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[MRG] ENH remove mix of multilabel input format #2024

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[MRG] ENH remove mix of multilabel input format #2024

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29 changes: 7 additions & 22 deletions sklearn/metrics/metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -930,11 +930,7 @@ def jaccard_similarity_score(y_true, y_pred, normalize=True):

# Handle mix representation
if type(y_true) != type(y_pred):
labels = unique_labels(y_true, y_pred)
lb = LabelBinarizer()
lb.fit([labels.tolist()])
y_true = lb.transform(y_true)
y_pred = lb.transform(y_pred)
raise ValueError("Mix of multilabel type input")

if is_label_indicator_matrix(y_true):
try:
Expand Down Expand Up @@ -1052,11 +1048,8 @@ def accuracy_score(y_true, y_pred, normalize=True):

# Handle mix representation
if type(y_true) != type(y_pred):
labels = unique_labels(y_true, y_pred)
lb = LabelBinarizer()
lb.fit([labels.tolist()])
y_true = lb.transform(y_true)
y_pred = lb.transform(y_pred)
raise ValueError("Mix of multilabel type input")


if is_label_indicator_matrix(y_true):
score = (y_pred != y_true).sum(axis=1) == 0
Expand Down Expand Up @@ -1416,11 +1409,8 @@ def _tp_tn_fp_fn(y_true, y_pred, labels=None):
if is_multilabel(y_true):
# Handle mix representation
if type(y_true) != type(y_pred):
labels = unique_labels(y_true, y_pred)
lb = LabelBinarizer()
lb.fit([labels.tolist()])
y_true = lb.transform(y_true)
y_pred = lb.transform(y_pred)
raise ValueError("Mix of multilabel type input")


if is_label_indicator_matrix(y_true):
true_pos = np.sum(np.logical_and(y_true == 1,
Expand Down Expand Up @@ -1644,11 +1634,7 @@ def precision_recall_fscore_support(y_true, y_pred, beta=1.0, labels=None,
if is_multilabel(y_true):
# Handle mix representation
if type(y_true) != type(y_pred):
labels = unique_labels(y_true, y_pred)
lb = LabelBinarizer()
lb.fit([labels.tolist()])
y_true = lb.transform(y_true)
y_pred = lb.transform(y_pred)
raise ValueError("Mix of multilabel type input")

if is_label_indicator_matrix(y_true):
y_true_pos_label = y_true == 1
Expand Down Expand Up @@ -2210,8 +2196,7 @@ def hamming_loss(y_true, y_pred, classes=None):
lb.fit([classes.tolist()])

if type(y_true) != type(y_pred):
y_true = lb.transform(y_true)
y_pred = lb.transform(y_pred)
raise ValueError("Mix of multilabel type input")

if is_label_indicator_matrix(y_true):
return np.mean(y_true != y_pred)
Expand Down
24 changes: 7 additions & 17 deletions sklearn/metrics/tests/test_metrics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1046,12 +1046,12 @@ def test_multilabel_representation_invariance():
# allows to return the shuffled tuple.
rng = check_random_state(42)
shuffled = lambda x: sorted(x, key=lambda *args: rng.rand())
y1_shuffle = [shuffled(x) for x in y1]
y2_shuffle = [shuffled(x) for x in y2]
y1_shuffle = tuple(shuffled(x) for x in y1)
y2_shuffle = tuple(shuffled(x) for x in y2)

# Let's have redundant labels
y1_redundant = [x * rng.randint(1, 4) for x in y1]
y2_redundant = [x * rng.randint(1, 4) for x in y2]
y1_redundant = tuple(x * rng.randint(1, 4) for x in y1)
y2_redundant = tuple(x * rng.randint(1, 4) for x in y2)

# Binary indicator matrix format
lb = LabelBinarizer().fit([range(n_classes)])
Expand Down Expand Up @@ -1099,19 +1099,9 @@ def test_multilabel_representation_invariance():
% name)

# Check invariance with mix input representation
assert_almost_equal(metric(y1, y2_binary_indicator), measure,
err_msg="%s failed mix input representation "
"invariance: y_true in list of list of "
"labels format and y_pred in dense binary "
"indicator format"
% name)

assert_almost_equal(metric(y1_binary_indicator, y2), measure,
err_msg="%s failed mix input representation "
"invariance: y_true in dense binary "
"indicator format and y_pred in list of "
"list of labels format."
% name)
print name
assert_raises(ValueError, metric, y1, y2_binary_indicator)
assert_raises(ValueError, metric, y1_binary_indicator, y2)


def test_multilabel_zero_one_loss_subset():
Expand Down