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FeatureSelector

FeatureSelector

A model type for constructing a feature selector, based on unknown.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

FeatureSelector = @load FeatureSelector pkg=unknown

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

Use this model to select features (columns) of a table, usually as part of a model Pipeline.

Training data

In MLJ or MLJBase, bind an instance model to data with

mach = machine(model, X)

where

  • X: any table of input features, where "table" is in the sense of Tables.jl

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

Hyper-parameters

  • features: one of the following, with the behavior indicated:

    • [] (empty, the default): filter out all features (columns) which were not encountered in training
    • non-empty vector of feature names (symbols): keep only the specified features (ignore=false) or keep only unspecified features (ignore=true)
    • function or other callable: keep a feature if the callable returns true on its name. For example, specifying FeatureSelector(features = name -> name in [:x1, :x3], ignore = true) has the same effect as FeatureSelector(features = [:x1, :x3], ignore = true), namely to select all features, with the exception of :x1 and :x3.

  • ignore: whether to ignore or keep specified features, as explained above

Operations

  • transform(mach, Xnew): select features from the table Xnew as specified by the model, taking features seen during training into account, if relevant

Fitted parameters

The fields of fitted_params(mach) are:

  • features_to_keep: the features that will be selected

Example

using MLJ

X = (ordinal1 = [1, 2, 3],
     ordinal2 = coerce(["x", "y", "x"], OrderedFactor),
     ordinal3 = [10.0, 20.0, 30.0],
     ordinal4 = [-20.0, -30.0, -40.0],
     nominal = coerce(["Your father", "he", "is"], Multiclass));

selector = FeatureSelector(features=[:ordinal3, ], ignore=true);

julia> transform(fit!(machine(selector, X)), X)
(ordinal1 = [1, 2, 3],
 ordinal2 = CategoricalValue{Symbol,UInt32}["x", "y", "x"],
 ordinal4 = [-20.0, -30.0, -40.0],
 nominal = CategoricalValue{String,UInt32}["Your father", "he", "is"],)