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 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 setUpClass(cls):
cls.n_users = 15
cls.n_items = 30
cls.interactions, cls.user_features, cls.item_features = 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,
return_datasets=True)
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 test_fit(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)
# Ensure that the nodes have been built
self.assertIsNotNone(model.tf_prediction_dense)
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 setUpClass(cls):
cls.n_users = 15
cls.n_items = 30
cls.interactions, cls.user_features, cls.item_features = 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.standard_model = TensorRec(
n_components=10,
n_tastes=3,
user_repr_graph=NormalizedLinearRepresentationGraph(),
attention_graph=LinearRepresentationGraph())
cls.standard_model.fit(cls.interactions,
cls.user_features,
cls.item_features,
epochs=10)
cls.unbiased_model = TensorRec(
n_components=10,
n_tastes=3,
biased=False,
user_repr_graph=NormalizedLinearRepresentationGraph(),
attention_graph=LinearRepresentationGraph())
cls.unbiased_model.fit(cls.interactions,
cls.user_features,
cls.item_features,
epochs=10)
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.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
)
cls.standard_model = TensorRec(n_components=10)
cls.standard_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(
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 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 setUpClass(cls):
cls.n_users = 15
cls.n_items = 30
cls.interactions, cls.user_features, cls.item_features = 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,
return_datasets=True
)
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 test_fit(self, name, user_repr, item_repr, n_user_features,
n_item_features, n_components):
interactions, user_features, item_features = generate_dummy_data(
num_users=15,
num_items=30,
interaction_density=.5,
num_user_features=n_user_features,
num_item_features=n_item_features,
n_features_per_user=20,
n_features_per_item=20,
pos_int_ratio=.5)
model = TensorRec(n_components=n_components,
user_repr_graph=user_repr(),
item_repr_graph=item_repr())
model.fit(interactions, user_features, item_features, epochs=10)
# Ensure that the nodes have been built
self.assertIsNotNone(model.tf_prediction)
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 setUpClass(cls):
cls.n_users = 15
cls.n_items = 30
cls.interactions, cls.user_features, cls.item_features = 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.standard_model = TensorRec(n_components=10,
n_tastes=3,
user_repr_graph=NormalizedLinearRepresentationGraph())
cls.standard_model.fit(cls.interactions, cls.user_features, cls.item_features, epochs=10)
cls.unbiased_model = TensorRec(n_components=10,
n_tastes=3,
biased=False,
user_repr_graph=NormalizedLinearRepresentationGraph())
cls.unbiased_model.fit(cls.interactions, cls.user_features, cls.item_features, epochs=10)
def test_fit_fail_on_bad_dims(self):
interactions, user_features, item_features = generate_dummy_data(
num_users=15,
num_items=30,
interaction_density=.5,
num_user_features=30,
num_item_features=20,
n_features_per_user=20,
n_features_per_item=20,
pos_int_ratio=.5)
with self.assertRaises(ValueError):
model = TensorRec(
n_components=25,
user_repr_graph=FeaturePassThroughRepresentationGraph(),
item_repr_graph=LinearRepresentationGraph())
model.fit(interactions, user_features, item_features, epochs=10)
with self.assertRaises(ValueError):
model = TensorRec(
n_components=25,
user_repr_graph=LinearRepresentationGraph(),
item_repr_graph=FeaturePassThroughRepresentationGraph())
model.fit(interactions, user_features, item_features, epochs=10)
def setUpClass(cls):
cls.interactions, cls.user_features, cls.item_features = generate_dummy_data(
num_users=10, num_items=20, interaction_density=.5)
