[python]使用PyQt4创建windows应用窗体
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数据挖掘实训课，作业要求：测试使用分类工具、聚类工具以及创建数据挖掘算法的演示程序。前两个都算好说，最后一个任务居然是设计一个界面演示数据挖掘的部分算法。怀着对python面向对象编程和面向窗体编程极不信任的态度，真的在网上找到了python专用的windows窗体开发组件PyQt4 for Python 2.7 and windows 64bit。
1.安装PtQt4
与想象的不同，PyQt4安装非常方便，只要和python版本以及windows版本一致，安装时会在Python的跟目录下安装，测试：
import sys
from PyQt4 import QtGui
没有报错即安装完成
2.PyQt4的简单调用
PyQt4创建窗体的过程为：创建application对象->创建窗体对象->显示窗体对象->调用完全退出监听服务
import sys
from PyQt4 import QtGui
app = QtGui.QApplication(sys.argv)
widget = QtGui.QWidget()
widget.resize(250, 150)
widget.setWindowTitle('simple')
widget.show()
sys.exit(app.exec_())
3.使用PyQt4创建演示程序
关于PyQt4各个组件的成员和方法就不记录了，直接上代码，做出来还是挺有意思的
#-*- coding:utf-8 -*-
from PyQt4.QtGui import *
from PyQt4.QtCore import *
from PyQt4.QtGui import QMessageBox
import sys
QTextCodec.setCodecForTr(QTextCodec.codecForName("utf8"))
class LayoutDialog(QDialog):
def __init__(self,parent=None):
super(LayoutDialog,self).__init__(parent)
self.setWindowTitle(self.tr("数据挖掘算法演示"))
self.setGeometry(400, 200, 1200, 600)
data_label=QLabel(self.tr("选择数据集:"))
classify_label=QLabel(self.tr("分类器:"))
normalization_label=QLabel(self.tr("归一化:"))
parameter_label=QLabel(self.tr("输入参数:"))
validation_label=QLabel(self.tr("验证比例:"))
otherLabel=QLabel(self.tr("备注:输入的参数以分号结束，回车换行 例：c=1.0;"))
otherLabel.setFrameStyle(QFrame.Panel|QFrame.Sunken)
self.data_comboBox=QComboBox()
self.data_comboBox.insertItem(0,self.tr("iris"))
self.data_comboBox.insertItem(1,self.tr("digits"))
self.classify_comboBox=QComboBox()
self.classify_comboBox.insertItem(0,self.tr("knn"))
self.classify_comboBox.insertItem(1,self.tr("tree"))
self.classify_comboBox.insertItem(2,self.tr("bayes"))
self.classify_comboBox.insertItem(3,self.tr("logistic"))
self.classify_comboBox.insertItem(4,self.tr("svm"))
self.normalization_comboBox=QComboBox()
self.normalization_comboBox.insertItem(0,self.tr("none"))
self.normalization_comboBox.insertItem(1,self.tr("standard"))
self.normalization_comboBox.insertItem(2,self.tr("max_min"))
self.parameter_text=QTextEdit()
self.validation_text=QLineEdit()
labelCol=0
contentCol=1
leftLayout=QGridLayout()
leftLayout.addWidget(data_label,1,labelCol)
leftLayout.addWidget(self.data_comboBox,1,contentCol)
leftLayout.addWidget(classify_label,2,labelCol)
leftLayout.addWidget(self.classify_comboBox,2,contentCol)
leftLayout.addWidget(normalization_label,3,labelCol)
leftLayout.addWidget(self.normalization_comboBox,3,contentCol)
leftLayout.addWidget(parameter_label,4,labelCol)
leftLayout.addWidget(self.parameter_text,4,contentCol)
leftLayout.addWidget(validation_label,5,labelCol)
leftLayout.addWidget(self.validation_text,5,contentCol)
leftLayout.addWidget(otherLabel,6,labelCol,1,2)
leftLayout.setColumnStretch(0,1)
leftLayout.setColumnStretch(1,3)
result_label=QLabel(self.tr("预测结果:"))
self.descTextEdit=QTextEdit()
rightLayout=QVBoxLayout()
rightLayout.setMargin(10)
rightLayout.addWidget(result_label)
rightLayout.addWidget(self.descTextEdit)
OKPushButton=QPushButton(self.tr("确定"))
CancelPushButton=QPushButton(self.tr("退出"))
bottomLayout=QHBoxLayout()
bottomLayout.addStretch()
bottomLayout.addWidget(OKPushButton)
bottomLayout.addWidget(CancelPushButton)
mainLayout=QGridLayout(self)
mainLayout.setMargin(15)
mainLayout.setSpacing(10)
mainLayout.addLayout(leftLayout,0,0)
mainLayout.addLayout(rightLayout,0,1,1,40)
mainLayout.addLayout(bottomLayout,1,0,1,5)
mainLayout.setSizeConstraint(QLayout.SetFixedSize)
'''事件'''
OKPushButton.clicked.connect(self.OKPushButton_OnClick)
CancelPushButton.clicked.connect(self.CancelPushButton_OnClick)
'''初始值'''
self.data_comboBox.setCurrentIndex(0)
self.classify_comboBox.setCurrentIndex(0)
self.normalization_comboBox.setCurrentIndex(0)
self.validation_text.setText('0.2')
def CancelPushButton_OnClick(self):
QCoreApplication.instance().quit()
def OKPushButton_OnClick(self):
data_name = self.data_comboBox.currentText()
classify_name = self.classify_comboBox.currentText()
normalization_name = self.normalization_comboBox.currentText()
parameter_string = self.parameter_text.toPlainText()
validation_string = self.validation_text.text()
y_test,y_pred = self.load_datasets_and_classify(data_name,classify_name,normalization_name,parameter_string,validation_string)
from sklearn.metrics import classification_report
self.descTextEdit.setText(classification_report(y_test, y_pred))
def load_datasets_and_classify(self,data_name,classify_name,normalization_name,parameter_string,validation_string):
from sklearn import datasets
from sklearn.model_selection import train_test_split
rate=0.2
if validation_string !='':
try:
rate=float(validation_string)
except:
self.alert_info(u"输入的数值必须是0~1的浮点数")
return
if data_name == 'iris':
origin_data=datasets.load_iris()
elif data_name == 'digits':
origin_data=datasets.load_digits()
else:
self.alert_info(u"未知数据集")
return
data=origin_data.data
target=origin_data.target
x_train,x_test,y_train,y_test=train_test_split(data,target,test_size=rate)
if normalization_name=='standard':
from sklearn import preprocessing
scaler = preprocessing.StandardScaler().fit(x_train)
scaler.transform(x_train)
scaler.transform(x_test)
elif normalization_name=='max_min':
from sklearn import preprocessing
min_max_scaler = preprocessing.MinMaxScaler()
x_train = min_max_scaler.fit_transform(x_train)
x_test = min_max_scaler.fit_transform(x_test)
elif normalization_name=='none':
pass
else:
self.alert_info(u"未知归一化方式")
return
if classify_name=='svm':
from sklearn.svm import SVC
clf = SVC()
elif classify_name=='bayes':
from sklearn import naive_bayes
clf = naive_bayes.GaussianNB()
elif classify_name=='tree':
from sklearn import tree
clf = tree.DecisionTreeClassifier()
elif classify_name=='knn':
from sklearn import neighbors
clf = neighbors.KNeighborsClassifier()
elif classify_name=='logistic':
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
else:
self.alert_info(u"未知分类器")
return
clf.fit(x_train,y_train)
y_pred=clf.predict(x_test)
return y_test,y_pred
def alert_info(self,info):
QMessageBox.information(self,u"警告",info)
if __name__ == '__main__':
app=QApplication(sys.argv)
dialog=LayoutDialog()
dialog.show()
app.exec_()
演示效果