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Linear and Logistic Regression Refactoring- ML From Scratch 04

In this Machine Learning from Scratch Tutorial, we are going to refactor the code from the previous two videos. We will implement Linear and Logistic Regression in only 60 lines.


In this Machine Learning from Scratch Tutorial, we are going to refactor the code from the previous two videos. We will implement Linear and Logistic Regression in only 60 lines of Python, with the help of a Base Regression class.

All algorithms from this course can be found on GitHub together with example tests.

IMPORTANT: In the video I forgot to call the `_approximation() in the Base class at the end. Please check the correct full code below or in the repo.

Implementation

import numpy as np

class BaseRegression:

    def __init__(self, learning_rate=0.001, n_iters=1000):
        self.lr = learning_rate
        self.n_iters = n_iters
        self.weights = None
        self.bias = None

    def fit(self, X, y):
        n_samples, n_features = X.shape

        # init parameters
        self.weights = np.zeros(n_features)
        self.bias = 0

        # gradient descent
        for _ in range(self.n_iters):
            y_predicted = self._approximation(X, self.weights, self.bias)

            # compute gradients
            dw = (1 / n_samples) * np.dot(X.T, (y_predicted - y))
            db = (1 / n_samples) * np.sum(y_predicted - y)

            # update parameters
            self.weights -= self.lr * dw
            self.bias -= self.lr * db

    def predict(self, X):
        return self._predict(X, self.weights, self.bias)

    def _predict(self, X, w, b):
        raise NotImplementedError()

    def _approximation(self, X, w, b):
        raise NotImplementedError()

class LinearRegression(BaseRegression):

    def _approximation(self, X, w, b):
        return np.dot(X, w) + b

    def _predict(self, X, w, b):
        return np.dot(X, w) + b


class LogisticRegression(BaseRegression):

    def _approximation(self, X, w, b):
        linear_model = np.dot(X, w) + b
        return self._sigmoid(linear_model)

    def _predict(self, X, w, b):
        linear_model = np.dot(X, w) + b
        y_predicted = self._sigmoid(linear_model)
        y_predicted_cls = [1 if i > 0.5 else 0 for i in y_predicted]
        return np.array(y_predicted_cls)

    def _sigmoid(self, x):
        return 1 / (np.exp(-x) + 1)

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