Revision 680619b96c0dc2f5a1cb12e0a06005d76f857be5 authored by pat-s on 06 December 2019, 11:37:28 UTC, committed by pat-s on 06 December 2019, 11:37:28 UTC
Build URL: https://circleci.com/gh/mlr-org/mlr/992 Commit:
1 parent 88a0774
RLearner_regr_cvglmnet.R
#' @export
makeRLearner.regr.cvglmnet = function() {
makeRLearnerRegr(
cl = "regr.cvglmnet",
package = "glmnet",
par.set = makeParamSet(
makeDiscreteLearnerParam(id = "family", default = "gaussian", values = c("gaussian", "poisson")),
makeNumericLearnerParam(id = "alpha", default = 1, lower = 0, upper = 1),
makeIntegerLearnerParam(id = "nfolds", default = 10L, lower = 3L),
makeDiscreteLearnerParam(id = "type.measure", values = c("mse", "mae"), default = "mse"),
makeDiscreteLearnerParam(id = "s", values = c("lambda.1se", "lambda.min"), default = "lambda.1se", when = "predict"),
makeIntegerLearnerParam(id = "nlambda", default = 100L, lower = 1L),
makeNumericLearnerParam(id = "lambda.min.ratio", lower = 0, upper = 1),
makeLogicalLearnerParam(id = "standardize", default = TRUE),
makeLogicalLearnerParam(id = "intercept", default = TRUE),
makeNumericLearnerParam(id = "thresh", default = 1e-07, lower = 0),
makeIntegerLearnerParam(id = "dfmax", lower = 0L),
makeIntegerLearnerParam(id = "pmax", lower = 0L),
makeIntegerVectorLearnerParam(id = "exclude", lower = 1L),
makeNumericVectorLearnerParam(id = "penalty.factor", lower = 0, upper = 1),
makeNumericVectorLearnerParam(id = "lower.limits", upper = 0),
makeNumericVectorLearnerParam(id = "upper.limits", lower = 0),
makeIntegerLearnerParam(id = "maxit", default = 100000L, lower = 1L),
makeNumericLearnerParam(id = "fdev", default = 1.0e-5, lower = 0, upper = 1),
makeNumericLearnerParam(id = "devmax", default = 0.999, lower = 0, upper = 1),
makeNumericLearnerParam(id = "eps", default = 1.0e-6, lower = 0, upper = 1),
makeNumericLearnerParam(id = "big", default = 9.9e35),
makeIntegerLearnerParam(id = "mnlam", default = 5, lower = 1),
makeNumericLearnerParam(id = "pmin", default = 1.0e-9, lower = 0, upper = 1),
makeNumericLearnerParam(id = "exmx", default = 250.0),
makeNumericLearnerParam(id = "prec", default = 1e-10),
makeIntegerLearnerParam(id = "mxit", default = 100L, lower = 1L),
makeUntypedLearnerParam(id = "offset", default = NULL),
# FIXME Data dependent default. If n.features < 500 'covariance', 'naive' otherwise
makeDiscreteLearnerParam(id = "type.gaussian", values = c("covariance", "naive"), requires = quote(family == "gaussian")),
makeNumericVectorLearnerParam(id = "gamma", lower = 0, upper = 1, requires = quote(relax == TRUE)),
makeLogicalLearnerParam(id = "relax", default = FALSE)
),
properties = c("numerics", "factors", "weights"),
name = "GLM with Lasso or Elasticnet Regularization (Cross Validated Lambda)",
short.name = "cvglmnet",
note = "Factors automatically get converted to dummy columns, ordered factors to integer.
glmnet uses a global control object for its parameters. mlr resets all control parameters to their defaults
before setting the specified parameters and after training.
If you are setting glmnet.control parameters through glmnet.control,
you need to save and re-set them after running the glmnet learner.",
callees = c("cv.glmnet", "glmnet", "glmnet.control", "predict.cv.glmnet")
)
}
#' @export
trainLearner.regr.cvglmnet = function(.learner, .task, .subset, .weights = NULL, ...) {
d = getTaskData(.task, .subset, target.extra = TRUE)
info = getFixDataInfo(d$data, factors.to.dummies = TRUE, ordered.to.int = TRUE)
args = c(list(x = as.matrix(fixDataForLearner(d$data, info)), y = d$target), list(...))
rm(d)
if (!is.null(.weights)) {
args$weights = .weights
}
glmnet::glmnet.control(factory = TRUE)
saved.ctrl = glmnet::glmnet.control()
is.ctrl.arg = names(args) %in% names(saved.ctrl)
if (any(is.ctrl.arg)) {
on.exit(glmnet::glmnet.control(factory = TRUE))
do.call(glmnet::glmnet.control, args[is.ctrl.arg])
args = args[!is.ctrl.arg]
}
attachTrainingInfo(do.call(glmnet::cv.glmnet, args), info)
}
#' @export
predictLearner.regr.cvglmnet = function(.learner, .model, .newdata, ...) {
info = getTrainingInfo(.model)
.newdata = as.matrix(fixDataForLearner(.newdata, info))
p = drop(predict(.model$learner.model, newx = .newdata, type = "response", ...))
p
}
![swh spinner](/static/img/swh-spinner.gif)
Computing file changes ...