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deepray-0.1.2
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مشاهده بیشترتوضیحات
A new Modular, Scalable, Configurable, Easy-to-Use and Extend infrastructure for Deep Learning based classification.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | deepray-0.1.2 |
| نام | deepray |
| نسخه کتابخانه | 0.1.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Hailin Fu |
| ایمیل نویسنده | hailinfufu@outlook.com |
| آدرس صفحه اصلی | https://github.com/fuhailin/deepray |
| آدرس اینترنتی | https://pypi.org/project/deepray/ |
| مجوز | Apache-2.0 |
**DeePray** (`深度祈祷`): A new Modular, Scalable, Configurable, Easy-to-Use and Extend infrastructure for Deep Learning based Recommendation.
[](https://deepray.readthedocs.io/en/latest/?badge=latest)
[](https://badge.fury.io/py/deepray)
[](https://badge.fury.io/gh/fuhailin%2Fdeepray)
## Introduction
The DeePray library offers state-of-the-art algorithms for [deep learning recommendation].
DeePray is built on latest [TensorFlow 2][(https://tensorflow.org/)] and designed with modular structure，
making it easy to discover patterns and answer questions about tabular-structed data.
The main goals of DeePray:
- Easy to use, newbies can get hands dirty with deep learning quickly
- Good performance with web-scale data
- Easy to extend, Modular architecture let you build your Neural network like playing LEGO!
Let's Get Started! Please refer to the official docs at https://deepray.readthedocs.io/en/latest/.
## Installation
#### Install DeePray using PyPI:
To install DeePray library from [PyPI](https://pypi.org/) using `pip`, execute the following command:
```
pip install deepray
```
#### Install DeePray from Github source:
First, clone the DeePray repository using `git`:
```
git clone https://github.com/fuhailin/deepray.git
```
Then, `cd` to the deepray folder, and install the library by executing the following commands:
```
cd deepray
pip install .
```
## Tutorial
### Census Adult Data Set
#### Data preparation
In your tabular data, specify **NUMERICAL** for your continue features, **CATEGORY** for categorical features, **VARIABLE** for variable length features, and obviously **LABEL** for label column. Then process them to to [TFRecord](https://www.tensorflow.org/tutorials/load_data/tfrecord) format into order to get good performance with large-scale dataset.
```python
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
from deepray.utils.converter import CSV2TFRecord
# http://archive.ics.uci.edu/ml/datasets/Adult
train_data = 'DeePray/examples/census/data/raw_data/adult_data.csv'
df = pd.read_csv(train_data)
df['income_label'] = (df["income_bracket"].apply(lambda x: ">50K" in x)).astype(int)
df.pop('income_bracket')
NUMERICAL_FEATURES = ['age', 'fnlwgt', 'hours_per_week', 'capital_gain', 'capital_loss', 'education_num']
CATEGORY_FEATURES = [col for col in df.columns if col != LABEL and col not in NUMERICAL_FEATURES]
LABEL = ['income_label']
for feat in CATEGORY_FEATURES:
lbe = LabelEncoder()
df[feat] = lbe.fit_transform(df[feat])
# Feature normilization
mms = MinMaxScaler(feature_range=(0, 1))
df[NUMERICAL_FEATURES] = mms.fit_transform(df[NUMERICAL_FEATURES])
prebatch = 1 # flags.prebatch
converter = CSV2TFRecord(LABEL, NUMERICAL_FEATURES, CATEGORY_FEATURES, VARIABLE_FEATURES=[], gzip=False)
converter.write_feature_map(df, './data/feature_map.csv')
train_df, valid_df = train_test_split(df, test_size=0.2)
converter(train_df, out_file='./data/train.tfrecord', prebatch=prebatch)
converter(valid_df, out_file='./data/valid.tfrecord', prebatch=prebatch)
```
You will get a feature map file like that:
```
9,workclass,CATEGORICAL
16,education,CATEGORICAL
7,marital_status,CATEGORICAL
15,occupation,CATEGORICAL
6,relationship,CATEGORICAL
5,race,CATEGORICAL
2,gender,CATEGORICAL
42,native_country,CATEGORICAL
1,hours_per_week,NUMERICAL
1,capital_gain,NUMERICAL
1,age,NUMERICAL
1,fnlwgt,NUMERICAL
1,capital_loss,NUMERICAL
1,education_num,NUMERICAL
2,income_label,LABEL
```
And then create two txt file `train`and `valid` separately to record train set TFRecords and valid set TFRecords file path.
### Choose your model, Training and evaluation
```python
"""
build and train model
"""
import sys
from absl import app, flags
import deepray as dp
from deepray.base.trainer import train
from deepray.model.build_model import BuildModel
FLAGS = flags.FLAGS
def main(flags=None):
FLAGS(flags, known_only=True)
flags = FLAGS
model = BuildModel(flags)
history = train(model)
print(history)
argv = [
sys.argv[0],
'--model=lr',
'--train_data=/Users/vincent/Projects/DeePray/examples/census/data/train',
'--valid_data=/Users/vincent/Projects/DeePray/examples/census/data/valid',
'--feature_map=/Users/vincent/Projects/DeePray/examples/census/data/feature_map.csv',
'--learning_rate=0.01',
'--epochs=10',
'--batch_size=64',
]
main(flags=argv)
```
## Models List
| Titile | Booktitle | Resources |
| ------------------------------------------------------------ | :---------: | ------------------------------------------------------------ |
| **FM**: Factorization Machines | ICDM'2010 | [[pdf]](https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_fm.py) |
| **FFM**: Field-aware Factorization Machines for CTR Prediction | RecSys'2016 | [[pdf]](https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_ffm.py) |
| **FNN**: Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction | ECIR'2016 | [[pdf]](https://arxiv.org/abs/1601.02376)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_fnn.py) |
| **PNN**: Product-based Neural Networks for User Response Prediction | ICDM'2016 | [[pdf]](https://arxiv.org/abs/1611.00144)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_pnn.py) |
| **Wide&Deep**: Wide & Deep Learning for Recommender Systems | DLRS'2016 | [[pdf]](https://arxiv.org/pdf/1606.07792)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_wdl.py) |
| **AFM**: Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networks | IJCAI'2017 | [[pdf]](https://arxiv.org/abs/1708.04617)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_afm.py) |
| **NFM**: Neural Factorization Machines for Sparse Predictive Analytics | SIGIR'2017 | [[pdf]](https://arxiv.org/abs/1708.05027)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_nfm.py) |
| **DeepFM**: DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[C] | IJCAI'2017 | [[pdf]](https://arxiv.org/abs/1703.04247) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_deepfm.py) |
| **DCN**: Deep & Cross Network for Ad Click Predictions | ADKDD'2017 | [[pdf]](https://arxiv.org/abs/1708.05123) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_dcn.py) |
| **xDeepFM**: xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems | KDD'2018 | [[pdf]](https://arxiv.org/abs/1803.05170) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_xdeepfm.py) |
| **DIN**: DIN: Deep Interest Network for Click-Through Rate Prediction | KDD'2018 | [[pdf]](https://arxiv.org/abs/1706.06978) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_dien.py) |
| **DIEN**: DIEN: Deep Interest Evolution Network for Click-Through Rate Prediction | AAAI'2019 | [[pdf]](https://arxiv.org/abs/1809.03672) [[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_dien.py) |
| **DSIN**: Deep Session Interest Network for Click-Through Rate Prediction | IJCAI'2019 | [[pdf]](https://arxiv.org/abs/1905.06482)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_dsin.py) |
| **AutoInt**: Automatic Feature Interaction Learning via Self-Attentive Neural Networks | CIKM'2019 | [[pdf]](https://arxiv.org/abs/1810.11921)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_autoint.py) |
| **FLEN**: Leveraging Field for Scalable CTR Prediction | AAAI'2020 | [[pdf]](https://arxiv.org/pdf/1911.04690.pdf)[[code]](https://github.com/fuhailin/DeePray/blob/master/deepray/model/model_flen.py) |
| **DFN**: Deep Feedback Network for Recommendation | IJCAI'2020 | [[pdf]]()[[code]](TODO) |
# How to build your own model with DeePray
Inheriting `BaseCTRModel` class from `from deepray.model.model_ctr`, and implement your own `build_network()` method!
# Contribution
DeePray is still under development, and call for contributions!
```
* Hailin Fu (`Hailin <https://github.com/fuhailin>`)
* Call for contributions!
```
让DeePray成为推荐算法新基建需要你的贡献
# Citing
DeePray is designed, developed and supported by [Hailin](https://github.com/fuhailin/).
If you use any part of this library in your research, please cite it using the following BibTex entry
```latex
@misc{DeePray,
author = {Hailin Fu},
title = {DeePray: A new Modular, Scalable, Configurable, Easy-to-Use and Extend infrastructure for Deep Learning based Recommendation},
year = {2020},
publisher = {GitHub},
journal = {GitHub Repository},
howpublished = {\url{https://github.com/fuhailin/deepray}},
}
```
# License
Copyright (c) Copyright © 2020 The DeePray Authors<Hailin Fu>. All Rights Reserved.
Licensed under the [Apach](LICENSE) License.
# Reference
https://github.com/shenweichen/DeepCTR
https://github.com/aimetrics/jarvis
https://github.com/shichence/AutoInt
# Contact
If you want cooperation or have any questions, please follow my wechat offical account:
公众微信号ID：【StateOfTheArt】
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl deepray-0.1.2:
pip install deepray-0.1.2.whl
نصب پکیج tar.gz deepray-0.1.2:
pip install deepray-0.1.2.tar.gz