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🔒 🤖 CI Update lock files for main CI build(s) 🔒 🤖 #28569

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scikit-learn-bot
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Update lock files.

Note

If the CI tasks fail, create a new branch based on this PR and add the required fixes to that branch.

@scikit-learn-bot scikit-learn-bot force-pushed the auto-update-lock-files-main branch from f9848e4 to 2e0c18c Compare March 4, 2024 05:13
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github-actions bot commented Mar 4, 2024

✔️ Linting Passed

All linting checks passed. Your pull request is in excellent shape! ☀️

Generated for commit: 2e0c18c. Link to the linter CI: here

@adrinjalali
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related issue: pytest-dev/pytest#11779

will try to figure out the fix

@lesteve
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lesteve commented Mar 4, 2024

Also the doc build failure is likely due to #28352 (comment)

@adrinjalali adrinjalali mentioned this pull request Mar 4, 2024
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@adrinjalali
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Therandom_search.cv_results_ in examples/model_selection/plot_grid_search_text_feature_extraction.py before and after the cv result dtype PR:

45,57c45,127
<  'param_vect__ngram_range': masked_array(data=[(1, 2), (1, 2), (1, 1), (1, 2), (1, 2), (1, 2), (1, 2),
<                     (1, 2), (1, 2), (1, 2), (1, 2), (1, 2), (1, 2), (1, 1),
<                     (1, 2), (1, 2), (1, 1), (1, 1), (1, 2), (1, 2), (1, 1),
<                     (1, 2), (1, 2), (1, 2), (1, 1), (1, 2), (1, 2), (1, 2),
<                     (1, 1), (1, 1), (1, 2), (1, 2), (1, 2), (1, 2), (1, 1),
<                     (1, 1), (1, 2), (1, 2), (1, 2), (1, 2)],
<               mask=[False, False, False, False, False, False, False, False,
<                     False, False, False, False, False, False, False, False,
<                     False, False, False, False, False, False, False, False,
<                     False, False, False, False, False, False, False, False,
<                     False, False, False, False, False, False, False, False],
<         fill_value='?',
<              dtype=object),
---
>  'param_vect__ngram_range': masked_array(
>    data=[[1, 2],
>          [1, 2],
>          [1, 1],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 1],
>          [1, 2],
>          [1, 2],
>          [1, 1],
>          [1, 1],
>          [1, 2],
>          [1, 2],
>          [1, 1],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 1],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 1],
>          [1, 1],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 1],
>          [1, 1],
>          [1, 2],
>          [1, 2],
>          [1, 2],
>          [1, 2]],
>    mask=[[False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False],
>          [False, False]],
>    fill_value='?',
>    dtype=object),
66,67c136
<         fill_value='?',
<              dtype=object),
---
>         fill_value=999999),
77,78c146
<         fill_value='?',
<              dtype=object),
---
>         fill_value=1e+20),
90,91c158
<         fill_value='?',
<              dtype=object),
---
>         fill_value=1e+20),

cc @MarcoGorelli @thomasjpfan

@MarcoGorelli
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Also the doc build failure is likely due to #28352 (comment)

taking a look - running the example with n_iter=3, on main:

(Pdb) random_search.cv_results_['param_vect__ngram_range']
masked_array(
  data=[[1, 1],
        [1, 2],
        [1, 2]],
  mask=[[False, False],
        [False, False],
        [False, False]],
  fill_value='?',
  dtype=object)
(Pdb) random_search.cv_results_['param_vect__ngram_range'].dtype
dtype('O')
(Pdb) random_search.cv_results_['param_vect__ngram_range'].shape
(3, 2)

Before that:

(Pdb) random_search.cv_results_['param_vect__ngram_range']
masked_array(data=[(1, 1), (1, 2), (1, 2)],
             mask=[False, False, False],
       fill_value='?',
            dtype=object)
(Pdb) random_search.cv_results_['param_vect__ngram_range'].dtype
dtype('O')
(Pdb) random_search.cv_results_['param_vect__ngram_range'].shape
(3,)

@adrinjalali
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@MarcoGorelli do you think you'd have the bandwidth to submit a hotfix?

@MarcoGorelli
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I can do, yes! Just checking, which is the expected output? A 1-d object array of tuples, or a 2d array on ints? Maybe the latter?

@adrinjalali
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I think a 1D array, to make sure one can always convert the dict to a pandas DF.

@MarcoGorelli
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MarcoGorelli commented Mar 4, 2024 •
edited
Loading

Maybe this example is a good use-case for Polars, because that can handle conversion from a dict with 2-d arrays because of its List dtype

object datatype means you can't really do anything efficient with it later anyway

EDIT: ValueError: data type List(Int64) not supported by the interchange protocol 🤦

@MarcoGorelli MarcoGorelli mentioned this pull request Mar 4, 2024
Merged
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@scikit-learn-bot @adrinjalali @lesteve @MarcoGorelli @web-flow