59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
from chefboost import Chefboost as chef
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from multiprocessing import freeze_support
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import pandas as pd
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from numpy import random
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# decision tree create
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###############
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class DecisionTrees:
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def create_model(self):
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model = chef.fit(pd.read_csv("data/db.txt"), {'algorithm': 'ID3'})
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return model
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def return_predict(self, mod):
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# read data
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#df = pd.read_csv("data/db.txt")
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# Header of df looks like:
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header = ['Size(bigger_more_difficult)', 'Year(older_more_difficult)', 'Protection_from_defuse',
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'Meters_under_the_ground', 'Random_detonation_chance', 'Detonation_power_in_m',
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'Decision']
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# print data
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# print(df.head())
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# ID3 config
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#config = {'algorithm': 'ID3'}
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# create decision tree
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# print predict
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# print(chef.predict(model, [1, 2022, 0, 0, 0, 10]))
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# random generate characteristics for mine
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size = random.randint(1, 10)
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year = random.randint(1941, 2022)
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protection = 0
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if year >= 2000:
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protection = random.choice([1, 0, 1])
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m_under_the_ground = random.randint(0, 10)
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detonation_chance = random.randint(0, 100)
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detonation_power_in_m = random.randint(0, 10)
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detonation_power_in_m = detonation_power_in_m - m_under_the_ground
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if detonation_power_in_m <= 0:
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detonation_power_in_m = 0
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mine_characteristics = [size, year, protection, m_under_the_ground, detonation_chance, detonation_power_in_m]
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# print data about mine
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print("Mine characteristics : ")
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cnt = 0
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for i in mine_characteristics:
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print(header[cnt], " = ", i)
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cnt += 1
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# return prediction
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return chef.predict(mod, mine_characteristics)
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