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Changelog

Source: NEWS.md

randomPlantedForest 0.5.0

Breaking changes

  • rpf() now defaults to max_interaction = 2, main effects and pairwise interactions, instead of 1, an additive model (#66). Values above the number of predictors are capped silently instead of with a message.
  • rpf() now defaults to loss = "exponential" for classification instead of "L2", so classification fits give proper probability estimates by default; regression still uses "L2" (#66).
  • rpf() arguments are reordered by purpose (forest size, split search, loss, other). Pass arguments after the data arguments by name (#66).
  • rpf(), predict() and purify() error on unknown arguments, such as misspelled ones, instead of silently ignoring them (#66).
  • purify() is a regular function instead of an S3 generic (#66).
  • preprocess_predictors_predict() is no longer exported; it is an internal step of predict() (#66).
  • The package now requires R >= 4.1.0 (#66).
  • For a given seed, classification models differ from 0.4.0 due to the fixes and speedup below, but regression models are unchanged (#57, #66).
  • For direct users of the C++ object ($fit): set_data() no longer fits, call fit() afterwards, and an unknown classification loss is an error instead of a silent fallback to L2 (@jyliuu, #57).
  • rpf() no longer has a cv argument, which never had an effect: the C++ cross-validation was a no-op (#66).

Bug fixes

  • predict_components() returns one intercept per target level for multiclass classification, named by target_levels. Previously only the first level’s intercept was returned, so components plus intercept did not sum to the prediction for other levels.
  • predict() and predict_components() error on missing values in new_data, naming the affected columns. Previously such rows silently received arbitrary predictions, as rpf() has no handling for missing values. rpf() likewise reports missing values in the training data by column (#66).
  • Classification fits with a fixed seed could differ between runs. When a split replaced a leaf, its new split candidates could be lost or attached to the wrong leaf, depending on memory layout (#66).
  • predict(type = "class") breaks probability ties by level order instead of at random, so class predictions are deterministic and no longer advance the random number generator (#66).
  • purify() returns its input invisibly, as documented (#66).
  • Logical predictors in the formula interface are used as a single 0/1 column, as in the x/y interface. Previously they were expanded into two redundant indicator columns, which also counted against max_interaction (#66).

Performance

  • Prediction is much faster: leaves are scanned from flat, cache-friendly arrays (~15x single-threaded), and rows are split across threads (#65). predict() gains nthreads, defaulting to the nthreads used for fitting.
  • predict_components() is much faster: purified forests look up only the trees of the requested component, and each distinct input row is predicted once, which pays off for low-cardinality features (#59, #65).
  • Classification fits are slightly faster, as redundant work during model construction was removed (#57).

Other improvements

  • New “Getting started” vignette covering regression, classification, component decomposition, recipes, and saving models (#66).
  • The print() method is overhauled and gains a format() counterpart: it reports the forest size, interaction degree, purification state, split search settings and number of threads (#66).
  • deterministic = TRUE with ntrees > 1 warns, as all tree families are then identical (#66).
  • Rewritten documentation for rpf(), purify() and predict_components(), including guidance on choosing parameters and on how purification interacts with predict_components() (#66).

Internals

  • The C++ core no longer depends on Rcpp: it uses standard C++ types and exceptions, and an Rcpp layer (src/rcpp_interface.*) converts at the boundary. This is groundwork for bindings in other languages such as Python (@jyliuu, #57).
  • cli and rlang are now imported, mvtnorm is only needed to build the pkgdown site (#66).

randomPlantedForest 0.4.0

It is now possible to serialize and de-serialize an rpf object via marshalling, meaning a fitted model can be saved to disk or dispatched to a worker process for parallelization or encapsulation, as is done in mlr3. This also re-opens the door for the mlr3extralearners wrapper, which was removed due to the lack of serialization support.

  • New rpf_marshal() / rpf_unmarshal() serialize a fitted forest to a plain R list and back, making saveRDS()-based storage of rpf models possible (#52). Purified forests restore their purified state directly; training data is only embedded with include_data = TRUE (required to purify() after restoring). Blobs record the blob format version and the package version they were created with; restoring under an older package than the one that saved the model warns. Malformed or corrupt blobs error instead of reading out of bounds.
  • New rpf_is_valid() checks whether an rpf object’s internal model is usable, predict(), purify() and predict_components() now give an actionable error for rpf objects restored via readRDS() without marshaling.
  • New bundle::bundle() method for rpf models wrapping the marshaling API.

Behavior changes

  • The default for delta changed from 0 to 0.001. With delta = 0, splits producing single-class nodes have infinite logit loss and are always rejected, which also degraded binary logit fits.

Fixes and improvements

  • Fixed multiclass classification with loss = "logit", which effectively never split and predicted near-uniform class probabilities (#40). The C++ logit loss is a reference-class multinomial formulation expecting K-1 indicator columns, but the R wrapper passed a full K-column one-hot matrix, pinning the implicit reference-class probability to zero. Multiclass logit outcomes are now encoded with the first factor level as reference class:
    • predict(type = "prob") reconstructs all K class probabilities from the K-1 logits; rows sum to 1 exactly.
    • predict(type = "numeric"/"link") now returns K-1 columns named after the non-reference levels (previously K columns).
    • predict_components() on multiclass logit fits returns per-class components for the K-1 non-reference levels; target_levels reflects this.

randomPlantedForest 0.3.0

Major changes (#61)

  • New rpf() arguments controlling split-candidate sampling:
    • split_structure = "leaves": Defines what a split candidate is and how candidates are drawn. One of "leaves" (default), "hist", "cur_trees_1", "cur_trees_2", or "res_trees"; see ?rpf for details.
    • max_candidates = 50: Maximum number of split candidates sampled per iteration.
    • split_decay_rate = 0.1: Exponential aging of repeatedly drawn but unchosen split candidates. split_decay_rate = 0 corresponds to no aging and uniform sampling.
    • delete_leaves = TRUE: Whether a parent leaf is deleted when splitting along an existing dimension.
  • Fitting results change: The new candidate-sampling defaults and a reworked internal RNG mean that fits are not reproducible against previous versions, even with the same seed. Install an older commit if exact reproduction of previous results is required.
  • Seeded fits are now reproducible regardless of nthreads: per-tree seeds are drawn from R’s RNG, so set.seed() gives identical forests for serial and multithreaded fits.
  • Substantial speedups in fitting (cached per-leaf orderings, prefix sums) and reduced memory use (training-only buffers are released after each tree family is built).
  • purify() gains arguments:
    • mode = 2: Purification algorithm; 2 is a new fast exact method, 1 is the legacy grid-based path.
    • nthreads = NULL: Purification is now multithreaded, defaulting to the fit’s nthreads setting.
    • maxp_interaction = NULL: Optionally only compute purified components up to this interaction order.
  • New rpf() argument export_forest = FALSE: The flattened forest is no longer stored in the fitted object by default, so rpf_object$forest is NULL unless export_forest = TRUE. This reduces object size; predict(), purify(), and predict_components() are unaffected.
  • preprocess_predictors_predict() is now exported.
  • Fixed a memory bug in the legacy purification path where the grid was sized one element too large, causing out-of-bounds reads (crashes on Windows, silently wrong purification results elsewhere).
  • Fixed a crash on Windows when fitting with nthreads > 1, caused by a thread_local buffer with a non-trivial destructor being destroyed at thread exit.

Other changes

  • Internals in src/ have been refactored into modular sub-files (#53)
  • rpf() now errors if a regression target is combined with a loss other than "L2".
  • Allow features of type logical, which are now converted via as.integer.
  • The parallel = TRUE|FALSE argument in rpf() has been substituted by an nthreads = 1L argument, allowing for more flexible parallelization. The previous behavior only allowed for either no parallelization or using n-1 of n available cores. The new implementation should be reasonably robust and the default behavior remains serial execution.
  • Remove SystemRequirements field from DESCRIPTION: Now the default C++ version is C++17 and with a minor change to internal use of random numbers, randomPlantedForest is now compatible with C++11 through C++23.
  • Add remainder term to predict_components output for case where max_interaction supplied is smaller than max_interaction in rpf fit. In that case, the m values don’t sum up to the global predictions, so we add a remainder to allow reconstruction of that property.

randomPlantedForest 0.2.1

  • Add glex class to output of predict_components(), for extended functionality available with glex.
  • Add target_levels vector to output of predict_components() to aid multiclass handling. Keeping track of levels is somewhat awkward since column names of $m need to be identifiable regarding the target level.

randomPlantedForest 0.2.0

  • Added a NEWS.md file to track changes to the package.