Question

在GitHub上看到了这个心脏病检测程序，我想知道是否可以使用PyQt将生成的图形显示到GUI。我尝试将其显示在PyQt窗口上，到目前为止，它确实显示在弹出窗口中，但是该图显示在python IDE的控制台上。这是我在玩的原始代码：

#This code performs the classification  of heart  disease by labeling the predicted values
# in various classes, namely 0 for absence and 1 to 4 for presence and also try  
# to check the model performance by comparing it against other Classifiers

from numpy import genfromtxt
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from sklearn.svm import LinearSVC
from sklearn.decomposition import PCA
import pylab as pl
from itertools import cycle
from sklearn import cross_validation
from sklearn.svm import SVC 
from IPython import get_ipython

class problem:
#Loading and pruning the data
    dataset = genfromtxt('cleveland_data.csv',dtype = float, delimiter=',')
    #print dataset
    X = dataset[:,0:12] #Feature Set
    y = dataset[:,13]   #Label Set

    #Method to plot the graph for reduced Dimesions
    def plot_2D(data, target, target_names):
         colors = cycle('rgbcmykw')
         target_ids = range(len(target_names))
         plt.figure()
         for i, c, label in zip(target_ids, colors, target_names):
             plt.scatter(data[target == i, 0], data[target == i, 1],
                        c=c, label=label)
         plt.legend()
         plt.savefig('Reduced_PCA_Graph')

    # Classifying the data using a Linear SVM and predicting the probability of disease belonging to a particular class
    modelSVM = LinearSVC(C=0.001)
    pca = PCA(n_components=5, whiten=True).fit(X)
    X_new = pca.transform(X)

    # calling plot_2D
    target_names = ['0','1','2','3','4']
    plot_2D(X_new, y, target_names)

    #Applying cross validation on the training and test set for validating our Linear SVM Model
    X_train, X_test, y_train, y_test = cross_validation.train_test_split(X_new, y, test_size=0.4, train_size=0.6, random_state=0)
    modelSVM = modelSVM.fit(X_train, y_train)
    print("Testing  Linear SVC values using Split")
    print(modelSVM.score(X_test, y_test))

    # prediction score based on X_new
    modelSVMRaw = LinearSVC(C=0.001)
    modelSVMRaw = modelSVMRaw.fit(X_new, y)
    cnt = 0
    for i in modelSVMRaw.predict(X_new):
        if i == y[i]:
           cnt = cnt+1
    print("Score without any split")
    print(float(cnt)/303)


    # printing the Likelihood of disease belonging to a particular class
    # predicting the outcome
    count0 = 0
    count1 = 0
    count2 = 0
    count3 = 0
    count4 = 0
    for i in modelSVM.predict(X_new):
            if i == 0:
                    count0 = count0+1;
            elif i == 1:
                    count1 = count1+1;
            elif i == 2:
                    count2 = count2+1;
            elif i == 3:
                    count3 = count3+1;
            elif modelSVM.predict(i) ==4:
                    count4 = count4+1
    total = count0+count1+count2+count3+count4
    #Predicting the Likelihood
    print("The prediction is as follows:")
    print(" Likelihood of belonging to Class 0 is", float(count0)/total)
    print(" Likelihood of belonging to Class 1 is", float(count1)/total)
    print(" Likelihood of belonging to Class 2 is", float(count2)/total)
    print(" Likelihood of belonging to Class 3 is", float(count3)/total)
    print(" Likelihood of belonging to Class 4 is", float(count4)/total)


    #Applying the Principal Component Analysis on the data features
    modelSVM2 = SVC(C=0.001,kernel='rbf')

    #Applying cross validation on the training and test set for validating our Linear SVM Model
    X_train1, X_test1, y_train1, y_test1 = cross_validation.train_test_split(X_new, y, test_size=0.4, train_size=0.6, random_state=0)
    modelSVM2 = modelSVM2.fit(X_train1, y_train1)
    print("Testing with RBF using split")
    print(modelSVM2.score(X_test1, y_test1))

    modelSVM2Raw = SVC(C=0.001,kernel='rbf')
    modelSVM2Raw = modelSVM2Raw.fit(X_new, y)
    cnt1 = 0
    for i in modelSVM2Raw.predict(X_new):
            if i == y[i]:
               cnt1 = cnt1+1
    print("RBF Score without split")
    print(float(cnt1)/303)
    #Using Stratified K Fold
    skf = cross_validation.StratifiedKFold(y, n_folds=5)
    for train_index, test_index in skf:
       # print("TRAIN:", train_index, "TEST:", test_index)
        X_train3, X_test3 = X[train_index], X[test_index]
        y_train3, y_test3 = y[train_index], y[test_index]
    modelSVM3 = SVC(C=0.001,kernel='rbf')
    modelSVM3 = modelSVM3.fit(X_train3, y_train3)
    print("Testing using stratified with K folds")
    print(modelSVM3.score(X_test3, y_test3))

    modelSVM3Raw = SVC(C=0.001,kernel='rbf')
    modelSVM3Raw = modelSVM3Raw.fit(X_new, y)
    cnt2 = 0
    for i in modelSVM3Raw.predict(X_new):
            if i == y[i]:
               cnt2 = cnt2+1
    print("Stratified K Fold score on X_New")
    print(float(cnt2)/303)

    fig.savefig('plot.pdf')

    def HandleQuestion(self):
        pic = QtGui.QLabel(self)
        pic.setPixmap(QtGui.QPixmap("Reduced_PCA_Graph.png"))

        pic.show() # You were missing this.

        self.lbl3.move(0,190)
        self.SketchPad.resize(250,80)
        self.SketchPad.move(0,220)

如果执行了此程序，它将在项目的控制台上生成一个散点图。现在我想发生的是，散点图应该在PyQt窗口中。最好的方法是什么？

Answer 1

（此处为 Spyder维护程序）要获得所需的内容，需要两件事：

在您的代码中删除或注释此行
```
matplotlib.use('Agg')
```
您需要转到Spyder中的此菜单项

Tools > Preferences > IPython console > Graphics > Graphics backend

将名为Backend的选项从Inline更改为Automatic，然后重新启动Spyder。

在Spyder中运行代码时在PyQt中显示Matplotlib图

1 个答案: