1.0.1 candidate has changes (many previously published elsehwere) for

setup.py

@@ -59,7 +59,7 @@ def main():
         ],
         url='https://github.com/nok/sklearn-porter',
         install_requires=[
-            'scikit-learn>=0.17,<=0.22a0',
+            'scikit-learn>=1.5',
             'jinja2>=2.11',
             'joblib>=1',
             'loguru>=0.5',

sklearn_porter/Estimator.py

@@ -371,7 +371,7 @@ def _load_est(estimator):
 
         # Classifiers:
         if name == 'DecisionTreeClassifier':
-            from sklearn.tree.tree import (
+            from sklearn.tree import (
                 DecisionTreeClassifier as DecisionTreeClassifierClass,
             )
 
@@ -382,7 +382,7 @@ def _load_est(estimator):
 
                 return DecisionTreeClassifier(est)
         elif name == 'AdaBoostClassifier':
-            from sklearn.ensemble.weight_boosting import (
+            from sklearn.ensemble import (
                 AdaBoostClassifier as AdaBoostClassifierClass,
             )
 
@@ -393,7 +393,7 @@ def _load_est(estimator):
 
                 return AdaBoostClassifier(est)
         elif name == 'RandomForestClassifier':
-            from sklearn.ensemble.forest import (
+            from sklearn.ensemble import (
                 RandomForestClassifier as RandomForestClassifierClass,
             )
 
@@ -404,7 +404,7 @@ def _load_est(estimator):
 
                 return RandomForestClassifier(est)
         elif name == 'ExtraTreesClassifier':
-            from sklearn.ensemble.forest import (
+            from sklearn.ensemble import (
                 ExtraTreesClassifier as ExtraTreesClassifierClass,
             )
 
@@ -462,7 +462,7 @@ def _load_est(estimator):
                 return BernoulliNB(est)
         elif name == 'MLPClassifier':
             try:
-                from sklearn.neural_network.multilayer_perceptron import (
+                from sklearn.neural_network import (
                     MLPClassifier as MLPClassifierClass,
                 )
             except ImportError:
@@ -480,7 +480,7 @@ def _load_est(estimator):
         # Regressors:
         elif name == 'MLPRegressor':
             try:
-                from sklearn.neural_network.multilayer_perceptron import (
+                from sklearn.neural_network import (
                     MLPRegressor as MLPRegressorClass,
                 )
             except ImportError:
@@ -851,14 +851,14 @@ def classifiers() -> Tuple:
         """
 
         # scikit-learn version < 0.18.0
-        from sklearn.tree.tree import DecisionTreeClassifier
-        from sklearn.ensemble.weight_boosting import AdaBoostClassifier
-        from sklearn.ensemble.forest import RandomForestClassifier
-        from sklearn.ensemble.forest import ExtraTreesClassifier
-        from sklearn.svm.classes import LinearSVC
-        from sklearn.svm.classes import SVC
-        from sklearn.svm.classes import NuSVC
-        from sklearn.neighbors.classification import KNeighborsClassifier
+        from sklearn.tree import DecisionTreeClassifier
+        from sklearn.ensemble import AdaBoostClassifier
+        from sklearn.ensemble import RandomForestClassifier
+        from sklearn.ensemble import ExtraTreesClassifier
+        from sklearn.svm import LinearSVC
+        from sklearn.svm import SVC
+        from sklearn.svm import NuSVC
+        from sklearn.neighbors import KNeighborsClassifier
         from sklearn.naive_bayes import GaussianNB
         from sklearn.naive_bayes import BernoulliNB
 
@@ -877,7 +877,7 @@ def classifiers() -> Tuple:
 
         # scikit-learn version >= 0.18.0
         try:
-            from sklearn.neural_network.multilayer_perceptron import (
+            from sklearn.neural_network import (
                 MLPClassifier,
             )
         except ImportError:
@@ -903,7 +903,7 @@ def regressors() -> Tuple:
 
         # scikit-learn version >= 0.18.0
         try:
-            from sklearn.neural_network.multilayer_perceptron import (
+            from sklearn.neural_network import (
                 MLPRegressor,
             )
         except ImportError:

sklearn_porter/__version__.txt

@@ -1 +1 @@
-1.0.0
+1.0.1

sklearn_porter/estimator/DecisionTreeClassifier/__init__.py

@@ -7,7 +7,7 @@
 from loguru import logger as L
 
 # scikit-learn
-from sklearn.tree.tree import \
+from sklearn.tree import \
     DecisionTreeClassifier as DecisionTreeClassifierClass
 
 # sklearn-porter
@@ -67,14 +67,15 @@ def __init__(self, estimator: DecisionTreeClassifierClass):
 
         # Is the estimator fitted?
         try:
-            getattr(est, 'n_features_')  # for sklearn >  0.19
+            getattr(est, 'n_features_in_')  # for sklearn >= 1.50
+            #getattr(est, 'n_features_')  # for sklearn >  0.19
             getattr(est.tree_, 'value')  # for sklearn <= 0.18
         except AttributeError:
             raise exception.NotFittedEstimatorError(self.estimator_name)
 
         # Extract and save meta information:
         self.meta_info = dict(
-            n_features=est.n_features_,
+            n_features=est.n_features_in_,
             n_classes=len(est.tree_.value.tolist()[0][0]),
         )
         L.info('Meta info (keys): {}'.format(self.meta_info.keys()))

sklearn_porter/estimator/DecisionTreeClassifier/templates/c/attached.class.jinja2

@@ -64,6 +64,7 @@ void predict_proba(double *result, double features[N_FEATURES], int node) {
     }
 }
 
+#ifdef STANDALONE
 int main(int argc, const char *argv[]) {
 
     /* Features: */
@@ -98,4 +99,5 @@ int main(int argc, const char *argv[]) {
 
     return 0;
 }
-{% endblock %}
+#endif
+{% endblock %}