Helper to make parsnip model specs from a dials parameter grid

Helper to make parsnip model specs from a dials parameter grid

Usage

create_model_grid(grid, f_model_spec, engine_name, ..., engine_params = list())

Arguments

grid

A tibble that forms a grid of parameters to adjust

f_model_spec

A function name (quoted or unquoted) that specifies a parsnip model specification function

engine_name

A name of an engine to use. Gets passed to parsnip::set_engine().

...

Static parameters that get passed to the f_model_spec

engine_params

A list of additional parameters that can be passed to the engine via parsnip::set_engine(...).

Value

Tibble with a new colum named .models

Details

This is a helper function that combines dials grids with parsnip model specifications. The intent is to make it easier to generate workflowset objects for forecast evaluations with modeltime_fit_workflowset().

The process follows:

1. Generate a grid (hyperparemeter combination)

2. Use create_model_grid() to apply the parameter combinations to a parsnip model spec and engine.

The output contains ".model" column that can be used as a list of models inside the workflow_set() function.

See also

• dials::grid_regular(): For making parameter grids.

• workflowsets::workflow_set(): For creating a workflowset from the .models list stored in the ".models" column.

• modeltime_fit_workflowset(): For fitting a workflowset to forecast data.

Examples

library(tidymodels)

# Parameters that get optimized
grid_tbl <- grid_regular(
    learn_rate(),
    levels = 3
)

# Generate model specs
grid_tbl %>%
    create_model_grid(
        f_model_spec = boost_tree,
        engine_name  = "xgboost",
        # Static boost_tree() args
        mode = "regression",
        # Static set_engine() args
        engine_params = list(
            max_depth = 5
        )
    )
#> # A tibble: 3 × 2
#>     learn_rate .models  
#>          <dbl> <list>   
#> 1 0.0000000001 <spec[+]>
#> 2 0.00000316   <spec[+]>
#> 3 0.1          <spec[+]>