classification.py

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy as sc
############### data ###############
path = "C:\\ML\\Classification\\First_Project_Classification\\data.txt"
data = pd.read_csv(path,header= None,names=["Exam1","Exam2","Admitted"])

data.insert(0,"Bais",1)

# separate featurs and output 
col =data.shape[1] # output number of columns for data 
x = data.iloc[ : , 0 : col - 1]
y = data.iloc[ : , col - 1 : col]

# separate positive and negative data
positive = data[data["Admitted"].isin([1])]
negative = data[data["Admitted"].isin([0])]

# draw positive and negative data
fig,ax = plt.subplots()
ax.scatter(positive["Exam1"],positive["Exam2"],marker='o',c='b') 
ax.scatter(negative["Exam1"],negative["Exam2"],marker='x',c='r')
plt.show()

# convert x , y to matrix
x = np.matrix(x.values)
y= np.matrix(y.values)
theta = np.zeros((x.shape[1],1))

# implement Sigmoid Function
def Sigmoid(z):
    return 1 / (1 + np.exp(-z))

#implement Cost Function
def Cost(theta,x, y):
    m = len(x)
    prediction = Sigmoid(x @ theta)
    epsilon = 1e-5
    cost = (-1/m) * (np.sum(np.multiply(y, np.log(prediction + epsilon).T) + np.multiply((1 - y), np.log(1 - prediction + epsilon)).T))
    return cost

# implement Gradiant Function
def Gradient(theta,x,y):
    m = len(y)
    theta = np.matrix(theta).reshape(-1, 1)
    parameters = x.shape[1]
    grad = np.zeros((parameters,1)) # 
    error =  Sigmoid(x @ theta) - y # 
    grad = (1/m) * (x.T @ error)
    return grad

def predict(x, theta):
    p = Sigmoid(x @ newTheta)
    for i in range(96):
        if p[0,i] > 0.5:
            p[0,i] = 1
        else:
            p[0,i] = 0
    return p

""""""""" Training Data """""""""

result = sc.optimize.fmin_tnc(func=Cost, x0=theta, args=(x, y), fprime=Gradient)


newTheta = result[0]

print(f"newtheta : {newTheta.shape}")
print(f"x : {x.shape}")
print(f"y : {y.shape}")

cost0 = Cost(theta,x,y)
print(f"Cost Before = {cost0}")
print("============================")

cost1 = Cost(newTheta,x,y)
print(f"Cost After: {cost1}")

prediction_values = predict(x,newTheta)
prediction_values = prediction_values.reshape(96,1)

print(prediction_values.shape)
print(y.shape)
compare = np.zeros((96, 2))
compare[:, 0] = prediction_values.flatten()
compare[:, 1] = y.flatten()
print(compare)
a= 0
for i in range(96):
    if compare[i,0] == compare[i,1]:
        a += 1
accurecy = (a/96) * 100
print(f"Accurecy : {accurecy}%")
print("============================")