Python plotROC示例

示例#1

def testHolic():
    datArr,labelArr = adaboost.loadDataSet('horseColicTraining2.txt')
    classifierArray, classifierEst = adaboost.adaBoostTrainDS(datArr, labelArr, 10)

    testArr, testLabelArr = adaboost.loadDataSet('horseColicTest2.txt')
    prediction10 = adaboost.adaClassify(testArr, classifierArray)
    print("prediction:", prediction10)

    errArr = mat(ones((67,1)))
    errCnt = errArr[prediction10 != mat(testLabelArr).T].sum()
    print("err count:%d error rate:%.2f" % (errCnt, float(errCnt)/67))

    adaboost.plotROC(classifierEst.T, labelArr)

示例#2

import adaboost
from numpy import *
#datMat, classLabels = adaboost.loadSimpleData()
datMat, classLabels = adaboost.loadDataSet('horseColicTraining2.txt')
classifierArray, aggClassEst = adaboost.adaBoostTrainDS(
    datMat, classLabels, 10)
adaboost.plotROC(aggClassEst.T, classLabels)

示例#3

文件： shell.py 项目： zack624/Machine-Learning-In-Action

# -*- coding: utf-8 -*-

import adaboost
from numpy import *
da, la = adaboost.loadDataSet('horseColicTraining.txt')
ca = adaboost.adaBoostTrainDS(da, la, 10)
tda, tla = adaboost.loadDataSet('horseColicTest.txt')
prediction10 = adaboost.adaClassify(tda, ca)
errArr = mat(ones((67, 1)))
errArr[prediction10 != mat(tla).T].sum()

reload(adaboost)
da, la = adaboost.loadDataSet('horseColicTraining.txt')
ca, ace = adaboost.adaBoostTrainDS(da, la, 40)
adaboost.plotROC(ace.T, la)

示例#4

# coding:utf-8

import adaboost
from numpy import *

#datMat,classLabels=adaboost.loadSimpData()

# D=mat(ones((5,1))/5)
# result=adaboost.buildStump(datMat,classLabels,D)
# print result

# classifierArray=adaboost.adaBoostTrainDS(datMat,classLabels,9)
# print classifierArray
#
# reload(adaboost)
# result= adaboost.adaClassify([0,0],classifierArray)
# print result

# dataArr,labelArr=adaboost.loadDataSet('horseColicTraining2.txt')
# classifierArray=adaboost.adaBoostTrainDS(dataArr,labelArr,10)
# testArr,testLabelArr=adaboost.loadDataSet('horseColicTest2.txt')
# prediction10=adaboost.adaClassify(testArr,classifierArray)
# errArr=mat(ones((67,1)))
# errArr[prediction10!=mat(testLabelArr).T].sum()

dataArr,labelArr=adaboost.loadDataSet('horseColicTraining2.txt')
classifierArray,aggClassEst=adaboost.adaBoostTrainDS_plt(dataArr,labelArr,10)
adaboost.plotROC(aggClassEst.T,labelArr)

示例#5

文件： test_2.py 项目： wang4yu6peng13/MachineLearningInAction

import adaboost
from numpy import *

datArr, labelArr = adaboost.loadDataSet('horseColicTraining2.txt')
#classifierArray = adaboost.adaBoostTrainDS(datArr, labelArr, 10)
#testArr, testLabelArr = adaboost.loadDataSet('horseColicTest2.txt')
#prediction10 = adaboost.adaClassify(testArr, classifierArray)
#errArr = mat(ones((67, 1)))
#print errArr[prediction10!=mat(testLabelArr).T].sum()

classifierArray, aggClassEst = adaboost.adaBoostTrainDS(datArr, labelArr, 10)
adaboost.plotROC(aggClassEst.T, labelArr)

示例#6

文件： testBoost.py 项目： Mic-JasonTang/Machine_Learing_In_Action

# print(bestStump)
# print(minError)
# print(bestClasEst)

# classifierArr = adaboost.adaBoostTrainDS(dataMat, labelMat, 40)

# print(classifierArr)

# result = adaboost.adaClassify([[1, 5], [2, 4]], classifierArr)

# print(result)

dataMat, labelMat = adaboost.loadDataSet("horseColicTraining2.txt")

classifierArr, aggClassEst = adaboost.adaBoostTrainDS(dataMat, labelMat, 50)

print(classifierArr)
print(aggClassEst.T.shape)

adaboost.plotROC(aggClassEst.T, labelMat)

# dataMat, labelMat = adaboost.loadDataSet("horseColicTest2.txt")
# pred = adaboost.adaClassify(dataMat, classifierArr)

# print(np.mat(labelMat).shape)
# print(np.mat(labelMat).T.shape)
# print(len(dataMat))
# errorMat = np.mat(np.ones((len(dataMat), 1)))
# rate = (errorMat[pred != np.mat(labelMat).T].sum() / len(dataMat))

# print(rate)

示例#7

文件： a.py 项目： furongw/machine_learning_in_action

import adaboost
import numpy as np
#dataMat ,classlabel = adaboost.loadSimpData()
#print(dataMat)
#D = np.mat(np.ones((5,1)))/5
#print(adaboost.buildStump(dataMat,classlabel,D))
#classifierArray,aggest = adaboost.adaBoostTrainDS(dataMat,classlabel,9)
#print(aggest)

file = open('data.txt', 'r')
datalist = []
classlabel = []
for line in file.readlines():
    data = line.split()[:-4]
    label = int(line.split()[-1])
    datalist.append(list(map(float, data)))
    classlabel.append(label)
dataMat = np.mat(datalist)
classlabels = np.mat(classlabel)
classifierArray, aggest = adaboost.adaBoostTrainDS(dataMat, classlabel, 40,
                                                   100)
print(classifierArray)

adaboost.plotROC(aggest.T, classlabel)