tlverse / sl3

Compare 1563918 ... +249 ... 20834ae

Coverage Reach
sl3_Task.R Lrnr_base.R Lrnr_cv.R Lrnr_sl.R Lrnr_h2o_grid.R utils.R Stack.R Lrnr_lstm_keras.R Lrnr_gru_keras.R variable_type.R Lrnr_xgboost.R Lrnr_density_semiparametric.R Lrnr_gam.R Lrnr_glm_fast.R Lrnr_lightgbm.R importance.R loss_functions.R Lrnr_solnp.R Pipeline.R Lrnr_screener_coefs.R process_data.R Lrnr_stratified.R Lrnr_multiple_ts.R Lrnr_glm.R Lrnr_h2o_glm.R learner_helpers.R Lrnr_glmnet.R Lrnr_screener_correlation.R zzz.R Lrnr_bilstm.R Lrnr_bayesglm.R Lrnr_optim.R Lrnr_ts_weights.R Lrnr_density_discretize.R Lrnr_randomForest.R prediction_plot.R Lrnr_arima.R Lrnr_haldensify.R Shared_Data.R Lrnr_caret.R Lrnr_solnp_density.R Lrnr_independent_binomial.R survival_utils.R Lrnr_multivariate.R process_missing.R Lrnr_ranger.R Lrnr_hal9001.R Lrnr_screener_importance.R Lrnr_svm.R Lrnr_polspline.R multinomial_helpers.R Lrnr_mean.R Lrnr_harmonicReg.R Lrnr_pooled_hazards.R Lrnr_bartMachine.R Lrnr_pkg_SuperLearner_screener.R Lrnr_nnls.R Lrnr_gbm.R Lrnr_dbarts.R Lrnr_pkg_SuperLearner.R Lrnr_earth.R Lrnr_hts.R Lrnr_tsDyn.R Lrnr_rpart.R Lrnr_cv_selector.R Lrnr_gts.R Lrnr_nnet.R Lrnr_bound.R Lrnr_grf.R default_metalearner.R debug_helpers.R density_utils.R Lrnr_rugarch.R Lrnr_density_hse.R Lrnr_revere_task.R sampling.R Lrnr_pca.R Lrnr_pkg_SuperLearner_method.R revere_task.R list_learners.R Custom_chain.R metalearners.R Lrnr_screener_augment.R reindex_folds.R CV_Lrnr_sl.R ts_utils.R Lrnr_expSmooth.R Lrnr_define_interactions.R make_learner_stack.R Lrnr_subset_covariates.R

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Showing 70 of 300 files from the diff.
Newly tracked file
R/Lrnr_lightgbm.R created.
Newly tracked file
R/Lrnr_bayesglm.R created.
Other files ignored by Codecov
NAMESPACE has changed.
man/Lrnr_gam.Rd has changed.
.travis.yml was deleted.
docs/sitemap.xml has changed.
R/Lrnr_lstm.R was deleted.
Makefile has changed.
docs/pkgdown.yml has changed.
man/Lrnr_glm.Rd has changed.
.Rbuildignore has changed.
man/Lrnr_arima.Rd has changed.
man/Lrnr_gbm.Rd has changed.
man/Lrnr_caret.Rd has changed.
man/Lrnr_earth.Rd has changed.
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DESCRIPTION has changed.
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inst/CITATION has changed.
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man/Lrnr_base.Rd has changed.
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README.Rmd has changed.

@@ -42,10 +42,8 @@
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      super$initialize(params = params, ...)
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    }
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  ),
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  private = list(
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    .properties = c("density"),
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    .train = function(task) {
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      discretized <- discretize_variable(task$Y,
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        type = self$params$type,
@@ -72,7 +70,6 @@
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      )
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      return(fit_object)
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    },
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    .predict = function(task) {
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      # make discretized task
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      discretized <- discretize_variable(task$Y,

@@ -85,16 +85,14 @@
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                          honesty = TRUE,
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                          alpha = 0.05,
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                          imbalance.penalty = 0,
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                          num.threads = 1,
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                          num.threads = 1L,
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                          quantiles_pred = 0.5,
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                          ...) {
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      super$initialize(params = args_to_list(), ...)
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    }
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  ),
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  private = list(
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    .properties = c("continuous", "binomial", "categorical", "weights"),
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    .train = function(task) {
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      args <- self$params
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      outcome_type <- self$get_outcome_type(task)
@@ -120,17 +118,17 @@
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      fit_object <- call_with_args(grf::quantile_forest, args)
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      return(fit_object)
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    },
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    .predict = function(task) {
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      # quantiles for which to predict
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      quantiles_pred <- private$.params$quantiles_pred
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      # generate predictions and output
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      predictions <- stats::predict(
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      predictions_list <- stats::predict(
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        private$.fit_object,
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        new_data = data.frame(task$X),
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        quantiles = quantiles_pred
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      )
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      predictions <- as.numeric(predictions_list$predictions)
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      return(predictions)
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    },
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    .required_packages = c("grf")

@@ -91,11 +91,9 @@
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      params <- list(learner = learner, folds = folds, full_fit = full_fit, ...)
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      super$initialize(params = params, ...)
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    },
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    cv_risk = function(loss_fun) {
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      return(cv_risk(self, loss_fun))
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    },
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    print = function() {
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      print("Lrnr_cv")
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      print(self$params$learner)
@@ -175,7 +173,6 @@
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        column_names = new_col_names
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      ))
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    },
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    update = function(task, drop_old = FALSE) {
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      if (!self$is_trained) {
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        return(self$base_train(task))
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      return(new_object)
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    }
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  ),
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  active = list(
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    name = function() {
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      name <- paste("CV", self$params$learner$name, sep = "_")
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    }
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  ),
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  private = list(
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    .properties = c("wrapper", "cv"),
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    .train_sublearners = function(task) {
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      verbose <- getOption("sl3.verbose")
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@@ -281,7 +275,6 @@
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      results <- list(full_fit = full_fit, fold_fits = bundle_delayed(cv_results))
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      return(bundle_delayed(results))
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    },
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    .train = function(task, trained_sublearners) {
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      # prefer folds from params, but default to folds from task
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      folds <- self$params$folds
@@ -340,7 +333,6 @@
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      )
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      return(fit_object)
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    },
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    .predict = function(task) {
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      folds <- task$folds
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      fold_fits <- private$.fit_object$fold_fits

@@ -58,7 +58,6 @@
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      "continuous", "binomial", "categorical", "weights",
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      "offset"
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    ),
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    .train = function(task) {
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      verbose <- getOption("sl3.verbose")
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      params <- self$params
@@ -125,7 +124,6 @@
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      fit_object$name <- "solnp"
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      return(fit_object)
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    },
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    .predict = function(task = NULL) {
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      verbose <- getOption("sl3.verbose")
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      X <- as.matrix(task$X)

@@ -82,7 +82,6 @@
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      private$.fit_object$is_error <- is_error
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    }
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  ),
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  active = list(
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    name = function() {
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      # learners = self$params$learners
@@ -96,11 +95,9 @@
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      return(result)
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    }
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  ),
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  private = list(
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    # modified names of learners
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    .learner_names = NULL,
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    .train_sublearners = function(task) {
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      # generate training subtasks
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      learners <- self$params$learners

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Learn more Showing 8 files with coverage changes found.

Changes in R/density_utils.R
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Files Coverage
R +3.49% 78.36%
Project Totals (90 files) 78.36%
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