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A python implementation of Factorization Machines / Field-aware Factorization Machines with a simple interface.
(FFM fully functioning/tested, FM still not fully tested)
Supports classification and regression.
Project Structure
pyffm
├── engine
│ ├── model
│ │ ├── __init__.py
│ │ ├── base_model.py
│ │ ├── ffm_model.py
│ │ └── fm_model.py
│ ├── __init__.py
│ ├── base_engine.py
│ ├── ffm_engine.py
│ └── fm_engine.py
├── test
│ ├── data
│ │ ├── __init__.py
│ │ ├── bigdata.te.txt
│ │ ├── bigdata.tr.txt
│ │ └── small_sample_train.csv
│ ├── __init__.py
│ ├── test_ctrengine.py
│ ├── test_pyffm.py
│ └── test_utils.py
├── __init__.py
├── pyffm.py
└── util.py
LICENSE
README.md
setup.py
Installation:
pip install pyffmBasic example:
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/
