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EvoSplineRegressor

EvoSplineRegressor(; kwargs...)

A model type for constructing a EvoSplineRegressor, based on EvoLinear.jl, and implementing both an internal API and the MLJ model interface.

Keyword arguments

  • loss=:mse: loss function to be minimised. Can be one of:

    • :mse
    • :logistic
    • :poisson
    • :gamma
    • :tweedie

  • nrounds=10: maximum number of training rounds.

  • eta=1: Learning rate. Typically in the range [1e-2, 1].

  • L1=0: Regularization penalty applied by shrinking to 0 weight update if update is < L1. No penalty if update > L1. Results in sparse feature selection. Typically in the [0, 1] range on normalized features.

  • L2=0: Regularization penalty applied to the squared of the weight update value. Restricts large parameter values. Typically in the [0, 1] range on normalized features.

  • rng=123: random seed. Not used at the moment.

  • updater=:all: training method. Only :all is supported at the moment. Gradients for each feature are computed simultaneously, then bias is updated based on all features update.

  • device=:cpu: Only :cpu is supported at the moment.

Internal API

Do config = EvoSplineRegressor() to construct an hyper-parameter struct with default hyper-parameters. Provide keyword arguments as listed above to override defaults, for example:

EvoSplineRegressor(loss=:logistic, L1=1e-3, L2=1e-2, nrounds=100)

Training model

A model is built using fit:

config = EvoSplineRegressor()
m = fit(config; x, y, w)

Inference

Fitted results is an EvoLinearModel which acts as a prediction function when passed a features matrix as argument. 

preds = m(x)

MLJ Interface

From MLJ, the type can be imported using:

EvoSplineRegressor = @load EvoSplineRegressor pkg=EvoLinear

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

Training model

In MLJ or MLJBase, bind an instance model to data with mach = machine(model, X, y) where:

  • X: any table of input features (eg, a DataFrame) whose columns each have one of the following element scitypes: Continuous, Count, or <:OrderedFactor; check column scitypes with schema(X)
  • y: is the target, which can be any AbstractVector whose element scitype is <:Continuous; check the scitype with scitype(y)

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

Operations

  • predict(mach, Xnew): return predictions of the target given

features Xnew having the same scitype as X above. Predictions are deterministic.

Fitted parameters

The fields of fitted_params(mach) are:

  • :fitresult: the SplineModel object returned by EvoSplineRegressor fitting algorithm.

Report

The fields of report(mach) are:

  • :coef: Vector of coefficients (βs) associated to each of the features.
  • :bias: Value of the bias.
  • :names: Names of each of the features.