def test_random_split(interactions_to_split):
train_expected_df = pd.DataFrame(
data={
'user_id': [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 4, 4],
'item_id': [0, 1, 2, 3, 4, 5, 8, 1, 3, 4, 1, 3, 4, 2, 2, 4],
'rating': [1, 2, 3, 4, 5, 4, 1, 1, 3, 4, 2, 4, 5, 5, 3, 5],
})
train_expected = Interactions(
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,
check_num_negative_samples_is_valid=False,
)
validate_expected_df = pd.DataFrame(data={
'user_id': [3, 4, 4],
'item_id': [1, 1, 5],
'rating': [1, 2, 4]
})
validate_expected = Interactions(
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,
check_num_negative_samples_is_valid=False,
)
test_expected_df = pd.DataFrame(
data={
'user_id': [0, 0, 1, 2, 3],
'item_id': [6, 7, 2, 2, 4],
'rating': [3, 2, 2, 3, 4],
})
test_expected = Interactions(
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,
check_num_negative_samples_is_valid=False,
)
(train_actual, validate_actual,
test_actual) = random_split(interactions=interactions_to_split,
val_p=0.1,
test_p=0.2,
seed=42)
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)
def test_test_p_negative_random(self, interactions_to_split):
with pytest.raises(ValueError):
random_split(interactions=interactions_to_split, test_p=-0.7)
def test_random_split_with_user_with_only_one_interaction(
interactions_to_split_with_a_user_with_only_one_interaction, ):
# unlike for ``stratified_split``, this should work without error
random_split(interactions=
interactions_to_split_with_a_user_with_only_one_interaction, )
def test_test_p_equal_one_random(self, interactions_to_split):
with pytest.raises(ValueError):
random_split(interactions=interactions_to_split, test_p=1)
def test_test_p_too_large_random(self, interactions_to_split):
with pytest.raises(ValueError):
random_split(interactions=interactions_to_split, test_p=1.1)
def test_val_negative_but_combined_good_random(self,
interactions_to_split):
with pytest.raises(ValueError):
random_split(interactions=interactions_to_split,
val_p=-0.1,
test_p=0.3)
def test_combined_equal_one_random(self, interactions_to_split):
with pytest.raises(ValueError):
random_split(interactions=interactions_to_split,
val_p=0.7,
test_p=0.3)
def test_combined_too_large_random(self, interactions_to_split):
with pytest.raises(ValueError):
random_split(interactions=interactions_to_split,
val_p=0.9,
test_p=0.2)
