hunga bunga

Author: Yam Peleg

.idea/workspace.xml

@@ -180,7 +180,7 @@ def run_all_classifiers(x, y, small = True, normalize_x = True, n_jobs=cpu_count
 
 
 class HungaBungaClassifier(ClassifierMixin):
-    def __init__(self, brain=False, test_size = 0.2, n_splits = 5, random_state=None, upsample=True, scoring=None, verbose=True, normalize_x = True, n_jobs =cpu_count() - 1):
+    def __init__(self, brain=False, test_size = 0.2, n_splits = 5, random_state=None, upsample=True, scoring=None, verbose=False, normalize_x = True, n_jobs =cpu_count() - 1):
         self.model = None
         self.brain = brain
         self.test_size = test_size

hunga_bunga/classification.py

@@ -291,11 +291,11 @@ def gen_reg_data(x_mu=10., x_sigma=1., num_samples=100, num_features=3, y_formul
 
 def run_all_regressors(x, y, small = True, normalize_x = True, n_jobs=cpu_count()-1, brain=False, test_size=0.2, n_splits=5, upsample=True, scoring=None, verbose=False):
     all_params = (linear_models_n_params_small if small else linear_models_n_params) + (nn_models_n_params_small if small else nn_models_n_params) + ([] if small else gaussianprocess_models_n_params) + neighbor_models_n_params + (svm_models_n_params_small if small else svm_models_n_params) + (tree_models_n_params_small if small else tree_models_n_params)
-    return main_loop(all_params, StandardScaler().fit_transform(x) if normalize_x else x, y, isClassification=False, n_jobs=n_jobs, brain=brain)
+    return main_loop(all_params, StandardScaler().fit_transform(x) if normalize_x else x, y, isClassification=False, n_jobs=n_jobs, brain=brain, test_size=test_size, n_splits=n_splits, upsample=upsample, scoring=scoring, verbose=verbose)
 
 
 class HungaBungaRegressor(RegressorMixin):
-    def __init__(self, brain=False, test_size = 0.2, n_splits = 5, random_state=None, upsample=True, scoring=None, verbose=True, normalize_x = True, n_jobs =cpu_count() - 1):
+    def __init__(self, brain=False, test_size = 0.2, n_splits = 5, random_state=None, upsample=True, scoring=None, verbose=False, normalize_x = True, n_jobs =cpu_count() - 1):
         self.model = None
         self.brain = brain
         self.test_size = test_size