scikit-learn
diff --git a/‎sklearn/decomposition/pca.py
Lines changed: 11 additions & 0 deletions b/‎sklearn/decomposition/pca.py
Lines changed: 11 additions & 0 deletions
diff --git a/‎sklearn/decomposition/tests/test_pca.py
Lines changed: 10 additions & 0 deletions b/‎sklearn/decomposition/tests/test_pca.py
Lines changed: 10 additions & 0 deletions
@@ -11,6 +11,7 @@
 # License: BSD 3 clause
 
 from math import log, sqrt
+import numbers
 
 import numpy as np
 from scipy import linalg
@@ -421,6 +422,12 @@ def _fit_full(self, X, n_components):
                              "min(n_samples, n_features)=%r with "
                              "svd_solver='full'"
                              % (n_components, min(n_samples, n_features)))
+        elif n_components >= 1:
+            if not isinstance(n_components, (numbers.Integral, np.integer)):
+                raise ValueError("n_components=%r must be of type int "
+                                 "when greater than or equal to 1, "
+                                 "was of type=%r"
+                                 % (n_components, type(n_components)))
 
         # Center data
         self.mean_ = np.mean(X, axis=0)
@@ -481,6 +488,10 @@ def _fit_truncated(self, X, n_components, svd_solver):
                              "svd_solver='%s'"
                              % (n_components, min(n_samples, n_features),
                                 svd_solver))
+        elif not isinstance(n_components, (numbers.Integral, np.integer)):
+            raise ValueError("n_components=%r must be of type int "
+                             "when greater than or equal to 1, was of type=%r"
+                             % (n_components, type(n_components)))
         elif svd_solver == 'arpack' and n_components == min(n_samples,
                                                             n_features):
             raise ValueError("n_components=%r must be strictly less than "
 
@@ -10,6 +10,7 @@
 from sklearn.utils.testing import assert_raise_message
 from sklearn.utils.testing import assert_raises
 from sklearn.utils.testing import assert_raises_regex
+from sklearn.utils.testing import assert_raise_message
 from sklearn.utils.testing import assert_no_warnings
 from sklearn.utils.testing import assert_warns_message
 from sklearn.utils.testing import ignore_warnings
@@ -390,6 +391,15 @@ def test_pca_validation():
                                     PCA(n_components, svd_solver=solver)
                                     .fit, data)
 
+        n_components = 1.0
+        type_ncom = type(n_components)
+        assert_raise_message(ValueError,
+                             "n_components={} must be of type int "
+                             "when greater than or equal to 1, was of type={}"
+                             .format(n_components, type_ncom),
+                             PCA(n_components, svd_solver=solver).fit, data)
+
+
 
 def test_n_components_none():
     # Ensures that n_components == None is handled correctly