tune 0.1.4

• Fixed an issue in finalize_recipe() which failed during tuning of recipe steps that contain multiple tune() parameters in an single step.

• Changed conf_mat_resampled() to return the same type of object as yardstick::conf_mat() when tidy = FALSE (#370).

• The automatic parameter machinery for sample_size with the C5.0 engine was changes to use dials::sample_prop().

tune 0.1.3

• The rsample::pretty() methods were extended to tune_results objects.

• Added pillar methods for formatting tune objects in list columns.

• A method for .get_fingerprint() was added. This helps determine if tune objects used the same resamples.

tune 0.1.2

Bug Fixes

• 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.

Other Changes

• 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().

tune 0.1.1

Breaking Changes

• 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.

Other Changes

• 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.

tune 0.1.0

Breaking Changes

• The arguments to the main tuning/fitting functions (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.

Other Changes

• 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.

Bug Fixes

• 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).

last version before removing references to external workflow functions

old-workflows

added a vignette page describing optimizations

v0.0.1

Data release