def test_fit_from_datasets(self):
uf_as_dataset = create_tensorrec_dataset_from_sparse_matrix(
self.user_features)
if_as_dataset = create_tensorrec_dataset_from_sparse_matrix(
self.item_features)
int_as_dataset = create_tensorrec_dataset_from_sparse_matrix(
self.interactions)
model = TensorRec(n_components=10)
model.fit(int_as_dataset, uf_as_dataset, if_as_dataset, epochs=10)
def test_fit_interactions_as_dataset(self):
int_as_dataset = create_tensorrec_dataset_from_sparse_matrix(
self.interactions, False)
model = TensorRec(n_components=10)
model.fit(int_as_dataset,
self.user_features,
self.item_features,
epochs=10)
def test_fit_fail_batching_dataset(self):
model = TensorRec(n_components=10)
interactions_as_dataset = create_tensorrec_dataset_from_sparse_matrix(
self.interactions)
with self.assertRaises(BatchNonSparseInputException):
model.fit(interactions_as_dataset,
self.user_features,
self.item_features,
epochs=10,
user_batch_size=2)
model = TensorRec(n_components=2,
biased=False,
loss_graph=BalancedWMRBLossGraph(),
item_repr_graph=ReLURepresentationGraph(),
n_tastes=3)
# Make some random selections of movies and users we want to plot
movies_to_plot = np.random.choice(a=item_features.shape[0],
size=50,
replace=False)
user_to_plot = np.random.choice(a=user_features.shape[0],
size=100,
replace=False)
# Coerce data to datasets for faster fitting
train_interactions_ds = create_tensorrec_dataset_from_sparse_matrix(
train_interactions)
user_features_ds = create_tensorrec_dataset_from_sparse_matrix(user_features)
item_features_ds = create_tensorrec_dataset_from_sparse_matrix(item_features)
# Iterate through 1000 epochs, outputting a JPG plot each epoch
for epoch in range(epochs):
model.fit_partial(interactions=train_interactions_ds,
user_features=user_features_ds,
item_features=item_features_ds,
**fit_kwargs)
# The position of a movie or user is that movie's/user's 2-dimensional representation.
movie_positions = model.predict_item_representation(item_features_ds)
user_positions = model.predict_user_representation(user_features_ds)
# Handle multiple tastes, if applicable. If there are more than 1 taste per user, only the first of each user's
fit_kwargs = {'epochs': 1, 'alpha': 0.0001, 'verbose': True, 'learning_rate': .01,
'n_sampled_items': int(item_features.shape[0] * .1)}
# Build the TensorRec model
model = TensorRec(n_components=2,
biased=False,
loss_graph=BalancedWMRBLossGraph(),
item_repr_graph=ReLURepresentationGraph(),
n_tastes=3)
# Make some random selections of movies and users we want to plot
movies_to_plot = np.random.choice(a=item_features.shape[0], size=50, replace=False)
user_to_plot = np.random.choice(a=user_features.shape[0], size=100, replace=False)
# Coerce data to datasets for faster fitting
train_interactions_ds = create_tensorrec_dataset_from_sparse_matrix(train_interactions)
user_features_ds = create_tensorrec_dataset_from_sparse_matrix(user_features)
item_features_ds = create_tensorrec_dataset_from_sparse_matrix(item_features)
# Iterate through 1000 epochs, outputting a JPG plot each epoch
for epoch in range(epochs):
model.fit_partial(interactions=train_interactions_ds,
user_features=user_features_ds,
item_features=item_features_ds,
**fit_kwargs)
# The position of a movie or user is that movie's/user's 2-dimensional representation.
movie_positions = model.predict_item_representation(item_features_ds)
user_positions = model.predict_user_representation(user_features_ds)
# Handle multiple tastes, if applicable. If there are more than 1 taste per user, only the first of each user's
def test_fit_from_datasets(self):
uf_as_dataset = create_tensorrec_dataset_from_sparse_matrix(self.user_features)
if_as_dataset = create_tensorrec_dataset_from_sparse_matrix(self.item_features)
int_as_dataset = create_tensorrec_dataset_from_sparse_matrix(self.interactions)
model = TensorRec(n_components=10)
model.fit(int_as_dataset, uf_as_dataset, if_as_dataset, epochs=10)
def test_fit_fail_batching_dataset(self):
model = TensorRec(n_components=10)
interactions_as_dataset = create_tensorrec_dataset_from_sparse_matrix(self.interactions)
with self.assertRaises(ValueError):
model.fit(interactions_as_dataset, self.user_features, self.item_features, epochs=10, user_batch_size=2)
model = TensorRec(n_components=2,
biased=False,
loss_graph=BalancedWMRBLossGraph(),
item_repr_graph=ReLURepresentationGraph(),
n_tastes=3)
# Make some random selections of movies and users we want to plot
movies_to_plot = np.random.choice(a=item_features.shape[0],
size=50,
replace=False)
user_to_plot = np.random.choice(a=user_features.shape[0],
size=100,
replace=False)
# Coerce data to datasets for faster fitting
train_interactions_ds = create_tensorrec_dataset_from_sparse_matrix(
train_interactions, is_coo=False)
user_features_ds = create_tensorrec_dataset_from_sparse_matrix(user_features,
is_coo=True)
item_features_ds = create_tensorrec_dataset_from_sparse_matrix(item_features,
is_coo=True)
# Iterate through 1000 epochs, outputting a JPG plot each epoch
for epoch in range(epochs):
model.fit_partial(interactions=train_interactions_ds,
user_features=user_features_ds,
item_features=item_features_ds,
**fit_kwargs)
# The position of a movie or user is that movie's/user's 2-dimensional representation.
movie_positions = model.predict_item_representation(item_features_ds)
user_positions = model.predict_user_representation(user_features_ds)
