def test_save_and_load_model(self):
model = TensorRec(n_components=10)
model.fit(self.interactions,
self.user_features,
self.item_features,
epochs=10)
predictions = model.predict(user_features=self.user_features,
item_features=self.item_features)
ranks = model.predict_rank(user_features=self.user_features,
item_features=self.item_features)
model.save_model(directory_path=self.test_dir)
# Check that, after saving, the same predictions come back
predictions_after_save = model.predict(
user_features=self.user_features, item_features=self.item_features)
ranks_after_save = model.predict_rank(user_features=self.user_features,
item_features=self.item_features)
self.assertTrue((predictions == predictions_after_save).all())
self.assertTrue((ranks == ranks_after_save).all())
# Blow away the session
set_session(None)
tf.reset_default_graph()
# Reload the model, predict, and check for equal predictions
new_model = TensorRec.load_model(directory_path=self.test_dir)
new_predictions = new_model.predict(user_features=self.user_features,
item_features=self.item_features)
new_ranks = new_model.predict_rank(user_features=self.user_features,
item_features=self.item_features)
self.assertTrue((predictions == new_predictions).all())
self.assertTrue((ranks == new_ranks).all())
def setUpClass(cls):
cls.n_user_features = 200
cls.n_item_features = 150
cls.interactions, cls.user_features, cls.item_features = generate_dummy_data(
num_users=15,
num_items=30,
interaction_density=.5,
num_user_features=cls.n_user_features,
num_item_features=cls.n_item_features,
n_features_per_user=20,
n_features_per_item=20,
pos_int_ratio=.5)
set_session(None)
cls.temp_dir = tempfile.mkdtemp()
cls.interactions_path = os.path.join(cls.temp_dir,
'interactions.tfrecord')
cls.user_features_path = os.path.join(cls.temp_dir,
'user_features.tfrecord')
cls.item_features_path = os.path.join(cls.temp_dir,
'item_features.tfrecord')
write_tfrecord_from_sparse_matrix(cls.user_features_path,
cls.user_features)
write_tfrecord_from_sparse_matrix(cls.item_features_path,
cls.item_features)
write_tfrecord_from_sparse_matrix(cls.interactions_path,
cls.interactions)
def test_fit_from_tfrecords(self):
set_session(None)
model = TensorRec(n_components=10)
model.fit(self.interactions_path,
self.user_features_path,
self.item_features_path,
epochs=10)
def test_save_and_load_model(self):
model = TensorRec(n_components=10)
model.fit(self.interactions, self.user_features, self.item_features, epochs=10)
predictions = model.predict(user_features=self.user_features, item_features=self.item_features)
ranks = model.predict_rank(user_features=self.user_features, item_features=self.item_features)
model.save_model(directory_path=self.test_dir)
# Check that, after saving, the same predictions come back
predictions_after_save = model.predict(user_features=self.user_features, item_features=self.item_features)
ranks_after_save = model.predict_rank(user_features=self.user_features, item_features=self.item_features)
self.assertTrue((predictions == predictions_after_save).all())
self.assertTrue((ranks == ranks_after_save).all())
# Blow away the session
set_session(None)
tf.reset_default_graph()
# Reload the model, predict, and check for equal predictions
new_model = TensorRec.load_model(directory_path=self.test_dir)
new_predictions = new_model.predict(user_features=self.user_features, item_features=self.item_features)
new_ranks = new_model.predict_rank(user_features=self.user_features, item_features=self.item_features)
self.assertTrue((predictions == new_predictions).all())
self.assertTrue((ranks == new_ranks).all())
def setUpClass(cls):
cls.interactions, cls.user_features, cls.item_features = generate_dummy_data(
num_users=15, num_items=30, interaction_density=.5, num_user_features=200, num_item_features=200,
n_features_per_user=20, n_features_per_item=20, pos_int_ratio=.5
)
tf.reset_default_graph()
set_session(None)
def setUpClass(cls):
# Blow away an existing session to avoid 'tf_map_func not found' error
set_session(None)
cls.n_users = 15
cls.n_items = 30
int_ds, uf_ds, if_ds = generate_dummy_data(
num_users=cls.n_users, num_items=cls.n_items, interaction_density=.5, num_user_features=200,
num_item_features=200, n_features_per_user=20, n_features_per_item=20, pos_int_ratio=.5
)
cls.temp_dir = tempfile.mkdtemp()
cls.interactions = os.path.join(cls.temp_dir, 'interactions.tfrecord')
cls.user_features = os.path.join(cls.temp_dir, 'user_features.tfrecord')
cls.item_features = os.path.join(cls.temp_dir, 'item_features.tfrecord')
write_tfrecord_from_sparse_matrix(cls.interactions, int_ds)
write_tfrecord_from_sparse_matrix(cls.user_features, uf_ds)
write_tfrecord_from_sparse_matrix(cls.item_features, if_ds)
cls.standard_model = TensorRec(n_components=10)
cls.standard_model.fit(cls.interactions, cls.user_features, cls.item_features, epochs=10)
cls.unbiased_model = TensorRec(n_components=10, biased=False)
cls.unbiased_model.fit(cls.interactions, cls.user_features, cls.item_features, epochs=10)
def setUpClass(cls):
cls.n_user_features = 200
cls.n_item_features = 150
cls.interactions, cls.user_features, cls.item_features = generate_dummy_data(
num_users=15, num_items=30, interaction_density=.5, num_user_features=cls.n_user_features,
num_item_features=cls.n_item_features, n_features_per_user=20, n_features_per_item=20, pos_int_ratio=.5
)
set_session(None)
cls.temp_dir = tempfile.mkdtemp()
cls.interactions_path = os.path.join(cls.temp_dir, 'interactions.tfrecord')
cls.user_features_path = os.path.join(cls.temp_dir, 'user_features.tfrecord')
cls.item_features_path = os.path.join(cls.temp_dir, 'item_features.tfrecord')
write_tfrecord_from_sparse_matrix(cls.user_features_path, cls.user_features)
write_tfrecord_from_sparse_matrix(cls.item_features_path, cls.item_features)
write_tfrecord_from_sparse_matrix(cls.interactions_path, cls.interactions)
def test_fit_from_tfrecords(self):
set_session(None)
model = TensorRec(n_components=10)
model.fit(self.interactions_path, self.user_features_path, self.item_features_path, epochs=10)
