Public API

fit_evotree

MLJModelInterface.fit — Function

fit(
    params::EvoTypes, 
    dtrain;
    target_name,
    feature_names=nothing,
    weight_name=nothing,
    offset_name=nothing,
    deval=nothing,
    print_every_n=9999,
    verbosity=1

)

Main training function. Performs model fitting given configuration params, dtrain, target_name and other optional kwargs.

Arguments

params::EvoTypes: configuration info providing hyper-paramters. EvoTypes can be one of:
dtrain: A Tables compatible training data (named tuples, DataFrame...) containing features and target variables.

Keyword arguments

target_name: name of the target variable.
feature_names = nothing: the names dtrain variables to use as features. If not provided, it deafults to all variables that aren't one of target, weight or offset`.
weight_name = nothing: name of the variable containing weights. If nothing, common weights on one will be used.
offset_name = nothing: name of the offset variable.
deval: A Tables compatible evaluation data containing features and target variables.
print_every_n: sets at which frequency logging info should be printed.
verbosity: set to 1 to print logging info during training.

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fit(
    params::EvoTypes{L};
    x_train::AbstractMatrix, 
    y_train::AbstractVector, 
    w_train=nothing, 
    offset_train=nothing,
    x_eval=nothing, 
    y_eval=nothing, 
    w_eval=nothing, 
    offset_eval=nothing,
    feature_names=nothing,
    early_stopping_rounds=9999,
    print_every_n=9999,
    verbosity=1)

Main training function. Performs model fitting given configuration params, x_train, y_train and other optional kwargs.

Arguments

params::EvoTypes: configuration info providing hyper-paramters. EvoTypes can be one of:

Keyword arguments

x_train::Matrix: training data of size [#observations, #features].
y_train::Vector: vector of train targets of length #observations.
w_train::Vector: vector of train weights of length #observations. If nothing, a vector of ones is assumed.
offset_train::VecOrMat: offset for the training data. Should match the size of the predictions.
x_eval::Matrix: evaluation data of size [#observations, #features].
y_eval::Vector: vector of evaluation targets of length #observations.
w_eval::Vector: vector of evaluation weights of length #observations. Defaults to nothing (assumes a vector of 1s).
offset_eval::VecOrMat: evaluation data offset. Should match the size of the predictions.
feature_names = nothing: the names of the x_train features. If provided, should be a vector of string with length(feature_names) = size(x_train, 2).
print_every_n: sets at which frequency logging info should be printed.
verbosity: set to 1 to print logging info during training.

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predict

MLJModelInterface.predict — Function

predict(m::EvoTree, data; ntree_limit=length(m.trees), device=:cpu)

Predictions from an EvoTree model - sums the predictions from all trees composing the model. Use ntree_limit=N to only predict with the first N trees.

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importance

EvoTrees.importance — Function

importance(model::EvoTree; feature_names=model.info[:feature_names])

Sorted normalized feature importance based on loss function gain. Feature names associated to the model are stored in model.info[:feature_names] as a string Vector and can be updated at any time. Eg: model.info[:feature_names] = new_feature_names_vec.

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