orsf_vs now returns a column that contains
non-reference coded variable names (see
https://github.com/ropensci/aorsf/pull/52).
orsf_vs no longer throws an error when
n_predictor_min = 1 is used (see
https://github.com/ropensci/aorsf/pull/58).
orsf_summarize_uni now allows specification of a
class to summarize for oblique classification forests (see
https://github.com/ropensci/aorsf/pull/57).
fixed an issue where orsf would throw an
uninformative error when all predictors were categorical (see
https://github.com/ropensci/aorsf/pull/56)
oblique random forests can now compute out-of-bag predictions on modified versions of their training data (see https://github.com/ropensci/aorsf/pull/54)
Setting oobag_pred_type to 'none' when
growing a forest no longer necessitates the specification of
pred_type when calling predict later (see
https://github.com/ropensci/aorsf/pull/48).
Setting sample_fraction to 1 will no longer result
in empty oobag_rows in the forest object (this would cause
R to crash when the forest was passed to C++; see
https://github.com/ropensci/aorsf/pull/48)
Re-worked the creation and maintenance of
oobag_denom in C++ routines (see
https://github.com/ropensci/aorsf/pull/48).
Restricted mean survival time is now used for
pred_type = 'time' instead of median survival time (See
https://github.com/ropensci/aorsf/pull/46).
Allowed option "time" for pred_type in
predict and partial dependence to predict survival time
(see https://github.com/ropensci/aorsf/issues/37).
Added pred_spec_auto() for more convenient
specification of variables for partial dependence.
Partial dependence now runs much faster with multiple threads.
Added orsf_vint() to compute variable interaction
scores using partial dependence.
Added orsf_update(), which can copy and modify an
obliqueForest or modify it in place.
Added orsf_control functions for classification,
regression, and survival
(https://github.com/ropensci/aorsf/pull/25).
optimization implemented for matrix multiplication during prediction (https://github.com/ropensci/aorsf/pull/20)
Fixed an uninitialized value for pd_type
Fixed various issues related to memory leaks
Re-worked internal C++ routines following the design of
ranger.
Re-worked how progress is printed to console when
verbose_progress is TRUE, following the design
of ranger. Messages now indicate the action being taken,
the % complete, and the approximate time until finishing the
action.
Improved variable importance, following the design of
ranger. Importance is now computed tree-by-tree instead of
by aggregate. Additionally, mortality is the type of prediction used for
importance with survival trees, since mortality does not depend on
pred_horizon.
Allowed multi-threading to be performed in orsf(),
predict.orsf_fit(), and functions in the
orsf_vi() and orsf_pd() family.
Allowed sampling without replacement and sampling a specific
fraction of observations in orsf()
Included Harrell’s C-statistic as an option for assessing goodness of splits while growing trees.
Fixed an issue where an uninformative error message would occur
when pred_horizon was > max(time) for
orsf_summarize_uni. Thanks to @JyHao1 and @DustinMLong for finding this!
orsf() no longer throws errors or warnings when you
try to give it a single predictor. A note was added to the documentation
in the details of ?orsf that explains why using a single
predictor with orsf() is somewhat useless. This was done to
resolve
https://github.com/mlr-org/mlr3extralearners/issues/259.
predict.orsf_fit now accepts
pred_horizon = 0 and returns sensible values. Thanks to
@mattwarkentin
for the feature request.
added a function to perform variable selection,
orsf_vs().
Made variable importance consistent with respect to
group_factors. Originally, the output from
orsf would have ungrouped VI values while
orsf_vi would have grouped values. With this update,
orsf defaults to grouped values. The ungrouped values can
still be recovered.
Fixed an issue in orsf_pd functions where output
data were not being returned on the original scale.
orsf formulas now accepts Surv objects
(see https://github.com/ropensci/aorsf/issues/11)
Added verbose_progress input to orsf,
which prints messages to console indicating progress.
Allowance of missing values for orsf. Mean and mode
imputation is performed for observations with missing data. These values
can also be used to impute new data with missing values.
Centering and scaling of predictors is now done prior to growing the forest.
Included rOpenSci reviewers Christopher Jackson, Marvin N Wright,
and Lukas Burk in DESCRIPTION as reviewers. Thank
you!
Added clarification to docs about pros/cons of different variable importance techniques
Added regression tests for aorsf versus
obliqueRSF (they should be similar)
Additional support and tests for functions with long right hand sides
Updated out-of-bag vignette with more appropriate custom functions.
Allow status values in input data to be more general, i.e., not just 0 and 1.
Allow missing values in predict functions, including
partial dependence.
Added orsf_control_custom(), which allows users to
submit custom functions for identifying linear combinations of inputs
while growing oblique decision trees.
Added weights input to orsf, allowing
users to over or under fit orsf to specific data in their
training set.
Added chf and mort options to
predict.orsf_fit(). Mortality predictions are not fully
implemented yet - they are not supported in partial dependence or
out-of-bag error estimates. These features will be added in a future
update.
Core features implemented: fit, interpret, and predict using oblique random survival forests.
Vignettes + Readme covering usage of core features.
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