step_umap() has gained initial and target_weight arguments. (#213)
Calling ?tidy.step_*() now sends you to the documentation for step_*() where the outcome is documented. (#216)
Documentation for tidy methods for all steps has been improved to describe the return value more accurately. (#217)
{keras} and {tensorflow} have been moved to Suggests instead of Imports. (#218)
step_collapse_stringdist() will now return predictors as factors. (#204)
Fixed regression from 1.1.2 in step_lencode_glm() where it couldn’t be used on multiple columns.
The keep_original_cols argument has been added to step_woe(). This change should mean that every step that produces new columns has the keep_original_cols argument. (#194)
Many internal changes to improve consistency and slight speed increases.
step_pca_sparse(), step_pca_truncated() and step_pca_sparse_bayes() now returns data unaltered if num_comp = 0. This is done to be consistent with recipes steps of the same nature. (#190)Fixed bug where step_pca_truncated() didn’t work with zero selection. (#181)
The tidy() methods for step_discretize_cart(), step_discretize_xgb(), step_embed(), step_feature_hash(), step_lencode_bayes(), step_lencode_glm(), step_lencode_mixed(), step_pca_sparse(), step_pca_sparse_bayes(), step_pca_truncated(), step_umap(), and step_woe() now correctly return zero-row tibbles when used with empty selections. (#181)
step_pca_truncated() has been added. This step only calculates the components that are required, and will be a speedup in cases where it is used on many variables. (#82)step_collapse_stringdist() has gained method and options arguments to allow for different types of string distance calculations. (#152)step_umap() has gained the argument metric. (#154)
step_embed() has gained the keep_original_cols argument. (#176)
All steps now have required_pkgs() methods.
Steps with tunable arguments now have those arguments listed in the documentation.
All steps that add new columns will now informatively error if name collision occurs.
step_collapse_cart() can pool a predictor’s factor levels using a tree-based method.
step_collapse_stringdist() can pool a predictor’s factor levels using string distances.
Case weights support have been added to step_discretize_cart(), step_discretize_xgb(), step_lencode_bayes(), step_lencode_glm(), and step_lencode_mixed().
step_embed() now correctly defaults to have a random id with the word “embed”. (#102)
step_feature_hash() is soft deprecated in embed in favor of step_dummy_hash() in textrecipes. (#95)
Steps now have a dedicated subsection detailing what happens when tidy() is applied. (#105)
Reorganize documentation for all recipe step tidy methods (#115).
Fixed a bug where woe_table() and step_woe() didn’t respect the factor levels of the outcome. (109)
Re-licensed package from GPL-2 to MIT. See consent from copyright holders here.
The tunable parameter ranges for step_umap() were changed for neighbors, num_comp, and min_dist to prevent uwot segmentation faults. The step also check to see if the data dimensions are consistent with the argument values.
Two new PCA steps were added, each using sparse techniques for estimation: step_pca_sparse() and step_pca_sparse_bayes().
Updated to use recipes_eval_select() from recipes 0.1.17 (#85).
Added prefix argument to step_umap() to harmonize with other recipes steps (#93).
All embed recipe steps now officially support empty selections to be more aligned with recipes, dplyr and other packages that use tidyselect.
step_woe() no longer warns about high-cardinality predictors when the recipe is estimated. Instead it warns when categories have fewer than 10 data points in the training set. (#74)
Minor release with changes to test for cases when CRAN cannot get xgboost to work on their Solaris configuration.
lme4 and rstanarm are now in the Suggests list so they are not automatically installed with embed. A message is written to the console if those packages are missing and their associated steps functions are invoked.
Changes to tests to get out of archive jail.
Updated the plumbing behind step_woe().
Due to a bug in tensorflow, added a “warm start” to instigate a TF session if one does not currently exist.
dplyr 1.0.0step_discretize_xgb() and step_discretize_cart() can be used to convert numeric predictors to categorical using supervised binning methods based on tree models. Thanks to Konrad Semsch for the contribution.
Added step_feature_hash() for creating dummy variables using feature hashing.
tidy.step_woe() now has column names consistent with other recipe steps.stringsAsFactors change.embed 0.0.5The example data are now in the modeldata package.
Small TF updates to step_embed().
embed 0.0.4Methods were added for a future generic called tunable(). This outlines which parameters in a step can/could be tuned.
Small updates to work with different versions of tidyr.
embed 0.0.3step_umap() was added for both supervised and unsupervised encodings.step_woe() created weight of evidence encodings.embed 0.0.2A mostly maintainence release to be compatible with version 0.1.3 of recipes.
The package now depends on the generics pacakge to get the broom tidy methods.
Karim Lahrichi added the ability to use callbacks when fitting tensorflow models. PR
embed 0.0.1First CRAN version