from chefboost import Chefboost as chef from multiprocessing import freeze_support import pandas as pd from numpy import random # decision tree create ############### class DecisionTrees: def create_model(self): model = chef.fit(pd.read_csv("data/db.txt"), {'algorithm': 'ID3'}) return model def return_predict(self, mod): # read data #df = pd.read_csv("data/db.txt") # Header of df 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'] # print data # print(df.head()) # ID3 config #config = {'algorithm': 'ID3'} # create decision tree # print predict # print(chef.predict(model, [1, 2022, 0, 0, 0, 10])) # random generate characteristics for mine 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 data about mine print("Mine characteristics : ") cnt = 0 for i in mine_characteristics: print(header[cnt], " = ", i) cnt += 1 # return prediction return chef.predict(mod, mine_characteristics)