Describe a model the framework can fit.

Describe a model the framework can fit.

Usage

model_spec(
  name,
  input,
  fit,
  predict,
  default_grid = NULL,
  count_params = NULL,
  description = "",
  fit_args = NULL
)

Arguments

name

Unique identifier, e.g. "rf", "mlp", "cnn".

input

"table" or "patches" – which view of the fold cache the model consumes.

fit

function(x, y, cfg, ...) returning a fitted object.

predict

function(object, x, ...) returning a numeric vector.

default_grid

function(tune_length, seed, x, y) returning a tibble with a config_id column, or NULL if the model has nothing to tune.

x AND y ARE THE REAL TRAINING DATA OF THE FIRST FOLD, and they are in the signature because some grid generators need them. mtry is a fraction of ncol(x); glmnet's lambda path is computed from the VALUES. A grid built against a synthetic matrix of the right width is correct for the first kind and quietly wrong for the second, so the framework hands over the real thing and a generator takes what it needs.

A generator that needs neither still has to accept them – an argument it ignores costs nothing, and a signature that varies per model is a signature the runner cannot call.

A PATCH model's generator may also take windows, the patch sizes the loaded store holds, and n_train, the training points of the smallest fold. dsm_train() passes each one the generator declares, so a default grid never asks for a window the store does not have – the CNN's grid used to draw 3/9/15, the SOC example's, from any store – or for a batch no fold can fill.

count_params

function(object) returning the number of free parameters, used as the complexity axis in one_se(). NULL means the model cannot report it and one_se() falls back to its other rule.

description

One line, printed by list_models().

fit_args

The arguments a caller may hand fit() through dsm_train()'s .... NULL derives them from fit()'s formals, less the ones the framework supplies itself (x, y, cfg, x_val, y_val, device, n_cores, points_valid, transform, model_name) and .... dsm_train() refuses any other: a misspelt option for a table model used to vanish into fit()'s ... and train as if it had not been given.

Value

A model_spec.

Examples

mean_only <- model_spec(
  "mean_only", input = "table",
  fit = function(x, y, cfg, ...) list(mu = mean(y)),
  predict = function(object, x, ...) rep(object$mu, nrow(x)),
  description = "the training mean: a floor every model must beat")
mean_only
#> <model_spec> mean_only
#>   input       : table
#>   tunable     : no
#>   reports size: no
#>   the training mean: a floor every model must beat