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wide_deep

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models/rank/wide_deep/README.md
浏览文件 @ 246a3535
......@@ -3,16 +3,19 @@
以下是本例的简要目录结构及说明：
```
├── data # 文档
├── train #训练数据
├── train_data.txt
├── create_data.sh
├── data_preparation.py
├── get_slot_data.py
├── run.sh
├── data # 数据
├── sample_data #示例数据
├── train #训练数据
├── train_data.txt
├── create_data.sh #数据下载脚本
├── data_preparation.py #数据处理程序
├── get_slot_data.py #数据处理程序
├── run.sh #一键数据下载脚本
├── args.py ## 脚本参数
├── __init__.py
├── config.yaml #配置文件
├── model.py #模型文件
├── README.md #文档
```
注：在阅读该示例前，建议您先了解以下内容：
......@@ -21,13 +24,13 @@
## 内容
- [模型简介](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/wide_deep#模型简介)
- [数据准备](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/wide_deep#数据准备)
- [运行环境](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/wide_deep#运行环境)
- [快速开始](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/wide_deep#快速开始)
- [论文复现](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/wide_deep#论文复现)
- [进阶使用](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/wide_deep#进阶使用)
- [FAQ](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/wide_deep#FAQ)
- [模型简介](#模型简介)
- [数据准备](#数据准备)
- [运行环境](#运行环境)
- [快速开始](#快速开始)
- [论文复现](#论文复现)
- [进阶使用](#进阶使用)
- [FAQ](#FAQ)
## 模型简介
......@@ -36,12 +39,6 @@
1. 效果上，在Google Play 进行线上A/B实验，wide&deep模型相比高度优化的Wide浅层模型，app下载率+3.9%。相比deep模型也有一定提升。
2. 性能上，通过切分一次请求需要处理的app 的Batch size为更小的size，并利用多线程并行请求达到提高处理效率的目的。单次响应耗时从31ms下降到14ms。
本例在paddlepaddle上实现wide&deep并在开源数据集Census-income Data上验证模型效果，在测试集上的平均acc和auc分别为：
> mean_acc: 0.76195
>
> mean_auc: 0.90577
若进行精度验证，请参考[论文复现](https://github.com/PaddlePaddle/PaddleRec/tree/master/models/rank/wide_deep#论文复现)部分。
本项目支持功能
......@@ -52,12 +49,44 @@
## 数据准备
本例在paddlerec上实现wide&deep并在开源数据集Census-income Data上验证模型效果
数据地址：
[adult.data](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data)
[adult.test](https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test)
您可以在进入models/rank/wide_deep/data目录，直接运行一键数据生成脚本run.sh获取数据。
```
sh run.sh
```
在本例中需要调用pandas库，如环境中没有提前安装，可以使用命令 pip install pandas 安装。
运行的结果示例如下：
```
--2020-09-27 16:57:38-- https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data
Resolving archive.ics.uci.edu... 128.195.10.252
Connecting to archive.ics.uci.edu|128.195.10.252|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 3974305 (3.8M) [application/x-httpd-php]
Saving to: data/adult.data
100%[===================================================================================================================>] 3,974,305 12.6K/s in 6m 17s
2020-09-27 17:03:57 (10.3 KB/s) - data/adult.data saved [3974305/3974305]
--2020-09-27 17:03:57-- https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test
Resolving archive.ics.uci.edu... 128.195.10.252
Connecting to archive.ics.uci.edu|128.195.10.252|:443... connected.
HTTP request sent, awaiting response... 200 OK
Length: 2003153 (1.9M) [application/x-httpd-php]
Saving to: data/adult.test
100%[==================================================================================================================>] 2,003,153 12.7K/s in 51s
2020-09-27 17:08:04 (13.5 KB/s) - data/adult.test saved [2003153/2003153]
```
## 运行环境
PaddlePaddle>=1.7.2
......@@ -68,60 +97,98 @@ PaddleRec >=0.1
os : windows/linux/macos
## 快速开始
本文提供了样例数据可以供您快速体验，在paddlerec目录下执行下面的命令即可快速启动训练：
### 单机训练
CPU环境
在config.yaml文件中设置好设备，epochs等。
```sh
dataset:
- name: sample_1
type: QueueDataset
batch_size: 5
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "label"
dense_slots: "wide_input:8 deep_input:58"
- name: infer_sample
type: QueueDataset
batch_size: 5
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "label"
dense_slots: "wide_input:8 deep_input:58"
```
### 单机预测
CPU环境
在config.yaml文件中设置好epochs、device等参数。
python -m paddlerec.run -m models/rank/wide_deep/config.yaml
```
- name: infer_runner
class: infer
device: cpu
init_model_path: "increment/0"
使用样例数据快速跑通的结果实例:
```
PaddleRec: Runner train_runner Begin
Executor Mode: train
processor_register begin
Running SingleInstance.
Running SingleNetwork.
Warning:please make sure there are no hidden files in the dataset folder and check these hidden files:[]
Running SingleStartup.
Running SingleRunner.
I0927 17:16:18.305258 3437 parallel_executor.cc:440] The Program will be executed on CPU using ParallelExecutor, 1 cards are used, so 1 programs are executed in parallel.
I0927 17:16:18.310783 3437 build_strategy.cc:365] SeqOnlyAllReduceOps:0, num_trainers:1
I0927 17:16:18.314724 3437 parallel_executor.cc:307] Inplace strategy is enabled, when build_strategy.enable_inplace = True
I0927 17:16:18.317752 3437 parallel_executor.cc:375] Garbage collection strategy is enabled, when FLAGS_eager_delete_tensor_gb = 0
2020-09-27 17:16:18,475-INFO: [Train] batch: 20, time_each_interval: 0.18s, ACC: [0.6], BATCH_AUC: [0.41666667], AUC: [0.61538462]
2020-09-27 17:16:18,583-INFO: [Train] batch: 40, time_each_interval: 0.11s, ACC: [0.8], BATCH_AUC: [0.875], AUC: [0.59693471]
2020-09-27 17:16:18,625-INFO: [Train] batch: 60, time_each_interval: 0.04s, ACC: [0.4], BATCH_AUC: [1.], AUC: [0.59405999]
2020-09-27 17:16:18,666-INFO: [Train] batch: 80, time_each_interval: 0.04s, ACC: [0.8], BATCH_AUC: [0.5], AUC: [0.56687606]
epoch 0 done, use time: 0.503633022308, global metrics: ACC=[1.], BATCH_AUC=[0.], AUC=[0.56696623]
PaddleRec Finish
```
## 论文复现
用原论文的完整数据复现论文效果需要在config.yaml中修改batch_size=40, thread_num=8, epoch_num=40
本例在paddlepaddle上实现wide&deep并在开源数据集Census-income Data上验证模型效果，在测试集上的平均acc和auc分别为：
mean_acc: 0.76195 , mean_auc: 0.90577
修改后运行方案：修改config.yaml中的'workspace'为config.yaml的目录位置，执行
为了方便使用者能够快速的跑通每一个模型，我们在每个模型下都提供了样例数据。如果需要复现readme中的效果,请按如下步骤依次操作即可。
在全量数据下模型的指标如下：
| 模型 | auc | acc | batch_size | thread_num| epoch_num| Time of each epoch |
| :------| :------ | :------ | :------| :------ | :------| :------ |
| wide_deep | 0.8987 | 0.775 | 40 | 1 | 80 | 约10s |
1. 确认您当前所在目录为PaddleRec/models/rank/wide_deep
2. 在data目录下运行数据一键处理脚本，命令如下：
```
cd data
sh run.sh
cd ..
```
3. 退回deepfm目录中，打开文件config.yaml,更改其中的参数
将workspace改为您当前的绝对路径。（可用pwd命令获取绝对路径）
将train_sample中的batch_size从5改为40
将train_sample中的data_path改为{workspace}/data/slot_train_data
将infer_sample中的batch_size从5改为40
将infer_sample中的data_path改为{workspace}/data/slot_test_data
将train_runner中的epochs改为80
将infer_runner中的init_model_path改为increment/79
4. 运行命令，模型会进行80个epoch的训练，然后预测最后一个epoch，并获得相应auc和acc指标
```
python -m paddlerec.run -m ./config.yaml
```
5. 经过全量数据训练后，执行预测的结果示例如下：
```
python -m paddlerec.run -m /home/your/dir/config.yaml #调试模式 直接指定本地config的绝对路径
PaddleRec: Runner infer_runner Begin
Executor Mode: infer
processor_register begin
Running SingleInstance.
Running SingleNetwork.
Warning:please make sure there are no hidden files in the dataset folder and check these hidden files:[]
Running SingleInferStartup.
Running SingleInferRunner.
load persistables from increment/79
2020-09-27 17:37:17,679-INFO: [Infer] batch: 20, time_each_interval: 0.77s, ACC: [0.8], AUC: [0.89880283]
2020-09-27 17:37:18,452-INFO: [Infer] batch: 40, time_each_interval: 0.77s, ACC: [0.825], AUC: [0.89879974]
2020-09-27 17:37:19,023-INFO: [Infer] batch: 60, time_each_interval: 0.57s, ACC: [0.7], AUC: [0.89880376]
2020-09-27 17:37:19,591-INFO: [Infer] batch: 80, time_each_interval: 0.57s, ACC: [0.925], AUC: [0.89879592]
2020-09-27 17:37:20,195-INFO: [Infer] batch: 100, time_each_interval: 0.60s, ACC: [0.725], AUC: [0.89879213]
2020-09-27 17:37:20,822-INFO: [Infer] batch: 120, time_each_interval: 0.63s, ACC: [0.775], AUC: [0.89879757]
2020-09-27 17:37:21,303-INFO: [Infer] batch: 140, time_each_interval: 0.48s, ACC: [0.775], AUC: [0.89879296]
2020-09-27 17:37:21,798-INFO: [Infer] batch: 160, time_each_interval: 0.49s, ACC: [0.875], AUC: [0.89879267]
2020-09-27 17:37:22,265-INFO: [Infer] batch: 180, time_each_interval: 0.47s, ACC: [0.85], AUC: [0.89879272]
2020-09-27 17:37:22,835-INFO: [Infer] batch: 200, time_each_interval: 0.57s, ACC: [0.725], AUC: [0.89878928]
2020-09-27 17:37:23,364-INFO: [Infer] batch: 220, time_each_interval: 0.53s, ACC: [0.825], AUC: [0.89878807]
2020-09-27 17:37:23,859-INFO: [Infer] batch: 240, time_each_interval: 0.49s, ACC: [0.7], AUC: [0.8987825]
2020-09-27 17:37:24,337-INFO: [Infer] batch: 260, time_each_interval: 0.48s, ACC: [0.775], AUC: [0.89878314]
2020-09-27 17:37:24,877-INFO: [Infer] batch: 280, time_each_interval: 0.54s, ACC: [0.875], AUC: [0.89877827]
2020-09-27 17:37:25,410-INFO: [Infer] batch: 300, time_each_interval: 0.53s, ACC: [0.75], AUC: [0.89877518]
2020-09-27 17:37:25,985-INFO: [Infer] batch: 320, time_each_interval: 0.57s, ACC: [0.75], AUC: [0.89876936]
2020-09-27 17:37:26,447-INFO: [Infer] batch: 340, time_each_interval: 0.46s, ACC: [0.775], AUC: [0.89876268]
2020-09-27 17:37:26,725-INFO: [Infer] batch: 360, time_each_interval: 0.28s, ACC: [0.75], AUC: [0.8987574]
2020-09-27 17:37:26,889-INFO: [Infer] batch: 380, time_each_interval: 0.16s, ACC: [0.8], AUC: [0.89874688]
2020-09-27 17:37:27,065-INFO: [Infer] batch: 400, time_each_interval: 0.18s, ACC: [0.8], AUC: [0.89875484]
Infer infer_phase of epoch increment/79 done, use time: 10.2139520645, global metrics: ACC=[0.775], AUC=[0.89875217]
PaddleRec Finish
```
## 进阶使用
## FAQ
## FAQ
models/rank/wide_deep/config.yaml
浏览文件 @ 246a3535
......@@ -19,13 +19,13 @@ workspace: "models/rank/wide_deep"
dataset:
- name: sample_1
type: QueueDataset
type: DataLoader # or QueueDataset
batch_size: 5
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "label"
dense_slots: "wide_input:8 deep_input:58"
- name: infer_sample
type: QueueDataset
type: DataLoader # or QueueDataset
batch_size: 5
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "label"
......@@ -33,14 +33,14 @@ dataset:
hyper_parameters:
optimizer:
class: SGD
learning_rate: 0.0001
class: adam
learning_rate: 0.001
hidden1_units: 75
hidden2_units: 50
hidden3_units: 25
mode: train_runner
mode: train_runner
# if infer, change mode to "infer_runner" and change phase to "infer_phase"
runner:
......@@ -53,17 +53,19 @@ runner:
save_inference_interval: 1
save_checkpoint_path: "increment"
save_inference_path: "inference"
phases: phase1
- name: infer_runner
class: infer
device: cpu
init_model_path: "increment/0"
phases: infer_phase
phase:
- name: phase1
model: "{workspace}/model.py"
dataset_name: sample_1
thread_num: 1
#- name: infer_phase
# model: "{workspace}/model.py"
# dataset_name: infer_sample
# thread_num: 1
- name: infer_phase
model: "{workspace}/model.py"
dataset_name: infer_sample
thread_num: 1
models/rank/wide_deep/data/args.py 0 → 100644
浏览文件 @ 246a3535
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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 absolute_import
from __future__ import division
from __future__ import print_function
import argparse
import distutils.util
import sys
def parse_args():
parser = argparse.ArgumentParser(description=__doc__)
parser.add_argument("--epochs", type=int, default=40, help="epochs")
parser.add_argument(
"--batch_size", type=int, default=40, help="batch_size")
parser.add_argument(
'--use_gpu', type=int, default=0, help='whether using gpu')
parser.add_argument(
'--test_epoch', type=str, default='39', help='test_epoch')
parser.add_argument(
'--train_path', type=str, default='data/adult.data', help='train_path')
parser.add_argument(
'--test_path', type=str, default='data/adult.test', help='test_path')
parser.add_argument(
'--train_data_path',
type=str,
default='train_data/train_data.csv',
help='train_data_path')
parser.add_argument(
'--test_data_path',
type=str,
default='test_data/test_data.csv',
help='test_data_path')
parser.add_argument(
'--model_dir', type=str, default='model_dir', help='test_data_path')
parser.add_argument(
'--hidden1_units', type=int, default=75, help='hidden1_units')
parser.add_argument(
'--hidden2_units', type=int, default=50, help='hidden2_units')
parser.add_argument(
'--hidden3_units', type=int, default=25, help='hidden3_units')
args = parser.parse_args()
return args
models/rank/wide_deep/data/create_data.sh
浏览文件 @ 246a3535
mkdir train_data
mkdir test_data
train_path="adult.data"
test_path="adult.test"
train_path="data/adult.data"
test_path="data/adult.test"
train_data_path="./train_data/train_data.csv"
test_data_path="./test_data/test_data.csv"
pip install -r requirements.txt
#pip install -r requirements.txt
wget -P data/ https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data
wget -P data/ https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.test
......
models/rank/wide_deep/data/data_preparation.py
浏览文件 @ 246a3535
......@@ -109,6 +109,7 @@ def build_model_columns(train_data_path, test_data_path):
train_df[categorical_columns], columns=categorical_columns)
test_df_temp = pd.get_dummies(
test_df[categorical_columns], columns=categorical_columns)
train_df = train_df.join(train_df_temp)
test_df = test_df.join(test_df_temp)
......@@ -121,22 +122,26 @@ def build_model_columns(train_data_path, test_data_path):
lambda x: 1 if x == '>50K' else 0)
test_df['label'] = test_df['income_bracket'].apply(
lambda x: 1 if x == '>50K' else 0)
'''
with io.open('train_data/columns.txt', 'w') as f:
write_str = str(len(wide_columns)) + '\n' + str(len(
deep_columns)) + '\n'
f.write(write_str)
f.write(u"{}".format(write_str))
f.close()
with io.open('test_data/columns.txt', 'w') as f:
write_str = str(len(wide_columns)) + '\n' + str(len(
deep_columns)) + '\n'
f.write(write_str)
f.close()
f.write(u"{}".format(write_str))
train_df[wide_columns + deep_columns + ['label']].fillna(0).to_csv(
train_data_path, index=False)
test_df[wide_columns + deep_columns + ['label']].fillna(0).to_csv(
test_data_path, index=False)
f.close()
'''
train_df = train_df[wide_columns + deep_columns + ['label']]
train_df = train_df.fillna(0).astype(int)
train_df.to_csv(train_data_path, index=False, header=0)
test_df = test_df[wide_columns + deep_columns + ['label']]
test_df = test_df.fillna(0).astype(int)
test_df.to_csv(test_data_path, index=False, header=0)
def clean_file(train_path, test_path, train_data_path, test_data_path):
......
models/rank/wide_deep/data/get_slot_data.py
浏览文件 @ 246a3535
......@@ -29,7 +29,7 @@ class Reader(dg.MultiSlotDataGenerator):
def _process_line(self, line):
line = line.strip().split(',')
features = list(map(float, line))
features = list(map(int, map(float, line)))
wide_feat = features[0:8]
deep_feat = features[8:58 + 8]
label = features[-1]
......
models/rank/wide_deep/data/run.sh
浏览文件 @ 246a3535
......@@ -7,6 +7,7 @@ do
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
......
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