item_seq_len) - test_items_emb = self.dense_layer_i(torch.cat((self.item_embedding.weight, self.entity_embedding.weight), -1)) # [n_items H] + test_items_emb = self.dense_layer_i( + torch.cat((self.item_embedding.weight, self.entity_embedding.weight), -1)) # [n_items H] scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, n_items] return scores diff --git a/recbole/model/sequential_recommender/narm.py b/recbole/model/sequential_recommender/narm.py index 562f74a12..76f5594b7 100644 --- a/recbole/model/sequential_recommender/narm.py +++ b/recbole/model/sequential_recommender/narm.py @@ -24,8 +24,8 @@ from torch import nn from torch.nn.init import xavier_normal_, constant_ -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss class NARM(SequentialRecommender): @@ -52,7 +52,7 @@ def __init__(self, config, dataset): self.a_2 = nn.Linear(self.hidden_size, self.hidden_size, bias=False) self.v_t = nn.Linear(self.hidden_size, 1, bias=False) self.ct_dropout = nn.Dropout(self.dropout_probs[1]) - self.b = nn.Linear(2*self.hidden_size, self.embedding_size, bias=False) + self.b = nn.Linear(2 * self.hidden_size, self.embedding_size, bias=False) self.loss_type = config['loss_type'] if self.loss_type == 'BPR': self.loss_fct = BPRLoss() diff --git a/recbole/model/sequential_recommender/nextitnet.py b/recbole/model/sequential_recommender/nextitnet.py index c55851d6f..9b2aba98e 100644 --- a/recbole/model/sequential_recommender/nextitnet.py +++ b/recbole/model/sequential_recommender/nextitnet.py @@ -22,8 +22,8 @@ from torch.nn import functional as F from torch.nn.init import uniform_, xavier_normal_, constant_ -from recbole.model.loss import RegLoss, BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import RegLoss, BPRLoss class NextItNet(SequentialRecommender): @@ -54,8 +54,9 @@ def __init__(self, config, dataset): self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) # residual blocks dilations in blocks:[1,2,4,8,1,2,4,8,...] - rb = [ResidualBlock_b(self.residual_channels, self.residual_channels, kernel_size=self.kernel_size, - dilation=dilation) for dilation in self.dilations] + rb = [ResidualBlock_b(self.residual_channels, self.residual_channels, + kernel_size=self.kernel_size, dilation=dilation) + for dilation in self.dilations] self.residual_blocks = nn.Sequential(*rb) # fully-connected layer @@ -82,11 +83,11 @@ def _init_weights(self, module): constant_(module.bias.data, 0.1) def forward(self, item_seq): - item_seq_emb = self.item_embedding(item_seq) # [batch_size, seq_len, embed_size] + item_seq_emb = self.item_embedding(item_seq) # [batch_size, seq_len, embed_size] # Residual locks dilate_outputs = self.residual_blocks(item_seq_emb) hidden = dilate_outputs[:, -1, :].view(-1, self.residual_channels) # [batch_size, embed_size] - seq_output = self.final_layer(hidden) # [batch_size, embedding_size] + seq_output = self.final_layer(hidden) # [batch_size, embedding_size] return seq_output def reg_loss_rb(self): @@ -97,7 +98,7 @@ def reg_loss_rb(self): if self.reg_weight > 0.0: for name, parm in self.residual_blocks.named_parameters(): if name.endswith('weight'): - loss_rb += torch.norm(parm,2) + loss_rb += torch.norm(parm, 2) return self.reg_weight * loss_rb def calculate_loss(self, interaction): @@ -116,7 +117,7 @@ def calculate_loss(self, interaction): test_item_emb = self.item_embedding.weight logits = torch.matmul(seq_output, test_item_emb.transpose(0, 1)) loss = self.loss_fct(logits, pos_items) - reg_loss = self.reg_loss([self.item_embedding.weight,self.final_layer.weight]) + reg_loss = self.reg_loss([self.item_embedding.weight, self.final_layer.weight]) loss = loss + self.reg_weight * reg_loss + self.reg_loss_rb() return loss @@ -141,14 +142,14 @@ class ResidualBlock_a(nn.Module): r""" Residual block (a) in the paper """ + def __init__(self, in_channel, out_channel, kernel_size=3, dilation=None): super(ResidualBlock_a, self).__init__() - half_channel = out_channel//2 + half_channel = out_channel // 2 self.ln1 = nn.LayerNorm(out_channel, eps=1e-8) self.conv1 = nn.Conv2d(in_channel, half_channel, kernel_size=(1, 1), padding=0) - self.ln2 = nn.LayerNorm(half_channel, eps=1e-8) self.conv2 = nn.Conv2d(half_channel, half_channel, kernel_size=(1, kernel_size), padding=0, dilation=dilation) @@ -176,12 +177,13 @@ def forward(self, x): # x: [batch_size, seq_len, embed_size] def conv_pad(self, x, dilation): # x: [batch_size, seq_len, embed_size] r""" Dropout-mask: To avoid the future information leakage problem, this paper proposed a masking-based dropout trick for the 1D dilated convolution to prevent the network from seeing the future items. - Also the One-dimensional transformation is completed in this funtion. + Also the One-dimensional transformation is completed in this function. """ - inputs_pad = x.permute(0, 2, 1) # [batch_size, embed_size, seq_len] + inputs_pad = x.permute(0, 2, 1) # [batch_size, embed_size, seq_len] inputs_pad = inputs_pad.unsqueeze(2) # [batch_size, embed_size, 1, seq_len] - pad = nn.ZeroPad2d(((self.kernel_size - 1) * dilation, 0, 0, 0)) # padding opration args：(left,right,top,bottom) - inputs_pad = pad(inputs_pad) # [batch_size, embed_size, 1, seq_len+(self.kernel_size-1)*dilations] + pad = nn.ZeroPad2d( + ((self.kernel_size - 1) * dilation, 0, 0, 0)) # padding operation args：(left,right,top,bottom) + inputs_pad = pad(inputs_pad) # [batch_size, embed_size, 1, seq_len+(self.kernel_size-1)*dilations] return inputs_pad @@ -189,22 +191,24 @@ class ResidualBlock_b(nn.Module): r""" Residual block (b) in the paper """ + def __init__(self, in_channel, out_channel, kernel_size=3, dilation=None): super(ResidualBlock_b, self).__init__() self.conv1 = nn.Conv2d(in_channel, out_channel, kernel_size=(1, kernel_size), padding=0, dilation=dilation) self.ln1 = nn.LayerNorm(out_channel, eps=1e-8) - self.conv2 = nn.Conv2d(out_channel, out_channel, kernel_size=(1, kernel_size), padding=0, dilation=dilation*2) + self.conv2 = nn.Conv2d(out_channel, out_channel, kernel_size=(1, kernel_size), padding=0, dilation=dilation * 2) self.ln2 = nn.LayerNorm(out_channel, eps=1e-8) self.dilation = dilation self.kernel_size = kernel_size def forward(self, x): # x: [batch_size, seq_len, embed_size] - x_pad = self.conv_pad(x, self.dilation) # [batch_size, embed_size, 1, seq_len+(self.kernel_size-1)*dilations] - out = self.conv1(x_pad).squeeze(2).permute(0, 2, 1) # [batch_size, seq_len+(self.kernel_size-1)*dilations-kernel_size+1, embed_size] + x_pad = self.conv_pad(x, self.dilation) # [batch_size, embed_size, 1, seq_len+(self.kernel_size-1)*dilations] + out = self.conv1(x_pad).squeeze(2).permute(0, 2, 1) + # [batch_size, seq_len+(self.kernel_size-1)*dilations-kernel_size+1, embed_size] out = F.relu(self.ln1(out)) - out_pad = self.conv_pad(out, self.dilation*2) + out_pad = self.conv_pad(out, self.dilation * 2) out2 = self.conv2(out_pad).squeeze(2).permute(0, 2, 1) out2 = F.relu(self.ln2(out2)) return out2 + x @@ -212,7 +216,7 @@ def forward(self, x): # x: [batch_size, seq_len, embed_size] def conv_pad(self, x, dilation): r""" Dropout-mask: To avoid the future information leakage problem, this paper proposed a masking-based dropout trick for the 1D dilated convolution to prevent the network from seeing the future items. - Also the One-dimensional transformation is completed in this funtion. + Also the One-dimensional transformation is completed in this function. """ inputs_pad = x.permute(0, 2, 1) inputs_pad = inputs_pad.unsqueeze(2) diff --git a/recbole/model/sequential_recommender/npe.py b/recbole/model/sequential_recommender/npe.py index f24374e44..29674241a 100644 --- a/recbole/model/sequential_recommender/npe.py +++ b/recbole/model/sequential_recommender/npe.py @@ -19,6 +19,7 @@ import torch import torch.nn as nn from torch.nn.init import xavier_normal_ + from recbole.model.abstract_recommender import SequentialRecommender from recbole.model.loss import BPRLoss diff --git a/recbole/model/sequential_recommender/repeatnet.py b/recbole/model/sequential_recommender/repeatnet.py index 794329517..5e0b5d6bf 100644 --- a/recbole/model/sequential_recommender/repeatnet.py +++ b/recbole/model/sequential_recommender/repeatnet.py @@ -19,11 +19,11 @@ import torch from torch import nn +from torch.nn import functional as F from torch.nn.init import xavier_normal_, constant_ -from torch.nn import functional as F -from recbole.utils import InputType from recbole.model.abstract_recommender import SequentialRecommender +from recbole.utils import InputType class RepeatNet(SequentialRecommender): diff --git a/recbole/model/sequential_recommender/s3rec.py b/recbole/model/sequential_recommender/s3rec.py index 5e1357f93..1fcdbf413 100644 --- a/recbole/model/sequential_recommender/s3rec.py +++ b/recbole/model/sequential_recommender/s3rec.py @@ -23,8 +23,8 @@ from torch import nn from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss from recbole.model.layers import TransformerEncoder +from recbole.model.loss import BPRLoss class S3Rec(SequentialRecommender): @@ -179,7 +179,7 @@ def forward(self, item_seq, bidirectional=True): trm_output = self.trm_encoder(input_emb, attention_mask, output_all_encoded_layers=True) - seq_output = trm_output[-1] # [B L H] + seq_output = trm_output[-1] # [B L H] return seq_output def pretrain(self, features, masked_item_sequence, pos_items, neg_items, @@ -231,13 +231,12 @@ def pretrain(self, features, masked_item_sequence, pos_items, neg_items, pos_segment_score = self._segment_prediction(segment_context, pos_segment_emb) neg_segment_score = self._segment_prediction(segment_context, neg_segment_emb) sp_distance = torch.sigmoid(pos_segment_score - neg_segment_score) - sp_loss = torch.sum(self.loss_fct(sp_distance, - torch.ones_like(sp_distance, dtype=torch.float32))) + sp_loss = torch.sum(self.loss_fct(sp_distance, torch.ones_like(sp_distance, dtype=torch.float32))) - pretrain_loss = self.aap_weight*aap_loss \ - + self.mip_weight*mip_loss \ - + self.map_weight*map_loss \ - + self.sp_weight*sp_loss + pretrain_loss = self.aap_weight * aap_loss \ + + self.mip_weight * mip_loss \ + + self.map_weight * map_loss \ + + self.sp_weight * sp_loss return pretrain_loss @@ -250,7 +249,7 @@ def _neg_sample(self, item_set): # [ , ] def _padding_zero_at_left(self, sequence): # had truncated according to the max_length pad_len = self.max_seq_length - len(sequence) - sequence = [0]*pad_len + sequence + sequence = [0] * pad_len + sequence return sequence def reconstruct_pretrain_data(self, item_seq, item_seq_len): @@ -260,7 +259,7 @@ def reconstruct_pretrain_data(self, item_seq, item_seq_len): # We don't need padding for features item_feature_seq = self.item_feat[self.FEATURE_FIELD][item_seq] - 1 - + end_index = item_seq_len.cpu().numpy().tolist() item_seq = item_seq.cpu().numpy().tolist() item_feature_seq = item_feature_seq.cpu().numpy().tolist() @@ -268,7 +267,7 @@ def reconstruct_pretrain_data(self, item_seq, item_seq_len): # we will padding zeros at the left side # these will be train_instances, after will be reshaped to batch sequence_instances = [] - associated_features = [] # For Associated Attribute Prediction and Masked Attribute Prediction + associated_features = [] # For Associated Attribute Prediction and Masked Attribute Prediction long_sequence = [] for i, end_i in enumerate(end_index): sequence_instances.append(item_seq[i][:end_i]) @@ -343,7 +342,6 @@ def reconstruct_pretrain_data(self, item_seq, item_seq_len): return associated_features, masked_item_sequence, pos_items, neg_items, \ masked_segment_list, pos_segment_list, neg_segment_list - def calculate_loss(self, interaction): item_seq = interaction[self.ITEM_SEQ] item_seq_len = interaction[self.ITEM_SEQ_LEN] @@ -390,6 +388,6 @@ def full_sort_predict(self, interaction): item_seq_len = interaction[self.ITEM_SEQ_LEN] seq_output = self.forward(item_seq, bidirectional=False) seq_output = self.gather_indexes(seq_output, item_seq_len - 1) - test_items_emb = self.item_embedding.weight[:self.n_items-1] # delete masked token + test_items_emb = self.item_embedding.weight[:self.n_items - 1] # delete masked token scores = torch.matmul(seq_output, test_items_emb.transpose(0, 1)) # [B, n_items] return scores diff --git a/recbole/model/sequential_recommender/sasrec.py b/recbole/model/sequential_recommender/sasrec.py index 564250dbf..274f097d7 100644 --- a/recbole/model/sequential_recommender/sasrec.py +++ b/recbole/model/sequential_recommender/sasrec.py @@ -19,8 +19,8 @@ from torch import nn from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss from recbole.model.layers import TransformerEncoder +from recbole.model.loss import BPRLoss class SASRec(SequentialRecommender): @@ -29,7 +29,7 @@ class SASRec(SequentialRecommender): NOTE: In the author's implementation, the Point-Wise Feed-Forward Network (PFFN) is implemented - by CNN with 1x1 kernel. In this implementation, we follows the original BERT implmentation + by CNN with 1x1 kernel. In this implementation, we follows the original BERT implementation using Fully Connected Layer to implement the PFFN. """ @@ -39,7 +39,7 @@ def __init__(self, config, dataset): # load parameters info self.n_layers = config['n_layers'] self.n_heads = config['n_heads'] - self.hidden_size = config['hidden_size'] # same as embedding_size + self.hidden_size = config['hidden_size'] # same as embedding_size self.inner_size = config['inner_size'] # the dimensionality in feed-forward layer self.hidden_dropout_prob = config['hidden_dropout_prob'] self.attn_dropout_prob = config['attn_dropout_prob'] @@ -50,7 +50,7 @@ def __init__(self, config, dataset): self.loss_type = config['loss_type'] # define layers and loss - self.item_embedding = nn.Embedding(self.n_items, self.hidden_size , padding_idx=0) + self.item_embedding = nn.Embedding(self.n_items, self.hidden_size, padding_idx=0) self.position_embedding = nn.Embedding(self.max_seq_length, self.hidden_size) self.trm_encoder = TransformerEncoder(n_layers=self.n_layers, n_heads=self.n_heads, hidden_size=self.hidden_size, inner_size=self.inner_size, @@ -116,7 +116,7 @@ def forward(self, item_seq, item_seq_len): output_all_encoded_layers=True) output = trm_output[-1] output = self.gather_indexes(output, item_seq_len - 1) - return output # [B H] + return output # [B H] def calculate_loss(self, interaction): item_seq = interaction[self.ITEM_SEQ] diff --git a/recbole/model/sequential_recommender/sasrecf.py b/recbole/model/sequential_recommender/sasrecf.py index 8c7f90c17..71242d026 100644 --- a/recbole/model/sequential_recommender/sasrecf.py +++ b/recbole/model/sequential_recommender/sasrecf.py @@ -12,8 +12,8 @@ from torch import nn from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss from recbole.model.layers import TransformerEncoder, FeatureSeqEmbLayer +from recbole.model.loss import BPRLoss class SASRecF(SequentialRecommender): @@ -130,7 +130,7 @@ def forward(self, item_seq, item_seq_len): output_all_encoded_layers=True) output = trm_output[-1] seq_output = self.gather_indexes(output, item_seq_len - 1) - return seq_output # [B H] + return seq_output # [B H] def calculate_loss(self, interaction): item_seq = interaction[self.ITEM_SEQ] diff --git a/recbole/model/sequential_recommender/shan.py b/recbole/model/sequential_recommender/shan.py index d970a624d..b9caf8707 100644 --- a/recbole/model/sequential_recommender/shan.py +++ b/recbole/model/sequential_recommender/shan.py @@ -13,12 +13,13 @@ """ +import numpy as np import torch import torch.nn as nn -import numpy as np +from torch.nn.init import normal_, uniform_ + from recbole.model.abstract_recommender import SequentialRecommender from recbole.model.loss import BPRLoss -from torch.nn.init import normal_, uniform_ class SHAN(SequentialRecommender): diff --git a/recbole/model/sequential_recommender/srgnn.py b/recbole/model/sequential_recommender/srgnn.py index 665e8791a..91d9e6e1a 100644 --- a/recbole/model/sequential_recommender/srgnn.py +++ b/recbole/model/sequential_recommender/srgnn.py @@ -15,16 +15,16 @@ https://github.com/CRIPAC-DIG/SR-GNN """ -import numpy as np import math +import numpy as np import torch from torch import nn from torch.nn import Parameter from torch.nn import functional as F -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss class GNN(nn.Module): @@ -58,7 +58,7 @@ def GNNCell(self, A, hidden): [batch_size, max_session_len, embedding_size] Returns: - torch.FloatTensor:Latent vectors of nodes,shape of [batch_size, max_session_len, embedding_size] + torch.FloatTensor: Latent vectors of nodes,shape of [batch_size, max_session_len, embedding_size] """ @@ -67,16 +67,16 @@ def GNNCell(self, A, hidden): # [batch_size, max_session_len, embedding_size * 2] inputs = torch.cat([input_in, input_out], 2) - # gi.size equals to gh.size, shape of [batch_size, max_session_len, embdding_size * 3] + # gi.size equals to gh.size, shape of [batch_size, max_session_len, embedding_size * 3] gi = F.linear(inputs, self.w_ih, self.b_ih) gh = F.linear(hidden, self.w_hh, self.b_hh) # (batch_size, max_session_len, embedding_size) i_r, i_i, i_n = gi.chunk(3, 2) h_r, h_i, h_n = gh.chunk(3, 2) - resetgate = torch.sigmoid(i_r + h_r) - inputgate = torch.sigmoid(i_i + h_i) - newgate = torch.tanh(i_n + resetgate * h_n) - hy = (1 - inputgate) * hidden + inputgate * newgate + reset_gate = torch.sigmoid(i_r + h_r) + input_gate = torch.sigmoid(i_i + h_i) + new_gate = torch.tanh(i_n + reset_gate * h_n) + hy = (1 - input_gate) * hidden + input_gate * new_gate return hy def forward(self, A, hidden): @@ -90,7 +90,7 @@ class SRGNN(SequentialRecommender): In addition to considering the connection between the item and the adjacent item, it also considers the connection with other interactive items. - Such as: A example of a session sequence(eg:item1, item2, item3, item2, item4) and the connecion matrix A + Such as: A example of a session sequence(eg:item1, item2, item3, item2, item4) and the connection matrix A Outgoing edges: === ===== ===== ===== ===== diff --git a/recbole/model/sequential_recommender/stamp.py b/recbole/model/sequential_recommender/stamp.py index 71fbce412..f9982734e 100644 --- a/recbole/model/sequential_recommender/stamp.py +++ b/recbole/model/sequential_recommender/stamp.py @@ -21,8 +21,8 @@ from torch import nn from torch.nn.init import normal_ -from recbole.model.loss import BPRLoss from recbole.model.abstract_recommender import SequentialRecommender +from recbole.model.loss import BPRLoss class STAMP(SequentialRecommender): diff --git a/recbole/model/sequential_recommender/transrec.py b/recbole/model/sequential_recommender/transrec.py index fd4a20c0f..5e431c8bd 100644 --- a/recbole/model/sequential_recommender/transrec.py +++ b/recbole/model/sequential_recommender/transrec.py @@ -15,10 +15,10 @@ import torch from torch import nn -from recbole.utils import InputType from recbole.model.abstract_recommender import SequentialRecommender -from recbole.model.loss import BPRLoss, EmbLoss, RegLoss from recbole.model.init import xavier_normal_initialization +from recbole.model.loss import BPRLoss, EmbLoss, RegLoss +from recbole.utils import InputType class TransRec(SequentialRecommender): @@ -41,8 +41,8 @@ def __init__(self, config, dataset): self.user_embedding = nn.Embedding(self.n_users, self.embedding_size, padding_idx=0) self.item_embedding = nn.Embedding(self.n_items, self.embedding_size, padding_idx=0) - self.bias = nn.Embedding(self.n_items, 1, padding_idx=0) # Beta popularity bias - self.T = nn.Parameter(torch.zeros(self.embedding_size)) # average user representation 'global' + self.bias = nn.Embedding(self.n_items, 1, padding_idx=0) # Beta popularity bias + self.T = nn.Parameter(torch.zeros(self.embedding_size)) # average user representation 'global' self.bpr_loss = BPRLoss() self.emb_loss = EmbLoss() @@ -52,21 +52,21 @@ def __init__(self, config, dataset): self.apply(xavier_normal_initialization) def _l2_distance(self, x, y): - return torch.sqrt(torch.sum((x - y)**2, dim=-1, keepdim=True)) # [B 1] + return torch.sqrt(torch.sum((x - y) ** 2, dim=-1, keepdim=True)) # [B 1] def gather_last_items(self, item_seq, gather_index): - "Gathers the last_item at the spexific positions over a minibatch" + """Gathers the last_item at the specific positions over a minibatch""" gather_index = gather_index.view(-1, 1) - last_items = item_seq.gather(index=gather_index, dim=1) # [B 1] - return last_items.squeeze(-1) # [B] + last_items = item_seq.gather(index=gather_index, dim=1) # [B 1] + return last_items.squeeze(-1) # [B] def forward(self, user, item_seq, item_seq_len): # the last item at the last position - last_items = self.gather_last_items(item_seq, item_seq_len - 1) # [B] - user_emb = self.user_embedding(user) # [B H] + last_items = self.gather_last_items(item_seq, item_seq_len - 1) # [B] + user_emb = self.user_embedding(user) # [B H] last_items_emb = self.item_embedding(last_items) # [B H] - T = self.T.expand_as(user_emb) # [B H] - seq_output = user_emb + T + last_items_emb # [B H] + T = self.T.expand_as(user_emb) # [B H] + seq_output = user_emb + T + last_items_emb # [B H] return seq_output def calculate_loss(self, interaction): @@ -117,13 +117,13 @@ def full_sort_predict(self, interaction): seq_output = self.forward(user, item_seq, item_seq_len) # [B H] - test_items_emb = self.item_embedding.weight # [item_num H] - test_items_emb = test_items_emb.repeat(seq_output.size(0), 1, 1) # [user_num item_num H] + test_items_emb = self.item_embedding.weight # [item_num H] + test_items_emb = test_items_emb.repeat(seq_output.size(0), 1, 1) # [user_num item_num H] - user_hidden = seq_output.unsqueeze(1).expand_as(test_items_emb) # [user_num item_num H] - test_bias = self.bias.weight # [item_num 1] - test_bias = test_bias.repeat(user_hidden.size(0), 1, 1) # [user_num item_num 1] + user_hidden = seq_output.unsqueeze(1).expand_as(test_items_emb) # [user_num item_num H] + test_bias = self.bias.weight # [item_num 1] + test_bias = test_bias.repeat(user_hidden.size(0), 1, 1) # [user_num item_num 1] - scores = test_bias - self._l2_distance(user_hidden, test_items_emb) # [user_num item_num 1] + scores = test_bias - self._l2_distance(user_hidden, test_items_emb) # [user_num item_num 1] scores = scores.squeeze(-1) # [B n_items] return scores From 8f31215aa9ee363ae6a0737da2202cb93cd24d2f Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 19:42:38 +0800 Subject: [PATCH 247/249] FORMAT: spelling correction --- recbole/config/eval_setting.py | 4 ++-- recbole/data/dataloader/neg_sample_mixin.py | 2 +- recbole/data/dataset/kg_dataset.py | 2 +- recbole/data/interaction.py | 5 +++-- 4 files changed, 7 insertions(+), 6 deletions(-) diff --git a/recbole/config/eval_setting.py b/recbole/config/eval_setting.py index 27d910c5a..030a1136f 100644 --- a/recbole/config/eval_setting.py +++ b/recbole/config/eval_setting.py @@ -57,7 +57,7 @@ class EvalSetting(object): Usually records are sorted by timestamp, or shuffled. split_args (dict): Args about splitting. - usually records are splitted by ratio (eg. 8:1:1), + usually records are split by ratio (eg. 8:1:1), or by 'leave one out' strategy, which means the last purchase record of one user is used for evaluation. @@ -173,7 +173,7 @@ def sort_by(self, field, ascending=None): Args: field (str or list of str): Name or list of names ascending (bool or list of bool): Sort ascending vs. descending. Specify list for multiple sort orders. - If this is a list of bools, must match the length of the field + If this is a list of bool, must match the length of the field """ if not isinstance(field, list): field = [field] diff --git a/recbole/data/dataloader/neg_sample_mixin.py b/recbole/data/dataloader/neg_sample_mixin.py index 53cea7840..bf9f34bdc 100644 --- a/recbole/data/dataloader/neg_sample_mixin.py +++ b/recbole/data/dataloader/neg_sample_mixin.py @@ -29,7 +29,7 @@ class NegSampleMixin(AbstractDataLoader): batch_size (int, optional): The batch_size of dataloader. Defaults to ``1``. dl_format (InputType, optional): The input type of dataloader. Defaults to :obj:`~recbole.utils.InputType.POINTWISE`. - shuffle (bool, optional): Whether the dataloader will be shuffle after a round. Defaluts to ``False``. + shuffle (bool, optional): Whether the dataloader will be shuffle after a round. Defaults to ``False``. """ dl_type = DataLoaderType.NEGSAMPLE diff --git a/recbole/data/dataset/kg_dataset.py b/recbole/data/dataset/kg_dataset.py index 610cea132..ea27412ed 100644 --- a/recbole/data/dataset/kg_dataset.py +++ b/recbole/data/dataset/kg_dataset.py @@ -58,7 +58,7 @@ class KnowledgeBasedDataset(Dataset): Note: :attr:`entity_field` doesn't exist exactly. It's only a symbol, - representing entitiy features. E.g. it can be written into ``config['fields_in_same_space']``. + representing entity features. E.g. it can be written into ``config['fields_in_same_space']``. ``[UI-Relation]`` is a special relation token. """ diff --git a/recbole/data/interaction.py b/recbole/data/interaction.py index 532f7388e..3e5360d6b 100644 --- a/recbole/data/interaction.py +++ b/recbole/data/interaction.py @@ -150,14 +150,15 @@ def to(self, device, selected_field=None): ret = {} if isinstance(selected_field, str): selected_field = [selected_field] - try: + + if selected_field is not None: selected_field = set(selected_field) for k in self.interaction: if k in selected_field: ret[k] = self.interaction[k].to(device) else: ret[k] = self.interaction[k] - except Exception: + else: for k in self.interaction: ret[k] = self.interaction[k].to(device) return Interaction(ret) From ad5e34db52a7f17f403da827d3229cd635a05ea8 Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 19:47:03 +0800 Subject: [PATCH 248/249] DOC: change `np.ndarray` to `numpy.ndarray` --- recbole/data/dataloader/general_dataloader.py | 8 +++---- recbole/data/dataloader/neg_sample_mixin.py | 4 ++-- .../data/dataloader/sequential_dataloader.py | 14 +++++------ recbole/data/dataset/dataset.py | 8 +++---- recbole/evaluator/evaluators.py | 16 ++++++------- recbole/evaluator/utils.py | 10 ++++---- recbole/sampler/sampler.py | 24 +++++++++---------- recbole/utils/case_study.py | 4 ++-- 8 files changed, 44 insertions(+), 44 deletions(-) diff --git a/recbole/data/dataloader/general_dataloader.py b/recbole/data/dataloader/general_dataloader.py index 0b071e722..baea05fe5 100644 --- a/recbole/data/dataloader/general_dataloader.py +++ b/recbole/data/dataloader/general_dataloader.py @@ -174,14 +174,14 @@ def _neg_sample_by_point_wise_sampling(self, inter_feat, neg_iids): def get_pos_len_list(self): """ Returns: - np.ndarray: Number of positive item for each user in a training/evaluating epoch. + numpy.ndarray: Number of positive item for each user in a training/evaluating epoch. """ return self.uid2items_num[self.uid_list] def get_user_len_list(self): """ Returns: - np.ndarray: Number of all item for each user in a training/evaluating epoch. + numpy.ndarray: Number of all item for each user in a training/evaluating epoch. """ return self.uid2items_num[self.uid_list] * self.times @@ -285,13 +285,13 @@ def _neg_sampling(self, user_df): def get_pos_len_list(self): """ Returns: - np.ndarray: Number of positive item for each user in a training/evaluating epoch. + numpy.ndarray: Number of positive item for each user in a training/evaluating epoch. """ return self.uid2items_num[self.uid_list] def get_user_len_list(self): """ Returns: - np.ndarray: Number of all item for each user in a training/evaluating epoch. + numpy.ndarray: Number of all item for each user in a training/evaluating epoch. """ return np.full(self.pr_end, self.item_num) diff --git a/recbole/data/dataloader/neg_sample_mixin.py b/recbole/data/dataloader/neg_sample_mixin.py index bf9f34bdc..9ea6d05ed 100644 --- a/recbole/data/dataloader/neg_sample_mixin.py +++ b/recbole/data/dataloader/neg_sample_mixin.py @@ -67,14 +67,14 @@ def _neg_sampling(self, inter_feat): def get_pos_len_list(self): """ Returns: - np.ndarray: Number of positive item for each user in a training/evaluating epoch. + numpy.ndarray: Number of positive item for each user in a training/evaluating epoch. """ raise NotImplementedError('Method [get_pos_len_list] should be implemented.') def get_user_len_list(self): """ Returns: - np.ndarray: Number of all item for each user in a training/evaluating epoch. + numpy.ndarray: Number of all item for each user in a training/evaluating epoch. """ raise NotImplementedError('Method [get_user_len_list] should be implemented.') diff --git a/recbole/data/dataloader/sequential_dataloader.py b/recbole/data/dataloader/sequential_dataloader.py index 21f0b2b46..88c643b65 100644 --- a/recbole/data/dataloader/sequential_dataloader.py +++ b/recbole/data/dataloader/sequential_dataloader.py @@ -116,9 +116,9 @@ def augmentation(self, item_list_index, target_index, item_list_length): """Data augmentation. Args: - item_list_index (np.ndarray): the index of history items list in interaction. - target_index (np.ndarray): the index of items to be predicted in interaction. - item_list_length (np.ndarray): history list length. + item_list_index (numpy.ndarray): the index of history items list in interaction. + target_index (numpy.ndarray): the index of items to be predicted in interaction. + item_list_length (numpy.ndarray): history list length. Returns: dict: the augmented data. @@ -219,14 +219,14 @@ def _neg_sample_by_point_wise_sampling(self, data, neg_iids): def get_pos_len_list(self): """ Returns: - np.ndarray: Number of positive item for each user in a training/evaluating epoch. + numpy.ndarray: Number of positive item for each user in a training/evaluating epoch. """ return np.ones(self.pr_end, dtype=np.int64) def get_user_len_list(self): """ Returns: - np.ndarray: Number of all item for each user in a training/evaluating epoch. + numpy.ndarray: Number of all item for each user in a training/evaluating epoch. """ return np.full(self.pr_end, self.times) @@ -278,13 +278,13 @@ def _next_batch_data(self): def get_pos_len_list(self): """ Returns: - np.ndarray or list: Number of positive item for each user in a training/evaluating epoch. + numpy.ndarray or list: Number of positive item for each user in a training/evaluating epoch. """ return np.ones(self.pr_end, dtype=np.int64) def get_user_len_list(self): """ Returns: - np.ndarray: Number of all item for each user in a training/evaluating epoch. + numpy.ndarray: Number of all item for each user in a training/evaluating epoch. """ return np.full(self.pr_end, self.item_num) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index fbddaec0f..38c343fc4 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -1040,10 +1040,10 @@ def token2id(self, field, tokens): Args: field (str): Field of external tokens. - tokens (str, list or np.ndarray): External tokens. + tokens (str, list or numpy.ndarray): External tokens. Returns: - int or np.ndarray: The internal ids of external tokens. + int or numpy.ndarray: The internal ids of external tokens. """ if isinstance(tokens, str): if tokens in self.field2token_id[field]: @@ -1061,10 +1061,10 @@ def id2token(self, field, ids): Args: field (str): Field of internal ids. - ids (int, list, np.ndarray or torch.Tensor): Internal ids. + ids (int, list, numpy.ndarray or torch.Tensor): Internal ids. Returns: - str or np.ndarray: The external tokens of internal ids. + str or numpy.ndarray: The external tokens of internal ids. """ try: return self.field2id_token[field][ids] diff --git a/recbole/evaluator/evaluators.py b/recbole/evaluator/evaluators.py index 22e5c6af4..eb7cb6871 100644 --- a/recbole/evaluator/evaluators.py +++ b/recbole/evaluator/evaluators.py @@ -105,11 +105,11 @@ def _calculate_metrics(self, pos_len_list, topk_index): """integrate the results of each batch and evaluate the topk metrics by users Args: - pos_len_list (np.ndarray): a list of users' positive items - topk_index (np.ndarray): a matrix which contains the index of the topk items for users + pos_len_list (numpy.ndarray): a list of users' positive items + topk_index (numpy.ndarray): a matrix which contains the index of the topk items for users Returns: - np.ndarray: a matrix which contains the metrics result + numpy.ndarray: a matrix which contains the metrics result """ pos_idx_matrix = (topk_index < pos_len_list.reshape(-1, 1)) @@ -245,11 +245,11 @@ def _calculate_metrics(self, user_len_list, pos_len_list, pos_rank_sum): """integrate the results of each batch and evaluate the topk metrics by users Args: - pos_len_list (np.ndarray): a list of users' positive items - topk_index (np.ndarray): a matrix which contains the index of the topk items for users + pos_len_list (numpy.ndarray): a list of users' positive items + topk_index (numpy.ndarray): a matrix which contains the index of the topk items for users Returns: - np.ndarray: a matrix which contains the metrics result + numpy.ndarray: a matrix which contains the metrics result """ result_list = [] @@ -328,8 +328,8 @@ def _calculate_metrics(self, trues, preds): """get metrics result Args: - trues (np.ndarray): the true scores' list - preds (np.ndarray): the predict scores' list + trues (numpy.ndarray): the true scores' list + preds (numpy.ndarray): the predict scores' list Returns: list: a list of metrics result diff --git a/recbole/evaluator/utils.py b/recbole/evaluator/utils.py index 650703301..392873493 100644 --- a/recbole/evaluator/utils.py +++ b/recbole/evaluator/utils.py @@ -53,14 +53,14 @@ def trunc(scores, method): """Round the scores by using the given method Args: - scores (np.ndarray): scores + scores (numpy.ndarray): scores method (str): one of ['ceil', 'floor', 'around'] Raises: NotImplementedError: method error Returns: - np.ndarray: processed scores + numpy.ndarray: processed scores """ try: @@ -75,11 +75,11 @@ def cutoff(scores, threshold): """cut of the scores based on threshold Args: - scores (np.ndarray): scores + scores (numpy.ndarray): scores threshold (float): between 0 and 1 Returns: - np.ndarray: processed scores + numpy.ndarray: processed scores """ return np.where(scores > threshold, 1, 0) @@ -92,7 +92,7 @@ def _binary_clf_curve(trues, preds): preds (numpy.ndarray): the predict scores' list Returns: - fps (np.ndarray): A count of false positives, at index i being the number of negative + fps (numpy.ndarray): A count of false positives, at index i being the number of negative samples assigned a score >= thresholds[i] preds (numpy.ndarray): An increasing count of true positives, at index i being the number of positive samples assigned a score >= thresholds[i]. diff --git a/recbole/sampler/sampler.py b/recbole/sampler/sampler.py index 668ad51ba..76deec7cd 100644 --- a/recbole/sampler/sampler.py +++ b/recbole/sampler/sampler.py @@ -59,14 +59,14 @@ def set_distribution(self, distribution): def get_random_list(self): """ Returns: - np.ndarray or list: Random list of value_id. + numpy.ndarray or list: Random list of value_id. """ raise NotImplementedError('method [get_random_list] should be implemented') def get_used_ids(self): """ Returns: - np.ndarray: Used ids. Index is key_id, and element is a set of value_ids. + numpy.ndarray: Used ids. Index is key_id, and element is a set of value_ids. """ raise NotImplementedError('method [get_used_ids] should be implemented') @@ -104,7 +104,7 @@ def sample_by_key_ids(self, key_ids, num): """Sampling by key_ids. Args: - key_ids (np.ndarray or list): Input key_ids. + key_ids (numpy.ndarray or list): Input key_ids. num (int): Number of sampled value_ids for each key_id. Returns: @@ -190,7 +190,7 @@ def __init__(self, phases, datasets, distribution='uniform'): def get_random_list(self): """ Returns: - np.ndarray or list: Random list of item_id. + numpy.ndarray or list: Random list of item_id. """ if self.distribution == 'uniform': return np.arange(1, self.n_items) @@ -206,7 +206,7 @@ def get_used_ids(self): """ Returns: dict: Used item_ids is the same as positive item_ids. - Key is phase, and value is a np.ndarray which index is user_id, and element is a set of item_ids. + Key is phase, and value is a numpy.ndarray which index is user_id, and element is a set of item_ids. """ used_item_id = dict() last = [set() for _ in range(self.n_users)] @@ -244,7 +244,7 @@ def sample_by_user_ids(self, user_ids, num): """Sampling by user_ids. Args: - user_ids (np.ndarray or list): Input user_ids. + user_ids (numpy.ndarray or list): Input user_ids. num (int): Number of sampled item_ids for each user_id. Returns: @@ -286,7 +286,7 @@ def __init__(self, dataset, distribution='uniform'): def get_random_list(self): """ Returns: - np.ndarray or list: Random list of entity_id. + numpy.ndarray or list: Random list of entity_id. """ if self.distribution == 'uniform': return np.arange(1, self.entity_num) @@ -298,7 +298,7 @@ def get_random_list(self): def get_used_ids(self): """ Returns: - np.ndarray: Used entity_ids is the same as tail_entity_ids in knowledge graph. + numpy.ndarray: Used entity_ids is the same as tail_entity_ids in knowledge graph. Index is head_entity_id, and element is a set of tail_entity_ids. """ used_tail_entity_id = np.array([set() for _ in range(self.entity_num)]) @@ -315,7 +315,7 @@ def sample_by_entity_ids(self, head_entity_ids, num=1): """Sampling by head_entity_ids. Args: - head_entity_ids (np.ndarray or list): Input head_entity_ids. + head_entity_ids (numpy.ndarray or list): Input head_entity_ids. num (int, optional): Number of sampled entity_ids for each head_entity_id. Defaults to ``1``. Returns: @@ -361,7 +361,7 @@ def __init__(self, phases, dataset, distribution='uniform'): def get_random_list(self): """ Returns: - np.ndarray or list: Random list of item_id. + numpy.ndarray or list: Random list of item_id. """ if self.distribution == 'uniform': return np.arange(1, self.n_items) @@ -373,7 +373,7 @@ def get_random_list(self): def get_used_ids(self): """ Returns: - np.ndarray: Used item_ids is the same as positive item_ids. + numpy.ndarray: Used item_ids is the same as positive item_ids. Index is user_id, and element is a set of item_ids. """ return np.array([set() for _ in range(self.n_users)]) @@ -382,7 +382,7 @@ def sample_by_user_ids(self, user_ids, num): """Sampling by user_ids. Args: - user_ids (np.ndarray or list): Input user_ids. + user_ids (numpy.ndarray or list): Input user_ids. num (int): Number of sampled item_ids for each user_id. Returns: diff --git a/recbole/utils/case_study.py b/recbole/utils/case_study.py index 45d15a2bc..45d39f6a8 100644 --- a/recbole/utils/case_study.py +++ b/recbole/utils/case_study.py @@ -23,7 +23,7 @@ def get_scores(uid_series, model, test_data): The score of [pad] and history items will be set into -inf. Args: - uid_series (np.ndarray): User id series + uid_series (numpy.ndarray): User id series model (AbstractRecommender): Model to predict test_data (AbstractDataLoader): The test_data of model @@ -69,7 +69,7 @@ def get_topk(uid_series, model, test_data, k): """Calculate the top-k items' scores and ids for each user in uid_series. Args: - uid_series (np.ndarray): User id series + uid_series (numpy.ndarray): User id series model (AbstractRecommender): Model to predict test_data (AbstractDataLoader): The test_data of model k (int): The top-k items. From 5f2dedeb717231d885182e6fb6d2b5fe862e74db Mon Sep 17 00:00:00 2001 From: chenyushuo <297086016@qq.com> Date: Sat, 2 Jan 2021 20:31:42 +0800 Subject: [PATCH 249/249] FIX: fix bug in merge --- recbole/data/dataset/dataset.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/recbole/data/dataset/dataset.py b/recbole/data/dataset/dataset.py index b9a4eb351..38c343fc4 100644 --- a/recbole/data/dataset/dataset.py +++ b/recbole/data/dataset/dataset.py @@ -1192,7 +1192,7 @@ def copy(self, new_inter_feat): :class:`~Dataset`: the new :class:`~Dataset` object, whose interaction feature has been updated. """ nxt = copy.copy(self) - nxt.inter_feat = pd.DataFrame(new_inter_feat) + nxt.inter_feat = new_inter_feat return nxt def _drop_unused_col(self):