This section of the ml tutorial: https://mlr.mlr-org.com/articles/tutorial/nested_resampling.html#filter-methods-with-tuning explains how to use a TuneWrapper with a FilterWrapper to tune the threshold for the filter. But what if my filter has hyperparameters that need tuning as well, such as a random forest variable importance filter? I don't seem to be able to tune any parameters except the threshold.
For example:
library(survival)
library(mlr)
data(veteran)
set.seed(24601)
task_id = "MAS"
mas.task <- makeSurvTask(id = task_id, data = veteran, target = c("time", "status"))
mas.task <- createDummyFeatures(mas.task)
tuning = makeResampleDesc("CV", iters=5, stratify=TRUE) # Tuning: 5-fold CV, no repeats
cox.filt.rsfrc.lrn = makeTuneWrapper(
makeFilterWrapper(
makeLearner(cl="surv.coxph", id = "cox.filt.rfsrc", predict.type="response"),
fw.method="randomForestSRC_importance",
cache=TRUE,
ntree=2000
),
resampling = tuning,
par.set = makeParamSet(
makeIntegerParam("fw.abs", lower=2, upper=10),
makeIntegerParam("mtry", lower = 5, upper = 15),
makeIntegerParam("nodesize", lower=3, upper=25)
),
control = makeTuneControlRandom(maxit=20),
show.info = TRUE)
produces the error message:
Error in checkTunerParset(learner, par.set, measures, control) :
Can only tune parameters for which learner parameters exist: mtry,nodesize
Is there any way to tune the hyperparameters of the random forest?
EDIT: Other attempts following suggestion in comments:
Wrap tuner around base learner before feeding to filter (filter not shown) - fails
cox.lrn = makeLearner(cl="surv.coxph", id = "cox.filt.rfsrc", predict.type="response")
cox.tune = makeTuneWrapper(cox.lrn,
resampling = tuning,
measures=list(cindex),
par.set = makeParamSet(
makeIntegerParam("mtry", lower = 5, upper = 15),
makeIntegerParam("nodesize", lower=3, upper=25),
makeIntegerParam("fw.abs", lower=2, upper=10)
),
control = makeTuneControlRandom(maxit=20),
show.info = TRUE)
Error in checkTunerParset(learner, par.set, measures, control) :
Can only tune parameters for which learner parameters exist: mtry,nodesize,fw.abs
Two levels of tuning - fails
cox.lrn = makeLearner(cl="surv.coxph", id = "cox.filt.rfsrc", predict.type="response")
cox.filt = makeFilterWrapper(cox.lrn,
fw.method="randomForestSRC_importance",
cache=TRUE,
ntree=2000)
cox.tune = makeTuneWrapper(cox.filt,
resampling = tuning,
measures=list(cindex),
par.set = makeParamSet(
makeIntegerParam("fw.abs", lower=2, upper=10)
),
control = makeTuneControlRandom(maxit=20),
show.info = TRUE)
cox.tune2 = makeTuneWrapper(cox.tune,
resampling = tuning,
measures=list(cindex),
par.set = makeParamSet(
makeIntegerParam("mtry", lower = 5, upper = 15),
makeIntegerParam("nodesize", lower=3, upper=25)
),
control = makeTuneControlRandom(maxit=20),
show.info = TRUE)
Error in makeBaseWrapper(id, learner$type, learner, learner.subclass = c(learner.subclass, :
Cannot wrap a tuning wrapper around another optimization wrapper!
It looks like that you currently can not tune hyperparameters of filters. You can manually change certain parameters by passing them in makeFilterWrapper() but not tune them.
You can only tune one of fw.abs, fw.perc or fw.tresh when it comes to filtering.
I do not know how big the effect will be on the ranking when using different hyperpars for the RandomForest filter. One way to check the robustness would be to compare the rankings of single RF model fits using different settings for mtry and friends with the help of getFeatureImportance(). If there is a very high rank correlation between these, you can safely ignore the tuning of the RF filter. (Maybe you want to use a different filter which does not come with this issue at all?)
If you insist on having this feature, you might need to raise PR for the package :)
lrn = makeLearner(cl = "surv.coxph", id = "cox.filt.rfsrc", predict.type = "response")
filter_wrapper = makeFilterWrapper(
lrn,
fw.method = "randomForestSRC_importance",
cache = TRUE,
ntrees = 2000
)
cox.filt.rsfrc.lrn = makeTuneWrapper(
filter_wrapper,
resampling = tuning,
par.set = makeParamSet(
makeIntegerParam("fw.abs", lower = 2, upper = 10)
),
control = makeTuneControlRandom(maxit = 20),
show.info = TRUE)
Related
How I can make a list of learners including autofselector and autotuner in benchmark and compare their performance?
I wonder how to rank learners stratified by task when we have multiple tasks
library(mlr3verse)
mod1 = AutoTuner$new(
learner = lrn("surv.svm", type = "hybrid", diff.meth = "makediff3",
gamma.mu = c(0.1, 0.1)),
resampling = rsmp("holdout"),
measure = msr("surv.cindex"),
terminator = trm("evals", n_evals = 10),
tuner = tnr("random_search"))
mod2 = AutoFSelector$new(
learner = as_learner(
po("imputemedian", affect_columns = selector_type("numeric")) %>>%
po("imputemode", affect_columns = selector_type("factor")) %>>%
po("scale") %>>%
po("encode", method = "one-hot") %>>%
lrn("surv.coxph")) ,
resampling = rsmp("holdout"),
measure = msr("surv.cindex"),
terminator = trm("evals", n_evals = 100),
fselector = fs("sequential", strategy ="sbs"))
lrns = c(mod1, mod1)
design = benchmark_grid(tasks = tsks(c("actg", "rats")),
learners = lrns,
resamplings = rsmp("holdout"))
bmr = benchmark(design, store_models = TRUE, store_backends = TRUE)
As im very new to XGBoost, I am trying to tune the parameters using mlr library and model but after using setHayperPars() learning using train() throws an error (in particular when i run xgmodel line): Error in colnames(x) : argument "x" is missing, with no default, and i can't recognize what's this error means, below is the code:
library(mlr)
library(dplyr)
library(caret)
library(xgboost)
set.seed(12345)
n=dim(mydata)[1]
id=sample(1:n, floor(n*0.6))
train=mydata[id,]
test=mydata[-id,]
traintask = makeClassifTask (data = train,target = "label")
testtask = makeClassifTask (data = test,target = "label")
#create learner
lrn = makeLearner("classif.xgboost",
predict.type = "response")
lrn$par.vals = list( objective="multi:softprob",
eval_metric="merror")
#set parameter space
params = makeParamSet( makeIntegerParam("max_depth",lower = 3L,upper = 10L),
makeIntegerParam("nrounds",lower = 20L,upper = 100L),
makeNumericParam("eta",lower = 0.1, upper = 0.3),
makeNumericParam("min_child_weight",lower = 1L,upper = 10L),
makeNumericParam("subsample",lower = 0.5,upper = 1),
makeNumericParam("colsample_bytree",lower = 0.5,upper = 1))
#set resampling strategy
configureMlr(show.learner.output = FALSE, show.info = FALSE)
rdesc = makeResampleDesc("CV",stratify = T,iters=5L)
# set the search optimization strategy
ctrl = makeTuneControlRandom(maxit = 10L)
# parameter tuning
set.seed(12345)
mytune = tuneParams(learner = lrn, task = traintask,
resampling = rdesc, measures = acc,
par.set = params, control = ctrl,
show.info = FALSE)
# build model using the tuned paramters
#set hyperparameters
lrn_tune = setHyperPars(lrn,par.vals = mytune$x)
#train model
xgmodel = train(learner = lrn_tune,task = traintask)
Could anyone tell me what's wrong!?
You have to be very careful when loading multiple packages that may involve methods with the same name - here caret and mlr, which both include a train method. Moreover, the order of the library statements is significant: here, as caret is loaded after mlr, it masks functions with the same name from it (and possibly every other package loaded previously), like train.
In your case, where you obviously want to use the train method from mlr (and not from caret), you should declare this explicitly in your code:
xgmodel = mlr::train(learner = lrn_tune,task = traintask)
I am tuning more than 2 hyperparameters, while Generate hyperparameter effect data using the function generateHyperParsEffectData I set partial.dep = TRUE, while plotting plotHyperParsEffect i am getting error for classification learner, its requiring regressor learner
This is my task and learner for classification
classif.task <- makeClassifTask(id = "rfh2o.task", data = Train_clean, target = "Action")
rfh20.lrn.base = makeLearner("classif.h2o.randomForest", predict.type = "prob",fix.factors.prediction=TRUE)
rfh20.lrn <- makeFilterWrapper(rfh20.lrn.base, fw.method = "chi.squared", fw.perc = 0.5)
This is my tuning
rdesc <- makeResampleDesc("CV", iters = 3L, stratify = TRUE)
ps<- makeParamSet(makeDiscreteParam("fw.perc", values = seq(0.2, 0.8, 0.1)),
makeIntegerParam("mtries", lower = 2, upper = 10),
makeIntegerParam("ntrees", lower = 20, upper = 50)
)
Tuned_rf <- tuneParams(rfh20.lrn, task = QBE_classif.task, resampling = rdesc.h2orf, par.set = ps.h2orf, control = makeTuneControlGrid())
While plotting the tune
h2orf_data = generateHyperParsEffectData(Tuned_rf, partial.dep = TRUE)
plotHyperParsEffect(h2orf_data, x = "iteration", y = "mmce.test.mean", plot.type = "line", partial.dep.learn =rfh20.lrn)
I am getting the Error
Error in checkLearner(partial.dep.learn, "regr") :
Learner 'classif.h2o.randomForest.filtered' must be of type 'regr', not: 'classif'
I would expect to see the plot for any more tuning requirement so I can add more hyper tuning, am I missing some thing.
The partial.dep.learn parameter needs a regression learner; see the documentation.
I would like to compare simple logistic regressions models where each model considers a specified set of features only. I would like to perform comparisons of these regression models on resamples of the data.
The R package mlr allows me to select columns at the task level using dropFeatures. The code would be something like:
full_task = makeClassifTask(id = "full task", data = my_data, target = "target")
reduced_task = dropFeatures(full_task, setdiff( getTaskFeatureNames(full_task), list_feat_keep))
Then I can do benchmark experiments where I have a list of tasks.
lrn = makeLearner("classif.logreg", predict.type = "prob")
rdesc = makeResampleDesc(method = "Bootstrap", iters = 50, stratify = TRUE)
bmr = benchmark(lrn, list(full_task, reduced_task), rdesc, measures = auc, show.info = FALSE)
How can I generate a learner that only considers a specified set of features.
As far as I know the filter or selection methods always apply some statistical
procedure but do not allow to select the features directly. Thank you!
The first solution is lazy and also not optimal because the filter calculation is still carried out:
library(mlr)
task = sonar.task
sel.feats = c("V1", "V10")
lrn = makeLearner("classif.logreg", predict.type = "prob")
lrn.reduced = makeFilterWrapper(learner = lrn, fw.method = "variance", fw.abs = 2, fw.mandatory.feat = sel.feats)
bmr = benchmark(list(lrn, lrn.reduced), task, cv3, measures = auc, show.info = FALSE)
The second one uses the preprocessing wrapper to filter the data and should be the fastest solution and is also more flexible:
lrn.reduced.2 = makePreprocWrapper(
learner = lrn,
train = function(data, target, args) list(data = data[, c(sel.feats, target)], control = list()),
predict = function(data, target, args, control) data[, sel.feats]
)
bmr = benchmark(list(lrn, lrn.reduced.2), task, cv3, measures = auc, show.info = FALSE)
I am trying to use the MLR package to tune the hyper-parameters of a decision tree built with the rpart package. Even if I can tune the basic parameters of the decision tree (e.g. minsplit, maxdepth and so on), I am not able to properly set the values of the parameter param. Specifically, I would like to try different priors in the grid search.
Here the code I written (dat is the dataframe I am using, and target is my class variable):
# Create a task
dat.task = makeClassifTask(id = "tree", data = dat, target = "target")
# Define the model
resamp = makeResampleDesc("CV", iters = 4L)
# Create the learner
lrn = makeLearner("classif.rpart")
# Create the grid params
control.grid = makeTuneControlGrid()
ps = makeParamSet(
makeDiscreteParam("cp", values = seq(0.001, 0.006, 0.002)),
makeDiscreteParam("minsplit", values = c(1, 5, 10, 50)),
makeDiscreteParam("maxdepth", values = c(20, 30, 50)),
makeDiscreteParam("parms", values = list(prior=list(c(.6, .4),
c(.5, .5))))
)
When I try to execute the tuning, with:
# Actual tuning, with accuracy as evaluation metric
tuned = tuneParams(lrn, task = dat.task,
resampling = resamp,
control = control.grid,
par.set = ps, measures = acc)
I get the error
Error in get(paste("rpart", method, sep = "."), envir = environment())(Y, : The parms list must have names
I also tried to define parms as an UntypedParam with
makeUntypedParam("parms", special.vals = list(prior=list(c(.6, .4), c(.5,.5))))
This was because by typing getParamSet("classif.rpart"), it seems to me that the tuning accepts an "untyped variable" rather than a discrete one.
However, when I try this, I get the error:
Error in makeOptPath(par.set, y.names, minimize, add.transformed.x, include.error.message, :
OptPath can currently only be used for: numeric,integer,numericvector,integervector,logical,logicalvector,discrete,discretevector,character,charactervector
Can anybody help?
You have to define the Parameter "parms" like this:
makeDiscreteParam("parms", values = list(a = list(prior = c(.6, .4)), b = list(prior = c(.5, .5))))
a and b can be arbitrary names that just reflect what the actual value says.