MultitargetKNNRegressor

MultitargetKNNRegressor

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

From MLJ, the type can be imported using

MultitargetKNNRegressor = @load MultitargetKNNRegressor pkg=NearestNeighborModels

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

Multi-target K-Nearest Neighbors regressor (MultitargetKNNRegressor) is a variation of KNNRegressor that assumes the target variable is vector-valued with Continuous components. (Target data must be presented as a table, however.)

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 table of responses whose element scitype is Continuous; check column scitypes with schema(y).
w is the observation weights which can either be nothing(default) or an AbstractVector whoose element scitype is Count or Continuous. This is different from weights kernel which is an hyperparameter to the model, 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 Union{Distances.Chebyshev, Distances.Cityblock, Distances.Euclidean, Distances.Minkowski, Distances.WeightedCityblock, Distances.WeightedEuclidean, Distances.WeightedMinkowski} 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.

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

## Create Data
X, y = make_regression(10, 5, n_targets=2)

## load MultitargetKNNRegressor
MultitargetKNNRegressor = @load MultitargetKNNRegressor pkg=NearestNeighborModels

## view possible kernels
NearestNeighborModels.list_kernels()

## MutlitargetKNNRegressor instantiation
model = MultitargetKNNRegressor(weights = NearestNeighborModels.Inverse())

## Wrap model and required data in an MLJ machine and fit.
mach = machine(model, X, y) |> fit! 

## Predict
y_hat = predict(mach, X)