运行pyqt5时，点击按钮不出结果报Unhandled Python exception问题
数据文件
https://wwc.lanzoui.com/iXiw4wyknba
密码:hbt1
https://wwc.lanzoui.com/iIMsKwykncb
密码:h8n4
adaboost：
import numpy as np
import matplotlib.pyplot as plt
def loadDataSet(fileName):
numFeat = len((open(fileName).readline().split('\t')))
dataMat = []
labelMat = []
fr = open(fileName)
for line in fr.readlines():
lineArr = []
curLine = line.strip().split('\t')
for i in range(numFeat - 1):
lineArr.append(float(curLine[i]))
dataMat.append(lineArr)
labelMat.append(float(curLine[-1]))
return dataMat, labelMat
def stumpClassify(dataMatrix, dimen, threshVal, threshIneq):
"""
单层决策树分类函数
Parameters:
dataMatrix - 数据矩阵
dimen - 第dimen列，也就是第几个特征
threshVal - 阈值
threshIneq - 标志
Returns:
retArray - 分类结果
"""
retArray = np.ones((np.shape(dataMatrix)[0], 1)) # 初始化retArray为1
if threshIneq == 'lt':
retArray[dataMatrix[:, dimen] <= threshVal] = -1.0 # 如果小于阈值,则赋值为-1
else:
retArray[dataMatrix[:, dimen] > threshVal] = -1.0 # 如果大于阈值,则赋值为-1
return retArray
def buildStump(dataArr, classLabels, D):
"""
找到数据集上最佳的单层决策树
Parameters:
dataArr - 数据矩阵
classLabels - 数据标签
D - 样本权重
Returns:
bestStump - 最佳单层决策树信息
minError - 最小误差
bestClasEst - 最佳的分类结果
"""
dataMatrix = np.mat(dataArr)
labelMat = np.mat(classLabels).T
m, n = np.shape(dataMatrix)
numSteps = 10.0
bestStump = {}
bestClasEst = np.mat(np.zeros((m, 1)))
minError = float('inf') # 最小误差初始化为正无穷大
for i in range(n): # 遍历所有特征
rangeMin = dataMatrix[:, i].min()
rangeMax = dataMatrix[:, i].max() # 找到特征中最小的值和最大值
stepSize = (rangeMax - rangeMin) / numSteps # 计算步长
for j in range(-1, int(numSteps) + 1):
for inequal in ['lt', 'gt']: # 大于和小于的情况，均遍历。lt:less than，gt:greater than
threshVal = (rangeMin + float(j) * stepSize) # 计算阈值
predictedVals = stumpClassify(dataMatrix, i, threshVal, inequal) # 计算分类结果
errArr = np.mat(np.ones((m, 1))) # 初始化误差矩阵
errArr[predictedVals == labelMat] = 0 # 分类正确的,赋值为0
weightedError = D.T * errArr # 计算误差
# print("split: dim %d, thresh %.2f, thresh ineqal: %s, the weighted error is %.3f" % (i, threshVal, inequal, weightedError))
if weightedError < minError: # 找到误差最小的分类方式
minError = weightedError
bestClasEst = predictedVals.copy()
bestStump['dim'] = i
bestStump['thresh'] = threshVal
bestStump['ineq'] = inequal
return bestStump, minError, bestClasEst
def adaBoostTrainDS(dataArr, classLabels, numIt=40):
"""
使用AdaBoost算法提升弱分类器性能
Parameters:
dataArr - 数据矩阵
classLabels - 数据标签
numIt - 最大迭代次数
Returns:
weakClassArr - 训练好的分类器
aggClassEst - 类别估计累计值
"""
weakClassArr = []
m = np.shape(dataArr)[0]
D = np.mat(np.ones((m, 1)) / m) # 初始化权重
aggClassEst = np.mat(np.zeros((m, 1)))
for i in range(numIt):
bestStump, error, classEst = buildStump(dataArr, classLabels, D) # 构建单层决策树
# print("D:",D.T)
alpha = float(0.5 * np.log((1.0 - error) / max(error, 1e-16))) # 计算弱学习算法权重alpha,使error不等于0,因为分母不能为0
bestStump['alpha'] = alpha # 存储弱学习算法权重
weakClassArr.append(bestStump) # 存储单层决策树
# print("classEst: ", classEst.T)
expon = np.multiply(-1 * alpha * np.mat(classLabels).T, classEst) # 计算e的指数项
D = np.multiply(D, np.exp(expon))
D = D / D.sum() # 根据样本权重公式，更新样本权重
# 计算AdaBoost误差，当误差为0的时候，退出循环
aggClassEst += alpha * classEst # 计算类别估计累计值
# print("aggClassEst: ", aggClassEst.T)
aggErrors = np.multiply(np.sign(aggClassEst) != np.mat(classLabels).T, np.ones((m, 1))) # 计算误差
errorRate = aggErrors.sum() / m
# print("total error: ", errorRate)
if errorRate == 0.0: break # 误差为0，退出循环
return weakClassArr, aggClassEst
def adaClassify(datToClass, classifierArr):
"""
AdaBoost分类函数
Parameters:
datToClass - 待分类样例
classifierArr - 训练好的分类器
Returns:
分类结果
"""
dataMatrix = np.mat(datToClass)
m = np.shape(dataMatrix)[0]
aggClassEst = np.mat(np.zeros((m, 1)))
for i in range(len(classifierArr)): # 遍历所有分类器，进行分类
classEst = stumpClassify(dataMatrix, classifierArr[i]['dim'], classifierArr[i]['thresh'],
classifierArr[i]['ineq'])
aggClassEst += classifierArr[i]['alpha'] * classEst
# print(aggClassEst)
return np.sign(aggClassEst)
def main():
dataArr, LabelArr = loadDataSet('horseColicTraining2.txt')
weakClassArr, aggClassEst = adaBoostTrainDS(dataArr, LabelArr)
testArr, testLabelArr = loadDataSet('horseColicTest2.txt')
#print(weakClassArr)
predictions = adaClassify(dataArr, weakClassArr)
errArr = np.mat(np.ones((len(dataArr), 1)))
print('训练集的错误率:%.3f%%' % float(errArr[predictions != np.mat(LabelArr).T].sum() / len(dataArr) * 100))
predictions = adaClassify(testArr, weakClassArr)
errArr = np.mat(np.ones((len(testArr), 1)))
print('测试集的错误率:%.3f%%' % float(errArr[predictions != np.mat(testLabelArr).T].sum() / len(testArr) * 100))
if __name__ == '__main__':
main()
mian()
from adaboost import adaboost, adaboost2
from fenlei import Ui_MainWindow
from bayes import naive_bayes, naive_bayes2
from decision_tree import decision_tree
from GBDT import GBDT_sklearn
from knn import knn, knn2
from logistic import Logistic, Logistic2
from random_forest import random_fotest
import sys
from PyQt5 import QtWidgets
from PyQt5 import QtCore, QtGui
from PyQt5.QtCore import QEventLoop, QTimer
from PyQt5.QtWidgets import QApplication, QMainWindow
from PyQt5.QtGui import QTextCursor
class EmittingStr(QtCore.QObject):
textWritten = QtCore.pyqtSignal(str) # 定义一个发送str的信号
def write(self, text):
self.textWritten.emit(str(text))
class ControlBoard(QMainWindow, Ui_MainWindow):
def __init__(self):
super(ControlBoard, self).__init__()
self.setupUi(self)
# 下面将输出重定向到textBrowser中
sys.stdout = EmittingStr(textWritten=self.outputWritten)
sys.stderr = EmittingStr(textWritten=self.outputWritten)
self.adaboost_shouxie.clicked.connect(self.ada_shouxie_bClicked)
self.adaboost_sklearn.clicked.connect(self.ada_sklearn_bClicked)
self.byes_shouxie.clicked.connect(self.bayes_shouxie_bClicked)
self.byes_sklearn.clicked.connect(self.bayes_sklearn_bClicked)
self.decision_tree_skearn.clicked.connect(self.decision_tree_bClicked)
self.GBDT_sklearn.clicked.connect(self.GBDT_bClicked)
self.knn_shouxie.clicked.connect(self.knn_shouxie_bClicked)
self.knn_sklearn.clicked.connect(self.knn_sklearn_bClicked)
self.logistic_shouxie.clicked.connect(self.logistic_shouxie_bClicked)
self.logistic_sklearn.clicked.connect(self.logistic_sklearn_bClicked)
self.random_forest_sklearn.clicked.connect(self.random_sklearn_bClicked)
def outputWritten(self, text):
cursor = self.shu_chu_kuang.textCursor()
cursor.movePosition(QtGui.QTextCursor.End)
cursor.insertText(text)
self.shu_chu_kuang.setTextCursor(cursor)
self.shu_chu_kuang.ensureCursorVisible()
def ada_shouxie_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.adaboost_shouxie1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
def ada_sklearn_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.adaboost_sklearn1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
def bayes_shouxie_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.byes_shouxie1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
def bayes_sklearn_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.byes_sklearn1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
def decision_tree_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.decision_tree1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
def GBDT_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.GBDT_sklearn1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
def knn_shouxie_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.knn_shouxie1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
def knn_sklearn_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.knn_sklearn1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
def logistic_shouxie_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.logistic_shouxie1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
def logistic_sklearn_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.logistic_sklearn1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
def random_sklearn_bClicked(self):
"""Runs the main function."""
print('Begin')
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
self.random_sklearn1()
loop = QEventLoop()
QTimer.singleShot(1000, loop.quit)
loop.exec_()
print("End")
"adaboost"
def adaboost_shouxie1(self):
adaboost.main()
def adaboost_sklearn1(self):
adaboost2.main()
"bayes"
def byes_shouxie1(self):
naive_bayes.main()
def byes_sklearn1(self):
naive_bayes2.main()
"decision_tree"
def decision_tree1(self):
decision_tree.mian()
"GBDT"
def GBDT_sklearn1(self):
GBDT_sklearn.mian()
"knn"
def knn_shouxie1(self):
knn.main()
def knn_sklearn1(self):
knn2.main()
"Logistic"
def logistic_shouxie1(self):
Logistic.colicTest()
def logistic_sklearn1(self):
Logistic2.colicSklearn()
"random_forest"
def random_sklearn1(self):
random_fotest.main()
if __name__ == "__main__":
app = QApplication(sys.argv)
win = ControlBoard()
win.show()
sys.exit(app.exec_())
直接运行adaboost可以
但是连接到pyqt5的时候点击按钮就直接关闭，报Unhandled Python exception问题
用pyqt5连接一个简单的函数试试pyqt5有没有问题