InteligentnySaper/classes/decisionTrees.py

45 lines
1.5 KiB
Python

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 looks like:
# 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())
# 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]))
size = random.randint(1, 10)
year = random.randint(1941, 2022)
protection = 0
if year >= 2000:
protection = random.choice([1, 0, 1])
m_under_the_ground = random.randint(0, 10)
detonation_chance = random.randint(0, 100)
detonation_power_in_m = random.randint(0, 10)
detonation_power_in_m = detonation_power_in_m - m_under_the_ground
if detonation_power_in_m <= 0:
detonation_power_in_m = 0
mine_characteristics = [size, year, protection, m_under_the_ground, detonation_chance, detonation_power_in_m]
print(mine_characteristics)
return chef.predict(model, mine_characteristics)