README.md

K-Nearest Neighbours

The aim the exercise is to implement the KNN algorithm and evaluated using cross-validation on the Iris data set. The implementation will also be evaluated by comparing it with the scikit-learn implementation.

Part 1.

The first step is to implement the kNN algorithm. The kNN algorithm uses a distance calculation of some sort to determine which points are neighbours.

You can use tools.plot_points() to see a scatter plot of the first two dimensions of all points in the Iris set:

d, t, classes = load_iris()
plot_points(d, t)

This results in the following image

Part 1.1

Implement a distance calculation so that we get the distance between a specific point to all the other points in the set. Use the Euclidean distance:

$$ d_n=\sqrt{\sum_{j=1}^D(x_j-x^{train~n}_j)^2} $$

wher $x^{train~n}$ is the $n$-th vector in the training set. The outcome of this step is a vector containing all $d_n$.

Create a function euclidian_distance(x, y) that calculates the euclidian distance between x and y.

Example inputs and outputs:

First seperate one point from the iris dataset. This will also be used in the following examples

d, t, classes = load_iris()
x, points = d[0,:], d[1:, :]
x_target, point_targets = t[0], t[1:]

1. euclidian_distance(x, points[0]) -> 0.5385164807134502
2. euclidian_distance(x, points[50])-> 3.6166282640050254

Part 1.2

The kNN algorithm calculates the euclidian distance between a point x and all other points. Create a new function euclidian_distances(x, points) that uses your euclidian_distance to perform this calculation.

Example inputs and outputs: euclidian_distances(x, points) -> [0.53851648 0.50990195 0.64807407 0.14142136 0.6164414 0.51961524 ..., 4.14004831]

Part 1.3

The next step in the algorithm is to find the $k$ nearest point to x. Using your euclidian_distances, create a function k_nearest(x, points, k) that returns the indices of the $k$ nearest points.

Example inputs and outputs:

1. k_nearest(x, points, 1) -> [16]
2. k_nearest(x, points, 3) -> [16 3 38]

Hint: Use np.argsort

Part 1.4

The final step in the kNN algorithm is the vote. After finding $k$-nearest points, we vote for the most common class label among the nearest points.

Create a function vote(targets, classes) that returns the most common class label.

Example inputs and outputs:

1. vote(np.array([0,0,1,2]), np.array([0,1,2])) -> 0
2. vote(np.array([1,1,1,1]), np.array([0,1])) -> 1

Part 1.5

You have now made all the building blocks for the kNN algorithm and it's time to put them together.

Create a function knn(x, points, point_targets, classes, k) where:

1. x is a test point
2. points are all other points in the dataset
3. point_targets are the class labels for all points
4. classes are all possible class labels
5. k is the number of required nearest neighbours.

The function should return a single value, the class label that your algorithm guesses is the class label of the test point.

Example input and output:

1. knn(x, points, point_targets, classes, 1) -> 0
2. knn(x, points, point_targets, classes, 5) -> 0
3. knn(x, points, point_targets, classes, 150) -> 1

Is your algorithm correct for the value of k you choose (i.e. does it correspond to x_target)?

Part 2

We will now evaluate the performance of the kNN algorithm on the Iris dataset by calculating the accuracy of class predictions.

Part 2.1

Create a function knn_predict(points, point_targets, classes, k) which uses your knn function to predict a class label for each point in points. help.remove_one(points, i) might be helpful here. This function should achieve the same as your knn function but on multiple points and should therefore call your knn function.

We should be evaluating the algorithm on a seperated test set and we can use tools.split_train_test for that:

Example inputs and outputs:

d, t, classes = load_iris()
(d_train, t_train), (d_test, t_test) = split_train_test(d, t, train_ratio=0.8)

1. predictions = knn_predict(d_test, t_test, classes, 10) -> [2 2 2 2 0 1 0 1 1 0 1 2 1 2 2 0 1 0 2 1 1 1 1 1 2 0 1 1 1]
2. predictions = knn_predict(d_test, t_test, classes, 5) -> [2 2 2 2 0 1 0 1 1 0 1 2 1 2 2 0 1 0 2 2 1 1 2 1 2 0 1 1 2]

Part 2.2

Create a function knn_accuracy(targets, point_targets, classes, k) that calculates the accuracy of your predictions. You should of course use your knn_predict for this.

Example inputs and outputs:

1. knn_accuracy(d_test, t_test, classes, 10) -> 0.8275862068965517
2. knn_accuracy(d_test, t_test, classes, 5) -> 0.9310344827586207

Part 2.3

Create a function knn_confusion_matrix(points, point_targets, classes, k) that should build a confusion matrix similarily to how we made the confusion matrix in assignment 1.

Example inputs and outputs:

1. knn_confusion_matrix(d_test, t_test, classes, 10)

[[ 6.  0.  0.]
 [ 0. 10.  4.]
 [ 0.  1.  8.]]

2. knn_confusion_matrix(d_test, t_test, classes, 20)

[[ 0.  0.  0.]
 [ 6.  8.  1.]
 [ 0.  3. 11.]]

Part 2.4

Based on the examples above, it seems like the choice of k is an important influence on the accuracy of the classifier. We therefore want to pick a good value of $k$, but how?

The way to do this is to evaluate multiple values of $k$ on the training set and pick the one that works best. Create a function best_k(points, point_targets, classes) that returns the value of $k$ that corresponds to the highest accuracy on the point set. You should test values of $K$ in the interval $[1, N-1]$ where $N$ is the number of points in the point set.

Example inputs and outputs.

best_k(d_train, t_train, classes) -> 9

Part 2.5

Create a function knn_plot_points(points, point_targets, classes, k). The function should look very similar to tools.plot_points but differs in that:

1. This function should get a prediction for each point
2. Set the edgecolor argument to green if the prediction is correct, otherwise red.

Example input and output: Calling knn_plot_points(d, t, classes, 3) results in the following image

Submit yours as 2_5_1.png.

Bonus Part

Note: This is a pre-formulated bonus question. In future assignments you will be asked to demonstrate your capability to add relevant insight to your assignment. The work suggested below is examplary of the type of insight you might add in future assignments. To get full marks on this assignment you must complete this bonus part.

The Weighted Nearest Neighbour algorithm (wkNN) expands on the kNN algorithm by weighing votes.

The intuition used in wkNN is that neighbours that are really close should have a bigger say in the classfication than neighbours far away.

In wkNN, the prediction $\hat{y}_i$ for a given point $x_i$ is

$$ \hat{y}i = \frac{c_1y_1 + c_2y_2 + ... + c_ky_k}{\sum{j=1}^{k} c_j} $$

where $y_j$ is the vote made by neighour $i$ and $c_j$ is the corresponding weight. But how should the weights be formalized? The weight should be large when the neighbour is close to $x_i$ and small when far away. The simplest way to achieve this is to say that

$$ c_j = \frac{1}{\text{distance}(x_i, x_j)} $$

B.1

Create the function weighted_vote(targets, distances, classes) that returns the class label with the highest vote mass according to the scheme above.

B.2

Create the function wknn(x, points, point_targets, classes, k) that achieves the same as your knn function but uses weighted_vote instead

B.3

Create the function wknn_predict(points, point_targets, classes, k) that achieves the same as knn_predict but uses wknn instead of knn.

B.4

Finally we will compare the accuracy of kNN and wkNN as a function of $k$. Create the function compare_knns(points, targets, classes) that calculates the accuracies of knn and wknn for $k\in(1; n)$ where $n$ is the number of points. Then plot both types of accuracies as a function of $k$ in the same plot.

Your plot should look something like the following. Submit your plot as b_4_1.png.

B.Theoretical

What explains this difference in accuracy between kNN and wkNN when $k$ increases?