From 938c36bea97c22ff6826517a0694dfa92794f18b Mon Sep 17 00:00:00 2001 From: Stefanie Senger <91849487+StefanieSenger@users.noreply.github.com> Date: Sat, 6 Jul 2024 00:06:11 +0200 Subject: [PATCH 01/20] FIX `TransformedTargetRegressor` warns when `set_output` expects dataframe (#29401) --- doc/whats_new/v1.5.rst | 17 +++++++++++++++++ sklearn/compose/_target.py | 4 ++++ sklearn/compose/tests/test_target.py | 19 ++++++++++++++++++- 3 files changed, 39 insertions(+), 1 deletion(-) diff --git a/doc/whats_new/v1.5.rst b/doc/whats_new/v1.5.rst index 20184bbd2a551..059875eec12d6 100644 --- a/doc/whats_new/v1.5.rst +++ b/doc/whats_new/v1.5.rst @@ -13,6 +13,23 @@ For a short description of the main highlights of the release, please refer to .. include:: changelog_legend.inc +.. _changes_1_5_2: + +Version 1.5.2 +============= + +**release date of 1.5.2** + +Changelog +--------- + +:mod:`sklearn.compose` +...................... + +- |Fix| Fixed :class:`compose.TransformedTargetRegressor` not to raise `UserWarning` if + transform output is set to `pandas` or `polars`, since it isn't a transformer. + :pr:`29401` by :user:`Stefanie Senger `. + .. _changes_1_5_1: Version 1.5.1 diff --git a/sklearn/compose/_target.py b/sklearn/compose/_target.py index c766c6ff2f3d4..c2ef2572ab786 100644 --- a/sklearn/compose/_target.py +++ b/sklearn/compose/_target.py @@ -193,6 +193,10 @@ def _fit_transformer(self, y): validate=True, check_inverse=self.check_inverse, ) + # We are transforming the target here and not the features, so we set the + # output of FunctionTransformer() to be a numpy array (default) and to not + # depend on the global configuration: + self.transformer_.set_output(transform="default") # XXX: sample_weight is not currently passed to the # transformer. However, if transformer starts using sample_weight, the # code should be modified accordingly. At the time to consider the diff --git a/sklearn/compose/tests/test_target.py b/sklearn/compose/tests/test_target.py index a971553b64739..fd885459e76d1 100644 --- a/sklearn/compose/tests/test_target.py +++ b/sklearn/compose/tests/test_target.py @@ -1,7 +1,9 @@ +import warnings + import numpy as np import pytest -from sklearn import datasets +from sklearn import config_context, datasets from sklearn.base import BaseEstimator, TransformerMixin, clone from sklearn.compose import TransformedTargetRegressor from sklearn.dummy import DummyRegressor @@ -393,3 +395,18 @@ def test_transform_target_regressor_pass_extra_predict_parameters(): regr.fit(X, y) regr.predict(X, check_input=False) assert regr.regressor_.predict_called + + +@pytest.mark.parametrize("output_format", ["pandas", "polars"]) +def test_transform_target_regressor_not_warns_with_global_output_set(output_format): + """Test that TransformedTargetRegressor will not raise warnings if + set_config(transform_output="pandas"/"polars") is set globally; regression test for + issue #29361.""" + X, y = datasets.make_regression() + y = np.abs(y) + 1 + with config_context(transform_output=output_format): + with warnings.catch_warnings(): + warnings.simplefilter("error") + TransformedTargetRegressor( + regressor=LinearRegression(), func=np.log, inverse_func=np.exp + ).fit(X, y) From 0dcc36457c992d38d0601ced2d2b4ba0179999af Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 8 Jul 2024 01:12:41 -0700 Subject: [PATCH 02/20] :lock: :robot: CI Update lock files for cirrus-arm CI build(s) :lock: :robot: (#29427) Co-authored-by: Lock file bot --- .../cirrus/pymin_conda_forge_linux-aarch64_conda.lock | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock index edc83e3689ccc..5de3fa818fd24 100644 --- a/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock +++ b/build_tools/cirrus/pymin_conda_forge_linux-aarch64_conda.lock @@ -2,7 +2,7 @@ # platform: linux-aarch64 # input_hash: 2d8c526ab7c0c2f0ca509bfec3f035e5bd33b8096f194f0747f167c8aff66383 @EXPLICIT -https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.6.2-hcefe29a_0.conda#3ef6b1a30375f8a973a593698e317191 +https://conda.anaconda.org/conda-forge/linux-aarch64/ca-certificates-2024.7.4-hcefe29a_0.conda#c4c784a1336d72fff54f6b207f3dd75f https://conda.anaconda.org/conda-forge/linux-aarch64/ld_impl_linux-aarch64-2.40-h9fc2d93_7.conda#1b0feef706f4d03eff0b76626ead64fc https://conda.anaconda.org/conda-forge/linux-aarch64/python_abi-3.9-4_cp39.conda#c191905a08694e4a5cb1238e90233878 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 @@ -45,7 +45,7 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/libtiff-4.6.0-hf980d43_3.co https://conda.anaconda.org/conda-forge/linux-aarch64/llvm-openmp-18.1.8-hb063fc5_0.conda#f0cf07feda9ed87092833cd8fca012f5 https://conda.anaconda.org/conda-forge/linux-aarch64/python-3.9.19-h4ac3b42_0_cpython.conda#1501507cd9451472ec8900d587ce872f https://conda.anaconda.org/conda-forge/linux-aarch64/brotli-1.1.0-h31becfc_1.conda#e41f5862ac746428407f3fd44d2ed01f -https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.9.1-h6552966_0.conda#758b202f61f6bbfd2c6adf0fde043276 +https://conda.anaconda.org/conda-forge/linux-aarch64/ccache-4.10.1-ha3bccff_0.conda#7cd24a038d2727b5e6377975237a6cfa https://conda.anaconda.org/conda-forge/noarch/certifi-2024.6.2-pyhd8ed1ab_0.conda#8821ec1c8fcdc9e1d291d7b9f6e9968a https://conda.anaconda.org/conda-forge/noarch/colorama-0.4.6-pyhd8ed1ab_0.tar.bz2#3faab06a954c2a04039983f2c4a50d99 https://conda.anaconda.org/conda-forge/noarch/cycler-0.12.1-pyhd8ed1ab_0.conda#5cd86562580f274031ede6aa6aa24441 @@ -70,13 +70,13 @@ https://conda.anaconda.org/conda-forge/linux-aarch64/tornado-6.4.1-py39ha3e8b56_ https://conda.anaconda.org/conda-forge/linux-aarch64/unicodedata2-15.1.0-py39h898b7ef_0.conda#8c072c9329aeea97a46005625267a851 https://conda.anaconda.org/conda-forge/noarch/wheel-0.43.0-pyhd8ed1ab_1.conda#0b5293a157c2b5cd513dd1b03d8d3aae https://conda.anaconda.org/conda-forge/noarch/zipp-3.19.2-pyhd8ed1ab_0.conda#49808e59df5535116f6878b2a820d6f4 -https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.53.0-py39he257ee7_0.conda#de1dc44f6b1a4393b37f4264d8e31e2a +https://conda.anaconda.org/conda-forge/linux-aarch64/fonttools-4.53.1-py39he257ee7_0.conda#e30df3a3431af304f87bbd0cd07d5674 https://conda.anaconda.org/conda-forge/noarch/importlib_resources-6.4.0-pyhd8ed1ab_0.conda#c5d3907ad8bd7bf557521a1833cf7e6d https://conda.anaconda.org/conda-forge/noarch/joblib-1.4.2-pyhd8ed1ab_0.conda#25df261d4523d9f9783bcdb7208d872f https://conda.anaconda.org/conda-forge/linux-aarch64/libcblas-3.9.0-22_linuxaarch64_openblas.conda#fbe7fe553f2cc78a0311e009b26f180d https://conda.anaconda.org/conda-forge/linux-aarch64/liblapack-3.9.0-22_linuxaarch64_openblas.conda#8c709d281609792c39b1d5c0241f90f1 https://conda.anaconda.org/conda-forge/noarch/meson-1.4.1-pyhd8ed1ab_0.conda#714ca123839eeebb25d12b443067ea64 -https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.3.0-py39h4a8821f_1.conda#12b4464ffbad162e28b7bf6e81129501 +https://conda.anaconda.org/conda-forge/linux-aarch64/pillow-10.4.0-py39h4a8821f_0.conda#318861157594972acc05a8715d3018a8 https://conda.anaconda.org/conda-forge/noarch/pip-24.0-pyhd8ed1ab_0.conda#f586ac1e56c8638b64f9c8122a7b8a67 https://conda.anaconda.org/conda-forge/noarch/pyproject-metadata-0.8.0-pyhd8ed1ab_0.conda#573fe09d7bd0cd4bcc210d8369b5ca47 https://conda.anaconda.org/conda-forge/noarch/pytest-8.2.2-pyhd8ed1ab_0.conda#0f3f49c22c7ef3a1195fa61dad3c43be From a408a5904cab304b43e62becdf7a36af825668b2 Mon Sep 17 00:00:00 2001 From: Edoardo Abati <29585319+EdAbati@users.noreply.github.com> Date: Mon, 8 Jul 2024 10:21:25 +0200 Subject: [PATCH 03/20] fix: `mps` device support in `entropy` (#29321) --- sklearn/metrics/cluster/_supervised.py | 6 +++--- sklearn/utils/_array_api.py | 12 ++++++++++++ sklearn/utils/tests/test_array_api.py | 10 ++++++++++ 3 files changed, 25 insertions(+), 3 deletions(-) diff --git a/sklearn/metrics/cluster/_supervised.py b/sklearn/metrics/cluster/_supervised.py index c854e79c481fb..295a176cfcf26 100644 --- a/sklearn/metrics/cluster/_supervised.py +++ b/sklearn/metrics/cluster/_supervised.py @@ -15,7 +15,7 @@ import numpy as np from scipy import sparse as sp -from ...utils._array_api import get_namespace +from ...utils._array_api import _max_precision_float_dtype, get_namespace_and_device from ...utils._param_validation import Interval, StrOptions, validate_params from ...utils.multiclass import type_of_target from ...utils.validation import check_array, check_consistent_length @@ -1275,12 +1275,12 @@ def entropy(labels): ----- The logarithm used is the natural logarithm (base-e). """ - xp, is_array_api_compliant = get_namespace(labels) + xp, is_array_api_compliant, device_ = get_namespace_and_device(labels) labels_len = labels.shape[0] if is_array_api_compliant else len(labels) if labels_len == 0: return 1.0 - pi = xp.astype(xp.unique_counts(labels)[1], xp.float64) + pi = xp.astype(xp.unique_counts(labels)[1], _max_precision_float_dtype(xp, device_)) # single cluster => zero entropy if pi.size == 1: diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py index f9fde2e97e6d3..542a8136da661 100644 --- a/sklearn/utils/_array_api.py +++ b/sklearn/utils/_array_api.py @@ -609,6 +609,18 @@ def _add_to_diagonal(array, value, xp): array[i, i] += value +def _max_precision_float_dtype(xp, device): + """Return the float dtype with the highest precision supported by the device.""" + # TODO: Update to use `__array_namespace__info__()` from array-api v2023.12 + # when/if that becomes more widespread. + xp_name = xp.__name__ + if xp_name in {"array_api_compat.torch", "torch"} and ( + str(device).startswith("mps") + ): # pragma: no cover + return xp.float32 + return xp.float64 + + def _find_matching_floating_dtype(*arrays, xp): """Find a suitable floating point dtype when computing with arrays. diff --git a/sklearn/utils/tests/test_array_api.py b/sklearn/utils/tests/test_array_api.py index 33b309d3a5f1d..beff36499fb92 100644 --- a/sklearn/utils/tests/test_array_api.py +++ b/sklearn/utils/tests/test_array_api.py @@ -17,6 +17,7 @@ _estimator_with_converted_arrays, _is_numpy_namespace, _isin, + _max_precision_float_dtype, _nanmax, _nanmin, _NumPyAPIWrapper, @@ -510,6 +511,15 @@ def test_indexing_dtype(namespace, _device, _dtype): assert indexing_dtype(xp) == xp.int64 +@pytest.mark.parametrize( + "namespace, _device, _dtype", yield_namespace_device_dtype_combinations() +) +def test_max_precision_float_dtype(namespace, _device, _dtype): + xp = _array_api_for_tests(namespace, _device) + expected_dtype = xp.float32 if _device == "mps" else xp.float64 + assert _max_precision_float_dtype(xp, _device) == expected_dtype + + @pytest.mark.parametrize( "array_namespace, device, _", yield_namespace_device_dtype_combinations() ) From 56dbfd0a7cc8dd6efd273ceacf8f5f359669273e Mon Sep 17 00:00:00 2001 From: EmilyXinyi <52259856+EmilyXinyi@users.noreply.github.com> Date: Mon, 8 Jul 2024 04:31:13 -0400 Subject: [PATCH 04/20] extreme_stable case for mean_tweedie_deviance (#29258) --- sklearn/metrics/_regression.py | 18 ++++++++++-------- sklearn/metrics/tests/test_common.py | 2 ++ 2 files changed, 12 insertions(+), 8 deletions(-) diff --git a/sklearn/metrics/_regression.py b/sklearn/metrics/_regression.py index a66664029294a..36a4638718118 100644 --- a/sklearn/metrics/_regression.py +++ b/sklearn/metrics/_regression.py @@ -1284,12 +1284,14 @@ def _mean_tweedie_deviance(y_true, y_pred, sample_weight, power): """Mean Tweedie deviance regression loss.""" xp, _ = get_namespace(y_true, y_pred) p = power + zero = xp.asarray(0, dtype=y_true.dtype) if p < 0: # 'Extreme stable', y any real number, y_pred > 0 dev = 2 * ( - xp.pow(xp.where(y_true > 0, y_true, 0), 2 - p) / ((1 - p) * (2 - p)) - - y_true * xp.pow(y_pred, 1 - p) / (1 - p) - + xp.pow(y_pred, 2 - p) / (2 - p) + xp.pow(xp.where(y_true > 0, y_true, zero), xp.asarray(2 - p)) + / ((1 - p) * (2 - p)) + - y_true * xp.pow(y_pred, xp.asarray(1 - p)) / (1 - p) + + xp.pow(y_pred, xp.asarray(2 - p)) / (2 - p) ) elif p == 0: # Normal distribution, y and y_pred any real number @@ -1302,9 +1304,9 @@ def _mean_tweedie_deviance(y_true, y_pred, sample_weight, power): dev = 2 * (xp.log(y_pred / y_true) + y_true / y_pred - 1) else: dev = 2 * ( - xp.pow(y_true, 2 - p) / ((1 - p) * (2 - p)) - - y_true * xp.pow(y_pred, 1 - p) / (1 - p) - + xp.pow(y_pred, 2 - p) / (2 - p) + xp.pow(y_true, xp.asarray(2 - p)) / ((1 - p) * (2 - p)) + - y_true * xp.pow(y_pred, xp.asarray(1 - p)) / (1 - p) + + xp.pow(y_pred, xp.asarray(2 - p)) / (2 - p) ) return float(_average(dev, weights=sample_weight)) @@ -1384,14 +1386,14 @@ def mean_tweedie_deviance(y_true, y_pred, *, sample_weight=None, power=0): message = f"Mean Tweedie deviance error with power={power} can only be used on " if power < 0: # 'Extreme stable', y any real number, y_pred > 0 - if (y_pred <= 0).any(): + if xp.any(y_pred <= 0): raise ValueError(message + "strictly positive y_pred.") elif power == 0: # Normal, y and y_pred can be any real number pass elif 1 <= power < 2: # Poisson and compound Poisson distribution, y >= 0, y_pred > 0 - if (y_true < 0).any() or (y_pred <= 0).any(): + if xp.any(y_true < 0) or xp.any(y_pred <= 0): raise ValueError(message + "non-negative y and strictly positive y_pred.") elif power >= 2: # Gamma and Extreme stable distribution, y and y_pred > 0 diff --git a/sklearn/metrics/tests/test_common.py b/sklearn/metrics/tests/test_common.py index c247e5f87f7d3..6110cbd3d1d13 100644 --- a/sklearn/metrics/tests/test_common.py +++ b/sklearn/metrics/tests/test_common.py @@ -1991,6 +1991,8 @@ def check_array_api_metric_pairwise(metric, array_namespace, device, dtype_name) check_array_api_multilabel_classification_metric, ], mean_tweedie_deviance: [check_array_api_regression_metric], + partial(mean_tweedie_deviance, power=-0.5): [check_array_api_regression_metric], + partial(mean_tweedie_deviance, power=1.5): [check_array_api_regression_metric], r2_score: [ check_array_api_regression_metric, check_array_api_regression_metric_multioutput, From 1e3c7be5ff7017550438036c42f0af502b9a2331 Mon Sep 17 00:00:00 2001 From: Drew Craeton <102687650+craetona@users.noreply.github.com> Date: Mon, 8 Jul 2024 05:21:39 -0700 Subject: [PATCH 05/20] DOC Add link to plot_regression.py (#29232) Co-authored-by: adrinjalali --- examples/neighbors/plot_regression.py | 21 ++++++++++++--------- sklearn/neighbors/_regression.py | 4 ++++ 2 files changed, 16 insertions(+), 9 deletions(-) diff --git a/examples/neighbors/plot_regression.py b/examples/neighbors/plot_regression.py index 3cd048c84d51b..431540e81761b 100644 --- a/examples/neighbors/plot_regression.py +++ b/examples/neighbors/plot_regression.py @@ -6,25 +6,26 @@ Demonstrate the resolution of a regression problem using a k-Nearest Neighbor and the interpolation of the target using both barycenter and constant weights. - """ # Authors: The scikit-learn developers # SPDX-License-Identifier: BSD-3-Clause - # %% # Generate sample data # -------------------- +# Here we generate a few data points to use to train the model. We also generate +# data in the whole range of the training data to visualize how the model would +# react in that whole region. import matplotlib.pyplot as plt import numpy as np from sklearn import neighbors -np.random.seed(0) -X = np.sort(5 * np.random.rand(40, 1), axis=0) -T = np.linspace(0, 5, 500)[:, np.newaxis] -y = np.sin(X).ravel() +rng = np.random.RandomState(0) +X_train = np.sort(5 * rng.rand(40, 1), axis=0) +X_test = np.linspace(0, 5, 500)[:, np.newaxis] +y = np.sin(X_train).ravel() # Add noise to targets y[::5] += 1 * (0.5 - np.random.rand(8)) @@ -32,15 +33,17 @@ # %% # Fit regression model # -------------------- +# Here we train a model and visualize how `uniform` and `distance` +# weights in prediction effect predicted values. n_neighbors = 5 for i, weights in enumerate(["uniform", "distance"]): knn = neighbors.KNeighborsRegressor(n_neighbors, weights=weights) - y_ = knn.fit(X, y).predict(T) + y_ = knn.fit(X_train, y).predict(X_test) plt.subplot(2, 1, i + 1) - plt.scatter(X, y, color="darkorange", label="data") - plt.plot(T, y_, color="navy", label="prediction") + plt.scatter(X_train, y, color="darkorange", label="data") + plt.plot(X_test, y_, color="navy", label="prediction") plt.axis("tight") plt.legend() plt.title("KNeighborsRegressor (k = %i, weights = '%s')" % (n_neighbors, weights)) diff --git a/sklearn/neighbors/_regression.py b/sklearn/neighbors/_regression.py index 2ef95247e83d1..98a5734c5140b 100644 --- a/sklearn/neighbors/_regression.py +++ b/sklearn/neighbors/_regression.py @@ -43,6 +43,10 @@ class KNeighborsRegressor(KNeighborsMixin, RegressorMixin, NeighborsBase): Uniform weights are used by default. + See the following example for a demonstration of the impact of + different weighting schemes on predictions: + :ref:`sphx_glr_auto_examples_neighbors_plot_regression.py`. + algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto' Algorithm used to compute the nearest neighbors: From 4c1747bdfb2637b17cf40139456b43ae0453b1f0 Mon Sep 17 00:00:00 2001 From: Martin Helm <54892009+MSHelm@users.noreply.github.com> Date: Mon, 8 Jul 2024 14:55:33 +0200 Subject: [PATCH 06/20] DOC Correct minimum adjusted rand index in User Guide (#29410) --- doc/modules/clustering.rst | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/doc/modules/clustering.rst b/doc/modules/clustering.rst index 2de39d0317bf5..b72b8f5ed0312 100644 --- a/doc/modules/clustering.rst +++ b/doc/modules/clustering.rst @@ -1370,7 +1370,7 @@ will not necessarily be close to zero.:: - **Bounded range**: Lower values indicate different labelings, similar clusterings have a high (adjusted or unadjusted) Rand index, 1.0 is the perfect match score. The score range is [0, 1] for the unadjusted Rand index - and [-1, 1] for the adjusted Rand index. + and [-0.5, 1] for the adjusted Rand index. - **No assumption is made on the cluster structure**: The (adjusted or unadjusted) Rand index can be used to compare all kinds of clustering @@ -1444,6 +1444,8 @@ will not necessarily be close to zero.:: * `Wikipedia entry for the Rand index `_ + * :doi:`Minimum adjusted Rand index for two clusterings of a given size, 2022, J. E. Chacón and A. I. Rastrojo <10.1007/s11634-022-00491-w>` + .. _mutual_info_score: From ca7b42b4b7532ec4551dd5e35d699a143b939f19 Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Mon, 8 Jul 2024 15:17:30 +0200 Subject: [PATCH 07/20] TST workaround arpack-ng regression in [scipy-dev] (#29432) --- sklearn/utils/estimator_checks.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/sklearn/utils/estimator_checks.py b/sklearn/utils/estimator_checks.py index 2108d33d6ad77..d1fe1d5ab8c56 100644 --- a/sklearn/utils/estimator_checks.py +++ b/sklearn/utils/estimator_checks.py @@ -1028,9 +1028,9 @@ def check_array_api_input_and_values( def _check_estimator_sparse_container(name, estimator_orig, sparse_type): rng = np.random.RandomState(0) X = rng.uniform(size=(40, 3)) - X[X < 0.8] = 0 + X[X < 0.6] = 0 X = _enforce_estimator_tags_X(estimator_orig, X) - y = (4 * rng.uniform(size=40)).astype(int) + y = (4 * rng.uniform(size=X.shape[0])).astype(np.int32) # catch deprecation warnings with ignore_warnings(category=FutureWarning): estimator = clone(estimator_orig) From 1e338bb9f86a94bd7e7b114df0170f38bc0790c7 Mon Sep 17 00:00:00 2001 From: scikit-learn-bot Date: Mon, 8 Jul 2024 08:53:59 -0700 Subject: [PATCH 08/20] :lock: :robot: CI Update lock files for scipy-dev CI build(s) :lock: :robot: (#29428) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Lock file bot Co-authored-by: Loïc Estève --- build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock index 502d425d2adb7..71d55df86a35d 100644 --- a/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_scipy_dev_linux-64_conda.lock @@ -28,7 +28,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py312h06a4308_0.conda# https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py312h06a4308_0.conda#6d9697bb8b9f3212be10b3b8e01a12b9 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb -# pip certifi @ https://files.pythonhosted.org/packages/5b/11/1e78951465b4a225519b8c3ad29769c49e0d8d157a070f681d5b6d64737f/certifi-2024.6.2-py3-none-any.whl#sha256=ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56 +# pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 # pip charset-normalizer @ https://files.pythonhosted.org/packages/ee/fb/14d30eb4956408ee3ae09ad34299131fb383c47df355ddb428a7331cfa1e/charset_normalizer-3.3.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=90d558489962fd4918143277a773316e56c72da56ec7aa3dc3dbbe20fdfed15b # pip coverage @ https://files.pythonhosted.org/packages/88/52/7054710a881b09d295e93b9889ac204c241a6847a8c05555fc6e1d8799d5/coverage-7.5.4-cp312-cp312-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=5013ed890dc917cef2c9f765c4c6a8ae9df983cd60dbb635df8ed9f4ebc9f555 # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 From 0dba98f1eb7edc893e26c36d54519386a265467d Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 9 Jul 2024 08:59:47 +0200 Subject: [PATCH 09/20] MNT Update main-ci lock files (#29388) --- build_tools/azure/debian_atlas_32bit_lock.txt | 2 +- ...latest_conda_forge_mkl_linux-64_conda.lock | 162 ++++++------ ...pylatest_conda_forge_mkl_osx-64_conda.lock | 54 ++-- ...test_conda_mkl_no_openmp_osx-64_conda.lock | 16 +- ...st_pip_openblas_pandas_linux-64_conda.lock | 30 +-- ...onda_defaults_openblas_linux-64_conda.lock | 10 +- .../pymin_conda_forge_mkl_win-64_conda.lock | 56 ++-- ...e_openblas_ubuntu_2204_linux-64_conda.lock | 138 +++++----- build_tools/circle/doc_environment.yml | 2 +- build_tools/circle/doc_linux-64_conda.lock | 242 +++++++++--------- .../doc_min_dependencies_linux-64_conda.lock | 219 +++++++++------- .../update_environments_and_lock_files.py | 7 + doc/conftest.py | 6 +- doc/modules/feature_extraction.rst | 33 +-- doc/modules/multiclass.rst | 6 + sklearn/conftest.py | 4 + sklearn/feature_extraction/image.py | 3 + sklearn/utils/_indexing.py | 8 +- sklearn/utils/validation.py | 6 +- 19 files changed, 534 insertions(+), 470 deletions(-) diff --git a/build_tools/azure/debian_atlas_32bit_lock.txt b/build_tools/azure/debian_atlas_32bit_lock.txt index 9da822ecd9cb8..2fd38ec1c8f81 100644 --- a/build_tools/azure/debian_atlas_32bit_lock.txt +++ b/build_tools/azure/debian_atlas_32bit_lock.txt @@ -6,7 +6,7 @@ # attrs==23.2.0 # via pytest -coverage==7.5.3 +coverage==7.5.4 # via pytest-cov cython==3.0.10 # via -r build_tools/azure/debian_atlas_32bit_requirements.txt diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index 09249ad17160b..d7d27d3039c43 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -1,66 +1,51 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 50fed47bc507d9ee3dbf5ff7a2247cb88944928bd5797e534ebdf8ece2d858ec +# input_hash: 29e3d76c436ee8cb86d5976aa41ef8822f991c469202b8d12fc27e43576ec805 @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 -https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.6.2-hbcca054_0.conda#847c3c2905cc467cea52c24f9cfa8080 +https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda#23ab7665c5f63cfb9f1f6195256daac6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-dejavu-sans-mono-2.37-hab24e00_0.tar.bz2#0c96522c6bdaed4b1566d11387caaf45 https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed37_0.tar.bz2#34893075a5c9e55cdafac56607368fc6 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f -https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_3.conda#7c1062eaa78dec4ea8a9a988dbda6045 -https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-13.2.0-hc0a3c3a_7.conda#53ebd4c833fa01cb2c6353e99f905406 +https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7.conda#b80f2f396ca2c28b8c14c437a4ed1e74 https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65 https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar.bz2#562b26ba2e19059551a811e72ab7f793 -https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-13.2.0-h77fa898_7.conda#72ec1b1b04c4d15d4204ece1ecea5978 -https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.11-hd590300_1.conda#0bb492cca54017ea314b809b1ee3a176 +https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h77fa898_0.conda#ca0fad6a41ddaef54a153b78eccb5037 +https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda#7ed427f0871fd41cb1d9c17727c17589 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.0-hd590300_0.conda#71b89db63b5b504e7afc8ad901172e1e https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.28.1-hd590300_0.conda#dcde58ff9a1f30b0037a2315d1846d1f https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-h59595ed_2.conda#985f2f453fb72408d6b6f1be0f324033 -https://conda.anaconda.org/conda-forge/linux-64/gflags-2.2.2-he1b5a44_1004.tar.bz2#cddaf2c63ea4a5901cf09524c490ecdc -https://conda.anaconda.org/conda-forge/linux-64/graphite2-1.3.13-h59595ed_1003.conda#f87c7b7c2cb45f323ffbce941c78ab7c -https://conda.anaconda.org/conda-forge/linux-64/icu-73.2-h59595ed_0.conda#cc47e1facc155f91abd89b11e48e72ff https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2#a8832b479f93521a9e7b5b743803be51 -https://conda.anaconda.org/conda-forge/linux-64/lerc-4.0.0-h27087fc_0.tar.bz2#76bbff344f0134279f225174e9064c8f -https://conda.anaconda.org/conda-forge/linux-64/libabseil-20230125.3-cxx17_h59595ed_0.conda#d1db1b8be7c3a8983dcbbbfe4f0765de -https://conda.anaconda.org/conda-forge/linux-64/libasprintf-0.22.5-h661eb56_2.conda#dd197c968bf9760bba0031888d431ede https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.0.9-h166bdaf_9.conda#61641e239f96eae2b8492dc7e755828c -https://conda.anaconda.org/conda-forge/linux-64/libcrc32c-1.1.2-h9c3ff4c_0.tar.bz2#c965a5aa0d5c1c37ffc62dff36e28400 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda#8e88f9389f1165d7c0936fe40d9a9a79 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d https://conda.anaconda.org/conda-forge/linux-64/libffi-3.4.2-h7f98852_5.tar.bz2#d645c6d2ac96843a2bfaccd2d62b3ac3 https://conda.anaconda.org/conda-forge/linux-64/libgettextpo-0.22.5-h59595ed_2.conda#172bcc51059416e7ce99e7b528cede83 -https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-13.2.0-hca663fb_7.conda#c0bd771f09a326fdcd95a60b617795bf +https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.1.0-hc5f4f2c_0.conda#6456c2620c990cd8dde2428a27ba0bc5 https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8 https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7 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https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda#57d7dc60e9325e3de37ff8dffd18e814 -https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.9.4-hcb278e6_0.conda#318b08df404f9c9be5712aaa5a6f0bb0 -https://conda.anaconda.org/conda-forge/linux-64/mpg123-1.32.6-h59595ed_0.conda#9160cdeb523a1b20cf8d2a0bf821f45d https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.5-h59595ed_0.conda#fcea371545eda051b6deafb24889fc69 -https://conda.anaconda.org/conda-forge/linux-64/ninja-1.12.1-h297d8ca_0.conda#3aa1c7e292afeff25a0091ddd7c69b72 -https://conda.anaconda.org/conda-forge/linux-64/nspr-4.35-h27087fc_0.conda#da0ec11a6454ae19bff5b02ed881a2b1 -https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_0.conda#a41fa0e391cc9e0d6b78ac69ca047a6c -https://conda.anaconda.org/conda-forge/linux-64/pixman-0.43.2-h59595ed_0.conda#71004cbf7924e19c02746ccde9fd7123 +https://conda.anaconda.org/conda-forge/linux-64/openssl-3.3.1-h4ab18f5_1.conda#b1e9d076f14e8d776213fd5047b4c3d9 https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h36c2ea0_1001.tar.bz2#22dad4df6e8630e8dff2428f6f6a7036 -https://conda.anaconda.org/conda-forge/linux-64/rdma-core-28.9-h59595ed_1.conda#aeffb7c06b5f65e55e6c637408dc4100 -https://conda.anaconda.org/conda-forge/linux-64/re2-2023.03.02-h8c504da_0.conda#206f8fa808748f6e90599c3368a1114e https://conda.anaconda.org/conda-forge/linux-64/sleef-3.5.1-h9b69904_2.tar.bz2#6e016cf4c525d04a7bd038cee53ad3fd -https://conda.anaconda.org/conda-forge/linux-64/snappy-1.1.10-hdb0a2a9_1.conda#78b8b85bdf1f42b8a2b3cb577d8742d1 https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h7f98852_1002.tar.bz2#4b230e8381279d76131116660f5a241a 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b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock index d38d4fcd82d6f..a5342c25efa97 100644 --- a/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock +++ b/build_tools/azure/pylatest_conda_mkl_no_openmp_osx-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: osx-64 -# input_hash: 33a102d2ccde4e14e315a98b50340349af349f802403dd49589375b2b889f2d3 +# input_hash: 197b213ba4980b3e95dcd438da09eb9b55e4bf5a4d8e6d3ddab29275aa22556a @EXPLICIT https://repo.anaconda.com/pkgs/main/osx-64/blas-1.0-mkl.conda#cb2c87e85ac8e0ceae776d26d4214c8a https://repo.anaconda.com/pkgs/main/osx-64/bzip2-1.0.8-h6c40b1e_6.conda#96224786021d0765ce05818fa3c59bdb @@ -26,7 +26,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/libgfortran5-11.3.0-h9dfd629_28.conda https://repo.anaconda.com/pkgs/main/osx-64/libpng-1.6.39-h6c40b1e_0.conda#a3c824835f53ad27aeb86d2b55e47804 https://repo.anaconda.com/pkgs/main/osx-64/lz4-c-1.9.4-hcec6c5f_1.conda#aee0efbb45220e1985533dbff48551f8 https://repo.anaconda.com/pkgs/main/osx-64/ninja-base-1.10.2-haf03e11_5.conda#c857c13129710a61395270656905c4a2 -https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.13-hca72f7f_2.conda#4f840ec6217dff98040ff6be19cf3afb +https://repo.anaconda.com/pkgs/main/osx-64/openssl-3.0.14-h46256e1_0.conda#d722280df65b3308e1b8b1b7777a3305 https://repo.anaconda.com/pkgs/main/osx-64/readline-8.2-hca72f7f_0.conda#971667436260e523f6f7355fdfa238bf https://repo.anaconda.com/pkgs/main/osx-64/tbb-2021.8.0-ha357a0b_0.conda#fb48530a3eea681c11dafb95b3387c0f https://repo.anaconda.com/pkgs/main/osx-64/tk-8.6.14-h4d00af3_0.conda#a2c03940c2ae54614301ec82e6a98d75 @@ -38,18 +38,18 @@ https://repo.anaconda.com/pkgs/main/osx-64/sqlite-3.45.3-h6c40b1e_0.conda#2edf90 https://repo.anaconda.com/pkgs/main/osx-64/zstd-1.5.5-hc035e20_2.conda#c033bf68c12f8c71fd916f000f3dc118 https://repo.anaconda.com/pkgs/main/osx-64/brotli-1.0.9-h6c40b1e_8.conda#10f89677a3898d0113dc354adf643df3 https://repo.anaconda.com/pkgs/main/osx-64/libtiff-4.5.1-hcec6c5f_0.conda#e127a800ffd9d300ed7d5e1b026944ec -https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.3-hd58486a_1.conda#cdc61e8f6c2d77b3b263e720048c4b54 +https://repo.anaconda.com/pkgs/main/osx-64/python-3.12.4-hcd54a6c_1.conda#753d941593548d5e26518c51b3e3b10d https://repo.anaconda.com/pkgs/main/osx-64/coverage-7.2.2-py312h6c40b1e_0.conda#b6e4b9fba325047c07f3c9211ae91d1c https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab https://repo.anaconda.com/pkgs/main/noarch/execnet-1.9.0-pyhd3eb1b0_0.conda#f895937671af67cebb8af617494b3513 https://repo.anaconda.com/pkgs/main/noarch/iniconfig-1.1.1-pyhd3eb1b0_0.tar.bz2#e40edff2c5708f342cef43c7f280c507 -https://repo.anaconda.com/pkgs/main/osx-64/joblib-1.4.0-py312hecd8cb5_0.conda#0af12a3a87d9c8051ae6ba2ed2c3882a +https://repo.anaconda.com/pkgs/main/osx-64/joblib-1.4.2-py312hecd8cb5_0.conda#8ab03dfa447b4e0bfa0bd3d25930f3b6 https://repo.anaconda.com/pkgs/main/osx-64/kiwisolver-1.4.4-py312hcec6c5f_0.conda#2ba6561ddd1d05936fe74f5d118ce7dd https://repo.anaconda.com/pkgs/main/osx-64/lcms2-2.12-hf1fd2bf_0.conda#697aba7a3308226df7a93ccfeae16ffa https://repo.anaconda.com/pkgs/main/osx-64/mkl-service-2.4.0-py312h6c40b1e_1.conda#b1ef860be9043b35c5e8d9388b858514 https://repo.anaconda.com/pkgs/main/osx-64/ninja-1.10.2-hecd8cb5_5.conda#a0043b325fb08db82477ae433668e684 -https://repo.anaconda.com/pkgs/main/osx-64/openjpeg-2.4.0-h66ea3da_0.conda#882833bd7befc5e60e6fba9c518c1b79 -https://repo.anaconda.com/pkgs/main/osx-64/packaging-23.2-py312hecd8cb5_0.conda#2b4e331c8f6df5d95a5dd3af37a34d89 +https://repo.anaconda.com/pkgs/main/osx-64/openjpeg-2.4.0-h7231236_1.conda#90f2c6bc7fe1384a4c1ba069a101ad9e +https://repo.anaconda.com/pkgs/main/osx-64/packaging-24.1-py312hecd8cb5_0.conda#6130dafc4d26d55e93ceab460d2a72b5 https://repo.anaconda.com/pkgs/main/osx-64/pluggy-1.0.0-py312hecd8cb5_1.conda#647fada22f1697691fdee90b52c99bcb https://repo.anaconda.com/pkgs/main/osx-64/pyparsing-3.0.9-py312hecd8cb5_0.conda#d85cf2b81c6d9326a57a6418e14db258 https://repo.anaconda.com/pkgs/main/noarch/python-tzdata-2023.3-pyhd3eb1b0_0.conda#479c037de0186d114b9911158427624e @@ -57,7 +57,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/pytz-2024.1-py312hecd8cb5_0.conda#2b2 https://repo.anaconda.com/pkgs/main/osx-64/setuptools-69.5.1-py312hecd8cb5_0.conda#5c7c7ef1e0762e3ca1f543d28310946f https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a -https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.3.3-py312h6c40b1e_0.conda#49173b5a36c9134865221f29d4a73fb6 +https://repo.anaconda.com/pkgs/main/osx-64/tornado-6.4.1-py312h46256e1_0.conda#ff2efd781e1b1af38284aeda9d676d42 https://repo.anaconda.com/pkgs/main/osx-64/unicodedata2-15.1.0-py312h6c40b1e_0.conda#65bd2cb787fc99662d9bb6e6520c5826 https://repo.anaconda.com/pkgs/main/osx-64/wheel-0.43.0-py312hecd8cb5_0.conda#c0bdd5748b170523232e8ad1d667136c https://repo.anaconda.com/pkgs/main/osx-64/fonttools-4.51.0-py312h6c40b1e_0.conda#8f55fa86b73e8a7f4403503f9b7a9959 @@ -77,7 +77,7 @@ https://repo.anaconda.com/pkgs/main/osx-64/mkl_random-1.2.4-py312ha357a0b_0.cond https://repo.anaconda.com/pkgs/main/osx-64/numpy-1.26.4-py312hac873b0_0.conda#3150bac1e382156f82a153229e1ebd06 https://repo.anaconda.com/pkgs/main/osx-64/numexpr-2.8.7-py312hac873b0_0.conda#6303ba071636ef57fddf69eb6f440ec1 https://repo.anaconda.com/pkgs/main/osx-64/scipy-1.11.4-py312h81688c2_0.conda#7d57b4c21a9261f97fa511e0940c5d93 -https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.1-py312he282a81_0.conda#021b70a1e40efb75b89eb8ebdb347132 +https://repo.anaconda.com/pkgs/main/osx-64/pandas-2.2.2-py312h77d3abe_0.conda#463868c40d8ff98bec263f1fd57a8d97 https://repo.anaconda.com/pkgs/main/osx-64/pyamg-4.2.3-py312h44cbcf4_0.conda#3bdc7be74087b3a5a83c520a74e1e8eb # pip cython @ https://files.pythonhosted.org/packages/d5/6d/06c08d75adb98cdf72af18801e193d22580cc86ca553610f430f18ea26b3/Cython-3.0.10-cp312-cp312-macosx_10_9_x86_64.whl#sha256=8f2864ab5fcd27a346f0b50f901ebeb8f60b25a60a575ccfd982e7f3e9674914 # pip meson @ https://files.pythonhosted.org/packages/44/b2/d4433391a7c5e94a39b50ca7295a8ceba736e7c72c455752a60122f52453/meson-1.4.1-py3-none-any.whl#sha256=d5acc3abae2dad3c70ddcbd10acac92b78b144d34d43f40f5b8ac31dfd8a826a diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index 449ca9a70121d..e2ffa14d39b43 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 37f8029b6bb116e0d5856093424791a8c1ddc3f493e20fcb5d02cd32d516523d +# input_hash: af52e4ce613b7668e1e28daaea07461722275d345395a5eaced4e07a16998179 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7 @@ -12,7 +12,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/_openmp_mutex-5.1-1_gnu.conda#71d28 https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-11.2.0-h1234567_1.conda#a87728dabf3151fb9cfa990bd2eb0464 https://repo.anaconda.com/pkgs/main/linux-64/libffi-3.4.4-h6a678d5_1.conda#70646cc713f0c43926cfdcfe9b695fe0 https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_2.conda#0019453c25f5e72129f99236e60febaa +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.14-h5eee18b_0.conda#37b6dad6aa49000a4230a9f0cad172f6 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -25,14 +25,14 @@ https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py39h06a4308_0.conda#4 https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce3af15cfecd12e4dda8c5cef5fb7 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb -# pip certifi @ https://files.pythonhosted.org/packages/5b/11/1e78951465b4a225519b8c3ad29769c49e0d8d157a070f681d5b6d64737f/certifi-2024.6.2-py3-none-any.whl#sha256=ddc6c8ce995e6987e7faf5e3f1b02b302836a0e5d98ece18392cb1a36c72ad56 +# pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 # pip charset-normalizer @ https://files.pythonhosted.org/packages/98/69/5d8751b4b670d623aa7a47bef061d69c279e9f922f6705147983aa76c3ce/charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796 # pip cycler @ https://files.pythonhosted.org/packages/e7/05/c19819d5e3d95294a6f5947fb9b9629efb316b96de511b418c53d245aae6/cycler-0.12.1-py3-none-any.whl#sha256=85cef7cff222d8644161529808465972e51340599459b8ac3ccbac5a854e0d30 # pip cython @ https://files.pythonhosted.org/packages/a7/f5/3dde4d96076888ceaa981827b098274c2b45ddd4b20d75a8cfaa92b91eec/Cython-3.0.10-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=651a15a8534ebfb9b58cb0b87c269c70984b6f9c88bfe65e4f635f0e3f07dfcd # pip docutils @ https://files.pythonhosted.org/packages/8f/d7/9322c609343d929e75e7e5e6255e614fcc67572cfd083959cdef3b7aad79/docutils-0.21.2-py3-none-any.whl#sha256=dafca5b9e384f0e419294eb4d2ff9fa826435bf15f15b7bd45723e8ad76811b2 # pip exceptiongroup @ https://files.pythonhosted.org/packages/01/90/79fe92dd413a9cab314ef5c591b5aa9b9ba787ae4cadab75055b0ae00b33/exceptiongroup-1.2.1-py3-none-any.whl#sha256=5258b9ed329c5bbdd31a309f53cbfb0b155341807f6ff7606a1e801a891b29ad # pip execnet @ https://files.pythonhosted.org/packages/43/09/2aea36ff60d16dd8879bdb2f5b3ee0ba8d08cbbdcdfe870e695ce3784385/execnet-2.1.1-py3-none-any.whl#sha256=26dee51f1b80cebd6d0ca8e74dd8745419761d3bef34163928cbebbdc4749fdc -# pip fonttools @ https://files.pythonhosted.org/packages/c1/cb/b1877d606dfa1daca70324bf37afec2b0a386138c467580027b9b51188a8/fonttools-4.53.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=ba9f09ff17f947392a855e3455a846f9855f6cf6bec33e9a427d3c1d254c712f +# pip fonttools @ https://files.pythonhosted.org/packages/7b/30/ad4483dfc5a1999f26b7bc5edc311576f433a3e00dd8aea01f2099c3a29f/fonttools-4.53.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=84ec3fb43befb54be490147b4a922b5314e16372a643004f182babee9f9c3407 # pip idna @ https://files.pythonhosted.org/packages/e5/3e/741d8c82801c347547f8a2a06aa57dbb1992be9e948df2ea0eda2c8b79e8/idna-3.7-py3-none-any.whl#sha256=82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0 # pip imagesize @ https://files.pythonhosted.org/packages/ff/62/85c4c919272577931d407be5ba5d71c20f0b616d31a0befe0ae45bb79abd/imagesize-1.4.1-py2.py3-none-any.whl#sha256=0d8d18d08f840c19d0ee7ca1fd82490fdc3729b7ac93f49870406ddde8ef8d8b # pip iniconfig @ https://files.pythonhosted.org/packages/ef/a6/62565a6e1cf69e10f5727360368e451d4b7f58beeac6173dc9db836a5b46/iniconfig-2.0.0-py3-none-any.whl#sha256=b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374 @@ -42,9 +42,9 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce # pip meson @ https://files.pythonhosted.org/packages/44/b2/d4433391a7c5e94a39b50ca7295a8ceba736e7c72c455752a60122f52453/meson-1.4.1-py3-none-any.whl#sha256=d5acc3abae2dad3c70ddcbd10acac92b78b144d34d43f40f5b8ac31dfd8a826a # pip networkx @ https://files.pythonhosted.org/packages/d5/f0/8fbc882ca80cf077f1b246c0e3c3465f7f415439bdea6b899f6b19f61f70/networkx-3.2.1-py3-none-any.whl#sha256=f18c69adc97877c42332c170849c96cefa91881c99a7cb3e95b7c659ebdc1ec2 # pip ninja @ https://files.pythonhosted.org/packages/6d/92/8d7aebd4430ab5ff65df2bfee6d5745f95c004284db2d8ca76dcbfd9de47/ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl#sha256=84502ec98f02a037a169c4b0d5d86075eaf6afc55e1879003d6cab51ced2ea4b -# pip numpy @ https://files.pythonhosted.org/packages/54/30/c2a907b9443cf42b90c17ad10c1e8fa801975f01cb9764f3f8eb8aea638b/numpy-1.26.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=f870204a840a60da0b12273ef34f7051e98c3b5961b61b0c2c1be6dfd64fbcd3 +# pip numpy @ https://files.pythonhosted.org/packages/87/d3/74e627205462a170f39e7d7ddd2b4166a0d8ab163377592c7f4fa935cc8c/numpy-2.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=821eedb7165ead9eebdb569986968b541f9908979c2da8a4967ecac4439bae3d # pip packaging @ https://files.pythonhosted.org/packages/08/aa/cc0199a5f0ad350994d660967a8efb233fe0416e4639146c089643407ce6/packaging-24.1-py3-none-any.whl#sha256=5b8f2217dbdbd2f7f384c41c628544e6d52f2d0f53c6d0c3ea61aa5d1d7ff124 -# pip pillow @ https://files.pythonhosted.org/packages/f5/6d/52e82352670e850f468de9e6bccced4202a09f58e7ea5ecdbf08283d85cb/pillow-10.3.0-cp39-cp39-manylinux_2_28_x86_64.whl#sha256=1dfc94946bc60ea375cc39cff0b8da6c7e5f8fcdc1d946beb8da5c216156ddd8 +# pip pillow @ https://files.pythonhosted.org/packages/bc/a8/8655557c9c7202b8abbd001f61ff36711cefaf750debcaa1c24d154ef602/pillow-10.4.0-cp39-cp39-manylinux_2_28_x86_64.whl#sha256=b2724fdb354a868ddf9a880cb84d102da914e99119211ef7ecbdc613b8c96b3c # pip pluggy @ https://files.pythonhosted.org/packages/88/5f/e351af9a41f866ac3f1fac4ca0613908d9a41741cfcf2228f4ad853b697d/pluggy-1.5.0-py3-none-any.whl#sha256=44e1ad92c8ca002de6377e165f3e0f1be63266ab4d554740532335b9d75ea669 # pip pygments @ https://files.pythonhosted.org/packages/f7/3f/01c8b82017c199075f8f788d0d906b9ffbbc5a47dc9918a945e13d5a2bda/pygments-2.18.0-py3-none-any.whl#sha256=b8e6aca0523f3ab76fee51799c488e38782ac06eafcf95e7ba832985c8e7b13a # pip pyparsing @ https://files.pythonhosted.org/packages/9d/ea/6d76df31432a0e6fdf81681a895f009a4bb47b3c39036db3e1b528191d52/pyparsing-3.1.2-py3-none-any.whl#sha256=f9db75911801ed778fe61bb643079ff86601aca99fcae6345aa67292038fb742 @@ -61,12 +61,12 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce # pip threadpoolctl @ https://files.pythonhosted.org/packages/4b/2c/ffbf7a134b9ab11a67b0cf0726453cedd9c5043a4fe7a35d1cefa9a1bcfb/threadpoolctl-3.5.0-py3-none-any.whl#sha256=56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467 # pip tomli @ https://files.pythonhosted.org/packages/97/75/10a9ebee3fd790d20926a90a2547f0bf78f371b2f13aa822c759680ca7b9/tomli-2.0.1-py3-none-any.whl#sha256=939de3e7a6161af0c887ef91b7d41a53e7c5a1ca976325f429cb46ea9bc30ecc # pip tzdata @ https://files.pythonhosted.org/packages/65/58/f9c9e6be752e9fcb8b6a0ee9fb87e6e7a1f6bcab2cdc73f02bb7ba91ada0/tzdata-2024.1-py2.py3-none-any.whl#sha256=9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252 -# pip urllib3 @ https://files.pythonhosted.org/packages/a2/73/a68704750a7679d0b6d3ad7aa8d4da8e14e151ae82e6fee774e6e0d05ec8/urllib3-2.2.1-py3-none-any.whl#sha256=450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d +# pip urllib3 @ https://files.pythonhosted.org/packages/ca/1c/89ffc63a9605b583d5df2be791a27bc1a42b7c32bab68d3c8f2f73a98cd4/urllib3-2.2.2-py3-none-any.whl#sha256=a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472 # pip zipp @ https://files.pythonhosted.org/packages/20/38/f5c473fe9b90c8debdd29ea68d5add0289f1936d6f923b6b9cc0b931194c/zipp-3.19.2-py3-none-any.whl#sha256=f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c # pip contourpy @ https://files.pythonhosted.org/packages/31/a2/2f12e3a6e45935ff694654b710961b03310b0e1ec997ee9f416d3c873f87/contourpy-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=e1d59258c3c67c865435d8fbeb35f8c59b8bef3d6f46c1f29f6123556af28445 -# pip coverage @ https://files.pythonhosted.org/packages/07/e0/0e30ca5c6c5bcae86df9583c30807ff26e0b991e76f266b81224410663e4/coverage-7.5.3-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=2e079c9ec772fedbade9d7ebc36202a1d9ef7291bc9b3a024ca395c4d52853d7 -# pip imageio @ https://files.pythonhosted.org/packages/a3/b6/39c7dad203d9984225f47e0aa39ac3ba3a47c77a02d0ef2a7be691855a06/imageio-2.34.1-py3-none-any.whl#sha256=408c1d4d62f72c9e8347e7d1ca9bc11d8673328af3913868db3b828e28b40a4c -# pip importlib-metadata @ https://files.pythonhosted.org/packages/2d/0a/679461c511447ffaf176567d5c496d1de27cbe34a87df6677d7171b2fbd4/importlib_metadata-7.1.0-py3-none-any.whl#sha256=30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570 +# pip coverage @ https://files.pythonhosted.org/packages/c4/b4/0cbc18998613f8caaec793ad5878d2450382dfac80e65d352fb7cd9cc1dc/coverage-7.5.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=dbc5958cb471e5a5af41b0ddaea96a37e74ed289535e8deca404811f6cb0bc3d +# pip imageio @ https://files.pythonhosted.org/packages/3d/84/f1647217231f6cc46883e5d26e870cc3e1520d458ecd52d6df750810d53c/imageio-2.34.2-py3-none-any.whl#sha256=a0bb27ec9d5bab36a9f4835e51b21d2cb099e1f78451441f94687ff3404b79f8 +# pip importlib-metadata @ https://files.pythonhosted.org/packages/dc/ef/38766b2edb096260d9b1b6ad35adaa0bce3b0567abb452b21eb074af88c4/importlib_metadata-8.0.0-py3-none-any.whl#sha256=15584cf2b1bf449d98ff8a6ff1abef57bf20f3ac6454f431736cd3e660921b2f # pip importlib-resources @ https://files.pythonhosted.org/packages/75/06/4df55e1b7b112d183f65db9503bff189e97179b256e1ea450a3c365241e0/importlib_resources-6.4.0-py3-none-any.whl#sha256=50d10f043df931902d4194ea07ec57960f66a80449ff867bfe782b4c486ba78c # pip jinja2 @ https://files.pythonhosted.org/packages/31/80/3a54838c3fb461f6fec263ebf3a3a41771bd05190238de3486aae8540c36/jinja2-3.1.4-py3-none-any.whl#sha256=bc5dd2abb727a5319567b7a813e6a2e7318c39f4f487cfe6c89c6f9c7d25197d # pip lazy-loader @ https://files.pythonhosted.org/packages/83/60/d497a310bde3f01cb805196ac61b7ad6dc5dcf8dce66634dc34364b20b4f/lazy_loader-0.4-py3-none-any.whl#sha256=342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc @@ -75,14 +75,14 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce # pip python-dateutil @ https://files.pythonhosted.org/packages/ec/57/56b9bcc3c9c6a792fcbaf139543cee77261f3651ca9da0c93f5c1221264b/python_dateutil-2.9.0.post0-py2.py3-none-any.whl#sha256=a8b2bc7bffae282281c8140a97d3aa9c14da0b136dfe83f850eea9a5f7470427 # pip requests @ https://files.pythonhosted.org/packages/f9/9b/335f9764261e915ed497fcdeb11df5dfd6f7bf257d4a6a2a686d80da4d54/requests-2.32.3-py3-none-any.whl#sha256=70761cfe03c773ceb22aa2f671b4757976145175cdfca038c02654d061d6dcc6 # pip scipy @ https://files.pythonhosted.org/packages/35/f5/d0ad1a96f80962ba65e2ce1de6a1e59edecd1f0a7b55990ed208848012e0/scipy-1.13.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=637e98dcf185ba7f8e663e122ebf908c4702420477ae52a04f9908707456ba4d -# pip tifffile @ https://files.pythonhosted.org/packages/d9/6c/740c07588434e86028c24b0653c1eb6b46904d9ce585a20f07590620ec41/tifffile-2024.5.22-py3-none-any.whl#sha256=e281781c15d7d197d7e12749849c965651413aa905f97a48b0f84bd90a3b4c6f -# pip lightgbm @ https://files.pythonhosted.org/packages/ba/11/cb8b67f3cbdca05b59a032bb57963d4fe8c8d18c3870f30bed005b7f174d/lightgbm-4.3.0-py3-none-manylinux_2_28_x86_64.whl#sha256=104496a3404cb2452d3412cbddcfbfadbef9c372ea91e3a9b8794bcc5183bf07 -# pip matplotlib @ https://files.pythonhosted.org/packages/d3/6d/45837c5b3d0005a5a9b04729b218a16bf3aa195701c6b33b2cc39ae943b6/matplotlib-3.9.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=81c40af649d19c85f8073e25e5806926986806fa6d54be506fbf02aef47d5a89 +# pip tifffile @ https://files.pythonhosted.org/packages/d2/d7/ca95f347442e82700f591f3608e336596ee607daecbcad6a7ebd16ff5de4/tifffile-2024.7.2-py3-none-any.whl#sha256=5a2ee608c9cc1f2e044d943dacebddc71d4827b6fad150ef4c644b7aefbe2d1a +# pip lightgbm @ https://files.pythonhosted.org/packages/f2/3d/4f152cf694aec100ab63b4a5547f2dbfbea59ab39d9375c89bed9775e47d/lightgbm-4.4.0-py3-none-manylinux_2_28_x86_64.whl#sha256=8700b41f637717d36763a282d280b8d4722a87103030b7f0f373b96da0225022 +# pip matplotlib @ https://files.pythonhosted.org/packages/8e/67/e75134cb83d2e533e46d72e2033a413772efdc18291beb981f5d574a829f/matplotlib-3.9.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=4db17fea0ae3aceb8e9ac69c7e3051bae0b3d083bfec932240f9bf5d0197a049 # pip meson-python @ https://files.pythonhosted.org/packages/91/c0/104cb6244c83fe6bc3886f144cc433db0c0c78efac5dc00e409a5a08c87d/meson_python-0.16.0-py3-none-any.whl#sha256=842dc9f5dc29e55fc769ff1b6fe328412fe6c870220fc321060a1d2d395e69e8 # pip pandas @ https://files.pythonhosted.org/packages/bb/30/f6f1f1ac36250f50c421b1b6af08c35e5a8b5a84385ef928625336b93e6f/pandas-2.2.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=66b479b0bd07204e37583c191535505410daa8df638fd8e75ae1b383851fe921 -# pip pyamg @ https://files.pythonhosted.org/packages/68/a9/aed9f557e7eb779d2cb4fa090663f8540979e0c04dadd16e9a0bdc9632c5/pyamg-5.1.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=5817d4567fb240dab4779bb1630bbb3035b3827731fcdaeb9ecc9c8814319995 +# pip pyamg @ https://files.pythonhosted.org/packages/de/b6/411b3de91fb23aebee8082af5c970f8345e06d8357ce1c33464837130770/pyamg-5.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=40acf38eb733dd33f054f2b9a6aa3cee1786b7a5420b602787f2d38eca8051ac # pip pytest-cov @ https://files.pythonhosted.org/packages/78/3a/af5b4fa5961d9a1e6237b530eb87dd04aea6eb83da09d2a4073d81b54ccf/pytest_cov-5.0.0-py3-none-any.whl#sha256=4f0764a1219df53214206bf1feea4633c3b558a2925c8b59f144f682861ce652 # pip pytest-xdist @ https://files.pythonhosted.org/packages/6d/82/1d96bf03ee4c0fdc3c0cbe61470070e659ca78dc0086fb88b66c185e2449/pytest_xdist-3.6.1-py3-none-any.whl#sha256=9ed4adfb68a016610848639bb7e02c9352d5d9f03d04809919e2dafc3be4cca7 -# pip scikit-image @ https://files.pythonhosted.org/packages/a3/7e/4cd853a855ac34b4ef3ef6a5c3d1c2e96eaca1154fc6be75db55ffa87393/scikit_image-0.22.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=3b7a6c89e8d6252332121b58f50e1625c35f7d6a85489c0b6b7ee4f5155d547a +# pip scikit-image @ https://files.pythonhosted.org/packages/f0/cc/1a58efefb9b17c60d15626b33416728003028d5d51f0521482151a222560/scikit_image-0.24.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=4688c18bd7ec33c08d7bf0fd19549be246d90d5f2c1d795a89986629af0a1e83 # pip sphinx @ https://files.pythonhosted.org/packages/b4/fa/130c32ed94cf270e3d0b9ded16fb7b2c8fea86fa7263c29a696a30c1dde7/sphinx-7.3.7-py3-none-any.whl#sha256=413f75440be4cacf328f580b4274ada4565fb2187d696a84970c23f77b64d8c3 # pip numpydoc @ https://files.pythonhosted.org/packages/f0/fa/dcfe0f65660661db757ee9ebd84e170ff98edd5d80235f62457d9088f85f/numpydoc-1.7.0-py3-none-any.whl#sha256=5a56419d931310d79a06cfc2a126d1558700feeb9b4f3d8dcae1a8134be829c9 diff --git a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock index a83d0cef3e063..5eb168a898f32 100644 --- a/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock +++ b/build_tools/azure/pymin_conda_defaults_openblas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: be0f080ab1974d224952262cd9179ff999d10108421d7e7ded2435e6f08edf0e +# input_hash: 244b6a064d3785ea62baaf9436848821d153846b455c2976f5e811182e848c83 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/blas-1.0-openblas.conda#9ddfcaef10d79366c90128f5dc444be8 @@ -28,7 +28,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/libwebp-base-1.3.2-h5eee18b_0.conda https://repo.anaconda.com/pkgs/main/linux-64/libxcb-1.15-h7f8727e_0.conda#ada518dcadd6aaee9aae47ba9a671553 https://repo.anaconda.com/pkgs/main/linux-64/lz4-c-1.9.4-h6a678d5_1.conda#2ee58861f2b92b868ce761abb831819d https://repo.anaconda.com/pkgs/main/linux-64/ncurses-6.4-h6a678d5_0.conda#5558eec6e2191741a92f832ea826251c -https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.13-h7f8727e_2.conda#0019453c25f5e72129f99236e60febaa +https://repo.anaconda.com/pkgs/main/linux-64/openssl-3.0.14-h5eee18b_0.conda#37b6dad6aa49000a4230a9f0cad172f6 https://repo.anaconda.com/pkgs/main/linux-64/xz-5.4.6-h5eee18b_1.conda#1562802f843297ee776a50b9329597ed https://repo.anaconda.com/pkgs/main/linux-64/zlib-1.2.13-h5eee18b_1.conda#92e42d8310108b0a440fb2e60b2b2a25 https://repo.anaconda.com/pkgs/main/linux-64/ccache-3.7.9-hfe4627d_0.conda#bef6fc681c273bb7bd0c67d1a591365e @@ -54,7 +54,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/glib-tools-2.78.4-h6a678d5_0.conda# https://repo.anaconda.com/pkgs/main/linux-64/lcms2-2.12-h3be6417_0.conda#719db47afba9f6586eecb5eacac70bff https://repo.anaconda.com/pkgs/main/linux-64/libclang-14.0.6-default_hc6dbbc7_1.conda#8f12583c4027b2861cff470f6b8837c4 https://repo.anaconda.com/pkgs/main/linux-64/libpq-12.17-hdbd6064_0.conda#6bed363e25859faff66bf546a11c10e8 -https://repo.anaconda.com/pkgs/main/linux-64/openjpeg-2.4.0-h3ad879b_0.conda#86baecb47ecaa7f7ff2657a1f03b90c9 +https://repo.anaconda.com/pkgs/main/linux-64/openjpeg-2.4.0-h9ca470c_1.conda#dfd4b36eb8ddaffeca0ab412de63c3e2 https://repo.anaconda.com/pkgs/main/linux-64/python-3.9.19-h955ad1f_1.conda#4b453281859c293c9d577271f3b18a0d https://repo.anaconda.com/pkgs/main/linux-64/certifi-2024.6.2-py39h06a4308_0.conda#738daf43271605d7291ecae0e8cac41c https://repo.anaconda.com/pkgs/main/noarch/cycler-0.11.0-pyhd3eb1b0_0.conda#f5e365d2cdb66d547eb8c3ab93843aab @@ -67,7 +67,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/joblib-1.2.0-py39h06a4308_0.conda#a https://repo.anaconda.com/pkgs/main/linux-64/kiwisolver-1.4.4-py39h6a678d5_0.conda#3d57aedbfbd054ce57fb3c1e4448828c https://repo.anaconda.com/pkgs/main/linux-64/mysql-5.7.24-h721c034_2.conda#dfc19ca2466d275c4c1f73b62c57f37b https://repo.anaconda.com/pkgs/main/linux-64/numpy-base-1.21.6-py39h375b286_1.conda#0061d9193658774ab79fc85d143a94fc -https://repo.anaconda.com/pkgs/main/linux-64/packaging-23.2-py39h06a4308_0.conda#b3f88f45f31bde016e49be3e941e5272 +https://repo.anaconda.com/pkgs/main/linux-64/packaging-24.1-py39h06a4308_0.conda#e80d41ffc9450162ef10cbbb9b4ec7e9 https://repo.anaconda.com/pkgs/main/linux-64/pillow-10.3.0-py39h5eee18b_0.conda#b346d6c71267c1553b6c18d3db5fdf6d https://repo.anaconda.com/pkgs/main/linux-64/pluggy-1.0.0-py39h06a4308_1.conda#fb4fed11ed43cf727dbd51883cc1d9fa https://repo.anaconda.com/pkgs/main/linux-64/ply-3.11-py39h06a4308_0.conda#6c89bf6d2fdf6d24126e34cb83fd10f1 @@ -77,7 +77,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py39h06a4308_0.co https://repo.anaconda.com/pkgs/main/noarch/six-1.16.0-pyhd3eb1b0_1.conda#34586824d411d36af2fa40e799c172d0 https://repo.anaconda.com/pkgs/main/noarch/toml-0.10.2-pyhd3eb1b0_0.conda#cda05f5f6d8509529d1a2743288d197a https://repo.anaconda.com/pkgs/main/linux-64/tomli-2.0.1-py39h06a4308_0.conda#b06dffe7ddca2645ed72f5116f0a087d 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https://conda.anaconda.org/conda-forge/win-64/libzlib-1.3.1-h2466b09_1.conda#d4483ca8afc57ddf1f6dded53b36c17f https://conda.anaconda.org/conda-forge/win-64/m2w64-gcc-libgfortran-5.3.0-6.tar.bz2#066552ac6b907ec6d72c0ddab29050dc https://conda.anaconda.org/conda-forge/win-64/ninja-1.12.1-hc790b64_0.conda#a557dde55343e03c68cd7e29e7f87279 -https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.1-h2466b09_0.conda#27fe798366ef3a81715b13eedf699e2f +https://conda.anaconda.org/conda-forge/win-64/openssl-3.3.1-h2466b09_1.conda#aa36aca82d1ffd26bee88ac7dc9e1ee3 https://conda.anaconda.org/conda-forge/win-64/pthreads-win32-2.9.1-hfa6e2cd_3.tar.bz2#e2da8758d7d51ff6aa78a14dfb9dbed4 https://conda.anaconda.org/conda-forge/win-64/tk-8.6.13-h5226925_1.conda#fc048363eb8f03cd1737600a5d08aafe https://conda.anaconda.org/conda-forge/win-64/xz-5.2.6-h8d14728_0.tar.bz2#515d77642eaa3639413c6b1bc3f94219 -https://conda.anaconda.org/conda-forge/win-64/krb5-1.21.2-heb0366b_0.conda#6e8b0f22b4eef3b3cb3849bb4c3d47f9 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https://conda.anaconda.org/conda-forge/noarch/sphinx-gallery-0.16.0-pyhd8ed1ab_0.conda#add28691ee89e875b190eda07929d5d4 https://conda.anaconda.org/conda-forge/noarch/sphinx-prompt-1.4.0-pyhd8ed1ab_0.tar.bz2#88ee91e8679603f2a5bd036d52919cc2 https://conda.anaconda.org/conda-forge/noarch/sphinx-remove-toctrees-1.0.0.post1-pyhd8ed1ab_0.conda#6dee8412218288a17f99f2cfffab334d @@ -273,10 +279,10 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip attrs @ https://files.pythonhosted.org/packages/e0/44/827b2a91a5816512fcaf3cc4ebc465ccd5d598c45cefa6703fcf4a79018f/attrs-23.2.0-py3-none-any.whl#sha256=99b87a485a5820b23b879f04c2305b44b951b502fd64be915879d77a7e8fc6f1 # pip cloudpickle @ https://files.pythonhosted.org/packages/96/43/dae06432d0c4b1dc9e9149ad37b4ca8384cf6eb7700cd9215b177b914f0a/cloudpickle-3.0.0-py3-none-any.whl#sha256=246ee7d0c295602a036e86369c77fecda4ab17b506496730f2f576d9016fd9c7 # pip defusedxml @ 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https://files.pythonhosted.org/packages/7d/39/472f2554a0f1e825bd7c5afc11c817cd7a2f3657460f7159f691fbb37c51/PyYAML-6.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=bc1bf2925a1ecd43da378f4db9e4f799775d6367bdb94671027b73b393a7c42c # pip rfc3986-validator @ https://files.pythonhosted.org/packages/9e/51/17023c0f8f1869d8806b979a2bffa3f861f26a3f1a66b094288323fba52f/rfc3986_validator-0.1.1-py2.py3-none-any.whl#sha256=2f235c432ef459970b4306369336b9d5dbdda31b510ca1e327636e01f528bfa9 @@ -299,25 +304,24 @@ https://conda.anaconda.org/conda-forge/noarch/sphinxext-opengraph-0.9.1-pyhd8ed1 # pip webencodings @ https://files.pythonhosted.org/packages/f4/24/2a3e3df732393fed8b3ebf2ec078f05546de641fe1b667ee316ec1dcf3b7/webencodings-0.5.1-py2.py3-none-any.whl#sha256=a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78 # pip websocket-client @ 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terminado @ https://files.pythonhosted.org/packages/6a/9e/2064975477fdc887e47ad42157e214526dcad8f317a948dee17e1659a62f/terminado-0.18.1-py3-none-any.whl#sha256=a4468e1b37bb318f8a86514f65814e1afc977cf29b3992a4500d9dd305dcceb0 # pip tinycss2 @ https://files.pythonhosted.org/packages/2c/4d/0db5b8a613d2a59bbc29bc5bb44a2f8070eb9ceab11c50d477502a8a0092/tinycss2-1.3.0-py3-none-any.whl#sha256=54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7 -# pip argon2-cffi-bindings @ https://files.pythonhosted.org/packages/ec/f7/378254e6dd7ae6f31fe40c8649eea7d4832a42243acaf0f1fff9083b2bed/argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae +# pip argon2-cffi @ https://files.pythonhosted.org/packages/a4/6a/e8a041599e78b6b3752da48000b14c8d1e8a04ded09c88c714ba047f34f5/argon2_cffi-23.1.0-py3-none-any.whl#sha256=c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea # pip isoduration 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jupyterlite-core @ https://files.pythonhosted.org/packages/05/d2/1d59d9a70d684b1eb3eb3a0b80a36b4e1d691e94af5d53aee56b1ad5240b/jupyterlite_core-0.3.0-py3-none-any.whl#sha256=247cc34ae6fedda41b15ce4778997164508b2039bc92480665cadfe955193467 -# pip pyzmq @ https://files.pythonhosted.org/packages/64/b8/1c181c13e118cabccfd25bd3e169e44958c649180b0d78b798a66899e08b/pyzmq-26.0.3-cp39-cp39-manylinux_2_12_x86_64.manylinux2010_x86_64.whl#sha256=b3cd31f859b662ac5d7f4226ec7d8bd60384fa037fc02aee6ff0b53ba29a3ba8 -# pip argon2-cffi @ https://files.pythonhosted.org/packages/a4/6a/e8a041599e78b6b3752da48000b14c8d1e8a04ded09c88c714ba047f34f5/argon2_cffi-23.1.0-py3-none-any.whl#sha256=c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea # pip jsonschema @ https://files.pythonhosted.org/packages/c8/2f/324fab4be6fe37fb7b521546e8a557e6cf08c1c1b3d0b4839a00f589d9ef/jsonschema-4.22.0-py3-none-any.whl#sha256=ff4cfd6b1367a40e7bc6411caec72effadd3db0bbe5017de188f2d6108335802 -# pip jupyter-client @ 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+https://conda.anaconda.org/conda-forge/linux-64/pywavelets-1.6.0-py39hd92a3bb_0.conda#32e26e16f60c568b17a82e3033a4d309 https://conda.anaconda.org/conda-forge/linux-64/scipy-1.6.0-py39hee8e79c_0.tar.bz2#3afcb78281836e61351a2924f3230060 https://conda.anaconda.org/conda-forge/linux-64/blas-2.122-mkl.conda#ead856637ff8a7feba572e2cf23b453b https://conda.anaconda.org/conda-forge/linux-64/matplotlib-3.3.4-py39hf3d152e_0.tar.bz2#cbaec993375a908bbe506dc7328d747c https://conda.anaconda.org/conda-forge/linux-64/pyamg-4.2.3-py39hac2352c_1.tar.bz2#6fb0628d6195d8b6caa2422d09296399 https://conda.anaconda.org/conda-forge/noarch/seaborn-base-0.12.2-pyhd8ed1ab_0.conda#cf88f3a1c11536bc3c10c14ad00ccc42 https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.13.2-py39hd257fcd_0.tar.bz2#bd7cdadf70e34a19333c3aacc40206e8 -https://conda.anaconda.org/conda-forge/noarch/tifffile-2020.6.3-py_0.tar.bz2#1fb771bb25b2eecbc73abf5143fa35bd +https://conda.anaconda.org/conda-forge/noarch/tifffile-2024.6.18-pyhd8ed1ab_0.conda#7c3077529bfe3b86f9425d526d73bd24 https://conda.anaconda.org/conda-forge/linux-64/scikit-image-0.17.2-py39hde0f152_4.tar.bz2#2a58a7e382317b03f023b2fddf40f8a1 https://conda.anaconda.org/conda-forge/noarch/seaborn-0.12.2-hd8ed1ab_0.conda#50847a47c07812f88581081c620f5160 https://conda.anaconda.org/conda-forge/noarch/numpydoc-1.2-pyhd8ed1ab_0.tar.bz2#025ad7ca2c7f65007ab6b6f5d93a56eb diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 2f6263cdd961d..87b4c6478a3f4 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -367,6 +367,13 @@ def remove_from(alist, to_remove): ], "package_constraints": { "python": "3.9", + # TODO: this needs to be adapted when matplotlib 3.11 is out. In + # the meantime, this avoids a warning in matplotlib 3.9 boxplot + # labels has been renamed to tick_labels. Possible options: + # - bump minimum matplotlib supported versions to 3.9 at one point + # - complicate the example code to do the right thing depending on + # maplotlib version + "matplotlib": "<3.9", }, }, { diff --git a/doc/conftest.py b/doc/conftest.py index 9a888898014bf..f2c0eaa490665 100644 --- a/doc/conftest.py +++ b/doc/conftest.py @@ -10,7 +10,7 @@ from sklearn.datasets._base import _pkl_filepath from sklearn.datasets._twenty_newsgroups import CACHE_NAME from sklearn.utils._testing import SkipTest, check_skip_network -from sklearn.utils.fixes import np_base_version, parse_version +from sklearn.utils.fixes import np_base_version, parse_version, sp_version def setup_labeled_faces(): @@ -177,6 +177,10 @@ def pytest_collection_modifyitems(config, items): reason = "Due to NEP 51 numpy scalar repr has changed in numpy 2" skip_doctests = True + if sp_version < parse_version("1.14"): + reason = "Scipy sparse matrix repr has changed in scipy 1.14" + skip_doctests = True + # Normally doctest has the entire module's scope. Here we set globs to an empty dict # to remove the module's scope: # https://docs.python.org/3/library/doctest.html#what-s-the-execution-context diff --git a/doc/modules/feature_extraction.rst b/doc/modules/feature_extraction.rst index 2181014644e15..2dd670891b434 100644 --- a/doc/modules/feature_extraction.rst +++ b/doc/modules/feature_extraction.rst @@ -106,8 +106,8 @@ suitable for feeding into a classifier (maybe after being piped into a >>> vec = DictVectorizer() >>> pos_vectorized = vec.fit_transform(pos_window) >>> pos_vectorized - <1x6 sparse matrix of type '<... 'numpy.float64'>' - with 6 stored elements in Compressed Sparse ... format> + >>> pos_vectorized.toarray() array([[1., 1., 1., 1., 1., 1.]]) >>> vec.get_feature_names_out() @@ -324,8 +324,8 @@ corpus of text documents:: ... ] >>> X = vectorizer.fit_transform(corpus) >>> X - <4x9 sparse matrix of type '<... 'numpy.int64'>' - with 19 stored elements in Compressed Sparse ... format> + The default configuration tokenizes the string by extracting words of at least 2 letters. The specific function that does this step can be @@ -507,8 +507,8 @@ Again please see the :ref:`reference documentation ... >>> tfidf = transformer.fit_transform(counts) >>> tfidf - <6x3 sparse matrix of type '<... 'numpy.float64'>' - with 9 stored elements in Compressed Sparse ... format> + >>> tfidf.toarray() array([[0.81940995, 0. , 0.57320793], @@ -592,8 +592,8 @@ Again please see the :ref:`reference documentation >>> from sklearn.feature_extraction.text import TfidfVectorizer >>> vectorizer = TfidfVectorizer() >>> vectorizer.fit_transform(corpus) - <4x9 sparse matrix of type '<... 'numpy.float64'>' - with 19 stored elements in Compressed Sparse ... format> + While the tf-idf normalization is often very useful, there might be cases where the binary occurrence markers might offer better @@ -755,15 +755,16 @@ span across words:: >>> ngram_vectorizer = CountVectorizer(analyzer='char_wb', ngram_range=(5, 5)) >>> ngram_vectorizer.fit_transform(['jumpy fox']) - <1x4 sparse matrix of type '<... 'numpy.int64'>' - with 4 stored elements in Compressed Sparse ... format> + + >>> ngram_vectorizer.get_feature_names_out() array([' fox ', ' jump', 'jumpy', 'umpy '], ...) >>> ngram_vectorizer = CountVectorizer(analyzer='char', ngram_range=(5, 5)) >>> ngram_vectorizer.fit_transform(['jumpy fox']) - <1x5 sparse matrix of type '<... 'numpy.int64'>' - with 5 stored elements in Compressed Sparse ... format> + >>> ngram_vectorizer.get_feature_names_out() array(['jumpy', 'mpy f', 'py fo', 'umpy ', 'y fox'], ...) @@ -829,8 +830,8 @@ meaning that you don't have to call ``fit`` on it:: >>> from sklearn.feature_extraction.text import HashingVectorizer >>> hv = HashingVectorizer(n_features=10) >>> hv.transform(corpus) - <4x10 sparse matrix of type '<... 'numpy.float64'>' - with 16 stored elements in Compressed Sparse ... format> + You can see that 16 non-zero feature tokens were extracted in the vector output: this is less than the 19 non-zeros extracted previously by the @@ -853,8 +854,8 @@ Let's try again with the default setting:: >>> hv = HashingVectorizer() >>> hv.transform(corpus) - <4x1048576 sparse matrix of type '<... 'numpy.float64'>' - with 19 stored elements in Compressed Sparse ... format> + We no longer get the collisions, but this comes at the expense of a much larger dimensionality of the output space. diff --git a/doc/modules/multiclass.rst b/doc/modules/multiclass.rst index b5f7611bdfd91..07d7c9d221a73 100644 --- a/doc/modules/multiclass.rst +++ b/doc/modules/multiclass.rst @@ -172,6 +172,9 @@ Valid :term:`multiclass` representations for >>> from scipy import sparse >>> y_sparse = sparse.csr_matrix(y_dense) >>> print(y_sparse) + + Coords Values (0, 0) 1 (1, 2) 1 (2, 0) 1 @@ -377,6 +380,9 @@ An example of the same ``y`` in sparse matrix form: >>> y_sparse = sparse.csr_matrix(y) >>> print(y_sparse) + + Coords Values (0, 0) 1 (0, 3) 1 (1, 2) 1 diff --git a/sklearn/conftest.py b/sklearn/conftest.py index 8dff8ac5ebf16..47b6c2887acef 100644 --- a/sklearn/conftest.py +++ b/sklearn/conftest.py @@ -210,6 +210,10 @@ def pytest_collection_modifyitems(config, items): reason = "Due to NEP 51 numpy scalar repr has changed in numpy 2" skip_doctests = True + if sp_version < parse_version("1.14"): + reason = "Scipy sparse matrix repr has changed in scipy 1.14" + skip_doctests = True + # Normally doctest has the entire module's scope. Here we set globs to an empty dict # to remove the module's scope: # https://docs.python.org/3/library/doctest.html#what-s-the-execution-context diff --git a/sklearn/feature_extraction/image.py b/sklearn/feature_extraction/image.py index 5c7d6efd7fa24..1189618f07feb 100644 --- a/sklearn/feature_extraction/image.py +++ b/sklearn/feature_extraction/image.py @@ -234,6 +234,9 @@ def grid_to_graph( >>> mask[[1, 2], [1, 2], :] = True >>> graph = grid_to_graph(*shape_img, mask=mask) >>> print(graph) + + Coords Values (0, 0) 1 (1, 1) 1 """ diff --git a/sklearn/utils/_indexing.py b/sklearn/utils/_indexing.py index ca2327f2bb109..11ecdfe0ecbd9 100644 --- a/sklearn/utils/_indexing.py +++ b/sklearn/utils/_indexing.py @@ -478,8 +478,8 @@ def resample(*arrays, replace=True, n_samples=None, random_state=None, stratify= [1., 0.]]) >>> X_sparse - <3x2 sparse matrix of type '<... 'numpy.float64'>' - with 4 stored elements in Compressed Sparse Row format> + >>> X_sparse.toarray() array([[1., 0.], @@ -616,8 +616,8 @@ def shuffle(*arrays, random_state=None, n_samples=None): [1., 0.]]) >>> X_sparse - <3x2 sparse matrix of type '<... 'numpy.float64'>' - with 3 stored elements in Compressed Sparse Row format> + >>> X_sparse.toarray() array([[0., 0.], diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py index 228fbe76a25e1..af9fdb4a79cba 100644 --- a/sklearn/utils/validation.py +++ b/sklearn/utils/validation.py @@ -501,7 +501,7 @@ def indexable(*iterables): ... [1, 2, 3], np.array([2, 3, 4]), None, csr_matrix([[5], [6], [7]]) ... ] >>> indexable(*iterables) - [[1, 2, 3], array([2, 3, 4]), None, <3x1 sparse matrix ...>] + [[1, 2, 3], array([2, 3, 4]), None, <...Sparse...dtype 'int64'...shape (3, 1)>] """ result = [_make_indexable(X) for X in iterables] @@ -1503,8 +1503,8 @@ def check_symmetric(array, *, tol=1e-10, raise_warning=True, raise_exception=Fal >>> from scipy.sparse import csr_matrix >>> sparse_symmetric_array = csr_matrix(symmetric_array) >>> check_symmetric(sparse_symmetric_array) - <3x3 sparse matrix of type '' - with 6 stored elements in Compressed Sparse Row format> + """ if (array.ndim != 2) or (array.shape[0] != array.shape[1]): raise ValueError( From cef803afa61830afb4193a0a69d334de365915ca Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 9 Jul 2024 04:25:24 -0400 Subject: [PATCH 10/20] FEA SLEP006: Metadata routing for `SelfTrainingClassifier` (#28494) --- doc/metadata_routing.rst | 2 +- doc/whats_new/v1.6.rst | 17 +- sklearn/semi_supervised/_self_training.py | 293 +++++++++++++++--- .../tests/test_self_training.py | 98 ++++-- sklearn/tests/metadata_routing_common.py | 33 +- .../test_metaestimators_metadata_routing.py | 20 +- sklearn/tests/test_pipeline.py | 4 +- 7 files changed, 382 insertions(+), 85 deletions(-) diff --git a/doc/metadata_routing.rst b/doc/metadata_routing.rst index b420c0b40abef..440300b7e44bb 100644 --- a/doc/metadata_routing.rst +++ b/doc/metadata_routing.rst @@ -315,6 +315,7 @@ Meta-estimators and functions supporting metadata routing: - :class:`sklearn.multioutput.RegressorChain` - :class:`sklearn.pipeline.FeatureUnion` - :class:`sklearn.pipeline.Pipeline` +- :class:`sklearn.semi_supervised.SelfTrainingClassifier` Meta-estimators and tools not supporting metadata routing yet: @@ -324,4 +325,3 @@ Meta-estimators and tools not supporting metadata routing yet: - :class:`sklearn.feature_selection.RFECV` - :class:`sklearn.feature_selection.SequentialFeatureSelector` - :class:`sklearn.model_selection.permutation_test_score` -- :class:`sklearn.semi_supervised.SelfTrainingClassifier` diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst index c98314d5ca1de..4f1ee132d95b5 100644 --- a/doc/whats_new/v1.6.rst +++ b/doc/whats_new/v1.6.rst @@ -71,7 +71,7 @@ more details. :class:`ensemble.StackingRegressor` now support metadata routing and pass ``**fit_params`` to the underlying estimators via their `fit` methods. :pr:`28701` by :user:`Stefanie Senger `. - + - |Feature| :class:`compose.TransformedTargetRegressor` now supports metadata routing in its `fit` and `predict` methods and routes the corresponding params to the underlying regressor. @@ -81,6 +81,14 @@ more details. the `fit` method of its estimator and for its underlying CV splitter and scorer. :pr:`29329` by :user:`Stefanie Senger `. +- |Feature| :class:`semi_supervised.SelfTrainingClassifier` + now supports metadata routing. The fit method now accepts ``**fit_params`` + which are passed to the underlying estimators via their `fit` methods. + In addition, the `predict`, `predict_proba`, `predict_log_proba`, `score` + and `decision_function` methods also accept ``**params`` which are + passed to the underlying estimators via their respective methods. + :pr:`28494` by :user:`Adam Li `. + Dropping official support for PyPy ---------------------------------- @@ -189,6 +197,13 @@ Changelog when duplicate values in the training data lead to inaccurate outlier detection. :pr:`28773` by :user:`Henrique Caroço `. +:mod:`sklearn.semi_supervised` +.............................. + +- |API| :class:`semi_supervised.SelfTrainingClassifier` + deprecated the `base_estimator` parameter in favor of `estimator`. + :pr:`28494` by :user:`Adam Li `. + Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.5, including: diff --git a/sklearn/semi_supervised/_self_training.py b/sklearn/semi_supervised/_self_training.py index 647f48204414a..b1ebea1061e4c 100644 --- a/sklearn/semi_supervised/_self_training.py +++ b/sklearn/semi_supervised/_self_training.py @@ -1,12 +1,19 @@ import warnings from numbers import Integral, Real +from warnings import warn import numpy as np from ..base import BaseEstimator, MetaEstimatorMixin, _fit_context, clone -from ..utils import safe_mask -from ..utils._param_validation import HasMethods, Interval, StrOptions -from ..utils.metadata_routing import _RoutingNotSupportedMixin +from ..utils import Bunch, safe_mask +from ..utils._param_validation import HasMethods, Hidden, Interval, StrOptions +from ..utils.metadata_routing import ( + MetadataRouter, + MethodMapping, + _raise_for_params, + _routing_enabled, + process_routing, +) from ..utils.metaestimators import available_if from ..utils.validation import check_is_fitted @@ -19,25 +26,23 @@ def _estimator_has(attr): """Check if we can delegate a method to the underlying estimator. - First, we check the fitted `base_estimator_` if available, otherwise we check - the unfitted `base_estimator`. We raise the original `AttributeError` if + First, we check the fitted `estimator_` if available, otherwise we check + the unfitted `estimator`. We raise the original `AttributeError` if `attr` does not exist. This function is used together with `available_if`. """ def check(self): - if hasattr(self, "base_estimator_"): - getattr(self.base_estimator_, attr) + if hasattr(self, "estimator_"): + getattr(self.estimator_, attr) else: - getattr(self.base_estimator, attr) + getattr(self.estimator, attr) return True return check -class SelfTrainingClassifier( - _RoutingNotSupportedMixin, MetaEstimatorMixin, BaseEstimator -): +class SelfTrainingClassifier(MetaEstimatorMixin, BaseEstimator): """Self-training classifier. This :term:`metaestimator` allows a given supervised classifier to function as a @@ -52,10 +57,22 @@ class SelfTrainingClassifier( Parameters ---------- + estimator : estimator object + An estimator object implementing `fit` and `predict_proba`. + Invoking the `fit` method will fit a clone of the passed estimator, + which will be stored in the `estimator_` attribute. + + .. versionadded:: 1.6 + `estimator` was added to replace `base_estimator`. + base_estimator : estimator object An estimator object implementing `fit` and `predict_proba`. Invoking the `fit` method will fit a clone of the passed estimator, - which will be stored in the `base_estimator_` attribute. + which will be stored in the `estimator_` attribute. + + .. deprecated:: 1.6 + `base_estimator` was deprecated in 1.6 and will be removed in 1.8. + Use `estimator` instead. threshold : float, default=0.75 The decision threshold for use with `criterion='threshold'`. @@ -85,12 +102,12 @@ class SelfTrainingClassifier( Attributes ---------- - base_estimator_ : estimator object + estimator_ : estimator object The fitted estimator. classes_ : ndarray or list of ndarray of shape (n_classes,) Class labels for each output. (Taken from the trained - `base_estimator_`). + `estimator_`). transduction_ : ndarray of shape (n_samples,) The labels used for the final fit of the classifier, including @@ -159,7 +176,13 @@ class SelfTrainingClassifier( _parameter_constraints: dict = { # We don't require `predic_proba` here to allow passing a meta-estimator # that only exposes `predict_proba` after fitting. - "base_estimator": [HasMethods(["fit"])], + # TODO(1.8) remove None option + "estimator": [None, HasMethods(["fit"])], + # TODO(1.8) remove + "base_estimator": [ + HasMethods(["fit"]), + Hidden(StrOptions({"deprecated"})), + ], "threshold": [Interval(Real, 0.0, 1.0, closed="left")], "criterion": [StrOptions({"threshold", "k_best"})], "k_best": [Interval(Integral, 1, None, closed="left")], @@ -169,25 +192,63 @@ class SelfTrainingClassifier( def __init__( self, - base_estimator, + estimator=None, + base_estimator="deprecated", threshold=0.75, criterion="threshold", k_best=10, max_iter=10, verbose=False, ): - self.base_estimator = base_estimator + self.estimator = estimator self.threshold = threshold self.criterion = criterion self.k_best = k_best self.max_iter = max_iter self.verbose = verbose + # TODO(1.8) remove + self.base_estimator = base_estimator + + def _get_estimator(self): + """Get the estimator. + + Returns + ------- + estimator_ : estimator object + The cloned estimator object. + """ + # TODO(1.8): remove and only keep clone(self.estimator) + if self.estimator is None and self.base_estimator != "deprecated": + estimator_ = clone(self.base_estimator) + + warn( + ( + "`base_estimator` has been deprecated in 1.6 and will be removed" + " in 1.8. Please use `estimator` instead." + ), + FutureWarning, + ) + # TODO(1.8) remove + elif self.estimator is None and self.base_estimator == "deprecated": + raise ValueError( + "You must pass an estimator to SelfTrainingClassifier." + " Use `estimator`." + ) + elif self.estimator is not None and self.base_estimator != "deprecated": + raise ValueError( + "You must pass only one estimator to SelfTrainingClassifier." + " Use `estimator`." + ) + else: + estimator_ = clone(self.estimator) + return estimator_ + @_fit_context( - # SelfTrainingClassifier.base_estimator is not validated yet + # SelfTrainingClassifier.estimator is not validated yet prefer_skip_nested_validation=False ) - def fit(self, X, y): + def fit(self, X, y, **params): """ Fit self-training classifier using `X`, `y` as training data. @@ -200,19 +261,31 @@ def fit(self, X, y): Array representing the labels. Unlabeled samples should have the label -1. + **params : dict + Parameters to pass to the underlying estimators. + + .. versionadded:: 1.6 + Only available if `enable_metadata_routing=True`, + which can be set by using + ``sklearn.set_config(enable_metadata_routing=True)``. + See :ref:`Metadata Routing User Guide ` for + more details. + Returns ------- self : object Fitted estimator. """ + _raise_for_params(params, self, "fit") + + self.estimator_ = self._get_estimator() + # we need row slicing support for sparse matrices, but costly finiteness check # can be delegated to the base estimator. X, y = self._validate_data( X, y, accept_sparse=["csr", "csc", "lil", "dok"], force_all_finite=False ) - self.base_estimator_ = clone(self.base_estimator) - if y.dtype.kind in ["U", "S"]: raise ValueError( "y has dtype string. If you wish to predict on " @@ -237,6 +310,11 @@ def fit(self, X, y): UserWarning, ) + if _routing_enabled(): + routed_params = process_routing(self, "fit", **params) + else: + routed_params = Bunch(estimator=Bunch(fit={})) + self.transduction_ = np.copy(y) self.labeled_iter_ = np.full_like(y, -1) self.labeled_iter_[has_label] = 0 @@ -247,13 +325,15 @@ def fit(self, X, y): self.max_iter is None or self.n_iter_ < self.max_iter ): self.n_iter_ += 1 - self.base_estimator_.fit( - X[safe_mask(X, has_label)], self.transduction_[has_label] + self.estimator_.fit( + X[safe_mask(X, has_label)], + self.transduction_[has_label], + **routed_params.estimator.fit, ) # Predict on the unlabeled samples - prob = self.base_estimator_.predict_proba(X[safe_mask(X, ~has_label)]) - pred = self.base_estimator_.classes_[np.argmax(prob, axis=1)] + prob = self.estimator_.predict_proba(X[safe_mask(X, ~has_label)]) + pred = self.estimator_.classes_[np.argmax(prob, axis=1)] max_proba = np.max(prob, axis=1) # Select new labeled samples @@ -291,14 +371,16 @@ def fit(self, X, y): if np.all(has_label): self.termination_condition_ = "all_labeled" - self.base_estimator_.fit( - X[safe_mask(X, has_label)], self.transduction_[has_label] + self.estimator_.fit( + X[safe_mask(X, has_label)], + self.transduction_[has_label], + **routed_params.estimator.fit, ) - self.classes_ = self.base_estimator_.classes_ + self.classes_ = self.estimator_.classes_ return self @available_if(_estimator_has("predict")) - def predict(self, X): + def predict(self, X, **params): """Predict the classes of `X`. Parameters @@ -306,22 +388,40 @@ def predict(self, X): X : {array-like, sparse matrix} of shape (n_samples, n_features) Array representing the data. + **params : dict of str -> object + Parameters to pass to the underlying estimator's ``predict`` method. + + .. versionadded:: 1.6 + Only available if `enable_metadata_routing=True`, + which can be set by using + ``sklearn.set_config(enable_metadata_routing=True)``. + See :ref:`Metadata Routing User Guide ` for + more details. + Returns ------- y : ndarray of shape (n_samples,) Array with predicted labels. """ check_is_fitted(self) + _raise_for_params(params, self, "predict") + + if _routing_enabled(): + # metadata routing is enabled. + routed_params = process_routing(self, "predict", **params) + else: + routed_params = Bunch(estimator=Bunch(predict={})) + X = self._validate_data( X, accept_sparse=True, force_all_finite=False, reset=False, ) - return self.base_estimator_.predict(X) + return self.estimator_.predict(X, **routed_params.estimator.predict) @available_if(_estimator_has("predict_proba")) - def predict_proba(self, X): + def predict_proba(self, X, **params): """Predict probability for each possible outcome. Parameters @@ -329,45 +429,85 @@ def predict_proba(self, X): X : {array-like, sparse matrix} of shape (n_samples, n_features) Array representing the data. + **params : dict of str -> object + Parameters to pass to the underlying estimator's + ``predict_proba`` method. + + .. versionadded:: 1.6 + Only available if `enable_metadata_routing=True`, + which can be set by using + ``sklearn.set_config(enable_metadata_routing=True)``. + See :ref:`Metadata Routing User Guide ` for + more details. + Returns ------- y : ndarray of shape (n_samples, n_features) Array with prediction probabilities. """ check_is_fitted(self) + _raise_for_params(params, self, "predict_proba") + + if _routing_enabled(): + # metadata routing is enabled. + routed_params = process_routing(self, "predict_proba", **params) + else: + routed_params = Bunch(estimator=Bunch(predict_proba={})) + X = self._validate_data( X, accept_sparse=True, force_all_finite=False, reset=False, ) - return self.base_estimator_.predict_proba(X) + return self.estimator_.predict_proba(X, **routed_params.estimator.predict_proba) @available_if(_estimator_has("decision_function")) - def decision_function(self, X): - """Call decision function of the `base_estimator`. + def decision_function(self, X, **params): + """Call decision function of the `estimator`. Parameters ---------- X : {array-like, sparse matrix} of shape (n_samples, n_features) Array representing the data. + **params : dict of str -> object + Parameters to pass to the underlying estimator's + ``decision_function`` method. + + .. versionadded:: 1.6 + Only available if `enable_metadata_routing=True`, + which can be set by using + ``sklearn.set_config(enable_metadata_routing=True)``. + See :ref:`Metadata Routing User Guide ` for + more details. + Returns ------- y : ndarray of shape (n_samples, n_features) - Result of the decision function of the `base_estimator`. + Result of the decision function of the `estimator`. """ check_is_fitted(self) + _raise_for_params(params, self, "decision_function") + + if _routing_enabled(): + # metadata routing is enabled. + routed_params = process_routing(self, "decision_function", **params) + else: + routed_params = Bunch(estimator=Bunch(decision_function={})) + X = self._validate_data( X, accept_sparse=True, force_all_finite=False, reset=False, ) - return self.base_estimator_.decision_function(X) + return self.estimator_.decision_function( + X, **routed_params.estimator.decision_function + ) @available_if(_estimator_has("predict_log_proba")) - def predict_log_proba(self, X): + def predict_log_proba(self, X, **params): """Predict log probability for each possible outcome. Parameters @@ -375,23 +515,44 @@ def predict_log_proba(self, X): X : {array-like, sparse matrix} of shape (n_samples, n_features) Array representing the data. + **params : dict of str -> object + Parameters to pass to the underlying estimator's + ``predict_log_proba`` method. + + .. versionadded:: 1.6 + Only available if `enable_metadata_routing=True`, + which can be set by using + ``sklearn.set_config(enable_metadata_routing=True)``. + See :ref:`Metadata Routing User Guide ` for + more details. + Returns ------- y : ndarray of shape (n_samples, n_features) Array with log prediction probabilities. """ check_is_fitted(self) + _raise_for_params(params, self, "predict_log_proba") + + if _routing_enabled(): + # metadata routing is enabled. + routed_params = process_routing(self, "predict_log_proba", **params) + else: + routed_params = Bunch(estimator=Bunch(predict_log_proba={})) + X = self._validate_data( X, accept_sparse=True, force_all_finite=False, reset=False, ) - return self.base_estimator_.predict_log_proba(X) + return self.estimator_.predict_log_proba( + X, **routed_params.estimator.predict_log_proba + ) @available_if(_estimator_has("score")) - def score(self, X, y): - """Call score on the `base_estimator`. + def score(self, X, y, **params): + """Call score on the `estimator`. Parameters ---------- @@ -401,16 +562,64 @@ def score(self, X, y): y : array-like of shape (n_samples,) Array representing the labels. + **params : dict of str -> object + Parameters to pass to the underlying estimator's ``score`` method. + + .. versionadded:: 1.6 + Only available if `enable_metadata_routing=True`, + which can be set by using + ``sklearn.set_config(enable_metadata_routing=True)``. + See :ref:`Metadata Routing User Guide ` for + more details. + Returns ------- score : float - Result of calling score on the `base_estimator`. + Result of calling score on the `estimator`. """ check_is_fitted(self) + _raise_for_params(params, self, "score") + + if _routing_enabled(): + # metadata routing is enabled. + routed_params = process_routing(self, "score", **params) + else: + routed_params = Bunch(estimator=Bunch(score={})) + X = self._validate_data( X, accept_sparse=True, force_all_finite=False, reset=False, ) - return self.base_estimator_.score(X, y) + return self.estimator_.score(X, y, **routed_params.estimator.score) + + def get_metadata_routing(self): + """Get metadata routing of this object. + + Please check :ref:`User Guide ` on how the routing + mechanism works. + + .. versionadded:: 1.6 + + Returns + ------- + routing : MetadataRouter + A :class:`~sklearn.utils.metadata_routing.MetadataRouter` encapsulating + routing information. + """ + router = MetadataRouter(owner=self.__class__.__name__) + router.add( + estimator=self.estimator, + method_mapping=( + MethodMapping() + .add(callee="fit", caller="fit") + .add(callee="score", caller="fit") + .add(callee="predict", caller="predict") + .add(callee="predict_proba", caller="predict_proba") + .add(callee="decision_function", caller="decision_function") + .add(callee="predict_log_proba", caller="predict_log_proba") + .add(callee="score", caller="score") + ), + ) + return router diff --git a/sklearn/semi_supervised/tests/test_self_training.py b/sklearn/semi_supervised/tests/test_self_training.py index 29b8f1ac6e87c..02244063994d5 100644 --- a/sklearn/semi_supervised/tests/test_self_training.py +++ b/sklearn/semi_supervised/tests/test_self_training.py @@ -12,6 +12,7 @@ from sklearn.neighbors import KNeighborsClassifier from sklearn.semi_supervised import SelfTrainingClassifier from sklearn.svm import SVC +from sklearn.tests.test_pipeline import SimpleEstimator from sklearn.tree import DecisionTreeClassifier # Authors: The scikit-learn developers @@ -43,25 +44,25 @@ def test_warns_k_best(): @pytest.mark.parametrize( - "base_estimator", + "estimator", [KNeighborsClassifier(), SVC(gamma="scale", probability=True, random_state=0)], ) @pytest.mark.parametrize("selection_crit", ["threshold", "k_best"]) -def test_classification(base_estimator, selection_crit): +def test_classification(estimator, selection_crit): # Check classification for various parameter settings. # Also assert that predictions for strings and numerical labels are equal. # Also test for multioutput classification threshold = 0.75 max_iter = 10 st = SelfTrainingClassifier( - base_estimator, max_iter=max_iter, threshold=threshold, criterion=selection_crit + estimator, max_iter=max_iter, threshold=threshold, criterion=selection_crit ) st.fit(X_train, y_train_missing_labels) pred = st.predict(X_test) proba = st.predict_proba(X_test) st_string = SelfTrainingClassifier( - base_estimator, max_iter=max_iter, criterion=selection_crit, threshold=threshold + estimator, max_iter=max_iter, criterion=selection_crit, threshold=threshold ) st_string.fit(X_train, y_train_missing_strings) pred_string = st_string.predict(X_test) @@ -112,15 +113,15 @@ def test_k_best(): def test_sanity_classification(): - base_estimator = SVC(gamma="scale", probability=True) - base_estimator.fit(X_train[n_labeled_samples:], y_train[n_labeled_samples:]) + estimator = SVC(gamma="scale", probability=True) + estimator.fit(X_train[n_labeled_samples:], y_train[n_labeled_samples:]) - st = SelfTrainingClassifier(base_estimator) + st = SelfTrainingClassifier(estimator) st.fit(X_train, y_train_missing_labels) - pred1, pred2 = base_estimator.predict(X_test), st.predict(X_test) + pred1, pred2 = estimator.predict(X_test), st.predict(X_test) assert not np.array_equal(pred1, pred2) - score_supervised = accuracy_score(base_estimator.predict(X_test), y_test) + score_supervised = accuracy_score(estimator.predict(X_test), y_test) score_self_training = accuracy_score(st.predict(X_test), y_test) assert score_self_training > score_supervised @@ -137,21 +138,21 @@ def test_none_iter(): @pytest.mark.parametrize( - "base_estimator", + "estimator", [KNeighborsClassifier(), SVC(gamma="scale", probability=True, random_state=0)], ) @pytest.mark.parametrize("y", [y_train_missing_labels, y_train_missing_strings]) -def test_zero_iterations(base_estimator, y): +def test_zero_iterations(estimator, y): # Check classification for zero iterations. # Fitting a SelfTrainingClassifier with zero iterations should give the # same results as fitting a supervised classifier. # This also asserts that string arrays work as expected. - clf1 = SelfTrainingClassifier(base_estimator, max_iter=0) + clf1 = SelfTrainingClassifier(estimator, max_iter=0) clf1.fit(X_train, y) - clf2 = base_estimator.fit(X_train[:n_labeled_samples], y[:n_labeled_samples]) + clf2 = estimator.fit(X_train[:n_labeled_samples], y[:n_labeled_samples]) assert_array_equal(clf1.predict(X_test), clf2.predict(X_test)) assert clf1.termination_condition_ == "max_iter" @@ -280,14 +281,14 @@ def test_k_best_selects_best(): assert row in added_by_st -def test_base_estimator_meta_estimator(): +def test_estimator_meta_estimator(): # Check that a meta-estimator relying on an estimator implementing # `predict_proba` will work even if it does not expose this method before being # fitted. # Non-regression test for: # https://github.com/scikit-learn/scikit-learn/issues/19119 - base_estimator = StackingClassifier( + estimator = StackingClassifier( estimators=[ ("svc_1", SVC(probability=True)), ("svc_2", SVC(probability=True)), @@ -296,12 +297,12 @@ def test_base_estimator_meta_estimator(): cv=2, ) - assert hasattr(base_estimator, "predict_proba") - clf = SelfTrainingClassifier(base_estimator=base_estimator) + assert hasattr(estimator, "predict_proba") + clf = SelfTrainingClassifier(estimator=estimator) clf.fit(X_train, y_train_missing_labels) clf.predict_proba(X_test) - base_estimator = StackingClassifier( + estimator = StackingClassifier( estimators=[ ("svc_1", SVC(probability=False)), ("svc_2", SVC(probability=False)), @@ -310,14 +311,14 @@ def test_base_estimator_meta_estimator(): cv=2, ) - assert not hasattr(base_estimator, "predict_proba") - clf = SelfTrainingClassifier(base_estimator=base_estimator) + assert not hasattr(estimator, "predict_proba") + clf = SelfTrainingClassifier(estimator=estimator) with pytest.raises(AttributeError): clf.fit(X_train, y_train_missing_labels) def test_self_training_estimator_attribute_error(): - """Check that we raise the proper AttributeErrors when the `base_estimator` + """Check that we raise the proper AttributeErrors when the `estimator` does not implement the `predict_proba` method, which is called from within `fit`, or `decision_function`, which is decorated with `available_if`. @@ -327,15 +328,15 @@ def test_self_training_estimator_attribute_error(): # `SVC` with `probability=False` does not implement 'predict_proba' that # is required internally in `fit` of `SelfTrainingClassifier`. We expect # an AttributeError to be raised. - base_estimator = SVC(probability=False, gamma="scale") - self_training = SelfTrainingClassifier(base_estimator) + estimator = SVC(probability=False, gamma="scale") + self_training = SelfTrainingClassifier(estimator) with pytest.raises(AttributeError, match="has no attribute 'predict_proba'"): self_training.fit(X_train, y_train_missing_labels) # `DecisionTreeClassifier` does not implement 'decision_function' and # should raise an AttributeError - self_training = SelfTrainingClassifier(base_estimator=DecisionTreeClassifier()) + self_training = SelfTrainingClassifier(estimator=DecisionTreeClassifier()) outer_msg = "This 'SelfTrainingClassifier' has no attribute 'decision_function'" inner_msg = "'DecisionTreeClassifier' object has no attribute 'decision_function'" @@ -343,3 +344,52 @@ def test_self_training_estimator_attribute_error(): self_training.fit(X_train, y_train_missing_labels).decision_function(X_train) assert isinstance(exec_info.value.__cause__, AttributeError) assert inner_msg in str(exec_info.value.__cause__) + + +# TODO(1.8): remove in 1.8 +def test_deprecation_warning_base_estimator(): + warn_msg = "`base_estimator` has been deprecated in 1.6 and will be removed" + with pytest.warns(FutureWarning, match=warn_msg): + SelfTrainingClassifier(base_estimator=DecisionTreeClassifier()).fit( + X_train, y_train_missing_labels + ) + + error_msg = "You must pass an estimator to SelfTrainingClassifier" + with pytest.raises(ValueError, match=error_msg): + SelfTrainingClassifier().fit(X_train, y_train_missing_labels) + + error_msg = "You must pass only one estimator to SelfTrainingClassifier." + with pytest.raises(ValueError, match=error_msg): + SelfTrainingClassifier( + base_estimator=DecisionTreeClassifier(), estimator=DecisionTreeClassifier() + ).fit(X_train, y_train_missing_labels) + + +# Metadata routing tests +# ================================================================= + + +@pytest.mark.filterwarnings("ignore:y contains no unlabeled samples:UserWarning") +@pytest.mark.parametrize( + "method", ["decision_function", "predict_log_proba", "predict_proba", "predict"] +) +def test_routing_passed_metadata_not_supported(method): + """Test that the right error message is raised when metadata is passed while + not supported when `enable_metadata_routing=False`.""" + est = SelfTrainingClassifier(estimator=SimpleEstimator()) + with pytest.raises( + ValueError, match="is only supported if enable_metadata_routing=True" + ): + est.fit([[1], [1]], [1, 1], sample_weight=[1], prop="a") + + est = SelfTrainingClassifier(estimator=SimpleEstimator()) + with pytest.raises( + ValueError, match="is only supported if enable_metadata_routing=True" + ): + # make sure that the estimator thinks it is already fitted + est.fitted_params_ = True + getattr(est, method)([[1]], sample_weight=[1], prop="a") + + +# End of routing tests +# ==================== diff --git a/sklearn/tests/metadata_routing_common.py b/sklearn/tests/metadata_routing_common.py index 0af522f9f9342..5fffec8fccecf 100644 --- a/sklearn/tests/metadata_routing_common.py +++ b/sklearn/tests/metadata_routing_common.py @@ -215,6 +215,17 @@ def predict(self, X): y_pred[len(X) // 2 :] = 1 return y_pred + def predict_proba(self, X): + # dummy probabilities to support predict_proba + y_proba = np.empty(shape=(len(X), 2)) + y_proba[: len(X) // 2, :] = np.asarray([1.0, 0.0]) + y_proba[len(X) // 2 :, :] = np.asarray([0.0, 1.0]) + return y_proba + + def predict_log_proba(self, X): + # dummy probabilities to support predict_log_proba + return self.predict_proba(X) + class NonConsumingRegressor(RegressorMixin, BaseEstimator): """A classifier which accepts no metadata on any method.""" @@ -291,13 +302,10 @@ def predict_proba(self, X, sample_weight="default", metadata="default"): return y_proba def predict_log_proba(self, X, sample_weight="default", metadata="default"): - pass # pragma: no cover - - # uncomment when needed - # record_metadata_not_default( - # self, sample_weight=sample_weight, metadata=metadata - # ) - # return np.zeros(shape=(len(X), 2)) + record_metadata_not_default( + self, sample_weight=sample_weight, metadata=metadata + ) + return np.zeros(shape=(len(X), 2)) def decision_function(self, X, sample_weight="default", metadata="default"): record_metadata_not_default( @@ -308,12 +316,11 @@ def decision_function(self, X, sample_weight="default", metadata="default"): y_score[: len(X) // 2] = 1 return y_score - # uncomment when needed - # def score(self, X, y, sample_weight="default", metadata="default"): - # record_metadata_not_default( - # self, sample_weight=sample_weight, metadata=metadata - # ) - # return 1 + def score(self, X, y, sample_weight="default", metadata="default"): + record_metadata_not_default( + self, sample_weight=sample_weight, metadata=metadata + ) + return 1 class ConsumingTransformer(TransformerMixin, BaseEstimator): diff --git a/sklearn/tests/test_metaestimators_metadata_routing.py b/sklearn/tests/test_metaestimators_metadata_routing.py index cf2bb130267a3..9aca241521ca0 100644 --- a/sklearn/tests/test_metaestimators_metadata_routing.py +++ b/sklearn/tests/test_metaestimators_metadata_routing.py @@ -390,6 +390,23 @@ def enable_slep006(): "y": y, "estimator_routing_methods": ["fit", "predict"], }, + { + "metaestimator": SelfTrainingClassifier, + "estimator_name": "estimator", + "estimator": "classifier", + "X": X, + "y": y, + "preserves_metadata": True, + "estimator_routing_methods": [ + "fit", + "predict", + "predict_proba", + "predict_log_proba", + "decision_function", + "score", + ], + "method_mapping": {"fit": ["fit", "score"]}, + }, ] """List containing all metaestimators to be tested and their settings @@ -433,7 +450,6 @@ def enable_slep006(): AdaBoostRegressor(), RFE(ConsumingClassifier()), RFECV(ConsumingClassifier()), - SelfTrainingClassifier(ConsumingClassifier()), SequentialFeatureSelector(ConsumingClassifier()), ] @@ -640,7 +656,7 @@ def test_error_on_missing_requests_for_sub_estimator(metaestimator): value=None, ) try: - # `fit` and `partial_fit` accept y, others don't. + # `fit`, `partial_fit`, 'score' accept y, others don't. method(X, y, **method_kwargs) except TypeError: method(X, **method_kwargs) diff --git a/sklearn/tests/test_pipeline.py b/sklearn/tests/test_pipeline.py index 273aa4e9d36e4..b9fba86d01e9b 100644 --- a/sklearn/tests/test_pipeline.py +++ b/sklearn/tests/test_pipeline.py @@ -1822,8 +1822,8 @@ class SimpleEstimator(BaseEstimator): # This class is used in this section for testing routing in the pipeline. # This class should have every set_{method}_request def fit(self, X, y, sample_weight=None, prop=None): - assert sample_weight is not None - assert prop is not None + assert sample_weight is not None, sample_weight + assert prop is not None, prop return self def fit_transform(self, X, y, sample_weight=None, prop=None): From 4bdd398d56a5d248c788a09c69febd22c4a0ccff Mon Sep 17 00:00:00 2001 From: m-maggi <124086916+m-maggi@users.noreply.github.com> Date: Tue, 9 Jul 2024 10:56:26 +0200 Subject: [PATCH 11/20] DOC Update doc for `min_samples` in HDBSCAN (#29263) --- sklearn/cluster/_hdbscan/_reachability.pyx | 4 ++-- sklearn/cluster/_hdbscan/hdbscan.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/sklearn/cluster/_hdbscan/_reachability.pyx b/sklearn/cluster/_hdbscan/_reachability.pyx index 7c37b795cbd14..86eb4612acc01 100644 --- a/sklearn/cluster/_hdbscan/_reachability.pyx +++ b/sklearn/cluster/_hdbscan/_reachability.pyx @@ -62,8 +62,8 @@ def mutual_reachability_graph( `CSR` format. min_samples : int, default=5 - The number of points in a neighbourhood for a point to be considered - a core point. + The parameter `k` used to calculate the distance between a point + `x_p` and its k-th nearest neighbor. max_distance : float, default=0.0 The distance which `np.inf` is replaced with. When the true mutual- diff --git a/sklearn/cluster/_hdbscan/hdbscan.py b/sklearn/cluster/_hdbscan/hdbscan.py index d20e745309fca..8caa639a6de69 100644 --- a/sklearn/cluster/_hdbscan/hdbscan.py +++ b/sklearn/cluster/_hdbscan/hdbscan.py @@ -441,8 +441,8 @@ class HDBSCAN(ClusterMixin, BaseEstimator): as noise. min_samples : int, default=None - The number of samples in a neighborhood for a point - to be considered as a core point. This includes the point itself. + The parameter `k` used to calculate the distance between a point + `x_p` and its k-th nearest neighbor. When `None`, defaults to `min_cluster_size`. cluster_selection_epsilon : float, default=0.0 From dddf2f0e4d7baac61f41f013d8e5c7d639b95b09 Mon Sep 17 00:00:00 2001 From: Adam Li Date: Tue, 9 Jul 2024 05:54:09 -0400 Subject: [PATCH 12/20] FEA Add missing-value support for ExtaTreeClassifier and ExtaTreeRegressor (#27966) --- doc/modules/tree.rst | 30 ++++- doc/whats_new/v1.6.rst | 9 ++ sklearn/ensemble/_iforest.py | 15 ++- sklearn/tree/_classes.py | 4 +- sklearn/tree/_splitter.pyx | 155 +++++++++++++++++++--- sklearn/tree/tests/test_monotonic_tree.py | 14 +- sklearn/tree/tests/test_tree.py | 122 +++++++++++++---- 7 files changed, 286 insertions(+), 63 deletions(-) diff --git a/doc/modules/tree.rst b/doc/modules/tree.rst index 9b475d6c09f5f..371cfccfffc1b 100644 --- a/doc/modules/tree.rst +++ b/doc/modules/tree.rst @@ -579,11 +579,21 @@ Note that it fits much slower than the MSE criterion. Missing Values Support ====================== -:class:`DecisionTreeClassifier` and :class:`DecisionTreeRegressor` -have built-in support for missing values when `splitter='best'` and criterion is +:class:`DecisionTreeClassifier`, :class:`DecisionTreeRegressor` +have built-in support for missing values using `splitter='best'`, where +the splits are determined in a greedy fashion. +:class:`ExtraTreeClassifier`, and :class:`ExtraTreeRegressor` have built-in +support for missing values for `splitter='random'`, where the splits +are determined randomly. For more details on how the splitter differs on +non-missing values, see the :ref:`Forest section `. + +The criterion supported when there are missing-values are `'gini'`, `'entropy`', or `'log_loss'`, for classification or `'squared_error'`, `'friedman_mse'`, or `'poisson'` for regression. +First we will describe how :class:`DecisionTreeClassifier`, :class:`DecisionTreeRegressor` +handle missing-values in the data. + For each potential threshold on the non-missing data, the splitter will evaluate the split with all the missing values going to the left node or the right node. @@ -634,6 +644,22 @@ Decisions are made as follows: >>> tree.predict(X_test) array([1]) +:class:`ExtraTreeClassifier`, and :class:`ExtraTreeRegressor` handle missing values +in a slightly different way. When splitting a node, a random threshold will be chosen +to split the non-missing values on. Then the non-missing values will be sent to the +left and right child based on the randomly selected threshold, while the missing +values will also be randomly sent to the left or right child. This is repeated for +every feature considered at each split. The best split among these is chosen. + +During prediction, the treatment of missing-values is the same as that of the +decision tree: + +- By default when predicting, the samples with missing values are classified + with the class used in the split found during training. + +- If no missing values are seen during training for a given feature, then during + prediction missing values are mapped to the child with the most samples. + .. _minimal_cost_complexity_pruning: Minimal Cost-Complexity Pruning diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst index 4f1ee132d95b5..d4f1d14fabe23 100644 --- a/doc/whats_new/v1.6.rst +++ b/doc/whats_new/v1.6.rst @@ -204,6 +204,15 @@ Changelog deprecated the `base_estimator` parameter in favor of `estimator`. :pr:`28494` by :user:`Adam Li `. +:mod:`sklearn.tree` +................... + +- |Feature| :class:`tree.ExtraTreeClassifier` and :class:`tree.ExtraTreeRegressor` now + support missing-values in the data matrix ``X``. Missing-values are handled by + randomly moving all of the samples to the left, or right child node as the tree is + traversed. + :pr:`27966` by :user:`Adam Li `. + Thanks to everyone who has contributed to the maintenance and improvement of the project since version 1.5, including: diff --git a/sklearn/ensemble/_iforest.py b/sklearn/ensemble/_iforest.py index 1405c1298a10a..b8df148f20a1a 100644 --- a/sklearn/ensemble/_iforest.py +++ b/sklearn/ensemble/_iforest.py @@ -315,7 +315,9 @@ def fit(self, X, y=None, sample_weight=None): self : object Fitted estimator. """ - X = self._validate_data(X, accept_sparse=["csc"], dtype=tree_dtype) + X = self._validate_data( + X, accept_sparse=["csc"], dtype=tree_dtype, force_all_finite=False + ) if issparse(X): # Pre-sort indices to avoid that each individual tree of the # ensemble sorts the indices. @@ -515,7 +517,13 @@ def score_samples(self, X): model.score(X) """ # Check data - X = self._validate_data(X, accept_sparse="csr", dtype=tree_dtype, reset=False) + X = self._validate_data( + X, + accept_sparse="csr", + dtype=tree_dtype, + reset=False, + force_all_finite=False, + ) return self._score_samples(X) @@ -627,7 +635,8 @@ def _more_tags(self): "check_sample_weights_invariance": ( "zero sample_weight is not equivalent to removing samples" ), - } + }, + "allow_nan": True, } diff --git a/sklearn/tree/_classes.py b/sklearn/tree/_classes.py index 675b58f48a2f4..2678781fc931c 100644 --- a/sklearn/tree/_classes.py +++ b/sklearn/tree/_classes.py @@ -1074,7 +1074,7 @@ def predict_log_proba(self, X): def _more_tags(self): # XXX: nan is only support for dense arrays, but we set this for common test to # pass, specifically: check_estimators_nan_inf - allow_nan = self.splitter == "best" and self.criterion in { + allow_nan = self.splitter in ("best", "random") and self.criterion in { "gini", "log_loss", "entropy", @@ -1405,7 +1405,7 @@ def _compute_partial_dependence_recursion(self, grid, target_features): def _more_tags(self): # XXX: nan is only support for dense arrays, but we set this for common test to # pass, specifically: check_estimators_nan_inf - allow_nan = self.splitter == "best" and self.criterion in { + allow_nan = self.splitter in ("best", "random") and self.criterion in { "squared_error", "friedman_mse", "poisson", diff --git a/sklearn/tree/_splitter.pyx b/sklearn/tree/_splitter.pyx index 6be286bf097c4..7bd0dbe71d0f0 100644 --- a/sklearn/tree/_splitter.pyx +++ b/sklearn/tree/_splitter.pyx @@ -19,6 +19,9 @@ from scipy.sparse import issparse cdef float64_t INFINITY = np.inf +# Allow for 32 bit float comparisons +cdef float32_t INFINITY_32t = np.inf + # Mitigate precision differences between 32 bit and 64 bit cdef float32_t FEATURE_THRESHOLD = 1e-7 @@ -479,6 +482,10 @@ cdef inline int node_split_best( current_split.threshold = feature_values[p_prev] current_split.n_missing = n_missing + + # if there are no missing values in the training data, during + # test time, we send missing values to the branch that contains + # the most samples during training time. if n_missing == 0: current_split.missing_go_to_left = n_left > n_right else: @@ -680,7 +687,13 @@ cdef inline int node_split_random( # Draw random splits and pick the best cdef intp_t start = splitter.start cdef intp_t end = splitter.end + cdef intp_t end_non_missing + cdef intp_t n_missing = 0 + cdef bint has_missing = 0 + cdef intp_t n_left, n_right + cdef bint missing_go_to_left + cdef intp_t[::1] samples = splitter.samples cdef intp_t[::1] features = splitter.features cdef intp_t[::1] constant_features = splitter.constant_features cdef intp_t n_features = splitter.n_features @@ -758,12 +771,22 @@ cdef inline int node_split_random( current_split.feature = features[f_j] - # Find min, max + # Find min, max as we will randomly select a threshold between them partitioner.find_min_max( current_split.feature, &min_feature_value, &max_feature_value ) + n_missing = partitioner.n_missing + end_non_missing = end - n_missing - if max_feature_value <= min_feature_value + FEATURE_THRESHOLD: + if ( + # All values for this feature are missing, or + end_non_missing == start or + # This feature is considered constant (max - min <= FEATURE_THRESHOLD) + max_feature_value <= min_feature_value + FEATURE_THRESHOLD + ): + # We consider this feature constant in this case. + # Since finding a split with a constant feature is not valuable, + # we do not consider this feature for splitting. features[f_j], features[n_total_constants] = features[n_total_constants], current_split.feature n_found_constants += 1 @@ -772,6 +795,8 @@ cdef inline int node_split_random( f_i -= 1 features[f_i], features[f_j] = features[f_j], features[f_i] + has_missing = n_missing != 0 + criterion.init_missing(n_missing) # Draw a random threshold current_split.threshold = rand_uniform( @@ -780,15 +805,38 @@ cdef inline int node_split_random( random_state, ) + if has_missing: + # If there are missing values, then we randomly make all missing + # values go to the right or left. + # + # Note: compared to the BestSplitter, we do not evaluate the + # edge case where all the missing values go to the right node + # and the non-missing values go to the left node. This is because + # this would indicate a threshold outside of the observed range + # of the feature. However, it is not clear how much probability weight should + # be given to this edge case. + missing_go_to_left = rand_int(0, 2, random_state) + else: + missing_go_to_left = 0 + criterion.missing_go_to_left = missing_go_to_left + if current_split.threshold == max_feature_value: current_split.threshold = min_feature_value # Partition - current_split.pos = partitioner.partition_samples(current_split.threshold) + current_split.pos = partitioner.partition_samples( + current_split.threshold + ) + + if missing_go_to_left: + n_left = current_split.pos - start + n_missing + n_right = end_non_missing - current_split.pos + else: + n_left = current_split.pos - start + n_right = end_non_missing - current_split.pos + n_missing # Reject if min_samples_leaf is not guaranteed - if (((current_split.pos - start) < min_samples_leaf) or - ((end - current_split.pos) < min_samples_leaf)): + if n_left < min_samples_leaf or n_right < min_samples_leaf: continue # Evaluate split @@ -817,16 +865,30 @@ cdef inline int node_split_random( current_proxy_improvement = criterion.proxy_impurity_improvement() if current_proxy_improvement > best_proxy_improvement: + current_split.n_missing = n_missing + + # if there are no missing values in the training data, during + # test time, we send missing values to the branch that contains + # the most samples during training time. + if has_missing: + current_split.missing_go_to_left = missing_go_to_left + else: + current_split.missing_go_to_left = n_left > n_right + best_proxy_improvement = current_proxy_improvement best_split = current_split # copy # Reorganize into samples[start:best.pos] + samples[best.pos:end] if best_split.pos < end: if current_split.feature != best_split.feature: - # TODO: Pass in best.n_missing when random splitter supports missing values. partitioner.partition_samples_final( - best_split.pos, best_split.threshold, best_split.feature, 0 + best_split.pos, + best_split.threshold, + best_split.feature, + best_split.n_missing ) + criterion.init_missing(best_split.n_missing) + criterion.missing_go_to_left = best_split.missing_go_to_left criterion.reset() criterion.update(best_split.pos) @@ -834,9 +896,13 @@ cdef inline int node_split_random( &best_split.impurity_left, &best_split.impurity_right ) best_split.improvement = criterion.impurity_improvement( - impurity, best_split.impurity_left, best_split.impurity_right + impurity, + best_split.impurity_left, + best_split.impurity_right ) + shift_missing_values_to_left_if_required(&best_split, samples, end) + # Respect invariant for constant features: the original order of # element in features[:n_known_constants] must be preserved for sibling # and child nodes @@ -941,29 +1007,68 @@ cdef class DensePartitioner: float32_t* min_feature_value_out, float32_t* max_feature_value_out, ) noexcept nogil: - """Find the minimum and maximum value for current_feature.""" + """Find the minimum and maximum value for current_feature. + + Missing values are stored at the end of feature_values. + The number of missing values observed in feature_values is stored + in self.n_missing. + """ cdef: - intp_t p + intp_t p, current_end float32_t current_feature_value const float32_t[:, :] X = self.X intp_t[::1] samples = self.samples - float32_t min_feature_value = X[samples[self.start], current_feature] - float32_t max_feature_value = min_feature_value + float32_t min_feature_value = INFINITY_32t + float32_t max_feature_value = -INFINITY_32t float32_t[::1] feature_values = self.feature_values + intp_t n_missing = 0 + const unsigned char[::1] missing_values_in_feature_mask = self.missing_values_in_feature_mask - feature_values[self.start] = min_feature_value + # We are copying the values into an array and + # finding min/max of the array in a manner which utilizes the cache more + # effectively. We need to also count the number of missing-values there are + if missing_values_in_feature_mask is not None and missing_values_in_feature_mask[current_feature]: + p, current_end = self.start, self.end - 1 + # Missing values are placed at the end and do not participate in the + # min/max calculation. + while p <= current_end: + # Finds the right-most value that is not missing so that + # it can be swapped with missing values towards its left. + if isnan(X[samples[current_end], current_feature]): + n_missing += 1 + current_end -= 1 + continue - for p in range(self.start + 1, self.end): - current_feature_value = X[samples[p], current_feature] - feature_values[p] = current_feature_value + # X[samples[current_end], current_feature] is a non-missing value + if isnan(X[samples[p], current_feature]): + samples[p], samples[current_end] = samples[current_end], samples[p] + n_missing += 1 + current_end -= 1 - if current_feature_value < min_feature_value: - min_feature_value = current_feature_value - elif current_feature_value > max_feature_value: - max_feature_value = current_feature_value + current_feature_value = X[samples[p], current_feature] + feature_values[p] = current_feature_value + if current_feature_value < min_feature_value: + min_feature_value = current_feature_value + elif current_feature_value > max_feature_value: + max_feature_value = current_feature_value + p += 1 + else: + min_feature_value = X[samples[self.start], current_feature] + max_feature_value = min_feature_value + + feature_values[self.start] = min_feature_value + for p in range(self.start + 1, self.end): + current_feature_value = X[samples[p], current_feature] + feature_values[p] = current_feature_value + + if current_feature_value < min_feature_value: + min_feature_value = current_feature_value + elif current_feature_value > max_feature_value: + max_feature_value = current_feature_value min_feature_value_out[0] = min_feature_value max_feature_value_out[0] = max_feature_value + self.n_missing = n_missing cdef inline void next_p(self, intp_t* p_prev, intp_t* p) noexcept nogil: """Compute the next p_prev and p for iteratiing over feature values. @@ -986,7 +1091,10 @@ cdef class DensePartitioner: # (feature_values[p] >= end) or (feature_values[p] > feature_values[p - 1]) p[0] += 1 - cdef inline intp_t partition_samples(self, float64_t current_threshold) noexcept nogil: + cdef inline intp_t partition_samples( + self, + float64_t current_threshold + ) noexcept nogil: """Partition samples for feature_values at the current_threshold.""" cdef: intp_t p = self.start @@ -1233,7 +1341,10 @@ cdef class SparsePartitioner: p_prev[0] = p[0] p[0] = p_next - cdef inline intp_t partition_samples(self, float64_t current_threshold) noexcept nogil: + cdef inline intp_t partition_samples( + self, + float64_t current_threshold + ) noexcept nogil: """Partition samples for feature_values at the current_threshold.""" return self._partition(current_threshold, self.start_positive) diff --git a/sklearn/tree/tests/test_monotonic_tree.py b/sklearn/tree/tests/test_monotonic_tree.py index 6478c2e2dfd85..6d89c4ae3f8bb 100644 --- a/sklearn/tree/tests/test_monotonic_tree.py +++ b/sklearn/tree/tests/test_monotonic_tree.py @@ -191,18 +191,22 @@ def test_multiple_output_raises(TreeClassifier): @pytest.mark.parametrize( - "DecisionTreeEstimator", [DecisionTreeClassifier, DecisionTreeRegressor] + "Tree", + [ + DecisionTreeClassifier, + DecisionTreeRegressor, + ExtraTreeClassifier, + ExtraTreeRegressor, + ], ) -def test_missing_values_raises(DecisionTreeEstimator): +def test_missing_values_raises(Tree): X, y = make_classification( n_samples=100, n_features=5, n_classes=2, n_informative=3, random_state=0 ) X[0, 0] = np.nan monotonic_cst = np.zeros(X.shape[1]) monotonic_cst[0] = 1 - est = DecisionTreeEstimator( - max_depth=None, monotonic_cst=monotonic_cst, random_state=0 - ) + est = Tree(max_depth=None, monotonic_cst=monotonic_cst, random_state=0) msg = "Input X contains NaN" with pytest.raises(ValueError, match=msg): diff --git a/sklearn/tree/tests/test_tree.py b/sklearn/tree/tests/test_tree.py index 6bf2d6f65b8ec..b59c857e3dccd 100644 --- a/sklearn/tree/tests/test_tree.py +++ b/sklearn/tree/tests/test_tree.py @@ -20,7 +20,7 @@ from sklearn.exceptions import NotFittedError from sklearn.impute import SimpleImputer from sklearn.metrics import accuracy_score, mean_poisson_deviance, mean_squared_error -from sklearn.model_selection import train_test_split +from sklearn.model_selection import cross_val_score, train_test_split from sklearn.pipeline import make_pipeline from sklearn.random_projection import _sparse_random_matrix from sklearn.tree import ( @@ -2392,8 +2392,8 @@ def test_min_sample_split_1_error(Tree): @pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse"]) -def test_missing_values_on_equal_nodes_no_missing(criterion): - """Check missing values goes to correct node during predictions""" +def test_missing_values_best_splitter_on_equal_nodes_no_missing(criterion): + """Check missing values goes to correct node during predictions.""" X = np.array([[0, 1, 2, 3, 8, 9, 11, 12, 15]]).T y = np.array([0.1, 0.2, 0.3, 0.2, 1.4, 1.4, 1.5, 1.6, 2.6]) @@ -2417,6 +2417,41 @@ def test_missing_values_on_equal_nodes_no_missing(criterion): assert_allclose(y_pred, [np.mean(y_equal[-4:])]) +@pytest.mark.parametrize("seed", range(3)) +@pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse"]) +def test_missing_values_random_splitter_on_equal_nodes_no_missing(criterion, seed): + """Check missing values go to the correct node during predictions for ExtraTree. + + Since ETC use random splits, we use different seeds to verify that the + left/right node is chosen correctly when the splits occur. + """ + X = np.array([[0, 1, 2, 3, 8, 9, 11, 12, 15]]).T + y = np.array([0.1, 0.2, 0.3, 0.2, 1.4, 1.4, 1.5, 1.6, 2.6]) + + etr = ExtraTreeRegressor(random_state=seed, max_depth=1, criterion=criterion) + etr.fit(X, y) + + # Get the left and right children of the root node + left_child = etr.tree_.children_left[0] + right_child = etr.tree_.children_right[0] + + # Get the number of samples for the left and right children + left_samples = etr.tree_.weighted_n_node_samples[left_child] + right_samples = etr.tree_.weighted_n_node_samples[right_child] + went_left = left_samples > right_samples + + # predictions + y_pred_left = etr.tree_.value[left_child][0] + y_pred_right = etr.tree_.value[right_child][0] + + # Goes to node with the most data points + y_pred = etr.predict([[np.nan]]) + if went_left: + assert_allclose(y_pred_left, y_pred) + else: + assert_allclose(y_pred_right, y_pred) + + @pytest.mark.parametrize("criterion", ["entropy", "gini"]) def test_missing_values_best_splitter_three_classes(criterion): """Test when missing values are uniquely present in a class among 3 classes.""" @@ -2466,7 +2501,7 @@ def test_missing_values_best_splitter_to_right(criterion): @pytest.mark.parametrize("criterion", ["entropy", "gini"]) -def test_missing_values_missing_both_classes_has_nan(criterion): +def test_missing_values_best_splitter_missing_both_classes_has_nan(criterion): """Check behavior of missing value when there is one missing value in each class.""" X = np.array([[1, 2, 3, 5, np.nan, 10, 20, 30, 60, np.nan]]).T y = np.array([0] * 5 + [1] * 5) @@ -2485,8 +2520,8 @@ def test_missing_values_missing_both_classes_has_nan(criterion): @pytest.mark.parametrize( "tree", [ - DecisionTreeClassifier(splitter="random"), DecisionTreeRegressor(criterion="absolute_error"), + ExtraTreeRegressor(criterion="absolute_error"), ], ) def test_missing_value_errors(sparse_container, tree): @@ -2502,7 +2537,8 @@ def test_missing_value_errors(sparse_container, tree): tree.fit(X, y) -def test_missing_values_poisson(): +@pytest.mark.parametrize("Tree", REG_TREES.values()) +def test_missing_values_poisson(Tree): """Smoke test for poisson regression and missing values.""" X, y = diabetes.data.copy(), diabetes.target @@ -2510,7 +2546,7 @@ def test_missing_values_poisson(): X[::5, 0] = np.nan X[::6, -1] = np.nan - reg = DecisionTreeRegressor(criterion="poisson", random_state=42) + reg = Tree(criterion="poisson", random_state=42) reg.fit(X, y) y_pred = reg.predict(X) @@ -2524,20 +2560,31 @@ def make_friedman1_classification(*args, **kwargs): @pytest.mark.parametrize( - "make_data,Tree", + "make_data, Tree, tolerance", [ - (datasets.make_friedman1, DecisionTreeRegressor), - (make_friedman1_classification, DecisionTreeClassifier), + # Due to the sine link between X and y, we expect the native handling of + # missing values to always be better than the naive mean imputation in the + # regression case. + # + # Due to randomness in ExtraTree, we expect the native handling of missing + # values to be sometimes better than the naive mean imputation, but not always + (datasets.make_friedman1, DecisionTreeRegressor, 0), + (datasets.make_friedman1, ExtraTreeRegressor, 0.07), + (make_friedman1_classification, DecisionTreeClassifier, 0.03), + (make_friedman1_classification, ExtraTreeClassifier, 0.12), ], ) @pytest.mark.parametrize("sample_weight_train", [None, "ones"]) def test_missing_values_is_resilience( - make_data, Tree, sample_weight_train, global_random_seed + make_data, Tree, sample_weight_train, global_random_seed, tolerance ): """Check that trees can deal with missing values have decent performance.""" n_samples, n_features = 5_000, 10 X, y = make_data( - n_samples=n_samples, n_features=n_features, random_state=global_random_seed + n_samples=n_samples, + n_features=n_features, + noise=1.0, + random_state=global_random_seed, ) X_missing = X.copy() @@ -2551,28 +2598,37 @@ def test_missing_values_is_resilience( else: sample_weight = None - native_tree = Tree(max_depth=10, random_state=global_random_seed) + # max_depth is used to avoid overfitting and also improve the runtime + # of the test. + max_depth = 10 + native_tree = Tree(max_depth=max_depth, random_state=global_random_seed) native_tree.fit(X_missing_train, y_train, sample_weight=sample_weight) score_native_tree = native_tree.score(X_missing_test, y_test) tree_with_imputer = make_pipeline( - SimpleImputer(), Tree(max_depth=10, random_state=global_random_seed) + SimpleImputer(), Tree(max_depth=max_depth, random_state=global_random_seed) ) tree_with_imputer.fit(X_missing_train, y_train) score_tree_with_imputer = tree_with_imputer.score(X_missing_test, y_test) - assert ( - score_native_tree > score_tree_with_imputer - ), f"{score_native_tree=} should be strictly greater than {score_tree_with_imputer}" + assert score_native_tree + tolerance > score_tree_with_imputer, ( + f"{score_native_tree=} + {tolerance} should be strictly greater than" + f" {score_tree_with_imputer}" + ) -def test_missing_value_is_predictive(): +# A single ExtraTree will randomly send missing values down the left, or right child, +# and therefore will not necessarily have the same performance as the greedy +# handling of missing values. +@pytest.mark.parametrize("Tree, expected_score", zip(CLF_TREES.values(), [0.85, 0.53])) +def test_missing_value_is_predictive(Tree, expected_score, global_random_seed): """Check the tree learns when only the missing value is predictive.""" rng = np.random.RandomState(0) - n_samples = 1000 + n_samples = 500 - X = rng.standard_normal(size=(n_samples, 10)) - y = rng.randint(0, high=2, size=n_samples) + X = rng.standard_normal(size=(n_samples, 20)) + y = np.concatenate([np.zeros(n_samples // 2), np.ones(n_samples // 2)]) + # y = rng.randint(0, high=2, size=n_samples) # Create a predictive feature using `y` and with some noise X_random_mask = rng.choice([False, True], size=n_samples, p=[0.95, 0.05]) @@ -2584,11 +2640,14 @@ def test_missing_value_is_predictive(): X[:, 5] = X_predictive - X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=rng) - tree = DecisionTreeClassifier(random_state=rng).fit(X_train, y_train) + tree = Tree(random_state=global_random_seed) - assert tree.score(X_train, y_train) >= 0.85 - assert tree.score(X_test, y_test) >= 0.85 + # Check that the tree can learn the predictive feature + # over an average of cross-validation fits. + tree_cv_score = cross_val_score(tree, X, y, cv=5).mean() + assert ( + tree_cv_score >= expected_score + ), f"Expected CV score: {expected_score} but got {tree_cv_score}" @pytest.mark.parametrize( @@ -2633,6 +2692,7 @@ def test_deterministic_pickle(): assert pickle1 == pickle2 +@pytest.mark.parametrize("Tree", [DecisionTreeRegressor, ExtraTreeRegressor]) @pytest.mark.parametrize( "X", [ @@ -2645,7 +2705,7 @@ def test_deterministic_pickle(): ], ) @pytest.mark.parametrize("criterion", ["squared_error", "friedman_mse"]) -def test_regression_tree_missing_values_toy(X, criterion): +def test_regression_tree_missing_values_toy(Tree, X, criterion): """Check that we properly handle missing values in regression trees using a toy dataset. @@ -2662,11 +2722,15 @@ def test_regression_tree_missing_values_toy(X, criterion): X = X.reshape(-1, 1) y = np.arange(6) - tree = DecisionTreeRegressor(criterion=criterion, random_state=0).fit(X, y) + tree = Tree(criterion=criterion, random_state=0).fit(X, y) tree_ref = clone(tree).fit(y.reshape(-1, 1), y) + assert all(tree.tree_.impurity >= 0) # MSE should always be positive - # Check the impurity match after the first split - assert_allclose(tree.tree_.impurity[:2], tree_ref.tree_.impurity[:2]) + + # Note: the impurity matches after the first split only on greedy trees + if Tree is DecisionTreeRegressor: + # Check the impurity match after the first split + assert_allclose(tree.tree_.impurity[:2], tree_ref.tree_.impurity[:2]) # Find the leaves with a single sample where the MSE should be 0 leaves_idx = np.flatnonzero( From 79afe9987cda921bb81c00381198217e9a32fac5 Mon Sep 17 00:00:00 2001 From: awwwyan <109622291+ovenpickled@users.noreply.github.com> Date: Tue, 9 Jul 2024 15:35:50 +0530 Subject: [PATCH 13/20] DOC Fix UserWarning in plot_gpr_prior_posterior (#29380) --- examples/gaussian_process/plot_gpr_prior_posterior.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/gaussian_process/plot_gpr_prior_posterior.py b/examples/gaussian_process/plot_gpr_prior_posterior.py index e0f1a05ad3ec9..df4ab89719678 100644 --- a/examples/gaussian_process/plot_gpr_prior_posterior.py +++ b/examples/gaussian_process/plot_gpr_prior_posterior.py @@ -126,8 +126,8 @@ def plot_gpr_samples(gpr_model, n_samples, ax): ) # %% -# Rational Quadradtic kernel -# .......................... +# Rational Quadratic kernel +# ......................... from sklearn.gaussian_process.kernels import RationalQuadratic kernel = 1.0 * RationalQuadratic(length_scale=1.0, alpha=0.1, alpha_bounds=(1e-5, 1e15)) @@ -200,7 +200,7 @@ def plot_gpr_samples(gpr_model, n_samples, ax): kernel = ConstantKernel(0.1, (0.01, 10.0)) * ( DotProduct(sigma_0=1.0, sigma_0_bounds=(0.1, 10.0)) ** 2 ) -gpr = GaussianProcessRegressor(kernel=kernel, random_state=0) +gpr = GaussianProcessRegressor(kernel=kernel, random_state=0, normalize_y=True) fig, axs = plt.subplots(nrows=2, sharex=True, sharey=True, figsize=(10, 8)) From 1fd6ca90604144c0e36159f9759da366b9eb290b Mon Sep 17 00:00:00 2001 From: notPlancha Date: Tue, 9 Jul 2024 12:07:55 +0100 Subject: [PATCH 14/20] DOC Update biclustering documentation to point to used scipy algorithm (#29371) --- doc/modules/biclustering.rst | 5 +++-- sklearn/metrics/cluster/_bicluster.py | 10 +++++++--- 2 files changed, 10 insertions(+), 5 deletions(-) diff --git a/doc/modules/biclustering.rst b/doc/modules/biclustering.rst index 503a535c408f0..4370c56f63e9d 100644 --- a/doc/modules/biclustering.rst +++ b/doc/modules/biclustering.rst @@ -288,7 +288,8 @@ available: 2. Assign biclusters from one set to another in a one-to-one fashion to maximize the sum of their similarities. This step is performed - using the Hungarian algorithm. + using :func:`scipy.optimize.linear_sum_assignment`, which uses a + modified Jonker-Volgenant algorithm. 3. The final sum of similarities is divided by the size of the larger set. @@ -302,4 +303,4 @@ are identical. * Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis for bicluster acquisition - `__. + `__. \ No newline at end of file diff --git a/sklearn/metrics/cluster/_bicluster.py b/sklearn/metrics/cluster/_bicluster.py index 713d0bee8fa2e..ad2153878b37e 100644 --- a/sklearn/metrics/cluster/_bicluster.py +++ b/sklearn/metrics/cluster/_bicluster.py @@ -57,8 +57,9 @@ def _pairwise_similarity(a, b, similarity): def consensus_score(a, b, *, similarity="jaccard"): """The similarity of two sets of biclusters. - Similarity between individual biclusters is computed. Then the - best matching between sets is found using the Hungarian algorithm. + Similarity between individual biclusters is computed. Then the best + matching between sets is found by solving a linear sum assignment problem, + using a modified Jonker-Volgenant algorithm. The final score is the sum of similarities divided by the size of the larger set. @@ -83,9 +84,12 @@ def consensus_score(a, b, *, similarity="jaccard"): Consensus score, a non-negative value, sum of similarities divided by size of larger set. + See Also + -------- + scipy.optimize.linear_sum_assignment : Solve the linear sum assignment problem. + References ---------- - * Hochreiter, Bodenhofer, et. al., 2010. `FABIA: factor analysis for bicluster acquisition `__. From df25e3747b36bf11d328fe2cc39561a7f933aefd Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Tue, 9 Jul 2024 15:13:13 +0200 Subject: [PATCH 15/20] DOC Add lock-file command to help debug CI issues (#29435) Co-authored-by: Arturo Amor <86408019+ArturoAmorQ@users.noreply.github.com> --- doc/developers/tips.rst | 51 +++++++++++++++++++++++++++++++++++++++++ 1 file changed, 51 insertions(+) diff --git a/doc/developers/tips.rst b/doc/developers/tips.rst index 3dbc35cec68d0..1c6ea5ba6f6f4 100644 --- a/doc/developers/tips.rst +++ b/doc/developers/tips.rst @@ -242,6 +242,57 @@ PR: Don't change unrelated Please do not change unrelated lines. It makes your contribution harder to review and may introduce merge conflicts to other pull requests. +.. _debugging_ci_issues: + +Debugging CI issues +------------------- + +CI issues may arise for a variety of reasons, so this is by no means a +comprehensive guide, but rather a list of useful tips and tricks. + +Using a lock-file to get an environment close to the CI ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ + +`conda-lock` can be used to create a conda environment with the exact same +conda and pip packages as on the CI. For example, the following command will +create a conda environment named `scikit-learn-doc` that is similar to the CI: + +.. prompt:: bash $ + + conda-lock install -n scikit-learn-doc build_tools/circle/doc_linux-64_conda.lock + +.. note:: + + It only works if you have the same OS as the CI build (check `platform:` in + the lock-file). For example, the previous command will only work if you are + on a Linux machine. Also this may not allow you to reproduce some of the + issues that are more tied to the particularities of the CI environment, for + example CPU architecture reported by OpenBLAS in `sklearn.show_versions()`. + +If you don't have the same OS as the CI build you can still create a conda +environment from the right environment yaml file, although it won't be as close +as the CI environment as using the associated lock-file. For example for the +doc build: + +.. prompt:: bash $ + + conda env create -n scikit-learn-doc -f build_tools/circle/doc_environment.yml -y + +This may not give you exactly the same package versions as in the CI for a +variety of reasons, for example: + +- some packages may have had new releases between the time the lock files were + last updated in the `main` branch and the time you run the `conda create` + command. You can always try to look at the version in the lock-file and + specify the versions by hand for some specific packages that you think would + help reproducing the issue. +- different packages may be installed by default depending on the OS. For + example, the default BLAS library when installing numpy is OpenBLAS on Linux + and MKL on Windows. + +Also the problem may be OS specific so the only way to be able to reproduce +would be to have the same OS as the CI build. + .. highlight:: default Debugging memory errors in Cython with valgrind From a6a5397bcb95c0653df449ea8ae982d6bc6566c0 Mon Sep 17 00:00:00 2001 From: Tim Head Date: Tue, 9 Jul 2024 16:37:18 +0200 Subject: [PATCH 16/20] CI Move CUDA CI to `pull_request` trigger (#29376) Co-authored-by: Olivier Grisel --- .github/workflows/cuda-gpu-ci.yml | 31 ++++++++++++++----------- .github/workflows/update-lock-files.yml | 8 +++---- 2 files changed, 22 insertions(+), 17 deletions(-) diff --git a/.github/workflows/cuda-gpu-ci.yml b/.github/workflows/cuda-gpu-ci.yml index 5175993d02e7e..802293f07189e 100644 --- a/.github/workflows/cuda-gpu-ci.yml +++ b/.github/workflows/cuda-gpu-ci.yml @@ -1,31 +1,36 @@ name: CUDA GPU + +# Only run this workflow when a Pull Request is labeled with the +# 'CUDA CI' label. on: - workflow_dispatch: - inputs: - commit_hash: - description: Commit hash to test - required: true + pull_request: + types: + - labeled -permissions: read-all +# In order to remove the "CUDA CI" label we need to have write permissions for PRs +permissions: + pull-requests: write jobs: tests: + if: contains(github.event.pull_request.labels.*.name, 'CUDA CI') runs-on: group: cuda-gpu-runner-group + # Set this high enough so that the tests can comforatble run. We set a + # timeout to make abusing this workflow less attractive. + timeout-minutes: 20 name: Run Array API unit tests steps: + - uses: actions-ecosystem/action-remove-labels@v1 + with: + labels: CUDA CI - uses: actions/setup-python@v5 with: # XXX: The 3.12.4 release of Python on GitHub Actions is corrupted: # https://github.com/actions/setup-python/issues/886 python-version: '3.12.3' - - uses: actions/checkout@v4 - with: - ref: ${{ inputs.commit_hash }} - - name: PRs associated with commit - run: | - echo "This commit belongs to PR(s):" - git ls-remote origin 'pull/*/head' | grep -F -f <(git rev-parse HEAD) | awk -F'/' '{print $3}' + - name: Checkout main repository + uses: actions/checkout@v4 - name: Cache conda environment id: cache-conda uses: actions/cache@v4 diff --git a/.github/workflows/update-lock-files.yml b/.github/workflows/update-lock-files.yml index 143d8d9ea28f9..4f149f58cac07 100644 --- a/.github/workflows/update-lock-files.yml +++ b/.github/workflows/update-lock-files.yml @@ -6,9 +6,9 @@ on: schedule: - cron: '0 5 * * 1' -# XXX Set the right permissions, per step?? -# Can we set read only at the global level here and then elevate to write for some steps? -#permissions: read-all +# In order to add the "CUDA CI" label we need to have write permissions for PRs +permissions: + pull-requests: write jobs: update_lock_files: @@ -68,7 +68,7 @@ jobs: env: GH_TOKEN: ${{ github.token }} run: | - gh workflow run .github/workflows/cuda-gpu-ci.yml -f commit_hash=`git rev-parse HEAD` + gh pr edit ${{steps.cpr.outputs.pull-request-number}} --add-label "CUDA CI" - name: Check Pull Request if: steps.cpr.outputs.pull-request-number != '' From a922568cafdf8aaa8c90530d76e85f99c41df6be Mon Sep 17 00:00:00 2001 From: Olivier Grisel Date: Tue, 9 Jul 2024 17:53:52 +0200 Subject: [PATCH 17/20] Fix tests for numpy 2 and array api compat (#29436) --- ...latest_pip_openblas_pandas_environment.yml | 2 + ...st_pip_openblas_pandas_linux-64_conda.lock | 4 +- .../update_environments_and_lock_files.py | 5 ++ sklearn/utils/_array_api.py | 21 ++++----- sklearn/utils/tests/test_array_api.py | 47 ++++++++++++------- 5 files changed, 50 insertions(+), 29 deletions(-) diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml index adb7add7622e1..c0d6aeaa717c0 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml +++ b/build_tools/azure/pylatest_pip_openblas_pandas_environment.yml @@ -27,3 +27,5 @@ dependencies: - numpydoc - lightgbm - scikit-image + - array-api-compat + - array-api-strict diff --git a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock index e2ffa14d39b43..ac4c92a671ed4 100644 --- a/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock +++ b/build_tools/azure/pylatest_pip_openblas_pandas_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: af52e4ce613b7668e1e28daaea07461722275d345395a5eaced4e07a16998179 +# input_hash: 11d97b96088b6b1eaf3b774050152e7899f0a6ab757350df2efd44b2de3a5f75 @EXPLICIT https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda#c3473ff8bdb3d124ed5ff11ec380d6f9 https://repo.anaconda.com/pkgs/main/linux-64/ca-certificates-2024.3.11-h06a4308_0.conda#08529eb3504712baabcbda266a19feb7 @@ -24,6 +24,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/setuptools-69.5.1-py39h06a4308_0.co https://repo.anaconda.com/pkgs/main/linux-64/wheel-0.43.0-py39h06a4308_0.conda#40bb60408c7433d767fd8c65b35bc4a0 https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce3af15cfecd12e4dda8c5cef5fb7 # pip alabaster @ https://files.pythonhosted.org/packages/32/34/d4e1c02d3bee589efb5dfa17f88ea08bdb3e3eac12bc475462aec52ed223/alabaster-0.7.16-py3-none-any.whl#sha256=b46733c07dce03ae4e150330b975c75737fa60f0a7c591b6c8bf4928a28e2c92 +# pip array-api-compat @ https://files.pythonhosted.org/packages/05/ae/2f11031bb9f819f6efaaa66b720b37928fbb0087161fcbae3465ae374a18/array_api_compat-1.7.1-py3-none-any.whl#sha256=6974f51775972f39edbca39e08f1c2e43c51401c093a0fea5ac7159875095d8a # pip babel @ https://files.pythonhosted.org/packages/27/45/377f7e32a5c93d94cd56542349b34efab5ca3f9e2fd5a68c5e93169aa32d/Babel-2.15.0-py3-none-any.whl#sha256=08706bdad8d0a3413266ab61bd6c34d0c28d6e1e7badf40a2cebe67644e2e1fb # pip certifi @ https://files.pythonhosted.org/packages/1c/d5/c84e1a17bf61d4df64ca866a1c9a913874b4e9bdc131ec689a0ad013fb36/certifi-2024.7.4-py3-none-any.whl#sha256=c198e21b1289c2ab85ee4e67bb4b4ef3ead0892059901a8d5b622f24a1101e90 # pip charset-normalizer @ https://files.pythonhosted.org/packages/98/69/5d8751b4b670d623aa7a47bef061d69c279e9f922f6705147983aa76c3ce/charset_normalizer-3.3.2-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=b261ccdec7821281dade748d088bb6e9b69e6d15b30652b74cbbac25e280b796 @@ -63,6 +64,7 @@ https://repo.anaconda.com/pkgs/main/linux-64/pip-24.0-py39h06a4308_0.conda#7f8ce # pip tzdata @ https://files.pythonhosted.org/packages/65/58/f9c9e6be752e9fcb8b6a0ee9fb87e6e7a1f6bcab2cdc73f02bb7ba91ada0/tzdata-2024.1-py2.py3-none-any.whl#sha256=9068bc196136463f5245e51efda838afa15aaeca9903f49050dfa2679db4d252 # pip urllib3 @ https://files.pythonhosted.org/packages/ca/1c/89ffc63a9605b583d5df2be791a27bc1a42b7c32bab68d3c8f2f73a98cd4/urllib3-2.2.2-py3-none-any.whl#sha256=a448b2f64d686155468037e1ace9f2d2199776e17f0a46610480d311f73e3472 # pip zipp @ https://files.pythonhosted.org/packages/20/38/f5c473fe9b90c8debdd29ea68d5add0289f1936d6f923b6b9cc0b931194c/zipp-3.19.2-py3-none-any.whl#sha256=f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c +# pip array-api-strict @ https://files.pythonhosted.org/packages/08/06/aba69bce257fd1cda0d1db616c12728af0f46878a5cc1923fcbb94201947/array_api_strict-2.0.1-py3-none-any.whl#sha256=f74cbf0d0c182fcb45c5ee7f28f9c7b77e6281610dfbbdd63be60b1a5a7872b3 # pip contourpy @ https://files.pythonhosted.org/packages/31/a2/2f12e3a6e45935ff694654b710961b03310b0e1ec997ee9f416d3c873f87/contourpy-1.2.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=e1d59258c3c67c865435d8fbeb35f8c59b8bef3d6f46c1f29f6123556af28445 # pip coverage @ https://files.pythonhosted.org/packages/c4/b4/0cbc18998613f8caaec793ad5878d2450382dfac80e65d352fb7cd9cc1dc/coverage-7.5.4-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl#sha256=dbc5958cb471e5a5af41b0ddaea96a37e74ed289535e8deca404811f6cb0bc3d # pip imageio @ https://files.pythonhosted.org/packages/3d/84/f1647217231f6cc46883e5d26e870cc3e1520d458ecd52d6df750810d53c/imageio-2.34.2-py3-none-any.whl#sha256=a0bb27ec9d5bab36a9f4835e51b21d2cb099e1f78451441f94687ff3404b79f8 diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 87b4c6478a3f4..12584da3c28b8 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -225,9 +225,14 @@ def remove_from(alist, to_remove): "pip_dependencies": ( remove_from(common_dependencies, ["python", "blas", "pip"]) + docstring_test_dependencies + # Test with some optional dependencies + ["lightgbm", "scikit-image"] + # Test array API on CPU without PyTorch + + ["array-api-compat", "array-api-strict"] ), "package_constraints": { + # XXX: we would like to use the latest version of Python but this makes + # the CI much slower. We need to investigate why. "python": "3.9", }, }, diff --git a/sklearn/utils/_array_api.py b/sklearn/utils/_array_api.py index 542a8136da661..a00d250ab31d2 100644 --- a/sklearn/utils/_array_api.py +++ b/sklearn/utils/_array_api.py @@ -672,16 +672,10 @@ def _average(a, axis=None, weights=None, normalize=True, xp=None): f"weights {tuple(weights.shape)} differ." ) - if weights.ndim != 1: - raise TypeError( - f"1D weights expected when a.shape={tuple(a.shape)} and " - f"weights.shape={tuple(weights.shape)} differ." - ) - - if size(weights) != a.shape[axis]: + if tuple(weights.shape) != (a.shape[axis],): raise ValueError( - f"Length of weights {size(weights)} not compatible with " - f" a.shape={tuple(a.shape)} and {axis=}." + f"Shape of weights weights.shape={tuple(weights.shape)} must be " + f"consistent with a.shape={tuple(a.shape)} and {axis=}." ) # If weights are 1D, add singleton dimensions for broadcasting @@ -839,9 +833,14 @@ def _estimator_with_converted_arrays(estimator, converter): return new_estimator -def _atol_for_type(dtype): +def _atol_for_type(dtype_or_dtype_name): """Return the absolute tolerance for a given numpy dtype.""" - return numpy.finfo(dtype).eps * 100 + if dtype_or_dtype_name is None: + # If no dtype is specified when running tests for a given namespace, we + # expect the same floating precision level as NumPy's default floating + # point dtype. + dtype_or_dtype_name = numpy.float64 + return numpy.finfo(dtype_or_dtype_name).eps * 100 def indexing_dtype(xp): diff --git a/sklearn/utils/tests/test_array_api.py b/sklearn/utils/tests/test_array_api.py index beff36499fb92..71f499f7a8dae 100644 --- a/sklearn/utils/tests/test_array_api.py +++ b/sklearn/utils/tests/test_array_api.py @@ -34,7 +34,7 @@ assert_array_equal, skip_if_array_api_compat_not_configured, ) -from sklearn.utils.fixes import _IS_32BIT, CSR_CONTAINERS +from sklearn.utils.fixes import _IS_32BIT, CSR_CONTAINERS, np_version, parse_version @pytest.mark.parametrize("X", [numpy.asarray([1, 2, 3]), [1, 2, 3]]) @@ -67,7 +67,12 @@ def test_get_namespace_ndarray_with_dispatch(): with config_context(array_api_dispatch=True): xp_out, is_array_api_compliant = get_namespace(X_np) assert is_array_api_compliant - assert xp_out is array_api_compat.numpy + if np_version >= parse_version("2.0.0"): + # NumPy 2.0+ is an array API compliant library. + assert xp_out is numpy + else: + # Older NumPy versions require the compatibility layer. + assert xp_out is array_api_compat.numpy @skip_if_array_api_compat_not_configured @@ -135,7 +140,7 @@ def test_asarray_with_order_ignored(): @pytest.mark.parametrize( - "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations() + "array_namespace, device_, dtype_name", yield_namespace_device_dtype_combinations() ) @pytest.mark.parametrize( "weights, axis, normalize, expected", @@ -167,19 +172,22 @@ def test_asarray_with_order_ignored(): ], ) def test_average( - array_namespace, device, dtype_name, weights, axis, normalize, expected + array_namespace, device_, dtype_name, weights, axis, normalize, expected ): - xp = _array_api_for_tests(array_namespace, device) + xp = _array_api_for_tests(array_namespace, device_) array_in = numpy.asarray([[1, 2, 3], [4, 5, 6]], dtype=dtype_name) - array_in = xp.asarray(array_in, device=device) + array_in = xp.asarray(array_in, device=device_) if weights is not None: weights = numpy.asarray(weights, dtype=dtype_name) - weights = xp.asarray(weights, device=device) + weights = xp.asarray(weights, device=device_) with config_context(array_api_dispatch=True): result = _average(array_in, axis=axis, weights=weights, normalize=normalize) - assert getattr(array_in, "device", None) == getattr(result, "device", None) + if np_version < parse_version("2.0.0") or np_version >= parse_version("2.1.0"): + # NumPy 2.0 has a problem with the device attribute of scalar arrays: + # https://github.com/numpy/numpy/issues/26850 + assert device(array_in) == device(result) result = _convert_to_numpy(result, xp) assert_allclose(result, expected, atol=_atol_for_type(dtype_name)) @@ -226,14 +234,15 @@ def test_average_raises_with_wrong_dtype(array_namespace, device, dtype_name): ( 0, [[1, 2]], - TypeError, - "1D weights expected", + # NumPy 2 raises ValueError, NumPy 1 raises TypeError + (ValueError, TypeError), + "weights", # the message is different for NumPy 1 and 2... ), ( 0, [1, 2, 3, 4], ValueError, - "Length of weights", + "weights", ), (0, [-1, 1], ZeroDivisionError, "Weights sum to zero, can't be normalized"), ), @@ -580,18 +589,18 @@ def test_get_namespace_and_device(): @pytest.mark.parametrize( - "array_namespace, device, dtype_name", yield_namespace_device_dtype_combinations() + "array_namespace, device_, dtype_name", yield_namespace_device_dtype_combinations() ) @pytest.mark.parametrize("csr_container", CSR_CONTAINERS) @pytest.mark.parametrize("axis", [0, 1, None, -1, -2]) @pytest.mark.parametrize("sample_weight_type", [None, "int", "float"]) def test_count_nonzero( - array_namespace, device, dtype_name, csr_container, axis, sample_weight_type + array_namespace, device_, dtype_name, csr_container, axis, sample_weight_type ): from sklearn.utils.sparsefuncs import count_nonzero as sparse_count_nonzero - xp = _array_api_for_tests(array_namespace, device) + xp = _array_api_for_tests(array_namespace, device_) array = numpy.array([[0, 3, 0], [2, -1, 0], [0, 0, 0], [9, 8, 7], [4, 0, 5]]) if sample_weight_type == "int": sample_weight = numpy.asarray([1, 2, 2, 3, 1]) @@ -602,12 +611,16 @@ def test_count_nonzero( expected = sparse_count_nonzero( csr_container(array), axis=axis, sample_weight=sample_weight ) - array_xp = xp.asarray(array, device=device) + array_xp = xp.asarray(array, device=device_) with config_context(array_api_dispatch=True): result = _count_nonzero( - array_xp, xp=xp, device=device, axis=axis, sample_weight=sample_weight + array_xp, xp=xp, device=device_, axis=axis, sample_weight=sample_weight ) assert_allclose(_convert_to_numpy(result, xp=xp), expected) - assert getattr(array_xp, "device", None) == getattr(result, "device", None) + + if np_version < parse_version("2.0.0") or np_version >= parse_version("2.1.0"): + # NumPy 2.0 has a problem with the device attribute of scalar arrays: + # https://github.com/numpy/numpy/issues/26850 + assert device(array_xp) == device(result) From 4cc331fae29e423f2d47d6f653d4f04559fd9d4e Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Lo=C3=AFc=20Est=C3=A8ve?= Date: Wed, 10 Jul 2024 09:52:33 +0200 Subject: [PATCH 18/20] CI Unpin PyTorch in pylatest_conda_forge_mkl build (#29445) --- ...latest_conda_forge_mkl_linux-64_conda.lock | 148 ++++++++++-------- ...t_conda_forge_mkl_linux-64_environment.yml | 2 +- .../update_environments_and_lock_files.py | 1 - 3 files changed, 88 insertions(+), 63 deletions(-) diff --git a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock index d7d27d3039c43..957622cc9fa50 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_conda.lock @@ -1,6 +1,6 @@ # Generated by conda-lock. # platform: linux-64 -# input_hash: 29e3d76c436ee8cb86d5976aa41ef8822f991c469202b8d12fc27e43576ec805 +# input_hash: 8a086d6ae339f1442cb02f643c8e4bc8c6eab83aa7c923d7021089b217bb7eaf @EXPLICIT https://conda.anaconda.org/conda-forge/linux-64/_libgcc_mutex-0.1-conda_forge.tar.bz2#d7c89558ba9fa0495403155b64376d81 https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2024.7.4-hbcca054_0.conda#23ab7665c5f63cfb9f1f6195256daac6 @@ -9,7 +9,8 @@ https://conda.anaconda.org/conda-forge/noarch/font-ttf-inconsolata-3.000-h77eed3 https://conda.anaconda.org/conda-forge/noarch/font-ttf-source-code-pro-2.038-h77eed37_0.tar.bz2#4d59c254e01d9cde7957100457e2d5fb https://conda.anaconda.org/conda-forge/noarch/font-ttf-ubuntu-0.83-h77eed37_2.conda#cbbe59391138ea5ad3658c76912e147f https://conda.anaconda.org/conda-forge/linux-64/ld_impl_linux-64-2.40-hf3520f5_7.conda#b80f2f396ca2c28b8c14c437a4ed1e74 -https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.11-4_cp311.conda#d786502c97404c94d7d58d258a445a65 +https://conda.anaconda.org/conda-forge/linux-64/mkl-include-2023.2.0-h84fe81f_50496.conda#7af9fd0b2d7219f4a4200a34561340f6 +https://conda.anaconda.org/conda-forge/linux-64/python_abi-3.12-4_cp312.conda#dccc2d142812964fcc6abdc97b672dff https://conda.anaconda.org/conda-forge/noarch/tzdata-2024a-h0c530f3_0.conda#161081fc7cec0bfda0d86d7cb595f8d8 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-forge-1-0.tar.bz2#f766549260d6815b0c52253f1fb1bb29 https://conda.anaconda.org/conda-forge/noarch/fonts-conda-ecosystem-1-0.tar.bz2#fee5683a3f04bd15cbd8318b096a27ab @@ -17,13 +18,13 @@ https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-2_kmp_llvm.tar https://conda.anaconda.org/conda-forge/linux-64/libgcc-ng-14.1.0-h77fa898_0.conda#ca0fad6a41ddaef54a153b78eccb5037 https://conda.anaconda.org/conda-forge/linux-64/alsa-lib-1.2.12-h4ab18f5_0.conda#7ed427f0871fd41cb1d9c17727c17589 https://conda.anaconda.org/conda-forge/linux-64/attr-2.5.1-h166bdaf_1.tar.bz2#d9c69a24ad678ffce24c6543a0176b00 -https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.0-hd590300_0.conda#71b89db63b5b504e7afc8ad901172e1e +https://conda.anaconda.org/conda-forge/linux-64/aws-c-common-0.9.23-h4ab18f5_0.conda#94d61ae2b2b701008a9d52ce6bbead27 https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hd590300_5.conda#69b8b6202a07720f448be700e300ccf4 https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.28.1-hd590300_0.conda#dcde58ff9a1f30b0037a2315d1846d1f https://conda.anaconda.org/conda-forge/linux-64/gettext-tools-0.22.5-h59595ed_2.conda#985f2f453fb72408d6b6f1be0f324033 https://conda.anaconda.org/conda-forge/linux-64/keyutils-1.6.1-h166bdaf_0.tar.bz2#30186d27e2c9fa62b45fb1476b7200e3 https://conda.anaconda.org/conda-forge/linux-64/lame-3.100-h166bdaf_1003.tar.bz2#a8832b479f93521a9e7b5b743803be51 -https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.0.9-h166bdaf_9.conda#61641e239f96eae2b8492dc7e755828c +https://conda.anaconda.org/conda-forge/linux-64/libbrotlicommon-1.1.0-hd590300_1.conda#aec6c91c7371c26392a06708a73c70e5 https://conda.anaconda.org/conda-forge/linux-64/libdeflate-1.20-hd590300_0.conda#8e88f9389f1165d7c0936fe40d9a9a79 https://conda.anaconda.org/conda-forge/linux-64/libev-4.33-hd590300_2.conda#172bf1cd1ff8629f2b1179945ed45055 https://conda.anaconda.org/conda-forge/linux-64/libexpat-2.6.2-h59595ed_0.conda#e7ba12deb7020dd080c6c70e7b6f6a3d @@ -33,12 +34,12 @@ https://conda.anaconda.org/conda-forge/linux-64/libgfortran5-14.1.0-hc5f4f2c_0.c https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.17-hd590300_2.conda#d66573916ffcf376178462f1b61c941e https://conda.anaconda.org/conda-forge/linux-64/libjpeg-turbo-3.0.0-hd590300_1.conda#ea25936bb4080d843790b586850f82b8 https://conda.anaconda.org/conda-forge/linux-64/libnsl-2.0.1-hd590300_0.conda#30fd6e37fe21f86f4bd26d6ee73eeec7 -https://conda.anaconda.org/conda-forge/linux-64/libnuma-2.0.18-h4ab18f5_2.conda#a263760479dbc7bc1f3df12707bd90dc https://conda.anaconda.org/conda-forge/linux-64/libogg-1.3.5-h4ab18f5_0.conda#601bfb4b3c6f0b844443bb81a56651e0 https://conda.anaconda.org/conda-forge/linux-64/libopus-1.3.1-h7f98852_1.tar.bz2#15345e56d527b330e1cacbdf58676e8f https://conda.anaconda.org/conda-forge/linux-64/libstdcxx-ng-14.1.0-hc0a3c3a_0.conda#1cb187a157136398ddbaae90713e2498 https://conda.anaconda.org/conda-forge/linux-64/libutf8proc-2.8.0-h166bdaf_0.tar.bz2#ede4266dc02e875fe1ea77b25dd43747 https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.38.1-h0b41bf4_0.conda#40b61aab5c7ba9ff276c41cfffe6b80b +https://conda.anaconda.org/conda-forge/linux-64/libuv-1.48.0-hd590300_0.conda#7e8b914b1062dd4386e3de4d82a3ead6 https://conda.anaconda.org/conda-forge/linux-64/libwebp-base-1.4.0-hd590300_0.conda#b26e8aa824079e1be0294e7152ca4559 https://conda.anaconda.org/conda-forge/linux-64/libxcrypt-4.4.36-hd590300_1.conda#5aa797f8787fe7a17d1b0821485b5adc https://conda.anaconda.org/conda-forge/linux-64/libzlib-1.3.1-h4ab18f5_1.conda#57d7dc60e9325e3de37ff8dffd18e814 @@ -55,19 +56,20 @@ https://conda.anaconda.org/conda-forge/linux-64/xorg-xextproto-7.3.0-h0b41bf4_10 https://conda.anaconda.org/conda-forge/linux-64/xorg-xf86vidmodeproto-2.3.1-h7f98852_1002.tar.bz2#3ceea9668625c18f19530de98b15d5b0 https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-h7f98852_1007.tar.bz2#b4a4381d54784606820704f7b5f05a15 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a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml index 30a2fe1d1812a..12fbd178dccb5 100644 --- a/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml +++ b/build_tools/azure/pylatest_conda_forge_mkl_linux-64_environment.yml @@ -23,7 +23,7 @@ dependencies: - pytest-cov - coverage - ccache - - pytorch=1.13 + - pytorch - pytorch-cpu - polars - pyarrow diff --git a/build_tools/update_environments_and_lock_files.py b/build_tools/update_environments_and_lock_files.py index 12584da3c28b8..a7f99c91a0735 100644 --- a/build_tools/update_environments_and_lock_files.py +++ b/build_tools/update_environments_and_lock_files.py @@ -129,7 +129,6 @@ def remove_from(alist, to_remove): ], "package_constraints": { "blas": "[build=mkl]", - "pytorch": "1.13", }, }, { From 775587b6b6bbc12d9e320b51ed007cabf28b576e Mon Sep 17 00:00:00 2001 From: Adam Li Date: Wed, 10 Jul 2024 07:53:49 -0400 Subject: [PATCH 19/20] FEA Support missing-values in `ExtraTrees*` (#28268) --- doc/whats_new/v1.6.rst | 5 +++++ sklearn/ensemble/tests/test_forest.py | 22 +++++++++++++++++----- sklearn/tree/_classes.py | 20 ++++++++++++++++++++ 3 files changed, 42 insertions(+), 5 deletions(-) diff --git a/doc/whats_new/v1.6.rst b/doc/whats_new/v1.6.rst index d4f1d14fabe23..62a6613e0a460 100644 --- a/doc/whats_new/v1.6.rst +++ b/doc/whats_new/v1.6.rst @@ -148,6 +148,11 @@ Changelog :pr:`28622` by :user:`Adam Li ` and :user:`Sérgio Pereira `. +- |Feature| :class:`ensemble.ExtraTreesClassifier` and :class:`ensemble.ExtraTreesRegressor` now support + missing-values in the data matrix `X`. Missing-values are handled by randomly moving all of + the samples to the left, or right child node as the tree is traversed. + :pr:`28268` by :user:`Adam Li `. + :mod:`sklearn.impute` ..................... diff --git a/sklearn/ensemble/tests/test_forest.py b/sklearn/ensemble/tests/test_forest.py index 14cd904521c3d..f2f687c2fb1d2 100644 --- a/sklearn/ensemble/tests/test_forest.py +++ b/sklearn/ensemble/tests/test_forest.py @@ -1767,6 +1767,8 @@ def test_estimators_samples(ForestClass, bootstrap, seed): [ (datasets.make_regression, RandomForestRegressor), (datasets.make_classification, RandomForestClassifier), + (datasets.make_regression, ExtraTreesRegressor), + (datasets.make_classification, ExtraTreesClassifier), ], ) def test_missing_values_is_resilient(make_data, Forest): @@ -1800,12 +1802,21 @@ def test_missing_values_is_resilient(make_data, Forest): assert score_with_missing >= 0.80 * score_without_missing -@pytest.mark.parametrize("Forest", [RandomForestClassifier, RandomForestRegressor]) +@pytest.mark.parametrize( + "Forest", + [ + RandomForestClassifier, + RandomForestRegressor, + ExtraTreesRegressor, + ExtraTreesClassifier, + ], +) def test_missing_value_is_predictive(Forest): """Check that the forest learns when missing values are only present for a predictive feature.""" rng = np.random.RandomState(0) n_samples = 300 + expected_score = 0.75 X_non_predictive = rng.standard_normal(size=(n_samples, 10)) y = rng.randint(0, high=2, size=n_samples) @@ -1835,19 +1846,20 @@ def test_missing_value_is_predictive(Forest): predictive_test_score = forest_predictive.score(X_predictive_test, y_test) - assert predictive_test_score >= 0.75 + assert predictive_test_score >= expected_score assert predictive_test_score >= forest_non_predictive.score( X_non_predictive_test, y_test ) -def test_non_supported_criterion_raises_error_with_missing_values(): +@pytest.mark.parametrize("Forest", FOREST_REGRESSORS.values()) +def test_non_supported_criterion_raises_error_with_missing_values(Forest): """Raise error for unsupported criterion when there are missing values.""" X = np.array([[0, 1, 2], [np.nan, 0, 2.0]]) y = [0.5, 1.0] - forest = RandomForestRegressor(criterion="absolute_error") + forest = Forest(criterion="absolute_error") - msg = "RandomForestRegressor does not accept missing values" + msg = ".*does not accept missing values" with pytest.raises(ValueError, match=msg): forest.fit(X, y) diff --git a/sklearn/tree/_classes.py b/sklearn/tree/_classes.py index 2678781fc931c..61c572554b3b6 100644 --- a/sklearn/tree/_classes.py +++ b/sklearn/tree/_classes.py @@ -1686,6 +1686,16 @@ def __init__( monotonic_cst=monotonic_cst, ) + def _more_tags(self): + # XXX: nan is only supported for dense arrays, but we set this for the + # common test to pass, specifically: check_estimators_nan_inf + allow_nan = self.splitter == "random" and self.criterion in { + "gini", + "log_loss", + "entropy", + } + return {"multilabel": True, "allow_nan": allow_nan} + class ExtraTreeRegressor(DecisionTreeRegressor): """An extremely randomized tree regressor. @@ -1929,3 +1939,13 @@ def __init__( ccp_alpha=ccp_alpha, monotonic_cst=monotonic_cst, ) + + def _more_tags(self): + # XXX: nan is only supported for dense arrays, but we set this for the + # common test to pass, specifically: check_estimators_nan_inf + allow_nan = self.splitter == "random" and self.criterion in { + "squared_error", + "friedman_mse", + "poisson", + } + return {"allow_nan": allow_nan} From fa14001fa19a262c7eb43b2ef3c0d6b56b4c8fad Mon Sep 17 00:00:00 2001 From: Lucy Liu Date: Wed, 10 Jul 2024 23:16:46 +1000 Subject: [PATCH 20/20] DOC Link to right user guide section in `StratifiedGroupKFold` docstring (#29447) --- sklearn/model_selection/_split.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/sklearn/model_selection/_split.py b/sklearn/model_selection/_split.py index d0a0252a9697e..2ab621e78dd69 100644 --- a/sklearn/model_selection/_split.py +++ b/sklearn/model_selection/_split.py @@ -866,15 +866,15 @@ class StratifiedGroupKFold(GroupsConsumerMixin, _BaseKFold): Each group will appear exactly once in the test set across all folds (the number of distinct groups has to be at least equal to the number of folds). - The difference between :class:`~sklearn.model_selection.GroupKFold` - and :class:`~sklearn.model_selection.StratifiedGroupKFold` is that + The difference between :class:`GroupKFold` + and `StratifiedGroupKFold` is that the former attempts to create balanced folds such that the number of distinct groups is approximately the same in each fold, whereas - StratifiedGroupKFold attempts to create folds which preserve the + `StratifiedGroupKFold` attempts to create folds which preserve the percentage of samples for each class as much as possible given the constraint of non-overlapping groups between splits. - Read more in the :ref:`User Guide `. + Read more in the :ref:`User Guide `. For visualisation of cross-validation behaviour and comparison between common scikit-learn split methods