Logistic Regression
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#!/usr/bin/python
# Logistic Regression from scratch
# In[62]:
# In[63]:
# 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
# get_ipython().run_line_magic('matplotlib', 'inline')
# In[67]:
# sigmoid function or logistic function is used as a hypothesis function in
# classification problems
def sigmoid_function(z: float | np.ndarray) -> float | np.ndarray:
"""
Also known as Logistic Function.
1
f(x) = -------
1 + e⁻ˣ
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:
>>> float(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])
"""
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(hθ(x)) - (1 - y) * log(1 - hθ(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
- hθ(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
"""
return float((-y * np.log(h) - (1 - y) * np.log(1 - h)).mean())
def log_likelihood(x, y, weights):
scores = np.dot(x, weights)
return np.sum(y * scores - np.log(1 + np.exp(scores)))
# 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])
for iterations in range(max_iterations):
z = np.dot(x, theta)
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)
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
return theta
# In[68]:
if __name__ == "__main__":
import doctest
doctest.testmod()
iris = datasets.load_iris()
x = iris.data[:, :2]
y = (iris.target != 0) * 1
alpha = 0.1
theta = logistic_reg(alpha, x, y, max_iterations=70000)
print("theta: ", theta) # printing the theta i.e our weights vector
def predict_prob(x):
return sigmoid_function(
np.dot(x, theta)
) # predicting the value of probability from the logistic regression algorithm
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())
(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)
plt.contour(xx1, xx2, probs, [0.5], linewidths=1, colors="black")
plt.legend()
plt.show()
À propos de cet Algorithme
import numpy as np
import pandas as pd
import sklearnImporting the MNIST Dataset to perform Multi-Class Classification.
MNIST is a large database of handwritten digits that is commonly used for training various image processing systems.
from sklearn.datasets import fetch_openml
X, y = fetch_openml('mnist_784', return_X_y=True)Shape of the dataset
X.shape(70000, 784)Visualizing the first 5 images in the dataset
We are reshaping the each array as a 28X28 Image so that we can plot it
import matplotlib.pyplot as plt
for i in range(5):
plt.imshow(X[i].reshape((28,28)))
plt.show()
Importing the Logistic Regression Model from Scikit-Learn and Data Processing
- We are decreasing the size of the dataset for training.
- Then, we scale the data.
- Finally, we split the dataset into training and test sets
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
# STEP 1
X, y = X[:10_000], y[:10_000]
# STEP 2
X_scaled = preprocessing.scale(X)
# STEP 3
X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, random_state = 42)Then, we finally train the Logistic Regression Model.
model = LogisticRegression(random_state=0, max_iter=2000)
model.fit(X_train, y_train)
print("Model Training Completed!")Model Training Completed!
We now measuring the accuracy score of the model that we have trained.
from sklearn.metrics import accuracy_score
preds = model.predict(X_test)
score = accuracy_score(y_test, preds)
print(f"Model's accuracy score is : {round(score*100, 2)} %")Model's accuracy score is : 89.76 %
Demonstrating the model's predictions on one of the image from our test-set.
pred = model.predict(X_test[45].reshape(-1, 784))
print(f"Model's Prediction : {pred}")
print("*"*30)
plt.imshow(X_test[45].reshape(28, 28))
plt.show()Model's Prediction : ['6']
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