Добавьте текстовый вывод из консоли в окно PyQT - PullRequest
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Добавьте текстовый вывод из консоли в окно PyQT

0 голосов
/ 19 января 2019

Как отобразить вывод текста из консоли в диалоговое окно PyQT вместе с сгенерированным графиком?

Приведенный ниже код запускает программу предикторов сердечных заболеваний, и ее результаты представлены графиком, который генерируется и отображается в окне PyQT. Однако нам также нужно, чтобы интерпретация результатов отображалась в диалоговом окне, но мы не можем этого понять. Пожалуйста помоги. 

import sys
from PyQt5.QtWidgets import QDialog, QApplication, QPushButton, QVBoxLayout

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 matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.backends.backend_qt5agg import NavigationToolbar2QT as NavigationToolbar
import matplotlib.pyplot as plt

import random

class Window(QDialog):
    def __init__(self, parent=None):
        super(Window, self).__init__(parent)

        # a figure instance to plot on
        self.figure = plt.figure()

        # this is the Canvas Widget that displays the `figure`
        # it takes the `figure` instance as a parameter to __init__
        self.canvas = FigureCanvas(self.figure)

        # this is the Navigation widget
        # it takes the Canvas widget and a parent
        self.toolbar = NavigationToolbar(self.canvas, self)

        # Just some button connected to `plot` method
        self.button = QPushButton('Plot the data')
        self.button.clicked.connect(self.my_model)

        # set the layout
        layout = QVBoxLayout()
        layout.addWidget(self.toolbar)
        layout.addWidget(self.canvas)
        layout.addWidget(self.button)
        self.setLayout(layout)

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

    def my_model(self):
        ''' plot some random stuff '''
        #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

        # 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']

        # create an axis
        ax = self.figure.add_subplot(111)

        # discards the old graph
        ax.clear()
        self.plot_2D(X_new, y, target_names,ax)


        # refresh canvas
        self.canvas.draw()

        #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))

        # 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

        #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))


        #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))

if __name__ == '__main__':
    app = QApplication(sys.argv)

    main = Window()
    main.show()

    sys.exit(app.exec_())

Приведенный выше код выводит граф предиктора в диалоговом окне, теперь все, что нам нужно, это его текстовая интерпретация, которая будет отображаться

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