流量预测（DNN、DNN + GRU + GRU + Attention、DNN + GRU + AIGRU）

[](https://travis-ci.org/GitHub-HongweiZhang/prediction-flow) [](https://badge.fury.io/py/prediction-flow) # 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 column_flow. The name parameter is used to index the column raw data from input data frame. The column_flow 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](https://arxiv.org/pdf/1606.07792.pdf) | | DeepFM | [IJCAI 2017][DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](http://www.ijcai.org/proceedings/2017/0239.pdf) | | DIN | [KDD 2018][Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf) | | DNN + GRU + GRU + Attention | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf) | | DNN + GRU + AIGRU | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf) | | DNN + GRU + AGRU | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf) | | DNN + GRU + AUGRU | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf) | | DIEN | [AAAI 2019][Deep Interest Evolution Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1809.03672.pdf) | | 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](examples/movielens/ml-1m/preprocess.ipynb) * Run the model. [movielens-1m.ipynb](examples/movielens/movielens-1m.ipynb) ### amazon * Prepare the dataset. [prepare_neg.ipynb](examples/amazon/prepare_neg.ipynb) * Run the model. [amazon.ipynb](examples/amazon/amazon.ipynb) * An example using [pytorch-lightning](https://github.com/williamFalcon/pytorch-lightning). [amazon-lightning.ipynb](examples/amazon/amazon-lightning.ipynb) **accuracy**  ## acknowledge and reference * Referring the design from [DeepCTR](https://github.com/shenweichen/DeepCTR), the features are divided into dense (class Number), sparse (class Category), sequence (class Sequence) types.