def test_algorithms_for_target_depression(self):
print("precision score")
print("\n\n\n depression_cutoff \n")
target = "depression_cutoff"
multiple_features_eda = EDAMultiFeatureBackend(
self.df, self.features + self.features_2, target)
print("\n depression_cutoff with resample\nn=10")
multiple_features_eda.model_checking(10, scoring='precision')
print("\n depression_cutoff without resample\nn=10")
multiple_features_eda.model_checking_without_resampling(
10, scoring='precision')
print("\n depression_cutoff with resample\nn=20")
multiple_features_eda.model_checking(20, scoring='precision')
print("\n depression_cutoff without resample\nn=20")
multiple_features_eda.model_checking_without_resampling(
20, scoring='precision')
print("\n depression_cutoff with resample\nn=30")
multiple_features_eda.model_checking(30, scoring='precision')
print("\n depression_cutoff without resample\nn=30")
multiple_features_eda.model_checking_without_resampling(
30, scoring='precision')
print("depression_cutoff logreg f1 score")
multiple_features_eda.logistic_regression_grid_search()
print("depression_cutoff logreg precision score")
multiple_features_eda.logistic_regression_grid_search(
scoring='precision')
def test_algorithms_for_target_diagnosis(self):
print("precision score")
print("\n\n\n diagnosis \n")
target = "diagnosis"
multiple_features_eda = EDAMultiFeatureBackend(self.df, self.features + self.features_2, target)
print("\n diagnosis with resample\nn=10")
multiple_features_eda.model_checking(10, scoring='precision')
print("\n diagnosis without resample\nn=10")
multiple_features_eda.model_checking_without_resampling(10, scoring='precision')
print("\n diagnosis with resample\nn=20")
multiple_features_eda.model_checking(20, scoring='precision')
print("\n diagnosis without resample\nn=20")
multiple_features_eda.model_checking_without_resampling(20, scoring='precision')
print("\n diagnosis with resample\nn=30")
multiple_features_eda.model_checking(30, scoring='precision')
print("\n diagnosis without resample\nn=30")
multiple_features_eda.model_checking_without_resampling(30, scoring='precision')
def test_algorithms_for_target_diagnosis(self):
print("precision score")
print("\n\n\n diagnosis \n")
target = "diagnosis"
multiple_features_eda = EDAMultiFeatureBackend(
self.df, self.features + self.features_2, target)
print("\n diagnosis with resample\nn=10")
multiple_features_eda.model_checking(10, scoring='precision')
print("\n diagnosis without resample\nn=10")
multiple_features_eda.model_checking_without_resampling(
10, scoring='precision')
print("\n diagnosis with resample\nn=20")
multiple_features_eda.model_checking(20, scoring='precision')
print("\n diagnosis without resample\nn=20")
multiple_features_eda.model_checking_without_resampling(
20, scoring='precision')
print("\n diagnosis with resample\nn=30")
multiple_features_eda.model_checking(30, scoring='precision')
print("\n diagnosis without resample\nn=30")
multiple_features_eda.model_checking_without_resampling(
30, scoring='precision')
def test_algorithms_for_target_depression(self):
print("precision score")
print("\n\n\n depression_cutoff \n")
target = "depression_cutoff"
multiple_features_eda = EDAMultiFeatureBackend(self.df, self.features + self.features_2, target)
print("\n depression_cutoff with resample\nn=10")
multiple_features_eda.model_checking(10, scoring='precision')
print("\n depression_cutoff without resample\nn=10")
multiple_features_eda.model_checking_without_resampling(10, scoring='precision')
print("\n depression_cutoff with resample\nn=20")
multiple_features_eda.model_checking(20, scoring='precision')
print("\n depression_cutoff without resample\nn=20")
multiple_features_eda.model_checking_without_resampling(20, scoring='precision')
print("\n depression_cutoff with resample\nn=30")
multiple_features_eda.model_checking(30, scoring='precision')
print("\n depression_cutoff without resample\nn=30")
multiple_features_eda.model_checking_without_resampling(30, scoring='precision')
print("depression_cutoff logreg f1 score")
multiple_features_eda.logistic_regression_grid_search()
print("depression_cutoff logreg precision score")
multiple_features_eda.logistic_regression_grid_search(scoring='precision')