Argument validation in make_multilabel_classification() (scikit-learn…

…#16006)

doc/whats_new/v0.23.rst

@@ -63,6 +63,10 @@ Changelog
   by :user:`Stephanie Andrews <gitsteph>` and
   :user:`Reshama Shaikh <reshamas>`.
 
+- |Fix| :func:`datasets.make_multilabel_classification` now generates
+  `ValueError` for arguments `n_classes < 1` OR `length < 1`.
+  :pr:`16006` by :user:`Rushabh Vasani <rushabh-v>`.
+
 :mod:`sklearn.feature_extraction`
 .................................
 

sklearn/datasets/_samples_generator.py

@@ -342,6 +342,17 @@ def make_multilabel_classification(n_samples=100, n_features=20, n_classes=5,
         Only returned if ``return_distributions=True``.
 
     """
+    if n_classes < 1:
+        raise ValueError(
+            "'n_classes' should be an integer greater than 0. Got {} instead."
+            .format(n_classes)
+            )
+    if length < 1:
+        raise ValueError(
+            "'length' should be an integer greater than 0. Got {} instead."
+            .format(length)
+            )
+
     generator = check_random_state(random_state)
     p_c = generator.rand(n_classes)
     p_c /= p_c.sum()

sklearn/datasets/tests/test_samples_generator.py

@@ -222,6 +222,18 @@ def test_make_multilabel_classification_return_indicator_sparse():
         assert sp.issparse(Y)
 
 
+@pytest.mark.parametrize(
+    "params, err_msg",
+    [
+        ({"n_classes": 0}, "'n_classes' should be an integer"),
+        ({"length": 0}, "'length' should be an integer")
+    ]
+)
+def test_make_multilabel_classification_valid_arguments(params, err_msg):
+    with pytest.raises(ValueError, match=err_msg):
+        make_multilabel_classification(**params)
+
+
 def test_make_hastie_10_2():
     X, y = make_hastie_10_2(n_samples=100, random_state=0)
     assert X.shape == (100, 10), "X shape mismatch"