def test_get(tcp_db_connection): main.create_tables() context = main.get_index_context() assert isinstance(context, dict) assert len(context.get("recent_votes")) >= 0 assert context.get("tab_count") >= 0 assert context.get("space_count") >= 0
def setUp(self): app = create_app("settings.TestingConfig") self.db = create_db(app) create_resources(app) self.app = app.test_client() with app.app_context(): create_tables()
from main import create_tables create_tables()
import main main.create_tables() main.create_fixture()
#!/usr/bin/env python import main main.create_tables() main.app.run()
plt.ylabel('True Positive Rate') plt.title(f'DNN model- {name}\nMax accuracy={round(max(accuracy),2)}, learning rate={lr}, epochs={epochs}') plt.legend(loc="lower right") plt.savefig('Log_ROC') # plt.show() # plt.savefig(f'/home/michal/MYOR Dropbox/R&D/Allergies Product Development/Prediction/Algorithm_Beta/18_01_2021_CARE_results/{name}-statistics-DNN.jpeg') # plt.show() # plt.savefig(f'/home/michal/MYOR Dropbox/R&D/Allergies Product Development/Prediction/Algorithm_Beta/18_01_2021_CARE_results/{name}-statistics-randomForest.jpeg') # plt.savefig('Log_ROC') if __name__ == '__main__': FA, label, name=Type('AD') merged_df=create_tables(run_tables_creation=False,FA=FA) y =merged_df[label] X = merged_df.drop(columns=[label]) X_train, X_test, y_train, y_test = train_test_split(X, np.where(y > 0, 1, 0), test_size=0.1, stratify=np.where(y > 0, 1, 0)) CARE_df=CARE_data() print(CARE_df.shape) CARE_df=CARE_df[X_train.columns] Random_forest_regress(X_train, X_test, y_train, y_test,CARE_df,n_estimators=200, name=name) DNN_regress(X_train, X_test, y_train, y_test,CARE_df, epochs=200, lr=0.0001,name=name) FA, label, name=Type('FA') merged_df=create_tables(run_tables_creation=False,FA=FA)