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finalize_model_tidyclust() and
finalize_workflow_tidyclust() are deprecated. Use
tune::finalize_model() and
tune::finalize_workflow() instead, which now support
cluster_spec objects natively. (#223)New db_clust() clustering specification for fitting
DBSCAN models, with engines "dbscan" and
"hdbscan". (#209, #238)
New gm_clust() clustering specification for fitting
Gaussian mixture models, with engine "mclust".
(#209)
New mean_shift() clustering specification for
fitting mean shift models, which iteratively shift observations toward
regions of high density and determine the number of clusters
automatically. Engines "LPCM" and "meanShiftR"
are supported. (#240, #244)
Added dials parameter constructors
radius(), min_points(),
circular(), zero_covariance(),
shared_orientation(), shared_shape(), and
shared_size() so that tuning parameters for
db_clust() and gm_clust() resolve to real
parameter objects rather than erroring on unexported
dials:: names.
Added a “Getting started with tidyclust” vignette
(vignette("tidyclust")). (#232)
Added butcher support for cluster_fit
objects. axe_data() removes the training data stored in the
fit, and axe_env() clears the environment reference from
the preprocessing terms. (#126)
contr_one_hot() is now exported, fixing the
indicators = "one_hot" code path in
.convert_form_to_x_fit() and
.convert_form_to_x_new(). (#218)
extract_cluster_assignment(),
extract_centroids(), and predict() gain a
labels argument, a character vector of cluster labels that
overrides the auto-generated prefix-based labels.
(#148)
hier_clust() gains a dist_fun argument
for specifying a custom distance function. (#70)
hier_clust() documentation now clarifies that
predict() may not match
extract_cluster_assignment() on training data:
predict() uses a distance-based heuristic while
extract_cluster_assignment() uses cutree()
based on the dendrogram structure. (#208)
The dist_fun argument accepted by cluster metrics is
now documented, including how to use {philentropy} to
supply custom distance methods. See
vignette("tuning_and_metrics", package = "tidyclust") for
examples. (#185)
tune_cluster() now supports parallel processing via
the mirai package in addition to future.
(#220)
tune_cluster() now warns when passed an
apparent() resample. Metrics from apparent resamples are
excluded by collect_metrics(summarize = TRUE) (the default)
since tune 1.2.0, which caused unexpected NA values. Use
collect_metrics(summarize = FALSE) to see per-resample
metrics. (#193)
The .notes column returned by
tune_cluster() now includes a trace column
containing backtraces for errors and warnings, making it easier to debug
failures. (#220)
Fixed bug when trying to tune the linkage_method
argument. (#206, @lgaborini)
silhouette_avg() now has
direction = "maximize" instead of
direction = "zero", so that show_best() and
select_best() correctly return models with the highest
silhouette values. (#212, @dnldelarosa)
sse_within_total() now correctly applies a custom
dist_fun when new_data is NULL by
using training data stored in the model. (#184)
The foreach package is no longer supported for
parallel processing in tune_cluster(). Use the
future or mirai packages instead. See
?tune::parallelism for details. (#220)
The .config column produced by
tune_cluster() has changed from the
Preprocessor{num}_Model{num} pattern to
pre{num}_mod{num}_post{num} to align with updates in the
tune package. (#220)
The clustMixType engine as been added to k_means().
This engine allows fitting of k-prototype models. (#63)
The klaR engine as been added to k_means(). This
engine allows fitting of k-modes models. (#63)
Fixed bug where engine specific arguments were passed along for
k_means() when the engine ClusterR. (#142)
Fixed bug where prefix argument wouldn’t be
correctly passed through extract_cluster_assignment(),
extract_centroids(), and predict()
(#145)
Metric functions now error informatively if used with unfit cluster specifications. (#146)
Fixed bug that caused cluster ordering in extract_fit_summary(). (#136)
Using extract_cluster_assignment(),
extract_centroids() and predict() on a fitted
hier_clust() model without specifying
num_clust or cut_height now gives more
informative error message. (#147)
k_means() now errors informatively if
fit() without num_clust specified.
(#134)
Fixed bug where levels didn’t match number of clusters if prediction on fewer number of observations. (#158)
Fixed bug where tune_cluster() would error if used
with an recipe that contained non-predictor variables such as id
variables. (#124)
Exported internal functions ClusterR_kmeans_fit(),
stats_kmeans_fit(), and hclust_fit() have been
renamed to .k_means_fit_ClusterR(),
.k_means_fit_stats(), and
.hier_clust_fit_stats() to reduce visibility for
users.
Cluster reordering is now done at the fitting time, not the extraction and prediction time. (#154)
generics::tune_args() and generics::tunable()
are now registered unconditionally (#115).Fixed bug where extract_cluster_assignment() and
predict() sometimes didn’t have agreement of clusters.
(#94)
silhouette() and silhouette_avg() now
return NAs instead of erroring when applied to a clustering object with
1 cluster. (#104)
Fixed bug where extract_cluster_assignment() doesn’t
work for hier_clust() models in workflows where
num_clusters is specified in
extract_cluster_assignment().
NEWS.md file to track changes to the
package.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.