create_model_grid: Helper to make 'parsnip' model specs from a 'dials' parameter...

View source: R/modeltime-fit-workflowset.R

create_model_gridR Documentation

Helper to make parsnip model specs from a dials parameter grid

Description

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(...).

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.

Value

Tibble with a new colum named .models

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)
library(modeltime)

# 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
        )
    )


modeltime documentation built on June 8, 2022, 1:07 a.m.