import predictor_knn as knn import utilitaires as util import best_formula as bf func_train = util.adaptData func_test = util.adaptData param = util.getParam() model = knn.model_knn if __name__ == '__main__': bf.best_formula(func_train,func_test,param,model)
cl2.fit(train[label], train["Survived"]) cl3.fit(train[label], train["Survived"]) cl4.fit(train[label], train["Survived"]) cl5.fit(train[label], train["Survived"]) cl6.fit(train[label], train["Survived"]) rf.fit(train[label], train["Survived"]) test_predict = pd.DataFrame.copy(test) test_predict["Survived"] = rf.predict(test_predict[label]) return test_predict func_train = util.adaptData func_test = util.adaptData na = util.getNumAdaptData() if na == 1: label = np.asarray(['Pclass', 'Sex', 'Age', 'SibSp', 'Parch']) elif na == 2: label = np.asarray(['Pclass', 'Sex', 'Age', 'Fare']) elif na == 5: label = np.asarray([ "Age", "Pclass", "Sex", "SibSp", "Parch", "Fare_bin", "EC", "EQ", "ES", "Family", "Title", "Deck", "ASP" ]) else: label = util.getParam() model = model_ensemble path = "../Predictions/ensemble.csv" p.predictor(func_test, func_test, label, model, path)