def main():
fr = open('lenses.txt')
lenses = [inst.strip().split('\t') for inst in fr.readlines()]
lensesLabels = ['age', 'prescript', 'astigmatic', 'tearRate']
lensesTree = trees.createTree(lenses, lensesLabels)
print lensesTree
plotTree.createPlot(lensesTree)
if type(dataSet[0][bestFeat]).__name__ == 'str':
currentlabel = labels_full.index(labels[bestFeat])
featValuesFull = [example[currentlabel] for example in data_full]
uniqueValsFull = set(featValuesFull)
del (labels[bestFeat])
#针对bestFeat的每个取值，划分出一个子树。
for value in uniqueVals:
subLabels = labels[:]
if type(dataSet[0][bestFeat]).__name__ == 'str':
uniqueValsFull.remove(value)
myTree[bestFeatLabel][value] = createTree(
splitDataSet(dataSet, bestFeat, value), subLabels, data_full,
labels_full)
if type(dataSet[0][bestFeat]).__name__ == 'str':
for value in uniqueValsFull:
myTree[bestFeatLabel][value] = majorityCnt(classList)
return myTree
df = pd.read_csv('watermelon_2.csv')
data = df.values[:11, 1:].tolist()
data_full = data[:]
labels = df.columns.values[1:-1].tolist()
labels_full = labels[:]
myTree = createTree(data, labels, data_full, labels_full)
print(myTree)
import plotTree
plotTree.createPlot(myTree)
tree = {FeatureLabel: {}}
del (labels[bestFeat])
featValues = [example[bestFeat] for example in data]
featValues = set(featValues) # Remove duplicate values
for value in featValues:
newLabels = labels[:]
tree[FeatureLabel][value] = buildTree(splitData(data, bestFeat, value), newLabels)
return tree
# use date & labels
tree = buildTree(data, labels)
print tree
import plotTree
plotTree.createPlot(tree)
def classify(tree, featLabels, test):
firstKey = tree.keys()[0]
secondDict = tree[firstKey]
featIndex = featLabels.index(firstKey)
for key in secondDict.keys():
if test[featIndex] == key:
if type(secondDict[key]).__name__ == "dict":
classLabel = classify(secondDict[key], featLabels, test)
else:
classLabel = secondDict[key]
return classLabel
data = [ord(rawData[0]) - ord('a'),ord(rawData[1]) - ord('1'),\
ord(rawData[2]) - ord('a'),ord(rawData[3]) - ord('1'),\
ord(rawData[4]) - ord('a'),ord(rawData[5]) - ord('1')]
if rawData[6] not in label2Index: #将分类用编号代替，因为感觉字符串的开销更高
label2Index[rawData[6]] = index
classifications.append(rawData[6])
index += 1
data.append(label2Index[rawData[6]])
dataset.append(data)
return [data[0:6] for data in dataset], [data[-1] for data in dataset]
def cutTree(tree, depth):
newTree = {}
if (depth == 4):
return "more"
if not isinstance(tree, dict):
return tree
for node in tree:
newTree[node] = cutTree(tree[node], depth + 1)
return newTree
if __name__ == '__main__':
(trainset, trainlabel) = readDataset("trainset.csv")
(testset, testlabel) = readDataset("testset.csv")
tree = createTree(trainset, trainlabel, testset, testlabel)
#print(tree)
#print(cutTree(tree,0))
plotTree.createPlot(cutTree(tree, 0))
def createDataSet(filename):
file = open(filename)
lines = file.readlines()
file.close()
dataSet = []
labels = []
features = ['age', 'prescript', 'astigmatic', 'tearRate']
for line in lines:
lst = line.split()
dataSet.append(lst[:4])
labels.append(' '.join(lst[4:]))
return dataSet, labels, features
# Create training data
dataSet, labels, features = createDataSet('data/lenses/training.txt')
tree = decisionTreeID3.buildTree(dataSet, labels)
plotTree.createPlot(tree, features)
# Create test data
testData, testLabels, _ = createDataSet('data/lenses/test.txt')
correct = 0
for i in range(len(testData)):
res = decisionTreeID3.evaluate(tree, testData[i])
if res == testLabels[i]:
correct += 1
print('Correctness: %d/%d' % (correct, len(testData)))
print("error: the testVect feat:%s, has the value: %s, but there is no the value in trainingDataSet" % (str(firstStr), str(testVect[featIndex])))
"""
使用pickle模块存储决策树
"""
def storeTree(inputTree, fileName):
#以二进制的形式存储
fw = open(fileName, 'wb')
pickle.dump(inputTree, fw)
fw.close()
"""
使用pickle模块读取决策树
"""
def grabTree(fileName):
#以二进制的形式读取
fr = open(fileName, 'rb')
return pickle.load(fr)
if __name__ == '__main__':
fr = open(r"lenses.txt")
dataSet = [line.strip().split('\t') for line in fr.readlines()]
classLables = ['age','prescript', 'astigmatic', 'tearRate']
lensesTree = createTree(dataSet, classLables)
print(lensesTree)
import plotTree as pt
pt.createPlot(lensesTree)
#storeTree(lensesTree, r"lensesTree.txt")
#print(grabTree(r"lensesTree.txt"))
import decideTree
import storeTree
import plotTree
if __name__ == '__main__':
#data = [[1,1,'是'],[1,1,'是'],[2,1,'否'],[2,2,'否'],[2,2,'否']]
# shannoEnt = calcshannoEnt(data)
# print(shannoEnt)
#print(classifyDataSetByFeature(data,0,2))
#dataset,labels = decideTree.createDataSet()
#decideTree = decideTree.createDecideTree(dataset,labels)
#print(decideTree)
#plotTree.createPlot()
#--------------------------------------------------------------
#storeTree.storeTree(decideTree,'myDecideTree.txt')
# myTree = storeTree.grabTree('myDecideTree.txt')
# print(myTree)
#--------------------------------------------------------------
file = open('lenses.txt')
labels = file.readline().strip().split('\t')
lenses = [example.strip().split('\t') for example in file.readlines()]
decideTree = decideTree.createDecideTree(lenses, labels)
print(decideTree)
plotTree.createPlot(decideTree)
import plotTree
import createDecisionTree
import computePara
if __name__ == '__main__':
data_set, labels = computePara.createDataset()
origin_labels = labels.copy()
decision_tree = createDecisionTree.createTree(data_set, labels)
test_label = createDecisionTree.classify(decision_tree, origin_labels,
[1, 0, 1])
print('该测试向量属于的类别是： ', test_label)
plotTree.createPlot(decision_tree)
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 06 14:21:43 2015
@author: Herbert
"""
import tree
import plotTree
import saveTree
fr = open('lenses.txt')
lensesData = [data.strip().split('\t') for data in fr.readlines()]
lensesLabel = ['age', 'prescript', 'astigmatic', 'tearRate']
lensesTree = tree.buildTree(lensesData, lensesLabel)
#print lensesData
print lensesTree
print plotTree.createPlot(lensesTree)
#!/usr/bin/python #coding = utf-8 import decision_tree import plotTree dataSet = [[1,1,'yes'],[1,0,'no'],[0,1,'no'],[0,1,'no'],[1,1,'yes']] labels = ['one', 'two', 'answer'] tree = decision_tree.createTree(dataSet, labels) plotTree.createPlot()
#shannonEnt = calcShannonEnt(dataset) #print shannonEnt #reduDataset = splitDataset(dataset, 0, 0) #print reduDataset #bestFeatIdx = chooseBestFeatureToSplitV2(dataset) #print bestFeatIdx #print dataset copyLabels = [] for label in labels: copyLabels.append(label) tree = createTree(dataset, copyLabels); print tree #classLabel = classify(tree, labels, [1, 0]) #print classLabel #classLabel = classify(tree, labels, [1, 1]) #print classLabel plotTree.createPlot(tree) #treeFile = 'storeTree.txt' #storeTree(tree, treeFile) #rebuildTree = grabTree(treeFile) #print rebuildTree
