# coding: utf-8
# This file test the xlearn python package.
# We create a ffm model for binary classification problem.
# The dataset comes from the criteo CTR.
from __future__ import absolute_import
import xlearn as xl

# Training task
ffm_model = xl.create_ffm() # Use field-aware factorization machine
ffm_model.setTrain("./small_train.txt")  # Training data
ffm_model.setValidate("./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("./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")
