def test_treePlotter1(self):
print('============test1=============')
mytree = treePlotter.retrieveTree(0)
print(mytree)
num = treePlotter.getNumLeafs(mytree)
print("num=" + str(num))
depth = treePlotter.getTreeDepth(mytree)
print("depth=" + str(depth))
self.assertTrue(num > 0, "num should be more than zero")
self.assertTrue(depth > 0, "depth should be more than zero")
def test_treePlotter5(self):
print("============使用决策树的分类函数==============")
myDat, labels = trees.createDataSet()
print(labels)
mytree = treePlotter.retrieveTree(0)
print(mytree)
result = trees.classify(mytree, labels, [1, 0])
print(result)
result = trees.classify(mytree, labels, [1, 1])
print(result)
# 存储树，也即序列化树
def storeTree(inputTree, filename):
# python模块 pickle可以序列化对象
import pickle
fw = open(filename, 'w')
# dump 倾倒，卸下
pickle.dump(inputTree, fw)
fw.close()
# 获取树，也即反序列化， grab：抢夺，抢先
def grabTree(filename):
import pickle
fr = open(filename)
return pickle.load(fr)
myTree = treePlt.retrieveTree(0)
# storeTree(myTree, 'classifierStorage1.txt')
# print grabTree('classifierStorage1.txt')
# 隐形眼镜决策树
fr = open('lenses.txt')
# 对于每行数据，根据 \t 分隔字符
lenses = [inst.strip().split('\t') for inst in fr.readlines()]
lensesLabels = ['age', 'prescript', 'astigmatic', 'tearRate']
lensesTree = createTree(lenses, lensesLabels)
print lensesTree
treePlt.createPlot(lensesTree)
def test_treePlotter3(self):
print('============test3=============')
mytree = treePlotter.retrieveTree(0)
mytree['no surfacing'][3] = 'maybe'
treePlotter.createPlot(mytree)
def test_treePlotter2(self):
print('============test2=============')
mytree = treePlotter.retrieveTree(0)
treePlotter.createPlot(mytree)