DeepFM-pytorch

re-implementation of DeepFM with pytorch 1.0

Usage

Input Format for fit method in model

This implementation requires model to receive data batches in the following format:

• label: target of each sample in the dataset (1/0 for classification)
• idxs: [[ind1_1, ind1_2, ...], [ind2_1, ind2_2, ...], ..., [indi_1, indi_2, ..., indi_j, ...], ...]
  • indi_j is the feature index of feature field j of sample i in the dataset
• vals: [[val1_1, val1_2, ...], [val2_1, val2_2, ...], ..., [vali_1, vali_2, ..., vali_j, ...], ...]
  • vali_j is the feature value of feature field j of sample i in the dataset
  • vali_j can be either binary (1/0, for binary/categorical features) or float (e.g., 12.34, for numerical features)

how to run

The main.py has already provided methods to process numeric and categorical features, so the only thing you need to do is changing data_path and hard coding the column names to tell the program which columns you want to re-format.

e.g.

• dummy_cols is the list of template codes to show result index;
• category_cols is the list of categorical column names
• Confirm that there is no other columns except columns mentioned above in your own dataframe, then the code will automatically extract the numeric_cols

data = pd.read_csv('./temp_data.csv').reset_index(drop=True)
category_cols = ['CODE_GENDER', 'FLAG_OWN_CAR', 'FLAG_OWN_REALTY']
dummy_cols = ['SK_ID_CURR']
target_col = 'TARGET'
numeric_cols = list(set(data.columns) - set(category_cols + dummy_cols + [target_col]))

Reference

DeepFM: A Factorization-Machine based Neural Network for CTR Prediction, Huifeng Guo, Ruiming Tang, Yunming Yey, Zhenguo Li, Xiuqiang He.