def complete_build(x_train, x_test, y_train, y_test): #Called function post label encoding lab_stats = create_stats(x_train, x_test, y_train, y_test, enc='labelencoder') #Prepare data for one hot encoding x_train, x_test, y_train, y_test = split_dataset(df) category_index = [ x for x in range(len(df.columns)) if df[df.columns[x]].dtype == 'object' ] #one hot encoding x_train, x_test = ohe_encode(x_train, x_test, category_index) #Called function post one hot encoding ohe_stats = create_stats(x_train, x_test, y_train, y_test, enc='oheencoder') final_stats = pd.concat([lab_stats, ohe_stats], axis=0) final_stats = final_stats[['c_val', 'rmse', 'mae', 'r2']] return final_stats
def complete_build(x_train, x_test, y_train, y_test): stats_label = create_stats(x_train, x_test, y_train, y_test) all_indices = list(range(0, x_train.shape[-1])) all_indices.remove(2) x_train_ohe, x_test_ohe = ohe_encode(x_train, x_test, all_indices) stats_ohe = create_stats(pd.DataFrame(x_train_ohe), pd.DataFrame(x_test_ohe), y_train, y_test, enc="ohe") model_plot = pd.concat([stats_label, stats_ohe]) model_plot.sort_values(['r2', 'rmse'], ascending=[0, 1]) return model_plot
def complete_build(x_train, x_test, y_train, y_test): category_index = [ x for x in range(len(x_train.columns)) if x_train[x_train.columns[x]].dtype == 'object' ] x_train_t, x_test_t = ohe_encode(x_train, x_test, category_index) train = pd.DataFrame(x_train_t) test = pd.DataFrame(x_test_t) train.columns = x_train.columns.values test.columns = x_test.columns.values complete_stats1 = create_stats(x_train, x_test, y_train, y_test) complete_stats = create_stats(train, test, y_train, y_test) return pd.concat([complete_stats1, complete_stats], axis=0)