提交 d17fad17 编写于 作者: Y yaoxuefeng

add ffm

上级 0dbbb781
# 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.
# 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
# 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
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