knn.py

'''
Implementation of a KNN algorithm.
Author - Sanket Patil
Requirements:
    - Numpy
Python:
    - 3.8.10
'''

import numpy as np
from sklearn.metrics import confusion_matrix, accuracy_score
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split


def euclidean_distance(x1, x2):
    return np.sqrt(np.sum((x1 - x2) ** 2))


class KNN:
    def __init__(self, k=3):
        self.k = k

    def fit(self, X, y):
        self.X_train = X
        self.y_train = y

    def predict(self, X):
        y_pred = [self._predict(x) for x in X]
        return np.array(y_pred)

    # Return the most common class label
    def _predict(self, x):
        distances = [euclidean_distance(x, x_train)
                     for x_train in self.X_train]
        k_indices = np.argsort(distances)[:self.k]
        k_nearest_labels = [self.y_train[i] for i in k_indices]
        most_common = np.argmax(np.bincount(k_nearest_labels))
        return most_common


if __name__ == '__main__':
    dataset = load_breast_cancer()
    X = dataset.data
    y = dataset.target

    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=0.2, random_state=0)

    model = KNN(k=3)
    model.fit(X_train, y_train)

    y_pred = model.predict(X_test)
    cm = confusion_matrix(y_test, y_pred)
    print(cm)
    print(accuracy_score(y_test, y_pred))