def test_fit(self):
"""
[recommendation.models.TensorCoFi] Test size of matrix after tensorCoFi fit
"""
tf = TensorCoFi(n_users=len(self.df.user.unique()), n_items=len(self.df.item.unique()), n_factors=2)
tf.fit(self.df)
#item and user are row vectors
#self.assertEqual(len(self.df.user.unique()), tf.factors[0].shape[0])
self.assertEqual(len(self.df.item.unique()), tf.factors[1].shape[0])
def test_tensor_score_against_testfm(self):
"""
[recommendation.models.TensorCoFi] Test tensorcofi scores with test.fm benchmark
"""
evaluator = Evaluator()
tc = TensorCoFi(n_users=len(self.df.user.unique()), n_items=len(self.df.item.unique()), n_factors=2)
ptc = PyTensorCoFi()
training, testing = testfm.split.holdoutByRandom(self.df, 0.9)
items = training.item.unique()
tc.fit(training)
ptc.fit(training)
tc_score = evaluator.evaluate_model(tc, testing, all_items=items)[0]
ptc_score = evaluator.evaluate_model(ptc, testing, all_items=items)[0]
assert abs(tc_score-ptc_score) < .15, \
"TensorCoFi score is not close enough to testfm benchmark (%.3f != %.3f)" % (tc_score, ptc_score)
