tune (version 0.1.2)

filter_parameters: Remove some tuning parameter results

Description

For objects produced by the tune_*() functions, there may only be a subset of tuning parameter combinations of interest. For large data sets, it might be helpful to be able to remove some results. This function trims the .metrics column of unwanted results as well as columns .predictions and .extracts (if they were requested).

Usage

filter_parameters(x, ..., parameters = NULL)

Arguments

x

An object of class tune_results that has multiple tuning parameters.

...

Expressions that return a logical value, and are defined in terms of the tuning parameter values. If multiple expressions are included, they are combined with the & operator. Only rows for which all conditions evaluate to TRUE are kept.

parameters

A tibble of tuning parameter values that can be used to filter the predicted values before processing. This tibble should only have columns for tuning parameter identifiers (e.g. "my_param" if tune("my_param") was used). There can be multiple rows and one or more columns. If used, this parameter must be named.

Value

A version of x where the lists columns only retain the parameter combinations in parameters or satisfied by the filtering logic.

Details

Removing some parameter combinations might affect the results of autoplot() for the object.

Examples

# NOT RUN {
library(dplyr)
library(tibble)

# For grid search:
data("example_ames_knn")

## -----------------------------------------------------------------------------
# select all combinations using the 'rank' weighting scheme

ames_grid_search %>%
 collect_metrics()

filter_parameters(ames_grid_search, weight_func == "rank") %>%
 collect_metrics()

rank_only <- tibble::tibble(weight_func = "rank")
filter_parameters(ames_grid_search, parameters = rank_only) %>%
 collect_metrics()

## -----------------------------------------------------------------------------
# Keep only the results from the numerically best combination

ames_iter_search %>%
 collect_metrics()

best_param <- select_best(ames_iter_search, metric = "rmse")
ames_iter_search %>%
 filter_parameters(parameters = best_param) %>%
 collect_metrics()
# }

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