classbound 1.0.1
Bug fixes
fit_model() now treats numeric or character class
labels as classes; previously a numeric response (such as Y
in data69_1) was fitted as a regression.fit_model() only checks the columns used in the
formula, so missing values in unrelated columns no longer stop
fitting.boundary_compute() can recompute a multi-model result,
and recomputing a boundary no longer repeats the
"classbound_boundary" class.plot_boundary(type = "disagreement") no longer fails
when obs_data is supplied.boundary_explore_nd() rescales features before its
k-nearest-neighbour fallback for models without class probabilities, as
classifly does; previously one large-scale feature could dominate and
boundary points were close to random. It also keeps non-syntactic
feature names and accepts character columns.fit_model() evaluates transformations in the formula
(such as log(x) or I(x^2)) with the formula
interface; previously the classifier failed with “object not found”. The
features of the model are the original variables.fit_model() now stops with a clear message when the
response has fewer than two classes, instead of failing inside the
classifier.plot_boundary() with obs_data and
classbound() accept a projection basis without row names
(such as one taken directly from tourr), assuming the rows
follow the order of the training features, instead of failing.classbound() takes the axes from the fitted model, so
formulas containing transformations work.print() on a classbound object prints the
fitted model on separate lines instead of a literal
\n.- The internal
tidymodels_bridge() now passes
resolution on.
Shiny application
- In Projection mode, the projection plane is recomputed when new data
are generated; previously the old plane was kept, which broke the plots
when the number of dimensions changed.
- Imported data keep rows whose missing values are only in columns the
app does not use, and data with fewer than two numeric features are
rejected up front.
- Custom models can declare
supports_prob = TRUE, and
fitted parsnip models from the workspace can show a probability
surface.
- Clear Canvas also resets the undo history and the number of base
classes.
- Exported grid predictions and the reproduce script now use the same
plot range as the app, and the script passes on the prediction arguments
(such as the PPtree rule). Each export uses its own temporary
folder.
- The export wizard creates its ZIP file with the ‘zip’ package when
it is installed, so exporting no longer needs a
zip program
(often missing on Windows).
- The reproduce script writes zoom limits with full precision, and the
exported
data.csv has an is_outlier column so
the script redraws the outliers as diamonds, as in the app.
- A zero-size brush no longer causes a division by zero when
zooming.
- An empty or invalid Point Density value no longer stops the app
while drawing.
- Outliers belonging to a class that has been removed no longer cause
ordinary observations to be drawn as outliers.
- Only installed models are preselected at start-up.
- The information table reports the 2D Slice mode correctly.
New features
lda and qda models from ‘MASS’ are
supported out of the box, with their posterior probabilities available
for the probability surface.
Documentation
- Projection views are now described as what they show: the predicted
regions on a plane through the data center (a planar section), with the
observations projected onto that plane.
boundary_explore_nd() credits the ‘classifly’ package
and Wickham, Cook and Hofmann (2015).- The
data69_1 help page now states that Y
is numeric with values 0, 1, 2 and that the data are the full UCI
dataset.
- Corrected the return values documented for
predict_adapter() and boundary_workflow_set(),
and Ignacio Alvarez-Castro’s e-mail address.
- The README and the getting-started vignette show installation from
CRAN, and the README has a CRAN status badge.
Tests
- Added regression tests for all of the fixes above (the suite now has
96 tests), tests for
classbound(), and tests of the Shiny
application’s server logic with shiny::testServer().
classbound 1.0.0