График генерируется для отображения в окне PyQt

Question

Я видел эту программу обнаружения сердечных заболеваний в GitHub, и мне было интересно, смогу ли я отобразить сгенерированный график в GUI, используя PyQt.

Это не мой код, это чей-то репозиторий в GitHub, и я просто пытаюсь поэкспериментировать с ним.

Я попытался отобразить его в окне PyQt, и пока оно отображается во всплывающем окне - только на консольном терминале IDE.

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)