"""
This module describes how to use the train_test_split() function.
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from amaze import SVD
from amaze import Dataset
from amaze import accuracy
from amaze.model_selection import train_test_split
# Load the movielens-100k dataset (download it if needed),
data = Dataset.load_builtin('ml-100k')
# sample random trainset and testset
# test set is made of 25% of the ratings.
trainset, testset = train_test_split(data, test_size=.25)
# We'll use the famous SVD algorithm.
algo = SVD()
# Train the algorithm on the trainset, and predict ratings for the testset
algo.fit(trainset)
predictions = algo.test(testset)
# Then compute RMSE
accuracy.rmse(predictions)
"""
from __future__ import (absolute_import, division, print_function,
unicode_literals)
from amaze import Dataset
from amaze import SVD
from amaze import accuracy
from amaze.model_selection import KFold
data = Dataset.load_builtin('ml-100k')
algo = SVD()
trainset = data.build_full_trainset()
algo.fit(trainset)
testset = trainset.build_testset()
predictions = algo.test(testset)
# RMSE should be low as we are biased
accuracy.rmse(predictions, verbose=True) # ~ 0.68 (which is low)
# We can also do this during a cross-validation procedure!
print('CV procedure:')
kf = KFold(n_splits=3)
for i, (trainset_cv, testset_cv) in enumerate(kf.split(data)):
print('fold number', i + 1)
algo.fit(trainset_cv)
print('On testset,', end=' ')