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AdaBoost in Python - ML From Scratch 13

Implement the AdaBoost algorithm using only built-in Python modules and numpy, and learn about the math behind this popular ML algorithm.


In this Machine Learning from Scratch Tutorial, we are going to implement the AdaBoost algorithm using only built-in Python modules and numpy. AdaBoost is an ensemble technique that attempts to create a strong classifier from a number of weak classifiers. We will first learn about the concept and the math behind this popular ML algorithm, and then we jump to the code.

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

Implementation

import numpy as np

# Decision stump used as weak classifier
class DecisionStump():
    def __init__(self):
        self.polarity = 1
        self.feature_idx = None
        self.threshold = None
        self.alpha = None

    def predict(self, X):
        n_samples = X.shape[0]
        X_column = X[:, self.feature_idx]
        predictions = np.ones(n_samples)
        if self.polarity == 1:
            predictions[X_column < self.threshold] = -1
        else:
            predictions[X_column > self.threshold] = -1

        return predictions


class Adaboost():

    def __init__(self, n_clf=5):
        self.n_clf = n_clf

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

        # Initialize weights to 1/N
        w = np.full(n_samples, (1 / n_samples))

        self.clfs = []
        # Iterate through classifiers
        for _ in range(self.n_clf):
            clf = DecisionStump()

            min_error = float('inf')
            # greedy search to find best threshold and feature
            for feature_i in range(n_features):
                X_column = X[:, feature_i]
                thresholds = np.unique(X_column)

                for threshold in thresholds:
                    # predict with polarity 1
                    p = 1
                    predictions = np.ones(n_samples)
                    predictions[X_column < threshold] = -1

                    # Error = sum of weights of misclassified samples
                    misclassified = w[y != predictions]
                    error = sum(misclassified)

                    if error > 0.5:
                        error = 1 - error
                        p = -1

                    # store the best configuration
                    if error < min_error:
                        clf.polarity = p
                        clf.threshold = threshold
                        clf.feature_idx = feature_i
                        min_error = error

            # calculate alpha
            EPS = 1e-10
            clf.alpha = 0.5 * np.log((1.0 - min_error + EPS) / (min_error + EPS))

            # calculate predictions and update weights
            predictions = clf.predict(X)

            w *= np.exp(-clf.alpha * y * predictions)
            # Normalize to one
            w /= np.sum(w)

            # Save classifier
            self.clfs.append(clf)

    def predict(self, X):
        clf_preds = [clf.alpha * clf.predict(X) for clf in self.clfs]
        y_pred = np.sum(clf_preds, axis=0)
        y_pred = np.sign(y_pred)

        return y_pred

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