KNNClassifier

KNNClassifier

A model type for constructing a K-nearest neighbor classifier, based on NearestNeighborModels.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

KNNClassifier = @load KNNClassifier pkg=NearestNeighborModels

Do model = KNNClassifier() to construct an instance with default hyper-parameters. Provide keyword arguments to override hyper-parameter defaults, as in KNNClassifier(K=...).

KNNClassifier implements K-Nearest Neighbors classifier which is non-parametric algorithm that predicts a discrete class distribution associated with a new point by taking a vote over the classes of the k-nearest points. Each neighbor vote is assigned a weight based on proximity of the neighbor point to the test point according to a specified distance metric.

For more information about the weighting kernels, see the paper by Geler et.al Comparison of different weighting schemes for the kNN classifier on time-series data.

Training data

In MLJ or MLJBase, bind an instance model to data with

mach = machine(model, X, y)

mach = machine(model, X, y, w)

Here:

X is any table of input features (eg, a DataFrame) whose columns are of scitype Continuous; check column scitypes with schema(X).
y is the target, which can be any AbstractVector whose element scitype is <:Finite (<:Multiclass or <:OrderedFactor will do); check the scitype with scitype(y)
w is the observation weights which can either be nothing (default) or an AbstractVector whose element scitype is Count or Continuous. This is different from weights kernel which is a model hyperparameter, see below.

Train the machine using fit!(mach, rows=...).

Hyper-parameters

K::Int=5 : number of neighbors
algorithm::Symbol = :kdtree : one of (:kdtree, :brutetree, :balltree)
metric::Metric = Euclidean() : any Metric from Distances.jl for the distance between points. For algorithm = :kdtree only metrics which are instances of Distances.UnionMinkowskiMetric are supported.
leafsize::Int = algorithm == 10 : determines the number of points at which to stop splitting the tree. This option is ignored and always taken as 0 for algorithm = :brutetree, since brutetree isn't actually a tree.
reorder::Bool = true : if true then points which are close in distance are placed close in memory. In this case, a copy of the original data will be made so that the original data is left unmodified. Setting this to true can significantly improve performance of the specified algorithm (except :brutetree). This option is ignored and always taken as false for algorithm = :brutetree.
weights::KNNKernel=Uniform() : kernel used in assigning weights to the k-nearest neighbors for each observation. An instance of one of the types in list_kernels(). User-defined weighting functions can be passed by wrapping the function in a UserDefinedKernel kernel (do ?NearestNeighborModels.UserDefinedKernel for more info). If observation weights w are passed during machine construction then the weight assigned to each neighbor vote is the product of the kernel generated weight for that neighbor and the corresponding observation weight.

Operations

predict(mach, Xnew): Return predictions of the target given features Xnew, which should have same scitype as X above. Predictions are probabilistic but uncalibrated.
predict_mode(mach, Xnew): Return the modes of the probabilistic predictions returned above.

Fitted parameters

The fields of fitted_params(mach) are:

tree: An instance of either KDTree, BruteTree or BallTree depending on the value of the algorithm hyperparameter (See hyper-parameters section above). These are data structures that stores the training data with the view of making quicker nearest neighbor searches on test data points.

Examples

using MLJ
KNNClassifier = @load KNNClassifier pkg=NearestNeighborModels
X, y = @load_crabs; ## a table and a vector from the crabs dataset
## view possible kernels
NearestNeighborModels.list_kernels()
## KNNClassifier instantiation
model = KNNClassifier(weights = NearestNeighborModels.Inverse())
mach = machine(model, X, y) |> fit! ## wrap model and required data in an MLJ machine and fit
y_hat = predict(mach, X)
labels = predict_mode(mach, X)