FIX Make `get_namespace` handle pandas dataframe input

#### [NAME_REDACTED]/PRs
Closes #32836
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#### What does this implement/fix? Explain your changes.

A pandas DataFrame or Series does not have a namespace associated to it in the array API. As such we should filter it out, like we do for sparse arrays, before trying to determine the namespace. This means that for `get_namespace(a_df)` (with array API enabled) we get the NumPy compat namespace back as `xp`. I think this makes sense.

#### AI usage disclosure
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I used AI assistance for:
- [x] Code generation (e.g., when writing an implementation or fixing a bug)
	- Cursor's tab complete helped adapt the test cases from `test_get_namespace_ndarray_with_dispatch` which I used as starting point

#### Any other comments?

How do we solve the problem that we can't import `_is_pandas_df_or_series` from `sklearn.utils.validation` as it causes a circular import. Duplicating it feels a bit weird, but I can't think of a great alternative. We could use `hasattr(array, "iloc")` to detect "pandas-ness"?

Before discussing how we best detect "pandas-ness" we should agree that filtering `DataFrame` and `Series`out (like sparse arrays) is the right approach.

cc [USER_REDACTED]

@@ -0,0 +1,2 @@
+- Estimators with array API support no longer reject dataframe inputs when array API support is enabled.
+  By :user:`[NAME_REDACTED] <betatim>`

@@ -20,6 +20,7 @@
 from sklearn.pipeline import _fit_transform_one, _name_estimators, _transform_one
 from sklearn.preprocessing import FunctionTransformer
 from sklearn.utils import Bunch
+from sklearn.utils._dataframe import is_pandas_df
 from sklearn.utils._indexing import (
     _determine_key_type,
     _get_column_indices,
@@ -47,7 +48,6 @@
     _check_feature_names_in,
     _check_n_features,
     _get_feature_names,
-    _is_pandas_df,
     _num_samples,
     check_array,
     check_is_fitted,
@@ -773,7 +773,7 @@ def _validate_output(self, result):
         except ImportError:
             return
         for Xs, name in zip(result, names):
-            if not _is_pandas_df(Xs):
+            if not is_pandas_df(Xs):
                 continue
             for col_name, dtype in Xs.dtypes.to_dict().items():
                 if getattr(dtype, "na_value", None) is not pd.NA:
@@ -1064,7 +1064,7 @@ def transform(self, X, **params):
         # were not present in fit time, and the order of the columns doesn't
         # matter.
         fit_dataframe_and_transform_dataframe = hasattr(self, "feature_names_in_") and (
-            _is_pandas_df(X) or hasattr(X, "__dataframe__")
+            is_pandas_df(X) or hasattr(X, "__dataframe__")
         )

         n_samples = _num_samples(X)

@@ -40,6 +40,7 @@
 from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import FunctionTransformer, LabelEncoder, OrdinalEncoder
 from sklearn.utils import check_random_state, compute_sample_weight, resample
+from sklearn.utils._dataframe import is_pandas_df
 from sklearn.utils._missing import is_scalar_nan
 from sklearn.utils._openmp_helpers import _openmp_effective_n_threads
 from sklearn.utils._param_validation import Interval, RealNotInt, StrOptions
@@ -48,7 +49,6 @@
     _check_monotonic_cst,
     _check_sample_weight,
     _check_y,
-    _is_pandas_df,
     check_array,
     check_consistent_length,
     check_is_fitted,
@@ -371,7 +371,7 @@ def _check_categorical_features(self, X):
         # fixed in main and maybe included in 2.2.1, see
         # https://github.com/[USER_REDACTED]ndas-dev/pandas/pull/57173.
         # Also pandas versions < 1.5.1 do not support the dataframe interchange
-        if _is_pandas_df(X):
+        if is_pandas_df(X):
             X_is_dataframe = True
             categorical_columns_mask = np.asarray(X.dtypes == "category")
         elif hasattr(X, "__dataframe__"):

@@ -12,11 +12,11 @@

 from sklearn.base import TransformerMixin
 from sklearn.utils import _safe_indexing, check_array, safe_sqr
+from sklearn.utils._dataframe import is_pandas_df
 from sklearn.utils._set_output import _get_output_config
 from sklearn.utils._tags import get_tags
 from sklearn.utils.validation import (
     _check_feature_names_in,
-    _is_pandas_df,
     check_is_fitted,
     validate_data,
 )
@@ -100,7 +100,7 @@ def transform(self, X):
         # Preserve X when X is a dataframe and the output is configured to
         # be pandas.
         output_config_dense = _get_output_config("transform", estimator=self)["dense"]
-        preserve_X = output_config_dense != "default" and _is_pandas_df(X)
+        preserve_X = output_config_dense != "default" and is_pandas_df(X)

         # note: we use get_tags instead of __sklearn_tags__ because this is a
         # public Mixin.

@@ -8,13 +8,12 @@
 from sklearn.base import is_regressor
 from sklearn.preprocessing import LabelEncoder
 from sklearn.utils import _safe_indexing
+from sklearn.utils._dataframe import is_pandas_df, is_polars_df
 from sklearn.utils._optional_dependencies import check_matplotlib_support
 from sklearn.utils._response import _get_response_values
 from sklearn.utils._set_output import _get_adapter_from_container
 from sklearn.utils.validation import (
     _is_arraylike_not_scalar,
-    _is_pandas_df,
-    _is_polars_df,
     _num_features,
     check_is_fitted,
 )
@@ -496,7 +495,7 @@ def from_estimator(
         )

         X_grid = np.c_[xx0.ravel(), xx1.ravel()]
-        if _is_pandas_df(X) or _is_polars_df(X):
+        if is_pandas_df(X) or is_polars_df(X):
             adapter = _get_adapter_from_container(X)
             X_grid = adapter.create_container(
                 X_grid,

@@ -7,6 +7,7 @@
 import numpy as np

 from sklearn.base import BaseEstimator, TransformerMixin, _fit_context
+from sklearn.utils._dataframe import is_pandas_df, is_polars_df
 from sklearn.utils._param_validation import StrOptions
 from sklearn.utils._repr_html.estimator import _VisualBlock
 from sklearn.utils._set_output import _get_adapter_from_container, _get_output_config
@@ -15,8 +16,6 @@
     _allclose_dense_sparse,
     _check_feature_names_in,
     _get_feature_names,
-    _is_pandas_df,
-    _is_polars_df,
     check_array,
     validate_data,
 )
@@ -302,9 +301,9 @@ def transform(self, X):
                 "a {0} DataFrame to follow the `set_output` API  or `feature_names_out`"
                 " should be defined."
             )
-            if output_config == "pandas" and not _is_pandas_df(out):
+            if output_config == "pandas" and not is_pandas_df(out):
                 warnings.warn(warn_msg.format("pandas"))
-            elif output_config == "polars" and not _is_polars_df(out):
+            elif output_config == "polars" and not is_polars_df(out):
                 warnings.warn(warn_msg.format("polars"))

         return out

@@ -16,6 +16,7 @@
 from sklearn.externals import array_api_compat
 from sklearn.externals import array_api_extra as xpx
 from sklearn.externals.array_api_compat import numpy as np_compat
+from sklearn.utils._dataframe import is_df_or_series
 from sklearn.utils.fixes import parse_version

 # TODO: complete __all__
@@ -320,6 +321,8 @@ def _remove_non_arrays(*arrays, remove_none=True, remove_types=(str,)):
             continue
         if sp.issparse(array):
             continue
+        if is_df_or_series(array):
+            continue
         filtered_arrays.append(array)

     return filtered_arrays

@@ -0,0 +1,123 @@
+"""Functions to determine if an object is a dataframe or series."""
+
+# Authors: The scikit-learn developers
+# SPDX-License-Identifier: BSD-3-Clause
+
+import sys
+
+
+def is_df_or_series(X):
+    """[NAME_REDACTED] if the X is a dataframe or series.
+
+    Parameters
+    ----------
+    X : {array-like, dataframe}
+        The array-like or dataframe object to check.
+
+    Returns
+    -------
+    bool
+        True if the X is a dataframe or series, False otherwise.
+    """
+    return is_pandas_df_or_series(X) or is_polars_df_or_series(X) or is_pyarrow_data(X)
+
+
+def is_pandas_df_or_series(X):
+    """[NAME_REDACTED] if the X is a pandas dataframe or series.
+
+    Parameters
+    ----------
+    X : {array-like, dataframe}
+        The array-like or dataframe object to check.
+
+    Returns
+    -------
+    bool
+        True if the X is a pandas dataframe or series, False otherwise.
+    """
+    try:
+        pd = sys.modules["pandas"]
+    except KeyError:
+        return False
+    return isinstance(X, (pd.DataFrame, pd.Series))
+
+
+def is_pandas_df(X):
+    """[NAME_REDACTED] if the X is a pandas dataframe.
+
+    Parameters
+    ----------
+    X : {array-like, dataframe}
+        The array-like or dataframe object to check.
+
+    Returns
+    -------
+    bool
+        True if the X is a pandas dataframe, False otherwise.
+    """
+    try:
+        pd = sys.modules["pandas"]
+    except KeyError:
+        return False
+    return isinstance(X, pd.DataFrame)
+
+
+def is_pyarrow_data(X):
+    """[NAME_REDACTED] if the X is a pyarrow Table, RecordBatch, Array or ChunkedArray.
+
+    Parameters
+    ----------
+    X : {array-like, dataframe}
+        The array-like or dataframe object to check.
+
+    Returns
+    -------
+    bool
+        True if the X is a pyarrow Table, RecordBatch, Array or ChunkedArray,
+        False otherwise.
+    """
+    try:
+        pa = sys.modules["pyarrow"]
+    except KeyError:
+        return False
+    return isinstance(X, (pa.Table, pa.RecordBatch, pa.Array, pa.ChunkedArray))
+
+
+def is_polars_df_or_series(X):
+    """[NAME_REDACTED] if the X is a polars dataframe or series.
+
+    Parameters
+    ----------
+    X : {array-like, dataframe}
+        The array-like or dataframe object to check.
+
+    Returns
+    -------
+    bool
+        True if the X is a polars dataframe or series, False otherwise.
+    """
+    try:
+        pl = sys.modules["polars"]
+    except KeyError:
+        return False
+    return isinstance(X, (pl.DataFrame, pl.Series))
+
+
+def is_polars_df(X):
+    """[NAME_REDACTED] if the X is a polars dataframe.
+
+    Parameters
+    ----------
+    X : {array-like, dataframe}
+        The array-like or dataframe object to check.
+
+    Returns
+    -------
+    bool
+        True if the X is a polarsdataframe, False otherwise.
+    """
+    try:
+        pl = sys.modules["polars"]
+    except KeyError:
+        return False
+    return isinstance(X, pl.DataFrame)

@@ -16,15 +16,17 @@
     get_namespace_and_device,
     move_to,
 )
+from sklearn.utils._dataframe import (
+    is_pandas_df,
+    is_polars_df_or_series,
+    is_pyarrow_data,
+)
 from sklearn.utils._param_validation import Interval, validate_params
 from sklearn.utils.extmath import _approximate_mode
 from sklearn.utils.fixes import PYARROW_VERSION_BELOW_17
 from sklearn.utils.validation import (
     _check_sample_weight,
     _is_arraylike_not_scalar,
-    _is_pandas_df,
-    _is_polars_df_or_series,
-    _is_pyarrow_data,
     _use_interchange_protocol,
     check_array,
     check_consistent_length,
@@ -325,21 +327,21 @@ def _safe_indexing(X, indices, *, axis=0):
     if (
         axis == 1
         and indices_dtype == "str"
-        and not (_is_pandas_df(X) or _use_interchange_protocol(X))
+        and not (is_pandas_df(X) or _use_interchange_protocol(X))
     ):
         raise ValueError(
             "Specifying the columns using strings is only supported for dataframes."
         )

     if hasattr(X, "iloc"):
-        # TODO: we should probably use _is_pandas_df_or_series(X) instead but:
+        # TODO: we should probably use is_pandas_df_or_series(X) instead but:
         # 1) Currently, it (probably) works for dataframes compliant to pandas' API.
         # 2) Updating would require updating some tests such as
         #    test_train_test_split_mock_pandas.
         return _pandas_indexing(X, indices, indices_dtype, axis=axis)
-    elif _is_polars_df_or_series(X):
+    elif is_polars_df_or_series(X):
         return _polars_indexing(X, indices, indices_dtype, axis=axis)
-    elif _is_pyarrow_data(X):
+    elif is_pyarrow_data(X):
         return _pyarrow_indexing(X, indices, indices_dtype, axis=axis)
     elif _use_interchange_protocol(X):  # pragma: no cover
         # Once the dataframe X is converted into its dataframe interchange protocol

@@ -43,6 +43,7 @@
 from sklearn.utils._testing import (
     SkipTest,
     _array_api_for_tests,
+    _convert_container,
     assert_array_equal,
     skip_if_array_api_compat_not_configured,
 )
@@ -84,6 +85,35 @@ def test_get_namespace_ndarray_with_dispatch():
         assert xp_out is np_compat

+[USER_REDACTED]_if_array_api_compat_not_configured
+[USER_REDACTED].mark.parametrize(
+    "constructor_name", ["pyarrow", "dataframe", "polars", "series"]
+)
+def test_get_namespace_df_with_dispatch(constructor_name):
+    """Test get_namespace on dataframes and series."""
+
+    df = _convert_container([[1, 4, 2], [3, 3, 6]], constructor_name)
+    with config_context(array_api_dispatch=True):
+        xp_out, is_array_api_compliant = get_namespace(df)
+        assert not is_array_api_compliant
+
+        # When operating on dataframes or series the Numpy namespace is
+        # the right thing to use.
+        assert xp_out is np_compat
+
+
+[USER_REDACTED]_if_array_api_compat_not_configured
+def test_get_namespace_sparse_with_dispatch():
+    """Test get_namespace on sparse arrays."""
+    with config_context(array_api_dispatch=True):
+        xp_out, is_array_api_compliant = get_namespace(sp.csr_array([[1, 2, 3]]))
+        assert not is_array_api_compliant
+
+        # When operating on sparse arrays the Numpy namespace is
+        # the right thing to use.
+        assert xp_out is np_compat
+
+
 [USER_REDACTED]_if_array_api_compat_not_configured
 def test_get_namespace_array_api(monkeypatch):
     """Test get_namespace for ArrayAPI arrays."""

@@ -0,0 +1,84 @@
+"""Tests for dataframe detection functions."""
+
+import numpy as np
+import pytest
+
+from sklearn._min_dependencies import dependent_packages
+from sklearn.utils._dataframe import is_df_or_series, is_pandas_df, is_polars_df
+from sklearn.utils._testing import _convert_container
+
+
+[USER_REDACTED].mark.parametrize("constructor_name", ["pyarrow", "dataframe", "polars"])
+def test_is_df_or_series(constructor_name):
+    df = _convert_container([[1, 4, 2], [3, 3, 6]], constructor_name)
+
+    assert is_df_or_series(df)
+    assert not is_df_or_series(np.asarray([1, 2, 3]))
+
+
+[USER_REDACTED].mark.parametrize("constructor_name", ["pyarrow", "dataframe", "polars"])
+def test_is_pandas_df_other_libraries(constructor_name):
+    df = _convert_container([[1, 4, 2], [3, 3, 6]], constructor_name)
+    if constructor_name in ("pyarrow", "polars"):
+        assert not is_pandas_df(df)
+    else:
+        assert is_pandas_df(df)
+
+
+def test_is_pandas_df():
+    """Check behavior of is_pandas_df when pandas is installed."""
+    pd = pytest.importorskip("pandas")
+    df = pd.DataFrame([[1, 2, 3]])
+    assert is_pandas_df(df)
+    assert not is_pandas_df(np.asarray([1, 2, 3]))
+    assert not is_pandas_df(1)
+
+
+def test_is_pandas_df_pandas_not_installed(hide_available_pandas):
+    """Check is_pandas_df when pandas is not installed."""
+
+    assert not is_pandas_df(np.asarray([1, 2, 3]))
+    assert not is_pandas_df(1)
+
+
+[USER_REDACTED].mark.parametrize(
+    "constructor_name, minversion",
+    [
+        ("pyarrow", dependent_packages["pyarrow"][0]),
+        ("dataframe", dependent_packages["pandas"][0]),
+        ("polars", dependent_packages["polars"][0]),
+    ],
+)
+def test_is_polars_df_other_libraries(constructor_name, minversion):
+    df = _convert_container(
+        [[1, 4, 2], [3, 3, 6]],
+        constructor_name,
+        minversion=minversion,
+    )
+    if constructor_name in ("pyarrow", "dataframe"):
+        assert not is_polars_df(df)
+    else:
+        assert is_polars_df(df)
+
+
+def test_is_polars_df_for_duck_typed_polars_dataframe():
+    """Check is_polars_df for object that looks like a polars dataframe"""
+
+    class NotAPolarsDataFrame:
+        def __init__(self):
+            self.columns = [1, 2, 3]
+            self.schema = "my_schema"
+
+    not_a_polars_df = NotAPolarsDataFrame()
+    assert not is_polars_df(not_a_polars_df)
+
+
+def test_is_polars_df():
+    """Check that is_polars_df return False for non-dataframe objects."""
+
+    class LooksLikePolars:
+        def __init__(self):
+            self.columns = ["a", "b"]
+            self.schema = ["a", "b"]
+
+    assert not is_polars_df(LooksLikePolars())

@@ -14,7 +14,6 @@

 import sklearn
 from sklearn._config import config_context
-from sklearn._min_dependencies import dependent_packages
 from sklearn.base import BaseEstimator
 from sklearn.datasets import make_blobs
 from sklearn.ensemble import RandomForestRegressor
@@ -77,8 +76,6 @@
     _estimator_has,
     _get_feature_names,
     _is_fitted,
-    _is_pandas_df,
-    _is_polars_df,
     _num_features,
     _num_samples,
     _to_object_array,
@@ -1995,63 +1992,6 @@ def test_get_feature_names_dataframe_protocol(constructor_name, minversion):
     assert_array_equal(feature_names, columns)

-[USER_REDACTED].mark.parametrize("constructor_name", ["pyarrow", "dataframe", "polars"])
-def test_is_pandas_df_other_libraries(constructor_name):
-    df = _convert_container([[1, 4, 2], [3, 3, 6]], constructor_name)
-    if constructor_name in ("pyarrow", "polars"):
-        assert not _is_pandas_df(df)
-    else:
-        assert _is_pandas_df(df)
-
-
-def test_is_pandas_df():
-    """Check behavior of is_pandas_df when pandas is installed."""
-    pd = pytest.importorskip("pandas")
-    df = pd.DataFrame([[1, 2, 3]])
-    assert _is_pandas_df(df)
-    assert not _is_pandas_df(np.asarray([1, 2, 3]))
-    assert not _is_pandas_df(1)
-
-
-def test_is_pandas_df_pandas_not_installed(hide_available_pandas):
-    """Check _is_pandas_df when pandas is not installed."""
-
-    assert not _is_pandas_df(np.asarray([1, 2, 3]))
-    assert not _is_pandas_df(1)
-
-
-[USER_REDACTED].mark.parametrize(
-    "constructor_name, minversion",
-    [
-        ("pyarrow", dependent_packages["pyarrow"][0]),
-        ("dataframe", dependent_packages["pandas"][0]),
-        ("polars", dependent_packages["polars"][0]),
-    ],
-)
-def test_is_polars_df_other_libraries(constructor_name, minversion):
-    df = _convert_container(
-        [[1, 4, 2], [3, 3, 6]],
-        constructor_name,
-        minversion=minversion,
-    )
-    if constructor_name in ("pyarrow", "dataframe"):
-        assert not _is_polars_df(df)
-    else:
-        assert _is_polars_df(df)
-
-
-def test_is_polars_df_for_duck_typed_polars_dataframe():
-    """Check _is_polars_df for object that looks like a polars dataframe"""
-
-    class NotAPolarsDataFrame:
-        def __init__(self):
-            self.columns = [1, 2, 3]
-            self.schema = "my_schema"
-
-    not_a_polars_df = NotAPolarsDataFrame()
-    assert not _is_polars_df(not_a_polars_df)
-
-
 def test_get_feature_names_numpy():
     """Get feature names return None for numpy arrays."""
     X = np.array([[1, 2, 3], [4, 5, 6]])
@@ -2322,17 +2262,6 @@ def test_column_or_1d():
                 column_or_1d(y)

-def test__is_polars_df():
-    """Check that _is_polars_df return False for non-dataframe objects."""
-
-    class LooksLikePolars:
-        def __init__(self):
-            self.columns = ["a", "b"]
-            self.schema = ["a", "b"]
-
-    assert not _is_polars_df(LooksLikePolars())
-
-
 def test_check_array_writeable_np():
     """Check the behavior of check_array when a writeable array is requested
     without copy if possible, on numpy arrays.

@@ -5,7 +5,6 @@

 import numbers
 import operator
-import sys
 import warnings
 from collections.abc import Sequence
 from contextlib import suppress
@@ -30,6 +29,7 @@
     get_namespace,
     get_namespace_and_device,
 )
+from sklearn.utils._dataframe import is_pandas_df, is_pandas_df_or_series
 from sklearn.utils._isfinite import FiniteStatus, cy_isfinite
 from sklearn.utils._tags import get_tags
 from sklearn.utils.fixes import (
@@ -312,7 +312,7 @@ def _use_interchange_protocol(X):
     to ensure strict behavioral backward compatibility with older versions of
     scikit-learn.
     """
-    return not _is_pandas_df(X) and hasattr(X, "__dataframe__")
+    return not is_pandas_df(X) and hasattr(X, "__dataframe__")

 def _num_features(X):
@@ -1129,7 +1129,7 @@ def is_sparse(dtype):
             # ensure that the output is writeable, even if avoidable, to not overwrite
             # the user's data by surprise.

-            if _is_pandas_df_or_series(array_orig):
+            if is_pandas_df_or_series(array_orig):
                 try:
                     # In pandas >= 3, np.asarray(df), called earlier in check_array,
                     # returns a read-only intermediate array. It can be made writeable
@@ -2307,51 +2307,6 @@ def _check_method_params(X, params, indices=None):
     return method_params_validated

-def _is_pandas_df_or_series(X):
-    """[NAME_REDACTED] if the X is a pandas dataframe or series."""
-    try:
-        pd = sys.modules["pandas"]
-    except KeyError:
-        return False
-    return isinstance(X, (pd.DataFrame, pd.Series))
-
-
-def _is_pandas_df(X):
-    """[NAME_REDACTED] if the X is a pandas dataframe."""
-    try:
-        pd = sys.modules["pandas"]
-    except KeyError:
-        return False
-    return isinstance(X, pd.DataFrame)
-
-
-def _is_pyarrow_data(X):
-    """[NAME_REDACTED] if the X is a pyarrow Table, RecordBatch, Array or ChunkedArray."""
-    try:
-        pa = sys.modules["pyarrow"]
-    except KeyError:
-        return False
-    return isinstance(X, (pa.Table, pa.RecordBatch, pa.Array, pa.ChunkedArray))
-
-
-def _is_polars_df_or_series(X):
-    """[NAME_REDACTED] if the X is a polars dataframe or series."""
-    try:
-        pl = sys.modules["polars"]
-    except KeyError:
-        return False
-    return isinstance(X, (pl.DataFrame, pl.Series))
-
-
-def _is_polars_df(X):
-    """[NAME_REDACTED] if the X is a polars dataframe."""
-    try:
-        pl = sys.modules["polars"]
-    except KeyError:
-        return False
-    return isinstance(X, pl.DataFrame)
-
-
 def _get_feature_names(X):
     """Get feature names from X.

@@ -2375,7 +2330,7 @@ def _get_feature_names(X):
     feature_names = None

     # extract feature names for support array containers
-    if _is_pandas_df(X):
+    if is_pandas_df(X):
         # Make sure we can inspect columns names from pandas, even with
         # versions too old to expose a working implementation of
         # __dataframe__.column_names() and avoid introducing any

Thanks. I think this is the correct fix but it needs to be generalized, see below.

LGTM as it is, but feel free to to implement https://github.com/[USER_REDACTED]n/scikit-learn/pull/32838/files#r2591717992 as well.

We probably need something similar for any type of dataframe/series: pandas, polars, pyarrow.

I would rather move that to a new `sklearn/utils/_dataframe.py` submodule instead of duplication.

```suggestion
        if _is_any_supported_df_or_series(array):
```

Investigate parametrizing this on pandas, pyarrow, polars instead of having one test per dataframe library.


CI: Make one tracking isssue per build for the unit-tests GHA

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Addresses this comment : https://github.com/[USER_REDACTED]n/scikit-learn/pull/32797#issuecomment-3589914268

#### What does this implement/fix? Explain your changes.

Instead of opening a single issue if any of the tests in the matrix failed, now independent issues for each matrix entry are generated.

Also allow the update tracking issue action to trigger on manually triggered actions.

#### AI usage disclosure
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I used AI assistance for:
- [x] Code generation (e.g., when writing an implementation or fixing a bug)
- [ ] Test/benchmark generation
- [ ] Documentation (including examples)
- [x] Research and understanding

#### Any other comments?

It is a bit more complex to achieve than what I anticipated. I could not do it using a job running after all the matrix jobs of unit-tests, as github does not allow to access all outptuts of a matrix of jobs, so only a global status of the whole matrix ("success" if all jobs succeeded, "failure" if at least one failed) can be obtained in another job.

Thus I had to use an extra step in the unit-tests job, but using another workflow (i.e. `uses: ./.github/workflows/update_tracking_issue.yml`) cannot be done as a step, only as a job, so I had to re-implement it, creating some code duplication. But it also allowed to tweak the CI log URL to point to the log of the specific job rather than the whole workflow. And since github does not expose the job id necessary for this URL, I also had to add a step to get this id using the REST API.

I tested it on my fork, for example it created and commented https://github.com/[USER_REDACTED]ncoisPgm/scikit-learn/issues/23
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@@ -205,11 +205,22 @@ jobs:
           token: ${{ secrets.CODECOV_TOKEN }}
           disable_search: true

-  update-tracker:
-    uses: ./.github/workflows/update_tracking_issue.yml
-    if: ${{ always() }}
-    needs: [unit-tests]
-    with:
-      job_status: ${{ needs.unit-tests.result }}
-    secrets:
-      BOT_GITHUB_TOKEN: ${{ secrets.BOT_GITHUB_TOKEN }}
+      - name: Update tracking issue
+        if: ${{ always() && (github.event_name == 'schedule' || github.event_name == 'workflow_dispatch')}}
+        run: |
+          set -ex
+          if [[ ${{ job.status }} == "success" ]]; then
+            TESTS_PASSED=true
+          else
+            TESTS_PASSED=false
+          fi
+
+          pip install defusedxml PyGithub
+          python maint_tools/update_tracking_issue.py \
+            ${{ secrets.BOT_GITHUB_TOKEN }} \
+            "$GITHUB_WORKFLOW ${{ matrix.name }}" \
+            "$GITHUB_REPOSITORY" \
+            https://github.com/$GITHUB_REPOSITORY/actions/runs/$GITHUB_RUN_ID \
+            --tests-passed $TESTS_PASSED \
+            --auto-close false \
+            --job-name "${{ matrix.name }}"

@@ -27,7 +27,7 @@ on:
 jobs:
   update_tracking_issue:
     runs-on: ubuntu-latest
-    if: github.repository == 'scikit-learn/scikit-learn' && github.event_name == 'schedule'
+    if: github.repository == 'scikit-learn/scikit-learn' && (github.event_name == 'schedule' || github.event_name == 'workflow_dispatch')
     steps:
       - uses: actions/checkout@v5
       - uses: actions/setup-python@v6

@@ -13,6 +13,7 @@

 import argparse
 import sys
+import warnings
 from datetime import datetime, timezone
 from pathlib import Path

@@ -28,12 +29,21 @@
 parser.add_argument("ci_name", help="Name of CI run instance")
 parser.add_argument("issue_repo", help="Repo to track issues")
 parser.add_argument("link_to_ci_run", help="URL to link to")
+parser.add_argument(
+    "--job-name",
+    help=(
+        "Name of the job. If provided the job ID will be added to the log URL so that"
+        " it points to log of the job and not the whole workflow."
+    ),
+    default=None,
+)
 parser.add_argument("--junit-file", help="JUnit file to determine if tests passed")
 parser.add_argument(
     "--tests-passed",
     help=(
         "If --tests-passed is true, then the original issue is closed if the issue "
-        "exists. If tests-passed is false, then the an issue is updated or created."
+        "exists, unless --auto-close is set to false. If tests-passed is false, then "
+        "the issue is updated or created."
     ),
 )
 parser.add_argument(
@@ -62,6 +72,23 @@
 title_query = f"CI failed on {args.ci_name}"
 title = f"⚠ {title_query} (last failure: {date_str}) ⚠"

+url = args.link_to_ci_run
+
+if args.job_name is not None:
+    run_id = int(args.link_to_ci_run.split("/")[-1])
+    workflow_run = issue_repo.get_workflow_run(run_id)
+    jobs = workflow_run.jobs()
+
+    for job in jobs:
+        if job.name == args.job_name:
+            url = f"{url}/job/{job.id}"
+            break
+    else:
+        warnings.warn(
+            f"Job '{args.job_name}' not found, the URL in the issue will link to the"
+            " whole workflow's log rather than the job's one."
+        )
+

 def get_issue():
     login = gh.get_user().login
@@ -76,7 +103,7 @@ def get_issue():

 def create_or_update_issue(body=""):
     # Interact with GitHub API to create issue
-    link = f"[{args.ci_name}]({args.link_to_ci_run})"
+    link = f"[{args.ci_name}]({url})"
     issue = get_issue()

     max_body_length = 60_000
@@ -107,9 +134,7 @@ def close_issue_if_opened():
     issue = get_issue()
     if issue is not None:
         header_str = "## CI is no longer failing!"
-        comment_str = (
-            f"{header_str} ✅\n\n[Successful run]({args.link_to_ci_run}) on {date_str}"
-        )
+        comment_str = f"{header_str} ✅\n\n[Successful run]({url}) on {date_str}"

         print(f"Commented on issue #{issue.number}")
         # New comment if "## CI is no longer failing!" comment does not exist

I think moving the logic to the Python script was a good move. Minor comments, and this can go in, thanks!

Thanks for the PR, this looks good to me!

I am wondering whether this can not made a bit simpler with `gh api`. Also I don't think you need to use `BOT_GITHUB_TOKEN` and can use the `GITHUB_TOKEN` which has the necessary read permissions.

I can look into it. Then do you think it'd be better to make a standalone python script for this step, or to merge it to the next step by modifying `maint_tools/update_tracking_issue.py` to accept a new flag that would tell it to grab the job id ?

I didn't know there were differences in permissions between the tokens, I believe it's set in repo settings that aren't public so I cannot really check and try to mimic those in my fork.

That's a good point, probably updating `update_tracking_issue.py` is preferrable indeed. Maybe `--append-job-id`, `--link-to-matrix-job-log` or a better name for the potential additional `job/$jobid` at the end of the URL? To be honest it almost feels like the logic for the job log URL should be done in the python script rather than provided externally but I don't know how easy this is.

This would make my point about tokens not relevant since you would use `GIHUB_BOT_TOKEN` for everything.

So with the new commits I implemented it, after a couple of tests on my fork it seems to work just fine.
I went for a `--job-name` flag because we need to provide the name of the matrix job to find its ID, it's not the most explicit name but I tried to be clear in the help message.

Also, I was wondering why the `update_tracking_issue.py` does not use `if __name__ == "__main__"`. If there's no specific reason, I'd be happy to do the refactor. I would find it much clearer. But maybe it's outside of the scope of this PR.

This is supposed to be a warning right not an exception? If that's the case use `warnings.warn(...)`

The help is slightly incorrect actually, `--test-passed` is about the message and the fact that it is closed or not depends on `--auto-close`.

To be honest this can be done in another PR, I let you choose.

> Also, I was wondering why the update_tracking_issue.py does not use if __name__ == "__main__". If there's no specific reason, I'd be happy to do the refactor. I would find it much clearer. But maybe it's outside of the scope of this PR.

I don't think there is a very good reason other than simplicity, easier debuggability (most of the variables are global variables and easily accessible), and maybe a zest of slopiness/laziness (which you know sometimes is a reasonable approach). Feel free to do a follow up PR improving this if you think this is important :wink:.

Yes I was thinking a warning is enough, then the URL will point to the whole workflow log. I think it's better than breaking the whole action.

I don't think it is really important, but I don't see a reason not to do it as I'm already working on it.

I might as well do it here and save you from having to review an extra PR ;)

Looking back at the code, I think the message is just missing the fact that to close the issue `--auto-close` must be true too, but otherwise, the fact that the issue is created/updated or closed is indeed determined by [NAME_REDACTED] `--test-passed` value. So it's not just about the message.

This is not needed anymore, right?

Ah yes, thanks for catching it !

Looks good now, thanks!

Just as a note for our future selves. This is not directly related to the PR, but this makes sure that when triggering wheels workflow manually via workflow_dispatch, it will also update the tracking issue. In particular this is consistent with the unit tests behaviour. I think this is fine to leave in this PR.


TST add tests for pyproject minimum dependency checks

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Related to https://github.com/[USER_REDACTED]n/scikit-learn/pull/32790#issuecomment-3589272892

#### What does this implement/fix? Explain your changes.
This adds a test using a minimal pyproject sample, to test that the tests actually work with upper bounds.
I had to restructure the code a bit in order to use a test string instead of the toml file.
Let me know if this is what you had in mind [USER_REDACTED].

#### Any other comments?

I noticed that there are also no tests testing the mismatch conditions of package versions. Adding this would be fairly straight forward now, but I'm not sure if these files are actually touched manually, and if this would make sense?

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@@ -2,6 +2,7 @@

 import os
 import re
+import tomllib
 from collections import defaultdict
 from pathlib import Path

@@ -11,18 +12,79 @@
 from sklearn._min_dependencies import dependent_packages
 from sklearn.utils.fixes import parse_version

-min_depencies_tag_to_packages_without_version = defaultdict(list)
-for package, (min_version, extras) in dependent_packages.items():
-    for extra in extras.split(", "):
-        min_depencies_tag_to_packages_without_version[extra].append(package)
+# minimal dependencies and pyproject definitions for testing the pyproject tests

-pyproject_section_to_min_dependencies_tag = {
-    "build-system.requires": "build",
-    "project.dependencies": "install",
+TOY_MIN_DEPENDENCIES_PY_INFO = {
+    "joblib": ("1.3.0", "install"),
+    "scipy": ("1.10.0", "build, install"),
+    "conda-lock": ("3.0.1", "maintenance"),
 }
-for tag in min_depencies_tag_to_packages_without_version:
-    section = f"project.optional-dependencies.{tag}"
-    pyproject_section_to_min_dependencies_tag[section] = tag
+
+TOY_MATCHING_PYPROJECT_SECTIONS = """
+[project]
+dependencies = ["joblib>=1.3.0", "scipy>=1.10.0"]
+[project.optional-dependencies]
+build = ["scipy>=1.10.0"]
+install = ["joblib>=1.3.0", "scipy>=1.10.0"]
+maintenance = ["conda-lock==3.0.1"]
+[build-system]
+requires = ["scipy>=1.10.0"]
+"""
+
+TOY_MATCHING_PYPROJECT_SECTIONS_WITH_UPPER_BOUND = """
+[project]
+dependencies = ["joblib>=1.3.0,<2.0", "scipy>=1.10.0"]
+[project.optional-dependencies]
+build = ["scipy>=1.10.0,<1.19.0"]
+install = ["joblib>=1.3.0,<2.0", "scipy>=1.10.0"]
+maintenance = ["conda-lock==3.0.1"]
+[build-system]
+requires = ["scipy>=1.10.0,<1.19.0"]
+"""
+
+TOY_WRONG_SYMBOL_PYPROJECT_SECTIONS = """
+[project]
+dependencies = ["scipy<1.10.0"]
+[project.optional-dependencies]
+build = ["scipy>=1.10.0"]
+install = ["scipy>=1.10.0"]
+maintenance = ["conda-lock==3.0.1"]
+[build-system]
+requires = ["scipy>=1.10.0"]
+"""
+
+TOY_MISSING_PACKAGE_PYPROJECT_SECTIONS = """
+[project]
+dependencies = ["scipy>=1.10.0"]
+[project.optional-dependencies]
+build = ["scipy>=1.10.0"]
+install = ["scipy>=1.10.0"]
+maintenance = ["conda-lock==3.0.1"]
+[build-system]
+requires = ["scipy>=1.10.0"]
+"""
+
+TOY_ADDITIONAL_PACKAGE_PYPROJECT_SECTIONS = """
+[project]
+dependencies = ["joblib>=1.3.0", "scipy>=1.10.0"]
+[project.optional-dependencies]
+build = ["scipy>=1.10.0", "package_not_in_min_dependencies_py_file>=4.2"]
+install = ["joblib>=1.3.0", "scipy>=1.10.0"]
+maintenance = ["conda-lock==3.0.1"]
+[build-system]
+requires = ["scipy>=1.10.0"]
+"""
+
+TOY_NON_MATCHING_VERSION_PYPROJECT_SECTIONS = """
+[project]
+dependencies = ["joblib>=1.42.0", "scipy>=1.10.0"]
+[project.optional-dependencies]
+build = ["scipy>=1.10.0"]
+install = ["joblib>=1.3.0", "scipy>=1.10.0"]
+maintenance = ["conda-lock==3.0.1"]
+[build-system]
+requires = ["scipy>=1.10.0"]
+"""

 def test_min_dependencies_readme():
@@ -66,18 +128,79 @@ def test_min_dependencies_readme():
                 assert version == min_version, message

-def check_pyproject_section(
-    pyproject_section, min_dependencies_tag, skip_version_check_for=None
-):
-    # tomllib is available in Python 3.11
-    tomllib = pytest.importorskip("tomllib")
+def extract_packages_and_pyproject_tags(dependencies):
+    min_depencies_tag_to_packages_without_version = defaultdict(list)
+    for package, (min_version, tags) in dependencies.items():
+        for t in tags.split(", "):
+            min_depencies_tag_to_packages_without_version[t].append(package)
+
+    pyproject_section_to_min_dependencies_tag = {
+        "build-system.requires": "build",
+        "project.dependencies": "install",
+    }
+    for tag in min_depencies_tag_to_packages_without_version:
+        section = f"project.optional-dependencies.{tag}"
+        pyproject_section_to_min_dependencies_tag[section] = tag
+
+    return (
+        min_depencies_tag_to_packages_without_version,
+        pyproject_section_to_min_dependencies_tag,
+    )
+
+
+def check_pyproject_sections(pyproject_toml, min_dependencies):
+    packages, pyproject_tags = extract_packages_and_pyproject_tags(min_dependencies)
+
+    for pyproject_section, min_dependencies_tag in pyproject_tags.items():
+        # Special situation for numpy: we have numpy>=2 in
+        # build-system.requires to make sure we build wheels against numpy>=2.
+        # TODO remove this when our minimum supported numpy version is >=2.
+        skip_version_check_for = (
+            ["numpy"] if pyproject_section == "build-system.requires" else []
+        )
+
+        expected_packages = packages[min_dependencies_tag]
+
+        pyproject_section_keys = pyproject_section.split(".")
+        info = pyproject_toml
+        # iterate through nested keys to get packages and version
+        for key in pyproject_section_keys:
+            info = info[key]
+
+        pyproject_build_min_versions = {}
+        # Assuming pyproject.toml build section has something like "my-package>=2.3.0"
+        pattern = r"([\w-]+)\s*[>=]=\s*([\d\w.]+)"
+        for requirement in info:
+            match = re.search(pattern, requirement)
+            if match is None:
+                raise NotImplementedError(
+                    f"{requirement} does not match expected regex {pattern!r}. "
+                    "Only >= and == are supported for version requirements"
+                )
+
+            package, version = match.group(1), match.group(2)

-    if skip_version_check_for is None:
-        skip_version_check_for = []
+            pyproject_build_min_versions[package] = version

-    expected_packages = min_depencies_tag_to_packages_without_version[
-        min_dependencies_tag
-    ]
+        msg = f"Packages in {pyproject_section} differ from _min_depencies.py"
+
+        assert sorted(pyproject_build_min_versions) == sorted(expected_packages), msg
+
+        for package, version in pyproject_build_min_versions.items():
+            version = parse_version(version)
+            expected_min_version = parse_version(min_dependencies[package][0])
+            if package in skip_version_check_for:
+                continue
+
+            message = (
+                f"{package} has inconsistent minimum versions in pyproject.toml and"
+                f" _min_depencies.py: {version} != {expected_min_version}"
+            )
+            assert version == expected_min_version, message
+
+
+def test_min_dependencies_pyproject_toml():
+    """Check versions in pyproject.toml is consistent with _min_dependencies."""

     root_directory = Path(sklearn.__file__).parent.parent
     pyproject_toml_path = root_directory / "pyproject.toml"
@@ -90,54 +213,53 @@ def check_pyproject_section(
     with pyproject_toml_path.open("rb") as f:
         pyproject_toml = tomllib.load(f)

-    pyproject_section_keys = pyproject_section.split(".")
-    info = pyproject_toml
-    for key in pyproject_section_keys:
-        info = info[key]
-
-    pyproject_build_min_versions = {}
-    # Assuming pyproject.toml build section has something like "my-package>=2.3.0"
-    # Warning: if you try to modify this regex, bear in mind that there can be upper
-    # bounds in release branches so "my-package>=2.3.0,<2.5.0"
-    pattern = r"([\w-]+)\s*[>=]=\s*([\d\w.]+)"
-    for requirement in info:
-        match = re.search(pattern, requirement)
-        if match is None:
-            raise NotImplementedError(
-                f"{requirement} does not match expected regex {pattern!r}. "
-                "Only >= and == are supported for version requirements"
-            )
-
-        package, version = match.group(1), match.group(2)
+    check_pyproject_sections(pyproject_toml, dependent_packages)

-        pyproject_build_min_versions[package] = version

-    assert sorted(pyproject_build_min_versions) == sorted(expected_packages)
+[USER_REDACTED].mark.parametrize(
+    "example_pyproject",
+    [
+        TOY_MATCHING_PYPROJECT_SECTIONS,
+        TOY_MATCHING_PYPROJECT_SECTIONS_WITH_UPPER_BOUND,
+    ],
+)
+def test_check_matching_pyproject_section(example_pyproject):
+    """Test the version check for matching packages."""

-    for package, version in pyproject_build_min_versions.items():
-        version = parse_version(version)
-        expected_min_version = parse_version(dependent_packages[package][0])
-        if package in skip_version_check_for:
-            continue
+    pyproject_toml = tomllib.loads(example_pyproject)

-        message = (
-            f"{package} has inconsistent minimum versions in pyproject.toml and"
-            f" _min_depencies.py: {version} != {expected_min_version}"
-        )
-        assert version == expected_min_version, message
+    check_pyproject_sections(pyproject_toml, TOY_MIN_DEPENDENCIES_PY_INFO)

 [USER_REDACTED].mark.parametrize(
-    "pyproject_section, min_dependencies_tag",
-    pyproject_section_to_min_dependencies_tag.items(),
+    "example_non_matching_pyproject, error_msg",
+    [
+        (
+            TOY_WRONG_SYMBOL_PYPROJECT_SECTIONS,
+            ".* does not match expected regex .*. "
+            "Only >= and == are supported for version requirements",
+        ),
+        (
+            TOY_MISSING_PACKAGE_PYPROJECT_SECTIONS,
+            "Packages in .* differ from _min_depencies.py",
+        ),
+        (
+            TOY_ADDITIONAL_PACKAGE_PYPROJECT_SECTIONS,
+            "Packages in .* differ from _min_depencies.py",
+        ),
+        (
+            TOY_NON_MATCHING_VERSION_PYPROJECT_SECTIONS,
+            ".* has inconsistent minimum versions in pyproject.toml and"
+            " _min_depencies.py: .* != .*",
+        ),
+    ],
 )
-def test_min_dependencies_pyproject_toml(pyproject_section, min_dependencies_tag):
-    """Check versions in pyproject.toml is consistent with _min_dependencies."""
-    # NumPy is more complex because build-time (>=1.25) and run-time (>=1.19.5)
-    # requirement currently don't match
-    skip_version_check_for = ["numpy"] if min_dependencies_tag == "build" else None
-    check_pyproject_section(
-        pyproject_section,
-        min_dependencies_tag,
-        skip_version_check_for=skip_version_check_for,
-    )
+def test_check_non_matching_pyproject_section(
+    example_non_matching_pyproject, error_msg
+):
+    """Test the version check for non-matching packages and versions."""
+
+    pyproject_toml = tomllib.loads(example_non_matching_pyproject)
+
+    with pytest.raises(Exception, match=error_msg):
+        check_pyproject_sections(pyproject_toml, TOY_MIN_DEPENDENCIES_PY_INFO)

Please find a pass of review below.

This can be removed as the previous comment was saying this is needed for Python<3.11 where tomllib was not part of the standard library but our minimum Python dependency is Python 3.11.

Thanks for the PR! This seems to go in the direction I had in mind, I'll try to have a closer look at one point.

General comment: the more you can reduce the diff, the more this will make the reviewer's life (my life in this particular case) easier 😉. For example, it seems like you move global variables `min_dependencies_tag_to_packages_without_version` and `pyproject_section_to_min_dependencies_tag` to a function but this function is called once.

Maybe you have a good reason to do it and I would be curious to hear about it, but as a reviewer, I am expecting an "easy" PR to review and I have to take 5-10 minutes to understand just this part and that doesn't help keeping this PR in the "easy to review" category 😅.

I moved the global variables into a function in order to create them either from the min_dependencies or the test_dependencies, meaning the function is only called once, but the parameter passed from `test_min_dependencies_pyproject_toml` and `test_check_pyproject_section_with_upper_bounds` differs.
One could integrate the code into `check_pyproject_sections`, but since it is sort of a pre-processing step, I decided to keep it as an own function.

I tried to move the function to the same place as the global variables to make it easier to compare, but github still aligned the wrong parts, so I'll try to mark the changes below instead.

Here, it now iterates over the argument `dependencies` instead of the global imported `dependent_packages`

(I took the liberty of also renaming `extras` to `tags`, because it took my a while to figure out what "extras" are.)

I replaced the parametrization in the existing test with a for-loop here, to enable iterating over either the whole actual `pyproject.toml` or the given test subset.

Because the tags are only available at this stage, the definition of the skip packages has to happen here now.

I added a comment here because it took me a while to understand the following loop and what `info` actually contains at the end.

The remainder of this function is almost unchanged except for one extra indentation because of the loop above and one other change marked below.

This parametrization is replaced by [NAME_REDACTED] `check_pyproject_sections(`

Reading in the actual pyproject.toml file is now happening here, and the parsed version is passed to `check_pyproject_sections` (to enable passing a test version in the new test)

Here, the version is compared to the given `min_dependencies`, not the global imported `dependent_packages` (as in line 119 in the old version)

I think we could use more explicit variable names to convey that those are not expected to match the currently used version numbers.

```suggestion
EXAMPLE_MIN_DEPENDENT_PACKAGES = {
```

```suggestion
EXAMPLE_EXPECTED_PYPROJECT_SECTIONS_WITH_UPPER_BOUND = """
```

Maybe add a similar example pyproject without the upper bounds to also test the check function against it?

Please also expand the test to also checks that a meaningful exception is raised when inconsistent arguments are passed to `check_pyproject_sections`.

I guess you can remove those 2 lines since there is a test checking this now? Basically I added this comment because there was not test before and to remind people of this edge case.
```suggestion
```

I was wondering, should we also check the upper bounds explicitly?
For now, the regex just ignores them and only the matching min versions are checked.

I would say YAGNI (not needed) for now.

I think it doesn't make too much sense to have `scipy==1.10.0` in some sections and `scipy>=1.10.0` in others.

I am trying to think hard to find a use case for mixing `>=` and `==` but I can not find one.

I think the only use case for `==` is for `conda-lock`. Here is what I would recommend as good/realistic enough:
- use scipy>=1.10.0 in all the `EXAMPLE_` variables
- add conda-lock==3.0.1 in all the `EXAMPLE_` variables

In an ideal world you would also check that `>=` and `==` is used consistently in all the sections for a given package. Right now I think this is not worth spending too much time on this.

I inserted the `==` randomly for one package to keep the examples as small as possible, but still have a check for both allowed operators. If you insist on realistic examples, I can apply your suggested change, but in terms of testing functionality, this doesn't really add anything.

And yes, testing for consistent use across pyproject sections could probably make sense at some point (but maybe even as a separate test).

I pushed some tweaks, hopefully you don't mind too much :sweat_smile:. Please double-check them to make sure I didn't miss something :pray:.

The main "important" change in my eyes is that the toy data (i.e. the `EXAMPLE_` variables) is realistic. Basically someone like me seeing that we use a mix of `scipy==` and `scipy>=` would
1. be surprised, why are we doing this is there a good reason? Minimizing suprise is a good thing in general.
2. maybe spend 1-2 hours looking at the history of `main` and previous release branches figuring out whether this surprising pattern corresponds to an edge case that we used at one point in the past.

Since I think the `==` is only used for conda-lock at this point, I added conda-lock in the toy data.

Avoiding confusion sounds like a valid goal. Thanks for adding the changes. LGTM.


Add new default max_samples=None in Bagging estimators

#### [NAME_REDACTED]/PRs

Fixes #32805. Follow-up to #31414.

#### What does this implement/fix? Explain your changes.

We add a new option `max_samples=None` in Bagging estimators, with the meaning of "draw exactly n_samples", and we make  it the new default. This ensures that the number of bootstrap samples used (a crucial hyperparameter in bagging) will be the same across the 1.7 -> 1.8 transition, and makes the changes introduced in #31414 less disruptive when fitting with sample_weight:
- in 1.7 and before, the default max_samples=1.0 was drawing `n_samples` (discarding sample_weight)
- in #31414, the default max_samples=1.0 draws `sample_weight.sum()`. This can potentially lead to catastrophic fits if for some reason the `sample_weight` are very small or normalized to one for example. We do raise an error in that case, but the changes may still be drastic and surprising to users in less extreme cases.
- here we add the new default `max_samples=None`, which always draws `n_samples` as in 1.7.

cc [USER_REDACTED] [USER_REDACTED] [USER_REDACTED]

@@ -1,7 +0,0 @@
-- :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor`
-  and :class:`ensemble.IsolationForest` now use `sample_weight` to draw
-  the samples instead of forwarding them multiplied by a uniformly sampled
-  mask to the underlying estimators. Furthermore, `max_samples` is now
-  interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]`
-  when passed as a float.
-  By :user:`[NAME_REDACTED] <antoinebaker>`.

@@ -0,0 +1,8 @@
+- :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` and
+  :class:`ensemble.IsolationForest` now use `sample_weight` to draw the samples
+  instead of forwarding them multiplied by a uniformly sampled mask to the
+  underlying estimators. Furthermore, when `max_samples` is a float, it is now
+  interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]`.
+  The new default `max_samples=None` draws `X.shape[0]` samples, irrespective
+  of `sample_weight`.
+  By :user:`[NAME_REDACTED] <antoinebaker>`. :pr:`31414` and

@@ -46,6 +46,63 @@
 MAX_INT = np.iinfo(np.int32).max

+def _get_n_samples_bootstrap(n_samples, max_samples, sample_weight):
+    """
+    Get the number of samples in a bootstrap sample.
+
+    Parameters
+    ----------
+    n_samples : int
+        Number of samples in the dataset.
+
+    max_samples : None, int or float
+        The maximum number of samples to draw.
+
+        - If None, then draw `n_samples` samples.
+        - If int, then draw `max_samples` samples.
+        - If float, then draw `max_samples * n_samples` unweighted samples or
+          `max_samples * sample_weight.sum()` weighted samples.
+
+    sample_weight : array of shape (n_samples,) or None
+        Sample weights with frequency semantics when `max_samples` is explicitly
+        set to a float or integer value. When keeping the `max_samples=None` default
+        value, the equivalence between fitting with integer weighted data points or
+        integer repeated data points is no longer guaranteed because the effective
+        bootstrap size is no longer guaranteed to be equivalent.
+
+    Returns
+    -------
+    n_samples_bootstrap : int
+        The total number of samples to draw for the bootstrap sample.
+    """
+    if max_samples is None:
+        return n_samples
+    elif isinstance(max_samples, Integral):
+        return max_samples
+
+    if sample_weight is None:
+        weighted_n_samples = n_samples
+        weighted_n_samples_msg = f"the number of samples is {weighted_n_samples} "
+    else:
+        weighted_n_samples = sample_weight.sum()
+        weighted_n_samples_msg = (
+            f"the total sum of sample weights is {weighted_n_samples} "
+        )
+
+    # max_samples Real fractional value relative to weighted_n_samples
+    n_samples_bootstrap = max(int(max_samples * weighted_n_samples), 1)
+    # Warn when number of bootstrap samples is suspiciously small
+    # This heuristic for "suspiciously small" might be adapted if found
+    # unsuitable in practice
+    if n_samples_bootstrap < max(10, n_samples ** (1 / 3)):
+        warn(
+            f"Using the fractional value {max_samples=} when {weighted_n_samples_msg}"
+            f"results in a low number ({n_samples_bootstrap}) of bootstrap samples. "
+            "We recommend passing `max_samples` as an integer instead."
+        )
+    return n_samples_bootstrap
+
+
 def _generate_indices(random_state, bootstrap, n_population, n_samples):
     """Draw randomly sampled indices."""
     # Draw sample indices
@@ -273,6 +330,7 @@ class BaseBagging(BaseEnsemble, metaclass=ABCMeta):
         "estimator": [HasMethods(["fit", "predict"]), None],
         "n_estimators": [Interval(Integral, 1, None, closed="left")],
         "max_samples": [
+            None,
             Interval(Integral, 1, None, closed="left"),
             Interval(RealNotInt, 0, 1, closed="right"),
         ],
@@ -295,7 +353,7 @@ def __init__(
         estimator=None,
         n_estimators=10,
         *,
-        max_samples=1.0,
+        max_samples=None,
         max_features=1.0,
         bootstrap=True,
         bootstrap_features=False,
@@ -340,7 +398,9 @@ def fit(self, X, y, sample_weight=None, **fit_params):
             Sample weights. If None, then samples are equally weighted. Used as
             probabilities to sample the training set. Note that the expected
             frequency semantics for the `sample_weight` parameter are only
-            fulfilled when sampling with replacement `bootstrap=True`.
+            fulfilled when sampling with replacement `bootstrap=True` and using
+            a float or integer `max_samples` (instead of the default
+            `max_samples=None`).

         **fit_params : dict
             Parameters to pass to the underlying estimators.
@@ -462,20 +522,7 @@ def _fit(
         if max_samples is None:
             max_samples = self.max_samples

-        if not isinstance(max_samples, numbers.Integral):
-            if sample_weight is None:
-                max_samples = max(int(max_samples * X.shape[0]), 1)
-            else:
-                sw_sum = np.sum(sample_weight)
-                if sw_sum <= 1:
-                    raise ValueError(
-                        f"The total sum of sample weights is {sw_sum}, which prevents "
-                        "resampling with a fractional value for max_samples="
-                        f"{max_samples}. Either pass max_samples as an integer or "
-                        "use a larger sample_weight."
-                    )
-                max_samples = max(int(max_samples * sw_sum), 1)
-
+        max_samples = _get_n_samples_bootstrap(X.shape[0], max_samples, sample_weight)
         if not self.bootstrap and max_samples > X.shape[0]:
             raise ValueError(
                 f"Effective max_samples={max_samples} must be <= n_samples="
@@ -728,13 +775,14 @@ class BaggingClassifier(ClassifierMixin, BaseBagging):
     n_estimators : int, default=10
         The number of base estimators in the ensemble.

-    max_samples : int or float, default=1.0
+    max_samples : int or float, default=None
         The number of samples to draw from X to train each base estimator (with
         replacement by default, see `bootstrap` for more details).

+        - If None, then draw `X.shape[0]` samples irrespective of `sample_weight`.
         - If int, then draw `max_samples` samples.
-        - If float, then draw `max_samples * X.shape[0]` unweighted samples
-          or `max_samples * sample_weight.sum()` weighted samples.
+        - If float, then draw `max_samples * X.shape[0]` unweighted samples or
+          `max_samples * sample_weight.sum()` weighted samples.

     max_features : int or float, default=1.0
         The number of features to draw from X to train each base estimator (
@@ -867,7 +915,7 @@ def __init__(
         estimator=None,
         n_estimators=10,
         *,
-        max_samples=1.0,
+        max_samples=None,
         max_features=1.0,
         bootstrap=True,
         bootstrap_features=False,
@@ -1239,12 +1287,14 @@ class BaggingRegressor(RegressorMixin, BaseBagging):
     n_estimators : int, default=10
         The number of base estimators in the ensemble.

-    max_samples : int or float, default=1.0
+    max_samples : int or float, default=None
         The number of samples to draw from X to train each base estimator (with
         replacement by default, see `bootstrap` for more details).

+        - If None, then draw `X.shape[0]` samples irrespective of `sample_weight`.
         - If int, then draw `max_samples` samples.
-        - If float, then draw `max_samples * X.shape[0]` samples.
+        - If float, then draw `max_samples * X.shape[0]` unweighted samples or
+          `max_samples * sample_weight.sum()` weighted samples.

     max_features : int or float, default=1.0
         The number of features to draw from X to train each base estimator (
@@ -1368,7 +1418,7 @@ def __init__(
         estimator=None,
         n_estimators=10,
         *,
-        max_samples=1.0,
+        max_samples=None,
         max_features=1.0,
         bootstrap=True,
         bootstrap_features=False,

@@ -6,6 +6,7 @@
 # SPDX-License-Identifier: BSD-3-Clause

 import re
+import warnings
 from itertools import cycle, product

 import joblib
@@ -26,6 +27,7 @@
     RandomForestClassifier,
     RandomForestRegressor,
 )
+from sklearn.ensemble._bagging import _get_n_samples_bootstrap
 from sklearn.feature_selection import SelectKBest
 from sklearn.linear_model import LogisticRegression, Perceptron
 from sklearn.model_selection import GridSearchCV, ParameterGrid, train_test_split
@@ -706,16 +708,17 @@ def test_warning_bootstrap_sample_weight():
 def test_invalid_sample_weight_max_samples_bootstrap_combinations():
     X, y = iris.data, iris.target

-    # Case 1: small weights and fractional max_samples would lead to sampling
-    # less than 1 sample, which is not allowed.
+    # Case 1: small weights and fractional max_samples lead to a small
+    # number of bootstrap samples, which raises a UserWarning.
     clf = BaggingClassifier(max_samples=1.0)
     sample_weight = np.ones_like(y) / (2 * len(y))
     expected_msg = (
-        r"The total sum of sample weights is 0.5(\d*), which prevents resampling with "
-        r"a fractional value for max_samples=1\.0\. Either pass max_samples as an "
-        r"integer or use a larger sample_weight\."
+        "Using the fractional value max_samples=1.0 when "
+        r"the total sum of sample weights is 0.5(\d*) "
+        r"results in a low number \(1\) of bootstrap samples. "
+        "We recommend passing `max_samples` as an integer."
     )
-    with pytest.raises(ValueError, match=expected_msg):
+    with pytest.warns(UserWarning, match=expected_msg):
         clf.fit(X, y, sample_weight=sample_weight)

     # Case 2: large weights and bootstrap=False would lead to sampling without
@@ -813,6 +816,55 @@ def test_draw_indices_using_sample_weight(
                 assert_allclose(estimator.y_, y[samples])

+def test_get_n_samples_bootstrap():
+    n_samples, max_samples, sample_weight = 10, None, "not_used"
+    assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == n_samples
+
+    n_samples, max_samples, sample_weight = 10, 5, "not_used"
+    assert (
+        _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == max_samples
+    )
+
+    n_samples, max_samples, sample_weight = 10, 1e-5, None
+    assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == 1
+
+    n_samples, max_samples, sample_weight = 10, 0.66, None
+    warning_msg = ".+the number of samples.+low number.+max_samples.+as an integer"
+    with pytest.warns(UserWarning, match=warning_msg):
+        assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == int(
+            max_samples * n_samples
+        )
+
+    n_samples, max_samples, sample_weight = 10, 1e-5, None
+    with pytest.warns(UserWarning, match=warning_msg):
+        assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == 1
+
+    warning_msg_with_weights = (
+        ".+the total sum of sample weights.+low number.+max_samples.+as an integer"
+    )
+    rng = np.random.default_rng(0)
+    n_samples, max_samples, sample_weight = 1_000_000, 1e-5, rng.uniform(size=1_000_000)
+    with pytest.warns(UserWarning, match=warning_msg_with_weights):
+        assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == int(
+            max_samples * sample_weight.sum()
+        )
+
+    sample_weight = np.ones(3)
+    with warnings.catch_warnings():
+        warnings.simplefilter("error")
+
+        n_samples, max_samples, sample_weight = 100, 30, None
+        assert (
+            _get_n_samples_bootstrap(n_samples, max_samples, sample_weight)
+            == max_samples
+        )
+
+        n_samples, max_samples, sample_weight = 100, 0.5, rng.uniform(size=100)
+        assert _get_n_samples_bootstrap(n_samples, max_samples, sample_weight) == int(
+            max_samples * sample_weight.sum()
+        )
+
+
 def test_oob_score_removed_on_warm_start():
     X, y = make_hastie_10_2(n_samples=100, random_state=1)

LGTM. A few comments/questions, but nothing blocking.

I agree, the behavior might be less surprising for `sample_weight` users. But we do lose the strict weight/repetition equivalence (frequency semantics) when using the default `max_samples=None`.

But's it's easy to restore the frequency semantics by [NAME_REDACTED] `max_samples=1.0` explicitly (or alternatively `max_samples=some_integer_of_choice`) after making a conscious decision, so maybe this is not a big deal.

Assuming that we are all ok with explicitly not implementing frequency weights by [NAME_REDACTED] estimator, I am fine with the contents of this PR.

At least the docstring should be explicit enough.

cc [USER_REDACTED] [USER_REDACTED].

Instead of raising an error when `sample_weigth.sum() < 1` as in #31414, here we always return something $\geq 1$, but we raise a warning if for any reason (small sample_weight, or small dataset or small max_samples value) the final number of bootstrap samples is "small" (now it is hardcoded in `_get_n_samples_bootstrap` than less than 10 is small).

Same function as in #31529. Will be refactored in #31529 once this PR is merged.

Is it ok to have two "conflicting" change logs in the same release?

Or would it be better to edit the previous change log?

```suggestion
    if n_samples_bootstrap < max(10, n_samples ** (1/3)):
```

I find that hard to believe someone would want to sample less than `cubic_root(n_samples)`.
I mean, if you made the effort to load and preprocess 1M rows, you probably want to use more than 100 samples in your model.
I even hesitate with `sqrt(n_samples)`: but maybe that would make this warning too frequent? Maybe a compromise at `n_samples ** 0.4`? 😅

nit: rename `n_samples_total` to `weighted_n_samples`?

Would it be ok to import this function in random forests even if it's private? (the logic will be the same right?)

For me the two changelogs are not in conflict, but complement each other.

31414.fix.rst states that:
> Furthermore, `max_samples` is now
  interpreted as a fraction of `sample_weight.sum()` instead of `X.shape[0]`
  when passed as a float.

It remains true in this PR (for float max_samples). This PR just adds the None max_samples option.

We can do a little refactoring in #31529 after this one is merged, as indeed I think both forest and bagging now share the same logic for the number of bootstrap samples. I think it's ok to have a few private function import , but we can make `_get_n_samples_bootstrap` public and maybe relocate it if need be in #31529.

No idea what a suspiciously "small" `n_samples_bootstrap` could be in general, I let you and other reviewers decide a good heuristic :)

```suggestion
            "We recommend passing `max_samples` as an integer instead."
```

I like the cubic root heuristic, floored by 10.

```suggestion
        - If None, then draw `X.shape[0]` samples irrespective of `sample_weight`.
```

You need this file to be a `.rst`, in an ideal world, CI would complain, but it looks like it does not ...

I noticed because I was not able to find the changelog entry in the rendered HTML ...

At the same time you can decide to merge the two changelog entries if that is simpler to understand from a user perspective. IIRC changelog entries are merged by [NAME_REDACTED] (not 100% sure) and depending on how much activity has gone into this module, they may not be close to each other.

One way to do this is something like I did in https://github.com/[USER_REDACTED]n/scikit-learn/pull/32831. Basically you use a single changelog entry and you mention one PR with `:pr:` in the text and the other one is added automatically based on the filename.

Maybe we can help advanced users make an informed choice about the `max_features` value here.

```suggestion
        Sample weights with frequency semantics when `max_features` is explicitly
        set to a float or integer value. When keeping the `max_features=None` default
        value, the equivalence between fitting with integer weighted data points or
        integer repeated data points is no longer guaranteed because the effective
        bootstrap size is no longer guaranteed to be equivalent.
```

[USER_REDACTED] good catch for the missing extension! I tried your hack by [NAME_REDACTED] in the new one (and removing the old one).  The output of towncrier seems okay:

```rst
:mod:`sklearn.ensemble`
-----------------------

- |Fix| :class:`ensemble.BaggingClassifier`, :class:`ensemble.BaggingRegressor` and
  :class:`ensemble.IsolationForest` now use `sample_weight` to draw the samples
  instead of forwarding them multiplied by a uniformly sampled mask to the
  underlying estimators. Furthermore, `max_samples` is now interpreted as a
  fraction of `sample_weight.sum()` instead of `X.shape[0]` when passed as a
  float. The new default `max_samples=None` draws `X.shape[0]` samples,
  irrespective of `sample_weight`.
  By :user:`[NAME_REDACTED] <antoinebaker>`. :pr:`31414` and :pr:`32825`
```

For reviewers, I updated the notebooks of our sample weight analyzer, in particular this want to highlight the fact that `max_samples=None` breaks frequency weights semantics:

https://github.com/[USER_REDACTED]nath-xoc/sample-weight-audit-nondet/blob/main/reports/sklearn_estimators_sample_weight_audit_investigative_plots.ipynb

The p-value is very small: the null-hypothesis that fitting with integer weights is equivalent to fitting with repeated data points is rejected.

Editing the cell to select either `max_samples=1.0` or `max_samples=100` restores large p-values, even when increasing `n_stochastic_fits` meaning that we cannot reject the null-hypothesis that frequency weights semantics are respected.

Alternatively, you could keep both `doc/whats_new/upcoming_changes/sklearn.ensemble/31414.fix.rst` and `doc/whats_new/upcoming_changes/sklearn.ensemble/32825.fix.rst`  and make sure that they have the same text and not PR number in them. As a result, the towncrier tool will merge this.

Yes, we could recommend:
1. `max_samples` = some_integer as the safest choice for `sample_weight` semantics (repeated/weighted equivalence for integer weights and safe with rescaled weights)
2. `max_samples` = some_float as the second best (repeated/weighted equivalence for integer weights but unsafe with rescaled weights)
3. default `max_samples = None` does not respect the repeated/weighted equivalence for integer weights

Yep, but personally as I said in https://github.com/[USER_REDACTED]n/scikit-learn/pull/32831 I find this towncrier functionality a bit too magical and prefer the `:pr:` way which feels simpler and more understandable ... I actually saw Christian use the `:pr:` way and quite liked it.

Just out of curiousity, do you have some kind of an explanation for cubic root for people without strong intuitions like me?

Right now this is what I am reading:
- 10 seems like a reasonable minimum samples in a bootstrap (curious why as well :wink:)
- for 1000 samples cubic root is 10 great that goes back to the 10 samples in a bootstrap heuristic
- for 1e6 samples cubic root is 100, that kind of seems right (curious why as well :wink:)

Looks like you may want to update the comment here and mentions that this is a heuristic to avoid bootstrap with too few samples. Bonus points if you add a few words about the heuristic 😉

Here is a proposal that you should not follow 😜

```py
# magic cubic root heuristic: you are not supposed to understand this, don't even try!
# Please don't touch this line or the universe may collapse
```

We want to detect a "suspiciously small" `n_samples_bootstrap`.

A "suspiciously small" `n_samples_bootstrap` depends on `n_samples`, so we want to find a threshold that depends on `n_samples`. So we're looking for a function `f(n_samples)` that should be:
- simple: it's just a warning, we want to keep a simple logic
- non-decreasing
- growing slower than a linear function: the more samples you have, the more you may want to brutally down-sample your dataset.

=> `n_samples ** alpha` is a good candidate.

Why taking 1/3 for this `alpha`? It's a trade-off between two things:
- raising the warning too often
- not raising it when `n_samples_bootstrap` is small by mistake

I could imagine my self wanting to use `n_samples_bootstrap = 10_000` for a 1B samples dataset, so 1/2 is too big. But I can't imagine wanting to use `n_samples_bootstrap = 1_000` in this case, so 1/3 is not too big.

It could be:

```Python
# raise a warning if n_samples_bootstrap is suspiciously small
# the heuristic for "suspiciously small " might be adapted if found unsuitable in practice
```


MNT: Fix `pre-commit run --all-files`

Currently `pre-commit run --all-files` fails, which damages the contributor experience

I know better than to get into discussions related to https://github.com/[USER_REDACTED]n/scikit-learn/issues/32500, so I've left that alone, but this at least ensures that running `pre-commit` in a current PR doesn't result in unrelated changes

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#### What does this implement/fix? Explain your changes.

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### ⚠ NOTE FOR AI TOOLS AND THEIR USERS: Opening a PR on scikit-learn requires **human
judgment, contextual understanding**, and **familiarity with scikit-learn’s structure
and goals**. It is **not suitable for automatic processing** by [NAME_REDACTED] code
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@@ -263,7 +263,7 @@ Bronze sponsors

   .. div:: text-box

-    `NVIDIA <https://nvidia.com>`_ supports scikit-learn through their sponsorship and employs full-time core maintainer [NAME_REDACTED].
+    `NVIDIA <https://nvidia.com>`_ supports scikit-learn through their sponsorship and employs full-time core maintainer [NAME_REDACTED].

   .. div:: image-box

@@ -525,7 +525,7 @@ list of events.
 Donating to the project
 =======================

-If you have found scikit-learn to be useful in your work, research, or company,
+If you have found scikit-learn to be useful in your work, research, or company,
 please consider making a donation to the project commensurate with your resources.
 There are several options for making donations:

@@ -558,21 +558,19 @@ There are several options for making donations:
   able to make a donation with a company match as high as 100%. Our project
   ID is `433725 <https://causes.benevity.org/projects/433725>`_.

-All donations are managed by `NumFOCUS <https://numfocus.org/>`_, a 501(c)(3)
+All donations are managed by `NumFOCUS <https://numfocus.org/>`_, a 501(c)(3)
 non-profit organization based in Austin, Texas, USA. The NumFOCUS board
-consists of `SciPy community members <https://numfocus.org/board.html>`_.
+consists of `SciPy community members <https://numfocus.org/board.html>`_.
 Contributions are tax-deductible to the extent allowed by law.

 .. rubric:: Notes

-Contributions support the maintenance of the project, including development,
-documentation, infrastructure and coding sprints.
+Contributions support the maintenance of the project, including development,
+documentation, infrastructure and coding sprints.

 scikit-learn Swag
 -----------------
 Official scikit-learn swag is available for purchase at the `NumFOCUS online store
 <https://numfocus.myspreadshop.com/scikit-learn+logo?idea=6335cad48f3f5268f5f42559>`_.
 A portion of the proceeds from each sale goes to support the scikit-learn project.
-
-

@@ -40,4 +40,4 @@
 - [NAME_REDACTED]
 - [NAME_REDACTED]
 - [NAME_REDACTED]
-- [NAME_REDACTED]
\ No newline at end of file
+- [NAME_REDACTED]

@@ -522,7 +522,7 @@ Formally, the loss function of classical MDS (strain) is given by

 where :math:`Z` is the :math:`n \times d` embedding matrix whose rows are
 :math:`z_i^T`, :math:`\|\cdot\|_F` denotes the Frobenius norm, and
-:math:`B` is the Gram matrix with elements :math:`b_{ij}`,
+:math:`B` is the Gram matrix with elements :math:`b_{ij}`,
 given by :math:`B = -\frac{1}{2}C\Delta C`.
 Here :math:`C\Delta C` is the double-centered matrix of squared dissimilarities,
 with :math:`\Delta` being the matrix of squared input dissimilarities

@@ -291,7 +291,7 @@ package = "sklearn"  # name of your package
         whatsnew_pattern = 'doc/whatsnew/upcoming_changes/[^/]+/\d+\.[^.]+\.rst'

 [tool.codespell]
-skip = ["./.git", "*.svg", "./.mypy_cache", "./sklearn/feature_extraction/_stop_words.py", "./sklearn/feature_extraction/tests/test_text.py", "./doc/_build", "./doc/auto_examples", "./doc/modules/generated"]
+skip = ["./.git", "*.svg", "./.mypy_cache", "*sklearn/feature_extraction/_stop_words.py", "*sklearn/feature_extraction/tests/test_text.py", "./doc/_build", "./doc/auto_examples", "./doc/modules/generated"]
 ignore-words = "build_tools/codespell_ignore_words.txt"

 [tool.towncrier]

Thank you, [USER_REDACTED]!

I checked the PR out locally and can confirm that all checks pass when running  `pre-commit run --all-files` (also after merging main, just to make sure).

this is the version in the dev guide https://github.com/[USER_REDACTED]n/scikit-learn/blob/a9876111036fca025c26448eff072b3ab6e6208b/doc/developers/development_setup.rst#L132

Good catch, I would guess we forgot to update the dev guide, so I would update the dev guide instead.

Can we do the same but in pyproject.toml where we already have a `tool.codespell` section?

In pyproject.toml we actually have this:
```
[tool.codespell]
skip = ["./.git", "*.svg", "./.mypy_cache", "./sklearn/feature_extraction/_stop_words.py", "./sklearn/feature_extraction/tests/test_text.py", "./doc/_build", "./doc/auto_examples", "./doc/modules/generated"]
```
but when [trying out pyrefly](https://github.com/[USER_REDACTED]n/scikit-learn/pull/32737) I also noticed that I had to remove the `./` from the two `.py` file paths for it to actually skip these files.

In pyproject.toml and _min_dependencies.py it is actually `0.11.7`. Should it be 0.12.2 everywhere (the most current version is 0.14.7)?
I can then open a separate PR to make the docs consistent (ruff is the only package for which a version is explicitly specified there).

thanks, can confirm that removing `./` fixes it (both when running `codespell` directly, and when running it via `pre-commit`)

😄 but not in CI by [NAME_REDACTED], taking a look

---

looks like a glob does the trick

I'm so confused as why these files are added here.

hmm something went wrong, updating now

Oh, I overlooked that they are added as new files here. I think they are still here because this PR was opened before the last release. `ruff` fixed some whitespace "issues" in them, but I guess they get removed once the release is out, so they should probably be removed here as well?

yup thanks, embarassing merge conflict (non-)resolution here 🙈 😳

have updated, thanks!


CI Move pylatest_pip_openblas_pandas to [NAME_REDACTED]

This moves `pylatest_pip_openblas_pandas` job from Azure to [NAME_REDACTED] on it together with [USER_REDACTED].

Part of #32434.

@@ -125,14 +125,31 @@ jobs:
             os: ubuntu-24.04-arm
             DISTRIB: conda
             LOCK_FILE: build_tools/github/pymin_conda_forge_arm_linux-aarch64_conda.lock
+
+          # Linux environment to test the latest available dependencies.
+          # It runs tests requiring lightgbm, pandas and PyAMG.
+          - name: Linux pylatest_pip_openblas_pandas
+            os: ubuntu-24.04
+            DISTRIB: conda
+            LOCK_FILE: build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock
+            SKLEARN_TESTS_GLOBAL_RANDOM_SEED: 3  # non-default seed
+            SCIPY_ARRAY_API: 1
+            CHECK_PYTEST_SOFT_DEPENDENCY: true
+            SKLEARN_WARNINGS_AS_ERRORS: 1
+            # disable pytest-xdist to have 1 job where OpenMP and BLAS are not single
+            # threaded because by [NAME_REDACTED] (sklearn/conftest.py)
+            # makes sure that they are single threaded in each xdist subprocess.
+            PYTEST_XDIST_VERSION: none
+            PIP_BUILD_ISOLATION: true
+
           - name: macOS pylatest_conda_forge_arm
             os: macOS-15
             DISTRIB: conda
             LOCK_FILE: build_tools/azure/pylatest_conda_forge_osx-arm64_conda.lock
             SKLEARN_TESTS_GLOBAL_RANDOM_SEED: 5  # non-default seed
             SCIPY_ARRAY_API: 1
             PYTORCH_ENABLE_MPS_FALLBACK: 1
-            CHECK_PYTEST_SOFT_DEPENDENCY: 'true'
+            CHECK_PYTEST_SOFT_DEPENDENCY: true

     env: ${{ matrix }}

@@ -183,20 +183,6 @@ jobs:
         SKLEARN_ENABLE_DEBUG_CYTHON_DIRECTIVES: '1'
         SKLEARN_RUN_FLOAT32_TESTS: '1'
         SKLEARN_TESTS_GLOBAL_RANDOM_SEED: '2'  # non-default seed
-      # Linux environment to test the latest available dependencies.
-      # It runs tests requiring lightgbm, pandas and PyAMG.
-      pylatest_pip_openblas_pandas:
-        DISTRIB: 'conda-pip-latest'
-        LOCK_FILE: './build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock'
-        CHECK_PYTEST_SOFT_DEPENDENCY: 'true'
-        SKLEARN_WARNINGS_AS_ERRORS: '1'
-        SKLEARN_TESTS_GLOBAL_RANDOM_SEED: '3'  # non-default seed
-        # disable pytest-xdist to have 1 job where OpenMP and BLAS are not single
-        # threaded because by [NAME_REDACTED] (sklearn/conftest.py)
-        # makes sure that they are single threaded in each xdist subprocess.
-        PYTEST_XDIST_VERSION: 'none'
-        PIP_BUILD_ISOLATION: 'true'
-        SCIPY_ARRAY_API: '1'

 - template: build_tools/azure/posix-docker.yml
   parameters:

LGTM, thanks!

I looked at `install.sh`, I think this is an historical quirk [^1], this can be replaced by [NAME_REDACTED]:
```suggestion
            DISTRIB: 'conda'
```

The fact that packages are installed with pip in already part of the build name `pylatest_pip_...` so I don't think there is much point of keeping it in the `DISTRIB`.

[^1]: probably we were handling things differently in the past in `install.sh` but now the fact that we are installing the packages with `pip` is part of the lock-file.

Yes, I see, it matches with `[[ "$DISTRIB" == "conda"* ]]` and there is no specific check if `DISTRIB == "conda-pip-latest"`.

Though I need to admit, before our discussion I would have been confused seeing "pip" in the job name while DISTRIB is set to something starting with "conda." I would have thought that's an error. Would renaming DISTRIB to something like INSTALL_METHOD help to clarify its more the purpose? Just a thought.

Yeah the naming is not ideal but at the same time it has been like this for a while (mostly for historical reasons) and it doesn't bother me too much ...

In my mind the `DISTRIB` is one of the variable to drive the possible variations in `build_tools/install.sh`.
- `DISTRIB=conda*` means the environment is installed from a conda-lock lock-file
  - sometimes we need additional tweaks on top of the lock-file e.g. `DISTRIB=conda-pip-scipy-dev` which installs development versions of numpy, scipy, etc ...
  - I guess `conda-pip-latest` also had additional tweaks at one point but not anymore since all the info comes from the lock-file
- sometimes we use the system Python and DISTRIB is kind of a Linux distribution (hence I guess the original name `DISTRIB` probably means "distribution") e.g. `ubuntu` or `debian-32`

Maybe it's something we could revisit once the Azure -> GHA migration is over?

> Maybe it's something we could revisit once the Azure -> GHA migration is over?

Yes, I think it will be useful to use a more speaking name sometime in the future.