Need help with prediction-flow?
Click the “chat” button below for chat support from the developer who created it, or find similar developers for support.

About the developer

GitHub-HongweiZhang
132 Stars 34 Forks MIT License 107 Commits 1 Opened issues

Description

Deep-Learning based CTR models implemented by PyTorch

Services available

!
?

Need anything else?

Contributors list

No Data

Build Status

PyPI version

prediction-flow

prediction-flow is a Python package providing modern Deep-Learning based CTR models. Models are implemented by PyTorch.

how to use

  • Install using pip.
    pip install prediction-flow
    

feature

how to define feature

There are two parameters for all feature types, name and columnflow. The name parameter is used to index the column raw data from input data frame. The columnflow parameter is a single transformer of a list of transformers. The transformer is used to pre-process the column data before training the model.

  • dense number feature
    Number('age', StandardScaler())
    Number('ctr', None)
    
  • sparse category feature
    Category('movieId', CategoryEncoder(min_cnt=1))
    
  • var length sequence feature
    Sequence('genres', SequenceEncoder(sep='|', min_cnt=1))
    

transformer

The following transformers are provided now.

| transformer | supported feature type | detail | |--|--|--| | StandardScaler | Number | Wrapper of scikit-learn's StandardScaler. Null value must be filled in advance. | | LogTransformer | Number | Log scaler. Null value must be filled in advance. | | CategoryEncoder | Category | Converting str value to int. Null value must be filled in advance using '__UNKNOWN__'. | | SequenceEncoder | Sequence | Converting sequence str value to int. Null value must be filled in advance using '__UNKNOWN__'. |

model

| model | reference | |--|--| | DNN | - | | Wide & Deep | [DLRS 2016]Wide & Deep Learning for Recommender Systems | | DeepFM | [IJCAI 2017]DeepFM: A Factorization-Machine based Neural Network for CTR Prediction | | DIN | [KDD 2018]Deep Interest Network for Click-Through Rate Prediction | | DNN + GRU + GRU + Attention | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction | | DNN + GRU + AIGRU | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction | | DNN + GRU + AGRU | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction | | DNN + GRU + AUGRU | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction | | DIEN | [AAAI 2019]Deep Interest Evolution Network for Click-Through Rate Prediction | | OTHER | TODO |

example

movielens-1M

This dataset is just used to test the code can run, accuracy does not make sense. * Prepare the dataset. preprocess.ipynb * Run the model. movielens-1m.ipynb

amazon

accuracy

benchmark

acknowledge and reference

  • Referring the design from DeepCTR, the features are divided into dense (class Number), sparse (class Category), sequence (class Sequence) types.

We use cookies. If you continue to browse the site, you agree to the use of cookies. For more information on our use of cookies please see our Privacy Policy.