print(f"X_train shape {X_train.shape}, y_train shape {y_train.shape}")
print(f"X_test shape {X_test.shape}, y_test shape {y_test.shape}")
#%% evaluate performance with training data
eval_reg = HistGradientBoostingRegressor(random_state=1129)
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())
print(f"X_train shape {X_train.shape}, y_train shape {y_train.shape}")
print(f"X_test shape {X_test.shape}, y_test shape {y_test.shape}")
#%% evaluate performance with training data
eval_reg = HistGradientBoostingRegressor(random_state=1129)
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, reg_out="adr")
#%%
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(["actual_adr"])
reg = HistGradientBoostingRegressor(random_state=1129)
reg.fit(X_df.to_numpy(), y_df["actual_adr"].to_numpy())