last_fit() and workflows::fit() will now give identical results for the same workflow when the underlying model uses random number generation (#300).
Fixed an issue where recipe tuning parameters could be randomly matched to the tuning grid incorrectly (#316).
last_fit() no longer accidentally adjusts the random seed (#264).
Fixed two bugs in the acquisition function calculations.
New parallel_over control argument to adjust the parallel processing method that tune uses.
The .config column that appears in the returned tibble from tuning and fitting resamples has changed slightly. It is now always of the form "Preprocessor<i>_Model<j>".
predict() can now be called on the workflow returned from last_fit() (#294, #295, #296).
tune now supports setting the event_level option from yardstick through the control objects (i.e. control_grid(event_level = "second")) (#240, #249).
tune now supports workflows created with the new workflows::add_variables() preprocessor.
Better control the random number streams in parallel for tune_grid() and fit_resamples() (#11)
Allow ... to pass options from tune_bayes() to GPfit::GP_fit().
Additional checks are done for the initial grid that is given to tune_bayes(). If the initial grid is small relative to the number of model terms, a warning is issued. If the grid is a single point, an error occurs. (#269)
Formatting of some messages created by tune_bayes() now respect the width and wrap lines using the new message_wrap() function.
tune functions (tune_grid(), tune_bayes(), etc) will now error if a model specification or model workflow are given as the first argument (the soft deprecation period is over).
An augment() method was added for objects generated by tune_*(), fit_resamples(), and last_fit().
autoplot.tune_results() now requires objects made by version 0.1.0 or higher of tune.
tune objects no longer keep the rset class that they have from the resamples argument.
autoplot.tune_results() now produces a different plot when the tuning grid is a regular grid (i.e. factorial or nearly factorial in nature). If there are 5+ parameters, the standard plot is produced. Non-regular grids are plotted in the same way (although see next bullet point). See ?autoplot.tune_results for more information.
autoplot.tune_results() now transforms the parameter values for the plot. For example, if the penalty parameter was used for a regularized regression, the points are plotted on the log-10 scale (its default transformation). For non-regular grids, the facet labels show the transformation type (e.g. "penalty (log-10)" or "cost (log-2)"). For regular grid, the x-axis is scaled using scale_x_continuous().
Finally, autoplot.tune_results() now shows the parameter labels in a plot. For example, if a k-nearest neighbors model was used with neighbors = tune(), the parameter will be labeled as "# Nearest Neighbors". When an ID was used, such as neighbors = tune("K"), this is used to identify the parameter.
In other plotting news, coord_obs_pred() has been included for regression models. When plotting the observed and predicted values from a model, this forces the x- and y-axis to be the same range and uses an aspect ratio of 1.
The outcome names are saved in an attribute called outcomes to objects with class tune_results. Also, several accessor functions (named `.get_tune_*()) were added to more easily access such attributes.
conf_mat_resampled() computes the average confusion matrix across resampling statistics for a single model.
show_best(), and the select_*() functions will now use the first metric in the metric set if no metric is supplied.
filter_parameters() can trim the .metrics column of unwanted results (as well as columns .predictions and .extracts) from tune_* objects.
In concert with dials > 0.0.7, tuning engine-specific arguments is possible. Many known engine-specific tuning parameters and handled automatically.
If a grid is given, parameters do not need to be finalized to be used in the tune_*() functions.
Added a save_workflow argument to control_* functions that will result in the workflow object used to carry out tuning/fitting (regardless of whether a formula or recipe was given as input to the function) to be appended to the resulting tune_results object in a workflow attribute. The new .get_tune_workflow() function can be used to access the workflow.
Many of the output columns in a tune_results object have an additional column called .config. This is meant to be a unique, qualitative value that used for sorting and merging. These values also correspond to the messages in the logging produced when verbose = TRUE.
tune_grid(), tune_bayes(), etc) have been reordered to better align with parsnip’s fit(). The first argument to all these functions is now a model specification or model workflow. The previous versions are soft-deprecated as of 0.1.0 and will be deprecated as of 0.1.2.Added more packages to be fully loaded in the workers when run in parallel using doParallel (#157), (#159), and (#160)
collect_predictions() gains two new arguments. parameters allows for pre-filtering of the hold-out predictions by tuning parameters values. If you are only interested in one sub-model, this makes things much faster. The other option is summarize and is used when the resampling method has training set rows that are predicted in multiple holdout sets.
select_best(), select_by_one_std_err(), and select_by_pct_loss() no longer have a redundant maximize argument (#176). Each metric set in yardstick now has a direction (maximize vs. minimize) built in.
tune_bayes() no longer errors with a recipe, which has tuning parameters, in combination with a parameter set, where the defaults contain unknown values (#168).CRAN release.
Changed license to MIT
The ... arguments of tune_grid() and tune_bayes() have been moved forward to force optional arguments to be named.
New fit_resamples() for fitting a set of resamples that don’t require any tuning.
Changed summarise.tune_results() back to estimate.tune_results()
NEWS.md file to track changes to the package.