Accessor Functions

LearnAPI.algorithm(model)
LearnAPI.algorithm(minimized_model)

Recover the algorithm used to train model or the output of minimize(model).

In other words, if model = fit(algorithm, data...), for some algorithm and data, then

LearnAPI.algorithm(model) == algorithm == LearnAPI.algorithm(minimize(model))

is true.

New implementations

Implementation is compulsory for new algorithm types. The behaviour described above is the only contract. If implemented, you must include algorithm in the tuple returned by the LearnAPI.functions trait.

LearnAPI.extras(model)

Return miscellaneous byproducts of an algorithm's computation, from the object model returned by a call of the form fit(algorithm, data).

For "static" algorithms (those without training data) it may be necessary to first call transform or predict on model.

See also fit.

New implementations

Implementation is discouraged for byproducts already covered by other LearnAPI.jl accessor functions: LearnAPI.algorithm, LearnAPI.coefficients, LearnAPI.intercept, LearnAPI.tree, LearnAPI.trees, LearnAPI.feature_importances, LearnAPI.training_labels, LearnAPI.training_losses, LearnAPI.training_scores and LearnAPI.components.

If implemented, you must include training_labels in the tuple returned by the LearnAPI.functions trait. .

LearnAPI.coefficients(model)

For a linear model, return the learned coefficients. The value returned has the form of an abstract vector of feature_or_class::Symbol => coefficient::Real pairs (e.g [:gender => 0.23, :height => 0.7, :weight => 0.1]) or, in the case of multi-targets, feature::Symbol => coefficients::AbstractVector{<:Real} pairs.

The model reports coefficients if LearnAPI.coefficients in LearnAPI.functions(Learn.algorithm(model)).

See also LearnAPI.intercept.

New implementations

Implementation is optional.

If implemented, you must include coefficients in the tuple returned by the LearnAPI.functions trait. .

LearnAPI.intercept(model)

For a linear model, return the learned intercept. The value returned is Real (single target) or an AbstractVector{<:Real} (multi-target).

The model reports intercept if LearnAPI.intercept in LearnAPI.functions(Learn.algorithm(model)).

See also LearnAPI.coefficients.

New implementations

Implementation is optional.

If implemented, you must include intercept in the tuple returned by the LearnAPI.functions trait. .

LearnAPI.tree(model)

Return a user-friendly tree, in the form of a root object implementing the following interface defined in AbstractTrees.jl:

• subtypes AbstractTrees.AbstractNode{T}
• implements AbstractTrees.children()
• implements AbstractTrees.printnode()

Such a tree can be visualized using the TreeRecipe.jl package, for example.

See also LearnAPI.trees.

New implementations

Implementation is optional.

If implemented, you must include tree in the tuple returned by the LearnAPI.functions trait. .

LearnAPI.trees(model)

For some ensemble model, return a vector of trees. See LearnAPI.tree for the form of such trees.

See also LearnAPI.tree.

New implementations

Implementation is optional.

If implemented, you must include trees in the tuple returned by the LearnAPI.functions trait. .

LearnAPI.feature_importances(model)

Return the algorithm-specific feature importances of a model output by fit(algorithm, ...) for some algorithm. The value returned has the form of an abstract vector of feature::Symbol => importance::Real pairs (e.g [:gender => 0.23, :height => 0.7, :weight => 0.1]).

The algorithm supports feature importances if LearnAPI.feature_importances in LearnAPI.functions(algorithm).

If an algorithm is sometimes unable to report feature importances then LearnAPI.feature_importances will return all importances as 0.0, as in [:gender => 0.0, :height => 0.0, :weight => 0.0].

New implementations

Implementation is optional.

If implemented, you must include feature_importances in the tuple returned by the LearnAPI.functions trait. .

LearnAPI.training_losses(model)

Return the training losses obtained when running model = fit(algorithm, ...) for some algorithm.

See also fit.

New implementations

Implement for iterative algorithms that compute and record training losses as part of training (e.g. neural networks).

If implemented, you must include training_losses in the tuple returned by the LearnAPI.functions trait. .

LearnAPI.training_scores(model)

Return the training scores obtained when running model = fit(algorithm, ...) for some algorithm.

See also fit.

New implementations

Implement for algorithms, such as outlier detection algorithms, which associate a score with each observation during training, where these scores are of interest in later processes (e.g, in defining normalized scores for new data).

If implemented, you must include training_scores in the tuple returned by the LearnAPI.functions trait. .

LearnAPI.training_labels(model)

Return the training labels obtained when running model = fit(algorithm, ...) for some algorithm.

See also fit.

New implementations

If implemented, you must include training_labels in the tuple returned by the LearnAPI.functions trait. .

LearnAPI.components(model)

For a composite model, return the component models (fit outputs). These will be in the form of a vector of named pairs, property_name::Symbol => component_model. Here property_name is the name of some algorithm-valued property (hyper-parameter) of algorithm = LearnAPI.algorithm(model).

A composite model is one for which the corresponding algorithm includes one or more algorithm-valued properties, and for which LearnAPI.is_composite(algorithm) is true.

See also is_composite.

New implementations

Implementent if and only if model is a composite model.

If implemented, you must include components in the tuple returned by the LearnAPI.functions trait. .