for mat

core/engine/engine.py

@@ -29,4 +29,3 @@ class Engine:
     @abc.abstractmethod
     def run(self):
         pass
-

core/engine/local_cluster.py

@@ -20,7 +20,6 @@ import os
 import sys
 import subprocess
 
-
 from paddlerec.core.engine.engine import Engine
 from paddlerec.core.utils import envs
 

core/metric.py

@@ -53,7 +53,7 @@ class Metric(object):
         pass
 
     @abc.abstractmethod
-    def get_result_to_string(self):
+    def __str__(self):
         """
         Return:
             result(string) : calculate result with string format, for output

core/metrics/auc_metrics.py

@@ -200,7 +200,7 @@ class AUCMetric(Metric):
         """ """
         return self._result
 
-    def get_result_to_string(self):
+    def __str__(self):
         """ """
         result = self.get_result()
         result_str = "%s AUC=%.6f BUCKET_ERROR=%.6f MAE=%.6f RMSE=%.6f " \

core/trainers/__init__.py

+# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""
+trainer implement.
+
+          ↗ (single/cluster) CtrTrainer
+Trainer
+                               ↗ (for single  training) SingleTrainer/TDMSingleTrainer
+          ↘ TranspilerTrainer →  (for cluster training) ClusterTrainer/TDMClusterTrainer
+                               ↘  (for online learning training) OnlineLearningTrainer
+
+"""
+
+

core/trainers/ctr_coding_trainer.py

@@ -23,7 +23,7 @@ from paddlerec.core.utils import envs
 from paddlerec.core.trainer import Trainer
 
 
-class CtrPaddleTrainer(Trainer):
+class CtrTrainer(Trainer):
     """R
     """
 

core/trainers/ctr_modul_trainer.py

@@ -72,7 +72,7 @@ def worker_numric_max(value, env="mpi"):
     return wroker_numric_opt(value, env, "max")
 
 
-class CtrPaddleTrainer(Trainer):
+class CtrTrainer(Trainer):
     """R
     """
 

core/trainers/online_learning_trainer.py

@@ -31,7 +31,7 @@ from paddlerec.core.utils import envs
 from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
 
 
-class ClusterTrainer(TranspileTrainer):
+class OnlineLearningTrainer(TranspileTrainer):
     def processor_register(self):
         role = PaddleCloudRoleMaker()
         fleet.init(role)

core/trainers/single_trainer.py

@@ -36,7 +36,8 @@ class SingleTrainer(TranspileTrainer):
         self.regist_context_processor('uninit', self.instance)
         self.regist_context_processor('init_pass', self.init)
         self.regist_context_processor('startup_pass', self.startup)
-        if envs.get_platform() == "LINUX" and envs.get_global_env("dataset_class", None, "train.reader") != "DataLoader":
+        if envs.get_platform() == "LINUX" and envs.get_global_env("dataset_class", None,
+                                                                  "train.reader") != "DataLoader":
             self.regist_context_processor('train_pass', self.dataset_train)
         else:
             self.regist_context_processor('train_pass', self.dataloader_train)
@@ -122,8 +123,8 @@ class SingleTrainer(TranspileTrainer):
                                          fetch_info=self.fetch_alias,
                                          print_period=self.fetch_period)
             end_time = time.time()
-            times = end_time-begin_time
-            print("epoch {} using time {}, speed {:.2f} lines/s".format(i, times, ins/times))
+            times = end_time - begin_time
+            print("epoch {} using time {}, speed {:.2f} lines/s".format(i, times, ins / times))
 
             self.save(i, "train", is_fleet=False)
         context['status'] = 'infer_pass'

core/trainers/tdm_cluster_trainer.py

@@ -27,7 +27,6 @@ from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import f
 from paddlerec.core.utils import envs
 from paddlerec.core.trainers.cluster_trainer import ClusterTrainer
 
-
 logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger("fluid")
 logger.setLevel(logging.INFO)

core/trainers/tdm_single_trainer.py

@@ -24,7 +24,6 @@ import paddle.fluid as fluid
 from paddlerec.core.trainers.single_trainer import SingleTrainer
 from paddlerec.core.utils import envs
 
-
 logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger("fluid")
 logger.setLevel(logging.INFO)

core/utils/dataset.py → core/utils/dataset_holder.py

@@ -22,7 +22,7 @@ from paddlerec.core.utils import fs as fs
 from paddlerec.core.utils import util as util
 
 
-class Dataset(object):
+class DatasetHolder(object):
     """
     Dataset Base
     """
@@ -62,7 +62,7 @@ class Dataset(object):
         pass
 
 
-class TimeSplitDataset(Dataset):
+class TimeSplitDatasetHolder(DatasetHolder):
     """
     Dataset with time split dir.  root_path/$DAY/$HOUR
     """
@@ -142,16 +142,6 @@ class TimeSplitDataset(Dataset):
             data_time = data_time + datetime.timedelta(minutes=self._split_interval)
         return data_file_list
 
-
-class FluidTimeSplitDataset(TimeSplitDataset):
-    """
-    A Dataset with time split for PaddleFluid
-    """
-
-    def __init__(self, config):
-        """ """
-        TimeSplitDataset.__init__(self, config)
-
     def _alloc_dataset(self, file_list):
         """ """
         dataset = fluid.DatasetFactory().create_dataset(self._config['dataset_type'])

core/utils/fs.py

@@ -95,11 +95,12 @@ class FileHandler(object):
     """
     A Smart file handler. auto judge local/afs by path 
     """
+
     def __init__(self, config):
         """R
         """
         if 'fs_name' in config:
-            hadoop_home="$HADOOP_HOME"
+            hadoop_home = "$HADOOP_HOME"
             hdfs_configs = {
                 "hadoop.job.ugi": config['fs_ugi'],
                 "fs.default.name": config['fs_name']
@@ -132,7 +133,8 @@ class FileHandler(object):
             if mode.find('a') >= 0:
                 org_content = self._hdfs_client.cat(dest_path)
             content = content + org_content
-            self._local_fs_client.write(content, temp_local_file, mode) #fleet hdfs_client only support upload, so write tmp file
+            self._local_fs_client.write(content, temp_local_file,
+                                        mode)  # fleet hdfs_client only support upload, so write tmp file
             self._hdfs_client.delete(dest_path + ".tmp")
             self._hdfs_client.upload(dest_path + ".tmp", temp_local_file)
             self._hdfs_client.delete(dest_path + ".bak")

models/contentunderstanding/classification/reader.py

@@ -38,6 +38,7 @@ class TrainReader(Reader):
             data = [int(i) for i in data]
             label = [int(i) for i in label]
             seq_len = [int(i) for i in seq_len]
-            print >>sys.stderr, str([('data', data), ('label', label), ('seq_len', seq_len)])
+            print >> sys.stderr, str([('data', data), ('label', label), ('seq_len', seq_len)])
             yield [('data', data), ('label', label), ('seq_len', seq_len)]
+
         return data_iter

models/contentunderstanding/tagspace/model.py

@@ -18,6 +18,7 @@ import paddle.fluid.layers.tensor as tensor
 import paddle.fluid.layers.control_flow as cf
 
 from paddlerec.core.model import Model as ModelBase
+from paddlerec.core.utils import envs
 
 
 class Model(ModelBase):
@@ -25,14 +26,13 @@ class Model(ModelBase):
         ModelBase.__init__(self, config)
         self.cost = None
         self.metrics = {}
-        self.vocab_text_size = 11447#envs.get_global_env("vocab_text_size", None, self._namespace)
-        self.vocab_tag_size = 4#envs.get_global_env("vocab_tag_size", None, self._namespace)
-        self.emb_dim = 10#envs.get_global_env("emb_dim", None, self._namespace)
-        self.hid_dim = 1000#envs.get_global_env("hid_dim", None, self._namespace)
-        self.win_size = 5#envs.get_global_env("win_size", None, self._namespace)
-        self.margin = 0.1#envs.get_global_env("margin", None, self._namespace)
-        self.neg_size = 3#envs.get_global_env("neg_size", None, self._namespace)
-        print self.emb_dim
+        self.vocab_text_size = envs.get_global_env("vocab_text_size", None, self._namespace)
+        self.vocab_tag_size = envs.get_global_env("vocab_tag_size", None, self._namespace)
+        self.emb_dim = envs.get_global_env("emb_dim", None, self._namespace)
+        self.hid_dim = envs.get_global_env("hid_dim", None, self._namespace)
+        self.win_size = envs.get_global_env("win_size", None, self._namespace)
+        self.margin = envs.get_global_env("margin", None, self._namespace)
+        self.neg_size = envs.get_global_env("neg_size", None, self._namespace)
 
     def train_net(self):
         """ network definition """
@@ -96,11 +96,9 @@ class Model(ModelBase):
         return self.metrics
 
     def optimizer(self):
-        learning_rate = 0.01#envs.get_global_env("hyper_parameters.base_lr", None, self._namespace)
+        learning_rate = envs.get_global_env("hyper_parameters.base_lr", None, self._namespace)
         sgd_optimizer = fluid.optimizer.Adagrad(learning_rate=learning_rate)
-        #sgd_optimizer.minimize(avg_cost)
         return sgd_optimizer
 
-
     def infer_net(self, parameter_list):
         self.train_net()

models/contentunderstanding/tagspace/reader.py

@@ -19,6 +19,7 @@ import numpy as np
 
 from paddlerec.core.reader import Reader
 
+
 class TrainReader(Reader):
     def init(self):
         pass
@@ -46,9 +47,6 @@ class TrainReader(Reader):
                 neg_index = rand_i
                 neg_tag.append(neg_index)
                 sum_n += 1
-       # if n > 0 and len(text) > n:
-       #    #yield None
-       #    return None, None, None
         return text, pos_tag, neg_tag
 
     def generate_sample(self, line):
@@ -58,4 +56,5 @@ class TrainReader(Reader):
                 yield None
                 return
             yield [('text', text), ('pos_tag', pos_tag), ('neg_tag', neg_tag)]
+
         return data_iter

models/match/dssm/model.py

@@ -28,7 +28,8 @@ class Model(ModelBase):
 
         self.query = fluid.data(name="query", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
         self.doc_pos = fluid.data(name="doc_pos", shape=[-1, TRIGRAM_D], dtype='float32', lod_level=0)
-        self.doc_negs = [fluid.data(name="doc_neg_" + str(i), shape=[-1, TRIGRAM_D], dtype="float32", lod_level=0) for i in range(Neg)]
+        self.doc_negs = [fluid.data(name="doc_neg_" + str(i), shape=[-1, TRIGRAM_D], dtype="float32", lod_level=0) for i
+                         in range(Neg)]
         self._data_var.append(self.query)
         self._data_var.append(self.doc_pos)
         for input in self.doc_negs:
@@ -38,7 +39,6 @@ class Model(ModelBase):
             self._data_loader = fluid.io.DataLoader.from_generator(
                 feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
 
-
     def net(self, is_infer=False):
         hidden_layers = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
         hidden_acts = envs.get_global_env("hyper_parameters.fc_acts", None, self._namespace)
@@ -46,7 +46,7 @@ class Model(ModelBase):
         def fc(data, hidden_layers, hidden_acts, names):
             fc_inputs = [data]
             for i in range(len(hidden_layers)):
-		xavier=fluid.initializer.Xavier(uniform=True, fan_in=fc_inputs[-1].shape[1], fan_out=hidden_layers[i])
+                xavier = fluid.initializer.Xavier(uniform=True, fan_in=fc_inputs[-1].shape[1], fan_out=hidden_layers[i])
                 out = fluid.layers.fc(input=fc_inputs[-1],
                                       size=hidden_layers[i],
                                       act=hidden_acts[i],
@@ -65,12 +65,13 @@ class Model(ModelBase):
 
         R_Q_D_ns = []
         for i, doc_neg in enumerate(self.doc_negs):
-	    doc_neg_fc_i = fc(doc_neg, hidden_layers, hidden_acts, ['doc_neg_l1_' + str(i), 'doc_neg_l2_' + str(i), 'doc_neg_l3_' + str(i)])
+            doc_neg_fc_i = fc(doc_neg, hidden_layers, hidden_acts,
+                              ['doc_neg_l1_' + str(i), 'doc_neg_l2_' + str(i), 'doc_neg_l3_' + str(i)])
             R_Q_D_ns.append(fluid.layers.cos_sim(query_fc, doc_neg_fc_i))
         concat_Rs = fluid.layers.concat(input=[self.R_Q_D_p] + R_Q_D_ns, axis=-1)
         prob = fluid.layers.softmax(concat_Rs, axis=1)
 
-        hit_prob = fluid.layers.slice(prob, axes=[0,1], starts=[0,0], ends=[4, 1])
+        hit_prob = fluid.layers.slice(prob, axes=[0, 1], starts=[0, 0], ends=[4, 1])
         loss = -fluid.layers.reduce_sum(fluid.layers.log(hit_prob))
         self.avg_cost = fluid.layers.mean(x=loss)
 

models/match/dssm/synthetic_reader.py

@@ -37,7 +37,7 @@ class TrainReader(Reader):
             neg_docs = []
             for i in range(len(features) - 2):
                 feature_names.append('doc_neg_' + str(i))
-                neg_docs.append(map(float, features[i+2].split(',')))
+                neg_docs.append(map(float, features[i + 2].split(',')))
 
             yield zip(feature_names, [query] + [pos_doc] + neg_docs)
 

models/match/multiview-simnet/evaluate_reader.py

@@ -51,4 +51,5 @@ class EvaluateReader(Reader):
                 else:
                     output[index][1].append(padding)
             yield output
+
         return data_iter

models/match/multiview-simnet/generate_synthetic_data.py

@@ -14,10 +14,12 @@
 
 import random
 
+
 class Dataset:
     def __init__(self):
         pass
 
+
 class SyntheticDataset(Dataset):
     def __init__(self, sparse_feature_dim, query_slot_num, title_slot_num, dataset_size=10000):
         # ids are randomly generated
@@ -50,7 +52,8 @@ class SyntheticDataset(Dataset):
                     for i in range(self.title_slot_num):
                         nt_slot = generate_ids(self.ids_per_slot,
                                                self.sparse_feature_dim)
-                        nt_slot = [str(fea) + ':' + str(i + self.query_slot_num + self.title_slot_num) for fea in nt_slot]
+                        nt_slot = [str(fea) + ':' + str(i + self.query_slot_num + self.title_slot_num) for fea in
+                                   nt_slot]
                         neg_title_slots += nt_slot
                     yield query_slots + pos_title_slots + neg_title_slots
                 else:
@@ -67,6 +70,7 @@ class SyntheticDataset(Dataset):
     def test(self):
         return self._reader_creator(False)
 
+
 if __name__ == '__main__':
     sparse_feature_dim = 1000001
     query_slots = 1

models/match/multiview-simnet/model.py

@@ -19,6 +19,7 @@ import paddle.fluid.layers.control_flow as cf
 from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase
 
+
 class BowEncoder(object):
     """ bow-encoder """
 
@@ -94,6 +95,7 @@ class SimpleEncoderFactory(object):
             rnn_encode = GrnnEncoder(hidden_size=enc_hid_size)
             return rnn_encode
 
+
 class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
@@ -140,7 +142,8 @@ class Model(ModelBase):
 
         self.nt_slots = [
             fluid.data(
-                name="%d" % (i + len(self.query_encoders) + len(self.title_encoders)), shape=[None, 1], lod_level=1, dtype='int64')
+                name="%d" % (i + len(self.query_encoders) + len(self.title_encoders)), shape=[None, 1], lod_level=1,
+                dtype='int64')
             for i in range(len(self.title_encoders))
         ]
 

models/match/multiview-simnet/reader.py

@@ -54,4 +54,5 @@ class TrainReader(Reader):
                 else:
                     output[index][1].append(padding)
             yield output
+
         return data_iter

models/multitask/esmm/esmm_infer_reader.py

@@ -18,14 +18,14 @@ from collections import defaultdict
 from paddlerec.core.reader import Reader
 
 
-
 class EvaluateReader(Reader):
     def init(self):
-        all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128', '129',
+        all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128',
+                        '129',
                         '205', '206', '207', '210', '216', '508', '509', '702', '853', '301']
         self.all_field_id_dict = defaultdict(int)
-        for i,field_id in enumerate(all_field_id):
-            self.all_field_id_dict[field_id] = [False,i]
+        for i, field_id in enumerate(all_field_id):
+            self.all_field_id_dict[field_id] = [False, i]
 
     def generate_sample(self, line):
         """
@@ -41,10 +41,10 @@ class EvaluateReader(Reader):
             cvr = int(features[2])
 
             padding = 0
-            output = [(field_id,[]) for field_id in self.all_field_id_dict]
+            output = [(field_id, []) for field_id in self.all_field_id_dict]
 
             for elem in features[4:]:
-                field_id,feat_id = elem.strip().split(':')
+                field_id, feat_id = elem.strip().split(':')
                 if field_id not in self.all_field_id_dict:
                     continue
                 self.all_field_id_dict[field_id][0] = True
@@ -52,7 +52,7 @@ class EvaluateReader(Reader):
                 output[index][1].append(int(feat_id))
 
             for field_id in self.all_field_id_dict:
-                visited,index = self.all_field_id_dict[field_id]
+                visited, index = self.all_field_id_dict[field_id]
                 if visited:
                     self.all_field_id_dict[field_id][0] = False
                 else:
@@ -60,4 +60,5 @@ class EvaluateReader(Reader):
             output.append(('ctr', [ctr]))
             output.append(('cvr', [cvr]))
             yield output
+
         return reader

models/multitask/esmm/esmm_reader.py

@@ -21,11 +21,12 @@ from paddlerec.core.reader import Reader
 
 class TrainReader(Reader):
     def init(self):
-        all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128', '129',
+        all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128',
+                        '129',
                         '205', '206', '207', '210', '216', '508', '509', '702', '853', '301']
         self.all_field_id_dict = defaultdict(int)
-        for i,field_id in enumerate(all_field_id):
-            self.all_field_id_dict[field_id] = [False,i]
+        for i, field_id in enumerate(all_field_id):
+            self.all_field_id_dict[field_id] = [False, i]
 
     def generate_sample(self, line):
         """
@@ -37,25 +38,25 @@ class TrainReader(Reader):
             This function needs to be implemented by the user, based on data format
             """
             features = line.strip().split(',')
-            #ctr = list(map(int, features[1]))
-            #cvr = list(map(int, features[2]))
+            # ctr = list(map(int, features[1]))
+            # cvr = list(map(int, features[2]))
             ctr = int(features[1])
             cvr = int(features[2])
 
             padding = 0
-            output = [(field_id,[]) for field_id in self.all_field_id_dict]
+            output = [(field_id, []) for field_id in self.all_field_id_dict]
 
             for elem in features[4:]:
-                field_id,feat_id = elem.strip().split(':')
+                field_id, feat_id = elem.strip().split(':')
                 if field_id not in self.all_field_id_dict:
                     continue
                 self.all_field_id_dict[field_id][0] = True
                 index = self.all_field_id_dict[field_id][1]
-                #feat_id = list(map(int, feat_id))    
+                # feat_id = list(map(int, feat_id))
                 output[index][1].append(int(feat_id))
 
             for field_id in self.all_field_id_dict:
-                visited,index = self.all_field_id_dict[field_id]
+                visited, index = self.all_field_id_dict[field_id]
                 if visited:
                     self.all_field_id_dict[field_id][0] = False
                 else:
@@ -63,4 +64,5 @@ class TrainReader(Reader):
             output.append(('ctr', [ctr]))
             output.append(('cvr', [cvr]))
             yield output
+
         return reader

models/multitask/esmm/model.py

@@ -23,13 +23,14 @@ class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
 
-    def fc(self,tag, data, out_dim, active='prelu'):
+    def fc(self, tag, data, out_dim, active='prelu'):
 
         init_stddev = 1.0
         scales = 1.0 / np.sqrt(data.shape[1])
 
         p_attr = fluid.param_attr.ParamAttr(name='%s_weight' % tag,
-                    initializer=fluid.initializer.NormalInitializer(loc=0.0, scale=init_stddev * scales))
+                                            initializer=fluid.initializer.NormalInitializer(loc=0.0,
+                                                                                            scale=init_stddev * scales))
 
         b_attr = fluid.ParamAttr(name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
 
@@ -37,13 +38,13 @@ class Model(ModelBase):
                               size=out_dim,
                               act=active,
                               param_attr=p_attr,
-                            bias_attr =b_attr,
+                              bias_attr=b_attr,
                               name=tag)
         return out
 
     def input_data(self):
         sparse_input_ids = [
-            fluid.data(name="field_" + str(i), shape=[-1, 1], dtype="int64", lod_level=1) for i in range(0,23)
+            fluid.data(name="field_" + str(i), shape=[-1, 1], dtype="int64", lod_level=1) for i in range(0, 23)
         ]
         label_ctr = fluid.data(name="ctr", shape=[-1, 1], dtype="int64")
         label_cvr = fluid.data(name="cvr", shape=[-1, 1], dtype="int64")
@@ -62,10 +63,11 @@ class Model(ModelBase):
                                        size=[vocab_size, embed_size],
                                        param_attr=fluid.ParamAttr(name='dis_emb',
                                                                   learning_rate=5,
-                                                            initializer=fluid.initializer.Xavier(fan_in=embed_size,fan_out=embed_size)
+                                                                  initializer=fluid.initializer.Xavier(
+                                                                      fan_in=embed_size, fan_out=embed_size)
                                                                   ),
                                        is_sparse=True)
-            field_emb = fluid.layers.sequence_pool(input=feat_emb,pool_type='sum')
+            field_emb = fluid.layers.sequence_pool(input=feat_emb, pool_type='sum')
             emb.append(field_emb)
         concat_emb = fluid.layers.concat(emb, axis=1)
 
@@ -78,7 +80,7 @@ class Model(ModelBase):
         # cvr
         cvr_fc1 = self.fc('cvr_fc1', concat_emb, 200, active)
         cvr_fc2 = self.fc('cvr_fc2', cvr_fc1, 80, active)
-        cvr_out = self.fc('cvr_out', cvr_fc2, 2,'softmax')
+        cvr_out = self.fc('cvr_out', cvr_fc2, 2, 'softmax')
 
         ctr_clk = inputs[-2]
         ctcvr_buy = inputs[-1]
@@ -87,7 +89,7 @@ class Model(ModelBase):
         cvr_prop_one = fluid.layers.slice(cvr_out, axes=[1], starts=[1], ends=[2])
 
         ctcvr_prop_one = fluid.layers.elementwise_mul(ctr_prop_one, cvr_prop_one)
-        ctcvr_prop = fluid.layers.concat(input=[1-ctcvr_prop_one,ctcvr_prop_one], axis = 1)
+        ctcvr_prop = fluid.layers.concat(input=[1 - ctcvr_prop_one, ctcvr_prop_one], axis=1)
 
         auc_ctr, batch_auc_ctr, auc_states_ctr = fluid.layers.auc(input=ctr_out, label=ctr_clk)
         auc_ctcvr, batch_auc_ctcvr, auc_states_ctcvr = fluid.layers.auc(input=ctcvr_prop, label=ctcvr_buy)
@@ -97,25 +99,21 @@ class Model(ModelBase):
             self._infer_results["AUC_ctcvr"] = auc_ctcvr
             return
 
-    
         loss_ctr = fluid.layers.cross_entropy(input=ctr_out, label=ctr_clk)
         loss_ctcvr = fluid.layers.cross_entropy(input=ctcvr_prop, label=ctcvr_buy)
         cost = loss_ctr + loss_ctcvr
         avg_cost = fluid.layers.mean(cost)
 
-    
         self._cost = avg_cost
         self._metrics["AUC_ctr"] = auc_ctr
         self._metrics["BATCH_AUC_ctr"] = batch_auc_ctr
         self._metrics["AUC_ctcvr"] = auc_ctcvr
         self._metrics["BATCH_AUC_ctcvr"] = batch_auc_ctcvr
 
-
     def train_net(self):
         input_data = self.input_data()
         self.net(input_data)
 
-
     def infer_net(self):
         self._infer_data_var = self.input_data()
         self._infer_data_loader = fluid.io.DataLoader.from_generator(

models/multitask/mmoe/census_reader.py

@@ -43,8 +43,8 @@ class TrainReader(Reader):
                 label_marital = [1, 0]
             elif int(l[0]) == 1:
                 label_marital = [0, 1]
-            #label_income = np.array(label_income)
-            #label_marital = np.array(label_marital)
+            # label_income = np.array(label_income)
+            # label_marital = np.array(label_marital)
             feature_name = ["input", "label_income", "label_marital"]
             yield zip(feature_name, [data] + [label_income] + [label_marital])
 

models/multitask/mmoe/model.py

@@ -49,8 +49,7 @@ class Model(ModelBase):
                                             name='expert_' + str(i))
             expert_outputs.append(expert_output)
         expert_concat = fluid.layers.concat(expert_outputs, axis=1)
-        expert_concat = fluid.layers.reshape(expert_concat,[-1, expert_num, expert_size])
-        
+        expert_concat = fluid.layers.reshape(expert_concat, [-1, expert_num, expert_size])
 
         # g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper
         output_layers = []
@@ -78,19 +77,22 @@ class Model(ModelBase):
         pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
         pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
 
-        
         label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
         label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
 
-        auc_income, batch_auc_1, auc_states_1  = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64'))
-        auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64'))
+        auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income,
+                                                                 label=fluid.layers.cast(x=label_income_1,
+                                                                                         dtype='int64'))
+        auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital,
+                                                                  label=fluid.layers.cast(x=label_marital_1,
+                                                                                          dtype='int64'))
         if is_infer:
             self._infer_results["AUC_income"] = auc_income
             self._infer_results["AUC_marital"] = auc_marital
             return
 
-        cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
-        cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
+        cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income, soft_label=True)
+        cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital, soft_label=True)
 
         avg_cost_income = fluid.layers.mean(x=cost_income)
         avg_cost_marital = fluid.layers.mean(x=cost_marital)
@@ -103,10 +105,8 @@ class Model(ModelBase):
         self._metrics["AUC_marital"] = auc_marital
         self._metrics["BATCH_AUC_marital"] = batch_auc_2
 
-
     def train_net(self):
         self.MMOE()
 
-
     def infer_net(self):
         self.MMOE(is_infer=True)

models/multitask/share-bottom/census_reader.py

@@ -43,8 +43,8 @@ class TrainReader(Reader):
                 label_marital = [1, 0]
             elif int(l[0]) == 1:
                 label_marital = [0, 1]
-            #label_income = np.array(label_income)
-            #label_marital = np.array(label_marital)
+            # label_income = np.array(label_income)
+            # label_marital = np.array(label_marital)
             feature_name = ["input", "label_income", "label_marital"]
             yield zip(feature_name, [data] + [label_income] + [label_marital])
 

models/multitask/share-bottom/model.py

@@ -46,7 +46,6 @@ class Model(ModelBase):
                                         bias_attr=fluid.ParamAttr(learning_rate=1.0),
                                         name='bottom_output')
 
-       
         # Build tower layer from bottom layer
         output_layers = []
         for index in range(tower_nums):
@@ -60,23 +59,26 @@ class Model(ModelBase):
                                            name='output_layer_' + str(index))
             output_layers.append(output_layer)
 
-
         pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
         pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
 
         label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
         label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
 
-        auc_income, batch_auc_1, auc_states_1  = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64'))
-        auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64'))
+        auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income,
+                                                                 label=fluid.layers.cast(x=label_income_1,
+                                                                                         dtype='int64'))
+        auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital,
+                                                                  label=fluid.layers.cast(x=label_marital_1,
+                                                                                          dtype='int64'))
 
         if is_infer:
             self._infer_results["AUC_income"] = auc_income
             self._infer_results["AUC_marital"] = auc_marital
             return
 
-        cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
-        cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
+        cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income, soft_label=True)
+        cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital, soft_label=True)
         cost = fluid.layers.elementwise_add(cost_income, cost_marital, axis=1)
 
         avg_cost = fluid.layers.mean(x=cost)
@@ -87,10 +89,8 @@ class Model(ModelBase):
         self._metrics["AUC_marital"] = auc_marital
         self._metrics["BATCH_AUC_marital"] = batch_auc_2
 
-
     def train_net(self):
         self.model()
 
-
     def infer_net(self):
         self.model(is_infer=True)

models/rank/dcn/criteo_reader.py

@@ -21,7 +21,6 @@ try:
 except ImportError:
     import pickle
 
-
 from paddlerec.core.reader import Reader
 from paddlerec.core.utils import envs
 
@@ -85,6 +84,7 @@ class TrainReader(Reader):
         """
         Read the data line by line and process it as a dictionary
         """
+
         def data_iter():
             label_feat_list = self._process_line(line)
             yield list(zip(self.label_feat_names, label_feat_list))

models/rank/dcn/model.py

@@ -142,7 +142,6 @@ class Model(ModelBase):
         self._metrics["AUC"] = auc_var
         self._metrics["BATCH_AUC"] = batch_auc_var
 
-
         # logloss
         logloss = fluid.layers.log_loss(self.prob, self.target_input)
         self.avg_logloss = fluid.layers.reduce_mean(logloss)

models/rank/deepfm/criteo_reader.py

@@ -67,6 +67,7 @@ class TrainReader(Reader):
         """
         Read the data line by line and process it as a dictionary
         """
+
         def data_iter():
             feat_idx, feat_value, label = self._process_line(line)
             yield [('feat_idx', feat_idx), ('feat_value', feat_value), ('label', label)]

models/rank/deepfm/model.py

@@ -33,10 +33,11 @@ class Model(ModelBase):
         # ------------------------- network input --------------------------
 
         num_field = envs.get_global_env("hyper_parameters.num_field", None, self._namespace)
-        raw_feat_idx = fluid.data(name='feat_idx', shape=[None, num_field], dtype='int64') # None * num_field(defalut:39)
+        raw_feat_idx = fluid.data(name='feat_idx', shape=[None, num_field],
+                                  dtype='int64')  # None * num_field(defalut:39)
         raw_feat_value = fluid.data(name='feat_value', shape=[None, num_field], dtype='float32')  # None * num_field
         self.label = fluid.data(name='label', shape=[None, 1], dtype='float32')  # None * 1
-        feat_idx = fluid.layers.reshape(raw_feat_idx,[-1, 1])  # (None * num_field) * 1
+        feat_idx = fluid.layers.reshape(raw_feat_idx, [-1, 1])  # (None * num_field) * 1
         feat_value = fluid.layers.reshape(raw_feat_value, [-1, num_field, 1])  # None * num_field * 1
 
         # ------------------------- set _data_var --------------------------
@@ -48,7 +49,7 @@ class Model(ModelBase):
             self._data_loader = fluid.io.DataLoader.from_generator(
                 feed_list=self._data_var, capacity=64, use_double_buffer=False, iterable=False)
 
-        #------------------------- first order term --------------------------
+        # ------------------------- first order term --------------------------
 
         reg = envs.get_global_env("hyper_parameters.reg", 1e-4, self._namespace)
         first_weights_re = fluid.embedding(
@@ -66,7 +67,7 @@ class Model(ModelBase):
             first_weights_re, shape=[-1, num_field, 1])  # None * num_field * 1
         y_first_order = fluid.layers.reduce_sum((first_weights * feat_value), 1)
 
-        #------------------------- second order term --------------------------
+        # ------------------------- second order term --------------------------
 
         feat_embeddings_re = fluid.embedding(
             input=feat_idx,
@@ -100,8 +101,7 @@ class Model(ModelBase):
             summed_features_emb_square - squared_sum_features_emb, 1,
             keep_dim=True)  # None * 1
 
-
-        #------------------------- DNN --------------------------
+        # ------------------------- DNN --------------------------
 
         layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes", None, self._namespace)
         act = envs.get_global_env("hyper_parameters.act", None, self._namespace)
@@ -129,21 +129,21 @@ class Model(ModelBase):
                 initializer=fluid.initializer.TruncatedNormalInitializer(
                     loc=0.0, scale=init_value_)))
 
-        #------------------------- DeepFM --------------------------
+        # ------------------------- DeepFM --------------------------
 
         self.predict = fluid.layers.sigmoid(y_first_order + y_second_order + y_dnn)
 
     def train_net(self):
         self.deepfm_net()
 
-        #------------------------- Cost(logloss) --------------------------
+        # ------------------------- Cost(logloss) --------------------------
 
         cost = fluid.layers.log_loss(input=self.predict, label=self.label)
         avg_cost = fluid.layers.reduce_sum(cost)
 
         self._cost = avg_cost
 
-        #------------------------- Metric(Auc) --------------------------
+        # ------------------------- Metric(Auc) --------------------------
 
         predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
         label_int = fluid.layers.cast(self.label, 'int64')

models/rank/din/reader.py

@@ -47,9 +47,6 @@ class TrainReader(Reader):
         self.batch_size = envs.get_global_env("batch_size", 32, "train.reader")
         self.group_size = self.batch_size * 20
 
-
-        
-
     def _process_line(self, line):
         line = line.strip().split(';')
         hist = line[0].split()
@@ -58,13 +55,13 @@ class TrainReader(Reader):
         cate = [int(i) for i in cate]
         return [hist, cate, [int(line[2])], [int(line[3])], [float(line[4])]]
 
-    
     def generate_sample(self, line):
         """
         Read the data line by line and process it as a dictionary
         """
+
         def data_iter():
-            #feat_idx, feat_value, label = self._process_line(line)
+            # feat_idx, feat_value, label = self._process_line(line)
             yield self._process_line(line)
 
         return data_iter
@@ -131,5 +128,3 @@ class TrainReader(Reader):
         data_set = self.base_read(files)
         random.shuffle(data_set)
         return self.batch_reader(data_set, self.batch_size, self.batch_size * 20)
-        
-        
\ No newline at end of file

models/rank/wide_deep/model.py

@@ -27,8 +27,12 @@ class Model(ModelBase):
     def wide_part(self, data):
         out = fluid.layers.fc(input=data,
                               size=1,
-                            param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0 / math.sqrt(data.shape[1])),
-                                                       regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=1e-4)),
+                              param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0,
+                                                                                                       scale=1.0 / math.sqrt(
+                                                                                                           data.shape[
+                                                                                                               1])),
+                                                         regularizer=fluid.regularizer.L2DecayRegularizer(
+                                                             regularization_coeff=1e-4)),
                               act=None,
                               name='wide')
         return out
@@ -36,7 +40,10 @@ class Model(ModelBase):
     def fc(self, data, hidden_units, active, tag):
         output = fluid.layers.fc(input=data,
                                  size=hidden_units,
-                            param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0 / math.sqrt(data.shape[1]))),
+                                 param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0,
+                                                                                                          scale=1.0 / math.sqrt(
+                                                                                                              data.shape[
+                                                                                                                  1]))),
                                  act=active,
                                  name=tag)
 
@@ -65,13 +72,15 @@ class Model(ModelBase):
 
         wide_model = fluid.layers.fc(input=wide_output,
                                      size=1,
-                        param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)),
+                                     param_attr=fluid.ParamAttr(
+                                         initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)),
                                      act=None,
                                      name='w_wide')
 
         deep_model = fluid.layers.fc(input=deep_output,
                                      size=1,
-                        param_attr=fluid.ParamAttr(initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)),
+                                     param_attr=fluid.ParamAttr(
+                                         initializer=fluid.initializer.TruncatedNormal(loc=0.0, scale=1.0)),
                                      act=None,
                                      name='w_deep')
 

models/rank/wide_deep/reader.py

@@ -30,7 +30,7 @@ class TrainReader(Reader):
         line = line.strip().split(',')
         features = list(map(float, line))
         wide_feat = features[0:8]
-        deep_feat = features[8:58+8]
+        deep_feat = features[8:58 + 8]
         label = features[-1]
         return wide_feat, deep_feat, [label]
 
@@ -38,6 +38,7 @@ class TrainReader(Reader):
         """
         Read the data line by line and process it as a dictionary
         """
+
         def data_iter():
             wide_feat, deep_deat, label = self._process_line(line)
             yield [('wide_input', wide_feat), ('deep_input', deep_deat), ('label', label)]

models/recall/gnn/evaluate_reader.py

@@ -122,8 +122,9 @@ class EvaluateReader(Reader):
                 else:
                     # Due to fixed batch_size, discard the remaining ins
                     return
-                    #cur_batch = remain_data[i:]
-                    #yield self.make_data(cur_batch, group_remain % batch_size)
+                    # cur_batch = remain_data[i:]
+                    # yield self.make_data(cur_batch, group_remain % batch_size)
+
         return _reader
 
     def generate_batch_from_trainfiles(self, files):
@@ -134,4 +135,5 @@ class EvaluateReader(Reader):
     def generate_sample(self, line):
         def data_iter():
             yield []
+
         return data_iter

models/recall/gnn/model.py

@@ -29,13 +29,13 @@ class Model(ModelBase):
 
     def init_config(self):
         self._fetch_interval = 1
-        self.items_num, self.ins_num = self.config_read(envs.get_global_env("hyper_parameters.config_path", None, self._namespace))
+        self.items_num, self.ins_num = self.config_read(
+            envs.get_global_env("hyper_parameters.config_path", None, self._namespace))
         self.train_batch_size = envs.get_global_env("batch_size", None, "train.reader")
         self.evaluate_batch_size = envs.get_global_env("batch_size", None, "evaluate.reader")
         self.hidden_size = envs.get_global_env("hyper_parameters.sparse_feature_dim", None, self._namespace)
         self.step = envs.get_global_env("hyper_parameters.gnn_propogation_steps", None, self._namespace)
 
-
     def config_read(self, config_path=None):
         if config_path is None:
             raise ValueError("please set train.model.hyper_parameters.config_path at first")
@@ -48,31 +48,31 @@ class Model(ModelBase):
         self.items = fluid.data(
             name="items",
             shape=[bs, -1],
-            dtype="int64") #[batch_size, uniq_max]
+            dtype="int64")  # [batch_size, uniq_max]
         self.seq_index = fluid.data(
             name="seq_index",
             shape=[bs, -1, 2],
-            dtype="int32") #[batch_size, seq_max, 2]
+            dtype="int32")  # [batch_size, seq_max, 2]
         self.last_index = fluid.data(
             name="last_index",
             shape=[bs, 2],
-            dtype="int32") #[batch_size, 2]
+            dtype="int32")  # [batch_size, 2]
         self.adj_in = fluid.data(
             name="adj_in",
             shape=[bs, -1, -1],
-            dtype="float32") #[batch_size, seq_max, seq_max]
+            dtype="float32")  # [batch_size, seq_max, seq_max]
         self.adj_out = fluid.data(
             name="adj_out",
             shape=[bs, -1, -1],
-            dtype="float32") #[batch_size, seq_max, seq_max]
+            dtype="float32")  # [batch_size, seq_max, seq_max]
         self.mask = fluid.data(
             name="mask",
             shape=[bs, -1, 1],
-            dtype="float32") #[batch_size, seq_max, 1]
+            dtype="float32")  # [batch_size, seq_max, 1]
         self.label = fluid.data(
             name="label",
             shape=[bs, 1],
-            dtype="int64") #[batch_size, 1] 
+            dtype="int64")  # [batch_size, 1]
 
         res = [self.items, self.seq_index, self.last_index, self.adj_in, self.adj_out, self.mask, self.label]
         return res
@@ -114,7 +114,7 @@ class Model(ModelBase):
                 param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                     low=-stdv, high=stdv)),
                 bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
-                    low=-stdv, high=stdv)))  #[batch_size, uniq_max, h]
+                    low=-stdv, high=stdv)))  # [batch_size, uniq_max, h]
             state_out = layers.fc(
                 input=pre_state,
                 name="state_out",
@@ -124,10 +124,10 @@ class Model(ModelBase):
                 param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
                     low=-stdv, high=stdv)),
                 bias_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
-                    low=-stdv, high=stdv)))  #[batch_size, uniq_max, h]
+                    low=-stdv, high=stdv)))  # [batch_size, uniq_max, h]
 
-            state_adj_in = layers.matmul(self.adj_in, state_in)  #[batch_size, uniq_max, h]
-            state_adj_out = layers.matmul(self.adj_out, state_out)   #[batch_size, uniq_max, h]
+            state_adj_in = layers.matmul(self.adj_in, state_in)  # [batch_size, uniq_max, h]
+            state_adj_out = layers.matmul(self.adj_out, state_out)  # [batch_size, uniq_max, h]
 
             gru_input = layers.concat([state_adj_in, state_adj_out], axis=2)
 
@@ -155,7 +155,7 @@ class Model(ModelBase):
             num_flatten_dims=2,
             param_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.Uniform(
-                low=-stdv, high=stdv)))  #[batch_size, seq_max, h]
+                    low=-stdv, high=stdv)))  # [batch_size, seq_max, h]
         last_fc = layers.fc(
             input=last,
             name="last_fc",
@@ -165,21 +165,21 @@ class Model(ModelBase):
             num_flatten_dims=1,
             param_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.Uniform(
-                low=-stdv, high=stdv)))  #[bathc_size, h]
+                    low=-stdv, high=stdv)))  # [bathc_size, h]
 
         seq_fc_t = layers.transpose(
-            seq_fc, perm=[1, 0, 2])  #[seq_max, batch_size, h]
+            seq_fc, perm=[1, 0, 2])  # [seq_max, batch_size, h]
         add = layers.elementwise_add(
-            seq_fc_t, last_fc)  #[seq_max, batch_size, h]
+            seq_fc_t, last_fc)  # [seq_max, batch_size, h]
         b = layers.create_parameter(
             shape=[hidden_size],
             dtype='float32',
-            default_initializer=fluid.initializer.Constant(value=0.0))  #[h]
-        add = layers.elementwise_add(add, b)  #[seq_max, batch_size, h]
+            default_initializer=fluid.initializer.Constant(value=0.0))  # [h]
+        add = layers.elementwise_add(add, b)  # [seq_max, batch_size, h]
 
-        add_sigmoid = layers.sigmoid(add) #[seq_max, batch_size, h] 
+        add_sigmoid = layers.sigmoid(add)  # [seq_max, batch_size, h]
         add_sigmoid = layers.transpose(
-            add_sigmoid, perm=[1, 0, 2])  #[batch_size, seq_max, h]
+            add_sigmoid, perm=[1, 0, 2])  # [batch_size, seq_max, h]
 
         weight = layers.fc(
             input=add_sigmoid,
@@ -190,13 +190,13 @@ class Model(ModelBase):
             bias_attr=False,
             param_attr=fluid.ParamAttr(
                 initializer=fluid.initializer.Uniform(
-                    low=-stdv, high=stdv)))  #[batch_size, seq_max, 1]
+                    low=-stdv, high=stdv)))  # [batch_size, seq_max, 1]
         weight *= self.mask
-        weight_mask = layers.elementwise_mul(seq, weight, axis=0) #[batch_size, seq_max, h]
-        global_attention = layers.reduce_sum(weight_mask, dim=1) #[batch_size, h]
+        weight_mask = layers.elementwise_mul(seq, weight, axis=0)  # [batch_size, seq_max, h]
+        global_attention = layers.reduce_sum(weight_mask, dim=1)  # [batch_size, h]
 
         final_attention = layers.concat(
-            [global_attention, last], axis=1)  #[batch_size, 2*h]
+            [global_attention, last], axis=1)  # [batch_size, 2*h]
         final_attention_fc = layers.fc(
             input=final_attention,
             name="final_attention_fc",
@@ -204,7 +204,7 @@ class Model(ModelBase):
             bias_attr=False,
             act=None,
             param_attr=fluid.ParamAttr(initializer=fluid.initializer.Uniform(
-                low=-stdv, high=stdv)))  #[batch_size, h]
+                low=-stdv, high=stdv)))  # [batch_size, h]
 
         # all_vocab = layers.create_global_var(
         #     shape=[items_num - 1],
@@ -221,13 +221,13 @@ class Model(ModelBase):
                 name="emb",
                 initializer=fluid.initializer.Uniform(
                     low=-stdv, high=stdv)),
-            size=[items_num, hidden_size])  #[all_vocab, h]
+            size=[items_num, hidden_size])  # [all_vocab, h]
 
         logits = layers.matmul(
             x=final_attention_fc, y=all_emb,
-            transpose_y=True)  #[batch_size, all_vocab]
+            transpose_y=True)  # [batch_size, all_vocab]
         softmax = layers.softmax_with_cross_entropy(
-            logits=logits, label=self.label)  #[batch_size, 1]
+            logits=logits, label=self.label)  # [batch_size, 1]
         self.loss = layers.reduce_mean(softmax)  # [1]
         self.acc = layers.accuracy(input=logits, label=self.label, k=20)
 

models/recall/gnn/reader.py

@@ -122,8 +122,9 @@ class TrainReader(Reader):
                 else:
                     # Due to fixed batch_size, discard the remaining ins
                     return
-                    #cur_batch = remain_data[i:]
-                    #yield self.make_data(cur_batch, group_remain % batch_size)
+                    # cur_batch = remain_data[i:]
+                    # yield self.make_data(cur_batch, group_remain % batch_size)
+
         return _reader
 
     def generate_batch_from_trainfiles(self, files):
@@ -134,4 +135,5 @@ class TrainReader(Reader):
     def generate_sample(self, line):
         def data_iter():
             yield []
+
         return data_iter

models/recall/gru4rec/model.py

@@ -86,10 +86,8 @@ class Model(ModelBase):
         self._metrics["cost"] = avg_cost
         self._metrics["acc"] = acc
 
-
     def train_net(self):
         self.all_vocab_network()
 
-
     def infer_net(self):
         self.all_vocab_network(is_infer=True)

models/recall/ssr/model.py

@@ -51,6 +51,7 @@ class GrnnEncoder(object):
             bias_attr=self.param_name + ".bias")
         return fluid.layers.sequence_pool(input=gru_h, pool_type='max')
 
+
 class PairwiseHingeLoss(object):
     def __init__(self, margin=0.8):
         self.margin = margin
@@ -67,6 +68,7 @@ class PairwiseHingeLoss(object):
             loss_part2)
         return loss_part3
 
+
 class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
@@ -77,7 +79,6 @@ class Model(ModelBase):
         return correct
 
     def train(self):
-        
         vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
         emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None, self._namespace)
         hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None, self._namespace)
@@ -126,11 +127,9 @@ class Model(ModelBase):
         self._metrics["correct"] = correct
         self._metrics["hinge_loss"] = hinge_loss
 
-
     def train_net(self):
         self.train()
 
-
     def infer(self):
         vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
         emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None, self._namespace)
@@ -170,6 +169,5 @@ class Model(ModelBase):
 
         self._infer_results['recall20'] = acc
 
-
     def infer_net(self):
         self.infer()

models/recall/ssr/ssr_infer_reader.py

@@ -20,12 +20,10 @@ from paddlerec.core.reader import Reader
 from paddlerec.core.utils import envs
 
 
-
 class EvaluateReader(Reader):
     def init(self):
         self.vocab_size = envs.get_global_env("vocab_size", 10, "train.model.hyper_parameters")
 
-
     def generate_sample(self, line):
         """
         Read the data line by line and process it as a dictionary
@@ -41,6 +39,6 @@ class EvaluateReader(Reader):
             src = conv_ids[:boundary]
             pos_tgt = [conv_ids[boundary]]
             feature_name = ["user", "all_item", "p_item"]
-            yield zip(feature_name, [src] + [np.arange(self.vocab_size).astype("int64").tolist()]+ [pos_tgt])
+            yield zip(feature_name, [src] + [np.arange(self.vocab_size).astype("int64").tolist()] + [pos_tgt])
 
         return reader

models/recall/ssr/ssr_reader.py

@@ -19,7 +19,6 @@ import random
 from paddlerec.core.reader import Reader
 
 
-
 class TrainReader(Reader):
     def init(self):
         pass
@@ -27,7 +26,6 @@ class TrainReader(Reader):
     def sample_neg_from_seq(self, seq):
         return seq[random.randint(0, len(seq) - 1)]
 
-
     def generate_sample(self, line):
         """
         Read the data line by line and process it as a dictionary

models/recall/word2vec/preprocess.py

@@ -20,11 +20,8 @@ import random
 import re
 import six
 
-
 import argparse
 
-
-
 prog = re.compile("[^a-z ]", flags=0)
 
 
@@ -78,7 +75,7 @@ def parse_args():
 
 
 def text_strip(text):
-    #English Preprocess Rule
+    # English Preprocess Rule
     return prog.sub("", text.lower())
 
 
@@ -120,7 +117,7 @@ def filter_corpus(args):
     word_all_count = 0
     id_counts = []
     word_id = 0
-    #read dict
+    # read dict
     with io.open(args.dict_path, 'r', encoding='utf-8') as f:
         for line in f:
             word, count = line.split()[0], int(line.split()[1])
@@ -130,13 +127,13 @@ def filter_corpus(args):
             id_counts.append(count)
             word_all_count += count
 
-    #write word2id file
+    # write word2id file
     print("write word2id file to : " + args.dict_path + "_word_to_id_")
     with io.open(
             args.dict_path + "_word_to_id_", 'w+', encoding='utf-8') as fid:
         for k, v in word_to_id_.items():
             fid.write(k + " " + str(v) + '\n')
-    #filter corpus and convert id
+    # filter corpus and convert id
     if not os.path.exists(args.output_corpus_dir):
         os.makedirs(args.output_corpus_dir)
     for file in os.listdir(args.input_corpus_dir):
@@ -205,7 +202,7 @@ def build_dict(args):
     for item in item_to_remove:
         unk_sum += word_count[item]
         del word_count[item]
-    #sort by count
+    # sort by count
     word_count[native_to_unicode('<UNK>')] = unk_sum
     word_count = sorted(
         word_count.items(), key=lambda word_count: -word_count[1])
@@ -233,12 +230,13 @@ def data_split(args):
     print("contents: ", str(len(contents)))
     print("lines_per_file: ", str(lines_per_file))
 
-    for i in range(1, num+1):
+    for i in range(1, num + 1):
         with open(os.path.join(new_data_dir, "part_" + str(i)), 'w') as fout:
-            data = contents[(i-1)*lines_per_file:min(i*lines_per_file,len(contents))]
+            data = contents[(i - 1) * lines_per_file:min(i * lines_per_file, len(contents))]
             for line in data:
                 fout.write(line)
 
+
 if __name__ == "__main__":
     args = parse_args()
     if args.build_dict:

models/recall/word2vec/w2v_evaluate_reader.py

@@ -49,18 +49,15 @@ class EvaluateReader(Reader):
                 return True
         return False
 
-    
     def _to_unicode(self, s, ignore_errors=False):
         if self._is_unicode(s):
             return s
         error_mode = "ignore" if ignore_errors else "strict"
         return s.decode("utf-8", errors=error_mode)
 
-    
     def strip_lines(self, line, vocab):
         return self._replace_oov(vocab, self.native_to_unicode(line))
 
-    
     def _replace_oov(self, original_vocab, line):
         """Replace out-of-vocab words with "<UNK>".
       This maintains compatibility with published results.
@@ -78,5 +75,7 @@ class EvaluateReader(Reader):
         def reader():
             features = self.strip_lines(line.lower(), self.word_to_id)
             features = features.split()
-            yield [('analogy_a', [self.word_to_id[features[0]]]), ('analogy_b', [self.word_to_id[features[1]]]), ('analogy_c', [self.word_to_id[features[2]]]), ('analogy_d', [self.word_to_id[features[3]]])]
+            yield [('analogy_a', [self.word_to_id[features[0]]]), ('analogy_b', [self.word_to_id[features[1]]]),
+                   ('analogy_c', [self.word_to_id[features[2]]]), ('analogy_d', [self.word_to_id[features[3]]])]
+
         return reader

models/recall/word2vec/w2v_reader.py

@@ -87,7 +87,7 @@ class TrainReader(Reader):
                     output = [('input_word', [int(target_id)]), ('true_label', [int(context_id)])]
                     if not self.with_shuffle_batch:
                         neg_array = self.cs.searchsorted(np.random.sample(self.neg_num))
-                        output += [('neg_label', [int(str(i)) for i in neg_array ])]
+                        output += [('neg_label', [int(str(i)) for i in neg_array])]
                     yield output
-        return reader
 
+        return reader

models/treebased/tdm/model.py

@@ -229,7 +229,7 @@ class Model(ModelBase):
                 act=self.act,
                 param_attr=fluid.ParamAttr(
                     name="trans.layer_fc.weight." + str(i)),
-                bias_attr=fluid.ParamAttr(name="trans.layer_fc.bias."+str(i)),
+                bias_attr=fluid.ParamAttr(name="trans.layer_fc.bias." + str(i)),
             ) for i in range(self.max_layers)
         ]
 
@@ -268,8 +268,8 @@ class Model(ModelBase):
                 num_flatten_dims=2,
                 act=self.act,
                 param_attr=fluid.ParamAttr(
-                    name="cls.concat_fc.weight."+str(i)),
-                bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias."+str(i))
+                    name="cls.concat_fc.weight." + str(i)),
+                bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias." + str(i))
             ) for i in range(self.max_layers)
         ]
 
@@ -458,7 +458,7 @@ class Model(ModelBase):
             param_attr=fluid.ParamAttr(
                 name="trans.layer_fc.weight." + str(layer_idx)),
             bias_attr=fluid.ParamAttr(
-                name="trans.layer_fc.bias."+str(layer_idx)),
+                name="trans.layer_fc.bias." + str(layer_idx)),
         )
         return input_layer_fc_out
 
@@ -479,6 +479,6 @@ class Model(ModelBase):
             num_flatten_dims=2,
             act=self.act,
             param_attr=fluid.ParamAttr(
-                name="cls.concat_fc.weight."+str(layer_idx)),
-            bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias."+str(layer_idx)))
+                name="cls.concat_fc.weight." + str(layer_idx)),
+            bias_attr=fluid.ParamAttr(name="cls.concat_fc.bias." + str(layer_idx)))
         return hidden_states_fc

models/treebased/tdm/tdm_evaluate_reader.py

@@ -28,6 +28,7 @@ class EvaluateReader(Reader):
         """
         Read the data line by line and process it as a dictionary
         """
+
         def reader():
             """
             This function needs to be implemented by the user, based on data format

models/treebased/tdm/tdm_reader.py

@@ -28,6 +28,7 @@ class TrainReader(Reader):
         """
         Read the data line by line and process it as a dictionary
         """
+
         def reader():
             """
             This function needs to be implemented by the user, based on data format

setup.py

@@ -36,7 +36,7 @@ about["__author__"] = "paddle-dev"
 about["__author_email__"] = "paddle-dev@baidu.com"
 about["__url__"] = "https://github.com/PaddlePaddle/PaddleRec"
 
-readme = "..."
+readme = ""
 
 
 def run_cmd(command):