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后剪枝中传入的特征集合应该是变化的 #15

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后剪枝中传入的特征集合应该是变化的 #15

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HTQ17double Sep 13, 2023
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HTQ17double Sep 13, 2023
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10 changes: 6 additions & 4 deletions tree.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
import operator
import treePlotter
from collections import Counter
from copy import deepcopy


pre_pruning = True
Expand Down Expand Up @@ -55,10 +56,10 @@ def cal_entropy(dataset):
labelCounts = {}
# 给所有可能分类创建字典
for featVec in dataset:
currentlabel = featVec[-1]
currentlabel = featVec[-1]
if currentlabel not in labelCounts.keys():
labelCounts[currentlabel] = 0
labelCounts[currentlabel] += 1
labelCounts[currentlabel] += 1
Ent = 0.0
for key in labelCounts:
p = float(labelCounts[key]) / numEntries
Expand Down Expand Up @@ -184,6 +185,7 @@ def ID3_createTree(dataset, labels, test_dataset):
print(u"此时最优索引为：" + (bestFeatLabel))

ID3Tree = {bestFeatLabel: {}}
labels_for_post_pruning = deepcopy(labels)
del (labels[bestFeat])
# 得到列表包括节点所有的属性值
featValues = [example[bestFeat] for example in dataset]
Expand Down Expand Up @@ -224,8 +226,8 @@ def ID3_createTree(dataset, labels, test_dataset):

if post_pruning:
tree_output = classifytest(ID3Tree,
featLabels=['年龄段', '有工作', '有自己的房子', '信贷情况'],
testDataSet=test_dataset)
featLabels=labels_for_post_pruning,
testDataSet=test_dataset)# 这里传入的数据集的特征集合是变化后的，所以应该传入变化的特征集
ans = []
for vec in test_dataset:
ans.append(vec[-1])
Expand Down