8000 [MRG+1] Scaling a sparse matrix along axis 0 should accept a csc by default by MechCoder · Pull Request #5791 · scikit-learn/scikit-learn · GitHub
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[MRG+1] Scaling a sparse matrix along axis 0 should accept a csc by default #5791

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Merged
merged 1 commit into from
Nov 13, 2015
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[MRG+1] Scaling a sparse matrix along axis 0 should accept a csc by default #5791

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15 changes: 4 additions & 11 deletions sklearn/preprocessing/data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -103,7 +103,7 @@ def scale(X, axis=0, with_mean=True, with_std=True, copy=True):
copy : boolean, optional, default True
set to False to perform inplace row normalization and avoid a
copy (if the input is already a numpy array or a scipy.sparse
CSR matrix and if axis is 1).
CSC matrix and if axis is 1).

Notes
-----
Expand All @@ -113,18 +113,18 @@ def scale(X, axis=0, with_mean=True, with_std=True, copy=True):

Instead the caller is expected to either set explicitly
`with_mean=False` (in that case, only variance scaling will be
performed on the features of the CSR matrix) or to call `X.toarray()`
performed on the features of the CSC matrix) or to call `X.toarray()`
if he/she expects the materialized dense array to fit in memory.

To avoid memory copy the caller should pass a CSR matrix.
To avoid memory copy the caller should pass a CSC matrix.

See also
--------
:class:`sklearn.preprocessing.StandardScaler` to perform centering and
scaling using the ``Transformer`` API (e.g. as part of a preprocessing
:class:`sklearn.pipeline.Pipeline`)
"""
X = check_array(X, accept_sparse='csr', copy=copy, ensure_2d=False,
X = check_array(X, accept_sparse='csc', copy=copy, ensure_2d=False,
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Why not ['csc, 'csr']?

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I did not write the initial code, but I think the idea is to convert it into a format where the column operations are sufficiently faster. I just maintained that convention because converting it to csr would be non-intuitive.

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Are you afraid that this will not be backward compatible?

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How much faster is converting to csc and multipying compared to not converting? ['csc', 'csr'] will not convert to CSR but will let CSR pass.

warn_on_dtype=True, estimator='the scale function',
dtype=FLOAT_DTYPES)
if sparse.issparse(X):
Expand All @@ -135,11 +135,6 @@ def scale(X, axis=0, with_mean=True, with_std=True, copy=True):
if axis != 0:
raise ValueError("Can only scale sparse matrix on axis=0, "
" got axis=%d" % axis)
if not sparse.isspmatrix_csr(X):
X = X.tocsr()
copy = False
if copy:
X = X.copy()
if with_std:
_, var = mean_variance_axis(X, axis=0)
var = _handle_zeros_in_scale(var, copy=False)
Expand All @@ -150,8 +145,6 @@ def scale(X, axis=0, with_mean=True, with_std=True, copy=True):
mean_ = np.mean(X, axis)
if with_std:
scale_ = np.std(X, axis)
if copy:
X = X.copy()
# Xr is a view on the original array that enables easy use of
# broadcasting on the axis in which we are interested in
Xr = np.rollaxis(X, axis)
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2 changes: 1 addition & 1 deletion sklearn/preprocessing/tests/test_data.py
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Expand Up @@ -843,7 +843,7 @@ def test_scale_function_without_centering():

# null scale
X_csr_scaled = scale(X_csr, with_mean=False, with_std=False, copy=True)
assert_array_almost_equal(X_csr.data, X_csr_scaled.data)
assert_array_almost_equal(X_csr.toarray(), X_csr_scaled.toarray())


def test_robust_scale_axis1():
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0