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Updating PolynomialFeatures.Transform docstring #13755

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May 1, 2019
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Updating PolynomialFeatures.Transform docstring #13755

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Updating PolynomailFeatures.Transform docstring
AWNystrom May 1, 2019
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24 changes: 14 additions & 10 deletions sklearn/preprocessing/data.py
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Expand Up @@ -1475,17 +1475,21 @@ def transform(self, X):

Parameters
----------
X : array-like or sparse matrix, shape [n_samples, n_features]
X : array-like or CSR/CSC sparse matrix, shape [n_samples, n_features]
The data to transform, row by row.
Sparse input should preferably be in CSR format (for speed),
but must be in CSC format if the degree is 4 or higher.

If the input matrix is in CSR format and the expansion is of
degree 2 or 3, the method described in the work "Leveraging
Sparsity to Speed Up Polynomial Feature Expansions of CSR
Matrices Using K-Simplex Numbers" by Andrew Nystrom and
John Hughes is used, which is much faster than the method
used on CSC input.

Prefer CSR over CSC for sparse input (for speed), but CSC is
required if the degree is 4 or higher. If the degree is less than
4 and the input format is CSC, it will be converted to CSR, have
its polynomial features generated, then converted back to CSC.

If the degree is 2 or 3, the method described in "Leveraging
Sparsity to Speed Up Polynomial Feature Expansions of CSR Matrices
Using K-Simplex Numbers" by Andrew Nystrom and John Hughes is
used, which is much faster than the method used on CSC input. For
this reason, a CSC input will be converted to CSR, and the output
will be converted back to CSC prior to being returned, hence the
preference of CSR.

Returns
-------
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