def check_binarized_results(y, classes, pos_label, neg_label, expected):
for sparse_output in [True, False]:
if (pos_label == 0 or neg_label != 0) and sparse_output:
with pytest.raises(ValueError):
label_binarize(
y,
classes=classes,
neg_label=neg_label,
pos_label=pos_label,
sparse_output=sparse_output,
)
continue
# check label_binarize
binarized = label_binarize(
y,
classes=classes,
neg_label=neg_label,
pos_label=pos_label,
sparse_output=sparse_output,
)
binarized = binarized.fetch()
if hasattr(binarized, "raw"):
binarized = binarized.raw
assert_array_equal(toarray(binarized), expected)
assert sp.issparse(binarized) == sparse_output
# check inverse
y_type = type_of_target(y)
if y_type == "multiclass":
inversed = _inverse_binarize_multiclass(binarized, classes=classes)
else:
inversed = _inverse_binarize_thresholding(
binarized,
output_type=y_type,
classes=classes,
threshold=((neg_label + pos_label) / 2.0),
)
assert_array_equal(toarray(inversed), toarray(y))
# Check label binarizer
lb = LabelBinarizer(neg_label=neg_label,
pos_label=pos_label,
sparse_output=sparse_output)
binarized = lb.fit_transform(y)
assert_array_equal(toarray(binarized), expected)
assert binarized.issparse() == sparse_output
inverse_output = lb.inverse_transform(binarized)
assert_array_equal(toarray(inverse_output), toarray(y))
assert inverse_output.issparse() == sp.issparse(y)
def test_inverse_binarize_multiclass():
got = _inverse_binarize_multiclass(
csr_matrix([[0, 1, 0], [-1, 0, -1], [0, 0, 0]]), np.arange(3))
assert_array_equal(got, np.array([1, 1, 0]))
