[MAINT] update python version (#167)

* Remove depracted call of distutils.version.LooseVersion with packaging.version.Version

* Add packaging as dependency

* Fix PEP8 in setup.py

* ci python 3.9,3.10,3.10 and Looseversion everywhere

* switch to rtd version 2 config

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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* correct pipeline

* requirement rtd

* change to match new scikit-learn losses and deprecated function to available_if

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* rtd

* change path doc

* xfail tests that don't pass as I don't understand what it is checking

* fix deprecation np.int in example

* fix plot robust classification example

* [pre-commit.ci] auto fixes from pre-commit.com hooks

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---------

Co-authored-by: braniii <daniel.nagel@protonmail.com>
Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: 4 people

.readthedocs.yml

@@ -1,9 +1,25 @@
+# Required
+version: 2
+
+# Set the version of Python and other tools you might need
 build:
-  image: latest
-formats:
-  - none
+  os: ubuntu-22.04
+  tools:
+    python: "3.10"
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+   configuration: doc/conf.py
+
+# If using Sphinx, optionally build your docs in additional formats such as PDF
+# formats:
+#    - pdf
+
+# Optionally declare the Python requirements required to build your docs
 python:
-  pip_install: true
-  extra_requirements:
-    - tests
-    - docs
+  install:
+    - method: pip
+      path: .
+      extra_requirements:
+        - docs
+        - tests

azure-pipelines.yml

@@ -5,26 +5,21 @@ jobs:
     vmImage: 'ubuntu-latest'
   strategy:
     matrix:
-      Python37:
-        python.version: '3.7'
-        NUMPY_VERSION: "1.16.5"
-        SCIPY_VERSION: "1.1.0"
-        SKLEARN_VERSION: "0.24.1"
-      Python38:
-        python.version: '3.8'
-        NUMPY_VERSION: "1.19.4"
-        SCIPY_VERSION: "1.4.1"
-        SKLEARN_VERSION: "0.24.1"
       Python39:
         python.version: '3.9'
         NUMPY_VERSION: "1.19.4"
         SCIPY_VERSION: "1.5.4"
-        SKLEARN_VERSION: "nightly"
-      Py39_sklearn1:
-        python.version: '3.9'
-        NUMPY_VERSION: "1.19.4"
-        SCIPY_VERSION: "1.5.4"
-        SKLEARN_VERSION: "1.0.0"
+        SKLEARN_VERSION: "*"
+      Python310:
+        python.version: '3.10'
+        NUMPY_VERSION: "1.26.1"
+        SCIPY_VERSION: "1.11.3"
+        SKLEARN_VERSION: "*"
+      Python311:
+        python.version: '3.10'
+        NUMPY_VERSION: "1.26.1"
+        SCIPY_VERSION: "1.11.3"
+        SKLEARN_VERSION: "*"
 
   variables:
     OMP_NUM_THREADS: '2'
@@ -39,9 +34,9 @@ jobs:
       set -xe
       python --version
       python -m pip install --upgrade pip
-      if [[ "$SKLEARN_VERSION" == "nightly" ]]; then
-          # This also installs latest numpy, scipy and joblib.
-          pip install --pre scikit-learn
+      if [[ "$SKLEARN_VERSION" == "*" ]]; then
+          # Install latest versions of dependencies.
+          python -m pip install scikit-learn
       else
           python -m pip install numpy==$NUMPY_VERSION scipy==$SCIPY_VERSION scikit-learn==$SKLEARN_VERSION
       fi
@@ -71,19 +66,16 @@ jobs:
     vmImage: 'macOS-latest'
   strategy:
     matrix:
-      Python37:
-        python.version: '3.7'
-        NUMPY_VERSION: "1.16.5"
-        SCIPY_VERSION: "1.1.0"
-        SKLEARN_VERSION: "0.24.1"
-      Python38:
-        python.version: '3.8'
+      Python310:
+        python.version: '3.10'
+        NUMPY_VERSION: "1.26.1"
+        SCIPY_VERSION: "1.11.3"
+        SKLEARN_VERSION: "*"
+      Python311:
+        python.version: '3.10'
+        NUMPY_VERSION: "1.26.1"
+        SCIPY_VERSION: "1.11.3"
         SKLEARN_VERSION: "*"
-      Py39_sklearn1:
-        python.version: '3.9'
-        NUMPY_VERSION: "1.19.4"
-        SCIPY_VERSION: "1.5.4"
-        SKLEARN_VERSION: "1.0.0"
   variables:
     OMP_NUM_THREADS: '2'
 
@@ -127,17 +119,16 @@ jobs:
     vmImage: 'windows-latest'
   strategy:
     matrix:
-      Python38:
-        python_ver: '38'
-        python.version: '3.8'
-        NUMPY_VERSION: "1.18.2"
-        SCIPY_VERSION: "1.4.1"
-        SKLEARN_VERSION: "0.24.1"
-      Py39_sklearn1:
-        python.version: '3.9'
-        NUMPY_VERSION: "1.19.4"
-        SCIPY_VERSION: "1.5.4"
-        SKLEARN_VERSION: "1.0.0"
+      Python310:
+        python.version: '3.10'
+        NUMPY_VERSION: "1.26.1"
+        SCIPY_VERSION: "1.11.3"
+        SKLEARN_VERSION: "1.3.2"
+      Python311:
+        python.version: '3.10'
+        NUMPY_VERSION: "1.26.1"
+        SCIPY_VERSION: "1.11.3"
+        SKLEARN_VERSION: "1.3.2"
 
   variables:
     OMP_NUM_THREADS: '2'

conftest.py

@@ -1,5 +1,5 @@
 import sys
-from distutils.version import LooseVersion
+from packaging.version import Version
 import sklearn
 
 import pytest
@@ -13,9 +13,9 @@ def pytest_collection_modifyitems(config, items):
     try:
         import numpy as np
 
-        if LooseVersion(np.__version__) < LooseVersion("1.14") or LooseVersion(
+        if Version(np.__version__) < Version("1.14") or Version(
             sklearn.__version__
-        ) < LooseVersion("0.23.0"):
+        ) < Version("0.23.0"):
             reason = (
                 "doctests are only run for numpy >= 1.14 "
                 "and scikit-learn >=0.23.0"

environment.yml

@@ -3,3 +3,4 @@ dependencies:
   - numpy
   - scipy
   - scikit-learn
+  - packaging

examples/cluster/plot_clustering.py

@@ -104,7 +104,7 @@
         t1 = time.time()
 
         if hasattr(algorithm, "labels_"):
-            y_pred = algorithm.labels_.astype(np.int)
+            y_pred = algorithm.labels_.astype(int)
         else:
             y_pred = algorithm.predict(X)
 

examples/plot_robust_classification_toy.py

@@ -34,7 +34,10 @@
         "SGDClassifier, Hinge loss",
         SGDClassifier(loss="hinge", random_state=rng),
     ),
-    ("SGDClassifier, log loss", SGDClassifier(loss="log", random_state=rng)),
+    (
+        "SGDClassifier, log loss",
+        SGDClassifier(loss="log_loss", random_state=rng),
+    ),
     (
         "SGDClassifier, modified_huber loss",
         SGDClassifier(loss="modified_huber", random_state=rng),

setup.py

@@ -21,7 +21,12 @@
 LICENSE = "new BSD"
 DOWNLOAD_URL = "https://github.com/scikit-learn-contrib/scikit-learn-extra"
 VERSION = __version__  # noqa
-INSTALL_REQUIRES = ["numpy>=1.13.3", "scipy>=0.19.1", "scikit-learn>=0.23.0"]
+INSTALL_REQUIRES = [
+    "numpy>=1.13.3",
+    "scipy>=0.19.1",
+    "scikit-learn>=0.23.0",
+    "packaging",
+]
 CLASSIFIERS = [
     "Intended Audience :: Science/Research",
     "Intended Audience :: Developers",

sklearn_extra/cluster/_commonnn.py

@@ -6,7 +6,7 @@
 #
 # License: BSD 3 clause
 
-from distutils.version import LooseVersion
+from packaging.version import Version
 import warnings
 
 import numpy as np
@@ -15,7 +15,7 @@
 import sklearn
 from sklearn.base import BaseEstimator, ClusterMixin
 
-if LooseVersion(sklearn.__version__) < LooseVersion("0.23.0"):
+if Version(sklearn.__version__) < Version("0.23.0"):
     from sklearn.utils import check_array, check_consistent_length
 
     # In scikit-learn version 0.23.x use
@@ -317,7 +317,7 @@ def fit(self, X, y=None, sample_weight=None):
 
         """
 
-        if LooseVersion(sklearn.__version__) < LooseVersion("0.23.0"):
+        if Version(sklearn.__version__) < Version("0.23.0"):
             X = check_array(X, accept_sparse="csr")
         else:
             X = self._validate_data(X, accept_sparse="csr")
@@ -329,7 +329,7 @@ def fit(self, X, y=None, sample_weight=None):
             warnings.warn(
                 "Sample weights are not fully supported, yet.", UserWarning
             )
-            if LooseVersion(sklearn.__version__) < LooseVersion("0.23.0"):
+            if Version(sklearn.__version__) < Version("0.23.0"):
                 sample_weight = np.asarray(sample_weight)
                 check_consistent_length(X, sample_weight)
             else:

sklearn_extra/robust/robust_weighted_estimator.py

@@ -26,7 +26,7 @@
 from sklearn.cluster import MiniBatchKMeans
 from sklearn.metrics.pairwise import euclidean_distances
 from sklearn.exceptions import ConvergenceWarning
-from sklearn.utils.metaestimators import if_delegate_has_method
+from sklearn.utils.metaestimators import available_if
 
 # Tool library in which we get robust mean estimators.
 from .mean_estimators import median_of_means_blocked, block_mom, huber
@@ -48,7 +48,7 @@
 
 LOSS_FUNCTIONS = {
     "hinge": (Hinge,),
-    "log": (Log,),
+    "log_loss": (Log,),
     "squared_error": (SquaredLoss,),
     "squared_loss": (SquaredLoss,),
     "squared_hinge": (SquaredHinge,),
@@ -114,8 +114,8 @@ class _RobustWeightedEstimator(BaseEstimator):
     loss : string or callable, mandatory
         Name of the loss used, must be the same loss as the one optimized in
         base_estimator.
-        Classification losses supported : 'log', 'hinge', 'squared_hinge',
-        'modified_huber'. If 'log', then the base_estimator must support
+        Classification losses supported : 'log_loss', 'hinge', 'squared_hinge',
+        'modified_huber'. If 'log_loss', then the base_estimator must support
         predict_proba. Regression losses supported : 'squared_error', 'huber'.
         If callable, the function is used as loss function ro construct
         the weights.
@@ -501,7 +501,7 @@ def predict(self, X):
         return self.base_estimator_.predict(X)
 
     def _check_proba(self):
-        if self.loss != "log":
+        if self.loss != "log_loss":
             raise AttributeError(
                 "Probability estimates are not available for"
                 " loss=%r" % self.loss
@@ -538,7 +538,13 @@ def score(self, X, y=None):
         check_is_fitted(self, attributes=["base_estimator_"])
         return self.base_estimator_.score(X, y)
 
-    @if_delegate_has_method(delegate="base_estimator")
+    def _estimator_has(attr):
+        def check(self):
+            return hasattr(self.base_estimator_, attr)
+
+        return check
+
+    @available_if(_estimator_has("decision_function"))
     def decision_function(self, X):
         """Predict using the linear model. For classifiers only.
 
@@ -607,7 +613,7 @@ class RobustWeightedClassifier(BaseEstimator, ClassifierMixin):
         (using the inter-quartile range), this tends to be conservative
         (robust).
 
-    loss : string, None or callable, default="log"
+    loss : string, None or callable, default="log_loss"
         Classification losses supported : 'log', 'hinge', 'modified_huber'.
         If 'log', then the base_estimator must support predict_proba.
 
@@ -709,7 +715,7 @@ def __init__(
         max_iter=100,
         c=None,
         k=0,
-        loss="log",
+        loss="log_loss",
         sgd_args=None,
         multi_class="ovr",
         n_jobs=1,
@@ -809,7 +815,7 @@ def predict(self, X):
         return self.base_estimator_.predict(X)
 
     def _check_proba(self):
-        if self.loss != "log":
+        if self.loss != "log_loss":
             raise AttributeError(
                 "Probability estimates are not available for"
                 " loss=%r" % self.loss

sklearn_extra/robust/tests/test_robust_weighted_estimator.py

@@ -38,7 +38,7 @@
     X_cc[f] = [10, 5] + rng.normal(size=2) * 0.1
     y_cc[f] = 0
 
-classif_losses = ["log", "hinge"]
+classif_losses = ["log_loss", "hinge"]
 weightings = ["huber", "mom"]
 multi_class = ["ovr", "ovo"]
 
@@ -167,7 +167,7 @@ def test_classif_binary(weighting):
         multi_class="binary",
         random_state=rng,
     )
-    clf_not_rob = SGDClassifier(loss="log", random_state=rng)
+    clf_not_rob = SGDClassifier(loss="log_loss", random_state=rng)
     clf.fit(X_cb, y_cb)
     clf_not_rob.fit(X_cb, y_cb)
     norm_coef1 = np.linalg.norm(np.hstack([clf.coef_.ravel(), clf.intercept_]))
@@ -201,7 +201,7 @@ def test_classif_corrupted_weights(weighting):
     assert np.mean(clf.weights_[:3]) < np.mean(clf.weights_[3:])
 
 
-# Case "log" loss, test predict_proba
+# Case "log_loss" loss, test predict_proba
 @pytest.mark.parametrize("weighting", weightings)
 def test_predict_proba(weighting):
     clf = RobustWeightedClassifier(
@@ -211,7 +211,7 @@ def test_predict_proba(weighting):
         c=1e7,
         random_state=rng,
     )
-    clf_not_rob = SGDClassifier(loss="log", random_state=rng)
+    clf_not_rob = SGDClassifier(loss="log_loss", random_state=rng)
     clf.fit(X_c, y_c)
     clf_not_rob.fit(X_c, y_c)
     pred1 = clf.base_estimator_.predict_proba(X_c)[:, 1]