Categorical Tokenizer

Tokenizes categorical features into a dense embedding. For an input of shape (batch, n_features) the output shape is (batch, n_features, d_token).

nn_module

Calls nn_tokenizer_categ() when trained where the parameter cardinalities is inferred. The output shape is (batch, n_features, d_token).

Parameters

• d_token :: integer(1)
  The dimension of the embedding.

• bias :: logical(1)
  Whether to use a bias. Is initialized to TRUE.

• initialization :: character(1)
  The initialization method for the embedding weights. Possible values are "uniform" (default) and "normal".

• cardinalities :: integer()
  The number of categories for each feature. Only needs to be provided when working with lazy_tensor inputs.

Input and Output Channels

One input channel called "input" and one output channel called "output". For an explanation see PipeOpTorch.

State

The state is the value calculated by the public method $shapes_out().

Super classes

mlr3pipelines::PipeOp -> PipeOpTorch -> PipeOpTorchTokenizerCateg

Methods

Public methods

• PipeOpTorchTokenizerCateg$new()

• PipeOpTorchTokenizerCateg$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()
• PipeOpTorch$shapes_out()

---

PipeOpTorchTokenizerCateg$new()

Creates a new instance of this R6 class.

Usage

PipeOpTorchTokenizerCateg$new(id = "nn_tokenizer_categ", param_vals = list())

Arguments

id

(character(1))
Identifier of the resulting object.

param_vals

(list())
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction.

---

PipeOpTorchTokenizerCateg$clone()

The objects of this class are cloneable with this method.

Usage

PipeOpTorchTokenizerCateg$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Construct the PipeOp
pipeop = nn("tokenizer_categ", d_token = 10)
pipeop
#> 
#> ── PipeOp <tokenizer_categ>: not trained ───────────────────────────────────────
#> Values: d_token=10, bias=TRUE, initialization=uniform
#> 
#> ── Input channels: 
#>    name           train predict
#>  <char>          <char>  <char>
#>   input ModelDescriptor    Task
#> 
#> ── Output channels: 
#>    name           train predict
#>  <char>          <char>  <char>
#>  output ModelDescriptor    Task
# The available parameters
pipeop$param_set
#> <ParamSet(4)>
#>                id    class lower upper nlevels        default   value
#>            <char>   <char> <num> <num>   <num>         <list>  <list>
#> 1:        d_token ParamInt     1   Inf     Inf <NoDefault[0]>      10
#> 2:           bias ParamLgl    NA    NA       2 <NoDefault[0]>    TRUE
#> 3: initialization ParamFct    NA    NA       2 <NoDefault[0]> uniform
#> 4:  cardinalities ParamUty    NA    NA     Inf <NoDefault[0]>  [NULL]