DOC Clarify RobustScaler behavior with sparse input (#8858)

naoyak · jnothman · commit f6c7080ee502 · 2017-07-29T22:23:46.000+10:00
diff --git a/doc/modules/preprocessing.rst b/doc/modules/preprocessing.rst
@@ -199,7 +199,7 @@ matrices  as input, as long as ``with_mean=False`` is explicitly passed
 to the constructor. Otherwise a ``ValueError`` will be raised as
 silently centering would break the sparsity and would often crash the
 execution by allocating excessive amounts of memory unintentionally.
-:class:`RobustScaler` cannot be fited to sparse inputs, but you can use
+:class:`RobustScaler` cannot be fitted to sparse inputs, but you can use
 the ``transform`` method on sparse inputs.
 
 Note that the scalers accept both Compressed Sparse Rows and Compressed
diff --git a/sklearn/preprocessing/data.py b/sklearn/preprocessing/data.py
@@ -945,9 +945,9 @@ class RobustScaler(BaseEstimator, TransformerMixin):
     and the 3rd quartile (75th quantile).
 
     Centering and scaling happen independently on each feature (or each
-    sample, depending on the `axis` argument) by computing the relevant
+    sample, depending on the ``axis`` argument) by computing the relevant
     statistics on the samples in the training set. Median and  interquartile
-    range are then stored to be used on later data using the `transform`
+    range are then stored to be used on later data using the ``transform``
     method.
 
     Standardization of a dataset is a common requirement for many
@@ -964,7 +964,7 @@ class RobustScaler(BaseEstimator, TransformerMixin):
     ----------
     with_centering : boolean, True by default
         If True, center the data before scaling.
-        This does not work (and will raise an exception) when attempted on
+        This will cause ``transform`` to raise an exception when attempted on
         sparse matrices, because centering them entails building a dense
         matrix which in common use cases is likely to be too large to fit in
         memory.
@@ -1059,11 +1059,14 @@ def fit(self, X, y=None):
         return self
 
     def transform(self, X):
-        """Center and scale the data
+        """Center and scale the data.
+
+        Can be called on sparse input, provided that ``RobustScaler`` has been
+        fitted to dense input and ``with_centering=False``.
 
         Parameters
         ----------
-        X : array-like
+        X : {array-like, sparse matrix}
             The data used to scale along the specified axis.
         """
         if self.with_centering:
