- Add Paramtest for all learners and check for missing params #96
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R/LearnerClassifKKNN.R:67:1: style: Lines should not be more than 100 characters. p = invoke(kknn::kknn, formula = model$formula, train = model$data, test = newdata, .args = model$pars)
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerClassifRanger.R:64:1: style: Lines should not be more than 100 characters. ParamFct$new("se.method", default = "infjack", levels = c("jack", "infjack"), tags = "predict")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrGlmnet.R:28:1: style: Lines should not be more than 100 characters. ParamFct$new("family", default = "gaussian", levels = c("gaussian", "poisson"), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrGlmnet.R:32:1: style: Lines should not be more than 100 characters. ParamFct$new("type.measure", levels = c("deviance", "class", "auc", "mse", "mae"), default = "deviance", tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrGlmnet.R:33:1: style: Lines should not be more than 100 characters. ParamDbl$new("s", lower = 0, special_vals = list("lambda.1se", "lambda.min"), default = "lambda.1se", tags = "predict"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrKKNN.R:32:1: style: Lines should not be more than 100 characters. ParamFct$new("kernel", levels = c("rectangular", "triangular", "epanechnikov", "biweight", "triweight", "cos", "inv", "gaussian", "rank", "optimal"), default = "optimal", tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrKKNN.R:61:1: style: Lines should not be more than 100 characters. p = invoke(kknn::kknn, formula = model$formula, train = model$data, test = newdata, .args = model$pars)
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrLM.R:68:1: style: Lines should not be more than 100 characters. PredictionRegr$new(task = task, response = predict(self$model, newdata = newdata, se.fit = FALSE))
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrRanger.R:63:1: style: Lines should not be more than 100 characters. ParamInt$new("seed", default = NULL, special_vals = list(NULL), tags = c("train", "predict")),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrRanger.R:67:1: style: Lines should not be more than 100 characters. ParamFct$new("se.method", default = "infjack", levels = c("jack", "infjack"), tags = "predict")
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrRanger.R:133:1: style: Lines should not be more than 100 characters. preds = mlr3misc::invoke(predict, self$model, data = newdata, type = self$predict_type, .args = pars)
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrSVM.R:24:1: style: Lines should not be more than 100 characters. ParamFct$new("type", default = "eps-regression", levels = c("eps-regression", "nu-regression"), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrSVM.R:25:1: style: Lines should not be more than 100 characters. ParamFct$new("kernel", default = "radial", levels = c("linear", "polynomial", "radial", "sigmoid"), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrXgboost.R:31:1: style: Lines should not be more than 100 characters. ParamFct$new("booster", default = "gbtree", levels = c("gbtree", "gblinear", "dart"), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrXgboost.R:49:1: style: Lines should not be more than 100 characters. ParamDbl$new("missing", default = NA, tags = c("train", "predict"), special_vals = list(NA, NA_real_, NULL)),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrXgboost.R:57:1: style: Lines should not be more than 100 characters. ParamInt$new("early_stopping_rounds", default = NULL, lower = 1L, special_vals = list(NULL), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrXgboost.R:59:1: style: Lines should not be more than 100 characters. ParamFct$new("sample_type", default = "uniform", levels = c("uniform", "weighted"), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrXgboost.R:60:1: style: Lines should not be more than 100 characters. ParamFct$new("normalize_type", default = "tree", levels = c("tree", "forest"), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrXgboost.R:64:1: style: Lines should not be more than 100 characters. ParamFct$new("tree_method", default = "auto", levels = c("auto", "exact", "approx", "hist", "gpu_hist"), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrXgboost.R:65:1: style: Lines should not be more than 100 characters. ParamFct$new("grow_policy", default = "depthwise", levels = c("depthwise", "lossguide"), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrXgboost.R:73:1: style: Lines should not be more than 100 characters. ParamFct$new("feature_selector", default = "cyclic", levels = c("cyclic", "shuffle", "random", "greedy", "thrifty"), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/LearnerRegrXgboost.R:75:1: style: Lines should not be more than 100 characters. ParamFct$new("predictor", default = "cpu_predictor", levels = c("cpu_predictor", "gpu_predictor"), tags = "train"),
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/zzz.R:8:1: style: Lines should not be more than 100 characters. #' More learners are available in the `mlr3learners` repository on Github (\url{https://github.com/mlr3learners}).
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/zzz.R:9:1: style: Lines should not be more than 100 characters. #' There also is a wiki page listing all currently available custom learners (\url{https://github.com/mlr-org/mlr3learners/wiki/Extra-Learners}).
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~R/zzz.R:10:1: style: Lines should not be more than 100 characters. #' A guide on how to create custom learners is covered in the book: \url{https://mlr3book.mlr-org.com}.
^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~tests/testthat/helper.R:3:1: style: Lines should not be more than 100 characters. lapply(list.files(system.file("testthat", package = "mlr3"), pattern = "^helper.*\\.[rR]$", full.names = TRUE), source)
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pat-s
changed the title
.lintrconfig file addedglmnet
kknn
lda
log_reg
qda
ranger
svm
xgboost
Here more are missing - however, all params passed via arg
paramscannot be checked against programatically :/