Package 'tabby'

Model predictions across many sub-models

Description

Model predictions across many sub-models

Usage

## S3 method for class ''_brulee_auto_int''
multi_predict(object, new_data, type = NULL, epochs = NULL, ...)

## S3 method for class ''_brulee_resnet''
multi_predict(object, new_data, type = NULL, epochs = NULL, ...)

## S3 method for class ''_brulee_rln''
multi_predict(object, new_data, type = NULL, epochs = NULL, ...)

Arguments

object	A model fit.
new_data	A rectangular data object, such as a data frame.
type	A single character value or NULL. Possible values are: regression: "numeric" classification: "class", "prob" censored regression: "survival", "time", "hazard", "linear_pred" quantile regression: "quantile" interval estimates: "conf_int", "pred_int" other: "raw" When NULL, predict() will choose an appropriate value based on the model's mode.
epochs	An integer vector for the number of training epochs.
...	Optional arguments to pass to predict.model_fit(type = "raw") such as type.

Value

A tibble with the same number of rows as new_data. Its .pred column is a list of tibbles, each containing the predictions for the corresponding row of new_data across the requested values of epochs.

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Working with Tuning Parameters that are Lists

Description

neural_net_grid_space_filling() takes the number of layers in a neural network as an input, creates a space-filling design with the multidimensional layer parameter(s), and collapses the grid into list parameters that the model function needs. expand_list_parameters() is a convenience function that converts the list columns to a wide structure for printing, visualization, etc.

Usage

neural_net_grid_space_filling(
  wflow,
  num_layers = 3,
  size = 10,
  param_info = NULL,
  range = c(2, 50),
  collapse = TRUE
)

expand_list_parameters(x, pattern = "*")

Arguments

wflow	A model specification or workflows::workflow() object.
num_layers	A single integer for the number of layers containing hidden units.
size	The minimum grid size. It may be adjusted upwards to find a feasible design.
param_info	A dials::parameters() object or NULL. If none is given, a parameters set is derived from other arguments. Passing this argument can be useful when parameter ranges need to be customized.
range	A range for the number of hidden units in each layer.
collapse	A single logical for whether to collapse the parameters into list columns.
x	A data frame of grid points, some of which are list columns.
pattern	A regular expression pattern to select which list columns should be expanded to a wide format.

Value

neural_net_grid_space_filling() returns a tibble with grid points, some of which are list-columns containing integer vectors. expand_list_parameters() returns a tibble where the selected list columns are replaced by one integer column per layer.

Examples

rn_spec <-
  tabular_resnet(hidden_units = tune(),
             bottleneck_units = tune(),
             penalty = tune())

rn_grid <- neural_net_grid_space_filling(rn_spec)
rn_grid

rn_grid |> expand_list_parameters()

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