OneHotEncoder

OneHotEncoder

A model type for constructing a one-hot encoder, based on MLJModels.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

OneHotEncoder = @load OneHotEncoder pkg=MLJModels

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

Use this model to one-hot encode the Multiclass and OrderedFactor features (columns) of some table, leaving other columns unchanged.

New data to be transformed may lack features present in the fit data, but no new features can be present.

Warning: This transformer assumes that levels(col) for any Multiclass or OrderedFactor column, col, is the same for training data and new data to be transformed.

To ensure all features are transformed into Continuous features, or dropped, use ContinuousEncoder instead.

Training data

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

mach = machine(model, X)

where

X: any Tables.jl compatible table. Columns can be of mixed type but only those with element scitype Multiclass or OrderedFactor can be encoded. Check column scitypes with schema(X).

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

Hyper-parameters

features: a vector of symbols (column names). If empty (default) then all Multiclass and OrderedFactor features are encoded. Otherwise, encoding is further restricted to the specified features (ignore=false) or the unspecified features (ignore=true). This default behavior can be modified by the ordered_factor flag.
ordered_factor=false: when true, OrderedFactor features are universally excluded
drop_last=true: whether to drop the column corresponding to the final class of encoded features. For example, a three-class feature is spawned into three new features if drop_last=false, but just two features otherwise.

Fitted parameters

The fields of fitted_params(mach) are:

all_features: names of all features encountered in training
fitted_levels_given_feature: dictionary of the levels associated with each feature encoded, keyed on the feature name
ref_name_pairs_given_feature: dictionary of pairs r => ftr (such as 0x00000001 => :grad__A) where r is a CategoricalArrays.jl reference integer representing a level, and ftr the corresponding new feature name; the dictionary is keyed on the names of features that are encoded

Report

The fields of report(mach) are:

features_to_be_encoded: names of input features to be encoded
new_features: names of all output features

Example

using MLJ

X = (name=categorical(["Danesh", "Lee", "Mary", "John"]),
     grade=categorical(["A", "B", "A", "C"], ordered=true),
     height=[1.85, 1.67, 1.5, 1.67],
     n_devices=[3, 2, 4, 3])

julia> schema(X)
┌───────────┬──────────────────┐
│ names     │ scitypes         │
├───────────┼──────────────────┤
│ name      │ Multiclass{4}    │
│ grade     │ OrderedFactor{3} │
│ height    │ Continuous       │
│ n_devices │ Count            │
└───────────┴──────────────────┘

hot = OneHotEncoder(drop_last=true)
mach = fit!(machine(hot, X))
W = transform(mach, X)

julia> schema(W)
┌──────────────┬────────────┐
│ names        │ scitypes   │
├──────────────┼────────────┤
│ name__Danesh │ Continuous │
│ name__John   │ Continuous │
│ name__Lee    │ Continuous │
│ grade__A     │ Continuous │
│ grade__B     │ Continuous │
│ height       │ Continuous │
│ n_devices    │ Count      │
└──────────────┴────────────┘