sunilkumardash9

import numpy as np 
from numpy import log,dot,exp,shape
import matplotlib.pyplot as plt
from sklearn.datasets import make_classification
X,y = make_classification(n_featues=4)
from sklearn.model_selection import train_test_split  
X_tr,X_te,y_tr,y_te = train_test_split(X,y,test_size=0.1

def standardize(X_tr):
    for i in range(shape(X_tr)[1]):

sunilkumardash9

def F1_score(y,y_hat):
    tp,tn,fp,fn = 0,0,0,0
    for i in range(len(y)):
        if y[i] == 1 and y_hat[i] == 1:
            tp += 1
        elif y[i] == 1 and y_hat[i] == 0:
            fn += 1
        elif y[i] == 0 and y_hat[i] == 1:
            fp += 1
        elif y[i] == 0 and y_hat[i] == 0:

sunilkumardash9

def predict(self,X):
    z = dot(self.initialize(X)[1],self.weights)
    lis = []
    for i in self.sigmoid(z):
        if i>0.5:
            lis.append(1)
        else:
            lis.append(0)
    return lis

sunilkumardash9

def fit(self,X,y,alpha=0.001,iter=400):
    weights,X = self.initialize(X)
    cost_list = np.zeros(iter,)
    for i in range(iter):
        weights = weights-alpha*dot(X.T,self.sigmoid(dot(X,weights))-np.reshape(y,(len(y),1)))
        cost_list[i] = cost(weights)
    self.weights = weights
    return cost_list

sunilkumardash9

def cost_func(self,theta):
    z = dot(X,theta)
    cost0 = y.T.dot(log(self.sigmoid(z)))
    cost1 = (1-y).T.dot(log(1-self.sigmoid(z)))
    cost = -(sum(cost1 + cost0))/len(y) 
    return cos

sunilkumardash9

def sigmoid(self,X,weights):
    z = np.dot(X,weights)
    sig = 1/(1+np.e**(-z))
    return sig

sunilkumardash9

fig,ax = plt.subplots(figsize=(12,8))
ax.set_ylabel('J(cost)')
ax.set_xlabel('iterations')
x = ax.plot(range(400),a,'b.')

sunilkumardash9

from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
model = LogisticRegression().fit(X_tr,y_tr)
y_pred = model.predict(X_te)
print(f1_score(y_te,y_pred))

sunilkumardash9

from sklearn.datasets import make_classification
X,y = make_classification(n_features=4)
#spliting train,test data
from sklearn.model_selection import train_test_split
X_tr,X_te,y_tr,y_te = train_test_split(X,y,test_size=0.15)

sunilkumardash9

def initialize(self,X):
    weights = np.zeros((shape(X)[1]+1,1))
    X = np.c_[np.ones((shape(X)[0],1)),X]
    return weights,X