Metadata-Version: 2.1
Name: pyFFM
Version: 0.0.4
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.
        
        Supports classification and regression.
        
        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)
        
        from pyffm.test.data import sample_df  # Small training data sample 
        
        # Make sure your file has a label column, default name is 'click' but you can either rename it or pass in label=label_column_name
        
        # Balance the dataset so we get some non-zero predictions (very small training sample)
        balanced_df = sample_df[sample_df['click'] == 1].append(sample_df[sample_df['click'] == 0].sample(n=1000)).sample(frac=1)
        
        train_data = balanced_df.sample(frac=0.9)
        predict_data = balanced_df.drop(train_data.index)
        
        pyffm.train(train_data)
        preds = pyffm.predict(predict_data.drop(columns='click'))
        
        
        ```
        
        Sample data from:  
        https://github.com/ycjuan/libffm  
        and:  
        https://www.kaggle.com/c/criteo-display-ad-challenge
        
        Created using the algorithm described in the original paper:  
        https://www.csie.ntu.edu.tw/~cjlin/libffm/
        
        
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