[MRG+2] Minimize the validation of X in adaboost

doc/whats_new/v0.21.rst

@@ -118,6 +118,9 @@ Support for Python 3.4 and below has been officially dropped.
   value of ``learning_rate`` in ``update_terminal_regions`` is not consistent
   with the document and the caller functions.
   :issue:`6463` by :user:`movelikeriver <movelikeriver>`.
+
+- |Enhancement| Minimized the validation of X in :class:`ensemble.AdaBoostClassifier`
+  and :class:`ensemble.AdaBoostRegressor` :issue:`13174` by :user:`Christos Aridas <chkoar>`.
 
 :mod:`sklearn.externals`
 ........................

sklearn/ensemble/tests/test_weight_boosting.py

@@ -471,7 +471,6 @@ def fit(self, X, y, sample_weight=None):
 def test_sample_weight_adaboost_regressor():
     """
     AdaBoostRegressor should work without sample_weights in the base estimator
-
     The random weighted sampling is done internally in the _boost method in
     AdaBoostRegressor.
     """
@@ -486,3 +485,49 @@ def predict(self, X):
     boost = AdaBoostRegressor(DummyEstimator(), n_estimators=3)
     boost.fit(X, y_regr)
     assert_equal(len(boost.estimator_weights_), len(boost.estimator_errors_))
+
+
+def test_multidimensional_X():
+    """
+    Check that the AdaBoost estimators can work with n-dimensional
+    data matrix
+    """
+    class DummyClassifier(BaseEstimator):
+
+        def fit(self, X, y, sample_weight=None):
+            self.classes_ = np.unique(y)
+            return self
+
+        def predict(self, X):
+            n_samples = len(X)
+            predictions = np.random.choice(self.classes_, n_samples)
+            return predictions
+
+        def predict_proba(self, X):
+            n_samples = len(X)
+            n_classes = len(self.classes_)
+            probas = np.random.randn(n_samples, n_classes)
+            return probas
+
+    class DummyRegressor(BaseEstimator):
+
+        def fit(self, X, y):
+            return self
+
+        def predict(self, X):
+            n_samples = len(X)
+            predictions = np.random.randn(n_samples)
+            return predictions
+
+    X = np.random.randn(50, 3, 3)
+    yc = np.random.choice([0, 1], 50)
+    yr = np.random.randn(50)
+
+    boost = AdaBoostClassifier(DummyClassifier())
+    boost.fit(X, yc)
+    boost.predict(X)
+    boost.predict_proba(X)
+
+    boost = AdaBoostRegressor(DummyRegressor())
+    boost.fit(X, yr)
+    boost.predict(X)