ENH: issue warning when minmax scaling integer data + test

Author: ogrisel

sklearn/preprocessing.py

@@ -184,6 +184,7 @@ def fit(self, X, y=None):
             used for later scaling along the features axis.
         """
         X = check_arrays(X, sparse_format="dense", copy=self.copy)[0]
+        warn_if_not_float(X, estimator=self)
         feature_range = self.feature_range
         if feature_range[0] >= feature_range[1]:
             raise ValueError("Minimum of desired feature range must be smaller"

sklearn/tests/test_preprocessing.py

@@ -1,3 +1,4 @@
+import warnings
 import numpy as np
 import numpy.linalg as la
 import scipy.sparse as sp
@@ -280,6 +281,19 @@ def test_scale_function_without_centering():
     assert_array_almost_equal(X_csr_scaled_std, X_scaled.std(axis=0))
 
 
+def test_warning_scaling_integers():
+    """Check warning when scaling integer data"""
+    X = np.array([[1, 2, 0],
+                  [0, 0, 0]], dtype=np.uint8)
+
+    with warnings.catch_warnings(record=True) as w:
+        StandardScaler().fit(X)
+        assert_equal(len(w), 1)
+
+    with warnings.catch_warnings(record=True) as w:
+        MinMaxScaler().fit(X)
+        assert_equal(len(w), 1)
+
 def test_normalizer_l1():
     rng = np.random.RandomState(0)
     X_dense = rng.randn(4, 5)