153 lines
3.7 KiB
Python
153 lines
3.7 KiB
Python
import random
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn.tree import DecisionTreeClassifier
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def evaluate_values(values):
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data = []
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if values[0] == 10:
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data.append(10)
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elif values[0] == 15:
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data.append(15)
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elif values[0] == 20:
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data.append(20)
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elif values[0] == 30:
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data.append(30)
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elif values[0] == 40:
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data.append(40)
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else:
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data.append(random.choice([10, 15, 20, 30, 40]))
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if values[1] is True:
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data.append(1)
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elif values[1] is False:
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data.append(0)
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else:
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data.append(random.choice([1, 0]))
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if values[2] == 1:
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data.append(1)
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elif values[2] == 2:
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data.append(2)
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elif values[2] == 3:
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data.append(3)
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elif values[2] == 4:
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data.append(4)
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elif values[2] == 5:
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data.append(5)
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elif values[2] == 6:
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data.append(6)
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elif values[2] == 7:
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data.append(7)
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elif values[2] == 8:
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data.append(8)
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elif values[2] == 9:
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data.append(9)
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elif values[2] == 10:
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data.append(10)
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else:
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data.append(random.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
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if values[3] == 'plastic':
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data.append(5)
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elif values[3] == 'wood':
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data.append(2)
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elif values[3] == 'metal':
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data.append(4)
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elif values[3] == 'glass':
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data.append(3)
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elif values[3] == 'paper':
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data.append(1)
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else:
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data.append(random.choice([1, 2, 3, 4, 5]))
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if values[4] == 'little':
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data.append(1)
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elif values[4] == 'medium':
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data.append(2)
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elif values[4] == 'huge':
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data.append(3)
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elif values[4] == 'large':
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data.append(4)
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else:
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data.append(random.choice([1, 2, 3, 4]))
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if values[5] == 1:
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data.append(1)
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elif values[5] == 2:
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data.append(2)
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elif values[5] == 3:
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data.append(3)
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elif values[5] == 4:
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data.append(4)
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elif values[5] == 5:
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data.append(5)
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elif values[5] == 6:
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data.append(6)
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elif values[5] == 7:
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data.append(7)
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elif values[5] == 8:
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data.append(8)
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elif values[5] == 9:
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data.append(9)
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elif values[5] == 10:
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data.append(10)
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else:
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data.append(random.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
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if values[6] == 1:
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data.append(1)
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elif values[6] == 2:
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data.append(2)
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elif values[6] == 3:
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data.append(3)
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elif values[6] == 4:
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data.append(4)
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elif values[6] == 5:
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data.append(5)
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elif values[6] == 6:
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data.append(6)
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elif values[6] == 7:
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data.append(7)
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elif values[6] == 8:
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data.append(8)
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elif values[6] == 9:
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data.append(9)
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elif values[6] == 10:
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data.append(10)
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else:
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data.append(random.choice([1, 2, 3, 4, 5, 6, 7, 8, 9, 10]))
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return data
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def trash_selection(prefer):
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df = pd.read_excel('data.xlsx', sheet_name='list1')
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# print(df)
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d = {'paper': 1, 'wood': 2, 'glass': 3, 'metal': 4, 'plastic': 5}
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df['material'] = df['material'].map(d)
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d = {'little': 1, 'medium': 2, 'huge': 3, 'large': 4}
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df['size'] = df['size'].map(d)
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d = {'leave': 0, 'pick up': 1}
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df['what to do'] = df['what to do'].map(d)
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features = ['weight', 'density', 'fragility', 'material', 'size', 'degradability', 'renewability']
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x = df[features]
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y = df['what to do']
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x_train, x_test, y_train, y_test = train_test_split(x, y)
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clf = DecisionTreeClassifier(criterion='entropy')
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# model = clf.fit(X, y)
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# text_representation = tree.export_text(clf)
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# print(text_representation)
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clf = clf.fit(x_train, y_train)
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return clf.predict([prefer])
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