Question

我在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在GUI中显示生成的图形。

这不是我的代码，它是GitHub中其他人的存储库，我只是尝试对其进行试验。

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-仅在IDE的控制台终端上。

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 

dataset = genfromtxt('cleveland_data.csv',dtype = float, delimiter=',')

X = dataset[:,0:12] 
y = dataset[:,13]   


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')


modelSVM = LinearSVC(C=0.001)
pca = PCA(n_components=5, whiten=True).fit(X)
X_new = pca.transform(X)

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

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))

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)


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

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)

modelSVM2 = SVC(C=0.001,kernel='rbf')

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)

skf = cross_validation.StratifiedKFold(y, n_folds=5)
for train_index, test_index in skf:

    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)

生成的图形显示在PyQt窗口中

0 个答案: