Merge pull request #666 from hyp1231/master

FIX: code format

recbole/data/dataloader/abstract_dataloader.py

@@ -55,13 +55,6 @@ def __init__(self, config, dataset, batch_size=1, dl_format=InputType.POINTWISE,
         if self.real_time is None:
             self.real_time = True
 
-        self.join = self.dataset.join
-        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:
                 flag = hasattr(self.dataset, dataset_attr)

recbole/data/dataset/dataset.py

@@ -1173,6 +1173,7 @@ def _check_field(self, *field_names):
             if getattr(self, field_name, None) is None:
                 raise ValueError(f'{field_name} isn\'t set.')
 
+    @dlapi.set()
     def join(self, df):
         """Given interaction feature, join user/item feature into it.
 
@@ -1429,6 +1430,7 @@ def save(self, filepath):
             if df is not None:
                 df.to_csv(os.path.join(filepath, f'{name}.csv'))
 
+    @dlapi.set()
     def get_user_feature(self):
         """
         Returns:
@@ -1440,6 +1442,7 @@ def get_user_feature(self):
         else:
             return self.user_feat
 
+    @dlapi.set()
     def get_item_feature(self):
         """
         Returns:
@@ -1536,6 +1539,7 @@ def _create_graph(self, tensor_feat, source_field, target_field, form='dgl', val
         else:
             raise NotImplementedError(f'Graph format [{form}] has not been implemented.')
 
+    @dlapi.set()
     def inter_matrix(self, form='coo', value_field=None):
         """Get sparse matrix that describe interactions between user_id and item_id.
 
@@ -1617,6 +1621,7 @@ def _history_matrix(self, row, value_field=None):
 
         return torch.LongTensor(history_matrix), torch.FloatTensor(history_value), torch.LongTensor(history_len)
 
+    @dlapi.set()
     def history_item_matrix(self, value_field=None):
         """Get dense matrix describe user's history interaction records.
 
@@ -1641,6 +1646,7 @@ def history_item_matrix(self, value_field=None):
         """
         return self._history_matrix(row='user', value_field=value_field)
 
+    @dlapi.set()
     def history_user_matrix(self, value_field=None):
         """Get dense matrix describe item's history interaction records.
 

recbole/data/utils.py

@@ -19,7 +19,7 @@
 from recbole.config import EvalSetting
 from recbole.data.dataloader import *
 from recbole.sampler import KGSampler, Sampler, RepeatableSampler
-from recbole.utils import ModelType
+from recbole.utils import ModelType, ensure_dir
 
 
 def create_dataset(config):
@@ -216,8 +216,7 @@ def save_datasets(save_path, name, dataset):
         raise ValueError(f'Length of name {name} should equal to length of dataset {dataset}.')
     for i, d in enumerate(dataset):
         cur_path = os.path.join(save_path, name[i])
-        if not os.path.isdir(cur_path):
-            os.makedirs(cur_path)
+        ensure_dir(cur_path)
         d.save(cur_path)
 
 

recbole/model/abstract_recommender.py

@@ -91,7 +91,6 @@ def __init__(self, config, dataset):
         self.n_items = dataset.num(self.ITEM_ID)
 
         # load parameters info
-        self.batch_size = config['train_batch_size']
         self.device = config['device']
 
 
@@ -145,7 +144,6 @@ def __init__(self, config, dataset):
         self.n_relations = dataset.num(self.RELATION_ID)
 
         # load parameters info
-        self.batch_size = config['train_batch_size']
         self.device = config['device']
 
 

recbole/model/general_recommender/dgcf.py

@@ -74,7 +74,7 @@ def __init__(self, config, dataset):
         self.n_layers = config['n_layers']
         self.reg_weight = config['reg_weight']
         self.cor_weight = config['cor_weight']
-        n_batch = dataset.dataset.inter_num // self.batch_size + 1
+        n_batch = dataset.dataset.inter_num // config['train_batch_size'] + 1
         self.cor_batch_size = int(max(self.n_users / n_batch, self.n_items / n_batch))
         # ensure embedding can be divided into <n_factors> intent
         assert self.embedding_size % self.n_factors == 0

recbole/model/sequential_recommender/gru4rec.py

@@ -70,8 +70,8 @@ def _init_weights(self, module):
         if isinstance(module, nn.Embedding):
             xavier_normal_(module.weight)
         elif isinstance(module, nn.GRU):
-            xavier_uniform_(self.gru_layers.weight_hh_l0)
-            xavier_uniform_(self.gru_layers.weight_ih_l0)
+            xavier_uniform_(module.weight_hh_l0)
+            xavier_uniform_(module.weight_ih_l0)
 
     def forward(self, item_seq, item_seq_len):
         item_seq_emb = self.item_embedding(item_seq)

recbole/model/sequential_recommender/ksr.py

@@ -84,8 +84,8 @@ def _init_weights(self, module):
         if isinstance(module, nn.Embedding):
             xavier_normal_(module.weight)
         elif isinstance(module, nn.GRU):
-            xavier_uniform_(self.gru_layers.weight_hh_l0)
-            xavier_uniform_(self.gru_layers.weight_ih_l0)
+            xavier_uniform_(module.weight_hh_l0)
+            xavier_uniform_(module.weight_ih_l0)
 
     def _get_kg_embedding(self, head):
         """Difference:

recbole/trainer/trainer.py

@@ -16,7 +16,6 @@
 from logging import getLogger
 from time import time
 
-import matplotlib.pyplot as plt
 import numpy as np
 import torch
 import torch.optim as optim
@@ -64,7 +63,7 @@ class Trainer(AbstractTrainer):
 
     Initializing the Trainer needs two parameters: `config` and `model`. `config` records the parameters information
     for controlling training and evaluation, such as `learning_rate`, `epochs`, `eval_step` and so on.
-    More information can be found in [placeholder]. `model` is the instantiated object of a Model Class.
+    `model` is the instantiated object of a Model Class.
 
     """
 
@@ -422,6 +421,7 @@ def plot_train_loss(self, show=True, save_path=None):
             save_path (str, optional): The data path to save the figure, default: None.
                                        If it's None, it will not be saved.
         """
+        import matplotlib.pyplot as plt
         epochs = list(self.train_loss_dict.keys())
         epochs.sort()
         values = [float(self.train_loss_dict[epoch]) for epoch in epochs]

recbole/utils/logger.py

@@ -11,7 +11,7 @@
 import logging
 import os
 
-from recbole.utils.utils import get_local_time
+from recbole.utils.utils import get_local_time, ensure_dir
 
 
 def init_logger(config):
@@ -30,8 +30,7 @@ def init_logger(config):
     """
     LOGROOT = './log/'
     dir_name = os.path.dirname(LOGROOT)
-    if not os.path.exists(dir_name):
-        os.makedirs(dir_name)
+    ensure_dir(dir_name)
 
     logfilename = '{}-{}.log'.format(config['model'], get_local_time())