提交 44b8928d 编写于 作者: F frankwhzhang

add listwise

上级 633ecc2c
......@@ -177,6 +177,7 @@ python -m paddlerec.run -m ./models/rank/dnn/config.yaml -b backend.yaml
| 多任务 | [ESMM](models/multitask/esmm/model.py) | ✓ | ✓ | ✓ |
| 多任务 | [MMOE](models/multitask/mmoe/model.py) | ✓ | ✓ | ✓ |
| 多任务 | [ShareBottom](models/multitask/share-bottom/model.py) | ✓ | ✓ | ✓ |
| 融合 | [Listwise](models/rerank/listwise/model.py) | ✓ | x | ✓ |
......
# 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.
# 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.
evaluate:
reader:
batch_size: 1
class: "{workspace}/random_infer_reader.py"
test_data_path: "{workspace}/data/train"
train:
trainer:
# for cluster training
strategy: "async"
epochs: 3
workspace: "paddlerec.models.rerank.listwise"
device: cpu
reader:
batch_size: 2
class: "{workspace}/random_reader.py"
train_data_path: "{workspace}/data/train"
dataset_class: "DataLoader"
model:
models: "{workspace}/model.py"
hyper_parameters:
hidden_size: 128
user_vocab: 200
item_vocab: 1000
item_len: 5
embed_size: 16
learning_rate: 0.01
optimizer: sgd
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
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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.
import math
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 input_data(self, is_infer=False):
item_len = envs.get_global_env("hyper_parameters.item_len", None,
self._namespace)
user_slot_names = fluid.data(
name='user_slot_names',
shape=[None, 1],
dtype='int64',
lod_level=1)
item_slot_names = fluid.data(
name='item_slot_names',
shape=[None, item_len],
dtype='int64',
lod_level=1)
lens = fluid.data(name='lens', shape=[None], dtype='int64')
labels = fluid.data(
name='labels', shape=[None, item_len], dtype='int64', lod_level=1)
inputs = [user_slot_names] + [item_slot_names] + [lens] + [labels]
if is_infer:
self._infer_data_var = inputs
self._infer_data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._infer_data_var,
capacity=64,
use_double_buffer=False,
iterable=False)
else:
self._data_var = inputs
self._data_loader = fluid.io.DataLoader.from_generator(
feed_list=self._data_var,
capacity=10000,
use_double_buffer=False,
iterable=False)
return inputs
def default_normal_initializer(self, nf=128):
return fluid.initializer.TruncatedNormal(
loc=0.0, scale=np.sqrt(1.0 / nf))
def default_regularizer(self):
return None
def default_fc(self, data, size, num_flatten_dims=1, act=None, name=None):
return fluid.layers.fc(
input=data,
size=size,
num_flatten_dims=num_flatten_dims,
param_attr=fluid.ParamAttr(
initializer=self.default_normal_initializer(size),
regularizer=self.default_regularizer()),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.0),
regularizer=self.default_regularizer()),
act=act,
name=name)
def default_embedding(self, data, vocab_size, embed_size):
reg = fluid.regularizer.L2Decay(
1e-5) # IMPORTANT, to prevent overfitting.
embed = fluid.embedding(
input=data,
size=[vocab_size, embed_size],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Xavier(), regularizer=reg),
is_sparse=True)
return embed
def default_drnn(self, data, nf, is_reverse, h_0):
return fluid.layers.dynamic_gru(
input=data,
size=nf,
param_attr=fluid.ParamAttr(
initializer=self.default_normal_initializer(nf),
regularizer=self.default_regularizer()),
bias_attr=fluid.ParamAttr(
initializer=fluid.initializer.Constant(value=0.0),
regularizer=self.default_regularizer()),
is_reverse=is_reverse,
h_0=h_0)
def fluid_sequence_pad(self, input, pad_value, maxlen=None):
"""
args:
input: (batch*seq_len, dim)
returns:
(batch, max_seq_len, dim)
"""
pad_value = fluid.layers.cast(
fluid.layers.assign(input=np.array([pad_value], 'float32')),
input.dtype)
input_padded, _ = fluid.layers.sequence_pad(
input, pad_value,
maxlen=maxlen) # (batch, max_seq_len, 1), (batch, 1)
# TODO, maxlen=300, used to solve issues: https://github.com/PaddlePaddle/Paddle/issues/14164
return input_padded
def fluid_sequence_get_pos(self, lodtensor):
"""
args:
lodtensor: lod = [[0,4,7]]
return:
pos: lod = [[0,4,7]]
data = [0,1,2,3,0,1,3]
shape = [-1, 1]
"""
lodtensor = fluid.layers.reduce_sum(lodtensor, dim=1, keep_dim=True)
assert lodtensor.shape == (-1, 1), (lodtensor.shape())
ones = fluid.layers.cast(lodtensor * 0 + 1,
'float32') # (batch*seq_len, 1)
ones_padded = self.fluid_sequence_pad(ones,
0) # (batch, max_seq_len, 1)
ones_padded = fluid.layers.squeeze(ones_padded,
[2]) # (batch, max_seq_len)
seq_len = fluid.layers.cast(
fluid.layers.reduce_sum(
ones_padded, 1, keep_dim=True), 'int64') # (batch, 1)
seq_len = fluid.layers.squeeze(seq_len, [1])
pos = fluid.layers.cast(
fluid.layers.cumsum(
ones_padded, 1, exclusive=True), 'int64')
pos = fluid.layers.sequence_unpad(pos, seq_len) # (batch*seq_len, 1)
pos.stop_gradient = True
return pos
def net(self, inputs, is_infer=False):
hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None,
self._namespace)
user_vocab = envs.get_global_env("hyper_parameters.user_vocab", None,
self._namespace)
item_vocab = envs.get_global_env("hyper_parameters.item_vocab", None,
self._namespace)
embed_size = envs.get_global_env("hyper_parameters.embed_size", None,
self._namespace)
#encode
user_embedding = self.default_embedding(inputs[0], user_vocab,
embed_size)
user_feature = self.default_fc(
data=user_embedding,
size=hidden_size,
num_flatten_dims=1,
act='relu',
name='user_feature_fc')
item_embedding = self.default_embedding(inputs[1], item_vocab,
embed_size)
item_embedding = fluid.layers.sequence_unpad(
x=item_embedding, length=inputs[2])
item_fc = self.default_fc(
data=item_embedding,
size=hidden_size,
num_flatten_dims=1,
act='relu',
name='item_fc')
pos = self.fluid_sequence_get_pos(item_fc)
pos_embed = self.default_embedding(pos, user_vocab, embed_size)
pos_embed = fluid.layers.squeeze(pos_embed, [1])
# item gru
gru_input = self.default_fc(
data=fluid.layers.concat([item_fc, pos_embed], 1),
size=hidden_size * 3,
num_flatten_dims=1,
act='relu',
name='item_gru_fc')
item_gru_forward = self.default_drnn(
data=gru_input, nf=hidden_size, h_0=user_feature, is_reverse=False)
item_gru_backward = self.default_drnn(
data=gru_input, nf=hidden_size, h_0=user_feature, is_reverse=True)
item_gru = fluid.layers.concat(
[item_gru_forward, item_gru_backward], axis=1)
out_click_fc1 = self.default_fc(
data=item_gru,
size=hidden_size,
num_flatten_dims=1,
act='relu',
name='out_click_fc1')
click_prob = self.default_fc(
data=out_click_fc1,
size=2,
num_flatten_dims=1,
act='softmax',
name='out_click_fc2')
labels = fluid.layers.sequence_unpad(x=inputs[3], length=inputs[2])
auc_val, batch_auc, auc_states = fluid.layers.auc(input=click_prob,
label=labels)
if is_infer:
self._infer_results["AUC"] = auc_val
return
loss = fluid.layers.reduce_mean(
fluid.layers.cross_entropy(
input=click_prob, label=labels))
self._cost = loss
self._metrics['auc'] = auc_val
def train_net(self):
input_data = self.input_data()
self.net(input_data)
def infer_net(self):
input_data = self.input_data(is_infer=True)
self.net(input_data, is_infer=True)
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
from collections import defaultdict
import paddle.fluid as fluid
import numpy as np
class EvaluateReader(Reader):
def init(self):
self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab",
None, "train.model")
self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab",
None, "train.model")
self.item_len = envs.get_global_env("hyper_parameters.item_len", None,
"train.model")
self.batch_size = envs.get_global_env("batch_size", None,
"train.reader")
def reader_creator(self):
def reader():
user_slot_name = []
for j in range(self.batch_size):
user_slot_name.append(
[int(np.random.randint(self.user_vocab))])
item_slot_name = np.random.randint(
self.item_vocab, size=(self.batch_size,
self.item_len)).tolist()
length = [self.item_len] * self.batch_size
label = np.random.randint(
2, size=(self.batch_size, self.item_len)).tolist()
output = []
output.append(user_slot_name)
output.append(item_slot_name)
output.append(length)
output.append(label)
yield output
return reader
def generate_batch_from_trainfiles(self, files):
return fluid.io.batch(
self.reader_creator(), batch_size=self.batch_size)
def generate_sample(self, line):
"""
the file is not used
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
pass
return reader
# 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.
from __future__ import print_function
from paddlerec.core.reader import Reader
from paddlerec.core.utils import envs
from collections import defaultdict
import paddle.fluid as fluid
import numpy as np
class TrainReader(Reader):
def init(self):
self.user_vocab = envs.get_global_env("hyper_parameters.user_vocab",
None, "train.model")
self.item_vocab = envs.get_global_env("hyper_parameters.item_vocab",
None, "train.model")
self.item_len = envs.get_global_env("hyper_parameters.item_len", None,
"train.model")
self.batch_size = envs.get_global_env("batch_size", None,
"train.reader")
def reader_creator(self):
def reader():
user_slot_name = []
for j in range(self.batch_size):
user_slot_name.append(
[int(np.random.randint(self.user_vocab))])
item_slot_name = np.random.randint(
self.item_vocab, size=(self.batch_size,
self.item_len)).tolist()
length = [self.item_len] * self.batch_size
label = np.random.randint(
2, size=(self.batch_size, self.item_len)).tolist()
output = []
output.append(user_slot_name)
output.append(item_slot_name)
output.append(length)
output.append(label)
yield output
return reader
def generate_batch_from_trainfiles(self, files):
return fluid.io.batch(
self.reader_creator(), batch_size=self.batch_size)
def generate_sample(self, line):
"""
the file is not used
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
pass
return reader
# 融合模型库
## 简介
我们提供了常见的多路排序融合使用的模型算法的PaddleRec实现, 单机训练&预测效果指标以及分布式训练&预测性能指标等。目前实现的模型是 [Listwise](listwise)
模型算法库在持续添加中,欢迎关注。
## 目录
* [整体介绍](#整体介绍)
* [融合模型列表](#融合模型列表)
* [使用教程](#使用教程)
* [训练 预测](#训练 预测)
* [效果对比](#效果对比)
* [模型效果列表](#模型效果列表)
## 整体介绍
### 融合模型列表
| 模型 | 简介 | 论文 |
| :------------------: | :--------------------: | :---------: |
| Listwise | Listwise | [Sequential Evaluation and Generation Framework for Combinatorial Recommender System](https://arxiv.org/pdf/1902.00245.pdf)(2019) |
下面是每个模型的简介(注:图片引用自链接中的论文)
[Listwise](https://arxiv.org/pdf/1902.00245.pdf):
<p align="center">
<img align="center" src="../../doc/imgs/listwise.png">
<p>
## 使用教程
### 训练 预测
```shell
python -m paddlerec.run -m paddlerec.models.rerank.listwise # listwise
```
## 效果对比
### 模型效果列表
| 数据集 | 模型 | loss | auc |
| :------------------: | :--------------------: | :---------: |:---------: |
| -- | Listwise | -- | -- |
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