Raise ValueError for non-integer absolute min_samples value

Author: ahojnnes

sklearn/linear_model/ransac.py

@@ -153,6 +153,9 @@ def fit(self, X, y):
         elif 0 < self.min_samples < 1:
             min_samples = np.ceil(self.min_samples * X.shape[0])
         elif self.min_samples >= 1:
+            if self.min_samples % 1 != 0:
+                raise ValueError("Absolute number of samples must be an "
+                                 "integer value.")
             min_samples = self.min_samples
         else:
             raise ValueError("Value for `min_samples` must be scalar and "

sklearn/linear_model/tests/test_ransac.py

@@ -201,12 +201,15 @@ def test_ransac_min_n_samples():
                                         residual_threshold=5, random_state=0)
     ransac_estimator3 = RANSACRegressor(base_estimator, min_samples=-1,
                                         residual_threshold=5, random_state=0)
+    ransac_estimator4 = RANSACRegressor(base_estimator, min_samples=5.2,
+                                        residual_threshold=5, random_state=0)
 
     ransac_estimator1.fit(X, y)
     ransac_estimator2.fit(X, y)
 
     assert_equal(ransac_estimator1.predict(X), ransac_estimator2.predict(X))
     assert_raises(ValueError, ransac_estimator3.fit, X, y)
+    assert_raises(ValueError, ransac_estimator4.fit, X, y)
 
 
 def test_ransac_multi_dimensional_targets():