Added quantile regression support to
predict().
tab_pfn()’s version argument now
accepts bare version numbers in addition to "v"-prefixed
strings. A "v" is prepended automatically, so
version = 2.5, version = "2.5", and
version = "v2.5" are all equivalent.
New tabpfn_initialize() eagerly loads the Python
tabpfn library (and PyTorch). Call it right after
library(tabpfn) and before other OpenMP-using packages to
avoid the segmentation fault described in #34.
When the "r-tabpfn" environment (created by
install_tabpfn()) is the Python installation reticulate
resolves to, the tabpfn Python library is now imported
eagerly at load time so that PyTorch claims OpenMP before other packages
can, avoiding a segmentation fault (#34).
New install_tabpfn() sets up a persistent
"r-tabpfn" Python virtual environment. It has a
version argument to pin a specific tabpfn
release and, by default, offers to upgrade an existing environment when
a newer release is available.
Added a type argument to be consistent with parsnip.
Defaults to NULL, which will produce all prediction
types.
The fitted tab_pfn object now records:
The underlying TabPFN model version in a version
element, which is also shown by the print method. It falls back to
"unknown" if the version cannot be determined.
The device(s) used to fit the model (e.g. "cpu",
"mps", or "cuda:0") in a device
element, which is also shown by the print method. It falls back to
"unknown" if the device cannot be determined.
Updated notes on License Requirements in
?tab_pfn.
control_tab_pfn() now accepts ... to
pass additional arguments directly to the TabPFN Python constructor
(e.g. model_path).
tab_pfn() gains a version argument to
select a specific TabPFN model version (e.g., "v2",
"v2.5"). When NULL, the Python library’s
current default is used (#15).
New tabpfn_list_versions() returns the model
versions supported by the currently installed Python tabpfn
library.
Added tabpfn_download_models() to close (#15) @frankiethull