import numpy as np
import pandas as pd
import os

def load_data(base_dir):
    print(f"base_dir = {base_dir}")
    print(os.listdir(f"{base_dir}"))
    x_train = None
    for i in range(1, 5):
        filename = f"{base_dir}/x_train_{i}.npz"
        with np.load(filename) as data:
            print(f"files in {filename}: {data.files}")
            temp_data = data[data.files[0]]
            if x_train is None:
                x_train = temp_data
            else:
                x_train = np.concatenate((x_train, temp_data))


    with np.load(f'{base_dir}/y_train.npz') as data:
        print(f"files in {base_dir}/y_train.npz: {data.files}")
        y_train = data[data.files[0]]

    with np.load(f'{base_dir}/x_test.npz') as data:
        print(f"files in {base_dir}/x_test.npz: {data.files}")
        x_test = data[data.files[0]]
    return x_train, y_train, x_test

def save_submission(submission, name="prediction.csv"):
    result = pd.DataFrame(submission)
    result = result.rename({0: "Label", }, axis=1)
    result.index.name = "Id"
    result.index += 1
    result.to_csv(name)

x, y, t = load_data("../input")


import catboost as cb

model = cb.CatBoostRegressor(loss_function='RMSE', iterations=2500, learning_rate=0.1, depth=7, l2_leaf_reg=0.07, task_type="GPU")

model.fit(x, y * 5)
submission -= np.min(submission)
submission /= np.max(submission)
submission *= 20
save_submission(submission)