@amueller : I hope this is what you had in mind

Apart from nitpicks and sparse matrix testing LGTM

Looks good :)

Since it is not in RobustScaler anymore, do we want to use _handle_zeros_in_scale here ?

Thanks for that 2nd review. I've implemented the changes you've suggested and squashed the commits.

Remove backticks, add what's new entry, squash commits, and you can haz merge. LGTM!

Thanks @untom, for your contribution and your perseverance!

Thanks for your review and in general for helping out with this!

I found a small problem (?) with MaxAbsScaler and not sure whether I should file a bug report for this (because 0.17 is not officially released)

So I have my collection of data scaled with MinMaxScaler. Then I need to transform a new sparse matrix with one row (sparse vector?), eg.

  (0, 56839)    0.462743526481
  (0, 55421)    0.469655562306
  (0, 54368)    0.203714596644
  (0, 54060)    0.0962621236939
  (0, 51441)    0.540495850676
  (0, 48518)    0.056152354043
  (0, 45181)    0.0652388777274
  (0, 38682)    0.230776053348
  (0, 31876)    0.199738544715
  (0, 14641)    0.280892719445
  (0, 434)  0.207189026352

However, if I attempt to transform the 1-row sparse matrix above (so I can do comparison with the collection), I get this assertion error

Traceback (most recent call last):
  File "./address-query.py", line 41, in <module>
    main()
  File "./address-query.py", line 28, in main
    query = scaler.transform(query_)[0].toarray()
  File "/Users/jeffrey04/.local/lib/python3.5/site-packages/sklearn/preprocessing/data.py", line 792, in transform
    inplace_row_scale(X, 1.0 / self.scale_)
  File "/Users/jeffrey04/.local/lib/python3.5/site-packages/sklearn/utils/sparsefuncs.py", line 200, in inplace_row_scale
    inplace_csr_row_scale(X, scale)
  File "/Users/jeffrey04/.local/lib/python3.5/site-packages/sklearn/utils/sparsefuncs.py", line 61, in inplace_csr_row_scale
    assert scale.shape[0] == X.shape[0]

The workaround to the problem is to use a matrix that has more than 1 row, due to this part of the code (if I am not mistaken)

        if sparse.issparse(X):
            if X.shape[0] == 1:
                inplace_row_scale(X, self.scale_)
            else:
                inplace_column_scale(X, self.scale_)

Could you please file this as a separate issue, and also report: X.shape
for the training data, X.shape for the new "sparse row vector", and
scaler.scale_.shape. Thank you.

On 19 October 2015 at 14:15, Jeffrey04 notifications@github.com wrote:

I found a small problem (?) with MaxAbsScaler and not sure whether I
should file a bug report for this

So I have my collection of data scaled with MinMaxScaler. Then I need to
transform a new sparse matrix with one row (sparse vector?), eg.

(0, 56839) 0.462743526481
(0, 55421) 0.469655562306
(0, 54368) 0.203714596644
(0, 54060) 0.0962621236939
(0, 51441) 0.540495850676
(0, 48518) 0.056152354043
(0, 45181) 0.0652388777274
(0, 38682) 0.230776053348
(0, 31876) 0.199738544715
(0, 14641) 0.280892719445
(0, 434) 0.207189026352

However, if I attempt to transform the 1-row sparse matrix above (so I can
do comparison with the collection), I get this assertion error

Traceback (most recent call last):
File "./address-query.py", line 41, in
main()
File "./address-query.py", line 28, in main
query = scaler.transform(query_)[0].toarray()
File "/Users/jeffrey04/.local/lib/python3.5/site-packages/sklearn/preprocessing/data.py", line 792, in transform
inplace_row_scale(X, 1.0 / self.scale_)
File "/Users/jeffrey04/.local/lib/python3.5/site-packages/sklearn/utils/sparsefuncs.py", line 200, in inplace_row_scale
inplace_csr_row_scale(X, scale)
File "/Users/jeffrey04/.local/lib/python3.5/site-packages/sklearn/utils/sparsefuncs.py", line 61, in inplace_csr_row_scale
assert scale.shape[0] == X.shape[0]

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#4828 (comment)
.

done, thanks (:

Just wanted to say thanks for this feature! I've already tested it out on several datasets and have found it super useful with sparse arrays. Thanks for all the hard work you've put into this, everyone!