Merge pull request #1 from neuroneural/sklearn_update

Sklearn update

polyssifier/poly_utils.py

@@ -125,14 +125,14 @@ def build_classifiers(exclude, scale, feature_selection, nCols):
     if 'Decision Tree' not in exclude:
         classifiers['Decision Tree'] = {
             'clf': DecisionTreeClassifier(max_depth=None,
-                                          max_features='auto'),
+                                          max_features='sqrt'),
             'parameters': {}}
 
     if 'Random Forest' not in exclude:
         classifiers['Random Forest'] = {
             'clf': RandomForestClassifier(max_depth=None,
                                           n_estimators=10,
-                                          max_features='auto'),
+                                          max_features='sqrt'),
             'parameters': {'n_estimators': list(range(5, 20))}}
 
     if 'Logistic Regression' not in exclude:

polyssifier/polyssifier.py

@@ -55,7 +55,7 @@ def poly(data, label, n_folds=10, scale=True, exclude=[],
     _le.fit(label)
     label = _le.transform(label)
     n_class = len(np.unique(label))
-    logger.info(f'Detected {n_class} classes in label')
+    logger.info('Detected ' + str(n_class) + ' classes in label')
 
     if save and not os.path.exists('poly_{}/models'.format(project_name)):
         os.makedirs('poly_{}/models'.format(project_name))
@@ -84,6 +84,7 @@ def poly(data, label, n_folds=10, scale=True, exclude=[],
     kf = list(skf.split(np.zeros(data.shape[0]), label))
 
     # Parallel processing of tasks
+
     manager = Manager()
     args = manager.list()
     args.append({})  # Store inputs

polyssifier/report.py

@@ -143,7 +143,7 @@ def plot_scores(scores, scoring='auc', file_name='temp', min_val=None):
             break
         ax1.text(rect.get_x() - rect.get_width() / 2., ymin + (1 - ymin) * .01,
                  data.index[n], ha='center', va='bottom',
-                 rotation='90', color='black', fontsize=15)
+                 rotation=90, color='black', fontsize=15)
     plt.tight_layout()
     plt.savefig(file_name + '.pdf')
     plt.savefig(file_name + '.svg', transparent=False)

setup.py

@@ -63,6 +63,12 @@
         'Programming Language :: Python :: 3.4',
         'Programming Language :: Python :: 3.5',
         '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',
+        'Programming Language :: Python :: 3.12',
     ],
 
     # What does your project relate to?
@@ -78,6 +84,6 @@
     # your project is installed. For an analysis of "install_requires" vs pip's
     # requirements files see:
     # https://packaging.python.org/en/latest/requirements.html
-    install_requires=['pandas', 'sklearn', 'numpy', 'matplotlib'],
+    install_requires=['pandas','scikit-learn', 'numpy', 'matplotlib','joblib'],
 
     zip_safe=False) # Override annoying default behavior of easy_install.

tests/test_classification.py

@@ -36,7 +36,7 @@ def test_run():
     report = poly(data, label, n_folds=2, verbose=1,
                   feature_selection=False,
                   save=False, project_name='test2')
-    for key, score in report.scores.mean().iteritems():
+    for key, score in report.scores.mean().items():
         assert score < 5, '{} score is too low'.format(key)
 
 
@@ -45,7 +45,7 @@ def test_multiclass():
     report = poly(data, label, n_folds=2, verbose=1,
                   feature_selection=False,
                   save=False, project_name='test3')
-    for key, score in report.scores.mean().iteritems():
+    for key, score in report.scores.mean().items():
         assert score < 5, '{} score is too low'.format(key)
 
 

tests/test_multiclass.py

@@ -26,7 +26,7 @@ def test_run():
     report = poly(data, label, n_folds=2, verbose=1,
                   feature_selection=False,
                   save=False, project_name='test2')
-    for key, score in report.scores.mean().iteritems():
+    for key, score in report.scores.mean().items():
         assert score < 5, '{} score is too low'.format(key)
 
 

tests/test_regression.py

@@ -25,7 +25,7 @@ def test_feature_selection_regression():
     assert (report_with_features.scores.mean()[:, 'train'] > 0.2).all(),\
         'train score below chance'
 
-    for key, ypred in report_with_features.predictions.iteritems():
+    for key, ypred in report_with_features.predictions.items():
         mse = np.linalg.norm(ypred - diabetes_target) / len(diabetes_target)
         assert mse < 5, '{} Prediction error is too high'.format(key)