def setUpClass(cls):
cls.interactions, cls.user_features, cls.item_features = generate_dummy_data_with_indicator(
num_users=10, num_items=12, interaction_density=.5)
model = TensorRec(n_components=10)
model.fit(cls.interactions, cls.user_features, cls.item_features, epochs=10)
cls.model = model
cls.ranks = model.predict_rank(user_features=cls.user_features, item_features=cls.item_features)
def setUpClass(cls):
cls.interactions, cls.user_features, cls.item_features = generate_dummy_data_with_indicator(
num_users=10, num_items=12, interaction_density=.5)
model = TensorRec(n_components=10)
model.fit(cls.interactions,
cls.user_features,
cls.item_features,
epochs=10)
cls.model = model
def metric_test(self):
""" uses tensorrec eval as benchmark for rating performance of various reco algorithms """
k = 10
latent_factor = 10
n_users = 10
n_items = 12
interactions, user_features, item_features = util.generate_dummy_data_with_indicator(
num_users=n_users, num_items=n_items, interaction_density=.5)
print("interactiosn shape={}".format(np.shape(interactions)))
print("user features shape={}".format(np.shape(
user_features.toarray())))
print("item features shape={}".format(np.shape(
item_features.toarray())))
model = TensorRec(n_components=latent_factor)
model.fit(interactions, user_features, item_features, epochs=19)
ranks = model.predict_rank(user_features=user_features,
item_features=item_features)
print("Ranks shape={}".format(np.shape(ranks)))
self.assertTrue(np.shape(interactions) == np.shape(ranks))
tr_recall_result = eval.recall_at_k(predicted_ranks=ranks,
test_interactions=interactions,
k=k,
preserve_rows=False)
# print (tr_recall_result.mean())
tr_precision_result = eval.precision_at_k(
predicted_ranks=ranks,
test_interactions=interactions,
k=k,
preserve_rows=False)
# print(tr_precision_result.mean())
# we need csr for interactions data
interactions_ = interactions.tocsr()
recall_result = metrics.recall_at_k(ranks,
interactions_,
k=k,
preserve_rows=False)
# print(recall_result.mean())
precision_result = metrics.precision_at_k(ranks,
interactions_,
k=k,
preserve_rows=False)
# print (precision_result.mean())
self.assertTrue(tr_recall_result.mean() == recall_result.mean())
self.assertTrue(tr_precision_result.mean() == precision_result.mean())
def setUpClass(cls):
cls.interactions, cls.user_features, cls.item_features = generate_dummy_data_with_indicator(
num_users=10, num_items=20, interaction_density=.5)
cls.standard_model = TensorRec(n_components=10,
normalize_items=True,
normalize_users=True)
cls.standard_model.fit(cls.interactions,
cls.user_features,
cls.item_features,
epochs=10)
cls.unbiased_model = TensorRec(n_components=10,
normalize_items=True,
normalize_users=True,
biased=False)
cls.unbiased_model.fit(cls.interactions,
cls.user_features,
cls.item_features,
epochs=10)
def setUpClass(cls): cls.interactions, cls.user_features, cls.item_features = generate_dummy_data_with_indicator( num_users=10, num_items=20, interaction_density=.5 )
def setUpClass(cls):
cls.interactions, cls.user_features, cls.item_features = generate_dummy_data_with_indicator(
num_users=10, num_items=12, interaction_density=.5)
