Mar-31-2020, 07:23 AM
Quote:I am working on a project called heart disease predictor.When i click the button predict heart disease.The below error is coming.But clf is defined global.Help me out asap.
Thank you.
import tkinter #importing tkinter library for GUI creation
from tkinter import *
from PIL import Image, ImageTk
import tkinter.messagebox
from tkinter import filedialog
#import mysql.connector
#from keras.models import Sequential
#from keras.layers import Dense
import pandas as pnd # importing pandas data analysis toolkit
import numpy as np # importing numpy library for array operations
from time import time # importing time library for time calculations
from sklearn.model_selection import train_test_split # importing module model_classification from scikit-learn library
print("hello")
header_row = ['age', 'sex', 'pain', 'BP', 'chol', 'fbs', 'ecg', 'maxhr','eiang', 'eist', 'slope', 'vessels', 'thal',
'diagnosis'] # Declaring the header row for getting data from the dataset files
# filter to only those diagnosed with heart disease
def cardiac():
global master
master = Tk() # Defining the Tkinter widget
master.wm_title("Heart Disease Prediction")
master.geometry('1500x600')
image = Image.open('c3.jpg')
image = image.resize((1500, 600))
photo_image = ImageTk.PhotoImage(image)
label = Label(master, image = photo_image)
label.place(x=0,y=0)
import sklearn # Importing scikit-learn functions
#Lab=Label(master,text=" Automatic Heart Disease Detection ") # Adding Label to the Tkinter widget
#Lab.place(x=600,y=50) # Packing the label data to the tkinter widget in user defined rows and columns
# Changing dimensions of the Label
#Lab=Label(master,text="")
#Lab.grid(row=2,column=5,columnspan=2)
#Lab1=Label(master,text="Classification Report")
#Lab1.place(x=170,y=330)
#Lab2=Label(master,text="Confusion Matrix")
#Lab2.place(x=930,y=330)
T = Text(master, height=6, width=40,font=("bold",10),highlightthickness=2,bg="white",relief=SUNKEN) # Declaring Text Widget for Result Displaying
T.place(x=55,y=350)
T1 = Text(master, height=6, width=35,font=("bold",10),highlightthickness=2,bg="white",relief=SUNKEN)
T1.place(x=1000,y=350)
var = StringVar(master)
var.set("Select Dataset") # initial value
option = OptionMenu(master, var, "Cleveland", "Hungarian", "VA", "all") # Declaring the OptionMenu (Drop-Down list) widget
option.config(bg = "violet")
option.config(fg = "black")
option.config(font=('algerian',10,'bold'))
option.config(width=12)
option.place(x=500,y=80)
'''field1="Age" # Defining the field names which user has to input for heart disease detection
field2="Sex"
field3="Pain"
field4="BP"
field5="Chol"
field6="FBS"
field7="ECG"
field8="Maxhr"
field9="Eiang"
field10="Eist"
field11="Slope"
field12="Vessels"
field13="Thal"'''
'''L1=Label(master,text=field1)
L1.grid(row = 4, column = 0, sticky='nsew')
L1.configure(width=14)
L2=Label(master,text=field2)
L2.grid(row = 4, column = 1, sticky='nsew')
L2.configure(width=14)
L3=Label(master,text=field3)
L3.grid(row = 4, column = 2, sticky='nsew')
L3.configure(width=14)
L4=Label(master,text=field4)
L4.grid(row = 4, column = 3, sticky='nsew')
L4.configure(width=14)
L5=Label(master,text=field5)
L5.grid(row = 4, column = 4, sticky='nsew')
L5.configure(width=14)
L6=Label(master,text=field6, )
L6.grid(row = 4, column = 5, sticky='nsew')
L6.configure(width=14)
L7=Label(master,text=field7)
L7.grid(row = 4, column = 6, sticky='nsew')
L7.configure(width=14)
L8=Label(master,text=field8)
L8.grid(row = 4, column = 7, sticky='nsew')
L8.configure(width=14)
L9=Label(master,text=field9)
L9.grid(row = 4, column = 8, sticky='nsew')
L9.configure(width=14)
L10=Label(master,text=field10)
L10.grid(row = 4, column = 9, sticky='nsew')
L10.configure(width=14)
L11=Label(master,text=field11)
L11.grid(row = 4, column = 10, sticky='nsew')
L11.configure(width=14)
L12=Label(master,text=field12)
L12.grid(row = 4, column = 11, sticky='nsew')
L12.configure(width=14)
L13=Label(master,text=field13)
L13.grid(row = 4, column = 12, sticky='nsew')
L13.configure(width=14)'''
E1=Entry(master,width=8,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E1.place(x=0, y=220)
E2=Entry(master,width=8,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E2.place(x=90, y=220)
E3=Entry(master,width=8,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E3.place(x=180, y=220)
E4=Entry(master,width=8,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E4.place(x=280, y=220)
E5=Entry(master,width=8,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E5.place(x=370, y=220)
E6=Entry(master,width=8,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E6.place(x=470, y=220)
E7=Entry(master,width=8,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E7.place(x=550, y=220)
E8=Entry(master,width=8,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E8.place(x=650, y=220)
E9=Entry(master,width=10,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E9.place(x=770, y=220)
E10=Entry(master,width=10,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E10.place(x=880, y=220)
E11=Entry(master,width=10,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E11.place(x=980, y=220)
E12=Entry(master,width=10,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E12.place(x=1100, y=220)
E13=Entry(master,width=10,font=("bold",10),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E13.place(x=1220, y=220)
lb1 = Label(master, text="patient",font=('algerian',15,'bold'),fg="BLACK",anchor='w')
lb1.place(x=0, y=150)
E0=Entry(master,width=10,font=("bold",15),highlightthickness=2,bg="WHITE",relief=SUNKEN)
E0.place(x=120, y=150)
'''Labx=Label(master,text="")
Labx.grid(row=21,column=4,columnspan=4)
Labx.visible=False'''
#T3 = Text(master, height=2, width=30) # Declaring Text Widget for Displaying Prediction
#T3.grid(row=23,column=4, columnspan=4, sticky= 'nsew')
def train_classifier(x_train,x_test,y_train,y_test,string): # Declaring the function for training classifiers and classification analysis
global clf # Declaring clf as a Global Variable for using throughot the code
global outclass
global a1,a2,a3
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
T.delete(1.0,END) # Deleting the text in the Text Widget
T1.delete(1.0,END)
if string=="CNN":
from keras.models import Sequential
from keras.layers import Dense
import matplotlib.pyplot as plt1
#import pandas as pnd # importing pandas data analysis toolkit
#import numpy as np
t1= time()
clf = Sequential() #initial creation
clf.add(Dense(13, input_dim=13, init='uniform', activation='relu')) #first hidden layer
clf.add(Dense(10, init='uniform', activation='relu'))
#model.add(Dense(8, init='uniform', activation='relu'))
#model.add(Dense(6, init='uniform', activation='relu'))
clf.add(Dense(1, init='uniform', activation='sigmoid')) #output layer
# compile the model
clf.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
# fitting data to model
clf.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=200, batch_size=5, verbose=0)
# evaluate the model
scores = clf.evaluate(x_test, y_test)
#printing accuracy
print("Accuracy: %.2f%%" % (scores[1]*100))
#p1=model.predict(Xv)
#print(p1)
a1=(scores[1]*100)
T.insert(END, "Accuracy")
T.insert(END, " ")
T.insert(END,(scores[1]*100) )
y_pred = clf.predict(x_test)
print(y_pred)
l1=list()
for i in y_pred:
if i<.49:
l1.append(0)
else:
l1.append(1)
print(l1)
count1=y_test.count()
l2=list(range(1,count1+1,1))
print(count1)
print(y_test.count())
#print(count(l1))
colormap=np.array(['lime','red'])
plt1.subplot(1,2,1)
plt1.scatter(l2,y_test,c=colormap[y_test])
plt1.suptitle("CNN Algorihm")
plt1.xlabel("X values")
plt1.ylabel("y tests values")
plt1.title("true Values")
plt1.subplot(1,2,2)
plt1.scatter(l2,l1,c=colormap[l1])
plt1.suptitle("CNN Algorihm")
plt1.xlabel("X values")
plt1.ylabel("Predictions")
plt1.title("Predictions")
plt1.show()
# Predict Results for Test Data
#title = "Learning Curves (SVM)"
#print(y_train)
#geterror(x_train,y_train,clf,title);
t= time()-t1
T.insert(END, "Accuracy")
T.insert(END, " ")
T.insert(END,(scores[1]*100) )
print("Training Complete")
elif string=="Naive Bayes":
from sklearn.naive_bayes import GaussianNB
import matplotlib.pyplot as plt2
t2=time()
clf = GaussianNB() # Initializing the Naive Bayes Classifier
clf.partial_fit(x_train, y_train, np.unique(y_train)) # Fitting the classifier to the training and testing the Naive Bayes Classifier
y_pred = clf.predict(x_test)
print(y_pred)
t = time() - t2
title = "Learning Curves (Naive Bayes)"
#geterror(x_train,y_train,clf,title);
classre=classification_report(y_test,y_pred) # Generating Classification Report
T.insert(END,classre[1:5]+classre[1:32]+classre[1:13]+classre[60:90]+classre[1:11]+classre[1:2]+classre[115:140]+classre[1:7]+classre[161:195]) # Printing Precision and Recall Results
print(classre)
confmat=confusion_matrix(y_test,y_pred) # Calculating the Confusion Matrix for the classification
T1.insert(END, confmat)
T.insert(END, classre[1:9])
T.insert(END, "Accuracy")
T.insert(END, classre[1:5])
T.insert(END, int(float((y_test==y_pred).sum())/len(y_test.T)*100))
T.insert(END, "%")
T.insert(END, classre[1:10]+classre[1:10])
T.insert(END, "Class. Time")
T.insert(END, classre[1:8])
#T.insert(END, t[0:4]+" sec")
a2=int(float((y_test==y_pred).sum())/len(y_test.T)*100)
count2=y_test.count()
l3=list(range(1,count2+1,1))
print(count2)
print(y_test.count())
#print(count(l1))
colormap=np.array(['lime','red'])
plt2.subplot(1,2,1)
plt2.scatter(l3,y_test,c=colormap[y_test])
plt2.suptitle("Naviee Bayesian Algorihm")
plt2.xlabel("X values")
plt2.ylabel("y tests values")
plt2.title("true Values")
plt2.subplot(1,2,2)
plt2.scatter(l3,y_pred,c=colormap[y_pred])
plt2.suptitle("Naviee Bayesian Algorihm")
plt2.xlabel("X values")
plt2.ylabel("Predictions")
plt2.title("Predictions")
plt2.show()
print("Training Complete")
elif string=="K-Nearesr Neighbour":
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt3
clf = KNeighborsClassifier(n_neighbors=5)
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
print(y_pred)
print(y_test)
classre=classification_report(y_test,y_pred) # Generating Classification Report
T.insert(END,classre[1:5]+classre[1:32]+classre[1:13]+classre[60:90]+classre[1:11]+classre[1:2]+classre[115:140]+classre[1:7]+classre[161:195]) # Printing Precision and Recall Results
print(classre)
confmat=confusion_matrix(y_test,y_pred) # Calculating the Confusion Matrix for the classification
T1.insert(END, confmat)
T.insert(END, classre[1:9])
T.insert(END, "Accuracy")
T.insert(END, classre[1:5])
T.insert(END, int(float((y_test==y_pred).sum())/len(y_test.T)*100))
T.insert(END, "%")
T.insert(END, classre[1:10]+classre[1:10])
#T.insert(END, "Class. Time")
#T.insert(END, classre[1:8])
#T.insert(END, t[0:4]+" sec")
a3=int(float((y_test==y_pred).sum())/len(y_test.T)*100)
count3=y_test.count()
l4=list(range(1,count3+1,1))
print(count3)
print(a1)
print(a2)
print(a3)
#plot_bar_x(a1,a2,a3);
print(y_test.count())
#print(count(l1))
colormap=np.array(['lime','red'])
plt3.subplot(1,2,1)
plt3.scatter(l4,y_test,c=colormap[y_test])
plt3.suptitle("K Nearest Neighbour Algorihm")
plt3.xlabel("X values")
plt3.ylabel("Y tests values")
plt3.title("True Values")
plt3.subplot(1,2,2)
plt3.scatter(l4,y_pred,c=colormap[y_pred])
plt3.suptitle("K Nearest Neighbour Algorihm")
plt3.xlabel("X values")
plt3.ylabel("Predictions")
plt3.title("Predictions")
plt3.show()
#print(a1)
#print(a2)
#print(a3)
plot_bar_x(a1,a2,a3)
print("Training Complete")
'''elif string=="Logistic Regression":
from sklearn.linear_model import LogisticRegression
t3=time()
clf=LogisticRegression(penalty='l2', dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, solver='liblinear', max_iter=100, verbose=0, warm_start=False, n_jobs=1) # Initializing the Logistic Regression Classifier
clf.fit(x_train,y_train)
y_pred = clf.predict(x_test)
print(y_pred)
t = time() - t3
title = "Learning Curves (Logistic Regression)"
# geterror(x_train,y_train,clf,title);
print("Training Complete")
t=str(t)'''
'''print(y_test)
classre=classification_report(y_test,y_pred) # Generating Classification Report
T.insert(END,classre[1:5]+classre[1:32]+classre[1:13]+classre[60:90]+classre[1:11]+classre[1:2]+classre[115:140]+classre[1:7]+classre[161:195]) # Printing Precision and Recall Results
print(classre)
confmat=confusion_matrix(y_test,y_pred) # Calculating the Confusion Matrix for the classification
T1.insert(END, confmat)
T.insert(END, classre[1:9])
T.insert(END, "Accuracy")
T.insert(END, classre[1:5])
T.insert(END, int(float((y_test==y_pred).sum())/len(y_test.T)*100))
T.insert(END, "%")
T.insert(END, classre[1:10]+classre[1:10])
T.insert(END, "Class. Time")
T.insert(END, classre[1:8])
T.insert(END, t[0:4]+" sec")
import matplotlib.pyplot as plt'''
def plot_bar_x(a1,a2,a3):
import matplotlib.pyplot as pltu
import numpy as np
print(a1)
print(a2)
print(a3)
label = ['CNN', 'NAIVE BAYESIAN', 'KNN']
no_movies = [a1,a2,a3]
# this is for plotting purpose
index = np.arange(len(label))
pltu.bar(index, no_movies)
pltu.xlabel('Algorithms', fontsize=15)
pltu.ylabel('Accuracies', fontsize=15)
pltu.xticks(index, label, fontsize=15, rotation=30)
pltu.title('Comparison of different algorithms used')
pltu.show()
def process_dataset(string):
if string=="Cleveland":
heart = pnd.read_csv('processed.cleveland.data', names=header_row) # Reading the dataset file in .data format using Pandas library function read_csv()
print("Unprocessed Cleveland Dataset")
print("************************************************************************")
print(heart.loc[:, 'age':'diagnosis'])
print("************************************************************************")
import numpy as np
has_hd_check = heart['diagnosis'] > 0 # Getting the indices of individuals having heart disease
has_hd_patients = heart[has_hd_check]
heart['vessels'] = heart['vessels'].apply(lambda vessels: 0.0 if vessels == "?" else vessels) # Replacing the unknown values in the dataset with float
heart['vessels'] = heart['vessels'].astype(float)
heart['thal'] = heart['thal'].apply(lambda thal: 0.0 if thal == "?" else thal)
heart['thal'] = heart['thal'].astype(float)
heart['diag_int'] = has_hd_check.astype(int)
ind1 = np.where((heart['diagnosis'] == 1)|(heart['diagnosis'] ==2));
ind2 = np.where((heart['diagnosis'] == 3)|(heart['diagnosis'] ==4));
temp = heart['diagnosis'];
temp.ix[ ind1 ] = 1;
temp.ix[ ind2 ] = 2;
heart['diagnosis'] = temp;
global x_train
global y_train
global x_test
global y_test
x_train, x_test, y_train, y_test = train_test_split(heart.loc[:, 'age':'thal'], heart.loc[:, 'diagnosis'], # Splitting the processed data into training data and testing data
test_size=0.20, random_state=42) # test_size = percent of data used for testing,
# random_state = for initializing the random number generator
print("Processed Cleveland Dataset")
print("************************************************************************")
print(heart.loc[:, 'age':'diagnosis'])
print("************************************************************************")
elif string=="VA":
import numpy as np
heart_va = pnd.read_csv('processed.va.data', names=header_row)
print("Unprocessed VA Dataset")
print("************************************************************************")
print(heart_va.loc[:, 'age':'diagnosis'])
print("************************************************************************")
has_hd_check = heart_va['diagnosis'] > 0
heart_va['diag_int'] = has_hd_check.astype(int)
heart_va = heart_va.replace(to_replace='?', value=0.0)
heart_va['diag_int'] = has_hd_check.astype(int)
ind1 = np.where((heart_va['diagnosis'] == 1)|(heart_va['diagnosis'] ==2));
ind2 = np.where((heart_va['diagnosis'] == 3)|(heart_va['diagnosis'] ==4));
temp = heart_va['diagnosis'];
temp.ix[ ind1 ] = 1;
temp.ix[ ind2 ] = 2;
heart_va['diagnosis'] = temp;
print("Processed VA Dataset")
print("************************************************************************")
print(heart_va.loc[:, 'age':'diagnosis'])
print("************************************************************************")
x_train, x_test, y_train, y_test = train_test_split(heart_va.loc[:, 'age':'thal'], heart_va.loc[:, 'diagnosis'],
test_size=0.30, random_state=42)
elif string=="Hungarian":
import numpy as np
heart_hu = pnd.read_csv('processed.hungarian.data', names=header_row)
print("Unprocessed Hungarian Dataset")
print("************************************************************************")
print(heart_hu.loc[:, 'age':'diagnosis'])
print("************************************************************************")
has_hd_check = heart_hu['diagnosis'] > 0
heart_hu['diag_int'] = has_hd_check.astype(int)
heart_hu = heart_hu.replace(to_replace='?', value=0.0)
ind1 = np.where((heart_hu['diagnosis'] == 1)|(heart_hu['diagnosis'] ==2));
ind2 = np.where((heart_hu['diagnosis'] == 3)|(heart_hu['diagnosis'] ==4));
temp = heart_hu['diagnosis'];
temp.ix[ ind1 ] = 1;
temp.ix[ ind2 ] = 2;
heart_hu['diagnosis'] = temp;
print("Processed Hungarian Dataset")
print("************************************************************************")
print(heart_hu.loc[:, 'age':'diagnosis'])
print("************************************************************************")
heart_hu['diag_int'] = has_hd_check.astype(int)
x_train, x_test, y_train, y_test = train_test_split(heart_hu.loc[:, 'age':'thal'], heart_hu.loc[:, 'diagnosis'],
test_size=0.30, random_state=42)
elif string=="all":
import numpy as np
heart_cl = pnd.read_csv('processed.cleveland.data', names=header_row)
print("Unprocessed Cleveland Dataset")
print("************************************************************************")
print(heart_cl.loc[:, 'age':'diagnosis'])
print("************************************************************************")
has_hd_check = heart_cl['diagnosis'] > 0
has_hd_patients = heart_cl[has_hd_check]
heart_cl['diag_int'] = has_hd_check.astype(int)
heart_cl['vessels'] = heart_cl['vessels'].apply(lambda vessels: 0.0 if vessels == "?" else vessels)
heart_cl['vessels'] = heart_cl['vessels'].astype(float)
heart_cl['thal'] = heart_cl['thal'].apply(lambda thal: 0.0 if thal == "?" else thal)
heart_cl['thal'] = heart_cl['thal'].astype(float)
ind1 = np.where((heart_cl['diagnosis'] == 1)|(heart_cl['diagnosis'] ==2));
ind2 = np.where((heart_cl['diagnosis'] == 3)|(heart_cl['diagnosis'] ==4));
temp = heart_cl['diagnosis'];
temp.ix[ ind1 ] = 1;
temp.ix[ ind2 ] = 2;
heart_cl['diagnosis'] = temp;
heart_va = pnd.read_csv('processed.va.data', names=header_row)
print("Unprocessed VA Dataset")
print("************************************************************************")
print(heart_va.loc[:, 'age':'diagnosis'])
print("************************************************************************")
has_hd_check = heart_va['diagnosis'] > 0
heart_va['diag_int'] = has_hd_check.astype(int)
heart_va = heart_va.replace(to_replace='?', value=0.0)
ind1 = np.where((heart_va['diagnosis'] == 1)|(heart_va['diagnosis'] ==2));
ind2 = np.where((heart_va['diagnosis'] == 3)|(heart_va['diagnosis'] ==4));
temp = heart_va['diagnosis'];
temp.ix[ ind1 ] = 1;
temp.ix[ ind2 ] = 2;
heart_va['diagnosis'] = temp;
print("Processed VA Dataset")
print("************************************************************************")
print(heart_va.loc[:, 'age':'diagnosis'])
print("************************************************************************")
heart_hu = pnd.read_csv('processed.hungarian.data', names=header_row)
print("Unprocessed Hungarian Dataset")
print("************************************************************************")
print(heart_hu.loc[:, 'age':'diagnosis'])
print("************************************************************************")
has_hd_check = heart_hu['diagnosis'] > 0
heart_hu['diag_int'] = has_hd_check.astype(int)
heart_hu = heart_hu.replace(to_replace='?', value=0.0)
ind1 = np.where((heart_hu['diagnosis'] == 1)|(heart_hu['diagnosis'] ==2));
ind2 = np.where((heart_hu['diagnosis'] == 3)|(heart_hu['diagnosis'] ==4));
temp = heart_hu['diagnosis'];
temp.ix[ ind1 ] = 1;
temp.ix[ ind2 ] = 2;
heart_hu['diagnosis'] = temp;
print("Processed Hungarian Dataset")
print("************************************************************************")
print(heart_hu.loc[:, 'age':'diagnosis'])
print("************************************************************************")
x_train1, x_test1, y_train1, y_test1 = train_test_split(heart_cl.loc[:, 'age':'thal'], heart_cl.loc[:, 'diagnosis'],
test_size=0.30, random_state=42)
x_train2, x_test2, y_train2, y_test2 = train_test_split(heart_va.loc[:, 'age':'thal'], heart_va.loc[:, 'diagnosis'],
test_size=0.30, random_state=42)
x_train3, x_test3, y_train3, y_test3 = train_test_split(heart_hu.loc[:, 'age':'thal'], heart_hu.loc[:, 'diagnosis'],
test_size=0.30, random_state=42)
# Combining the dataset for Cleveland, VA and Hungarian Dataset
x_train4= x_train1.append(x_train2);
x_train = x_train4.append(x_train3);
y_train4 = y_train1.append(y_train2);
y_train = y_train4.append(y_train3);
x_test4 = x_test1.append(x_test2);
x_test = x_test4.append(x_test3)
y_test4 = y_test1.append(y_test2);
y_test = y_test4.append(y_test3);
button = Button(master, text="Process Dataset",height=1,fg="black",font=('algerian',13,'bold'),bg="violet",justify='center', command=lambda: process_dataset(var.get())) #Defining the button in the Tkinter Widget
button.place(x=700,y=80)
var1 = StringVar(master)
var1.set("Select Classifier") # initial value
option1 = OptionMenu(master, var1, "CNN", "Naive Bayes","K-Nearesr Neighbour")
option1.place(x=500,y=120)
option1.config(bg = "violet")
option1.config(fg = "black")
option1.config(font=('algerian',10,'bold'))
option1.config(width=12)
#option.place ( relx=0.5, rely=0.1)
button1 = Button(master, text=" Train Classifier",height=1,fg="black",font=('algerian',13,'bold'),bg="violet",justify='center', command=lambda: train_classifier(x_train,x_test,y_train,y_test,var1.get()))
button1.place(x=700,y=120)
#e1.bind('<Button-1>',e1.delete(0,END))
def predres(clf): # Defining function to predict the result from the user input data
E14=E10.get() # Converting the Eist data according to sign and decimal point
if len(E14)==3 or len(E14)==4:
E15=float(E14)
else:
E16=E14+'.0'
E15=float(E16)
test=[float(E1.get()+'.0'),float(E2.get()+'.0'),float(E3.get()+'.0'),float(E4.get()+'.0'),float(E5.get()+'.0'),float(E6.get()+'.0'),float(E7.get()+'.0'),float(E8.get()+'.0'),float(E9.get()+'.0'),E15,float(E11.get()),float(E12.get()+'.0'),float(E13.get()+'.0')]
test=np.reshape(test,(1,-1))
#print(test)
print(clf)
print(clf.predict(test))
if clf.predict(test) < 0.49:
res="The Person does not have heart Disease"
Labx1=Label(master,text="The Person does not have heart Disease", bg='green')
Labx1.visible=False
Labx1.place(x=600,y=430)
Labx1.visible=True
#T3.insert(END,"The Person has Heart Disease")
elif clf.predict(test) == 2:
res="The Person has Severe heart Disease"
Labx1=Label(master,text="The Person has Severe heart Disease", bg='red')
Labx1.visible=False
Labx1.place(x=600,y=430)
Labx1.visible=True
else:
res="The Person has heart Disease"
Labx1=Label(master,text="The Person has heart Disease ", bg='red')
Labx1.visible=False
Labx1.place(x=600,y=430)
Labx1.visible=True
#T3.insert(END,"The Person does not have Heart Disease")
age= E1.get()
sex = E2.get()
pai= E3.get()
bp= E4.get()
chol = E5.get()
fbs = E6.get()
ecg = E7.get()
maxhr = E8.get()
eiang = E9.get()
eist = E10.get()
slope = E11.get()
vessels = E12.get()
thal = E13.get()
pana=E0.get()
aa = mysql.connector.connect(host='localhost', port=3306, user="root", passwd="root", db="cardiac")
mm = aa.cursor()
mm.execute("""INSERT INTO cardiac1 VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)""", (age,sex,pai,bp,chol,fbs,ecg,maxhr,eiang,eist,slope,vessels,thal,pana,res))
aa.commit()
#con.close()
def geterror(x_train,y_train,clf,title):
#global clf
#global outclass
import matplotlib.pyplot as plt
from sklearn.model_selection import learning_curve
from sklearn.model_selection import ShuffleSplit
clas=[];
def plot_learning_curve(estimator, title, X, y, ylim=None, cv=None,
n_jobs=1, train_sizes=np.linspace(.1, 1.0, 5)):
plt.figure()
plt.title(title)
if ylim is not None:
plt.ylim(*ylim)
plt.xlabel("Training examples")
plt.ylabel("Score")
train_sizes, train_scores, test_scores = learning_curve(
estimator, X, y, cv=cv, n_jobs=n_jobs, train_sizes=train_sizes)
train_scores_mean = np.mean(train_scores, axis=1)
train_scores_std = np.std(train_scores, axis=1)
test_scores_mean = np.mean(test_scores, axis=1)
test_scores_std = np.std(test_scores, axis=1)
plt.grid()
plt.fill_between(train_sizes, train_scores_mean - train_scores_std,
train_scores_mean + train_scores_std, alpha=0.1,
color="r")
plt.fill_between(train_sizes, test_scores_mean - test_scores_std,
test_scores_mean + test_scores_std, alpha=0.1, color="g")
plt.plot(train_sizes, train_scores_mean, 'o-', color="r",
label="Training score")
plt.plot(train_sizes, test_scores_mean, 'o-', color="g",
label="Cross-validation score")
plt.legend(loc="best")
plt.axis([ 0,len(y),0,1.1])
return plt
for index in range(len(y_train)):
#x_train1=np.reshape(x_train.iloc[index:],(1,-1));
x_train1=x_train.iloc[index].values.reshape(1,-1);
outclass = clf.predict(x_train1);
clas.append(outclass[0]);
ind3 = np.where((clas == y_train));
ind4 = np.where((clas != y_train));
l=0
m=0
cv = ShuffleSplit(n_splits=4, test_size=0.2, random_state=0)
plot_learning_curve(clf, title, x_train, y_train, (0.7, 1.01), cv=cv, n_jobs=1)
plt.show()
button2 = Button(master, text=" Predict Heart Disease ",width=20,height=1,fg="black",font=('algerian',13,'bold'),bg="violet",justify='center',command=lambda:predres(clf))
button2.place(x=600,y=300)
btn6=Button(master,text="LOGOUT",width=8,height=1,fg="black",font=('algerian',15,'bold'),bg="SKYBLUE",justify='center',command=cardes)
btn6.place(x=1100,y=80)
#button2.configure(width=14)
#button1.place(relx=0.1,rely=0.2)
master.mainloop()
def adminlogin():
def adminlogininto():
usernames = e1.get()
passwords = e2.get()
if e1.get() == "" or e2.get() == "":
tkinter.messagebox.showinfo("sorry","Please complete the required field")
elif e1.get() == "admin" and e2.get() == "admin":
#tkMessageBox.showinfo("yeh","logged in")
admindes()
else:
tkinter.messagebox.showinfo("Sorry" , "Wrong Password")
global window1
window1=Tk()
window1.title("LOGIN PAGE")
window1.geometry('700x500')
image = Image.open('photo.png')
image = image.resize((700, 600))
photo_image = ImageTk.PhotoImage(image)
label = Label(window1, image = photo_image)
label.place(x=0,y=0)
'''lb1=Label(window1,text="USERNAME",font=('algerian',25,'bold'),fg="BLACK",anchor='w')
lb1.place(x=150,y=400)'''
e1=Entry(window1,width=10,font=("bold",17),highlightthickness=2,bg="WHITE",relief=SUNKEN)
e1.place(x=250,y=150)
'''lb2=Label(window1,text="PASSWORD",font=('algerian',25,'bold'),fg="BLACK",anchor='w')
lb2.place(x=150,y=450)'''
e2=Entry(window1,width=10,show="*",font=("bold",17),highlightthickness=2,bg="WHITE",relief=SUNKEN)
e2.place(x=250,y=200)
btn6=Button(window1,text="LOGIN",width=8,height=1,fg="black",font=('algerian',15,'bold'),bg="SKYBLUE",justify='center',command=adminlogininto)
btn6.place(x=270,y=300)
window1.mainloop()
def admindes():
window1.destroy()
cardiac()
def cardes():
master.destroy()
adminlogin()
if __name__ == "__main__":
adminlogin()Error:Exception in Tkinter callback
Traceback (most recent call last):
File "C:\Users\Dell\AppData\Local\Programs\Python\Python37\lib\tkinter\__init__.py", line 1705, in __call__
return self.func(*args)
File "C:\Users\Dell\Desktop\finalproject\heartfinal.py", line 755, in <lambda>
button2 = Button(master, text=" Predict Heart Disease ",width=20,height=1,fg="black",font=('algerian',13,'bold'),bg="violet",justify='center',command=lambda:predres(clf))
NameError: name 'clf' is not defined