This update of SLOPE brings an entirely different C++ implementation of the underlying package based on the C++ library libslope. It comes with several large and breaking changes with respect to the previous version of the package.
We realized that this may throw off some users, and hope that you will be patient with dealing with the large number of breaking changes.
caretSLOPE() function that was deprecated has now been removed from the
package.unique, violations, and active_sets are no longer stored in the
SLOPE object. These fields were typically only used for debugging purposes.prox_method and method arguments in SLOPE() and sortedL1Prox(),
respectively, have been removed. The proximal operator is now always computed
using the fast stack-based algorithm. There was never any reason to use the
slower PAVA algorithm.SLOPE() with
solver = "admm" will now throws a warning and the value will be
automatically set to "auto".alpha is now scaled by n (the number of observations) and differences with
respect to the type of scaling are no longer taken into account.p times m (number of classes). The returned
coefficients from SLOPE() now have dimensions p times m (rather than
m - 1).coefficients from SLOPE() is now a list of sparse matrices
(rather than a three-dimensional array as before). Now it contains only the
coefficients and not the intercepts. The intercepts are instead stored in
intercepts in the returned object and are always present even if
intercept = FALSE.coef.SLOPE() has changed somewhat.q in SLOPE() has changed from
0.1 * min(1, NROW(x) / NCOL(x)) to 0.1.sigma, n_sigma, and lambda_min_ratio in SLOPE() that were
previously deprecated have been removed.SLOPE() now internally solves the problem normalized by scaling with the
number of observations, which means that values returned in deviance and
prmals and duals if diagnostics = TRUE are now scaled by n.path_length in SLOPE() now defaults to 100 (previously 20).tol_dev_ratio in SLOPE() now defaults to 0.999 (previously 0.995).plot.SLOPE() now use base R graphics rather than ggplot2. This
means that the plots are more difficult to customize but plot much more faster
when there are many variables and significantly reduces the dependency load of
the package. For plots of trained SLOPE objects, which used to be faceted on
the q parameter, the user now needs to use the standard base R graphics API
to facet plots via par(mfrow = c(1, 2)) or similar.tol_rel_gap, tol_infeas, tol_abs, tol_rel, tol_rel_coef in
SLOPE() are now deprecated. The solvers now all rely on the same tolerance
criterion, which is set by tol and uses the duality gap normalized by the
current primal value.screen and screen_alg are now deprecated and have no effect.
Feature screening is always used. These arguments were only used for
debugging.verbosity in SLOPE() is now defunct and has no effect.prox_method in SLOPE() and sortedL1Prox() is now defunct
and has no effect.x in SLOPE() is now allowed again.center and scale in SLOPE().solver = "hybrid"."auto", in which case the package automatically
chooses a solver.diagnostics = TRUE are now true duality
gaps, computed by guaranteeing that the dual variable is feasible (which was
not the case previously).scale in SLOPE() gains a new option "max_abs" which scales the columns
of x by their maximum absolute value.trainSLOPE().The new hybrid algorithm that's implemented in libslope and now used in the package constitutes a major upgrade in terms of performance.
We have made an effort to reduce the footprint of the package and reduce the number of dependencies.
ggplot2 is removed.vdiffr, tidyr, dplyr, bench, scales, and glmnet packages in
the Suggests field that were used for testing are now removed."gaussian" when the
number of observations is less than the number of variables.plot.SLOPE(), plot.trainSLOPE() and plotDiagnostics() have been
reimplemented in ggplot2.caretSLOPE() has been deprecated and will be made defunct in version 0.6.0.sortedL1Prox() is a new function that computes the proximal operator for the
sorted L1 norm (the penalty term in SLOPE).regularizationWeights() is a new function that returns the penalty weights
(lambda sequence) for SLOPE or OSCAR.SLOPE() gains two arguments: theta1 and theta2 to control the
behavior using the parametrization from L. W. Zhong and J. T. Kwok, “Efficient
sparse modeling with automatic feature grouping,” IEEE Transactions on Neural
Networks and Learning Systems, vol. 23, no. 9, pp. 1436–1447, Sep. 2012, doi:
10.1109/TNNLS.2012.2200262. q is no longer used with OSCAR models. Thanks,
Nuno Eusebio.SLOPE() has gained a new argument, prox_method, which allows the user to
select prox algorithm to use. There is no an additional algorithm in the
package, based on the PAVA algorithm used in isotonic regression, that can be
used. Note that this addition is mostly of academic interest and does not need
to be changed by the user.q parameter is no longer allowed to be smaller than 1e-6 to avoid
constructions of regularization paths with infinite lambda values.lambda argument in SLOPE() now also allowed the input "lasso" to
obtain the standard lasso.trainSLOPE()lambda = "gaussian" were incorrectly computed, increasing and then
decreasing. This is now fixed and regularization weights in this case are now
always non-increasing.trainSLOPE()
for multinomial models (thanks @jakubkala and @KrystynaGrzesiak)trainSLOPE() was previously hampered by erroneous refitting
of the models, which has been fixed now (thanks @jakubkala and
@KrystynaGrzesiak)yvar argument in plotDiagnostics() that was previously deprecated is now
defunct.missclass for the measure argument in trainSLOPE() has been
deprecated in favor of misclass.SLOPE().intercept = FALSE and family = "gaussian"
(#13, thanks, Patrick Tardivel).tol_rel_coef_change argument to SLOPE() as a convergence criterion
for the FISTA solver that sets a tolerance for the relative change in
coefficients across iterations.std::sqrt() in src/SLOPE.cpp.alpha (previously sigma) is now invariant to the number of
observations, which is achieved by scaling the penalty part of the objective
by the square root of the number of observations if scale = "l2" and the
number of observations if scale = "sd" or "none". No scaling is applied
when scale = "l1".sigma argument is deprecated in favor of alpha in SLOPE(),
coef.SLOPE(), and predict.SLOPE().n_sigma argument is deprecated in favor of path_length in SLOPE()lambda_min_ratio argument is deprecated in favor of alpha_min_ratio in
SLOPE()lambda in SLOPE() has changed from "gaussian"
to "bh".scale = "sd" now scales with the population standard deviation rather than
the sample standard deviation, i.e. the scaling factor now used is the number
of observations (and not the number of observations minus one as before).path_length has changed from 100 to 20.plot.SLOPE() has gained an argument x_variable that controls what is
plotted on the x axis.max_variables criterion is hit, the solution path returned will now
include also the last solution (which was not the case before). Thanks,
@straw-boy.rho instead of 1 is now used in the factorization part for the ADMM
solver.deviance() and SLOPE() that were taking too long to
execute have been removed or modified.This version of SLOPE represents a major change to the package. We have merged functionality from the owl package into this package, which means there are several changes to the API, including deprecated functions.
SLOPE_solver(), SLOPE_solver_matlab(), prox_sorted_L1(), and
create_lambda() have been deprecated (and will be defunct in the next
version of SLOPE)X, fdr, and normalize have been deprecated in SLOPE() and
replaced by x, q, scale and center, respectively"default" and "matlab" to argument solver in SLOPE() have been
deprecated and replaced with "fista" and "admm", which uses the
accelerated proximal gradient method FISTA and alternating direction of
multipliers method (ADMM) respectivelyfamily = "gaussian"family
argument in SLOPE())lambda is now scaled (divided by) the number of observations (rows)
in xscreen = TRUE in SLOPE(). The type of algorithm can also be set via
screen_alg.SLOPE() now returns an object of class "SLOPE" (and an additional class
depending on input to family in SLOPE()SLOPE objects gain coef() and plot() methods.SLOPE now uses screening rules to speed up model fitting in the
high-dimensional regimetrainSLOPE() trains SLOPE with repeated k-folds
cross-validationcaretSLOPE() enables model-tuning using the caret packageSLOPE() now fits an entire path of regularization sequences by defaultnormalize option to SLOPE() has been replaced by scale and center,
which allows granular options for standardizationdeviance() returns the deviance from the fitscore() can be used to assess model performance against new
dataplotDiagnostics() has been included to visualize data from
the solver (if diagnostics = TRUE in the call to SLOPE())lambda = "oscar" in the call toSLOPE()`