Python/machine_learning/logistic_regression.py

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#!/usr/bin/python
# Logistic Regression from scratch
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# importing all the required libraries
"""
Implementing logistic regression for classification problem
Helpful resources:
Coursera ML course
https://medium.com/@martinpella/logistic-regression-from-scratch-in-python-124c5636b8ac
"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn import datasets
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# get_ipython().run_line_magic('matplotlib', 'inline')
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# sigmoid function or logistic function is used as a hypothesis function in
# classification problems
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def sigmoid_function(z: float | np.ndarray) -> float | np.ndarray:
"""
Also known as Logistic Function.
1
f(x) = -------
1 + eˣ
The sigmoid function approaches a value of 1 as its input 'x' becomes
increasing positive. Opposite for negative values.
Reference: https://en.wikipedia.org/wiki/Sigmoid_function
@param z: input to the function
@returns: returns value in the range 0 to 1
Examples:
>>> sigmoid_function(4)
0.9820137900379085
>>> sigmoid_function(np.array([-3, 3]))
array([0.04742587, 0.95257413])
>>> sigmoid_function(np.array([-3, 3, 1]))
array([0.04742587, 0.95257413, 0.73105858])
>>> sigmoid_function(np.array([-0.01, -2, -1.9]))
array([0.49750002, 0.11920292, 0.13010847])
>>> sigmoid_function(np.array([-1.3, 5.3, 12]))
array([0.21416502, 0.9950332 , 0.99999386])
>>> sigmoid_function(np.array([0.01, 0.02, 4.1]))
array([0.50249998, 0.50499983, 0.9836975 ])
>>> sigmoid_function(np.array([0.8]))
array([0.68997448])
"""
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return 1 / (1 + np.exp(-z))
def cost_function(h: np.ndarray, y: np.ndarray) -> float:
"""
Cost function quantifies the error between predicted and expected values.
The cost function used in Logistic Regression is called Log Loss
or Cross Entropy Function.
J(θ) = (1/m) * Σ [ -y * log((x)) - (1 - y) * log(1 - (x)) ]
Where:
- J(θ) is the cost that we want to minimize during training
- m is the number of training examples
- Σ represents the summation over all training examples
- y is the actual binary label (0 or 1) for a given example
- (x) is the predicted probability that x belongs to the positive class
@param h: the output of sigmoid function. It is the estimated probability
that the input example 'x' belongs to the positive class
@param y: the actual binary label associated with input example 'x'
Examples:
>>> estimations = sigmoid_function(np.array([0.3, -4.3, 8.1]))
>>> cost_function(h=estimations,y=np.array([1, 0, 1]))
0.18937868932131605
>>> estimations = sigmoid_function(np.array([4, 3, 1]))
>>> cost_function(h=estimations,y=np.array([1, 0, 0]))
1.459999655669926
>>> estimations = sigmoid_function(np.array([4, -3, -1]))
>>> cost_function(h=estimations,y=np.array([1,0,0]))
0.1266663223365915
>>> estimations = sigmoid_function(0)
>>> cost_function(h=estimations,y=np.array([1]))
0.6931471805599453
References:
- https://en.wikipedia.org/wiki/Logistic_regression
"""
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return (-y * np.log(h) - (1 - y) * np.log(1 - h)).mean()
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def log_likelihood(x, y, weights):
scores = np.dot(x, weights)
return np.sum(y * scores - np.log(1 + np.exp(scores)))
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# here alpha is the learning rate, X is the feature matrix,y is the target matrix
def logistic_reg(alpha, x, y, max_iterations=70000):
theta = np.zeros(x.shape[1])
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for iterations in range(max_iterations):
z = np.dot(x, theta)
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h = sigmoid_function(z)
gradient = np.dot(x.T, h - y) / y.size
theta = theta - alpha * gradient # updating the weights
z = np.dot(x, theta)
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h = sigmoid_function(z)
j = cost_function(h, y)
if iterations % 100 == 0:
print(f"loss: {j} \t") # printing the loss after every 100 iterations
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return theta
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if __name__ == "__main__":
import doctest
doctest.testmod()
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iris = datasets.load_iris()
x = iris.data[:, :2]
y = (iris.target != 0) * 1
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alpha = 0.1
theta = logistic_reg(alpha, x, y, max_iterations=70000)
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print("theta: ", theta) # printing the theta i.e our weights vector
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def predict_prob(x):
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return sigmoid_function(
np.dot(x, theta)
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) # predicting the value of probability from the logistic regression algorithm
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plt.figure(figsize=(10, 6))
plt.scatter(x[y == 0][:, 0], x[y == 0][:, 1], color="b", label="0")
plt.scatter(x[y == 1][:, 0], x[y == 1][:, 1], color="r", label="1")
(x1_min, x1_max) = (x[:, 0].min(), x[:, 0].max())
(x2_min, x2_max) = (x[:, 1].min(), x[:, 1].max())
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(xx1, xx2) = np.meshgrid(np.linspace(x1_min, x1_max), np.linspace(x2_min, x2_max))
grid = np.c_[xx1.ravel(), xx2.ravel()]
probs = predict_prob(grid).reshape(xx1.shape)
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plt.contour(xx1, xx2, probs, [0.5], linewidths=1, colors="black")
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plt.legend()
plt.show()