def test_stratified_split_with_user_with_only_one_interaction(
interactions_to_split_with_a_user_with_only_one_interaction, ):
with pytest.raises(ValueError):
stratified_split(
interactions=
interactions_to_split_with_a_user_with_only_one_interaction,
test_p=0.2,
seed=42,
)
def train_val_explicit_data(movielens_explicit_interactions):
return stratified_split(
interactions=movielens_explicit_interactions,
val_p=0.,
test_p=0.2,
seed=42,
)
def test_splits_vary_number_of_processes(implicit_interactions_to_split):
train_1, test_1 = stratified_split(
interactions=implicit_interactions_to_split, seed=42, processes=-1)
train_2, test_2 = stratified_split(
interactions=implicit_interactions_to_split, seed=42, processes=0)
train_3, test_3 = stratified_split(
interactions=implicit_interactions_to_split, seed=42, processes=1)
train_4, test_4 = stratified_split(
interactions=implicit_interactions_to_split, seed=42, processes=2)
# transitive property in action here
np.testing.assert_array_equal(train_1.toarray(), train_2.toarray())
np.testing.assert_array_equal(train_2.toarray(), train_3.toarray())
np.testing.assert_array_equal(train_3.toarray(), train_4.toarray())
np.testing.assert_array_equal(test_1.toarray(), test_2.toarray())
np.testing.assert_array_equal(test_2.toarray(), test_3.toarray())
np.testing.assert_array_equal(test_3.toarray(), test_4.toarray())
def run_movielens_example(epochs: int = 20, gpus: int = 0) -> None:
"""
Retrieve and split data, train and evaluate a model, and save it.
From the terminal, you can run this script with:
.. code-block:: bash
python collie/movielens/run.py --epochs 20
Parameters
----------
epochs: int
Number of epochs for model training
gpus: int
Number of gpus to train on
"""
t = Timer()
t.timecheck(' 1.0 - retrieving MovieLens 100K dataset')
df = read_movielens_df(decrement_ids=True)
t.timecheck(' 1.0 complete')
t.timecheck(' 2.0 - splitting data')
df_imp = convert_to_implicit(df)
interactions = Interactions(users=df_imp['user_id'],
items=df_imp['item_id'],
allow_missing_ids=True)
train, val, test = stratified_split(interactions, val_p=0.1, test_p=0.1)
train_loader = InteractionsDataLoader(train, batch_size=1024, shuffle=True)
val_loader = InteractionsDataLoader(val, batch_size=1024, shuffle=False)
t.timecheck(' 2.0 complete')
t.timecheck(' 3.0 - training the model')
model = MatrixFactorizationModel(train=train_loader,
val=val_loader,
dropout_p=0.05,
loss='adaptive',
lr=5e-2,
embedding_dim=10,
optimizer='adam',
weight_decay=1e-7)
trainer = CollieTrainer(model=model,
gpus=gpus,
max_epochs=epochs,
deterministic=True,
logger=False,
checkpoint_callback=False,
callbacks=[EarlyStopping(monitor='val_loss_epoch', mode='min')],
weights_summary='full',
terminate_on_nan=True)
trainer.fit(model)
model.eval()
t.timecheck('\n 3.0 complete')
t.timecheck(' 4.0 - evaluating model')
auc_score, mrr_score, mapk_score = evaluate_in_batches([auc, mrr, mapk], test, model, k=10)
print(f'AUC: {auc_score}')
print(f'MRR: {mrr_score}')
print(f'MAP@10: {mapk_score}')
t.timecheck(' 4.0 complete')
t.timecheck(' 5.0 - saving model')
absolute_data_path = DATA_PATH / 'fitted_model'
model.save_model(absolute_data_path)
t.timecheck(' 5.0 complete')
def test_test_p_negative_stratified(self, implicit_interactions_to_split):
with pytest.raises(ValueError):
stratified_split(interactions=implicit_interactions_to_split,
test_p=-0.7)
def test_test_p_equal_one_stratified(self, implicit_interactions_to_split):
with pytest.raises(ValueError):
stratified_split(interactions=implicit_interactions_to_split,
test_p=1)
def test_val_negative_but_combined_good_stratified(
self, implicit_interactions_to_split):
with pytest.raises(ValueError):
stratified_split(interactions=implicit_interactions_to_split,
val_p=-0.1,
test_p=0.3)
def test_combined_equal_one_stratified(self,
implicit_interactions_to_split):
with pytest.raises(ValueError):
stratified_split(interactions=implicit_interactions_to_split,
val_p=0.7,
test_p=0.3)
def test_combined_too_large_stratified(self,
implicit_interactions_to_split):
with pytest.raises(ValueError):
stratified_split(interactions=implicit_interactions_to_split,
val_p=0.9,
test_p=0.2)
def test_bad_stratified_split_HDF5Interactions(hdf5_interactions):
with pytest.raises(AssertionError):
stratified_split(interactions=hdf5_interactions, )
def test_stratified_split(implicit_interactions_to_split,
explicit_interactions_to_split, data_type):
if data_type == 'implicit':
interactions_class = Interactions
interactions_kwargs = {
'check_num_negative_samples_is_valid': False,
}
interactions_to_split = implicit_interactions_to_split
else:
interactions_class = ExplicitInteractions
interactions_kwargs = {}
interactions_to_split = explicit_interactions_to_split
train_expected_df = pd.DataFrame(
data={
'user_id': [0, 0, 0, 0, 0, 0, 1, 1, 2, 2, 3, 4, 4],
'item_id': [1, 2, 3, 4, 6, 8, 1, 2, 3, 4, 2, 4, 5],
'rating': [2, 3, 4, 5, 3, 1, 1, 2, 4, 5, 5, 5, 4],
})
train_expected = interactions_class(
mat=coo_matrix(
(
train_expected_df['rating'],
(train_expected_df['user_id'], train_expected_df['item_id']),
),
shape=(interactions_to_split.num_users,
interactions_to_split.num_items),
),
allow_missing_ids=True,
**interactions_kwargs,
)
validate_expected_df = pd.DataFrame(
data={
'user_id': [0, 1, 2, 3, 4],
'item_id': [7, 3, 2, 1, 2],
'rating': [2, 3, 3, 1, 3],
})
validate_expected = interactions_class(
mat=coo_matrix(
(
validate_expected_df['rating'],
(validate_expected_df['user_id'],
validate_expected_df['item_id']),
),
shape=(interactions_to_split.num_users,
interactions_to_split.num_items),
),
allow_missing_ids=True,
**interactions_kwargs,
)
test_expected_df = pd.DataFrame(
data={
'user_id': [0, 0, 1, 2, 3, 4],
'item_id': [0, 5, 4, 1, 4, 1],
'rating': [1, 4, 4, 2, 4, 2],
})
test_expected = interactions_class(
mat=coo_matrix(
(
test_expected_df['rating'],
(test_expected_df['user_id'], test_expected_df['item_id']),
),
shape=(interactions_to_split.num_users,
interactions_to_split.num_items),
),
allow_missing_ids=True,
**interactions_kwargs,
)
(train_actual, validate_actual,
test_actual) = stratified_split(interactions=interactions_to_split,
val_p=0.1,
test_p=0.2,
seed=46)
np.testing.assert_array_equal(train_actual.toarray(),
train_expected.toarray())
np.testing.assert_array_equal(validate_actual.toarray(),
validate_expected.toarray())
np.testing.assert_array_equal(test_actual.toarray(),
test_expected.toarray())
assert (train_actual.num_users == train_expected.num_users ==
validate_actual.num_users == validate_expected.num_users ==
test_actual.num_users == test_expected.num_users)
assert (train_actual.num_items == train_expected.num_items ==
validate_actual.num_items == validate_expected.num_items ==
test_actual.num_items == test_expected.num_items)
assert (type(train_actual) == type(train_expected) == type(validate_actual)
== type(validate_expected) == type(test_actual) ==
type(test_expected) == interactions_class)
