DOC Fix GitHub regex labeler (#75)

* TST enable test docstring params for feature extraction module (scikit-learn#20188)

* DOC fix a reference in sklearn.ensemble.GradientBoostingRegressor (scikit-learn#20198)

* FIX mcc zero divsion  (scikit-learn#19977)

* TST Add TransformedTargetRegressor to test_meta_estimators_delegate_data_validation (scikit-learn#20175)

Co-authored-by: Guillaume Lemaitre <[email protected]>

* TST enable n_feature_in_ test for feature_extraction module

* FIX Uses points instead of pixels in plot_tree (scikit-learn#20023)

* MNT n_features_in through the multiclass module (scikit-learn#20193)

* CI Removes python 3.6 builds from wheel building (scikit-learn#20184)

* FIX Fix typo in error message in `fetch_openml` (scikit-learn#20201)

* FIX Fix error when using Calibrated with Voting (scikit-learn#20087)

* FIX Fix RandomForestRegressor doesn't accept max_samples=1.0 (scikit-learn#20159)

Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]>

* ENH Adds Poisson criterion in RandomForestRegressor (scikit-learn#19836)

Co-authored-by: Christian Lorentzen <[email protected]>
Co-authored-by: Alihan Zihna <[email protected]>
Co-authored-by: Alihan Zihna <[email protected]>
Co-authored-by: Chiara Marmo <[email protected]>
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: naozin555 <[email protected]>
Co-authored-by: Venkatachalam N <[email protected]>
Co-authored-by: Thomas J. Fan <[email protected]>

* TST Replace assert_warns from decomposition/tests (scikit-learn#20214)

* TST check n_features_in_ in pipeline module (scikit-learn#20192)

Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Jérémie du Boisberranger <[email protected]>
Co-authored-by: Olivier Grisel <[email protected]>

* Allow `n_knots=None` if knots are explicitly specified in `SplineTransformer` (scikit-learn#20191)


Co-authored-by: Olivier Grisel <[email protected]>

* FIX make check_complex_data deterministic (scikit-learn#20221)

* TST test_fit_docstring_attributes include properties (scikit-learn#20190)

* FIX Uses the color max for colormap in ConfusionMatrixDisplay (scikit-learn#19784)

* STY Changing .format method to f-string formatting (scikit-learn#20215)

* CI Adds permissions for label action

Co-authored-by: Jérémie du Boisberranger <[email protected]>
Co-authored-by: tsuga <[email protected]>
Co-authored-by: Conner Shen <[email protected]>
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: mlondschien <[email protected]>
Co-authored-by: Clément Fauchereau <[email protected]>
Co-authored-by: murata-yu <[email protected]>
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: Brian Sun <[email protected]>
Co-authored-by: Christian Lorentzen <[email protected]>
Co-authored-by: Alihan Zihna <[email protected]>
Co-authored-by: Alihan Zihna <[email protected]>
Co-authored-by: Chiara Marmo <[email protected]>
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: naozin555 <[email protected]>
Co-authored-by: Venkatachalam N <[email protected]>
Co-authored-by: Nanshan Li <[email protected]>
Co-authored-by: solosilence <[email protected]>

Author: 18 people

.github/workflows/labeler-title-regex.yml

@@ -3,6 +3,10 @@ on:
   pull_request_target:
     types: [opened, edited]
 
+permissions:
+  contents: read
+  pull-requests: write
+
 jobs:
 
   labeler:

.github/workflows/wheels.yml

@@ -36,7 +36,7 @@ jobs:
         name: Check build trigger
         run: bash build_tools/github/check_build_trigger.sh
 
-  # Build the wheels for Linux, Windows and macOS for Python 3.6 and newer
+  # Build the wheels for Linux, Windows and macOS for Python 3.7 and newer
   build_wheels:
     name: Build wheel for cp${{ matrix.python }}-${{ matrix.platform_id }}-${{ matrix.manylinux_image }}
     runs-on: ${{ matrix.os }}
@@ -48,7 +48,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [windows-latest, ubuntu-latest, macos-latest]
-        python: [36, 37, 38, 39]
+        python: [37, 38, 39]
         bitness: [32, 64]
         manylinux_image: [manylinux1, manylinux2010]
         include:
@@ -102,6 +102,7 @@ jobs:
           CIBW_TEST_REQUIRES: pytest pandas threadpoolctl
           CIBW_TEST_COMMAND: bash {project}/build_tools/github/test_wheels.sh
           CIBW_TEST_COMMAND_WINDOWS: bash {project}/build_tools/github/test_windows_wheels.sh ${{ matrix.python }} ${{ matrix.bitness }}
+          CIBW_BUILD_VERBOSITY: 1
 
         run: bash build_tools/github/build_wheels.sh
 

benchmarks/bench_20newsgroups.py

@@ -46,18 +46,16 @@
 
     print("20 newsgroups")
     print("=============")
-    print("X_train.shape = {0}".format(X_train.shape))
-    print("X_train.format = {0}".format(X_train.format))
-    print("X_train.dtype = {0}".format(X_train.dtype))
-    print("X_train density = {0}"
-          "".format(X_train.nnz / np.product(X_train.shape)))
-    print("y_train {0}".format(y_train.shape))
-    print("X_test {0}".format(X_test.shape))
-    print("X_test.format = {0}".format(X_test.format))
-    print("X_test.dtype = {0}".format(X_test.dtype))
-    print("y_test {0}".format(y_test.shape))
+    print(f"X_train.shape = {X_train.shape}")
+    print(f"X_train.format = {X_train.format}")
+    print(f"X_train.dtype = {X_train.dtype}")
+    print(f"X_train density = {X_train.nnz / np.product(X_train.shape)}")
+    print(f"y_train {y_train.shape}")
+    print(f"X_test {X_test.shape}")
+    print(f"X_test.format = {X_test.format}")
+    print(f"X_test.dtype = {X_test.dtype}")
+    print(f"y_test {y_test.shape}")
     print()
-
     print("Classifier Training")
     print("===================")
     accuracy, train_time, test_time = {}, {}, {}

doc/whats_new/v1.0.rst

@@ -138,6 +138,10 @@ Changelog
   :class:`calibration.CalibratedClassifierCV` can now properly be used on
   prefitted pipelines. :pr:`19641` by :user:`Alek Lefebvre <AlekLefebvre>`.
 
+- |Fix| Fixed an error when using a ::class:`ensemble.VotingClassifier`
+  as `base_estimator` in ::class:`calibration.CalibratedClassifierCV`.
+  :pr:`20087` by :user:`Clément Fauchereau <clement-f>`.
+
 :mod:`sklearn.cluster`
 ......................
 
@@ -266,6 +270,16 @@ Changelog
   :class:`ensemble.StackingClassifier` and :class:`ensemble.StackingRegressor`.
   :pr:`19564` by `Thomas Fan`_.
 
+- |Enhancement| Documented and tested support of the Poisson criterion for
+  :class:`ensemble.RandomForestRegressor`. :pr:`19836` by
+  :user:`Brian Sun <bsun94>`.
+
+- |Fix| Fixed the range of the argument max_samples to be (0.0, 1.0]
+  in :class:`ensemble.RandomForestClassifier`,
+  :class:`ensemble.RandomForestRegressor`, where `max_samples=1.0` is
+  interpreted as using all `n_samples` for bootstrapping. :pr:`20159` by
+  :user:`murata-yu`.
+
 :mod:`sklearn.feature_extraction`
 .................................
 
@@ -389,6 +403,9 @@ Changelog
   are integral.
   :pr:`9843` by :user:`Jon Crall <Erotemic>`.
 
+- |Fix| :meth:`metrics.ConfusionMatrixDisplay.plot` uses the correct max
+  for colormap. :pr:`19784` by `Thomas Fan`_.
+
 - |Fix| Samples with zero `sample_weight` values do not affect the results
   from :func:`metrics.det_curve`, :func:`metrics.precision_recall_curve`
   and :func:`metrics.roc_curve`.

pyproject.toml

@@ -9,7 +9,10 @@ requires = [
     # wheels on PyPI
     #
     # see: https://github.com/scipy/oldest-supported-numpy/blob/master/setup.cfg
-    "oldest-supported-numpy",
+    "oldest-supported-numpy; python_version!='3.7' or platform_machine=='aarch64' or platform_system=='AIX' or platform_python_implementation == 'PyPy'",
+
+    # Override oldest-supported-numpy setting because pandas 0.25.0 requires 1.14.6
+    "numpy==1.14.6; python_version=='3.7' and platform_machine!='aarch64' and platform_system!='AIX' and platform_python_implementation != 'PyPy'",
 
     "scipy>=1.1.0",
 ]

sklearn/calibration.py

@@ -257,9 +257,10 @@ def fit(self, X, y, sample_weight=None):
             check_is_fitted(self.base_estimator, attributes=["classes_"])
             self.classes_ = self.base_estimator.classes_
 
-            pred_method = _get_prediction_method(base_estimator)
+            pred_method, method_name = _get_prediction_method(base_estimator)
             n_classes = len(self.classes_)
-            predictions = _compute_predictions(pred_method, X, n_classes)
+            predictions = _compute_predictions(pred_method, method_name, X,
+                                               n_classes)
 
             calibrated_classifier = _fit_calibrator(
                 base_estimator, predictions, y, self.classes_, self.method,
@@ -310,12 +311,13 @@ def fit(self, X, y, sample_weight=None):
                 )
             else:
                 this_estimator = clone(base_estimator)
-                method_name = _get_prediction_method(this_estimator).__name__
+                _, method_name = _get_prediction_method(this_estimator)
                 pred_method = partial(
                     cross_val_predict, estimator=this_estimator, X=X, y=y,
                     cv=cv, method=method_name, n_jobs=self.n_jobs
                 )
-                predictions = _compute_predictions(pred_method, X, n_classes)
+                predictions = _compute_predictions(pred_method, method_name, X,
+                                                   n_classes)
 
                 if sample_weight is not None and supports_sw:
                     this_estimator.fit(X, y, sample_weight)
@@ -441,8 +443,9 @@ def _fit_classifier_calibrator_pair(estimator, X, y, train, test, supports_sw,
         estimator.fit(X_train, y_train)
 
     n_classes = len(classes)
-    pred_method = _get_prediction_method(estimator)
-    predictions = _compute_predictions(pred_method, X_test, n_classes)
+    pred_method, method_name = _get_prediction_method(estimator)
+    predictions = _compute_predictions(pred_method, method_name, X_test,
+                                       n_classes)
 
     calibrated_classifier = _fit_calibrator(
         estimator, predictions, y_test, classes, method, sample_weight=sw_test
@@ -465,18 +468,21 @@ def _get_prediction_method(clf):
     -------
     prediction_method : callable
         The prediction method.
+    method_name : str
+        The name of the prediction method.
     """
     if hasattr(clf, 'decision_function'):
         method = getattr(clf, 'decision_function')
+        return method, 'decision_function'
     elif hasattr(clf, 'predict_proba'):
         method = getattr(clf, 'predict_proba')
+        return method, 'predict_proba'
     else:
         raise RuntimeError("'base_estimator' has no 'decision_function' or "
                            "'predict_proba' method.")
-    return method
 
 
-def _compute_predictions(pred_method, X, n_classes):
+def _compute_predictions(pred_method, method_name, X, n_classes):
     """Return predictions for `X` and reshape binary outputs to shape
     (n_samples, 1).
 
@@ -485,6 +491,9 @@ def _compute_predictions(pred_method, X, n_classes):
     pred_method : callable
         Prediction method.
 
+    method_name: str
+        Name of the prediction method
+
     X : array-like or None
         Data used to obtain predictions.
 
@@ -498,10 +507,6 @@ def _compute_predictions(pred_method, X, n_classes):
         (X.shape[0], 1).
     """
     predictions = pred_method(X=X)
-    if hasattr(pred_method, '__name__'):
-        method_name = pred_method.__name__
-    else:
-        method_name = signature(pred_method).parameters['method'].default
 
     if method_name == 'decision_function':
         if predictions.ndim == 1:
@@ -634,8 +639,9 @@ def predict_proba(self, X):
             The predicted probabilities. Can be exact zeros.
         """
         n_classes = len(self.classes)
-        pred_method = _get_prediction_method(self.base_estimator)
-        predictions = _compute_predictions(pred_method, X, n_classes)
+        pred_method, method_name = _get_prediction_method(self.base_estimator)
+        predictions = _compute_predictions(pred_method, method_name, X,
+                                           n_classes)
 
         label_encoder = LabelEncoder().fit(self.classes)
         pos_class_indices = label_encoder.transform(

sklearn/cluster/_bicluster.py

@@ -255,6 +255,9 @@ class SpectralCoclustering(BaseSpectral):
     column_labels_ : array-like of shape (n_cols,)
         The bicluster label of each column.
 
+    biclusters_ : tuple of two ndarrays
+        The tuple contains the `rows_` and `columns_` arrays.
+
     Examples
     --------
     >>> from sklearn.cluster import SpectralCoclustering

sklearn/compose/_column_transformer.py

@@ -141,6 +141,12 @@ class ColumnTransformer(TransformerMixin, _BaseComposition):
 
         .. versionadded:: 1.0
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying transformers expose such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     Notes
     -----
     The order of the columns in the transformed feature matrix follows the

sklearn/compose/_target.py

@@ -82,6 +82,12 @@ class TransformedTargetRegressor(RegressorMixin, BaseEstimator):
     transformer_ : object
         Transformer used in ``fit`` and ``predict``.
 
+    n_features_in_ : int
+        Number of features seen during :term:`fit`. Only defined if the
+        underlying regressor exposes such an attribute when fit.
+
+        .. versionadded:: 0.24
+
     Examples
     --------
     >>> import numpy as np

sklearn/datasets/_openml.py

@@ -840,7 +840,7 @@ def fetch_openml(
             raise ValueError(
                 "Dataset data_id={} and version={} passed, but you can only "
                 "specify a numeric data_id or a version, not "
-                "both.".format(data_id, name))
+                "both.".format(data_id, version))
     else:
         raise ValueError(
             "Neither name nor data_id are provided. Please provide name or "