Merge branch 'develop' of http://gitlab.baidu.com/tangwei12/paddlerec into develop

core/engine/cluster/cloud/cluster.sh

 #!/bin/bash
+# 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.
+
 
 ###################################################
 # Usage: submit.sh

core/engine/cluster/cluster.py

@@ -15,10 +15,9 @@
 from __future__ import print_function
 from __future__ import unicode_literals
 
-import subprocess
-import sys
-import os
 import copy
+import os
+import subprocess
 
 from paddlerec.core.engine.engine import Engine
 from paddlerec.core.factory import TrainerFactory

core/engine/engine.py

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

core/engine/local_cluster.py

@@ -14,10 +14,11 @@
 
 from __future__ import print_function
 from __future__ import unicode_literals
-import subprocess
-import sys
-import os
+
 import copy
+import os
+import sys
+import subprocess
 
 from paddlerec.core.engine.engine import Engine
 from paddlerec.core.utils import envs

core/engine/local_mpi.py

@@ -14,10 +14,11 @@
 
 from __future__ import print_function
 from __future__ import unicode_literals
-import subprocess
-import sys
-import os
+
 import copy
+import os
+import sys
+import subprocess
 
 from paddlerec.core.engine.engine import Engine
 

core/layer.py

@@ -25,17 +25,8 @@ class Layer(object):
         """
         pass
 
-    def generate(self, mode, param):
-        """R
-        """
-        if mode == 'fluid':
-            return self.generate_fluid(param)
-
-        print('unsupport this mode: ' + mode)
-        return None, None
-
     @abc.abstractmethod
-    def generate_fluid(self, param):
+    def generate(self, param):
         """R
         """
         pass

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

@@ -13,8 +13,10 @@
 # limitations under the License.
 
 import math
+
 import numpy as np
 import paddle.fluid as fluid
+
 from paddlerec.core.metric import Metric
 
 
@@ -198,7 +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/model.py

@@ -47,7 +47,7 @@ class Model(object):
     def get_infer_results(self):
         return self._infer_results
 
-    def get_cost_op(self):
+    def get_avg_cost(self):
         """R
         """
         return self._cost

core/modules/modul/build.py

@@ -12,10 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import yaml
 import copy
+
 import paddle.fluid as fluid
 from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
+import yaml
 
 from paddlerec.core.model import Model
 from paddlerec.core.utils import table

core/modules/modul/layers.py

@@ -13,10 +13,11 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
+
 from paddlerec.core.layer import Layer
 
 
-class EmbeddingInputLayer(Layer):
+class EmbeddingFuseLayer(Layer):
     """R
     """
 
@@ -31,7 +32,7 @@ class EmbeddingInputLayer(Layer):
         self._emb_dim = self._mf_dim + 3  # append show ctr lr
         self._emb_layers = []
 
-    def generate_fluid(self, param):
+    def generate(self, param):
         """R
         """
         show_clk = fluid.layers.concat(
@@ -63,7 +64,7 @@ class LabelInputLayer(Layer):
         self._data_type = config.get('data_type', "int64")
         self._label_idx = config['label_idx']
 
-    def generate_fluid(self, param):
+    def generate(self, param):
         """R
         """
         label = fluid.layers.data(name=self._name, shape=[-1, self._dim], \
@@ -85,7 +86,7 @@ class TagInputLayer(Layer):
         self._dim = config.get('dim', 1)
         self._data_type = config['data_type']
 
-    def generate_fluid(self, param):
+    def generate(self, param):
         """R
         """
         output = fluid.layers.data(name=self._name, shape=[-1, self._dim], \
@@ -107,7 +108,7 @@ class ParamLayer(Layer):
         self._data_type = config.get('data_type', 'float32')
         self._config = config
 
-    def generate_fluid(self, param):
+    def generate(self, param):
         """R
         """
         return self._config, {'inference_param': {'name': 'param', 'params': [], 'table_id': self._table_id}}
@@ -125,7 +126,7 @@ class SummaryLayer(Layer):
         self._data_type = config.get('data_type', 'float32')
         self._config = config
 
-    def generate_fluid(self, param):
+    def generate(self, param):
         """R
         """
         return self._config, {'inference_param': {'name': 'summary', 'params': [], 'table_id': self._table_id}}
@@ -143,7 +144,7 @@ class NormalizetionLayer(Layer):
         self._summary = config['summary']
         self._table_id = config.get('table_id', -1)
 
-    def generate_fluid(self, param):
+    def generate(self, param):
         """R
         """
         input_layer = param['layer'][self._input[0]]
@@ -158,7 +159,7 @@ class NormalizetionLayer(Layer):
                                         'params': inference_param, 'table_id': summary_layer.get('table_id', -1)}}
 
 
-class NeuralLayer(Layer):
+class FCLayer(Layer):
     """R
     """
 
@@ -171,7 +172,7 @@ class NeuralLayer(Layer):
         self._bias = config.get('bias', True)
         self._act_func = config.get('act_func', None)
 
-    def generate_fluid(self, param):
+    def generate(self, param):
         """R
         """
         param_layer = param['layer'][self._param]
@@ -199,7 +200,7 @@ class NeuralLayer(Layer):
                                         'table_id': param_layer.get('table_id', -1)}}
 
 
-class SigmoidLossLayer(Layer):
+class LogLossLayer(Layer):
     """R
     """
 
@@ -230,7 +231,7 @@ class SigmoidLossLayer(Layer):
             }
         }
 
-    def generate_fluid(self, param):
+    def generate(self, param):
         """R
         """
         input_layer = param['layer'][self._input[0]]

core/trainer.py

@@ -12,14 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
-import sys
-
 import abc
+import os
 import time
+import sys
 import yaml
 
 from paddle import fluid
+
 from paddlerec.core.utils import envs
 
 

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/cluster_trainer.py

@@ -25,7 +25,6 @@ import paddle.fluid as fluid
 from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
 from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
 from paddle.fluid.incubate.fleet.base.role_maker import PaddleCloudRoleMaker
-from paddle.fluid.incubate.fleet.base.role_maker import MPISymetricRoleMaker
 
 from paddlerec.core.utils import envs
 from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
@@ -83,7 +82,7 @@ class ClusterTrainer(TranspileTrainer):
 
         strategy = self.build_strategy()
         optimizer = fleet.distributed_optimizer(optimizer, strategy)
-        optimizer.minimize(self.model.get_cost_op())
+        optimizer.minimize(self.model.get_avg_cost())
 
         if fleet.is_server():
             context['status'] = 'server_pass'
@@ -115,7 +114,7 @@ class ClusterTrainer(TranspileTrainer):
 
         program = fluid.compiler.CompiledProgram(
             fleet.main_program).with_data_parallel(
-            loss_name=self.model.get_cost_op().name,
+            loss_name=self.model.get_avg_cost().name,
             build_strategy=self.strategy.get_build_strategy(),
             exec_strategy=self.strategy.get_execute_strategy())
 

core/trainers/ctr_coding_trainer.py

@@ -11,9 +11,10 @@
 # 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.
+
 import os
-import numpy as np
 
+import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
 from paddle.fluid.incubate.fleet.base.role_maker import MPISymetricRoleMaker
@@ -22,7 +23,7 @@ from paddlerec.core.utils import envs
 from paddlerec.core.trainer import Trainer
 
 
-class CtrPaddleTrainer(Trainer):
+class CtrTrainer(Trainer):
     """R
     """
 
@@ -87,7 +88,7 @@ class CtrPaddleTrainer(Trainer):
         optimizer = self.model.optimizer()
 
         optimizer = fleet.distributed_optimizer(optimizer, strategy={"use_cvm": False})
-        optimizer.minimize(self.model.get_cost_op())
+        optimizer.minimize(self.model.get_avg_cost())
 
         if fleet.is_server():
             context['status'] = 'server_pass'

core/trainers/ctr_modul_trainer.py

@@ -13,12 +13,12 @@
 # limitations under the License.
 
 
+import datetime
+import json
 import sys
 import time
-import json
-import datetime
-import numpy as np
 
+import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.incubate.fleet.parameter_server.pslib import fleet
 from paddle.fluid.incubate.fleet.base.role_maker import GeneralRoleMaker
@@ -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
     """
 
@@ -129,7 +129,7 @@ class CtrPaddleTrainer(Trainer):
             model = self._exector_context[executor['name']]['model']
             self._metrics.update(model.get_metrics())
             runnnable_scope.append(scope)
-            runnnable_cost_op.append(model.get_cost_op())
+            runnnable_cost_op.append(model.get_avg_cost())
             for var in model._data_var:
                 if var.name in data_var_name_dict:
                     continue
@@ -146,7 +146,7 @@ class CtrPaddleTrainer(Trainer):
             model = self._exector_context[executor['name']]['model']
             program = model._build_param['model']['train_program']
             if not executor['is_update_sparse']:
-                program._fleet_opt["program_configs"][str(id(model.get_cost_op().block.program))]["push_sparse"] = []
+                program._fleet_opt["program_configs"][str(id(model.get_avg_cost().block.program))]["push_sparse"] = []
             if 'train_thread_num' not in executor:
                 executor['train_thread_num'] = self.global_config['train_thread_num']
             with fluid.scope_guard(scope):

core/trainers/online_learning_trainer.py

@@ -18,9 +18,9 @@ Training use fluid with one node only.
 
 from __future__ import print_function
 
+import datetime
 import os
 import time
-import datetime
 
 import paddle.fluid as fluid
 from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
@@ -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)
@@ -78,7 +78,7 @@ class ClusterTrainer(TranspileTrainer):
         optimizer = self.model.optimizer()
         strategy = self.build_strategy()
         optimizer = fleet.distributed_optimizer(optimizer, strategy)
-        optimizer.minimize(self.model.get_cost_op())
+        optimizer.minimize(self.model.get_avg_cost())
 
         if fleet.is_server():
             context['status'] = 'server_pass'

core/trainers/single_trainer.py

@@ -17,14 +17,14 @@ Training use fluid with one node only.
 """
 
 from __future__ import print_function
-import logging
+
 import time
+import logging
 
 import paddle.fluid as fluid
 
 from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
 from paddlerec.core.utils import envs
-import numpy as np
 
 logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger("fluid")
@@ -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)
@@ -47,7 +48,7 @@ class SingleTrainer(TranspileTrainer):
     def init(self, context):
         self.model.train_net()
         optimizer = self.model.optimizer()
-        optimizer.minimize((self.model.get_cost_op()))
+        optimizer.minimize((self.model.get_avg_cost()))
 
         self.fetch_vars = []
         self.fetch_alias = []
@@ -74,7 +75,7 @@ class SingleTrainer(TranspileTrainer):
 
         program = fluid.compiler.CompiledProgram(
             fluid.default_main_program()).with_data_parallel(
-            loss_name=self.model.get_cost_op().name)
+            loss_name=self.model.get_avg_cost().name)
 
         metrics_varnames = []
         metrics_format = []
@@ -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

@@ -17,17 +17,16 @@ Training use fluid with one node only.
 """
 
 from __future__ import print_function
+
 import logging
+
 import numpy as np
 import paddle.fluid as fluid
 from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler import fleet
-from paddle.fluid.incubate.fleet.parameter_server.distribute_transpiler.distributed_strategy import StrategyFactory
-from paddle.fluid.incubate.fleet.base.role_maker import PaddleCloudRoleMaker
 
 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

@@ -18,12 +18,11 @@ Training use fluid with one node only.
 
 from __future__ import print_function
 import logging
-import paddle.fluid as fluid
 
-from paddlerec.core.trainers.transpiler_trainer import TranspileTrainer
+import numpy as np
+import paddle.fluid as fluid
 from paddlerec.core.trainers.single_trainer import SingleTrainer
 from paddlerec.core.utils import envs
-import numpy as np
 
 logging.basicConfig(format="%(asctime)s - %(levelname)s - %(message)s")
 logger = logging.getLogger("fluid")

core/utils/dataloader_instance.py

@@ -14,7 +14,6 @@
 from __future__ import print_function
 
 import os
-import sys
 
 from paddlerec.core.utils.envs import lazy_instance_by_fliename
 from paddlerec.core.utils.envs import get_global_env

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

@@ -13,8 +13,8 @@
 # limitations under the License.
 
 import abc
-import time
 import datetime
+import time
 
 import paddle.fluid as fluid
 
@@ -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/dataset_instance.py

@@ -11,6 +11,7 @@
 # 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.
+
 from __future__ import print_function
 import sys
 

core/utils/envs.py

@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
+from contextlib import closing
 import copy
-import sys
+import os
 import socket
-from contextlib import closing
+import sys
 
 global_envs = {}
 

core/utils/fs.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import os
+
 from paddle.fluid.incubate.fleet.utils.hdfs import HDFSClient
 
 
@@ -94,11 +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']
@@ -131,7 +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")

core/utils/table.py

@@ -12,9 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import copy
-import yaml
-
 
 class TableMeta(object):
     """

core/utils/util.py

@@ -12,11 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import datetime
 import os
 import time
-import datetime
 
 from paddle import fluid
+
 from paddlerec.core.utils import fs as fs
 
 

doc/design.md

@@ -153,7 +153,7 @@ class Model(object):
     def infer_net(self):
         pass
 
-    def get_cost_op(self):
+    def get_avg_cost(self):
     return self._cost
 
 ```

models/contentunderstanding/classification/model.py

@@ -13,15 +13,9 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
-import math
 
-from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase
 
-import paddle.fluid as fluid
-import paddle.fluid.layers.nn as nn
-import paddle.fluid.layers.tensor as tensor
-import paddle.fluid.layers.control_flow as cf
 
 class Model(ModelBase):
     def __init__(self, config):
@@ -65,7 +59,7 @@ class Model(ModelBase):
         self.cost = avg_cost
         self._metrics["acc"] = acc
 
-    def get_cost_op(self):
+    def get_avg_cost(self):
         return self.cost
 
     def get_metrics(self):

models/contentunderstanding/classification/reader.py

@@ -12,20 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
+
 import sys
-import collections
-import os
-import six
-import time
-import numpy as np
-import paddle.fluid as fluid
-import paddle
-import csv
-import io
 
 from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
+
 
 class TrainReader(Reader):
     def init(self):
@@ -47,6 +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

@@ -12,30 +12,27 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import paddle.fluid as fluid
-import math
-
-from paddlerec.core.utils import envs
-from paddlerec.core.model import Model as ModelBase
-
 import paddle.fluid as fluid
 import paddle.fluid.layers.nn as nn
 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):
     def __init__(self, config):
         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 """
@@ -92,18 +89,16 @@ class Model(ModelBase):
         self.metrics["correct"] = correct
         self.metrics["cos_pos"] = cos_pos
 
-    def get_cost_op(self):
+    def get_avg_cost(self):
         return self.cost
 
     def get_metrics(self):
         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

@@ -12,20 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import re
+
 import sys
-import collections
-import os
-import six
-import time
+
 import numpy as np
-import paddle.fluid as fluid
-import paddle
-import csv
-import io
 
 from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
+
 
 class TrainReader(Reader):
     def init(self):
@@ -54,9 +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):
@@ -66,4 +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

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import math
 import paddle.fluid as fluid
 
 from paddlerec.core.utils import envs
@@ -29,7 +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:
@@ -39,7 +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)
@@ -47,7 +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],
@@ -66,12 +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_evaluate_reader.py

@@ -14,7 +14,6 @@
 from __future__ import print_function
 
 from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 
 
 class EvaluateReader(Reader):

models/match/dssm/synthetic_reader.py

@@ -11,10 +11,10 @@
 # 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.
+
 from __future__ import print_function
 
 from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 
 
 class TrainReader(Reader):
@@ -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/data_process.sh

 #! /bin/bash
 
+# 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.
+
+
 set -e
 echo "begin to prepare data"
 

models/match/multiview-simnet/evaluate_reader.py

@@ -11,10 +11,7 @@
 # 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.
-import numpy as np
-import io
-import copy
-import random
+
 from paddlerec.core.reader import Reader
 from paddlerec.core.utils import envs
 
@@ -54,4 +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

@@ -12,16 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy as np
-import math
 import paddle.fluid as fluid
-import paddle.fluid.layers as layers
 import paddle.fluid.layers.tensor as tensor
 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 """
 
@@ -97,6 +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)
@@ -143,7 +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

@@ -11,10 +11,7 @@
 # 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.
-import numpy as np
-import io
-import copy
-import random
+
 from paddlerec.core.reader import Reader
 from paddlerec.core.utils import envs
 
@@ -57,4 +54,5 @@ class TrainReader(Reader):
                 else:
                     output[index][1].append(padding)
             yield output
+
         return data_iter

models/multitask/esmm/esmm_infer_reader.py

@@ -13,19 +13,19 @@
 # limitations under the License.
 from __future__ import print_function
 
-from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 from collections import defaultdict
-import numpy as np
+
+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

@@ -11,21 +11,22 @@
 # 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.
+
 from __future__ import print_function
 
-from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 from collections import defaultdict
-import numpy as np
+
+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

@@ -12,25 +12,25 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import math
+import numpy as np
 import paddle.fluid as fluid
 
 from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase
-import numpy as np
 
 
 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))
 
@@ -38,13 +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")
@@ -63,10 +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)
 
@@ -79,7 +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]
@@ -88,7 +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)
@@ -98,25 +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_infer_reader.py

@@ -11,11 +11,10 @@
 # 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.
+
 from __future__ import print_function
 
 from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
-import numpy as np
 
 
 class EvaluateReader(Reader):

models/multitask/mmoe/census_reader.py

@@ -11,11 +11,10 @@
 # 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.
+
 from __future__ import print_function
 
 from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
-import numpy as np
 
 
 class TrainReader(Reader):
@@ -44,8 +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

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import math
 import paddle.fluid as fluid
 
 from paddlerec.core.utils import envs
@@ -50,8 +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 = []
@@ -79,19 +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)
@@ -104,10 +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_infer_reader.py

@@ -11,11 +11,10 @@
 # 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.
+
 from __future__ import print_function
 
 from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
-import numpy as np
 
 
 class EvaluateReader(Reader):

models/multitask/share-bottom/census_reader.py

@@ -11,11 +11,10 @@
 # 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.
+
 from __future__ import print_function
 
 from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
-import numpy as np
 
 
 class TrainReader(Reader):
@@ -44,8 +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

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import math
 import paddle.fluid as fluid
 
 from paddlerec.core.utils import envs
@@ -47,7 +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):
@@ -61,23 +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)
@@ -88,10 +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/criteo_reader.py

@@ -11,6 +11,7 @@
 # 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.
+
 from __future__ import print_function
 
 from paddlerec.core.reader import Reader

models/rank/dcn/criteo_reader.py

@@ -11,18 +11,19 @@
 # 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.
+
 from __future__ import print_function
 import math
-import sys
+import os
 
-from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 try:
     import cPickle as pickle
 except ImportError:
     import pickle
-from collections import Counter
-import os
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+
 
 class TrainReader(Reader):
     def init(self):
@@ -83,6 +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

@@ -12,12 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from collections import OrderedDict
+
 import paddle.fluid as fluid
-import math
 
 from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase
-from collections import OrderedDict
+
 
 class Model(ModelBase):
     def __init__(self, config):
@@ -141,7 +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

@@ -11,15 +11,18 @@
 # 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.
+
 from __future__ import print_function
 
-from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 try:
     import cPickle as pickle
 except ImportError:
     import pickle
 
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+
+
 class TrainReader(Reader):
     def init(self):
         self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
@@ -64,6 +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

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import paddle.fluid as fluid
 import math
 
+import paddle.fluid as fluid
+
 from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase
 
@@ -32,10 +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 --------------------------
@@ -47,7 +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(
@@ -65,7 +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,
@@ -99,8 +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)
@@ -128,21 +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/model.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
-import math
 
 from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase

models/rank/din/reader.py

@@ -13,16 +13,20 @@
 # limitations under the License.
 from __future__ import print_function
 
-from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
-import numpy as np
 import os
 import random
+
 try:
     import cPickle as pickle
 except ImportError:
     import pickle
 
+import numpy as np
+
+from paddlerec.core.reader import Reader
+from paddlerec.core.utils import envs
+
+
 class TrainReader(Reader):
     def init(self):
         self.train_data_path = envs.get_global_env("train_data_path", None, "train.reader")
@@ -43,9 +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()
@@ -54,13 +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
@@ -127,5 +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)
-        
-        

models/rank/dnn/model.py

@@ -13,6 +13,7 @@
 # limitations under the License.
 
 import math
+
 import paddle.fluid as fluid
 
 from paddlerec.core.utils import envs

models/rank/wide_deep/model.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import paddle.fluid as fluid
 import math
 
+import paddle.fluid as fluid
+
 from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase
 
@@ -26,8 +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
@@ -35,7 +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)
 
@@ -64,13 +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

@@ -11,15 +11,17 @@
 # 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.
+
 from __future__ import print_function
 
-from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 try:
     import cPickle as pickle
 except ImportError:
     import pickle
 
+from paddlerec.core.reader import Reader
+
+
 class TrainReader(Reader):
     def init(self):
         pass
@@ -28,7 +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]
 
@@ -36,6 +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/rank/xdeepfm/criteo_reader.py

@@ -11,15 +11,17 @@
 # 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.
+
 from __future__ import print_function
 
-from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 try:
     import cPickle as pickle
 except ImportError:
     import pickle
 
+from paddlerec.core.reader import Reader
+
+
 class TrainReader(Reader):
     def init(self):
         pass

models/rank/xdeepfm/model.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 
 import paddle.fluid as fluid
-import math
 
 from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase

models/recall/gnn/data_process.sh

 #! /bin/bash
 
+# 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.
+
 set -e
 echo "begin to download data"
 

models/recall/gnn/evaluate_reader.py

@@ -11,10 +11,12 @@
 # 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.
-import numpy as np
-import io
+
 import copy
 import random
+
+import numpy as np
+
 from paddlerec.core.reader import Reader
 from paddlerec.core.utils import envs
 
@@ -120,8 +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):
@@ -132,4 +135,5 @@ class EvaluateReader(Reader):
     def generate_sample(self, line):
         def data_iter():
             yield []
+
         return data_iter

models/recall/gnn/model.py

@@ -12,8 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import numpy as np
 import math
+import numpy as np
+
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 
@@ -28,13 +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")
@@ -47,31 +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
@@ -113,7 +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",
@@ -123,10 +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)
 
@@ -154,7 +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",
@@ -164,21 +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,
@@ -189,13 +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",
@@ -203,7 +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],
@@ -220,13 +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

@@ -11,10 +11,12 @@
 # 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.
-import numpy as np
-import io
+
 import copy
 import random
+
+import numpy as np
+
 from paddlerec.core.reader import Reader
 from paddlerec.core.utils import envs
 
@@ -120,8 +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):
@@ -132,4 +135,5 @@ class TrainReader(Reader):
     def generate_sample(self, line):
         def data_iter():
             yield []
+
         return data_iter

models/recall/gru4rec/model.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import math
 import paddle.fluid as fluid
 
 from paddlerec.core.utils import envs
@@ -87,10 +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/gru4rec/rsc15_infer_reader.py

@@ -11,10 +11,10 @@
 # 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.
+
 from __future__ import print_function
 
 from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 
 
 class EvaluateReader(Reader):

models/recall/gru4rec/rsc15_reader.py

@@ -11,10 +11,10 @@
 # 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.
+
 from __future__ import print_function
 
 from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 
 
 class TrainReader(Reader):

models/recall/ssr/model.py

@@ -12,15 +12,12 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import math
 import paddle.fluid as fluid
-
-from paddlerec.core.utils import envs
-from paddlerec.core.model import Model as ModelBase
 import paddle.fluid.layers.tensor as tensor
-import paddle.fluid.layers.io as io
 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):
@@ -54,6 +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
@@ -70,6 +68,7 @@ class PairwiseHingeLoss(object):
             loss_part2)
         return loss_part3
 
+
 class Model(ModelBase):
     def __init__(self, config):
         ModelBase.__init__(self, config)
@@ -80,7 +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)
@@ -129,11 +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)
@@ -173,6 +169,5 @@ class Model(ModelBase):
 
         self._infer_results['recall20'] = acc
 
-
     def infer_net(self):
         self.infer()

models/recall/ssr/ssr_infer_reader.py

@@ -11,19 +11,19 @@
 # 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.
+
 from __future__ import print_function
 
+import numpy as np
+
 from paddlerec.core.reader import Reader
 from paddlerec.core.utils import envs
-import random
-import numpy as np
 
 
 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
@@ -39,6 +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

@@ -11,12 +11,13 @@
 # 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.
+
 from __future__ import print_function
 
-from paddlerec.core.reader import Reader
-from paddlerec.core.utils import envs
 import random
 
+from paddlerec.core.reader import Reader
+
 
 class TrainReader(Reader):
     def init(self):
@@ -25,7 +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/model.py

@@ -12,7 +12,6 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import math
 import numpy as np
 import paddle.fluid as fluid
 

models/recall/word2vec/prepare_data.sh

 #! /bin/bash
 
+# 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.
+
+
 # download train_data
 mkdir raw_data
 wget --no-check-certificate https://paddlerec.bj.bcebos.com/word2vec/1-billion-word-language-modeling-benchmark-r13output.tar

models/recall/word2vec/preprocess.py

@@ -13,13 +13,15 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import io
+import math
 import os
 import random
 import re
 import six
+
 import argparse
-import io
-import math
+
 prog = re.compile("[^a-z ]", flags=0)
 
 
@@ -73,7 +75,7 @@ def parse_args():
 
 
 def text_strip(text):
-    #English Preprocess Rule
+    # English Preprocess Rule
     return prog.sub("", text.lower())
 
 
@@ -115,7 +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])
@@ -125,13 +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):
@@ -200,7 +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])
@@ -228,12 +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

@@ -11,9 +11,11 @@
 # 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.
-import numpy as np
+
 import io
+
 import six
+
 from paddlerec.core.reader import Reader
 from paddlerec.core.utils import envs
 
@@ -47,18 +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.
@@ -76,5 +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

@@ -11,8 +11,11 @@
 # 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.
-import numpy as np
+
 import io
+
+import numpy as np
+
 from paddlerec.core.reader import Reader
 from paddlerec.core.utils import envs
 
@@ -84,7 +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

@@ -14,8 +14,8 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
+
 import paddle.fluid as fluid
-import math
 
 from paddlerec.core.utils import envs
 from paddlerec.core.model import Model as ModelBase
@@ -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

run.py

@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import argparse
 import os
 import subprocess
-import tempfile
 
+import argparse
+import tempfile
 import yaml
 
 from paddlerec.core.factory import TrainerFactory

setup.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.
+
 """
 setup for paddle-rec.
 """
+
 import os
+
 from setuptools import setup, find_packages
-import tempfile
 import shutil
+import tempfile
+
 
 requires = [
     "paddlepaddle == 1.7.2",
@@ -19,7 +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):

tools/tools.py

@@ -12,15 +12,16 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import functools
 import os
-import time
+import platform
+import sys
 import shutil
+import time
+
 import requests
-import sys
 import tarfile
 import zipfile
-import platform
-import functools
 
 lasttime = time.time()
 FLUSH_INTERVAL = 0.1