def save_output(regs, X_df):
revenue_preds = []
for reg, models in regs:
X_w_pred_df = append_pred(models, X_df.copy())
revenue_pred = reg.predict(X_w_pred_df)
revenue_preds.append(revenue_pred)
revenue_pred = np.sum(revenue_preds, axis=0) / len(revenue_preds)
pred_df = X_df.copy()
pred_df["pred_revenue"] = revenue_pred
predict_df = data.to_label(pred_df)
fill_label(predict_df, "data/test_nolabel.csv")
print("*Saved prediction Ouput")
eval_reg.fit(X_train, y_train)
print("-" * 10, "regression report", "-" * 10)
report = regression_report(y_test, eval_reg.predict(X_test),
X_test.shape[1])
print(report)
print("-" * 10, "evaluation of label", "-" * 10)
label_df = data.get_true_label(
columns=["adr", "revenue", "is_canceled", "label"])
pred_label_df = data.predict_label(eval_reg, X_test_df)
print("[ label evaluation ]")
report_label = evaluate_by_label(pred_label_df, label_df, target="label")
print(report_label)
print("[ revenue_per_day evaluation ]")
report_revenue = evaluate_by_label(pred_label_df,
label_df,
target="revenue")
print(report_revenue)
#%% training with all data
X_df, y_df = data.processing(["revenue"])
reg = HistGradientBoostingRegressor(random_state=1129)
reg.fit(X_df.to_numpy(), y_df["revenue"].to_numpy())
#%% fill predict label to csv
test_X_df = data.processing_test_data("data/test.csv")
predict_df = data.predict_label(reg, test_X_df)
fill_label(predict_df, "data/test_nolabel.csv")