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[MRG] DOC Clarify RobustScaler behavior with sparse input #8858

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Jul 29, 2017
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[MRG] DOC Clarify RobustScaler behavior with sparse input #8858

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2 changes: 1 addition & 1 deletion doc/modules/preprocessing.rst
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Expand Up @@ -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
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Ah that was the typo, good catch

the ``transform`` method on sparse inputs.

Note that the scalers accept both Compressed Sparse Rows and Compressed
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13 changes: 8 additions & 5 deletions sklearn/preprocessing/data.py
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Expand Up @@ -909,9 +909,9 @@ 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
Expand All @@ -928,7 +928,7 @@ 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
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@lesteve lesteve May 11, 2017 •
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I think .fit raises an exception if X is sparse Edit: scratch this as well.

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Yeah, fiton sparse raises regardless of any option. You can transform on sparse after fitting on dense, apparently?

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.
Expand Down Expand Up @@ -1023,11 +1023,14 @@ 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
----------
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The type description for X here site mention sparse

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👍

X : array-like
X : {array-like, sparse matrix}
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@jnothman if I read your feedback correctly, does this address the comment?

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Yes. Sorry for the autocorrect typo.

The data used to scale along the specified axis.
"""
if self.with_centering:
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