使用Decision Tree对MNIST数据集进行实验
使用的Decision Tree中,对MNIST中的灰度值进行了0/1处理,方便来进行分类和计算熵。
使用较少的测试数据测试了在对灰度值进行多分类的情况下,分类结果的正确率如何。实验结果如下。
#Test change pixel data into more categories than 0/1:
#int(pixel)/50: 37%
#int(pixel)/64: 45.9%
#int(pixel)/96: 52.3%
#int(pixel)/128: 62.48%
#int(pixel)/152: 59.1%
#int(pixel)/176: 57.6%
#int(pixel)/192: 54.0%
可见,在对灰度数据进行二分类,也就是0/1处理时,效果是最好的。
使用0/1处理,最终结果如下:
#Result:
#Train with 10k, test with 60k: 77.79%
#Train with 60k, test with 10k: 87.3%
#Time cost: 3 hours.
最终结果是87.3%的正确率。与SVM和KNN的超过95%相比,差距不小。而且消耗时间更长。
需要注意的是,此次Decision Tree算法中,并未对决策树进行剪枝。因此,还有可以提升的空间。
python代码见最下面。其中:
calcShannonEntropy(dataSet):是对矩阵的熵进行计算,根据各个数据点的分类情况,使用香农定理计算;
splitDataSet(dataSet, axis, value): 是获取第axis维度上的值为value的所有行所组成的矩阵。对于第axis维度上的数据,分别计算他们的splitDataSet的矩阵的熵,并与该维度上数据的出现概率相乘求和,可以得到使用第axis维度构建决策树后,整体的熵。
chooseBestFeatureToSplit(dataSet): 根据splitDataSet函数,对比得到整体的熵与原矩阵的熵相比,熵的增量最大的维度。根据此维度feature来构建决策树。
createDecisionTree(dataSet, features): 递归构建决策树。若在叶子节点处没法分类,则采用majorityCnt(classList)方法统计出现最多次的class作为分类。
代码如下:
- #Decision tree for MNIST dataset by arthur503.
- #Data format: 'class label1:pixel label2:pixel ...'
- #Warning: without fix overfitting!
- #
- #Test change pixel data into more categories than 0/1:
- #int(pixel)/50: 37%
- #int(pixel)/64: 45.9%
- #int(pixel)/96: 52.3%
- #int(pixel)/128: 62.48%
- #int(pixel)/152: 59.1%
- #int(pixel)/176: 57.6%
- #int(pixel)/192: 54.0%
- #
- #Result:
- #Train with 10k, test with 60k: 77.79%
- #Train with 60k, test with 10k: 87.3%
- #Time cost: 3 hours.
- from numpy import *
- import operator
- def calcShannonEntropy(dataSet):
- numEntries = len(dataSet)
- labelCounts = {}
- for featureVec in dataSet:
- currentLabel = featureVec[0]
- if currentLabel not in labelCounts.keys():
- labelCounts[currentLabel] = 1
- else:
- labelCounts[currentLabel] += 1
- shannonEntropy = 0.0
- for key in labelCounts:
- prob = float(labelCounts[key])/numEntries
- shannonEntropy -= prob * log2(prob)
- return shannonEntropy
- #get all rows whose axis item equals value.
- def splitDataSet(dataSet, axis, value):
- subDataSet = []
- for featureVec in dataSet:
- if featureVec[axis] == value:
- reducedFeatureVec = featureVec[:axis]
- reducedFeatureVec.extend(featureVec[axis+1:]) #if axis == -1, this will cause error!
- subDataSet.append(reducedFeatureVec)
- return subDataSet
- def chooseBestFeatureToSplit(dataSet):
- #Notice: Actucally, index 0 of numFeatures is not feature(it is class label).
- numFeatures = len(dataSet[0])
- baseEntropy = calcShannonEntropy(dataSet)
- bestInfoGain = 0.0
- bestFeature = numFeatures - 1 #DO NOT use -1! or splitDataSet(dataSet, -1, value) will cause error!
- #feature index start with 1(not 0)!
- for i in range(numFeatures)[1:]:
- featureList = [example[i] for example in dataSet]
- featureSet = set(featureList)
- newEntropy = 0.0
- for value in featureSet:
- subDataSet = splitDataSet(dataSet, i, value)
- prob = len(subDataSet)/float(len(dataSet))
- newEntropy += prob * calcShannonEntropy(subDataSet)
- infoGain = baseEntropy - newEntropy
- if infoGain > bestInfoGain:
- bestInfoGain = infoGain
- bestFeature = i
- return bestFeature
- #classify on leaf of decision tree.
- def majorityCnt(classList):
- classCount = {}
- for vote in classList:
- if vote not in classCount:
- classCount[vote] = 0
- classCount[vote] += 1
- sortedClassCount = sorted(classCount.iteritems(), key=operator.itemgetter(1), reverse=True)
- return sortedClassCount[0][0]
- #Create Decision Tree.
- def createDecisionTree(dataSet, features):
- print 'create decision tree... length of features is:'+str(len(features))
- classList = [example[0] for example in dataSet]
- if classList.count(classList[0]) == len(classList):
- return classList[0]
- if len(dataSet[0]) == 1:
- return majorityCnt(classList)
- bestFeatureIndex = chooseBestFeatureToSplit(dataSet)
- bestFeatureLabel = features[bestFeatureIndex]
- myTree = {bestFeatureLabel:{}}
- del(features[bestFeatureIndex])
- featureValues = [example[bestFeatureIndex] for example in dataSet]
- featureSet = set(featureValues)
- for value in featureSet:
- subFeatures = features[:]
- myTree[bestFeatureLabel][value] = createDecisionTree(splitDataSet(dataSet, bestFeatureIndex, value), subFeatures)
- return myTree
- def line2Mat(line):
- mat = line.strip().split(' ')
- for i in range(len(mat)-1):
- pixel = mat[i+1].split(':')[1]
- #change MNIST pixel data into 0/1 format.
- mat[i+1] = int(pixel)/128
- return mat
- #return matrix as a list(instead of a matrix).
- #features is the 28*28 pixels in MNIST dataset.
- def file2Mat(fileName):
- f = open(fileName)
- lines = f.readlines()
- matrix = []
- for line in lines:
- mat = line2Mat(line)
- matrix.append(mat)
- f.close()
- print 'Read file '+str(fileName) + ' to array done! Matrix shape:'+str(shape(matrix))
- return matrix
- #Classify test file.
- def classify(inputTree, featureLabels, testVec):
- firstStr = inputTree.keys()[0]
- secondDict = inputTree[firstStr]
- featureIndex = featureLabels.index(firstStr)
- predictClass = '-1'
- for key in secondDict.keys():
- if testVec[featureIndex] == key:
- if type(secondDict[key]) == type({}):
- predictClass = classify(secondDict[key], featureLabels, testVec)
- else:
- predictClass = secondDict[key]
- return predictClass
- def classifyTestFile(inputTree, featureLabels, testDataSet):
- rightCnt = 0
- for i in range(len(testDataSet)):
- classLabel = testDataSet[i][0]
- predictClassLabel = classify(inputTree, featureLabels, testDataSet[i])
- if classLabel == predictClassLabel:
- rightCnt += 1
- if i % 200 == 0:
- print 'num '+str(i)+'. ratio: ' + str(float(rightCnt)/(i+1))
- return float(rightCnt)/len(testDataSet)
- def getFeatureLabels(length):
- strs = []
- for i in range(length):
- strs.append('#'+str(i))
- return strs
- #Normal file
- trainFile = 'train_60k.txt'
- testFile = 'test_10k.txt'
- #Scaled file
- #trainFile = 'train_60k_scale.txt'
- #testFile = 'test_10k_scale.txt'
- #Test file
- #trainFile = 'test_only_1.txt'
- #testFile = 'test_only_2.txt'
- #train decision tree.
- dataSet = file2Mat(trainFile)
- #Actually, the 0 item is class, not feature labels.
- featureLabels = getFeatureLabels(len(dataSet[0]))
- print 'begin to create decision tree...'
- myTree = createDecisionTree(dataSet, featureLabels)
- print 'create decision tree done.'
- #predict with decision tree.
- testDataSet = file2Mat(testFile)
- featureLabels = getFeatureLabels(len(testDataSet[0]))
- rightRatio = classifyTestFile(myTree, featureLabels, testDataSet)
- print 'total right ratio: ' + str(rightRatio)
使用Decision Tree对MNIST数据集进行实验的更多相关文章
- 使用libsvm对MNIST数据集进行实验
使用libsvm对MNIST数据集进行实验 在学SVM中的实验环节,老师介绍了libsvm的使用.当时看完之后感觉简单的说不出话来. 1. libsvm介绍 虽然原理要求很高的数学知识等,但是libs ...
- 使用libsvm对MNIST数据集进行实验---浅显易懂!
原文:http://blog.csdn.net/arthur503/article/details/19974057 在学SVM中的实验环节,老师介绍了libsvm的使用.当时看完之后感觉简单的说不出 ...
- 使用KNN对MNIST数据集进行实验
由于KNN的计算量太大,还没有使用KD-tree进行优化,所以对于60000训练集,10000测试集的数据计算比较慢.这里只是想测试观察一下KNN的效果而已,不调参. K选择之前看过貌似最好不要超过2 ...
- 决策树Decision Tree 及实现
Decision Tree 及实现 标签: 决策树熵信息增益分类有监督 2014-03-17 12:12 15010人阅读 评论(41) 收藏 举报 分类: Data Mining(25) Pyt ...
- 用于分类的决策树(Decision Tree)-ID3 C4.5
决策树(Decision Tree)是一种基本的分类与回归方法(ID3.C4.5和基于 Gini 的 CART 可用于分类,CART还可用于回归).决策树在分类过程中,表示的是基于特征对实例进行划分, ...
- (转)Decision Tree
Decision Tree:Analysis 大家有没有玩过猜猜看(Twenty Questions)的游戏?我在心里想一件物体,你可以用一些问题来确定我心里想的这个物体:如是不是植物?是否会飞?能游 ...
- 从零到一:caffe-windows(CPU)配置与利用mnist数据集训练第一个caffemodel
一.前言 本文会详细地阐述caffe-windows的配置教程.由于博主自己也只是个在校学生,目前也写不了太深入的东西,所以准备从最基础的开始一步步来.个人的计划是分成配置和运行官方教程,利用自己的数 ...
- CART分类与回归树与GBDT(Gradient Boost Decision Tree)
一.CART分类与回归树 资料转载: http://dataunion.org/5771.html Classification And Regression Tree(CART)是决策 ...
- class-决策树Decision Tree
顾名思义,决策树model是树形结构,在分类中,表示基于特征对实例进行分类的过程.可以认为是"if-else"的合集,也可以认为是特征空间,类空间上条件概率分布.主要优点是分类速度 ...
随机推荐
- set ver on/off
set verify(或ver) on/off可以设置是否显示替代变量被替代前后的语句 SQL> set verify on SQL> select &num from d ...
- uiwebview 兼容性 - IOS8及以上 WKWebView
@import WKWebView; WKWebView *webView = [[WKWebView alloc]init......]; 使用. WKWebView兼容 IOS 及 OSX.IOS ...
- layoutsubviews什么时候调用
layoutSubviews在以下情况下会被调用:1.init初始化不会触发layoutSubviews2.addSubview会触发layoutSubviews3.设置view的Frame会触发la ...
- keil 编译的一些错误
以前使用的是MDK4.5 但是没有stm32F3的元器件,果断的使用了4.6版本了.但是编译之后出现这样错误:linking....\Obj\prj.axf: Warning: L6373W: lib ...
- HashMap和HashTable区别
HashMap和HashTable区别 HashMap--->允许控制/线程安全 HashTable-->线程不安全
- list和map的区别
list和map的区别 list-->list是对象集合,允许对象重复 map-->map是键值对的集合,不允许key重复
- bzoj2743 [HEOI2012]采花
做法是每个询问先算出询问区间中花的种类减去区间中只有一朵花的花的种类,这两个子问题都不算难,具体看代码吧.询问可以离线处理,用树状数组维护,复杂度O(nlogn). 不知道是想的复杂了还是打的太low ...
- 3D语音天气球(源码分享)——完结篇
转载请注明本文出自大苞米的博客(http://blog.csdn.net/a396901990),谢谢支持! 开篇废话: 由于这篇文章是本系列最后一篇,有必要进行简单的回顾和思路整理. 这个程序是由两 ...
- 夺命雷公狗---node.js---7fs模块初步
目录结构如下所示: /** * Created by leigood on 2016/8/13. */ var http = require("http"); var fs = r ...
- knockout之各种数据绑定方法:text、attr、visible、html、css、style绑定
http://knockoutjs.com/documentation/attr-binding.html(Knockout官网文档) 1.text绑定 目的:text 绑定到DOM元素上,使得该元素 ...