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

In modeltime: The Tidymodels Extension for Time Series Modeling

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)

# 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 Oct. 23, 2024, 1:07 a.m.