from chefboost import Chefboost as chef
from multiprocessing import freeze_support
import pandas as pd
from numpy import random


class DecisionTrees:
    
    def return_predict(self):
        # header = ['Size(bigger_more_difficult)', 'Year(older_more_difficult)', 'Protection_from_defuse',
        #           'Meters_under_the_ground', 'Random_detonation_chance', 'Detonation_power_in_m',
        #           'Decision']

        # read data

        df = pd.read_csv("D:\\1 Python projects\Saper\data\db.txt")

        # print data
        # print(df.head())

        lines = []

        with open('D:\\1 Python projects\Saper\data\db.txt') as f:
            line = f.readline()
            for i in range(0, 200):
                line = f.readline()
                line = line.rstrip()
                line = line.replace(",detonate", "")
                line = line.replace(",defuse", "")
                lines.append(line)

        ss = []
        for line in lines:
            ss.append(line.split(","))

        normalized_data_for_predict = []
        for i in ss:
            normalized_data_for_predict.append(list(map(int, i)))
        print(normalized_data_for_predict)

        # ID3 config
        config = {'algorithm': 'ID3'}
        # create decision tree
        model = chef.fit(df, config)

        # print predict
        # print(chef.predict(model, [1, 2022, 0, 0, 0, 10]))

        predict = normalized_data_for_predict[random.randint(0, 199)]
        return chef.predict(model, predict)