diff --git a/.github/workflows/wheels.yml b/.github/workflows/wheels.yml
index ae8f182450202..b82a114bff1af 100644
--- a/.github/workflows/wheels.yml
+++ b/.github/workflows/wheels.yml
@@ -70,8 +70,6 @@ jobs:
- os: windows-latest
python: 312
platform_id: win_amd64
- # TODO: remove when Python 3.12 is released
- prerelease: "True"
# Linux 64 bit manylinux2014
- os: ubuntu-latest
@@ -97,8 +95,6 @@ jobs:
python: 312
platform_id: manylinux_x86_64
manylinux_image: manylinux2014
- # TODO: remove when Python 3.12 is released
- prerelease: "True"
# MacOS x86_64
- os: macos-latest
@@ -116,8 +112,6 @@ jobs:
- os: macos-latest
python: 312
platform_id: macosx_x86_64
- # TODO: remove when Python 3.12 is released
- prerelease: "True"
# MacOS arm64
# The wheel for the latest Python version is built and tested on
diff --git a/build_tools/cirrus/arm_wheel.yml b/build_tools/cirrus/arm_wheel.yml
index c5f5a34afa490..f210eea817601 100644
--- a/build_tools/cirrus/arm_wheel.yml
+++ b/build_tools/cirrus/arm_wheel.yml
@@ -25,8 +25,6 @@ macos_arm64_wheel_task:
# is actually tested on Cirrus CI.
- env:
CIBW_BUILD: cp312-macosx_arm64
- # TODO: remove when Python 3.12 is released
- CIBW_PRERELEASE_PYTHONS: True
conda_script:
- curl -L --retry 10 -o ~/mambaforge.sh https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-MacOSX-arm64.sh
@@ -78,8 +76,6 @@ linux_arm64_wheel_task:
CIBW_TEST_SKIP: "*_aarch64"
- env:
CIBW_BUILD: cp312-manylinux_aarch64
- # TODO: remove when Python 3.12 is released
- CIBW_PRERELEASE_PYTHONS: True
cibuildwheel_script:
- apt install -y python3 python-is-python3
diff --git a/doc/templates/index.html b/doc/templates/index.html
index 40f58f7bbefe3..b41ede9382a49 100644
--- a/doc/templates/index.html
+++ b/doc/templates/index.html
@@ -169,7 +169,9 @@ News
On-going development:
What's new (Changelog)
- September 2023. scikit-learn 1.3.1 is available for download (Changelog).
+ October 2023. scikit-learn 1.3.2 is available for download (Changelog).
+
+ September 2023. scikit-learn 1.3.1 is available for download (Changelog).
June 2023. scikit-learn 1.3.0 is available for download (Changelog).
diff --git a/doc/whats_new/v1.3.rst b/doc/whats_new/v1.3.rst
index 4999aba4c2e71..70d14ab285eea 100644
--- a/doc/whats_new/v1.3.rst
+++ b/doc/whats_new/v1.3.rst
@@ -2,6 +2,47 @@
.. currentmodule:: sklearn
+.. _changes_1_3_2:
+
+Version 1.3.2
+=============
+
+**October 2023**
+
+Changelog
+---------
+
+:mod:`sklearn.datasets`
+.......................
+
+- |Fix| All dataset fetchers now accept `data_home` as any object that implements
+ the :class:`os.PathLike` interface, for instance, :class:`pathlib.Path`.
+ :pr:`27468` by :user:`Yao Xiao `.
+
+:mod:`sklearn.decomposition`
+............................
+
+- |Fix| Fixes a bug in :class:`decomposition.KernelPCA` by forcing the output of
+ the internal :class:`preprocessing.KernelCenterer` to be a default array. When the
+ arpack solver is used, it expects an array with a `dtype` attribute.
+ :pr:`27583` by :user:`Guillaume Lemaitre `.
+
+:mod:`sklearn.metrics`
+......................
+
+- |Fix| Fixes a bug for metrics using `zero_division=np.nan`
+ (e.g. :func:`~metrics.precision_score`) within a paralell loop
+ (e.g. :func:`~model_selection.cross_val_score`) where the singleton for `np.nan`
+ will be different in the sub-processes.
+ :pr:`27573` by :user:`Guillaume Lemaitre `.
+
+:mod:`sklearn.tree`
+...................
+
+- |Fix| Do not leak data via non-initialized memory in decision tree pickle files and make
+ the generation of those files deterministic. :pr:`27580` by :user:`Loïc Estève `.
+
+
.. _changes_1_3_1:
Version 1.3.1
diff --git a/sklearn/__init__.py b/sklearn/__init__.py
index 8df2c91fb2ca9..48d907fa5ad23 100644
--- a/sklearn/__init__.py
+++ b/sklearn/__init__.py
@@ -38,7 +38,7 @@
# Dev branch marker is: 'X.Y.dev' or 'X.Y.devN' where N is an integer.
# 'X.Y.dev0' is the canonical version of 'X.Y.dev'
#
-__version__ = "1.3.1"
+__version__ = "1.3.2"
# On OSX, we can get a runtime error due to multiple OpenMP libraries loaded
diff --git a/sklearn/covariance/tests/test_graphical_lasso.py b/sklearn/covariance/tests/test_graphical_lasso.py
index 317bf2aa85124..aaee1919e8dcc 100644
--- a/sklearn/covariance/tests/test_graphical_lasso.py
+++ b/sklearn/covariance/tests/test_graphical_lasso.py
@@ -24,7 +24,12 @@
)
-def test_graphical_lasso(random_state=0):
+def test_graphical_lassos(random_state=1):
+ """Test the graphical lasso solvers.
+
+ This checks is unstable for some random seeds where the covariance found with "cd"
+ and "lars" solvers are different (4 cases / 100 tries).
+ """
# Sample data from a sparse multivariate normal
dim = 20
n_samples = 100
@@ -46,10 +51,11 @@ def test_graphical_lasso(random_state=0):
costs, dual_gap = np.array(costs).T
# Check that the costs always decrease (doesn't hold if alpha == 0)
if not alpha == 0:
- assert_array_less(np.diff(costs), 0)
+ # use 1e-12 since the cost can be exactly 0
+ assert_array_less(np.diff(costs), 1e-12)
# Check that the 2 approaches give similar results
- assert_array_almost_equal(covs["cd"], covs["lars"], decimal=4)
- assert_array_almost_equal(icovs["cd"], icovs["lars"], decimal=4)
+ assert_allclose(covs["cd"], covs["lars"], atol=1e-4)
+ assert_allclose(icovs["cd"], icovs["lars"], atol=1e-4)
# Smoke test the estimator
model = GraphicalLasso(alpha=0.25).fit(X)
diff --git a/sklearn/datasets/_base.py b/sklearn/datasets/_base.py
index b2d198ecf8c2f..5675798137824 100644
--- a/sklearn/datasets/_base.py
+++ b/sklearn/datasets/_base.py
@@ -57,7 +57,7 @@ def get_data_home(data_home=None) -> str:
----------
data_home : str or path-like, default=None
The path to scikit-learn data directory. If `None`, the default path
- is `~/sklearn_learn_data`.
+ is `~/scikit_learn_data`.
Returns
-------
@@ -84,7 +84,7 @@ def clear_data_home(data_home=None):
----------
data_home : str or path-like, default=None
The path to scikit-learn data directory. If `None`, the default path
- is `~/sklearn_learn_data`.
+ is `~/scikit_learn_data`.
"""
data_home = get_data_home(data_home)
shutil.rmtree(data_home)
diff --git a/sklearn/datasets/_california_housing.py b/sklearn/datasets/_california_housing.py
index b48e7e10bdc4b..3153f0dd03f72 100644
--- a/sklearn/datasets/_california_housing.py
+++ b/sklearn/datasets/_california_housing.py
@@ -23,7 +23,7 @@
import logging
import tarfile
-from os import makedirs, remove
+from os import PathLike, makedirs, remove
from os.path import exists
import joblib
@@ -53,7 +53,7 @@
@validate_params(
{
- "data_home": [str, None],
+ "data_home": [str, PathLike, None],
"download_if_missing": ["boolean"],
"return_X_y": ["boolean"],
"as_frame": ["boolean"],
@@ -76,7 +76,7 @@ def fetch_california_housing(
Parameters
----------
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
diff --git a/sklearn/datasets/_covtype.py b/sklearn/datasets/_covtype.py
index 557899bc88e97..7620e08c5ec92 100644
--- a/sklearn/datasets/_covtype.py
+++ b/sklearn/datasets/_covtype.py
@@ -65,7 +65,7 @@
@validate_params(
{
- "data_home": [str, None],
+ "data_home": [str, os.PathLike, None],
"download_if_missing": ["boolean"],
"random_state": ["random_state"],
"shuffle": ["boolean"],
@@ -98,7 +98,7 @@ def fetch_covtype(
Parameters
----------
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
diff --git a/sklearn/datasets/_kddcup99.py b/sklearn/datasets/_kddcup99.py
index 17c49161c3bc2..444bd01737901 100644
--- a/sklearn/datasets/_kddcup99.py
+++ b/sklearn/datasets/_kddcup99.py
@@ -50,7 +50,7 @@
@validate_params(
{
"subset": [StrOptions({"SA", "SF", "http", "smtp"}), None],
- "data_home": [str, None],
+ "data_home": [str, os.PathLike, None],
"shuffle": ["boolean"],
"random_state": ["random_state"],
"percent10": ["boolean"],
@@ -92,7 +92,7 @@ def fetch_kddcup99(
To return the corresponding classical subsets of kddcup 99.
If None, return the entire kddcup 99 dataset.
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
diff --git a/sklearn/datasets/_lfw.py b/sklearn/datasets/_lfw.py
index 345f56e89a03b..d06d29f21d0a5 100644
--- a/sklearn/datasets/_lfw.py
+++ b/sklearn/datasets/_lfw.py
@@ -10,7 +10,7 @@
import logging
from numbers import Integral, Real
-from os import listdir, makedirs, remove
+from os import PathLike, listdir, makedirs, remove
from os.path import exists, isdir, join
import numpy as np
@@ -234,7 +234,7 @@ def _fetch_lfw_people(
@validate_params(
{
- "data_home": [str, None],
+ "data_home": [str, PathLike, None],
"funneled": ["boolean"],
"resize": [Interval(Real, 0, None, closed="neither"), None],
"min_faces_per_person": [Interval(Integral, 0, None, closed="left"), None],
@@ -272,7 +272,7 @@ def fetch_lfw_people(
Parameters
----------
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
@@ -431,7 +431,7 @@ def _fetch_lfw_pairs(
@validate_params(
{
"subset": [StrOptions({"train", "test", "10_folds"})],
- "data_home": [str, None],
+ "data_home": [str, PathLike, None],
"funneled": ["boolean"],
"resize": [Interval(Real, 0, None, closed="neither"), None],
"color": ["boolean"],
@@ -480,7 +480,7 @@ def fetch_lfw_pairs(
official evaluation set that is meant to be used with a 10-folds
cross validation.
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify another download and cache folder for the datasets. By
default all scikit-learn data is stored in '~/scikit_learn_data'
subfolders.
diff --git a/sklearn/datasets/_olivetti_faces.py b/sklearn/datasets/_olivetti_faces.py
index 51710faccc417..8e1b3c91e254b 100644
--- a/sklearn/datasets/_olivetti_faces.py
+++ b/sklearn/datasets/_olivetti_faces.py
@@ -13,7 +13,7 @@
# Copyright (c) 2011 David Warde-Farley
# License: BSD 3 clause
-from os import makedirs, remove
+from os import PathLike, makedirs, remove
from os.path import exists
import joblib
@@ -36,7 +36,7 @@
@validate_params(
{
- "data_home": [str, None],
+ "data_home": [str, PathLike, None],
"shuffle": ["boolean"],
"random_state": ["random_state"],
"download_if_missing": ["boolean"],
@@ -67,7 +67,7 @@ def fetch_olivetti_faces(
Parameters
----------
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
diff --git a/sklearn/datasets/_openml.py b/sklearn/datasets/_openml.py
index 1c36dc8a25ce1..c9d09dc3ce46a 100644
--- a/sklearn/datasets/_openml.py
+++ b/sklearn/datasets/_openml.py
@@ -749,7 +749,7 @@ def _valid_data_column_names(features_list, target_columns):
"name": [str, None],
"version": [Interval(Integral, 1, None, closed="left"), StrOptions({"active"})],
"data_id": [Interval(Integral, 1, None, closed="left"), None],
- "data_home": [str, None],
+ "data_home": [str, os.PathLike, None],
"target_column": [str, list, None],
"cache": [bool],
"return_X_y": [bool],
@@ -769,7 +769,7 @@ def fetch_openml(
*,
version: Union[str, int] = "active",
data_id: Optional[int] = None,
- data_home: Optional[str] = None,
+ data_home: Optional[Union[str, os.PathLike]] = None,
target_column: Optional[Union[str, List]] = "default-target",
cache: bool = True,
return_X_y: bool = False,
@@ -815,7 +815,7 @@ def fetch_openml(
dataset. If data_id is not given, name (and potential version) are
used to obtain a dataset.
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify another download and cache folder for the data sets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
diff --git a/sklearn/datasets/_rcv1.py b/sklearn/datasets/_rcv1.py
index a807d8e311466..d9f392d872216 100644
--- a/sklearn/datasets/_rcv1.py
+++ b/sklearn/datasets/_rcv1.py
@@ -10,7 +10,7 @@
import logging
from gzip import GzipFile
-from os import makedirs, remove
+from os import PathLike, makedirs, remove
from os.path import exists, join
import joblib
@@ -74,7 +74,7 @@
@validate_params(
{
- "data_home": [str, None],
+ "data_home": [str, PathLike, None],
"subset": [StrOptions({"train", "test", "all"})],
"download_if_missing": ["boolean"],
"random_state": ["random_state"],
@@ -111,7 +111,7 @@ def fetch_rcv1(
Parameters
----------
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
diff --git a/sklearn/datasets/_species_distributions.py b/sklearn/datasets/_species_distributions.py
index 0bfc4bb0fdaf5..a1e654d41e071 100644
--- a/sklearn/datasets/_species_distributions.py
+++ b/sklearn/datasets/_species_distributions.py
@@ -39,7 +39,7 @@
import logging
from io import BytesIO
-from os import makedirs, remove
+from os import PathLike, makedirs, remove
from os.path import exists
import joblib
@@ -136,7 +136,7 @@ def construct_grids(batch):
@validate_params(
- {"data_home": [str, None], "download_if_missing": ["boolean"]},
+ {"data_home": [str, PathLike, None], "download_if_missing": ["boolean"]},
prefer_skip_nested_validation=True,
)
def fetch_species_distributions(*, data_home=None, download_if_missing=True):
@@ -146,7 +146,7 @@ def fetch_species_distributions(*, data_home=None, download_if_missing=True):
Parameters
----------
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify another download and cache folder for the datasets. By default
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
diff --git a/sklearn/datasets/_twenty_newsgroups.py b/sklearn/datasets/_twenty_newsgroups.py
index 637cf8e4fc8d4..5973e998c34b9 100644
--- a/sklearn/datasets/_twenty_newsgroups.py
+++ b/sklearn/datasets/_twenty_newsgroups.py
@@ -153,7 +153,7 @@ def strip_newsgroup_footer(text):
@validate_params(
{
- "data_home": [str, None],
+ "data_home": [str, os.PathLike, None],
"subset": [StrOptions({"train", "test", "all"})],
"categories": ["array-like", None],
"shuffle": ["boolean"],
@@ -191,7 +191,7 @@ def fetch_20newsgroups(
Parameters
----------
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify a download and cache folder for the datasets. If None,
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
@@ -351,7 +351,7 @@ def fetch_20newsgroups(
{
"subset": [StrOptions({"train", "test", "all"})],
"remove": [tuple],
- "data_home": [str, None],
+ "data_home": [str, os.PathLike, None],
"download_if_missing": ["boolean"],
"return_X_y": ["boolean"],
"normalize": ["boolean"],
@@ -411,7 +411,7 @@ def fetch_20newsgroups_vectorized(
ends of posts that look like signatures, and 'quotes' removes lines
that appear to be quoting another post.
- data_home : str, default=None
+ data_home : str or path-like, default=None
Specify an download and cache folder for the datasets. If None,
all scikit-learn data is stored in '~/scikit_learn_data' subfolders.
diff --git a/sklearn/datasets/tests/test_base.py b/sklearn/datasets/tests/test_base.py
index f31f20636c0c1..f84c275d67cf9 100644
--- a/sklearn/datasets/tests/test_base.py
+++ b/sklearn/datasets/tests/test_base.py
@@ -3,6 +3,7 @@
import tempfile
import warnings
from functools import partial
+from pathlib import Path
from pickle import dumps, loads
import numpy as np
@@ -31,6 +32,16 @@
from sklearn.utils.fixes import _is_resource
+class _DummyPath:
+ """Minimal class that implements the os.PathLike interface."""
+
+ def __init__(self, path):
+ self.path = path
+
+ def __fspath__(self):
+ return self.path
+
+
def _remove_dir(path):
if os.path.isdir(path):
shutil.rmtree(path)
@@ -67,13 +78,18 @@ def test_category_dir_2(load_files_root):
_remove_dir(test_category_dir2)
-def test_data_home(data_home):
+@pytest.mark.parametrize("path_container", [None, Path, _DummyPath])
+def test_data_home(path_container, data_home):
# get_data_home will point to a pre-existing folder
+ if path_container is not None:
+ data_home = path_container(data_home)
data_home = get_data_home(data_home=data_home)
assert data_home == data_home
assert os.path.exists(data_home)
# clear_data_home will delete both the content and the folder it-self
+ if path_container is not None:
+ data_home = path_container(data_home)
clear_data_home(data_home=data_home)
assert not os.path.exists(data_home)
diff --git a/sklearn/decomposition/_kernel_pca.py b/sklearn/decomposition/_kernel_pca.py
index ccf79e896f210..800b472a9b3a6 100644
--- a/sklearn/decomposition/_kernel_pca.py
+++ b/sklearn/decomposition/_kernel_pca.py
@@ -432,7 +432,7 @@ def fit(self, X, y=None):
raise ValueError("Cannot fit_inverse_transform with a precomputed kernel.")
X = self._validate_data(X, accept_sparse="csr", copy=self.copy_X)
self.gamma_ = 1 / X.shape[1] if self.gamma is None else self.gamma
- self._centerer = KernelCenterer()
+ self._centerer = KernelCenterer().set_output(transform="default")
K = self._get_kernel(X)
self._fit_transform(K)
diff --git a/sklearn/decomposition/tests/test_kernel_pca.py b/sklearn/decomposition/tests/test_kernel_pca.py
index 3c95454749b4a..fdaa71314f43f 100644
--- a/sklearn/decomposition/tests/test_kernel_pca.py
+++ b/sklearn/decomposition/tests/test_kernel_pca.py
@@ -4,7 +4,8 @@
import pytest
import scipy.sparse as sp
-from sklearn.datasets import make_blobs, make_circles
+import sklearn
+from sklearn.datasets import load_iris, make_blobs, make_circles
from sklearn.decomposition import PCA, KernelPCA
from sklearn.exceptions import NotFittedError
from sklearn.linear_model import Perceptron
@@ -550,3 +551,15 @@ def test_kernel_pca_inverse_correct_gamma():
X2_recon = kpca2.inverse_transform(kpca1.transform(X))
assert_allclose(X1_recon, X2_recon)
+
+
+def test_kernel_pca_pandas_output():
+ """Check that KernelPCA works with pandas output when the solver is arpack.
+
+ Non-regression test for:
+ https://github.com/scikit-learn/scikit-learn/issues/27579
+ """
+ pytest.importorskip("pandas")
+ X, _ = load_iris(as_frame=True, return_X_y=True)
+ with sklearn.config_context(transform_output="pandas"):
+ KernelPCA(n_components=2, eigen_solver="arpack").fit_transform(X)
diff --git a/sklearn/metrics/_classification.py b/sklearn/metrics/_classification.py
index f916b7f86da38..0819abb463e2b 100644
--- a/sklearn/metrics/_classification.py
+++ b/sklearn/metrics/_classification.py
@@ -1079,7 +1079,8 @@ def zero_one_loss(y_true, y_pred, *, normalize=True, sample_weight=None):
],
"sample_weight": ["array-like", None],
"zero_division": [
- Options(Real, {0.0, 1.0, np.nan}),
+ Options(Real, {0.0, 1.0}),
+ "nan",
StrOptions({"warn"}),
],
},
@@ -1260,7 +1261,8 @@ def f1_score(
],
"sample_weight": ["array-like", None],
"zero_division": [
- Options(Real, {0.0, 1.0, np.nan}),
+ Options(Real, {0.0, 1.0}),
+ "nan",
StrOptions({"warn"}),
],
},
@@ -1542,7 +1544,8 @@ def _check_set_wise_labels(y_true, y_pred, average, labels, pos_label):
"warn_for": [list, tuple, set],
"sample_weight": ["array-like", None],
"zero_division": [
- Options(Real, {0.0, 1.0, np.nan}),
+ Options(Real, {0.0, 1.0}),
+ "nan",
StrOptions({"warn"}),
],
},
@@ -1979,7 +1982,8 @@ class after being classified as negative. This is the case when the
],
"sample_weight": ["array-like", None],
"zero_division": [
- Options(Real, {0.0, 1.0, np.nan}),
+ Options(Real, {0.0, 1.0}),
+ "nan",
StrOptions({"warn"}),
],
},
@@ -2149,7 +2153,8 @@ def precision_score(
],
"sample_weight": ["array-like", None],
"zero_division": [
- Options(Real, {0.0, 1.0, np.nan}),
+ Options(Real, {0.0, 1.0}),
+ "nan",
StrOptions({"warn"}),
],
},
@@ -2412,7 +2417,8 @@ def balanced_accuracy_score(y_true, y_pred, *, sample_weight=None, adjusted=Fals
"digits": [Interval(Integral, 0, None, closed="left")],
"output_dict": ["boolean"],
"zero_division": [
- Options(Real, {0.0, 1.0, np.nan}),
+ Options(Real, {0.0, 1.0}),
+ "nan",
StrOptions({"warn"}),
],
},
diff --git a/sklearn/metrics/tests/test_classification.py b/sklearn/metrics/tests/test_classification.py
index cfcb08a312443..afa3b90d5e8a9 100644
--- a/sklearn/metrics/tests/test_classification.py
+++ b/sklearn/metrics/tests/test_classification.py
@@ -27,6 +27,7 @@
hinge_loss,
jaccard_score,
log_loss,
+ make_scorer,
matthews_corrcoef,
multilabel_confusion_matrix,
precision_recall_fscore_support,
@@ -35,7 +36,9 @@
zero_one_loss,
)
from sklearn.metrics._classification import _check_targets
+from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelBinarizer, label_binarize
+from sklearn.tree import DecisionTreeClassifier
from sklearn.utils._mocking import MockDataFrame
from sklearn.utils._testing import (
assert_allclose,
@@ -2802,3 +2805,27 @@ def test_classification_metric_pos_label_types(metric, classes):
y_pred = y_true.copy()
result = metric(y_true, y_pred, pos_label=pos_label)
assert not np.any(np.isnan(result))
+
+
+@pytest.mark.parametrize(
+ "scoring",
+ [
+ make_scorer(f1_score, zero_division=np.nan),
+ make_scorer(fbeta_score, beta=2, zero_division=np.nan),
+ make_scorer(precision_score, zero_division=np.nan),
+ make_scorer(recall_score, zero_division=np.nan),
+ ],
+)
+def test_classification_metric_division_by_zero_nan_validaton(scoring):
+ """Check that we validate `np.nan` properly for classification metrics.
+
+ With `n_jobs=2` in cross-validation, the `np.nan` used for the singleton will be
+ different in the sub-process and we should not use the `is` operator but
+ `math.isnan`.
+
+ Non-regression test for:
+ https://github.com/scikit-learn/scikit-learn/issues/27563
+ """
+ X, y = datasets.make_classification(random_state=0)
+ classifier = DecisionTreeClassifier(max_depth=3, random_state=0).fit(X, y)
+ cross_val_score(classifier, X, y, scoring=scoring, n_jobs=2, error_score="raise")
diff --git a/sklearn/tree/_tree.pyx b/sklearn/tree/_tree.pyx
index 22d3d38ce981a..c843ad7500480 100644
--- a/sklearn/tree/_tree.pyx
+++ b/sklearn/tree/_tree.pyx
@@ -771,11 +771,13 @@ cdef class Tree:
safe_realloc(&self.nodes, capacity)
safe_realloc(&self.value, capacity * self.value_stride)
- # value memory is initialised to 0 to enable classifier argmax
if capacity > self.capacity:
+ # value memory is initialised to 0 to enable classifier argmax
memset((self.value + self.capacity * self.value_stride), 0,
(capacity - self.capacity) * self.value_stride *
sizeof(double))
+ # node memory is initialised to 0 to ensure deterministic pickle (padding in Node struct)
+ memset((self.nodes + self.capacity), 0, (capacity - self.capacity) * sizeof(Node))
# if capacity smaller than node_count, adjust the counter
if capacity < self.node_count:
diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py
index 82c68b1327296..3dac1b38693c0 100644
--- a/sklearn/tree/tests/test_tree.py
+++ b/sklearn/tree/tests/test_tree.py
@@ -2626,3 +2626,16 @@ def test_sample_weight_non_uniform(make_data, Tree):
tree_samples_removed.fit(X[1::2, :], y[1::2])
assert_allclose(tree_samples_removed.predict(X), tree_with_sw.predict(X))
+
+
+def test_deterministic_pickle():
+ # Non-regression test for:
+ # https://github.com/scikit-learn/scikit-learn/issues/27268
+ # Uninitialised memory would lead to the two pickle strings being different.
+ tree1 = DecisionTreeClassifier(random_state=0).fit(iris.data, iris.target)
+ tree2 = DecisionTreeClassifier(random_state=0).fit(iris.data, iris.target)
+
+ pickle1 = pickle.dumps(tree1)
+ pickle2 = pickle.dumps(tree2)
+
+ assert pickle1 == pickle2
diff --git a/sklearn/utils/_param_validation.py b/sklearn/utils/_param_validation.py
index 0e30627ab06cc..bf063a1945621 100644
--- a/sklearn/utils/_param_validation.py
+++ b/sklearn/utils/_param_validation.py
@@ -46,6 +46,7 @@ def validate_parameter_constraints(parameter_constraints, params, caller_name):
- the string "boolean"
- the string "verbose"
- the string "cv_object"
+ - the string "nan"
- a MissingValues object representing markers for missing values
- a HasMethods object, representing method(s) an object must have
- a Hidden object, representing a constraint not meant to be exposed to the user
@@ -137,6 +138,8 @@ def make_constraint(constraint):
constraint = make_constraint(constraint.constraint)
constraint.hidden = True
return constraint
+ if isinstance(constraint, str) and constraint == "nan":
+ return _NanConstraint()
raise ValueError(f"Unknown constraint type: {constraint}")
diff --git a/sklearn/utils/tests/test_param_validation.py b/sklearn/utils/tests/test_param_validation.py
index ab6465538b863..2af84707cd2ed 100644
--- a/sklearn/utils/tests/test_param_validation.py
+++ b/sklearn/utils/tests/test_param_validation.py
@@ -23,6 +23,7 @@
_CVObjects,
_InstancesOf,
_IterablesNotString,
+ _NanConstraint,
_NoneConstraint,
_PandasNAConstraint,
_RandomStates,
@@ -387,6 +388,7 @@ def test_generate_valid_param(constraint):
(Real, 0.5),
("boolean", False),
("verbose", 1),
+ ("nan", np.nan),
(MissingValues(), -1),
(MissingValues(), -1.0),
(MissingValues(), None),
@@ -420,6 +422,7 @@ def test_is_satisfied_by(constraint_declaration, value):
(MissingValues(numeric_only=True), MissingValues),
(HasMethods("fit"), HasMethods),
("cv_object", _CVObjects),
+ ("nan", _NanConstraint),
],
)
def test_make_constraint(constraint_declaration, expected_constraint_class):