[MRG] MAINT explicit fail messages on non supported targets

imblearn/ensemble/_bagging.py

@@ -15,7 +15,7 @@
 from ..pipeline import Pipeline
 from ..under_sampling import RandomUnderSampler
 from ..under_sampling.base import BaseUnderSampler
-from ..utils import Substitution
+from ..utils import Substitution, check_target_type
 from ..utils._docstring import _random_state_docstring
 
 
@@ -240,6 +240,7 @@ def fit(self, X, y):
         self : object
             Returns self.
         """
+        check_target_type(y)
         # RandomUnderSampler is not supporting sample_weight. We need to pass
         # None.
         return self._fit(X, y, self.max_samples, sample_weight=None)

imblearn/ensemble/_easy_ensemble.py

@@ -17,7 +17,7 @@
 from .base import BaseEnsembleSampler
 from ..under_sampling import RandomUnderSampler
 from ..under_sampling.base import BaseUnderSampler
-from ..utils import Substitution
+from ..utils import Substitution, check_target_type
 from ..utils._docstring import _random_state_docstring
 from ..pipeline import Pipeline
 
@@ -290,6 +290,7 @@ def fit(self, X, y):
         self : object
             Returns self.
         """
+        check_target_type(y)
         # RandomUnderSampler is not supporting sample_weight. We need to pass
         # None.
         return self._fit(X, y, self.max_samples, sample_weight=None)

imblearn/ensemble/_weight_boosting.py

@@ -12,7 +12,7 @@
 from ..under_sampling.base import BaseUnderSampler
 from ..under_sampling import RandomUnderSampler
 from ..pipeline import make_pipeline
-from ..utils import Substitution
+from ..utils import Substitution, check_target_type
 from ..utils._docstring import _random_state_docstring
 
 
@@ -146,6 +146,7 @@ def fit(self, X, y, sample_weight=None):
             Returns self.
 
         """
+        check_target_type(y)
         self.samplers_ = []
         self.pipelines_ = []
         super().fit(X, y, sample_weight)

imblearn/ensemble/tests/test_weight_boosting.py

@@ -23,8 +23,7 @@ def imbalanced_dataset():
     [({"n_estimators": 'whatever'}, "n_estimators must be an integer"),
      ({"n_estimators": -100}, "n_estimators must be greater than zero")]
 )
-def test_balanced_random_forest_error(imbalanced_dataset, boosting_params,
-                                      err_msg):
+def test_rusboost_error(imbalanced_dataset, boosting_params, err_msg):
     rusboost = RUSBoostClassifier(**boosting_params)
     with pytest.raises(ValueError, match=err_msg):
         rusboost.fit(*imbalanced_dataset)

imblearn/over_sampling/tests/test_smote_nc.py

@@ -131,7 +131,8 @@ def test_smotenc_check_target_type():
         smote.fit_resample(X, y)
     rng = np.random.RandomState(42)
     y = rng.randint(2, size=(20, 3))
-    with pytest.raises(ValueError, match="'y' should encode the multiclass"):
+    msg = "Multilabel and multioutput targets are not supported."
+    with pytest.raises(ValueError, match=msg):
         smote.fit_resample(X, y)
 
 

imblearn/utils/_validation.py

@@ -88,8 +88,9 @@ def check_target_type(y, indicate_one_vs_all=False):
     if type_y == 'multilabel-indicator':
         if np.any(y.sum(axis=1) > 1):
             raise ValueError(
-                "When 'y' corresponds to '{}', 'y' should encode the "
-                "multiclass (a single 1 by row).".format(type_y))
+                "Imbalanced-learn currently supports binary, multiclass and "
+                "binarized encoded multiclasss targets. Multilabel and "
+                "multioutput targets are not supported.")
         y = y.argmax(axis=1)
 
     return (y, type_y == 'multilabel-indicator') if indicate_one_vs_all else y

imblearn/utils/estimator_checks.py

@@ -17,7 +17,7 @@
 from scipy import sparse
 
 from sklearn.base import clone
-from sklearn.datasets import make_classification
+from sklearn.datasets import make_classification, make_multilabel_classification  # noqa
 from sklearn.cluster import KMeans
 from sklearn.preprocessing import label_binarize
 from sklearn.utils.estimator_checks import check_estimator \
@@ -27,6 +27,7 @@
 from sklearn.utils.testing import set_random_state
 from sklearn.utils.multiclass import type_of_target
 
+from imblearn.base import BaseSampler
 from imblearn.over_sampling.base import BaseOverSampler
 from imblearn.under_sampling.base import BaseCleaningSampler, BaseUnderSampler
 from imblearn.ensemble.base import BaseEnsembleSampler
@@ -54,10 +55,18 @@ def _yield_sampler_checks(name, Estimator):
     yield check_samplers_sample_indices
 
 
+def _yield_classifier_checks(name, Estimator):
+    yield check_classifier_on_multilabel_or_multioutput_targets
+
+
 def _yield_all_checks(name, estimator):
     # trigger our checks if this is a SamplerMixin
     if hasattr(estimator, 'fit_resample'):
-        yield from _yield_sampler_checks(name, estimator)
+        for check in _yield_sampler_checks(name, estimator):
+            yield check
+    if hasattr(estimator, 'predict'):
+        for check in _yield_classifier_checks(name, estimator):
+            yield check
 
 
 def check_estimator(Estimator, run_sampler_tests=True):
@@ -99,7 +108,8 @@ def check_target_type(name, Estimator):
     # if the target is multilabel then we should raise an error
     rng = np.random.RandomState(42)
     y = rng.randint(2, size=(20, 3))
-    with pytest.raises(ValueError, match="'y' should encode the multiclass"):
+    msg = "Multilabel and multioutput targets are not supported."
+    with pytest.raises(ValueError, match=msg):
         estimator.fit_resample(X, y)
 
 
@@ -342,3 +352,11 @@ def check_samplers_sample_indices(name, Sampler):
         assert hasattr(sampler, 'sample_indices_') is sample_indices
     else:
         assert not hasattr(sampler, 'sample_indices_')
+
+
+def check_classifier_on_multilabel_or_multioutput_targets(name, Estimator):
+    estimator = Estimator()
+    X, y = make_multilabel_classification(n_samples=30)
+    msg = "Multilabel and multioutput targets are not supported."
+    with pytest.raises(ValueError, match=msg):
+        estimator.fit(X, y)