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Merged
merged 10 commits into from
Mar 22, 2023
Merged

FIX Fixes pandas extension arrays in check_array #25813

merged 10 commits into from
Mar 22, 2023

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thomasjpfan
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Reference Issues/PRs

Closes #25637
Follow up to #25638

What does this implement/fix? Explain your changes.

The duck typing with iloc does works with pandas Series, but not extension arrays directly:

import pandas as pd

y_true = pd.Series([1, 0, 0, 1, 0, 1, 1, 0, 1], dtype="Int64")
print(type(y_true))
# <class 'pandas.core.series.Series'>
assert hasattr(y_true, "iloc")

y_unique = y_true.unique()
print(type(y_unique))
# <class 'pandas.core.arrays.integer.IntegerArray'>
assert not hasattr(y_unique, "iloc")

This PR uses is_extension_array_dtype directly to detect the second class so it goes down the same code path as pd.Series.

@lorentzenchr
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If we start using the pattern

try:
    from pandas import XXX

anyway, should we also use it to identify an array-like as pandas instead of relying on hasattr?

Has anyone tested what happens with a pyarrow table or a polars dataframe? I guess, I better open a separate issue for it.

lorentzenchr
lorentzenchr approved these changes Mar 11, 2023
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LGTM

doc/whats_new/v1.3.rst Outdated
@@ -350,6 +353,9 @@ Changelog
:pr:`25733` by :user:`Brigitta Sipőcz <bsipocz>` and
:user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Fix| :func:`utils.validation.check_array` now suports extension arrays with object
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Suggested change
- |Fix| :func:`utils.validation.check_array` now suports extension arrays with object
- |Fix| :func:`utils.validation.check_array` now supports extension arrays with object

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What are extension arrays? Maybe a link to pandas docs or just adding „pandas“ in the sentence.

doc/whats_new/v1.3.rst Outdated
@@ -350,6 +353,9 @@ Changelog
:pr:`25733` by :user:`Brigitta Sipőcz <bsipocz>` and
:user:`Jérémie du Boisberranger <jeremiedbb>`.

- |Fix| :func:`utils.validation.check_array` now suports extension arrays with object
dtypes by return an ndarray with object dtype. :pr:`xxxxx` by `Thomas Fan`_.
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Suggested change
dtypes by return an ndarray with object dtype. :pr:`xxxxx` by `Thomas Fan`_.
dtypes. In that case, it returns an ndarray with object dtype. :pr:`xxxxx` by `Thomas Fan`_.

sklearn/preprocessing/tests/test_label.py

y_true = pd.Series([1, 0, 0, 1, 0, 1, 1, 0, 1], dtype=dtype)
if unique_first:
y_true = y_true.unique()
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Maybe add a comment that explains what the returned object / type of unique is.

@glemaitre
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Has anyone tested what happens with a pyarrow table or a polars dataframe?

This is really a relevant question because pandas 2.0 will come with Arrow dtype so we will encounter this road quite soon.

@glemaitre
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I am wondering if we should be considering refactoring check_array and splitting the conversion from the statistic check. Even in the conversion, we could split into library-type support. I don't know if it would be enough to make the code more readable.

@thomasjpfan
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thomasjpfan commented Mar 13, 2023 •
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Has anyone tested what happens with a pyarrow table or a polars dataframe?

In terms of check_array, polars dataframes return smoething but give the wrong ndarray:

import polars as pl
dataframe = pl.DataFrame({"integer": [1, 2, None], 
                          "float":[4.0, 5.0, 6.0]})

# This should be transposed.
check_array(dataframe, force_all_finite="allow-nan")
# array([[ 1.,  2., nan],
#        [ 4.,  5.,  6.]])

# This is because `np.asarray` returns the above:
np.asarray(dataframe)
# array([[ 1.,  2., nan],
#       [ 4.,  5.,  6.]])

The same story with PyArrow tables:

import pyarrow as pa
ints = pa.array([2, 4, 5, 100])
floats = pa.array([1.0, 2.0, 3.0, None])
table = pa.Table.from_arrays([ints, floats], names=["ints", "floats"])

# Again, this should be transposed.
check_array(table, force_all_finite="allow-nan")
# array([[  2.,   4.,   5., 100.],
#        [  1.,   2.,   3.,  nan]])

I don't know if it would be enough to make the code more readable.

I think it's possible to refactor check_array into something more readable and also support other DataFrames. In general, I'll want to support the DataFrame Protocol. The DataFrame Protocol is already implemented in many dataframes libraries such as Polars & pandas.

This is really a relevant question because pandas 2.0 will come with Arrow dtype so we will encounter this road quite soon.

The PyArrow types are extension arrays, so they "just work". For example, on main:

import pandas as pd
from sklearn.utils.validation import check_array


X_df = pd.DataFrame({
    "a": pd.Series([-1.5, 0.2, None], dtype="float32[pyarrow]"),
    "b": pd.Series([1, None, 2], dtype="int32[pyarrow]"),
    "c": pd.Series([2, 3, 4], dtype="int64[pyarrow]")
}) 

check_array(X_df, force_all_finite="allow-nan")
# array([[-1.5,  1. ,  2. ],
#       [ 0.2,  nan,  3. ],
#       [ nan,  2. ,  4. ]])

@glemaitre
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The PyArrow types are extension arrays, so they "just work"

Nice. I did not yet look at those.

@lorentzenchr lorentzenchr mentioned this pull request Mar 17, 2023
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@betatim
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betatim commented Mar 21, 2023

If we start using the pattern

try:
    from pandas import XXX

anyway, should we also use it to identify an array-like as pandas instead of relying on hasattr?

I think we should. There are a few "magic" if statements in the code base that deal with recognising inputs of a particular type without using/importing the library that provides that type. I'm not sure what the reasoning was to go down that road. To me having one central is_pandas(X) in utils or so would already be a good step forward. And it seems like a good idea to use import pandas as pd in that function, because if X is indeed a pandas type, we will be able to import pandas.

TL;DR: yes please.

jeremiedbb
jeremiedbb approved these changes Mar 21, 2023
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The fix looks acceptable for now. Though, I agree with the discussions and a refactoring of check_array to disentangle pandas (and maybe more general) dataframes and numpy arrays would make things a lot easier to maintain/enhance.

@jeremiedbb jeremiedbb merged commit 65dfab0 into scikit-learn:main Mar 22, 2023
Veghit pushed a commit to Veghit/scikit-learn that referenced this pull request Apr 15, 2023
@thomasjpfan
FIX Fixes pandas extension arrays in check_array (scikit-learn#25813)
f3cf046
MohitBurkule added a commit to MohitBurkule/scikit-learn that referenced this pull request May 7, 2023
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* MAINT Parameters validation for sklearn.metrics.pairwise.paired_euclidean_distances (scikit-learn#26073)

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Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr>

* MAINT Parameters validation for sklearn.preprocessing.normalize (scikit-learn#26069)

Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr>

* MAINT Parameter validation for metrics.cluster.fowlkes_mallows_score (scikit-learn#26080)

Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr>

* MAINT Parameters validation for compose.make_column_transformer (scikit-learn#25897)

Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr>

* MAINT Parameters validation for sklearn.metrics.pairwise.polynomial_kernel (scikit-learn#26070)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.pairwise.rbf_kernel (scikit-learn#26071)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.pairwise.sigmoid_kernel (scikit-learn#26072)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Param validation: constraint for numeric missing values (scikit-learn#26085)

* FIX Adds support for negative values in categorical features in gradient boosting (scikit-learn#25629)

Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
Co-authored-by: Tim Head <betatim@gmail.com>

* MAINT Fix C warning in Cython module splitting.pyx (scikit-learn#26051)

* MNT Updates _isotonic.pyx to use memoryviews instead of `cnp.ndarray` (scikit-learn#26068)

* FIX Fixes memory regression for inspecting extension arrays (scikit-learn#26106)

* PERF set openmp to use only physical cores by default (scikit-learn#26082)

* MNT Update black to 23.3.0 (scikit-learn#26110)

* MNT Adds black commit to git-blame-ignore-revs (scikit-learn#26111)

* MAINT Parameters validation for sklearn.metrics.pair_confusion_matrix (scikit-learn#26107)

* MAINT Parameters validation for sklearn.metrics.mean_poisson_deviance (scikit-learn#26104)

* DOC Use notebook style in plot_lof_outlier_detection.py (scikit-learn#26017)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

* MAINT utils._fast_dict uses types from utils._typedefs (scikit-learn#26025)

* DOC remove sparse-matrix for `y` in ElasticNet (scikit-learn#26127)

* ENH add exponential loss (scikit-learn#25965)

* MAINT Parameters validation for sklearn.preprocessing.robust_scale (scikit-learn#26086)

* MAINT Parameters validation for sklearn.datasets.fetch_rcv1 (scikit-learn#26126)

* MAINT Parameters validation for sklearn.metrics.adjusted_rand_score (scikit-learn#26134)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.calinski_harabasz_score  (scikit-learn#26135)

* MAINT Parameters validation for sklearn.metrics.davies_bouldin_score  (scikit-learn#26136)

* MAINT: remove `from numpy.math cimport` statements (scikit-learn#26143)

* MAINT Parameters validation for sklearn.inspection.permutation_importance (scikit-learn#26145)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.cluster.homogeneity_completeness_v_measure (scikit-learn#26137)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.rand_score (scikit-learn#26138)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* DOC update comment in metrics/tests/test_classification.py (scikit-learn#26150)

* CI small cleanup of Cirrus CI test script (scikit-learn#26168)

* MAINT remove deprecated is_categorical_dtype (scikit-learn#26156)

* DOC Add skforecast to related projects page (scikit-learn#26133)

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* FIX Keeps namedtuple's class when transform returns a tuple (scikit-learn#26121)

* DOC corrected letter case for better readability in sklearn/metrics/_classification.py / (scikit-learn#26169)

* MAINT Parameters validation for sklearn.preprocessing.power_transform (scikit-learn#26142)

* FIX `roc_auc_score` now uses `y_prob` instead of `y_pred` (scikit-learn#26155)

* MAINT Parameters validation for sklearn.datasets.load_iris (scikit-learn#26177)

* MAINT Parameters validation for sklearn.datasets.load_diabetes (scikit-learn#26166)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.datasets.load_breast_cancer (scikit-learn#26165)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.metrics.cluster.entropy (scikit-learn#26162)

* MAINT Parameters validation for sklearn.datasets.fetch_species_distributions (scikit-learn#26161)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* ASV Fix tol in SGDRegressorBenchmark (scikit-learn#26146)

Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

* MNT use api.openml.org URLs for fetch_openml (scikit-learn#26171)

* MAINT Parameters validation for sklearn.utils.resample (scikit-learn#26139)

* MAINT make it explicit that additive_chi2_kernel does not accept sparse matrix (scikit-learn#26178)

* MNT fix circleci link in README.rst (scikit-learn#26183)

* CI Fix circleci artifact redirector action (scikit-learn#26181)

* GOV introduce rights for groups as discussed in SLEP019 (scikit-learn#25753)

Co-authored-by: Julien <git@jjerphan.xyz>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* MAINT Parameters validation for sklearn.neighbors.sort_graph_by_row_values (scikit-learn#26173)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* FIX improve convergence criterion for LogisticRegression(penalty="l1", solver='liblinear') (scikit-learn#25214)

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* MAINT Fix several typos in src and doc files (scikit-learn#26187)

* PERF fix overhead of _rescale_data in LinearRegression (scikit-learn#26207)

* ENH add Huber loss (scikit-learn#25966)

* MAINT Refactor GraphicalLasso and graphical_lasso (scikit-learn#26033)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Cython linting (scikit-learn#25861)

* DOC Add JupyterLite button in example gallery (scikit-learn#25887)

* MAINT Parameters validation for sklearn.covariance.ledoit_wolf_shrinkage (scikit-learn#26200)

* MAINT Parameters validation for sklearn.datasets.load_linnerud (scikit-learn#26199)

* MAINT Parameters validation for sklearn.datasets.load_wine (scikit-learn#26196)

* DOC Added redirect to Provost paper + minor refactor (scikit-learn#26223)

* MAINT Parameter Validation for `covariance.graphical_lasso` (scikit-learn#25053)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.datasets.load_digits (scikit-learn#26195)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.preprocessing.quantile_transform (scikit-learn#26144)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.model_selection.cross_validate (scikit-learn#26129)

Co-authored-by: jeremiedbb <jeremiedbb@yahoo.fr>

* DOC Adds TargetEncoder example explaining the internal CV (scikit-learn#26185)

Co-authored-by: Tim Head <betatim@gmail.com>

* spelling mistake corrected in documentation for script `plot_document_clustering.py` (scikit-learn#26228)

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* FIX possible UnboundLocalError in fetch_openml (scikit-learn#26236)

* ENH Adds PyTorch support to LinearDiscriminantAnalysis (scikit-learn#25956)

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Co-authored-by: Tim Head <betatim@gmail.com>

* MNT Use fixed version of Pyodide (scikit-learn#26247)

* MNT Reset transform_output default in example to fix doc build build (scikit-learn#26269)

* DOC Update example plot_nearest_centroid.py (scikit-learn#26263)

* MNT reduce JupyterLite build size (scikit-learn#26246)

* DOC term -> meth in GradientBoosting (scikit-learn#26225)

* MNT speed-up html-noplot build (scikit-learn#26245)

Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* MNT Use copy=False when creating DataFrames (scikit-learn#26272)

* MAINT Parameters validation for sklearn.model_selection.permutation_test_score (scikit-learn#26230)

* MAINT Parameters validation for sklearn.datasets.clear_data_home (scikit-learn#26259)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.datasets.load_files (scikit-learn#26203)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.datasets.get_data_home (scikit-learn#26260)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* DOC Fix y-axis plot labels in permutation test score example (scikit-learn#26240)

* MAINT cython-lint ignores asv_benchmarks (scikit-learn#26282)

* MAINT Parameter validation for metrics.cluster._supervised (scikit-learn#26258)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* DOC Improve docstring for tol in SequentialFeatureSelector (scikit-learn#26271)

* MAINT Parameters validation for  sklearn.datasets.load_sample_image (scikit-learn#26226)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* DOC Consistent param type for pos_label (scikit-learn#26237)

* DOC Minor grammar fix to imputation docs (scikit-learn#26283)

* MAINT Parameters validation for sklearn.calibration.calibration_curve (scikit-learn#26198)

Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

* MAINT Parameters validation for sklearn.inspection.partial_dependence (scikit-learn#26209)

Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

* MAINT Parameters validation for sklearn.model_selection.validation_curve (scikit-learn#26229)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MAINT Parameters validation for sklearn.model_selection.learning_curve (scikit-learn#26227)

Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>

* MNT Remove deprecated pandas.api.types.is_sparse (scikit-learn#26287)

* CI Use Trusted Publishers for uploading wheels to PyPI (scikit-learn#26249)

* MAINT Parameters validation for sklearn.metrics.pairwise.manhattan_distances (scikit-learn#26122)

* PERF revert openmp use in csr_row_norms (scikit-learn#26275)

* MAINT Parameters validation for metrics.check_scoring (scikit-learn#26041)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>

* MNT Improve error message when checking classification target is of a non-regression type (scikit-learn#26281)

Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* DOC fix link to User Guide encoder_infrequent_categories (scikit-learn#26309)

* MNT remove unused args in _predict_regression_tree_inplace_fast_dense (scikit-learn#26314)

* ENH Adds missing value support for trees (scikit-learn#23595)

Co-authored-by: Tim Head <betatim@gmail.com>
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>

* CLN Clean up logic in validate_data and cast_to_ndarray (scikit-learn#26300)

* MAINT refactor scorer using _get_response_values (scikit-learn#26037)

Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>

* DOC Add HGBDT to "see also" section of random forests (scikit-learn#26319)

Co-authored-by: ArturoAmorQ <arturo.amor-quiroz@polytechnique.edu>
Co-authored-by: Tim Head <betatim@gmail.com>

* MNT Bump Github Action labeler version to use newer Node (scikit-learn#26302)

* FIX thresholds should not exceed 1.0 with probabilities in `roc_curve`  (scikit-learn#26194)

Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* ENH Allow for appropriate dtype us in `preprocessing.PolynomialFeatures` for sparse matrices (scikit-learn#23731)

Co-authored-by: Aleksandr Kokhaniukov <alexander.kohanyukov@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>
Co-authored-by: Julien Jerphanion <git@jjerphan.xyz>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

* DOC Fix minor typo (scikit-learn#26327)

* MAINT bump minimum version for pytest (scikit-learn#26184)

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Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>
Co-authored-by: Olivier Grisel <olivier.grisel@ensta.org>

* DOC fix return type in isotonic_regression (scikit-learn#26332)

* FIX fix available_if for MultiOutputRegressor.partial_fit (scikit-learn#26333)

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

* FIX make pipeline pass check_estimator (scikit-learn#26325)

* FEA Add multiclass support to `average_precision_score` (scikit-learn#24769)

Co-authored-by: Geoffrey <geoffrey.bolmier@gmail.com>
Co-authored-by: gbolmier <geoffrey.bolmier@volvocars.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>

---------

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Support nullable pandas dtypes in LabelBinarizer
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@thomasjpfan @lorentzenchr @glemaitre @betatim @jeremiedbb