如何使用 tkinter GUI 可视化 matplotlib 预测?

Question

我有一个场景，我对一个特定的数据集进行了预测。现在我想使用 Tkinter 可视化该预测图。

我的机器学习模型如下所示，由一个图形组成：

# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
# Importing the Batsmen Dataset
dataset = pd.read_csv('Batsmen/Batsmen.csv')
X = dataset.iloc[:, [1, 2, 3, 4, 5, 6]].values
# Using Elbow Method to find the optimal number of Clusters
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)
kmeans.fit(X)
wcss.append(kmeans.inertia_)
plt.plot(range(1, 11), wcss)
plt.title('The Elbow Method')
plt.xlabel('Number of Clusters')
plt.ylabel('WCSS')
plt.show()

我尝试了如下所示：

from tkinter import *
# these four imports are important
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
def plot():
# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
# Importing the Batsmen Dataset
dataset = pd.read_csv('Batsmen/Batsmen.csv')
X = dataset.iloc[:, [1, 2, 3, 4, 5, 6]].values
# Using Elbow Method to find the optimal number of Clusters
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)
kmeans.fit(X)
wcss.append(kmeans.inertia_)
root = Tk()
def app():
# initialise a window.
root = Tk()
root.config(background='white')
root.geometry("1000x700")
lab = Label(root, text="Live Plotting", bg = 'white').pack()
fig = Figure()
ax = fig.add_subplot(111)
ax.set_title('The Elbow Method')
ax.set_xlabel('Number of Clusters')
ax.set_ylabel('WCSS')
ax.grid()
graph = FigureCanvasTkAgg(fig, master=root)
graph.get_tk_widget().pack(side="top",fill='both',expand=True)
def plotter():
ax.cla()
ax.grid()
dpts = plot()
ax.plot(range(1, 11), wcss, marker='o', color='orange')
graph.draw()
time.sleep(1)
def gui_handler():
threading.Thread(target=plotter).start()
b = Button(root, text="Start/Stop", command=gui_handler, bg="red", fg="white")
b.pack()
root.mainloop()
if __name__ == '__main__':
app()

但它没有成功！

我只想在 Tkinter GUI 上显示预测，当我按下 GUI 中的预测按钮时，我希望图形显示在 GUI 画布内。所以，任何人都可以帮助我做同样的事情。

Answer 1

如果我使用root.after()而不是线程，您的代码对我有用。

可能在大多数 GUI 框架中，线程应该(或不能)更改 GUI 中的元素。

在我的计算机上，当我按下运行线程的按钮时，带有线程的 yoru 代码结束工作。

我运行plotter它不使用while和sleep，但after(1000, plotter)在 1000 毫秒(1 秒)后再次运行它

from tkinter import *
from random import randint
# these two imports are important
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
continuePlotting = False
def change_state():
global continuePlotting
if continuePlotting == True:
continuePlotting = False
else:
continuePlotting = True

def data_points():
f = open("data.txt", "w")
for i in range(10):
f.write(str(randint(0, 10))+'n')
f.close()
f = open("data.txt", "r")
data = f.readlines()
f.close()
l = []
for i in range(len(data)):
l.append(int(data[i].rstrip("n")))
return l
def app():
# initialise a window.
root = Tk()
root.config(background='white')
root.geometry("1000x700")
lab = Label(root, text="Live Plotting", bg = 'white').pack()
fig = Figure()
ax = fig.add_subplot(111)
ax.set_xlabel("X axis")
ax.set_ylabel("Y axis")
ax.grid()
graph = FigureCanvasTkAgg(fig, master=root)
graph.get_tk_widget().pack(side="top",fill='both',expand=True)
def plotter():
if continuePlotting:
ax.cla()
ax.grid()
dpts = data_points()
ax.plot(range(10), dpts, marker='o', color='orange')
graph.draw()
root.after(1000, plotter)
def gui_handler():
change_state()
plotter()
b = Button(root, text="Start/Stop", command=gui_handler, bg="red", fg="white")
b.pack()
root.mainloop()
if __name__ == '__main__':
app()

---

编辑：在新代码中，您不会运行循环，因此不需要线程或after()

但是您还有其他基本问题：

您有错误的缩进，并且mainloop()在app()之外 - 它是在app()之前执行

的在以前的版本中，plotter在app()内部 - 如果你需要在外部使用它，那么你的局部变量(如ax)就有问题，你必须使用global才能在其他函数中访问这些变量。或者你必须用这个值作为参数运行函数 - 即。plotter(ax, wcss, graph)

这个版本使用global，它对我有用。我没有你的CSV，我不想运行sklearn所以我放了一些假数据。

from tkinter import *
# these four imports are important
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
def plot():
# Importing Libraries
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import sys
# Importing the Batsmen Dataset
#    dataset = pd.read_csv('Batsmen/Batsmen.csv')
dataset = pd.DataFrame({
'a': range(10),
'b': range(10),
'c': range(10),
'd': range(10),
'e': range(10),
'f': range(10),
'g': range(10),
})
X = dataset.iloc[:, [1, 2, 3, 4, 5, 6]].values
# Using Elbow Method to find the optimal number of Clusters
from sklearn.cluster import KMeans
global wcss
wcss = range(1, 11)
#wcss = []
#    for i in range(1, 11):
#        kmeans = KMeans(n_clusters=i, init='k-means++', n_init=10, max_iter=300, random_state=0)
#        kmeans.fit(X)
#        wcss.append(kmeans.inertia_)
def plotter():
global wcss
global ax
global graph
ax.cla()
ax.grid()
dpts = plot()
ax.plot(range(1, 11), wcss, marker='o', color='orange')
graph.draw()
def gui_handler():
plotter()
def app():
global ax
global graph
# initialise a window.
root = Tk()
root.config(background='white')
root.geometry("1000x700")
lab = Label(root, text="Live Plotting", bg = 'white').pack()
fig = Figure()
ax = fig.add_subplot(111)
ax.set_title('The Elbow Method')
ax.set_xlabel('Number of Clusters')
ax.set_ylabel('WCSS')
ax.grid()
graph = FigureCanvasTkAgg(fig, master=root)
graph.get_tk_widget().pack(side="top",fill='both',expand=True)
b = Button(root, text="Start/Stop", command=gui_handler, bg="red", fg="white")
b.pack()
root.mainloop()
if __name__ == '__main__':
app()