Update for modern versions of sklearn and numpy

README.rst

@@ -27,8 +27,8 @@ Dependencies
 
 sklearn-genetic requires:
 
-- Python (>= 3.6)
-- scikit-learn (>= 0.23)
+- Python (>= 3.7)
+- scikit-learn (>= 1.0)
 - deap (>= 1.0.2)
 - numpy
 - multiprocess

docs/source/installation.rst

@@ -6,8 +6,8 @@ Dependencies
 
 sklearn-genetic requires:
 
-- Python (>= 3.6)
-- scikit-learn (>= 0.23)
+- Python (>= 3.7)
+- scikit-learn (>= 1.0)
 - deap (>= 1.0.2)
 - numpy
 - multiprocess

genetic_selection/gscv.py

@@ -19,7 +19,7 @@
 import multiprocess
 import numpy as np
 from sklearn.utils import check_X_y
-from sklearn.utils.metaestimators import if_delegate_has_method
+from sklearn.utils.metaestimators import available_if
 from sklearn.base import BaseEstimator
 from sklearn.base import MetaEstimatorMixin
 from sklearn.base import clone
@@ -122,7 +122,7 @@ def _evalFunction(individual, estimator, X, y, groups, cv, scorer, fit_params, m
     individual_tuple = tuple(individual)
     if caching and individual_tuple in scores_cache:
         return scores_cache[individual_tuple][0], individual_sum, scores_cache[individual_tuple][1]
-    X_selected = X[:, np.array(individual, dtype=np.bool)]
+    X_selected = X[:, np.array(individual, dtype=bool)]
     scores = cross_val_score(estimator=estimator, X=X_selected, y=y, groups=groups, scoring=scorer,
                              cv=cv, fit_params=fit_params)
     scores_mean = np.mean(scores)
@@ -132,6 +132,19 @@ def _evalFunction(individual, estimator, X, y, groups, cv, scorer, fit_params, m
     return scores_mean, individual_sum, scores_std
 
 
+def _estimator_has(attr):
+    """Check if we can delegate a method to the underlying estimator.
+
+    First, we check the first fitted estimator if available, otherwise we
+    check the unfitted estimator.
+    """
+    return lambda self: (
+        hasattr(self.estimator_, attr)
+        if hasattr(self, "estimator_")
+        else hasattr(self.estimator, attr)
+    )
+
+
 class GeneticSelectionCV(BaseEstimator, MetaEstimatorMixin, SelectorMixin):
     """Feature selection with genetic algorithm.
 
@@ -348,7 +361,7 @@ def _fit(self, X, y, groups=None):
             pool.join()
 
         # Set final attributes
-        support_ = np.array(hof, dtype=np.bool)[0]
+        support_ = np.array(hof, dtype=bool)[0]
         self.estimator_ = clone(self.estimator)
         self.estimator_.fit(X[:, support_], y)
 
@@ -358,7 +371,7 @@ def _fit(self, X, y, groups=None):
 
         return self
 
-    @if_delegate_has_method(delegate='estimator')
+    @available_if(_estimator_has("predict"))
     def predict(self, X):
         """Reduce X to the selected features and then predict using the underlying estimator.
 
@@ -374,7 +387,7 @@ def predict(self, X):
         """
         return self.estimator_.predict(self.transform(X))
 
-    @if_delegate_has_method(delegate='estimator')
+    @available_if(_estimator_has("score"))
     def score(self, X, y):
         """Reduce X to the selected features and return the score of the underlying estimator.
 
@@ -391,14 +404,14 @@ def score(self, X, y):
     def _get_support_mask(self):
         return self.support_
 
-    @if_delegate_has_method(delegate='estimator')
+    @available_if(_estimator_has("decision_function"))
     def decision_function(self, X):
         return self.estimator_.decision_function(self.transform(X))
 
-    @if_delegate_has_method(delegate='estimator')
+    @available_if(_estimator_has("predict_proba"))
     def predict_proba(self, X):
         return self.estimator_.predict_proba(self.transform(X))
 
-    @if_delegate_has_method(delegate='estimator')
+    @available_if(_estimator_has("predict_log_proba"))
     def predict_log_proba(self, X):
         return self.estimator_.predict_log_proba(self.transform(X))

requirements.txt

@@ -1,4 +1,4 @@
-scikit-learn>=0.23
+scikit-learn>=1.0
 deap>=1.0.2
 numpy
 multiprocess

setup.py

@@ -31,11 +31,13 @@
         'Topic :: Scientific/Engineering',
         'License :: OSI Approved :: GNU Lesser General Public License v3 (LGPLv3)',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.6',
         'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
+        'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
+        'Programming Language :: Python :: 3.11',
     ],
     packages=find_packages(),
-    python_requires='>=3.6',
-    install_requires=['scikit-learn>=0.23', 'deap>=1.0.2', 'numpy', 'multiprocess'],
+    python_requires='>=3.7',
+    install_requires=['scikit-learn>=1.0', 'deap>=1.0.2', 'numpy', 'multiprocess'],
 )