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xlearn_demo.py
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29 lines (24 loc) · 886 Bytes
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# -*- coding: utf-8 -*-
# Author: XuMing <[email protected]>
# Brief:
import xlearn as xl
# Training task
ffm_model = xl.create_ffm() # Use field-aware factorization machine
ffm_model.setTrain("../data/xlearn_data/small_train.txt") # Training data
ffm_model.setValidate("../data/xlearn_data/small_test.txt") # Validation data
# param:
# 0. binary classification
# 1. learning rate: 0.2
# 2. regular lambda: 0.002
# 3. evaluation metric: accuracy
param = {'task':'binary', 'lr':0.2,
'lambda':0.002, 'metric':'acc'}
# Start to train
# The trained model will be stored in model.out
ffm_model.fit(param, './model.out')
# Prediction task
ffm_model.setTest("../data/xlearn_data/small_test.txt") # Test data
ffm_model.setSigmoid() # Convert output to 0-1
# Start to predict
# The output result will be stored in output.txt
ffm_model.predict("./model.out", "./output.txt")