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Internal API

General

EvoTrees.EvoTree — Type
EvoTree{L,K}

An EvoTree holds the structure of a fitted gradient-boosted tree.

Fields

  • trees::Vector{Tree{L,K}}
  • info::Dict

EvoTree acts as a functor to perform inference on input data: 

pred = (m::EvoTree; ntree_limit=length(m.trees))(x)
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EvoTrees.check_parameter — Function
check_parameter(::Type{<:T}, value, min_value::Real, max_value::Real, label::Symbol) where {T<:Number}

Check model parameter if it's valid

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EvoTrees.check_args — Function
check_args(args::Dict{Symbol,Any})

Check model arguments if they are valid

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check_args(model::EvoTypes{L}) where {L}

Check model arguments if they are valid (eg, after mutation when tuning hyperparams) Note: does not check consistency of model type and loss selected

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Training utils

EvoTrees.init — Function
init(
    params::EvoTypes,
    dtrain,
    device::Type{<:Device}=CPU;
    target_name,
    fnames=nothing,
    w_name=nothing,
    offset_name=nothing
)

Initialise EvoTree

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init(
    params::EvoTypes,
    x_train::AbstractMatrix,
    y_train::AbstractVector,
    device::Type{<:Device}=CPU;
    fnames=nothing,
    w_train=nothing,
    offset_train=nothing
)

Initialise EvoTree

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EvoTrees.grow_evotree! — Function
grow_evotree!(evotree::EvoTree{L,K}, cache, params::EvoTypes{L}, ::Type{<:Device}=CPU) where {L,K}

Given a instantiate

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EvoTrees.update_gains! — Function
update_gains!(
    loss::L,
    node::TrainNode{T},
    js::Vector,
    params::EvoTypes, K, monotone_constraints) where {L,T,S}

Generic fallback

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EvoTrees.predict! — Function
predict!(pred::Matrix, tree::Tree, X)

Generic fallback to add predictions of tree to existing pred matrix.

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EvoTrees.subsample — Function
subsample(out::AbstractVector, mask::AbstractVector, rowsample::AbstractFloat)

Returns a view of selected rows ids.

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EvoTrees.split_set_chunk! — Function
Multi-threaded split_set!
    Take a view into left and right placeholders. Right ids are assigned at the end of the length of the current node set.
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Histogram

EvoTrees.get_edges — Function
get_edges(X::AbstractMatrix{T}; fnames, nbins, rng=Random.TaskLocalRNG()) where {T}
get_edges(df; fnames, nbins, rng=Random.TaskLocalRNG())

Get the histogram breaking points of the feature data.

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EvoTrees.binarize — Function
binarize(X::AbstractMatrix; fnames, edges)
binarize(df; fnames, edges)

Transform feature data into a UInt8 binarized matrix.

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