Metadata-Version: 2.1
Name: pyFFM
Version: 0.0.2
Summary: Python implementation of Factorization Machines (+ Field Aware)
Home-page: https://github.com/mascaroa/pyffm
Author: Aaron Mascaro
Author-email: mascaroa1@gmail.com
License: MIT
Description: ```
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        |__\__/|   |   |    |
        |   /       
        ```
        
        *** early stage testing! ***
        
        A python implementation of Factorization Machines / Field-aware Factorization Machines with a simple interface.
        
        Installation:
        ```shell script
        pip install pyffm
        ``` 
        
        Basic example:
        ```python
        import pandas as pd
        from pyffm import PyFFM
        training_params = {'epochs': 2, 'reg_lambda': 0.002}
        pyffm = PyFFM(model="ffm", training_params=training_params)
        
        file_path = 'path/to/csv/file'
        df_in = pd.read_csv(file_path)
        # Make sure your file has a label column, default name is 'click' but you can either rename it or pass in label
        df_in.rename(columns={'label': 'click'}, inplace=True)
        
        pyffm.train(df_in)
        preds = pyffm.predict(df_in)
        
        
        ```
        
        Sample data from:
        https://github.com/ycjuan/libffm
        
        Created using the algorithm described in the original paper:
        https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf
        
        
Platform: UNKNOWN
Description-Content-Type: text/markdown
