tune_bayes() uses models to generate new candidate tuning parameter
combinations based on previous results.
tune_bayes(object, ...) ## S3 method for class 'model_spec' tune_bayes( object, preprocessor, resamples, ..., iter = 10, param_info = NULL, metrics = NULL, objective = exp_improve(), initial = 5, control = control_bayes() ) ## S3 method for class 'workflow' tune_bayes( object, resamples, ..., iter = 10, param_info = NULL, metrics = NULL, objective = exp_improve(), initial = 5, control = control_bayes() )
Options to pass to
A traditional model formula or a recipe created using
The maximum number of search iterations.
A character string for what metric should be optimized or an acquisition function object.
An initial set of results in a tidy format (as would result
A control object created by
The optimization starts with a set of initial results, such as those
tune_grid(). If none exist, the function will create several
combinations and obtain their performance estimates.
Using one of the performance estimates as the model outcome, a Gaussian process (GP) model is created where the previous tuning parameter combinations are used as the predictors.
A large grid of potential hyperparameter combinations is predicted using
the model and scored using an acquisition function. These functions
usually combine the predicted mean and variance of the GP to decide the best
parameter combination to try next. For more information, see the
exp_improve() and the corresponding package vignette.
The best combination is evaluated using resampling and the process continues.
A tibble of results that mirror those generated by
However, these results contain an
.iter column and replicate the
object multiple times over iterations (at limited additional memory costs).
foreach package is used here. To execute the resampling iterations in
parallel, register a parallel backend function. See the documentation for
foreach::foreach() for examples.
For the most part, warnings generated during training are shown as they occur
and are associated with a specific resample when
control_bayes(verbose = TRUE). They are (usually) not aggregated until the
end of processing.
For Bayesian optimization, parallel processing is used to estimate the resampled performance values once a new candidate set of values are estimated.
The results of
tune_grid(), or a previous run of
tune_bayes() can be used
initial can also be a positive integer. In this
case, a space-filling design will be used to populate a preliminary set of
results. For good results, the number of initial values should be more than
the number of parameters being optimized.
In some cases, the tuning parameter values depend on the dimensions of the
data (they are said to contain unknown values). For
mtry in random forest models depends on the number of predictors.
In such cases, the unknowns in the tuning parameter object must be determined
beforehand and passed to the function via the
dials::finalize() can be used to derive the data-dependent parameters.
Otherwise, a parameter set can be created via
dials::parameters(), and the
update() function can be used to specify the ranges or values.
To use your own performance metrics, the
can be used to pick what should be measured for each model. If multiple
metrics are desired, they can be bundled. For example, to estimate the area
under the ROC curve as well as the sensitivity and specificity (under the
typical probability cutoff of 0.50), the
metrics argument could be given:
metrics = metric_set(roc_auc, sens, spec)
Each metric is calculated for each candidate model.
If no metric set is provided, one is created:
For regression models, the root mean squared error and coefficient of determination are computed.
For classification, the area under the ROC curve and overall accuracy are computed.
Note that the metrics also determine what type of predictions are estimated during tuning. For example, in a classification problem, if metrics are used that are all associated with hard class predictions, the classification probabilities are not created.
The out-of-sample estimates of these metrics are contained in a list column
.metrics. This tibble contains a row for each metric and columns
for the value, the estimator type, and so on.
collect_metrics() can be used for these objects to collapse the results
over the resampled (to obtain the final resampling estimates per tuning
control_bayes(save_pred = TRUE), the output tibble contains a list
.predictions that has the out-of-sample predictions for each
parameter combination in the grid and each fold (which can be very large).
The elements of the tibble are tibbles with columns for the tuning
parameters, the row number from the original data object (
outcome data (with the same name(s) of the original data), and any columns
created by the predictions. For example, for simple regression problems, this
function generates a column called
.pred and so on. As noted above, the
prediction columns that are returned are determined by the type of metric(s)
This list column can be
tidyr::unnest() or using the
extract control option will result in an additional function to be
.extracts. This is a list column that has tibbles
containing the results of the user's function for each tuning parameter
combination. This can enable returning each model and/or recipe object that
is created during resampling. Note that this could result in a large return
object, depending on what is returned.
The control function contains an option (
extract) that can be used to
retain any model or recipe that was created within the resamples. This
argument should be a function with a single argument. The value of the
argument that is given to the function in each resample is a workflow
workflows::workflow() for more information). Several
helper functions can be used to easily pull out the preprocessing
and/or model information from the workflow, such as
As an example, if there is interest in getting each parsnip model fit back, one could use:
extract = function (x) extract_fit_parsnip(x)
Note that the function given to the
extract argument is evaluated on
every model that is fit (as opposed to every model that is evaluated).
As noted above, in some cases, model predictions can be derived for
sub-models so that, in these cases, not every row in the tuning parameter
grid has a separate R object associated with it.
library(recipes) library(rsample) library(parsnip) # define resamples and minimal recipe on mtcars set.seed(6735) folds <- vfold_cv(mtcars, v = 5) car_rec <- recipe(mpg ~ ., data = mtcars) %>% step_normalize(all_predictors()) # define an svm with parameters to tune svm_mod <- svm_rbf(cost = tune(), rbf_sigma = tune()) %>% set_engine("kernlab") %>% set_mode("regression") # use a space-filling design with 6 points set.seed(3254) svm_grid <- tune_grid(svm_mod, car_rec, folds, grid = 6) show_best(svm_grid, metric = "rmse") # use bayesian optimization to evaluate at 6 more points set.seed(8241) svm_bayes <- tune_bayes(svm_mod, car_rec, folds, initial = svm_grid, iter = 6) # note that bayesian optimization evaluated parameterizations # similar to those that previously decreased rmse in svm_grid show_best(svm_bayes, metric = "rmse") # specifying `initial` as a numeric rather than previous tuning results # will result in `tune_bayes` initially evaluating an space-filling # grid using `tune_grid` with `grid = initial` set.seed(0239) svm_init <- tune_bayes(svm_mod, car_rec, folds, initial = 6, iter = 6) show_best(svm_init, metric = "rmse")
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