def test_padding_fill_value_transformer():
"""Test full fill padding."""
# load data
X_train, y_train = load_basic_motions(split="train", return_X_y=True)
padding_transformer = PaddingTransformer(pad_length=120, fill_value=1)
Xt = padding_transformer.fit_transform(X_train)
# when we tabularize the data it has 6 dimensions
# and we've padded them all to 120 long.
data = from_nested_to_2d_array(Xt)
assert len(data.columns) == 120 * 6
def test_padding_fill_value_transformer():
# load data
name = "JapaneseVowels"
X_train, y_train = _load_dataset(name, split="train", return_X_y=True)
X_test, y_test = _load_dataset(name, split="test", return_X_y=True)
# print(X_train)
padding_transformer = PaddingTransformer(pad_length=40, fill_value=1)
Xt = padding_transformer.fit_transform(X_train)
# when we tabulrize the data it has 12 dimensions
# and we've truncated them all to (10-2) long.
data = from_nested_to_2d_array(Xt)
assert len(data.columns) == 40 * 12
def test_padding_transformer():
# load data
name = "JapaneseVowels"
X_train, y_train = _load_dataset(name, split="train", return_X_y=True)
X_test, y_test = _load_dataset(name, split="test", return_X_y=True)
# print(X_train)
padding_transformer = PaddingTransformer()
Xt = padding_transformer.fit_transform(X_train)
# when we tabulrize the data it has 12 dimensions
# and we've padded them to there normal length of 29
data = from_nested_to_2d_array(Xt)
assert len(data.columns) == 29 * 12
def test_missing_unequal_tag_inference():
"""Test that ClassifierPipeline infers missing/unequal tags correctly."""
c = KNeighborsTimeSeriesClassifier()
c1 = ExponentTransformer() * PaddingTransformer() * ExponentTransformer(
) * c
c2 = ExponentTransformer() * ExponentTransformer() * c
c3 = Imputer() * ExponentTransformer() * c
c4 = ExponentTransformer() * Imputer() * c
assert c1.get_tag("capability:unequal_length")
assert not c2.get_tag("capability:unequal_length")
assert c3.get_tag("capability:missing_values")
assert not c4.get_tag("capability:missing_values")
def test_missing_unequal_tag_inference():
"""Test that TransformerPipeline infers missing/unequal tags correctly."""
t1 = ExponentTransformer() * PaddingTransformer() * ExponentTransformer()
t2 = ExponentTransformer() * ExponentTransformer()
t3 = Imputer() * ExponentTransformer()
t4 = ExponentTransformer() * Imputer()
assert t1.get_tag("capability:unequal_length")
assert t1.get_tag("capability:unequal_length:removes")
assert not t2.get_tag("capability:unequal_length:removes")
assert t3.get_tag("handles-missing-data")
assert t3.get_tag("capability:missing_values:removes")
assert not t4.get_tag("handles-missing-data")
assert not t4.get_tag("capability:missing_values:removes")