def test_join_and_check(self):
validator = InputValidator()
# Numpy Testing
y = np.array([2, 2, 3, 4, 5])
y_test = np.array([3, 4, 5, 6, 1])
joined = validator.join_and_check(y, y_test)
np.testing.assert_array_equal(
joined,
np.array([2, 2, 3, 4, 5, 3, 4, 5, 6, 1])
)
validator.validate_target(joined, is_classification=True)
y_encoded = validator.validate_target(y)
y_test_encoded = validator.validate_target(y_test)
# If a common encoding happened, then common elements
# should have a common encoding
self.assertEqual(y_encoded[2], y_test_encoded[0])
# Pandas Testing
validator = InputValidator()
joined = validator.join_and_check(
pd.DataFrame(y),
pd.DataFrame(y_test)
)
np.testing.assert_array_equal(
joined,
pd.DataFrame([2, 2, 3, 4, 5, 3, 4, 5, 6, 1])
)
# List Testing
validator = InputValidator()
joined = validator.join_and_check(
[2, 2, 3, 4, 5],
[3, 4, 5, 6, 1]
)
np.testing.assert_array_equal(
joined,
[2, 2, 3, 4, 5, 3, 4, 5, 6, 1]
)
# Make sure some messages are triggered
y = np.array([[1, 0, 0, 1], [0, 0, 1, 1], [0, 0, 0, 0]])
y_test = np.array([3, 4, 5, 6, 1])
with self.assertRaisesRegex(
ValueError,
'Train and test targets must have the same dimensionality'
):
joined = validator.join_and_check(y, y_test)
with self.assertRaisesRegex(
ValueError,
'Train and test targets must be of the same type'
):
joined = validator.join_and_check(y, pd.DataFrame(y_test))
def test_big_dataset_encoding2(self):
"""
Makes sure that when there are multiple classes,
and test/train targets differ, we proactively encode together
the data between test and train
"""
X, y = sklearn.datasets.fetch_openml(data_id=183, return_X_y=True, as_frame=True)
X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(
X,
y,
random_state=1
)
# Make sure this test makes sense, so that y_test
# and y_train have different classes
all_classes = set(np.unique(y_test)).union(set(np.unique(y_train)))
elements_in_test_only = np.setdiff1d(np.unique(y_test), np.unique(y_train))
self.assertGreater(len(elements_in_test_only), 0)
validator = InputValidator()
common = validator.join_and_check(
pd.DataFrame(y),
pd.DataFrame(y_test)
)
validator.validate_target(common, is_classification=True)
encoded_classes = validator.target_encoder.classes_
missing = all_classes - set(encoded_classes)
self.assertEqual(len(missing), 0)