def test_multilabel_binarizer_non_integer_labels():
tuple_classes = np.empty(3, dtype=object)
tuple_classes[:] = [(1, ), (2, ), (3, )]
inputs = [
([('2', '3'), ('1', ), ('1', '2')], ['1', '2', '3']),
([('b', 'c'), ('a', ), ('a', 'b')], ['a', 'b', 'c']),
([((2, ), (3, )), ((1, ), ), ((1, ), (2, ))], tuple_classes),
]
indicator_mat = np.array([[0, 1, 1], [1, 0, 0], [1, 1, 0]])
for inp, classes in inputs:
# fit_transform()
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
assert_array_equal(mlb.classes_, classes)
assert_array_equal(mlb.inverse_transform(indicator_mat), inp)
# fit().transform()
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
assert_array_equal(mlb.classes_, classes)
assert_array_equal(mlb.inverse_transform(indicator_mat), inp)
mlb = MultiLabelBinarizer()
with pytest.raises(TypeError):
mlb.fit_transform([({}), ({}, {'a': 'b'})])
def test_multilabel_binarizer_given_classes():
inp = [(2, 3), (1, ), (1, 2)]
indicator_mat = np.array([[0, 1, 1], [1, 0, 0], [1, 0, 1]])
# fit_transform()
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
assert_array_equal(mlb.classes_, [1, 3, 2])
# fit().transform()
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
assert_array_equal(mlb.classes_, [1, 3, 2])
# ensure works with extra class
mlb = MultiLabelBinarizer(classes=[4, 1, 3, 2])
assert_array_equal(mlb.fit_transform(inp),
np.hstack(([[0], [0], [0]], indicator_mat)))
assert_array_equal(mlb.classes_, [4, 1, 3, 2])
# ensure fit is no-op as iterable is not consumed
inp = iter(inp)
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
# ensure a ValueError is thrown if given duplicate classes
err_msg = "The classes argument contains duplicate classes. Remove " \
"these duplicates before passing them to MultiLabelBinarizer."
mlb = MultiLabelBinarizer(classes=[1, 3, 2, 3])
with pytest.raises(ValueError, match=err_msg):
mlb.fit(inp)
def test_multilabel_binarizer_non_integer_labels():
tuple_classes = _to_object_array([(1,), (2,), (3,)])
inputs = [
([("2", "3"), ("1",), ("1", "2")], ["1", "2", "3"]),
([("b", "c"), ("a",), ("a", "b")], ["a", "b", "c"]),
([((2,), (3,)), ((1,),), ((1,), (2,))], tuple_classes),
]
indicator_mat = np.array([[0, 1, 1], [1, 0, 0], [1, 1, 0]])
for inp, classes in inputs:
# fit_transform()
mlb = MultiLabelBinarizer()
inp = np.array(inp, dtype=object)
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
assert_array_equal(mlb.classes_, classes)
indicator_mat_inv = np.array(mlb.inverse_transform(indicator_mat), dtype=object)
assert_array_equal(indicator_mat_inv, inp)
# fit().transform()
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
assert_array_equal(mlb.classes_, classes)
indicator_mat_inv = np.array(mlb.inverse_transform(indicator_mat), dtype=object)
assert_array_equal(indicator_mat_inv, inp)
mlb = MultiLabelBinarizer()
with pytest.raises(TypeError):
mlb.fit_transform([({}), ({}, {"a": "b"})])
def test_multilabel_binarizer_multiple_calls():
inp = [(2, 3), (1, ), (1, 2)]
indicator_mat = np.array([[0, 1, 1], [1, 0, 0], [1, 0, 1]])
indicator_mat2 = np.array([[0, 1, 1], [1, 0, 0], [1, 1, 0]])
# first call
mlb = MultiLabelBinarizer(classes=[1, 3, 2])
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
# second call change class
mlb.classes = [1, 2, 3]
assert_array_equal(mlb.fit_transform(inp), indicator_mat2)
def test_multilabel_binarizer_empty_sample():
mlb = MultiLabelBinarizer()
y = [[1, 2], [1], []]
Y = np.array([[1, 1],
[1, 0],
[0, 0]])
assert_array_equal(mlb.fit_transform(y), Y)
def test_multilabel_binarizer():
# test input as iterable of iterables
inputs = [
lambda: [(2, 3), (1,), (1, 2)],
lambda: ({2, 3}, {1}, {1, 2}),
lambda: iter([iter((2, 3)), iter((1,)), {1, 2}]),
]
indicator_mat = np.array([[0, 1, 1],
[1, 0, 0],
[1, 1, 0]])
inverse = inputs[0]()
for inp in inputs:
# With fit_transform
mlb = MultiLabelBinarizer()
got = mlb.fit_transform(inp())
assert_array_equal(indicator_mat, got)
assert_array_equal([1, 2, 3], mlb.classes_)
assert mlb.inverse_transform(got) == inverse
# With fit
mlb = MultiLabelBinarizer()
got = mlb.fit(inp()).transform(inp())
assert_array_equal(indicator_mat, got)
assert_array_equal([1, 2, 3], mlb.classes_)
assert mlb.inverse_transform(got) == inverse
def test_multilabel_binarizer_inverse_validation():
inp = [(1, 1, 1, 0)]
mlb = MultiLabelBinarizer()
mlb.fit_transform(inp)
# Not binary
with pytest.raises(ValueError):
mlb.inverse_transform(np.array([[1, 3]]))
# The following binary cases are fine, however
mlb.inverse_transform(np.array([[0, 0]]))
mlb.inverse_transform(np.array([[1, 1]]))
mlb.inverse_transform(np.array([[1, 0]]))
# Wrong shape
with pytest.raises(ValueError):
mlb.inverse_transform(np.array([[1]]))
with pytest.raises(ValueError):
mlb.inverse_transform(np.array([[1, 1, 1]]))
def test_multilabel_binarizer_same_length_sequence():
# Ensure sequences of the same length are not interpreted as a 2-d array
inp = [[1], [0], [2]]
indicator_mat = np.array([[0, 1, 0], [1, 0, 0], [0, 0, 1]])
# fit_transform()
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
assert_array_equal(mlb.inverse_transform(indicator_mat), inp)
# fit().transform()
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit(inp).transform(inp), indicator_mat)
assert_array_equal(mlb.inverse_transform(indicator_mat), inp)
def test_sparse_output_multilabel_binarizer():
# test input as iterable of iterables
inputs = [
lambda: [(2, 3), (1,), (1, 2)],
lambda: ({2, 3}, {1}, {1, 2}),
lambda: iter([iter((2, 3)), iter((1,)), {1, 2}]),
]
indicator_mat = np.array([[0, 1, 1],
[1, 0, 0],
[1, 1, 0]])
inverse = inputs[0]()
for sparse_output in [True, False]:
for inp in inputs:
# With fit_transform
mlb = MultiLabelBinarizer(sparse_output=sparse_output)
got = mlb.fit_transform(inp())
assert issparse(got) == sparse_output
if sparse_output:
# verify CSR assumption that indices and indptr have same dtype
assert got.indices.dtype == got.indptr.dtype
got = got.toarray()
assert_array_equal(indicator_mat, got)
assert_array_equal([1, 2, 3], mlb.classes_)
assert mlb.inverse_transform(got) == inverse
# With fit
mlb = MultiLabelBinarizer(sparse_output=sparse_output)
got = mlb.fit(inp()).transform(inp())
assert issparse(got) == sparse_output
if sparse_output:
# verify CSR assumption that indices and indptr have same dtype
assert got.indices.dtype == got.indptr.dtype
got = got.toarray()
assert_array_equal(indicator_mat, got)
assert_array_equal([1, 2, 3], mlb.classes_)
assert mlb.inverse_transform(got) == inverse
with pytest.raises(ValueError):
mlb.inverse_transform(csr_matrix(np.array([[0, 1, 1],
[2, 0, 0],
[1, 1, 0]])))
def test_multilabel_binarizer_non_unique():
inp = [(1, 1, 1, 0)]
indicator_mat = np.array([[1, 1]])
mlb = MultiLabelBinarizer()
assert_array_equal(mlb.fit_transform(inp), indicator_mat)
