Merge pull request #877 from hyp1231/data

FEA: RS, benchmark for seq-rec & FIX: update argument_list

recbole/data/dataset/dataset.py

@@ -3,7 +3,7 @@
 # @Email  : houyupeng@ruc.edu.cn
 
 # UPDATE:
-# @Time   : 2020/10/28 2021/7/1, 2020/11/10
+# @Time   : 2021/7/11 2021/7/1, 2020/11/10
 # @Author : Yupeng Hou, Xingyu Pan, Yushuo Chen
 # @Email  : houyupeng@ruc.edu.cn, xy_pan@foxmail.com, chenyushuo@ruc.edu.cn
 
@@ -942,7 +942,7 @@ def _concat_remaped_tokens(self, remap_list):
             if ftype == FeatureType.TOKEN:
                 tokens.append(feat[field].values)
             elif ftype == FeatureType.TOKEN_SEQ:
-                tokens.append(feat[field].agg(np.concatenate))
+                tokens.append(feat[field].reset_index(drop=True).agg(np.concatenate))
         split_point = np.cumsum(list(map(len, tokens)))[:-1]
         tokens = np.concatenate(tokens)
         return tokens, split_point

recbole/data/dataset/sequential_dataset.py

@@ -3,7 +3,7 @@
 # @Email  : chenyushuo@ruc.edu.cn
 
 # UPDATE:
-# @Time   : 2020/9/16, 2021/7/1, 2021/7/9
+# @Time   : 2020/9/16, 2021/7/1, 2021/7/11
 # @Author : Yushuo Chen, Xingyu Pan, Yupeng Hou
 # @Email  : chenyushuo@ruc.edu.cn, xy_pan@foxmail.com, houyupeng@ruc.edu.cn
 
@@ -34,6 +34,8 @@ def __init__(self, config):
         self.max_item_list_len = config['MAX_ITEM_LIST_LENGTH']
         self.item_list_length_field = config['ITEM_LIST_LENGTH_FIELD']
         super().__init__(config)
+        if config['benchmark_filename'] is not None:
+            self._benchmark_presets()
 
     def _change_feat_format(self):
         """Change feat format from :class:`pandas.DataFrame` to :class:`Interaction`,
@@ -134,6 +136,15 @@ def data_augmentation(self):
         new_data.update(Interaction(new_dict))
         self.inter_feat = new_data
 
+    def _benchmark_presets(self):
+        list_suffix = self.config['LIST_SUFFIX']
+        for field in self.inter_feat:
+            if field + list_suffix in self.inter_feat:
+                list_field = field + list_suffix
+                setattr(self, f'{field}_list_field', list_field)
+        self.set_field_property(self.item_list_length_field, FeatureType.TOKEN, FeatureSource.INTERACTION, 1)
+        self.inter_feat[self.item_list_length_field] = self.inter_feat[self.item_id_list_field].agg(len)
+
     def inter_matrix(self, form='coo', value_field=None):
         """Get sparse matrix that describe interactions between user_id and item_id.
         Sparse matrix has shape (user_num, item_num).

recbole/properties/dataset/sample.yaml

@@ -28,8 +28,8 @@ additional_feat_suffix: ~
 rm_dup_inter: ~
 val_interval: ~
 filter_inter_by_user_or_item: True
-item_inter_num_interval: "[0,inf)"
 user_inter_num_interval: "[0,inf)"
+item_inter_num_interval: "[0,inf)"
 
 # Preprocessing
 alias_of_user_id: ~
@@ -52,9 +52,5 @@ TAIL_ENTITY_ID_FIELD: tail_id
 RELATION_ID_FIELD: relation_id
 ENTITY_ID_FIELD: entity_id
 
-# Social Model Needed
-SOURCE_ID_FIELD: source_id
-TARGET_ID_FIELD: target_id
-
 # Benchmark .inter
 benchmark_filename: ~

recbole/properties/overall.yaml

@@ -28,7 +28,6 @@ eval_args:
   group_by: user
   order: RO
   mode: full
-real_time_process: False
 metrics: ["Recall","MRR","NDCG","Hit","Precision"]
 topk: [10]
 valid_metric: MRR@10

recbole/sampler/sampler.py

@@ -222,7 +222,7 @@ def get_used_ids(self):
                 raise ValueError(
                     'Some users have interacted with all items, '
                     'which we can not sample negative items for them. '
-                    'Please set `max_user_inter_num` to filter those users.'
+                    'Please set `user_inter_num_interval` to filter those users.'
                 )
         return used_item_id
 

recbole/utils/argument_list.py

@@ -10,7 +10,9 @@
     'reproducibility',
     'state',
     'data_path',
+    'benchmark_filename',
     'show_progress',
+    'config_file'
 ]
 
 training_arguments = [
@@ -27,11 +29,8 @@
 ]
 
 evaluation_arguments = [
-    'eval_setting',
-    'group_by_user',
-    'split_ratio', 'leave_one_num',
-    'real_time_process',
-    'metrics', 'topk', 'valid_metric',
+    'eval_args',
+    'metrics', 'topk', 'valid_metric', 'valid_metric_bigger',
     'eval_batch_size',
     'metric_decimal_place'
 ]
@@ -45,8 +44,8 @@
     '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', 'unused_col', 'additional_feat_suffix',
-    'user_inter_num_interval', 'item_inter_num_interval ',
-    'val_interval',
+    'filter_inter_by_user_or_item', 'rm_dup_inter',
+    'val_interval', 'user_inter_num_interval', 'item_inter_num_interval',
     'alias_of_user_id', 'alias_of_item_id', 'alias_of_entity_id', 'alias_of_relation_id',
     'preload_weight',
     'normalize_field', 'normalize_all'

tests/config/test_config.py

@@ -48,7 +48,6 @@ def test_default_settings(self):
         self.assertIsInstance(config['checkpoint_dir'], str)
 
         self.assertIsInstance(config['eval_args'], dict)
-        self.assertIsInstance(config['real_time_process'], bool)
         self.assertIsInstance(config['metrics'], list)
         self.assertIsInstance(config['topk'], list)
         self.assertIsInstance(config['valid_metric'], str)

tests/config/test_overall.py

@@ -92,9 +92,6 @@ def test_checkpoint_dir(self):
     def test_eval_batch_size(self):
         self.assertTrue(run_parms({'eval_batch_size': [1, 100]}))
 
-    def test_real_time_process(self):
-        self.assertTrue(run_parms({'real_time_process':[False, True]}))
-
     def test_topk(self):
         settings = {
             'metrics': ["Recall", "MRR", "NDCG", "Hit", "Precision"],

tests/data/seq_benchmark/seq_benchmark.test.inter

@@ -0,0 +1,5 @@
+user_id:token	item_id:token	timestamp:float	item_id_list:token_seq	timestamp_list:float_seq
+1	7	7	1 2 3 4 5 6	1 2 3 4 5 6
+1	8	8	1 2 3 4 5 6 7	1 2 3 4 5 6 7
+2	8	8	4 5 6 7	4 5 6 7
+3	6	6	4 5	4 5

tests/data/seq_benchmark/seq_benchmark.train.inter

@@ -0,0 +1,8 @@
+user_id:token	item_id:token	timestamp:float	item_id_list:token_seq	timestamp_list:float_seq
+1	2	2	1	1
+1	3	3	1 2	1 2
+1	4	4	1 2 3	1 2 3
+4	4	4	3	3
+2	5	5	4	4
+2	6	6	4 5	4 5
+3	5	5	4	4

tests/data/seq_benchmark/seq_benchmark.valid.inter

@@ -0,0 +1,4 @@
+user_id:token	item_id:token	timestamp:float	item_id_list:token_seq	timestamp_list:float_seq
+1	5	5	1 2 3 4	1 2 3 4
+1	6	6	1 2 3 4 5	1 2 3 4 5
+2	7	7	4 5 6	4 5 6

tests/data/test_dataset.py

@@ -4,7 +4,7 @@
 # @Email  : chenyushuo@ruc.edu.cn
 
 # UPDATE
-# @Time    :   2020/1/3, 2021/7/1, 2021/7/9
+# @Time    :   2020/1/3, 2021/7/1, 2021/7/11
 # @Author  :   Yushuo Chen, Xingyu Pan, Yupeng Hou
 # @email   :   chenyushuo@ruc.edu.cn, xy_pan@foxmail.com, houyupeng@ruc.edu.cn
 
@@ -612,6 +612,88 @@ def test_seq_leave_one_out(self):
             [0., 0., 0., 0., 0., 0., 0., 0., 0.]
         ]).all()
 
+    def test_seq_split_by_ratio(self):
+        config_dict = {
+            'model': 'GRU4Rec',
+            'dataset': 'seq_dataset',
+            'data_path': current_path,
+            'load_col': None,
+            'training_neg_sample_num': 0,
+            'eval_args': {
+                'split': {'RS': [0.3, 0.3, 0.4]},
+                'order': 'TO'
+            }
+        }
+        train_dataset, valid_dataset, test_dataset = split_dataset(config_dict=config_dict)
+        assert (train_dataset.inter_feat[train_dataset.uid_field].numpy() == [1, 1, 1, 4, 2, 2, 3]).all()
+        assert (train_dataset.inter_feat[train_dataset.item_id_list_field][:,:3].numpy() == [
+            [1, 0, 0],
+            [1, 2, 0],
+            [1, 2, 3],
+            [3, 0, 0],
+            [4, 0, 0],
+            [4, 5, 0],
+            [4, 0, 0]]).all()
+        assert (train_dataset.inter_feat[train_dataset.iid_field].numpy() == [2, 3, 4, 4, 5, 6, 5]).all()
+        assert (train_dataset.inter_feat[train_dataset.item_list_length_field].numpy() == [1, 2, 3, 1, 1, 2, 1]).all()
+
+        assert (valid_dataset.inter_feat[valid_dataset.uid_field].numpy() == [1, 1, 2]).all()
+        assert (valid_dataset.inter_feat[valid_dataset.item_id_list_field][:,:5].numpy() == [
+            [1, 2, 3, 4, 0],
+            [1, 2, 3, 4, 5],
+            [4, 5, 6, 0, 0]]).all()
+        assert (valid_dataset.inter_feat[valid_dataset.iid_field].numpy() == [5, 6, 7]).all()
+        assert (valid_dataset.inter_feat[valid_dataset.item_list_length_field].numpy() == [4, 5, 3]).all()
+
+        assert (test_dataset.inter_feat[test_dataset.uid_field].numpy() == [1, 1, 2, 3]).all()
+        assert (test_dataset.inter_feat[test_dataset.item_id_list_field][:,:7].numpy() == [
+            [1, 2, 3, 4, 5, 6, 0],
+            [1, 2, 3, 4, 5, 6, 7],
+            [4, 5, 6, 7, 0, 0, 0],
+            [4, 5, 0, 0, 0, 0, 0]]).all()
+        assert (test_dataset.inter_feat[test_dataset.iid_field].numpy() == [7, 8, 8, 6]).all()
+        assert (test_dataset.inter_feat[test_dataset.item_list_length_field].numpy() == [6, 7, 4, 2]).all()
+
+    def test_seq_benchmark(self):
+        config_dict = {
+            'model': 'GRU4Rec',
+            'dataset': 'seq_benchmark',
+            'data_path': current_path,
+            'load_col': None,
+            'training_neg_sample_num': 0,
+            'benchmark_filename': ['train', 'valid', 'test'],
+            'alias_of_item_id': ['item_id_list']
+        }
+        train_dataset, valid_dataset, test_dataset = split_dataset(config_dict=config_dict)
+        assert (train_dataset.inter_feat[train_dataset.uid_field].numpy() == [1, 1, 1, 2, 3, 3, 4]).all()
+        assert (train_dataset.inter_feat[train_dataset.item_id_list_field][:,:3].numpy() == [
+            [8, 0, 0],
+            [8, 1, 0],
+            [8, 1, 2],
+            [2, 0, 0],
+            [3, 0, 0],
+            [3, 4, 0],
+            [3, 0, 0]]).all()
+        assert (train_dataset.inter_feat[train_dataset.iid_field].numpy() == [1, 2, 3, 3, 4, 5, 4]).all()
+        assert (train_dataset.inter_feat[train_dataset.item_list_length_field].numpy() == [1, 2, 3, 1, 1, 2, 1]).all()
+
+        assert (valid_dataset.inter_feat[valid_dataset.uid_field].numpy() == [1, 1, 3]).all()
+        assert (valid_dataset.inter_feat[valid_dataset.item_id_list_field][:,:5].numpy() == [
+            [8, 1, 2, 3, 0],
+            [8, 1, 2, 3, 4],
+            [3, 4, 5, 0, 0]]).all()
+        assert (valid_dataset.inter_feat[valid_dataset.iid_field].numpy() == [4, 5, 6]).all()
+        assert (valid_dataset.inter_feat[valid_dataset.item_list_length_field].numpy() == [4, 5, 3]).all()
+
+        assert (test_dataset.inter_feat[test_dataset.uid_field].numpy() == [1, 1, 3, 4]).all()
+        assert (test_dataset.inter_feat[test_dataset.item_id_list_field][:,:7].numpy() == [
+            [8, 1, 2, 3, 4, 5, 0],
+            [8, 1, 2, 3, 4, 5, 6],
+            [3, 4, 5, 6, 0, 0, 0],
+            [3, 4, 0, 0, 0, 0, 0]]).all()
+        assert (test_dataset.inter_feat[test_dataset.iid_field].numpy() == [6, 7, 7, 5]).all()
+        assert (test_dataset.inter_feat[test_dataset.item_list_length_field].numpy() == [6, 7, 4, 2]).all()
+
 
 class TestKGDataset:
     def test_kg_remap_id(self):

tests/model/test_model.yaml

@@ -38,16 +38,6 @@ load_col:
 
 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: ~
-
 # Preprocessing
 alias_of_user_id: ~
 alias_of_item_id: ~