import os.path

import numpy as np
import pandas as pd
import argparse

def rmsle(predictions, actuals):
    rmsle_confirmed = np.sqrt(np.mean((np.log1p(predictions['ConfirmedCases']) - np.log1p(actuals['ConfirmedCases'])) ** 2))
    rmsle_fatalities = np.sqrt(np.mean((np.log1p(predictions['Fatalities']) - np.log1p(actuals['Fatalities'])) ** 2))
    return (rmsle_confirmed + rmsle_fatalities) / 2

parser = argparse.ArgumentParser()
parser.add_argument('--path', type=str, required=True)
parser.add_argument('--name', type=str, required=True)
parser.add_argument('--answer_file', type=str, required=True)
parser.add_argument('--predict_file', type=str, required=True)

parser.add_argument('--value', type=str, default="count")

args = parser.parse_args()

answers = pd.read_csv(os.path.join(args.path, args.name, args.answer_file))
predictions = pd.read_csv(os.path.join(args.path, args.name, args.predict_file))

performance = rmsle(predictions, answers)

with open(os.path.join(args.path, args.name, "result.txt"), "w") as f:
    f.write(str(performance))