ContinuousEncoder

ContinuousEncoder

A model type for constructing a continuous encoder, based on MLJTransforms.jl, and implementing the MLJ model interface.

From MLJ, the type can be imported using

ContinuousEncoder = @load ContinuousEncoder pkg=MLJTransforms

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

Use this model to arrange all features (features) of a table to have Continuous element scitype, by applying the following protocol to each feature ftr:

If ftr is already Continuous retain it.
If ftr is Multiclass, one-hot encode it.
If ftr is OrderedFactor, replace it with coerce(ftr, Continuous) (vector of floating point integers), unless ordered_factors=false is specified, in which case one-hot encode it.
If ftr is Count, replace it with coerce(ftr, Continuous).
If ftr has some other element scitype, or was not observed in fitting the encoder, drop it from the table.

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 selectively one-hot-encode categorical features (without dropping features) use OneHotEncoder 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. features 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

drop_last=true: whether to drop the column corresponding to the final class of one-hot encoded features. For example, a three-class feature is spawned into three new features if drop_last=false, but two just features otherwise.
one_hot_ordered_factors=false: whether to one-hot any feature with OrderedFactor element scitype, or to instead coerce it directly to a (single) Continuous feature using the order

Fitted parameters

The fields of fitted_params(mach) are:

features_to_keep: names of features that will not be dropped from the table
one_hot_encoder: the OneHotEncoder model instance for handling the one-hot encoding
one_hot_encoder_fitresult: the fitted parameters of the OneHotEncoder model

Report

features_to_keep: names of input features that will not be dropped from the table
new_features: names of all output features

Example

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],
     comments=["the force", "be", "with you", "too"])

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

encoder = ContinuousEncoder(drop_last=true)
mach = fit!(machine(encoder, X))
W = transform(mach, X)

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

julia> setdiff(schema(X).names, report(mach).features_to_keep) ## dropped features
1-element Vector{Symbol}:
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