knn_predict.py

def knn_predict(X_train, X_test, y_train, y_test, k, p):
    
    # Counter to help with label voting
    from collections import Counter
    
    # Make predictions on the test data
    # Need output of 1 prediction per test data point
    y_hat_test = []

    for test_point in X_test:
        distances = []

        for train_point in X_train:
            distance = minkowski_distance(test_point, train_point, p=p)
            distances.append(distance)
        
        # Store distances in a dataframe
        df_dists = pd.DataFrame(data=distances, columns=['dist'], 
                                index=y_train.index)
        
        # Sort distances, and only consider the k closest points
        df_nn = df_dists.sort_values(by=['dist'], axis=0)[:k]

        # Create counter object to track the labels of k closest neighbors
        counter = Counter(y_train[df_nn.index])

        # Get most common label of all the nearest neighbors
        prediction = counter.most_common()[0][0]
        
        # Append prediction to output list
        y_hat_test.append(prediction)
        
    return y_hat_test


# Make predictions on test dataset
y_hat_test = knn_predict(X_train, X_test, y_train, y_test, k=5, p=1)

print(y_hat_test)