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logistic_regression.py
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logistic_regression.py
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#!/usr/bin/env python3
'''
Author : Yi Herng Ong
Purpose : Logistic regression for binary classification
'''
import pandas as pd
import numpy as np
import os, sys
import pdb
import matplotlib.pyplot as plt
import math
class Logistic_regression(object):
def __init__(self, x_train, x_test, y_train, y_test, prediction_feature, Lambda=1):
self.x_train = x_train
self.x_test = x_test
self.y_train = y_train
self.y_test = y_test
self.lr = Lambda # learning rate
self.prediction_feature = prediction_feature
def activation_function(self, z):
return 1 / (1 + np.exp(-z))
def train(self, steps=50000):
w = np.random.rand(self.x_train.shape[1])
for _ in range(steps):
# predict train x
# pdb.set_trace()
predictions = self.activation_function(np.dot(self.x_train, w))
# determine cost using Cross Entropy Error
# cost_1 = -self.y_train * np.log(predictions) # calc error when label = 1
# cost_0 = -(1-self.y_train) * np.log(1-predictions) # calc error when label = 0
# sum_cost = cost_1 + cost_0
# cost_avg = sum(sum_cost) / len(self.y_train)
# gradient of the error
cost = predictions - self.y_train
# update weight
grad = np.dot(self.x_train.T, cost)
grad /= self.x_train.shape[1]
grad *= self.lr
w -= grad
# print("cost:", cost)
self.w = w
def prediction(self):
count = 0
for i in range(len(self.y_test)):
pred = self.activation_function(np.dot(self.w, self.x_test[i]))
if pred > 0.5:
y_p = 1
else:
y_p = 0
if y_p == self.y_test[i]:
count += 1
print("percentage:", count / len(self.y_test))
return count / len(self.y_test)