def plotTree(myTree, parentpt, nodeTxt):
numLeafs = ID3Tree.getNumLeafs(myTree)
maxDepth = ID3Tree.getTreeDepth(myTree)
firstr = myTree.keys()[0]
cntrPt = (plotTree.xOff + (1.0 + float(numLeafs)) / 2.0 / plotTree.totalW,
plotTree.yOff)
# plot the text and the node
plotMidText(cntrPt, parentpt, nodeTxt)
plotNode(firstr, cntrPt, parentpt, decisionNode)
secondDict = myTree[firstr]
plotTree.yOff = plotTree.yOff - 1.0 / plotTree.totalD
# plot the tree by Recursive
for key in secondDict.keys():
if type(secondDict[key]).__name__ == "dict":
plotTree(secondDict[key], cntrPt, str(key))
else:
plotTree.xOff = plotTree.xOff + 1.0 / plotTree.totalW
plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt,
leafNode)
plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))
plotTree.yOff = plotTree.yOff + 1.0 / plotTree.totalD
def plotTree(myTree, parentpt, nodeTxt):
numLeafs = ID3Tree.getNumLeafs(myTree)
maxDepth = ID3Tree.getTreeDepth(myTree)
firstr = myTree.keys()[0]
cntrPt = (plotTree.xOff + (1.0 + float(numLeafs))/2.0/plotTree.totalW,
plotTree.yOff)
# plot the text and the node
plotMidText(cntrPt, parentpt, nodeTxt)
plotNode(firstr, cntrPt, parentpt, decisionNode)
secondDict = myTree[firstr]
plotTree.yOff = plotTree.yOff - 1.0 / plotTree.totalD
# plot the tree by Recursive
for key in secondDict.keys():
if type(secondDict[key]).__name__ == "dict":
plotTree(secondDict[key], cntrPt, str(key))
else:
plotTree.xOff = plotTree.xOff + 1.0 / plotTree.totalW
plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff),
cntrPt, leafNode)
plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))
plotTree.yOff = plotTree.yOff + 1.0 / plotTree.totalD
def createPlot(tree):
fig = plt.figure(1, facecolor='white')
fig.clf()
axprops = dict(xticks=[], yticks=[])
createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)
plotTree.totalW = float(ID3Tree.getNumLeafs(tree))
plotTree.totalD = float(ID3Tree.getTreeDepth(tree))
# init
plotTree.xOff = -0.5 / plotTree.totalW
plotTree.yOff = 1.0
plotTree(tree, (0.5, 1.0), '')
plt.show()
def createPlot(tree):
fig = plt.figure(1, facecolor='white')
fig.clf()
axprops = dict(xticks=[], yticks=[])
createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)
plotTree.totalW = float(ID3Tree.getNumLeafs(tree))
plotTree.totalD = float(ID3Tree.getTreeDepth(tree))
# init
plotTree.xOff = -0.5 / plotTree.totalW
plotTree.yOff = 1.0
plotTree(tree, (0.5, 1.0), '')
plt.show()
__author__ = 'wanghao'
"""
the glasses examples
"""
import ID3Tree
import TreePlot
fr = open('./lenses.txt')
DataList = fr.readlines()
DataSet = []
for data in DataList:
DataSet.append(data.strip().split('\t'))
print "The dateSet is ", DataSet
Labels = ['age', 'prescript', 'astigmatic', 'tearRate']
LenseTree = ID3Tree.createTree(DataSet, Labels)
print "the result ID3 Tree is ", LenseTree
TreePlot.createPlot(LenseTree)
__author__ = 'wanghao'
"""
Main function for the ID3 for test or run some functions
"""
import ID3Tree
import TreePlot
DataSet, Labels = ID3Tree.createDataSet()
shannon = ID3Tree.calShannonEnt(DataSet)
print "The base dataSet shannon Ent is ", shannon
print "---------------------------------------\n"
retDataSet = ID3Tree.splitDataSet(DataSet, 0, 1)
print retDataSet
print "---------------------------------------\n"
bestFeature = ID3Tree.chooseBestFeature(DataSet)
print "The base dataSet choose the best feature is ", Labels[bestFeature]
print "---------------------------------------\n"
classList = []
for oneData in DataSet:
classList.append(oneData[-1])
majorClass = ID3Tree.majorityCnt(classList)
print "The base dataSet the major Class is ", majorClass
print "---------------------------------------\n"
#coding:utf-8
'''
Created on 2016年10月21日
@author: TF
'''
from numpy import *
from ID3Tree import *
dtree = ID3Tree()
dtree.loadDataSet('dataset.dat', ['age', 'revenue', 'student', 'credit'])
dtree.train()
print dtree.tree
if line_count == 0:
feature_names = list(data for data in line_data if data != line_data[-1])
target_names.append(line_data[-1])
data_set = [[] for col in range(len(line_data) - 1)]
target_set = []
else:
for i in range(len(line_data) - 1):
data_set[i].append(line_data[i])
target_set.append(line_data[-1])
line_count = line_count + 1
target_names = list(set(target_set))
print('feature_names：', feature_names)
print('data_set:', data_set)
print('target_set:', target_set)
tree = ID3Tree.ID3Tree()
tree.create_tree(data_set, target_set, feature_names, target_names)
classifications = []
test_set = [[] for row in range(len(data_set[0]))]
for col in range(len(data_set)):
col_data = data_set[col]
for row in range(len(test_set)):
test_set[row].append(col_data[row])
for test_data in test_set:
classification = tree.predict(test_data)
classifications.append(classification)
print('classifications:', classifications)
