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 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 test_basic_usage(self):
# Build the model with default parameters
model = TensorRec()
# Generate some dummy data
interactions, user_features, item_features = generate_dummy_data(
num_users=100, num_items=150, interaction_density=.05)
# Fit the model
model.fit(interactions,
user_features,
item_features,
epochs=5,
verbose=True)
# Predict scores for user 75 on items 100, 101, and 102
predictions = model.predict(user_ids=[75, 75, 75],
item_ids=[100, 101, 102],
user_features=user_features,
item_features=item_features)
# Calculate and print the recall at 10
r_at_k = recall_at_k(model,
interactions,
k=10,
user_features=user_features,
item_features=item_features)
print(np.mean(r_at_k))
self.assertIsNotNone(predictions)
def test_fit_predict_unbiased(self): model = TensorRec(n_components=10, biased=False) 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) self.assertEqual(predictions.shape, (self.user_features.shape[0], self.item_features.shape[0]))
def test_predict(self):
interactions, user_features, item_features = generate_dummy_data(
num_users=10, num_items=20, interaction_density=.5)
model = TensorRec(n_components=10)
model.fit(interactions, user_features, item_features, epochs=10)
predictions = model.predict(user_features=user_features,
item_features=item_features)
self.assertEqual(predictions.shape,
(user_features.shape[0], item_features.shape[0]))
def test_predict(self):
interactions, user_features, item_features = generate_dummy_data(
num_users=10, num_items=10, interaction_density=.5)
model = TensorRec(n_components=10)
model.fit(interactions, user_features, item_features, epochs=10)
predictions = model.predict(user_ids=[1, 2, 3],
item_ids=[4, 5, 6],
user_features=user_features,
item_features=item_features)
self.assertEqual(len(predictions), 3)
def test_predict_fail_unfit(self):
model = TensorRec()
with self.assertRaises(ModelNotFitException):
model.predict(self.user_features, self.item_features)
with self.assertRaises(ModelNotFitException):
model.predict_rank(self.user_features, self.item_features)
with self.assertRaises(ModelNotFitException):
model.predict_user_representation(self.user_features)
with self.assertRaises(ModelNotFitException):
model.predict_item_representation(self.item_features)
with self.assertRaises(ModelNotFitException):
model.predict_user_attention_representation(self.user_features)
with self.assertRaises(ModelNotFitException):
model.predict_similar_items(self.item_features,
item_ids=[1],
n_similar=5)
with self.assertRaises(ModelNotFitException):
model.predict_item_bias(self.item_features)
with self.assertRaises(ModelNotFitException):
model.predict_user_bias(self.user_features)
def test_save_and_load_model_same_session(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) # 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.assertEqual(predictions.all(), new_predictions.all()) self.assertEqual(ranks.all(), new_ranks.all())
def test_movie_lens_fit_wmrb(self):
"""
This test checks whether the movielens getter works and that the resulting data is viable for fitting/testing a
TensorRec model.
"""
train_interactions, test_interactions, user_features, item_features = self.movielens_100k
model = TensorRec(loss_graph=wmrb_loss)
model.fit(interactions=train_interactions,
user_features=user_features,
item_features=item_features)
predictions = model.predict(user_features=user_features,
item_features=item_features)
self.assertIsNotNone(predictions)
def test_save_and_load_model_same_session(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)
# 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 test_movie_lens_fit(self):
"""
This test checks whether the movielens getter works and that the resulting data is viable for fitting/testing a
TensorRec model.
"""
train_interactions, test_interactions, user_features, item_features, _ = self.movielens_100k
model = TensorRec()
model.fit(interactions=train_interactions,
user_features=user_features,
item_features=item_features,
epochs=5)
predictions = model.predict(user_features=user_features,
item_features=item_features)
self.assertIsNotNone(predictions)