77 lines
1.9 KiB
Python
77 lines
1.9 KiB
Python
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#from sklearn.datasets import load_iris
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from sklearn.tree import export_text
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from sklearn.tree import DecisionTreeClassifier
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import joblib
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X1 = []
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view = []
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with open("decisionTree/database.txt", 'r') as f:
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for line in f:
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line = line.strip()
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test_list = [int(i) for i in line]
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x = []
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if line[0] == "0":
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x.append("clear")
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else:
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x.append("rainy")
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if line[1] == "0":
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x.append("not planted")
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else:
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x.append("planted")
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if line[2] == "0":
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x.append("bad")
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else:
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x.append("good")
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if line[3] == "0":
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x.append("good")
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else:
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x.append("too strong")
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if line[4] == "0":
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x.append("no snow")
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else:
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x.append("snow")
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if line[5] == "0":
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x.append("dry")
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else:
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x.append("moist")
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if line[6] == "0":
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x.append("healthy")
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else:
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x.append("rot")
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if line[7] == "0":
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x.append("morning")
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elif line[7] == "1":
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x.append("noon")
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elif line[7] == "2":
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x.append("sunset")
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else:
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x.append("night")
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view.append(x)
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X1.append(test_list)
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f = open("decisionTree/learning_set.txt", "w") #zapisuje atrybuty s<>ownie
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for i in view:
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f.write(str(i)+"\n")
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f.close()
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Y1 = []
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with open("decisionTree/decissions.txt", 'r') as f: #czyta decyzje
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for line in f:
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line = line.strip()
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test = int(line)
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Y1.append(test)
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dataset = X1
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decision = Y1
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labels = ['Rain','Plant','Temperature','Sun','Snow','Moisture','Rotten','Time']
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model = DecisionTreeClassifier(random_state=0, max_depth=20).fit(dataset, decision)
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filename = 'decisionTree/decisionTree.sav'
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print("Model trained")
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print("Decision tree:")
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print(export_text(model, feature_names=labels))
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joblib.dump(model, filename)
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