51 lines
1.5 KiB
Python
51 lines
1.5 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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class DecisionTrees:
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def return_predict(self):
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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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# read data
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df = pd.read_csv("D:\\1 Python projects\Saper\data\db.txt")
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# print data
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# print(df.head())
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lines = []
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with open('D:\\1 Python projects\Saper\data\db.txt') as f:
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line = f.readline()
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for i in range(0, 200):
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line = f.readline()
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line = line.rstrip()
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line = line.replace(",detonate", "")
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line = line.replace(",defuse", "")
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lines.append(line)
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ss = []
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for line in lines:
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ss.append(line.split(","))
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normalized_data_for_predict = []
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for i in ss:
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normalized_data_for_predict.append(list(map(int, i)))
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print(normalized_data_for_predict)
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# ID3 config
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config = {'algorithm': 'ID3'}
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# create decision tree
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model = chef.fit(df, config)
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# print predict
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# print(chef.predict(model, [1, 2022, 0, 0, 0, 10]))
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predict = normalized_data_for_predict[random.randint(0, 199)]
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return chef.predict(model, predict)
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