evaluate_on_trainset.py

"""
This module descibes how to test the performances of an algorithm on the
trainset.
"""

from __future__ import (absolute_import, division, print_function,
                        unicode_literals)

from surprise import Dataset
from surprise import SVD
from surprise import accuracy
from surprise.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='  ')
    predictions = algo.test(testset_cv)
    accuracy.rmse(predictions, verbose=True)

    print('On trainset,', end=' ')
    predictions = algo.test(trainset_cv.build_testset())
    accuracy.rmse(predictions, verbose=True)