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gui_v01.py
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#!/usr/bin/env python -W ignore::DeprecationWarning
import mlrose
import numpy as np
from datetime import datetime
import pandas as pd
from sklearn.metrics import accuracy_score
from datetime import datetime
import numpy as np
import sys
from tkinter import *
import warnings
warnings.filterwarnings("ignore", category=DeprecationWarning)
#warnings.filterwarnings("ignore", category=RuntimeWarning)
#print("\nStarting the execution now:\n")
def generateColumns(start, end):
for i in range(start, end+1):
l.extend([str(i)+'X', str(i)+'Y'])
return l
def genetic(iterations):
nn_model_genetic = mlrose.NeuralNetwork(
hidden_nodes = [4],
activation = 'relu',
algorithm = 'genetic_alg',
max_iters = iterations,
is_classifier = True,
learning_rate = 0.0001,
early_stopping = True,
clip_max = 5,
max_attempts = 100,
random_state = 3
)
nn_model_genetic.fit(X_train, y_train)
y_test_pred = nn_model_genetic.predict(X_test_scaled)
y_test_accuracy = accuracy_score(y_test, y_test_pred)
return (y_test_pred, y_test_accuracy)
def random_hill_climb(iterations):
nn_model1 = mlrose.NeuralNetwork(
hidden_nodes=[4],
activation ='relu',
algorithm='random_hill_climb',
max_iters=iterations,
bias=True,
is_classifier = True,
learning_rate=0.0001,
early_stopping = True,
clip_max = 5,
max_attempts=100,
random_state = 3)
nn_model1.fit(X_train_scaled, y_train)
y_test_pred = nn_model1.predict(X_test_scaled)
y_test_accuracy = accuracy_score(y_test, y_test_pred)
return(y_test_pred, y_test_accuracy)
def gradDesc(iterations):
nn_model_gradDesc = mlrose.NeuralNetwork(hidden_nodes = [4], activation = 'relu',
algorithm = 'gradient_descent',
max_iters = iterations, bias = True, is_classifier = True,
learning_rate = 0.0001, early_stopping = True,
clip_max = 5, max_attempts = 100, random_state = 3)
nn_model_gradDesc.fit(X_train_scaled, y_train)
# y_train_pred = nn_model.predict(X_train_scaled)
# y_train_accuracy = accuracy_score(y_train, y_train_pred)
# print("The Training accuracy is: ",y_train_accuracy*100,"%")
y_test_pred = nn_model_gradDesc.predict(X_test_scaled)
y_test_accuracy = accuracy_score(y_test, y_test_pred)
return (y_test_pred, y_test_accuracy)
# acc = y_test_accuracy*100
# for i in range(1, 10):
#running for 1 iteration
def eagle():
iters = 50
#print("\n")
dict = {}
acc3 = genetic(iters)
#print("\n\n\n\n")
#print("Genetic Algorithm gave: ",round(acc3[1]*100,2), "%")
dict['genetic'] = acc3[1]
acc1 = random_hill_climb(iters)
#print("Random Hill Climbing gave: ",round(acc1[1]*100,2), "%")
dict['random_hill_climb'] = acc1[1]
acc2 = gradDesc(iters)
#print("Gradient descent gave: ",round(acc2[1]*100,2), "%")
dict['gradDesc'] = acc2[1]
k = list(dict.keys())
v= list(dict.values())
max_acc_algo = k[v.index(max(v))]
max_acc = max(v)
# print(max_acc)
acc = max_acc
if max_acc_algo == 'gradDesc':
algo = 1
elif max_acc_algo == 'random_hill_climb':
algo = 2
else:
algo = 3
#print("Exploiting algorithm: ", max_acc_algo)
loop = 1
while (loop < 5 and acc < 98):
iters = iters + 550
if algo == 1:
y_test_accuracy = gradDesc(iters)
elif algo == 2:
y_test_accuracy = random_hill_climb(iters)
else:
try:
y_test_accuracy = genetic(iters)
except:
print("")
if ((y_test_accuracy[1] * 100) == acc):
loop = loop + 1
else:
loop = 0
acc = y_test_accuracy[1] * 100
#print("Exploiting the accuracy: ",acc, "In ", iters, "Iterations")
#print("Current execution time elapsed = ", datetime.now() - startTime)
#print("The final accuracy is: ", acc, "Which took ", iters,"Iterations")
label = Label(root, text="Execution time in seconds = "+str(datetime.now()-startTime)).pack()
#print("Execution time in seconds = "+str(datetime.now() - startTime))
root = Tk()
root.geometry("700x400")
startTime = datetime.now()
#root.configure('blue')
root.title("Artificial Neural Network")
initial_acc = 50
acc = 0
l = []
eyes = generateColumns(1, 12)
df = pd.read_csv('Eyes.csv')
first_column = df.columns[0]
df = df.drop([first_column],axis = 1)
# print("hello")
X = df[eyes]
y = df['truth_value'] #the actual class labels
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.30)
# Data Normalization
from sklearn.preprocessing import StandardScaler as SC
sc = SC()
X_train_scaled = sc.fit_transform(X_train)
X_test_scaled = sc.fit_transform(X_test)
#not scaling y since it's already 0s and 1s
X_train, y_train, X_test, y_test = np.array(X_train), np.array(y_train), np.array(X_test), np.array(y_test)
#converting all the scaled data to numpy arrays
button = Button(root, text = "Begin?",width = '10', height = '2',command=eagle).pack()
text = Text(root)
root.mainloop()