automl-tools: Tools for working with H2O AutoML results

In agua: 'tidymodels' Integration with 'h2o'

Tools for working with H2O AutoML results

Description

Functions that returns a tibble describing model performances.

• rank_results() ranks average cross validation performances of candidate models on each metric.

• collect_metrics() computes average statistics of performance metrics (summarized) for each model, or raw value in each resample (unsummarized).

• tidy() computes average performance for each model.

• member_weights() computes member importance for stacked ensemble models, i.e., the relative importance of base models in the meta-learner. This is typically the coefficient magnitude in the second-level GLM model.

extract_fit_engine() extracts single candidate model from auto_ml() results. When id is null, it returns the leader model.

refit() re-fits an existing AutoML model to add more candidates. The model to be re-fitted needs to have engine argument save_data = TRUE, and keep_cross_validation_predictions = TRUE if stacked ensembles is needed for later models.

Usage

## S3 method for class 'workflow'
rank_results(x, ...)

## S3 method for class ''_H2OAutoML''
rank_results(x, ...)

## S3 method for class 'H2OAutoML'
rank_results(x, n = NULL, id = NULL, ...)

## S3 method for class 'workflow'
collect_metrics(x, ...)

## S3 method for class ''_H2OAutoML''
collect_metrics(x, ...)

## S3 method for class 'H2OAutoML'
collect_metrics(x, summarize = TRUE, n = NULL, id = NULL, ...)

## S3 method for class ''_H2OAutoML''
tidy(x, n = NULL, id = NULL, keep_model = TRUE, ...)

get_leaderboard(x, n = NULL, id = NULL)

member_weights(x, ...)

## S3 method for class ''_H2OAutoML''
extract_fit_parsnip(x, id = NULL, ...)

## S3 method for class ''_H2OAutoML''
extract_fit_engine(x, id = NULL, ...)

## S3 method for class 'workflow'
refit(object, ...)

## S3 method for class ''_H2OAutoML''
refit(object, verbosity = NULL, ...)

Arguments

...	Not used.
n	An integer for the number of top models to extract from AutoML results, default to all.
id	A character vector of model ids to retrieve.
summarize	A logical; should metrics be summarized over resamples (TRUE) or return the values for each individual resample.
keep_model	A logical value for if the actual model object should be retrieved from the server. Defaults to TRUE.
object, x	A fitted auto_ml() model or workflow.
verbosity	Verbosity of the backend messages printed during training; Must be one of NULL (live log disabled), "debug", "info", "warn", "error". Defaults to NULL.

Details

H2O associates with each model in AutoML an unique id. This can be used for model extraction and prediction, i.e., extract_fit_engine(x, id = id) returns the model and predict(x, id = id) will predict for that model. extract_fit_parsnip(x, id = id) wraps the h2o model with parsnip parsnip model object is discouraged.

The algorithm column corresponds to the model family H2O use for a particular model, including xgboost ("XGBOOST"), gradient boosting ("GBM"), random forest and variants ("DRF", "XRT"), generalized linear model ("GLM"), and neural network ("deeplearning"). See the details section in h2o::h2o.automl() for more information.

Value

A tibble::tibble().

Examples

if (h2o_running()) {
 auto_fit <- auto_ml() %>%
   set_engine("h2o", max_runtime_secs = 5) %>%
   set_mode("regression") %>%
   fit(mpg ~ ., data = mtcars)

   rank_results(auto_fit, n = 5)
   collect_metrics(auto_fit, summarize = FALSE)
   tidy(auto_fit)
   member_weights(auto_fit)
}

agua documentation built on June 7, 2023, 5:07 p.m.