48 lines
1.5 KiB
Python
48 lines
1.5 KiB
Python
import pandas as pd
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def test_split(index, value, dataset):
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left, right = list(), list()
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for row in dataset:
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if row[index] < value:
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left.append(row)
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else:
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right.append(row)
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return left, right
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# Calculate the Gini index for a split dataset
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def gini_index(groups, classes):
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# count all samples at split point
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n_instances = float(sum([len(group) for group in groups]))
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# sum weighted Gini index for each group
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gini = 0.0
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for group in groups:
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size = float(len(group))
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# avoid divide by zero
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if size == 0:
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continue
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score = 0.0
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# score the group based on the score for each class
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for class_val in classes:
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p = [row[-1] for row in group].count(class_val) / size
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score += p * p
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# weight the group score by its relative size
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gini += (1.0 - score) * (size / n_instances)
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return gini
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# Select the best split point for a dataset
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def get_split(dataset):
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class_values = list(set(row[-1] for row in dataset))
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b_index, b_value, b_score, b_groups = 999, 999, 999, None
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for index in range(len(dataset[0])-1):
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for row in dataset:
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groups = test_split(index, row[index], dataset)
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gini = gini_index(groups, class_values)
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print('X%d < %.3f Gini=%.3f' % ((index+1), row[index], gini))
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if gini < b_score:
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b_index, b_value, b_score, b_groups = index, row[index], gini, groups
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return {'index':b_index, 'value':b_value, 'groups':b_groups}
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dataset = pd.read_excel('Train.xlsx', sheet_name="Sheet1").to_numpy()
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split = get_split(dataset)
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print('Split: [X%d < %.3f]' % ((split['index']+1), split['value'])) |