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Merge branch 'main' into pairwise-distances-argkmin-plug
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‎build_tools/azure/install.sh

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@@ -23,10 +23,10 @@ make_conda() {
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}
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setup_ccache() {
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echo "Setting up ccache"
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echo "Setting up ccache with CCACHE_DIR=${CCACHE_DIR}"
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mkdir /tmp/ccache/
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which ccache
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for name in gcc g++ cc c++ x86_64-linux-gnu-gcc x86_64-linux-gnu-c++; do
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for name in gcc g++ cc c++ clang clang++ i686-linux-gnu-gcc i686-linux-gnu-c++ x86_64-linux-gnu-gcc x86_64-linux-gnu-c++ x86_64-apple-darwin13.4.0-clang x86_64-apple-darwin13.4.0-clang++; do
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ln -s $(which ccache) "/tmp/ccache/${name}"
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done
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export PATH="/tmp/ccache/:${PATH}"

‎build_tools/azure/posix-docker.yml

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@@ -36,18 +36,31 @@ jobs:
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DISTRIB: ''
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DOCKER_CONTAINER: ''
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SHOW_SHORT_SUMMARY: 'false'
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CCACHE_DIR: $(Pipeline.Workspace)/ccache
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CCACHE_COMPRESS: '1'
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strategy:
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matrix:
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${{ insert }}: ${{ parameters.matrix }}
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steps:
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- task: Cache@2
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inputs:
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key: '"ccache-v1" | "$(Agent.JobName)" | "$(Build.BuildNumber)"'
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restoreKeys: |
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"ccache-v1" | "$(Agent.JobName)"
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path: $(CCACHE_DIR)
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displayName: ccache
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continueOnError: true
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- script: >
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mkdir -p $CCACHE_DIR
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# Container is detached and sleeping, allowing steps to run commands
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# in the container. The TEST_DIR is mapped allowing the host to access
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# the JUNITXML file
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- script: >
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docker container run --rm
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--volume $TEST_DIR:/temp_dir
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--volume $PWD:/io
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--volume $CCACHE_DIR:/ccache
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-w /io
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--detach
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--name skcontainer
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-e SKLEARN_SKIP_NETWORK_TESTS=$SKLEARN_SKIP_NETWORK_TESTS
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-e BLAS=$BLAS
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-e CPU_COUNT=$CPU_COUNT
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-e CCACHE_DIR=/ccache
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-e CCACHE_COMPRESS=$CCACHE_COMPRESS
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$DOCKER_CONTAINER
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sleep 1000000
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displayName: 'Start container'

‎build_tools/azure/posix.yml

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@@ -50,7 +50,9 @@ jobs:
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condition: startsWith(variables['DISTRIB'], 'conda')
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- task: Cache@2
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inputs:
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key: '"$(Agent.JobName)"'
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key: '"ccache-v1" | "$(Agent.JobName)" | "$(Build.BuildNumber)"'
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restoreKeys: |
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"ccache-v1" | "$(Agent.JobName)"
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path: $(CCACHE_DIR)
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displayName: ccache
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continueOnError: true

‎doc/glossary.rst

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@@ -1604,6 +1604,7 @@ functions or non-estimator constructors.
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number of different distinct random seeds. Popular integer
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random seeds are 0 and `42
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<https://en.wikipedia.org/wiki/Answer_to_the_Ultimate_Question_of_Life%2C_the_Universe%2C_and_Everything>`_.
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Integer values must be in the range `[0, 2**32 - 1]`.
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A :class:`numpy.random.RandomState` instance
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Use the provided random state, only affecting other users

‎doc/whats_new/v1.1.rst

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@@ -273,6 +273,13 @@ Changelog
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F-statistic).
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:pr:`17819` by :user:`Juan Carlos Alfaro Jiménez <alfaro96>`.
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:mod:`sklearn.gaussian_process`
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...............................
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- |Fix| :class:`gaussian_process.GaussianProcessClassifier` raises
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a more informative error if `CompoundKernel` is passed via `kernel`.
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:pr:`22223` by :user:`MarcoM <marcozzxx810>`.
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:mod:`sklearn.impute`
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.....................
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- |Enhancement| :class:`linear_model.QuantileRegressor` support sparse input
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for the highs based solvers.
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:pr:`21086` by :user:`Venkatachalam Natchiappan <venkyyuvy>`.
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In addition, those solvers now use the CSC matrix right from the
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beginning which speeds up fitting.
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:pr:`22206` by :user:`Christian Lorentzen <lorentzenchr>`.
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- |Enhancement| Rename parameter `base_estimator` to `estimator` in
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:class:`linear_model.RANSACRegressor` to improve readability and consistency.
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:pr:`21481` by :user:`Guillaume Lemaitre <glemaitre>` and
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:user:`Andrés Babino <ababino>`.
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- |Enhancement| :func:`linear_model.ElasticNet` and
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and other linear model classes using coordinate descent show error
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messages when non-finite parameter weights are produced. :pr:`22148`
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by :user:`Christian Ritter <chritter>` and :user:`Norbert Preining <norbusan>`.
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- |Fix| :class:`linear_model.ElasticNetCV` now produces correct
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warning when `l1_ratio=0`.
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:pr:`21724` by :user:`Yar Khine Phyo <yarkhinephyo>`.
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A deprecation cycle was introduced.
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:pr:`21576` by :user:`Paul-Emile Dugnat <pedugnat>`.
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- |API| The `"wminkowski"` metric of :class:`sklearn.metrics.DistanceMetric` is deprecated
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and will be removed in version 1.3. Instead the existing `"minkowski"` metric now takes
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in an optional `w` parameter for weights. This deprecation aims at remaining consistent
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with SciPy 1.8 convention. :pr:`21873` by :user:`Yar Khine Phyo <yarkhinephyo>`
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- |Fix| :func:`metrics.silhouette_score` now supports integer input for precomputed
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distances. :pr:`22108` by `Thomas Fan`_.
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splits failed. Similarly raise an error during grid-search when the fits for
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all the models and all the splits failed. :pr:`21026` by :user:`Loïc Estève <lesteve>`.
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- |Enhancement| it is now possible to pass `scoring="matthews_corrcoef"` to all
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model selection tools with a `scoring` argument to use the Matthews
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correlation coefficient (MCC). :pr:`22203` by :user:`Olivier Grisel
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<ogrisel>`.
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- |Fix| :class:`model_selection.GridSearchCV`,
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:class:`model_selection.HalvingGridSearchCV`
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now validate input parameters in `fit` instead of `__init__`.
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instead of `__init__`. :pr:`21430` by :user:`Desislava Vasileva <DessyVV>` and
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:user:`Lucy Jimenez <LucyJimenez>`.
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:mod:`sklearn.neural_network`
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.............................
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- |Enhancement| :func:`neural_network.MLPClassifier` and
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:func:`neural_network.MLPRegressor` show error
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messages when optimizers produce non-finite parameter weights. :pr:`22150`
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by :user:`Christian Ritter <chritter>` and :user:`Norbert Preining <norbusan>`.
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:mod:`sklearn.pipeline`
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.......................
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‎examples/applications/plot_model_complexity_influence.py

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@@ -165,7 +165,8 @@ def _count_nonzero_coefficients(estimator):
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"alpha": 0.001,
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"loss": "modified_huber",
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"fit_intercept": True,
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"tol": 1e-3,
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"tol": 1e-1,
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"n_iter_no_change": 2,
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},
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"changing_param": "l1_ratio",
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"changing_param_values": [0.25, 0.5, 0.75, 0.9],

‎sklearn/cluster/_optics.py

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@@ -673,13 +673,13 @@ def cluster_optics_xi(
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Parameters
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----------
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reachability : ndarray of shape (n_samples,)
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Reachability distances calculated by OPTICS (`reachability_`)
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Reachability distances calculated by OPTICS (`reachability_`).
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predecessor : ndarray of shape (n_samples,)
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Predecessors calculated by OPTICS.
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ordering : ndarray of shape (n_samples,)
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OPTICS ordered point indices (`ordering_`)
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OPTICS ordered point indices (`ordering_`).
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min_samples : int > 1 or float between 0 and 1
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The same as the min_samples given to OPTICS. Up and down steep regions

‎sklearn/cluster/tests/test_hierarchical.py

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@@ -410,6 +410,8 @@ def test_vector_scikit_single_vs_scipy_single(seed):
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assess_same_labelling(cut, cut_scipy)
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# TODO: Remove filterwarnings in 1.3 when wminkowski is removed
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@pytest.mark.filterwarnings("ignore:WMinkowskiDistance:FutureWarning:sklearn")
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@pytest.mark.parametrize("metric_param_grid", METRICS_DEFAULT_PARAMS)
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def test_mst_linkage_core_memory_mapped(metric_param_grid):
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"""The MST-LINKAGE-CORE algorithm must work on mem-mapped dataset.

‎sklearn/discriminant_analysis.py

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@@ -631,8 +631,10 @@ def transform(self, X):
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Returns
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-------
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X_new : ndarray of shape (n_samples, n_components)
635-
Transformed data.
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X_new : ndarray of shape (n_samples, n_components) or \
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(n_samples, min(rank, n_components))
636+
Transformed data. In the case of the 'svd' solver, the shape
637+
is (n_samples, min(rank, n_components)).
636638
"""
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if self.solver == "lsqr":
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raise NotImplementedError(

‎sklearn/ensemble/_voting.py

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@@ -157,8 +157,8 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):
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estimators : list of (str, estimator) tuples
158158
Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones
159159
of those original estimators that will be stored in the class attribute
160-
``self.estimators_``. An estimator can be set to ``'drop'``
161-
using ``set_params``.
160+
``self.estimators_``. An estimator can be set to ``'drop'`` using
161+
:meth:`set_params`.
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.. versionchanged:: 0.21
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``'drop'`` is accepted. Using None was deprecated in 0.22 and
@@ -254,6 +254,18 @@ class VotingClassifier(ClassifierMixin, _BaseVoting):
254254
>>> eclf2 = eclf2.fit(X, y)
255255
>>> print(eclf2.predict(X))
256256
[1 1 1 2 2 2]
257+
258+
To drop an estimator, :meth:`set_params` can be used to remove it. Here we
259+
dropped one of the estimators, resulting in 2 fitted estimators:
260+
261+
>>> eclf2 = eclf2.set_params(lr='drop')
262+
>>> eclf2 = eclf2.fit(X, y)
263+
>>> len(eclf2.estimators_)
264+
2
265+
266+
Setting `flatten_transform=True` with `voting='soft'` flattens output shape of
267+
`transform`:
268+
257269
>>> eclf3 = VotingClassifier(estimators=[
258270
... ('lr', clf1), ('rf', clf2), ('gnb', clf3)],
259271
... voting='soft', weights=[2,1,1],
@@ -434,7 +446,7 @@ class VotingRegressor(RegressorMixin, _BaseVoting):
434446
Invoking the ``fit`` method on the ``VotingRegressor`` will fit clones
435447
of those original estimators that will be stored in the class attribute
436448
``self.estimators_``. An estimator can be set to ``'drop'`` using
437-
``set_params``.
449+
:meth:`set_params`.
438450
439451
.. versionchanged:: 0.21
440452
``'drop'`` is accepted. Using None was deprecated in 0.22 and
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488500
>>> from sklearn.linear_model import LinearRegression
489501
>>> from sklearn.ensemble import RandomForestRegressor
490502
>>> from sklearn.ensemble import VotingRegressor
503+
>>> from sklearn.neighbors import KNeighborsRegressor
491504
>>> r1 = LinearRegression()
492505
>>> r2 = RandomForestRegressor(n_estimators=10, random_state=1)
506+
>>> r3 = KNeighborsRegressor()
493507
>>> X = np.array([[1, 1], [2, 4], [3, 9], [4, 16], [5, 25], [6, 36]])
494508
>>> y = np.array([2, 6, 12, 20, 30, 42])
495-
>>> er = VotingRegressor([('lr', r1), ('rf', r2)])
509+
>>> er = VotingRegressor([('lr', r1), ('rf', r2), ('r3', r3)])
496510
>>> print(er.fit(X, y).predict(X))
497-
[ 3.3 5.7 11.8 19.7 28. 40.3]
511+
[ 6.8... 8.4... 12.5... 17.8... 26... 34...]
512+
513+
In the following example, we drop the `'lr'` estimator with
514+
:meth:`~VotingRegressor.set_params` and fit the remaining two estimators:
515+
516+
>>> er = er.set_params(lr='drop')
517+
>>> er = er.fit(X, y)
518+
>>> len(er.estimators_)
519+
2
498520
"""
499521

500522
def __init__(self, estimators, *, weights=None, n_jobs=None, verbose=False):

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