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PaddleRec
未验证 提交 0ede1313 编写于 作者: C Chengmo 提交者： GitHub
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Merge branch 'master' into doc_v8

上级 015187c8 dfb74b32
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Showing with 2130 addition and 10 deletion
core/utils/envs.py
浏览文件 @ 0ede1313
......@@ -13,6 +13,7 @@
# limitations under the License.
from contextlib import closing
import yaml
import copy
import os
import socket
......
core/utils/validation.py
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......@@ -120,9 +120,7 @@ def register():
validations["train.engine"] = ValueFormat(
"str", ["single", "local_cluster", "cluster"], in_value_handler)
requires = [
"train.namespace", "train.device", "train.epochs", "train.engine"
]
requires = ["workspace", "dataset", "mode", "runner", "phase"]
return validations, requires
......
models/rank/afm/__init__.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
models/rank/afm/config.yaml 0 → 100755
浏览文件 @ 0ede1313
# 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.
# global settings
debug: false
workspace: "paddlerec.models.rank.afm"
dataset:
- name: train_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/../dataset/Criteo_data/sample_data/train"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
- name: infer_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/../dataset/Criteo_data/sample_data/train"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
# 用户自定义配置
hyper_parameters:
optimizer:
class: Adam
learning_rate: 0.0001
sparse_feature_number: 1086460
sparse_feature_dim: 16
is_sparse: False
reg: 0.001
num_field: 39
act: "relu"
hidden1_attention_size: 16
mode: train_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"
runner:
- name: train_runner
trainer_class: single_train
epochs: 1
device: cpu
init_model_path: ""
save_checkpoint_interval: 1
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 1
- name: infer_runner
trainer_class: single_infer
epochs: 1
device: cpu
init_model_path: "increment/0"
print_interval: 1
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: train_sample
thread_num: 1
#- name: infer_phase
# model: "{workspace}/model.py"
# dataset_name: infer_sample
# thread_num: 1
models/rank/afm/model.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import math
from collections import OrderedDict
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.is_distributed = True if envs.get_trainer(
) == "CtrTrainer" else False
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None)
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None)
self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
False)
self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
self.num_field = envs.get_global_env("hyper_parameters.num_field",
None)
self.hidden1_attention_size = envs.get_global_env(
"hyper_parameters.hidden1_attention_size", 16)
self.attention_act = envs.get_global_env("hyper_parameters.act",
"relu")
def net(self, inputs, is_infer=False):
raw_feat_idx = self._sparse_data_var[1] # (batch_size * num_field) * 1
raw_feat_value = self._dense_data_var[0] # batch_size * num_field
self.label = self._sparse_data_var[0] # batch_size * 1
init_value_ = 0.1
feat_idx = raw_feat_idx
feat_value = fluid.layers.reshape(
raw_feat_value,
[-1, self.num_field, 1]) # batch_size * num_field * 1
# ------------------------- first order term --------------------------
first_weights_re = fluid.embedding(
input=feat_idx,
is_sparse=self.is_sparse,
is_distributed=self.is_distributed,
dtype='float32',
size=[self.sparse_feature_number + 1, 1],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_),
regularizer=fluid.regularizer.L1DecayRegularizer(self.reg))
) # (batch_size * num_field) * 1 * 1(embedding_size)
first_weights = fluid.layers.reshape(
first_weights_re,
shape=[-1, self.num_field, 1]) # batch_size * num_field * 1
y_first_order = fluid.layers.reduce_sum((first_weights * feat_value),
1) # batch_size * 1
# ------------------------- Pair-wise Interaction Layer --------------------------
feat_embeddings_re = fluid.embedding(
input=feat_idx,
is_sparse=self.is_sparse,
is_distributed=self.is_distributed,
dtype='float32',
size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0,
scale=init_value_ /
math.sqrt(float(self.sparse_feature_dim))))
) # (batch_size * num_field) * 1 * embedding_size
feat_embeddings = fluid.layers.reshape(
feat_embeddings_re,
shape=[-1, self.num_field, self.sparse_feature_dim
]) # batch_size * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # batch_size * num_field * embedding_size
element_wise_product_list = []
for i in range(self.num_field):
for j in range(i + 1, self.num_field):
element_wise_product_list.append(
feat_embeddings[:, i, :] *
feat_embeddings[:,
j, :]) # list(batch_size * embedding_size)
stack_element_wise_product = fluid.layers.stack(
element_wise_product_list,
axis=0) # (num_field*(num_field-1)/2) * batch_size * embedding_size
stack_element_wise_product = fluid.layers.transpose(
stack_element_wise_product, perm=[1, 0, 2]
) # batch_size * (num_field*(num_field-1)/2) * embedding_size
# ------------------------- Attention-based Pooling --------------------------
attetion_mul = fluid.layers.fc(
input=fluid.layers.reshape(
stack_element_wise_product,
shape=[-1, self.sparse_feature_dim]),
size=self.hidden1_attention_size,
act=self.attention_act,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_))
) # (batch_size * (num_field*(num_field-1)/2)) * hidden1_attention_size
attention_h = fluid.layers.create_parameter(
shape=[self.hidden1_attention_size, 1], dtype="float32")
attention_out = fluid.layers.matmul(
attetion_mul,
attention_h) # (batch_size * (num_field*(num_field-1)/2)) * 1
attention_out = fluid.layers.softmax(
attention_out) # (batch_size * (num_field*(num_field-1)/2)) * 1
num_interactions = self.num_field * (self.num_field - 1) / 2
attention_out = fluid.layers.reshape(
attention_out,
shape=[-1, num_interactions,
1]) # batch_size * (num_field*(num_field-1)/2) * 1
attention_pooling = fluid.layers.matmul(
attention_out, stack_element_wise_product,
transpose_x=True) # batch_size * 1 * embedding_size
attention_pooling = fluid.layers.reshape(
attention_pooling,
shape=[-1, self.sparse_feature_dim]) # batch_size * embedding_size
y_AFM = fluid.layers.fc(
input=attention_pooling,
size=1,
act=None,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_))) # batch_size * 1
# ------------------------- Predict --------------------------
self.predict = fluid.layers.sigmoid(y_first_order + y_AFM)
cost = fluid.layers.log_loss(
input=self.predict, label=fluid.layers.cast(self.label,
"float32")) # log_loss
avg_cost = fluid.layers.reduce_sum(cost)
self._cost = avg_cost
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
label=label_int,
slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
self._infer_results["AUC"] = auc_var
models/rank/dataset/Criteo_data/download.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import os
import sys
import io
LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
sys.path.append(TOOLS_PATH)
from paddlerec.tools.tools import download_file_and_uncompress
if __name__ == '__main__':
trainfile = 'train.txt'
url = "https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz"
print("download and extract starting...")
download_file_and_uncompress(url)
print("download and extract finished")
count = 0
for _ in io.open(trainfile, 'r', encoding='utf-8'):
count += 1
print("total records: %d" % count)
print("done")
models/rank/dataset/Criteo_data/download_preprocess.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import os
import shutil
import sys
LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
sys.path.append(TOOLS_PATH)
from paddlerec.tools.tools import download_file_and_uncompress, download_file
if __name__ == '__main__':
url = "https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz"
url2 = "https://paddlerec.bj.bcebos.com/deepfm%2Ffeat_dict_10.pkl2"
print("download and extract starting...")
download_file_and_uncompress(url)
download_file(url2, "./sample_data/feat_dict_10.pkl2", True)
print("download and extract finished")
print("preprocessing...")
os.system("python preprocess.py")
print("preprocess done")
shutil.rmtree("raw_data")
print("done")
models/rank/dataset/Criteo_data/get_slot_data.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import yaml, os
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
import paddle.fluid.incubate.data_generator as dg
try:
import cPickle as pickle
except ImportError:
import pickle
class TrainReader(dg.MultiSlotDataGenerator):
def __init__(self, config):
dg.MultiSlotDataGenerator.__init__(self)
if os.path.isfile(config):
with open(config, 'r') as rb:
_config = yaml.load(rb.read(), Loader=yaml.FullLoader)
else:
raise ValueError("reader config only support yaml")
def init(self):
self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.cont_max_ = [
5775, 257675, 65535, 969, 23159456, 431037, 56311, 6047, 29019, 46,
231, 4008, 7393
]
self.cont_diff_ = [
self.cont_max_[i] - self.cont_min_[i]
for i in range(len(self.cont_min_))
]
self.continuous_range_ = range(1, 14)
self.categorical_range_ = range(14, 40)
# load preprocessed feature dict
self.feat_dict_name = "sample_data/feat_dict_10.pkl2"
self.feat_dict_ = pickle.load(open(self.feat_dict_name, 'rb'))
def _process_line(self, line):
features = line.rstrip('\n').split('\t')
feat_idx = []
feat_value = []
for idx in self.continuous_range_:
if features[idx] == '':
feat_idx.append(0)
feat_value.append(0.0)
else:
feat_idx.append(self.feat_dict_[idx])
feat_value.append(
(float(features[idx]) - self.cont_min_[idx - 1]) /
self.cont_diff_[idx - 1])
for idx in self.categorical_range_:
if features[idx] == '' or features[idx] not in self.feat_dict_:
feat_idx.append(0)
feat_value.append(0.0)
else:
feat_idx.append(self.feat_dict_[features[idx]])
feat_value.append(1.0)
label = [int(features[0])]
return feat_idx, feat_value, label
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)
s = ""
for i in [('feat_idx', feat_idx), ('feat_value', feat_value),
('label', label)]:
k = i[0]
v = i[1]
for j in v:
s += " " + k + ":" + str(j)
print s.strip()
yield None
return data_iter
reader = TrainReader("../config.yaml")
reader.init()
reader.run_from_stdin()
models/rank/dataset/Criteo_data/preprocess.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import os
import numpy
from collections import Counter
import shutil
import pickle
def get_raw_data():
if not os.path.isdir('raw_data'):
os.mkdir('raw_data')
fin = open('train.txt', 'r')
fout = open('raw_data/part-0', 'w')
for line_idx, line in enumerate(fin):
if line_idx % 200000 == 0 and line_idx != 0:
fout.close()
cur_part_idx = int(line_idx / 200000)
fout = open('raw_data/part-' + str(cur_part_idx), 'w')
fout.write(line)
fout.close()
fin.close()
def split_data():
split_rate_ = 0.9
dir_train_file_idx_ = 'aid_data/train_file_idx.txt'
filelist_ = [
'raw_data/part-%d' % x for x in range(len(os.listdir('raw_data')))
]
if not os.path.exists(dir_train_file_idx_):
train_file_idx = list(
numpy.random.choice(
len(filelist_), int(len(filelist_) * split_rate_), False))
with open(dir_train_file_idx_, 'w') as fout:
fout.write(str(train_file_idx))
else:
with open(dir_train_file_idx_, 'r') as fin:
train_file_idx = eval(fin.read())
for idx in range(len(filelist_)):
if idx in train_file_idx:
shutil.move(filelist_[idx], 'train_data')
else:
shutil.move(filelist_[idx], 'test_data')
def get_feat_dict():
freq_ = 10
dir_feat_dict_ = 'aid_data/feat_dict_' + str(freq_) + '.pkl2'
continuous_range_ = range(1, 14)
categorical_range_ = range(14, 40)
if not os.path.exists(dir_feat_dict_):
# print('generate a feature dict')
# Count the number of occurrences of discrete features
feat_cnt = Counter()
with open('train.txt', 'r') as fin:
for line_idx, line in enumerate(fin):
if line_idx % 100000 == 0:
print('generating feature dict', line_idx / 45000000)
features = line.rstrip('\n').split('\t')
for idx in categorical_range_:
if features[idx] == '': continue
feat_cnt.update([features[idx]])
# Only retain discrete features with high frequency
dis_feat_set = set()
for feat, ot in feat_cnt.items():
if ot >= freq_:
dis_feat_set.add(feat)
# Create a dictionary for continuous and discrete features
feat_dict = {}
tc = 1
# Continuous features
for idx in continuous_range_:
feat_dict[idx] = tc
tc += 1
for feat in dis_feat_set:
feat_dict[feat] = tc
tc += 1
# Save dictionary
with open(dir_feat_dict_, 'wb') as fout:
pickle.dump(feat_dict, fout, protocol=2)
print('args.num_feat ', len(feat_dict) + 1)
if __name__ == '__main__':
if not os.path.isdir('train_data'):
os.mkdir('train_data')
if not os.path.isdir('test_data'):
os.mkdir('test_data')
if not os.path.isdir('aid_data'):
os.mkdir('aid_data')
get_raw_data()
split_data()
get_feat_dict()
print('Done!')
models/rank/dataset/Criteo_data/run.sh 0 → 100644
浏览文件 @ 0ede1313
python download_preprocess.py
mkdir slot_train_data
for i in `ls ./train_data`
do
cat train_data/$i | python get_slot_data.py > slot_train_data/$i
done
mkdir slot_test_data
for i in `ls ./test_data`
do
cat test_data/$i | python get_slot_data.py > slot_test_data/$i
done
models/rank/dataset/Criteo_data/sample_data/train/sample_train.txt 0 → 100644
浏览文件 @ 0ede1313
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feat_idx:0 feat_idx:2 feat_idx:0 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:0 feat_idx:11 feat_idx:12 feat_idx:13 feat_idx:506931 feat_idx:123566 feat_idx:961529 feat_idx:810019 feat_idx:314332 feat_idx:615411 feat_idx:475867 feat_idx:795081 feat_idx:148475 feat_idx:697060 feat_idx:1069621 feat_idx:370551 feat_idx:696973 feat_idx:69630 feat_idx:396064 feat_idx:95177 feat_idx:854924 feat_idx:488825 feat_idx:0 feat_idx:0 feat_idx:581782 feat_idx:0 feat_idx:476211 feat_idx:289148 feat_idx:0 feat_idx:0 feat_value:0.0 feat_value:0.0066672358525 feat_value:0.0 feat_value:0.00103199174407 feat_value:0.000325784854359 feat_value:4.40797425743e-05 feat_value:0.000266377794747 feat_value:0.000165371258475 feat_value:0.00299803576967 feat_value:0.0 feat_value:0.030303030303 feat_value:0.0 feat_value:0.000135263086704 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 label:0
feat_idx:1 feat_idx:2 feat_idx:3 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:10 feat_idx:11 feat_idx:0 feat_idx:13 feat_idx:268086 feat_idx:704711 feat_idx:995515 feat_idx:139394 feat_idx:943087 feat_idx:0 feat_idx:546815 feat_idx:144963 feat_idx:148475 feat_idx:364765 feat_idx:552750 feat_idx:920037 feat_idx:816538 feat_idx:223357 feat_idx:790588 feat_idx:560935 feat_idx:13161 feat_idx:750233 feat_idx:734534 feat_idx:1047606 feat_idx:361734 feat_idx:0 feat_idx:122096 feat_idx:434883 feat_idx:502861 feat_idx:203213 feat_value:0.0 feat_value:0.000197921436832 feat_value:4.57770656901e-05 feat_value:0.00206398348813 feat_value:0.000625316933178 feat_value:0.000874634892132 feat_value:0.000142068157198 feat_value:0.000330742516951 feat_value:0.00975223129674 feat_value:0.0 feat_value:0.00865800865801 feat_value:0.0 feat_value:0.000270526173407 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 label:0
models/rank/dataset/__init__.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
models/rank/dcn/model.py
浏览文件 @ 0ede1313
......@@ -156,8 +156,3 @@ class Model(ModelBase):
l2_reg_cross_loss = self.l2_reg_cross * l2_reg_cross_loss
self.loss = self.avg_logloss + l2_reg_cross_loss
self._cost = self.loss
#def optimizer(self):
#
# optimizer = fluid.optimizer.Adam(self.learning_rate, lazy_mode=True)
# return optimizer
models/rank/deepfm/data/download_preprocess.py
浏览文件 @ 0ede1313
......@@ -28,7 +28,7 @@ if __name__ == '__main__':
print("download and extract starting...")
download_file_and_uncompress(url)
download_file(url2, "./aid_data/feat_dict_10.pkl2", True)
download_file(url2, "./sample_data/feat_dict_10.pkl2", True)
print("download and extract finished")
print("preprocessing...")
......
models/rank/ffm/__init__.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
models/rank/ffm/config.yaml 0 → 100755
浏览文件 @ 0ede1313
# 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.
# global settings
debug: false
workspace: "paddlerec.models.rank.fm"
dataset:
- name: train_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/../dataset/Criteo_data/sample_data/train"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
- name: infer_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/../dataset/Criteo_data/sample_data/train"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
hyper_parameters:
# 用户自定义配置
optimizer:
class: Adam
learning_rate: 0.0001
sparse_feature_number: 1086460
sparse_feature_dim: 9
is_sparse: False
reg: 0.001
num_field: 39
mode: train_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"
runner:
- name: train_runner
trainer_class: single_train
epochs: 1
device: cpu
init_model_path: ""
save_checkpoint_interval: 1
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 1
- name: infer_runner
trainer_class: single_infer
epochs: 1
device: cpu
init_model_path: "increment/0"
print_interval: 1
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: train_sample
thread_num: 1
#- name: infer_phase
# model: "{workspace}/model.py"
# dataset_name: infer_sample
# thread_num: 1
models/rank/ffm/model.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import math
from collections import OrderedDict
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.is_distributed = True if envs.get_trainer(
) == "CtrTrainer" else False
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None)
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None)
self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
self.num_field = envs.get_global_env("hyper_parameters.num_field",
None)
def net(self, inputs, is_infer=False):
raw_feat_idx = self._sparse_data_var[1] # (batch_size * num_field) * 1
raw_feat_value = self._dense_data_var[0] # batch_size * num_field
self.label = self._sparse_data_var[0] # batch_size * 1
init_value_ = 0.1
feat_idx = raw_feat_idx
feat_value = fluid.layers.reshape(
raw_feat_value,
[-1, self.num_field, 1]) # batch_size * num_field * 1
# ------------------------- first order term --------------------------
first_weights_re = fluid.embedding(
input=feat_idx,
is_sparse=True,
is_distributed=self.is_distributed,
dtype='float32',
size=[self.sparse_feature_number + 1, 1],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_),
regularizer=fluid.regularizer.L1DecayRegularizer(self.reg))
) # (batch_size * num_field) * 1 * 1(embedding_size)
first_weights = fluid.layers.reshape(
first_weights_re,
shape=[-1, self.num_field, 1]) # batch_size * num_field * 1
y_first_order = fluid.layers.reduce_sum((first_weights * feat_value),
1) # batch_size * 1
b_linear = fluid.layers.create_parameter(
shape=[1],
dtype='float32',
default_initializer=fluid.initializer.ConstantInitializer(
value=0)) # 1
# ------------------------- Field-aware second order term --------------------------
embedding_size_for_all_field = self.num_field * self.sparse_feature_dim
feat_embeddings_re = fluid.embedding(
input=feat_idx,
is_sparse=True,
is_distributed=self.is_distributed,
dtype='float32',
size=[
self.sparse_feature_number + 1, embedding_size_for_all_field
],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0,
scale=init_value_ /
math.sqrt(float(embedding_size_for_all_field))))
) # (batch_size * num_field) * 1 * embedding_size
feat_embeddings = fluid.layers.reshape(
feat_embeddings_re,
shape=[-1, self.num_field, embedding_size_for_all_field
]) # batch_size * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # batch_size * num_field * (embedding_size * num_field)
field_aware_feat_embedding = fluid.layers.reshape(
feat_embeddings,
shape=[
-1, self.num_field, self.num_field, self.sparse_feature_dim
])
field_aware_interaction_list = []
for i in range(self.num_field):
for j in range(i + 1, self.num_field):
field_aware_interaction_list.append(
fluid.layers.reduce_sum(
field_aware_feat_embedding[:, i, j, :] *
field_aware_feat_embedding[:, j, i, :],
dim=1,
keep_dim=True))
y_field_aware_second_order = fluid.layers.sum(
field_aware_interaction_list)
# ------------------------- Predict --------------------------
self.predict = fluid.layers.sigmoid(b_linear + y_first_order +
y_field_aware_second_order)
cost = fluid.layers.log_loss(
input=self.predict, label=fluid.layers.cast(self.label,
"float32")) # log_loss
avg_cost = fluid.layers.reduce_sum(cost)
self._cost = avg_cost
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
label=label_int,
slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
self._infer_results["AUC"] = auc_var
models/rank/fm/__init__.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
models/rank/fm/config.yaml 0 → 100755
浏览文件 @ 0ede1313
# 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.
# global settings
debug: false
workspace: "paddlerec.models.rank.fm"
dataset:
- name: train_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/../dataset/Criteo_data/sample_data/train"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
- name: infer_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/../dataset/Criteo_data/sample_data/train"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
hyper_parameters:
# 用户自定义配置
optimizer:
class: Adam
learning_rate: 0.0001
sparse_feature_number: 1086460
sparse_feature_dim: 9
is_sparse: False
reg: 0.001
num_field: 39
mode: train_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"
runner:
- name: train_runner
trainer_class: single_train
epochs: 1
device: cpu
init_model_path: ""
save_checkpoint_interval: 1
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 1
- name: infer_runner
trainer_class: single_infer
epochs: 1
device: cpu
init_model_path: "increment/0"
print_interval: 1
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: train_sample
thread_num: 1
#- name: infer_phase
# model: "{workspace}/model.py"
# dataset_name: infer_sample
# thread_num: 1
models/rank/fm/model.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import math
from collections import OrderedDict
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.is_distributed = True if envs.get_trainer(
) == "CtrTrainer" else False
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None)
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None)
self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
False)
self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
self.num_field = envs.get_global_env("hyper_parameters.num_field",
None)
def net(self, inputs, is_infer=False):
raw_feat_idx = self._sparse_data_var[1] # (batch_size * num_field) * 1
raw_feat_value = self._dense_data_var[0] # batch_size * num_field
self.label = self._sparse_data_var[0] # batch_size * 1
init_value_ = 0.1
feat_idx = raw_feat_idx
feat_value = fluid.layers.reshape(
raw_feat_value,
[-1, self.num_field, 1]) # batch_size * num_field * 1
# ------------------------- first order term --------------------------
first_weights_re = fluid.embedding(
input=feat_idx,
is_sparse=self.is_sparse,
is_distributed=self.is_distributed,
dtype='float32',
size=[self.sparse_feature_number + 1, 1],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_),
regularizer=fluid.regularizer.L1DecayRegularizer(self.reg))
) # (batch_size * num_field) * 1 * 1(embedding_size)
first_weights = fluid.layers.reshape(
first_weights_re,
shape=[-1, self.num_field, 1]) # batch_size * num_field * 1
y_first_order = fluid.layers.reduce_sum((first_weights * feat_value),
1) # batch_size * 1
b_linear = fluid.layers.create_parameter(
shape=[1],
dtype='float32',
default_initializer=fluid.initializer.ConstantInitializer(
value=0)) # 1
# ------------------------- second order term --------------------------
feat_embeddings_re = fluid.embedding(
input=feat_idx,
is_sparse=self.is_sparse,
is_distributed=self.is_distributed,
dtype='float32',
size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0,
scale=init_value_ /
math.sqrt(float(self.sparse_feature_dim))))
) # (batch_size * num_field) * 1 * embedding_size
feat_embeddings = fluid.layers.reshape(
feat_embeddings_re,
shape=[-1, self.num_field, self.sparse_feature_dim
]) # batch_size * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # batch_size * num_field * embedding_size
# sum_square part
summed_features_emb = fluid.layers.reduce_sum(
feat_embeddings, 1) # batch_size * embedding_size
summed_features_emb_square = fluid.layers.square(
summed_features_emb) # batch_size * embedding_size
# square_sum part
squared_features_emb = fluid.layers.square(
feat_embeddings) # batch_size * num_field * embedding_size
squared_sum_features_emb = fluid.layers.reduce_sum(
squared_features_emb, 1) # batch_size * embedding_size
y_FM = 0.5 * fluid.layers.reduce_sum(
summed_features_emb_square - squared_sum_features_emb,
dim=1,
keep_dim=True) # batch_size * 1
# ------------------------- Predict --------------------------
self.predict = fluid.layers.sigmoid(b_linear + y_first_order + y_FM)
cost = fluid.layers.log_loss(
input=self.predict, label=fluid.layers.cast(self.label,
"float32")) # log_loss
avg_cost = fluid.layers.reduce_sum(cost)
self._cost = avg_cost
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
label=label_int,
slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
self._infer_results["AUC"] = auc_var
models/rank/nfm/__init__.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
models/rank/nfm/config.yaml 0 → 100755
浏览文件 @ 0ede1313
# 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.
# global settings
debug: false
workspace: "paddlerec.models.rank.nfm"
dataset:
- name: train_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
- name: infer_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
hyper_parameters:
# 用户自定义配置
optimizer:
class: Adam
learning_rate: 0.0001
sparse_feature_number: 1086460
sparse_feature_dim: 9
is_sparse: False
use_batchnorm: False
use_dropout: False
dropout_prob: 0.9
fc_sizes: [400, 400, 400]
loss_type: "log_loss" # log_loss or square_loss
reg: 0.001
num_field: 39
act: "relu"
mode: train_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"
runner:
- name: train_runner
trainer_class: single_train
epochs: 1
device: cpu
init_model_path: ""
save_checkpoint_interval: 1
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 1
- name: infer_runner
trainer_class: single_infer
epochs: 1
device: cpu
init_model_path: "increment/0"
print_interval: 1
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: train_sample
thread_num: 1
#- name: infer_phase
# model: "{workspace}/model.py"
# dataset_name: infer_sample
# thread_num: 1
models/rank/nfm/data/download.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import os
import sys
import io
LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
sys.path.append(TOOLS_PATH)
from paddlerec.tools.tools import download_file_and_uncompress
if __name__ == '__main__':
trainfile = 'train.txt'
url = "https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz"
print("download and extract starting...")
download_file_and_uncompress(url)
print("download and extract finished")
count = 0
for _ in io.open(trainfile, 'r', encoding='utf-8'):
count += 1
print("total records: %d" % count)
print("done")
models/rank/nfm/data/download_preprocess.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import os
import shutil
import sys
LOCAL_PATH = os.path.dirname(os.path.abspath(__file__))
TOOLS_PATH = os.path.join(LOCAL_PATH, "..", "..", "tools")
sys.path.append(TOOLS_PATH)
from paddlerec.tools.tools import download_file_and_uncompress, download_file
if __name__ == '__main__':
url = "https://s3-eu-west-1.amazonaws.com/kaggle-display-advertising-challenge-dataset/dac.tar.gz"
url2 = "https://paddlerec.bj.bcebos.com/deepfm%2Ffeat_dict_10.pkl2"
print("download and extract starting...")
download_file_and_uncompress(url)
download_file(url2, "./sample_data/feat_dict_10.pkl2", True)
print("download and extract finished")
print("preprocessing...")
os.system("python preprocess.py")
print("preprocess done")
shutil.rmtree("raw_data")
print("done")
models/rank/nfm/data/get_slot_data.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import yaml, os
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
import paddle.fluid.incubate.data_generator as dg
try:
import cPickle as pickle
except ImportError:
import pickle
class TrainReader(dg.MultiSlotDataGenerator):
def __init__(self, config):
dg.MultiSlotDataGenerator.__init__(self)
if os.path.isfile(config):
with open(config, 'r') as rb:
_config = yaml.load(rb.read(), Loader=yaml.FullLoader)
else:
raise ValueError("reader config only support yaml")
def init(self):
self.cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
self.cont_max_ = [
5775, 257675, 65535, 969, 23159456, 431037, 56311, 6047, 29019, 46,
231, 4008, 7393
]
self.cont_diff_ = [
self.cont_max_[i] - self.cont_min_[i]
for i in range(len(self.cont_min_))
]
self.continuous_range_ = range(1, 14)
self.categorical_range_ = range(14, 40)
# load preprocessed feature dict
self.feat_dict_name = "sample_data/feat_dict_10.pkl2"
self.feat_dict_ = pickle.load(open(self.feat_dict_name, 'rb'))
def _process_line(self, line):
features = line.rstrip('\n').split('\t')
feat_idx = []
feat_value = []
for idx in self.continuous_range_:
if features[idx] == '':
feat_idx.append(0)
feat_value.append(0.0)
else:
feat_idx.append(self.feat_dict_[idx])
feat_value.append(
(float(features[idx]) - self.cont_min_[idx - 1]) /
self.cont_diff_[idx - 1])
for idx in self.categorical_range_:
if features[idx] == '' or features[idx] not in self.feat_dict_:
feat_idx.append(0)
feat_value.append(0.0)
else:
feat_idx.append(self.feat_dict_[features[idx]])
feat_value.append(1.0)
label = [int(features[0])]
return feat_idx, feat_value, label
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)
s = ""
for i in [('feat_idx', feat_idx), ('feat_value', feat_value),
('label', label)]:
k = i[0]
v = i[1]
for j in v:
s += " " + k + ":" + str(j)
print s.strip()
yield None
return data_iter
reader = TrainReader("../config.yaml")
reader.init()
reader.run_from_stdin()
models/rank/nfm/data/preprocess.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import os
import numpy
from collections import Counter
import shutil
import pickle
def get_raw_data():
if not os.path.isdir('raw_data'):
os.mkdir('raw_data')
fin = open('train.txt', 'r')
fout = open('raw_data/part-0', 'w')
for line_idx, line in enumerate(fin):
if line_idx % 200000 == 0 and line_idx != 0:
fout.close()
cur_part_idx = int(line_idx / 200000)
fout = open('raw_data/part-' + str(cur_part_idx), 'w')
fout.write(line)
fout.close()
fin.close()
def split_data():
split_rate_ = 0.9
dir_train_file_idx_ = 'aid_data/train_file_idx.txt'
filelist_ = [
'raw_data/part-%d' % x for x in range(len(os.listdir('raw_data')))
]
if not os.path.exists(dir_train_file_idx_):
train_file_idx = list(
numpy.random.choice(
len(filelist_), int(len(filelist_) * split_rate_), False))
with open(dir_train_file_idx_, 'w') as fout:
fout.write(str(train_file_idx))
else:
with open(dir_train_file_idx_, 'r') as fin:
train_file_idx = eval(fin.read())
for idx in range(len(filelist_)):
if idx in train_file_idx:
shutil.move(filelist_[idx], 'train_data')
else:
shutil.move(filelist_[idx], 'test_data')
def get_feat_dict():
freq_ = 10
dir_feat_dict_ = 'aid_data/feat_dict_' + str(freq_) + '.pkl2'
continuous_range_ = range(1, 14)
categorical_range_ = range(14, 40)
if not os.path.exists(dir_feat_dict_):
# print('generate a feature dict')
# Count the number of occurrences of discrete features
feat_cnt = Counter()
with open('train.txt', 'r') as fin:
for line_idx, line in enumerate(fin):
if line_idx % 100000 == 0:
print('generating feature dict', line_idx / 45000000)
features = line.rstrip('\n').split('\t')
for idx in categorical_range_:
if features[idx] == '': continue
feat_cnt.update([features[idx]])
# Only retain discrete features with high frequency
dis_feat_set = set()
for feat, ot in feat_cnt.items():
if ot >= freq_:
dis_feat_set.add(feat)
# Create a dictionary for continuous and discrete features
feat_dict = {}
tc = 1
# Continuous features
for idx in continuous_range_:
feat_dict[idx] = tc
tc += 1
for feat in dis_feat_set:
feat_dict[feat] = tc
tc += 1
# Save dictionary
with open(dir_feat_dict_, 'wb') as fout:
pickle.dump(feat_dict, fout, protocol=2)
print('args.num_feat ', len(feat_dict) + 1)
if __name__ == '__main__':
if not os.path.isdir('train_data'):
os.mkdir('train_data')
if not os.path.isdir('test_data'):
os.mkdir('test_data')
if not os.path.isdir('aid_data'):
os.mkdir('aid_data')
get_raw_data()
split_data()
get_feat_dict()
print('Done!')
models/rank/nfm/data/run.sh 0 → 100644
浏览文件 @ 0ede1313
python download_preprocess.py
mkdir slot_train_data
for i in `ls ./train_data`
do
cat train_data/$i | python get_slot_data.py > slot_train_data/$i
done
mkdir slot_test_data
for i in `ls ./test_data`
do
cat test_data/$i | python get_slot_data.py > slot_test_data/$i
done
models/rank/nfm/data/sample_data/train/sample_train.txt 0 → 100644
浏览文件 @ 0ede1313
feat_idx:1 feat_idx:2 feat_idx:3 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:10 feat_idx:11 feat_idx:0 feat_idx:13 feat_idx:695357 feat_idx:655161 feat_idx:0 feat_idx:1075467 feat_idx:314332 feat_idx:615411 feat_idx:733564 feat_idx:795081 feat_idx:148475 feat_idx:123424 feat_idx:582322 feat_idx:0 feat_idx:1082305 feat_idx:288355 feat_idx:328646 feat_idx:756244 feat_idx:13161 feat_idx:134834 feat_idx:734534 feat_idx:1047606 feat_idx:626828 feat_idx:0 feat_idx:476211 feat_idx:819217 feat_idx:502861 feat_idx:767167 feat_value:0.00017316017316 feat_value:1.55232499476e-05 feat_value:7.62951094835e-05 feat_value:0.0 feat_value:5.96732496653e-05 feat_value:9.27994580512e-06 feat_value:0.000266377794747 feat_value:0.000330742516951 feat_value:0.00623729280816 feat_value:0.0217391304348 feat_value:0.00865800865801 feat_value:0.0 feat_value:0.000270526173407 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 label:0
feat_idx:1 feat_idx:2 feat_idx:3 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:10 feat_idx:11 feat_idx:0 feat_idx:13 feat_idx:695357 feat_idx:328856 feat_idx:583609 feat_idx:356189 feat_idx:314332 feat_idx:404876 feat_idx:233441 feat_idx:144963 feat_idx:148475 feat_idx:954707 feat_idx:778340 feat_idx:598842 feat_idx:701804 feat_idx:223357 feat_idx:310528 feat_idx:805012 feat_idx:599055 feat_idx:683739 feat_idx:734534 feat_idx:94311 feat_idx:135625 feat_idx:0 feat_idx:476211 feat_idx:737768 feat_idx:502861 feat_idx:618666 feat_value:0.00034632034632 feat_value:1.16424374607e-05 feat_value:0.000671396963455 feat_value:0.00103199174407 feat_value:4.40424852812e-06 feat_value:1.85598916102e-05 feat_value:3.55170392996e-05 feat_value:0.000330742516951 feat_value:0.000137840725042 feat_value:0.0217391304348 feat_value:0.004329004329 feat_value:0.0 feat_value:0.000541052346815 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 label:0
feat_idx:1 feat_idx:2 feat_idx:3 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:10 feat_idx:11 feat_idx:12 feat_idx:13 feat_idx:125230 feat_idx:244091 feat_idx:428972 feat_idx:323226 feat_idx:314332 feat_idx:615411 feat_idx:655488 feat_idx:144963 feat_idx:148475 feat_idx:754940 feat_idx:989454 feat_idx:789125 feat_idx:274685 feat_idx:59528 feat_idx:142028 feat_idx:791919 feat_idx:339114 feat_idx:12934 feat_idx:0 feat_idx:0 feat_idx:128761 feat_idx:925828 feat_idx:476211 feat_idx:686449 feat_idx:0 feat_idx:0 feat_value:0.00034632034632 feat_value:1.16424374607e-05 feat_value:1.52590218967e-05 feat_value:0.0144478844169 feat_value:3.31182217752e-05 feat_value:0.000206478794164 feat_value:7.10340785992e-05 feat_value:0.000330742516951 feat_value:0.00844274440884 feat_value:0.0217391304348 feat_value:0.012987012987 feat_value:0.000748502994012 feat_value:0.00608683890166 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 label:0
feat_idx:0 feat_idx:2 feat_idx:0 feat_idx:0 feat_idx:5 feat_idx:0 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:0 feat_idx:11 feat_idx:0 feat_idx:0 feat_idx:695357 feat_idx:541890 feat_idx:0 feat_idx:1012660 feat_idx:314332 feat_idx:404876 feat_idx:1742 feat_idx:144963 feat_idx:148475 feat_idx:456917 feat_idx:220560 feat_idx:0 feat_idx:480237 feat_idx:59528 feat_idx:402233 feat_idx:0 feat_idx:763481 feat_idx:885529 feat_idx:0 feat_idx:0 feat_idx:0 feat_idx:0 feat_idx:476211 feat_idx:68781 feat_idx:0 feat_idx:0 feat_value:0.0 feat_value:0.00347720798826 feat_value:0.0 feat_value:0.0 feat_value:0.000189641760152 feat_value:0.0 feat_value:0.0 feat_value:0.0 feat_value:0.0 feat_value:0.0 feat_value:0.0 feat_value:0.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 feat_value:0.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 label:0
feat_idx:1 feat_idx:2 feat_idx:0 feat_idx:4 feat_idx:5 feat_idx:6 feat_idx:7 feat_idx:8 feat_idx:9 feat_idx:10 feat_idx:11 feat_idx:0 feat_idx:13 feat_idx:518052 feat_idx:52223 feat_idx:0 feat_idx:610088 feat_idx:314332 feat_idx:85900 feat_idx:253972 feat_idx:144963 feat_idx:148475 feat_idx:581401 feat_idx:921618 feat_idx:374454 feat_idx:576858 feat_idx:288355 feat_idx:526081 feat_idx:597631 feat_idx:763481 feat_idx:468634 feat_idx:0 feat_idx:0 feat_idx:360559 feat_idx:0 feat_idx:122096 feat_idx:604513 feat_idx:0 feat_idx:0 feat_value:0.000519480519481 feat_value:7.7616249738e-06 feat_value:0.0 feat_value:0.0 feat_value:8.63578142768e-08 feat_value:0.0 feat_value:5.32755589494e-05 feat_value:0.0 feat_value:0.0 feat_value:0.0217391304348 feat_value:0.004329004329 feat_value:0.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 feat_value:1.0 feat_value:0.0 feat_value:1.0 feat_value:1.0 feat_value:0.0 feat_value:0.0 label:0
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models/rank/nfm/model.py 0 → 100755
浏览文件 @ 0ede1313
# 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.
import math
from collections import OrderedDict
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.is_distributed = True if envs.get_trainer(
) == "CtrTrainer" else False
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None)
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None)
self.is_sparse = envs.get_global_env("hyper_parameters.is_sparse",
False)
self.use_batchnorm = envs.get_global_env(
"hyper_parameters.use_batchnorm", False)
self.use_dropout = envs.get_global_env("hyper_parameters.use_dropout",
False)
self.dropout_prob = envs.get_global_env(
"hyper_parameters.dropout_prob", None)
self.layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes",
None)
self.loss_type = envs.get_global_env("hyper_parameters.loss_type",
'logloss')
self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
self.num_field = envs.get_global_env("hyper_parameters.num_field",
None)
self.act = envs.get_global_env("hyper_parameters.act", None)
def net(self, inputs, is_infer=False):
raw_feat_idx = self._sparse_data_var[1] # (batch_size * num_field) * 1
raw_feat_value = self._dense_data_var[0] # batch_size * num_field
self.label = self._sparse_data_var[0] # batch_size * 1
init_value_ = 0.1
feat_idx = raw_feat_idx
feat_value = fluid.layers.reshape(
raw_feat_value,
[-1, self.num_field, 1]) # batch_size * num_field * 1
# ------------------------- first order term --------------------------
first_weights_re = fluid.embedding(
input=feat_idx,
is_sparse=self.is_sparse,
is_distributed=self.is_distributed,
dtype='float32',
size=[self.sparse_feature_number + 1, 1],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_),
regularizer=fluid.regularizer.L1DecayRegularizer(self.reg))
) # (batch_size * num_field) * 1 * 1(embedding_size)
first_weights = fluid.layers.reshape(
first_weights_re,
shape=[-1, self.num_field, 1]) # batch_size * num_field * 1
y_first_order = fluid.layers.reduce_sum((first_weights * feat_value),
1) # batch_size * 1
# ------------------------- second order term --------------------------
feat_embeddings_re = fluid.embedding(
input=feat_idx,
is_sparse=self.is_sparse,
is_distributed=self.is_distributed,
dtype='float32',
size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0,
scale=init_value_ /
math.sqrt(float(self.sparse_feature_dim))))
) # (batch_size * num_field) * 1 * embedding_size
feat_embeddings = fluid.layers.reshape(
feat_embeddings_re,
shape=[-1, self.num_field, self.sparse_feature_dim
]) # batch_size * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # batch_size * num_field * embedding_size
# sum_square part
summed_features_emb = fluid.layers.reduce_sum(
feat_embeddings, 1) # batch_size * embedding_size
summed_features_emb_square = fluid.layers.square(
summed_features_emb) # batch_size * embedding_size
# square_sum part
squared_features_emb = fluid.layers.square(
feat_embeddings) # batch_size * num_field * embedding_size
squared_sum_features_emb = fluid.layers.reduce_sum(
squared_features_emb, 1) # batch_size * embedding_size
y_FM = 0.5 * (summed_features_emb_square - squared_sum_features_emb
) # batch_size * embedding_size
if self.use_batchnorm:
y_FM = fluid.layers.batch_norm(input=y_FM, is_test=is_infer)
if self.use_dropout:
y_FM = fluid.layers.dropout(
x=y_FM, dropout_prob=self.dropout_prob, is_test=is_infer)
# ------------------------- DNN --------------------------
y_dnn = y_FM
for s in self.layer_sizes:
if self.use_batchnorm:
y_dnn = fluid.layers.fc(
input=y_dnn,
size=s,
act=self.act,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.
TruncatedNormalInitializer(
loc=0.0,
scale=init_value_ /
math.sqrt(float(10)))),
bias_attr=fluid.ParamAttr(initializer=fluid.initializer.
TruncatedNormalInitializer(
loc=0.0, scale=init_value_)))
y_dnn = fluid.layers.batch_norm(
input=y_dnn, act=self.act, is_test=is_infer)
else:
y_dnn = fluid.layers.fc(
input=y_dnn,
size=s,
act=self.act,
param_attr=fluid.ParamAttr(initializer=fluid.initializer.
TruncatedNormalInitializer(
loc=0.0,
scale=init_value_ /
math.sqrt(float(10)))),
bias_attr=fluid.ParamAttr(initializer=fluid.initializer.
TruncatedNormalInitializer(
loc=0.0, scale=init_value_)))
if self.use_dropout:
y_dnn = fluid.layers.dropout(
x=y_dnn, dropout_prob=self.dropout_prob, is_test=is_infer)
y_dnn = fluid.layers.fc(
input=y_dnn,
size=1,
act=None,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)))
# ------------------------- Predict --------------------------
self.predict = fluid.layers.sigmoid(y_first_order + y_dnn)
if self.loss_type == "squqre_loss":
cost = fluid.layers.mse_loss(
input=self.predict,
label=fluid.layers.cast(self.label, "float32"))
else:
cost = fluid.layers.log_loss(
input=self.predict,
label=fluid.layers.cast(self.label,
"float32")) # default log_loss
avg_cost = fluid.layers.reduce_sum(cost)
self._cost = avg_cost
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
label=label_int,
slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
self._infer_results["AUC"] = auc_var
models/rank/pnn/__init__.py 0 → 100755
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# 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.
models/rank/pnn/config.yaml 0 → 100755
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# 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.
# global settings
debug: false
workspace: "paddlerec.models.rank.nfm"
dataset:
- name: train_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/../dataset/Criteo_data/sample_data/train"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
- name: infer_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/../dataset/Criteo_data/sample_data/train"
sparse_slots: "label feat_idx"
dense_slots: "feat_value:39"
hyper_parameters:
# 用户自定义配置
optimizer:
class: Adam
learning_rate: 0.0001
sparse_feature_number: 1086460
sparse_feature_dim: 9
deep_input_size: 50
use_inner_product: True
num_field: 39
fc_sizes: [32, 32]
reg: 0.001
act: "relu"
mode: train_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"
runner:
- name: train_runner
trainer_class: single_train
epochs: 1
device: cpu
init_model_path: ""
save_checkpoint_interval: 1
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
print_interval: 1
- name: infer_runner
trainer_class: single_infer
epochs: 1
device: cpu
init_model_path: "increment/0"
print_interval: 1
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: train_sample
thread_num: 1
#- name: infer_phase
# model: "{workspace}/model.py"
# dataset_name: infer_sample
# thread_num: 1
models/rank/pnn/model.py 0 → 100755
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# 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.
#
# 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.
import math
from collections import OrderedDict
import paddle
import paddle.fluid as fluid
from paddlerec.core.utils import envs
from paddlerec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.is_distributed = True if envs.get_trainer(
) == "CtrTrainer" else False
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number", None)
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim", None)
self.deep_input_size = envs.get_global_env(
"hyper_parameters.deep_input_size", 50)
self.use_inner_product = envs.get_global_env(
"hyper_parameters.use_inner_product", None)
self.layer_sizes = envs.get_global_env("hyper_parameters.fc_sizes",
None)
self.reg = envs.get_global_env("hyper_parameters.reg", 1e-4)
self.num_field = envs.get_global_env("hyper_parameters.num_field",
None)
self.act = envs.get_global_env("hyper_parameters.act", None)
def net(self, inputs, is_infer=False):
raw_feat_idx = self._sparse_data_var[1] # (batch_size * num_field) * 1
raw_feat_value = self._dense_data_var[0] # batch_size * num_field
self.label = self._sparse_data_var[0] # batch_size * 1
init_value_ = 0.1
feat_idx = raw_feat_idx
feat_value = fluid.layers.reshape(
raw_feat_value,
[-1, self.num_field, 1]) # batch_size * num_field * 1
# ------------------------- first order term --------------------------
first_weights_re = fluid.embedding(
input=feat_idx,
is_sparse=True,
is_distributed=self.is_distributed,
dtype='float32',
size=[self.sparse_feature_number + 1, 1],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_),
regularizer=fluid.regularizer.L1DecayRegularizer(self.reg))
) # (batch_size * num_field) * 1 * 1(embedding_size)
first_weights = fluid.layers.reshape(
first_weights_re,
shape=[-1, self.num_field, 1]) # batch_size * num_field * 1
y_first_order = fluid.layers.reduce_sum((first_weights * feat_value),
1) # batch_size * 1
# ------------------------- Embedding --------------------------
feat_embeddings_re = fluid.embedding(
input=feat_idx,
is_sparse=True,
is_distributed=self.is_distributed,
dtype='float32',
size=[self.sparse_feature_number + 1, self.sparse_feature_dim],
padding_idx=0,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0,
scale=init_value_ /
math.sqrt(float(self.sparse_feature_dim))))
) # (batch_size * num_field) * 1 * embedding_size
feat_embeddings = fluid.layers.reshape(
feat_embeddings_re,
shape=[-1, self.num_field, self.sparse_feature_dim
]) # batch_size * num_field * embedding_size
feat_embeddings = feat_embeddings * feat_value # batch_size * num_field * embedding_size
# ------------------------- Linear Signal --------------------------
linear_input_size = self.num_field * self.sparse_feature_dim
flaten_feat_embedding = fluid.layers.reshape(
x=feat_embeddings, shape=[-1, linear_input_size])
w_z_linear_weights = fluid.layers.create_parameter(
shape=[linear_input_size, self.deep_input_size], dtype="float32")
linear_signal = fluid.layers.matmul(
flaten_feat_embedding,
w_z_linear_weights) # batch_size * deep_input_size
# ------------------------- Quardatic Singal --------------------------
quadratic_output = []
if self.use_inner_product:
w_p_quardatic_weights = fluid.layers.create_parameter(
shape=[self.deep_input_size, self.num_field], dtype="float32")
for i in range(self.deep_input_size):
transpose_embedding = fluid.layers.transpose(
feat_embeddings, perm=[0, 2, 1])
theta = fluid.layers.elementwise_mul(
transpose_embedding, w_p_quardatic_weights[i], axis=-1)
quadratic_output.append(
paddle.norm(
fluid.layers.reduce_sum(
theta, dim=1),
p=2,
axis=1,
keepdim=True))
else:
w_p_quardatic_weights_outer = fluid.layers.create_parameter(
shape=[
self.deep_input_size, self.sparse_feature_dim,
self.sparse_feature_dim
],
dtype="float32")
embedding_sum = fluid.layers.reduce_sum(feat_embeddings, dim=1)
p = fluid.layers.matmul(
fluid.layers.reshape(
embedding_sum, shape=[0, -1, 1]),
fluid.layers.reshape(
embedding_sum, shape=[0, 1, -1]))
for i in range(self.deep_input_size):
theta = fluid.layers.elementwise_mul(
p, w_p_quardatic_weights_outer[i, :, :], axis=-1)
quadratic_output.append(
fluid.layers.reshape(
fluid.layers.reduce_sum(
theta, dim=[1, 2]),
shape=[-1, 1]))
quadratic_signal = fluid.layers.concat(quadratic_output, axis=1)
y_dnn = linear_signal + quadratic_signal
y_dnn = fluid.layers.relu6(y_dnn, threshold=10000000.0)
for s in self.layer_sizes:
y_dnn = fluid.layers.fc(
input=y_dnn,
size=s,
act=self.act,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)))
y_dnn = fluid.layers.fc(
input=y_dnn,
size=1,
act=None,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.TruncatedNormalInitializer(
loc=0.0, scale=init_value_)))
# ------------------------- Predict --------------------------
self.predict = fluid.layers.sigmoid(y_first_order + y_dnn)
cost = fluid.layers.log_loss(
input=self.predict, label=fluid.layers.cast(self.label, "float32"))
avg_cost = fluid.layers.reduce_sum(cost)
self._cost = avg_cost
predict_2d = fluid.layers.concat([1 - self.predict, self.predict], 1)
label_int = fluid.layers.cast(self.label, 'int64')
auc_var, batch_auc_var, _ = fluid.layers.auc(input=predict_2d,
label=label_int,
slide_steps=0)
self._metrics["AUC"] = auc_var
self._metrics["BATCH_AUC"] = batch_auc_var
if is_infer:
self._infer_results["AUC"] = auc_var
models/rank/pnn/readme.md 0 → 100644
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# 注意
本模型默认实现要求paddle版本 >= 2.0, 后续提供基于paddle1.7版本
```
pip install paddlepaddle==2.0.0a0
```
models/rank/readme.md
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......@@ -23,9 +23,14 @@
| 模型 | 简介 | 论文 |
| :------------------: | :--------------------: | :---------: |
| DNN | 多层神经网络 | -- |
| Logistic Regression | 逻辑回归 | -- |
| FM | 因子分解机 | [Factorization Machine](https://ieeexplore.ieee.org/abstract/document/5694074)(2010) |
| PNN | Product Network | [Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf)(2016) |
| wide&deep | Deep + wide(LR) | [Wide & Deep Learning for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/2988450.2988454)(2016) |
| DeepFM | DeepFM | [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf)(2017) |
| DCN | Deep Cross Network | [Deep & Cross Network for Ad Click Predictions](https://dl.acm.org/doi/pdf/10.1145/3124749.3124754)(2017) |
| NFM | Neural Factorization Machines | [Neural Factorization Machines for Sparse Predictive Analytics](https://dl.acm.org/doi/pdf/10.1145/3077136.3080777)(2017) |
| AFM | Attentional Factorization Machines | [Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks](https://arxiv.org/pdf/1708.04617.pdf)(2017) |
| xDeepFM | xDeepFM | [xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems](https://dl.acm.org/doi/pdf/10.1145/3219819.3220023)(2018) |
| DIN | Deep Interest Network | [Deep Interest Network for Click-Through Rate Prediction](https://dl.acm.org/doi/pdf/10.1145/3219819.3219823)(2018) |
......
setup.py
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......@@ -63,7 +63,8 @@ def build(dirname):
models_copy = [
'data/*.txt', 'data/*/*.txt', '*.yaml', '*.sh', 'tree/*.npy',
'tree/*.txt', 'data/sample_data/*', 'data/sample_data/train/*',
'data/sample_data/infer/*', 'data/*/*.csv'
'data/sample_data/infer/*', 'data/*/*.csv', 'Criteo_data/*',
'Criteo_data/sample_data/train/*'
]
engine_copy = ['*/*.sh']
......
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