import predict

mlr-org · Dec 6, 2020 · 6b00dea · 6b00dea
1 parent 8b00da3
commit 6b00dea
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Showing 22 changed files with 27 additions and 25 deletions.
diff --git a/NAMESPACE b/NAMESPACE
@@ -31,4 +31,5 @@ importFrom(R6,R6Class)
 importFrom(mlr3,LearnerClassif)
 importFrom(mlr3,LearnerRegr)
 importFrom(mlr3,mlr_learners)
+importFrom(stats,predict)
 importFrom(utils,bibentry)
diff --git a/R/LearnerClassifCVGlmnet.R b/R/LearnerClassifCVGlmnet.R
@@ -126,13 +126,13 @@ LearnerClassifCVGlmnet = R6Class("LearnerClassifCVGlmnet",
       }
 
       if (self$predict_type == "response") {
-        response = mlr3misc::invoke(stats::predict, self$model,
+        response = mlr3misc::invoke(predict, self$model,
           newx = newdata, type = "class",
           .args = pars)
 
         list(response = drop(response))
       } else {
-        prob = mlr3misc::invoke(stats::predict, self$model,
+        prob = mlr3misc::invoke(predict, self$model,
           newx = newdata, type = "response",
           .args = pars)
 

diff --git a/R/LearnerClassifGlmnet.R b/R/LearnerClassifGlmnet.R
@@ -125,12 +125,12 @@ LearnerClassifGlmnet = R6Class("LearnerClassifGlmnet",
       }
 
       if (self$predict_type == "response") {
-        response = mlr3misc::invoke(stats::predict, self$model,
+        response = mlr3misc::invoke(predict, self$model,
           newx = newdata, type = "class",
           .args = pars)
         list(response = drop(response))
       } else {
-        prob = mlr3misc::invoke(stats::predict, self$model,
+        prob = mlr3misc::invoke(predict, self$model,
           newx = newdata, type = "response",
           .args = pars)
 

diff --git a/R/LearnerClassifLDA.R b/R/LearnerClassifLDA.R
@@ -74,7 +74,7 @@ LearnerClassifLDA = R6Class("LearnerClassifLDA",
         pars$predict.prior = NULL
       }
       newdata = task$data(cols = task$feature_names)
-      p = mlr3misc::invoke(stats::predict, self$model,
+      p = mlr3misc::invoke(predict, self$model,
         newdata = newdata,
         .args = self$param_set$get_values(tags = "predict"))
 

diff --git a/R/LearnerClassifLogReg.R b/R/LearnerClassifLogReg.R
@@ -71,7 +71,7 @@ LearnerClassifLogReg = R6Class("LearnerClassifLogReg",
     .predict = function(task) {
       newdata = task$data(cols = task$feature_names)
 
-      p = unname(stats::predict(self$model, newdata = newdata, type = "response"))
+      p = unname(predict(self$model, newdata = newdata, type = "response"))
       levs = levels(self$model$data[[task$target_names]])
 
       if (self$predict_type == "response") {

diff --git a/R/LearnerClassifMultinom.R b/R/LearnerClassifMultinom.R
@@ -65,10 +65,10 @@ LearnerClassifMultinom = R6Class("LearnerClassifMultinom",
       levs = task$class_names
 
       if (self$predict_type == "response") {
-        response = mlr3misc::invoke(stats::predict, self$model, newdata = newdata, type = "class")
+        response = mlr3misc::invoke(predict, self$model, newdata = newdata, type = "class")
         list(response = drop(response))
       } else {
-        prob = mlr3misc::invoke(stats::predict, self$model, newdata = newdata, type = "probs")
+        prob = mlr3misc::invoke(predict, self$model, newdata = newdata, type = "probs")
         if (length(levs) == 2L) {
           prob = matrix(c(1 - prob, prob), ncol = 2L, byrow = FALSE)
           colnames(prob) = levs

diff --git a/R/LearnerClassifNaiveBayes.R b/R/LearnerClassifNaiveBayes.R
@@ -52,12 +52,12 @@ LearnerClassifNaiveBayes = R6Class("LearnerClassifNaiveBayes",
       newdata = task$data(cols = task$feature_names)
 
       if (self$predict_type == "response") {
-        response = mlr3misc::invoke(stats::predict, self$model,
+        response = mlr3misc::invoke(predict, self$model,
           newdata = newdata,
           type = "class", .args = pars)
         list(response = response)
       } else {
-        prob = mlr3misc::invoke(stats::predict, self$model, newdata = newdata,
+        prob = mlr3misc::invoke(predict, self$model, newdata = newdata,
           type = "raw", .args = pars)
         list(prob = prob)
       }

diff --git a/R/LearnerClassifQDA.R b/R/LearnerClassifQDA.R
@@ -73,7 +73,7 @@ LearnerClassifQDA = R6Class("LearnerClassifQDA",
       }
 
       newdata = task$data(cols = task$feature_names)
-      p = mlr3misc::invoke(stats::predict, self$model, newdata = newdata, .args = pars)
+      p = mlr3misc::invoke(predict, self$model, newdata = newdata, .args = pars)
 
       if (self$predict_type == "response") {
         list(response = p$class)

diff --git a/R/LearnerClassifRanger.R b/R/LearnerClassifRanger.R
@@ -122,7 +122,7 @@ LearnerClassifRanger = R6Class("LearnerClassifRanger",
     .predict = function(task) {
       pars = self$param_set$get_values(tags = "predict")
       newdata = task$data(cols = task$feature_names)
-      p = mlr3misc::invoke(stats::predict, self$model,
+      p = mlr3misc::invoke(predict, self$model,
         data = newdata,
         predict.type = "response", .args = pars)
 

diff --git a/R/LearnerClassifSVM.R b/R/LearnerClassifSVM.R
@@ -80,7 +80,7 @@ LearnerClassifSVM = R6Class("LearnerClassifSVM",
       pars = self$param_set$get_values(tags = "predict")
       newdata = as.matrix(task$data(cols = task$feature_names))
       newdata = newdata[, self$state$feature_names, drop = FALSE]
-      p = mlr3misc::invoke(stats::predict, self$model,
+      p = mlr3misc::invoke(predict, self$model,
         newdata = newdata,
         probability = (self$predict_type == "prob"), .args = pars)
 

diff --git a/R/LearnerClassifXgboost.R b/R/LearnerClassifXgboost.R
@@ -223,7 +223,7 @@ LearnerClassifXgboost = R6Class("LearnerClassifXgboost",
 
       newdata = data.matrix(task$data(cols = task$feature_names))
       newdata = newdata[, model$feature_names, drop = FALSE]
-      pred = mlr3misc::invoke(stats::predict, model, newdata = newdata, .args = pars)
+      pred = mlr3misc::invoke(predict, model, newdata = newdata, .args = pars)
 
       if (nlvls == 2L) { # binaryclass
         if (pars$objective == "multi:softprob") {

diff --git a/R/LearnerRegrCVGlmnet.R b/R/LearnerRegrCVGlmnet.R
@@ -125,7 +125,7 @@ LearnerRegrCVGlmnet = R6Class("LearnerRegrCVGlmnet",
         pars$predict.gamma = NULL
       }
 
-      response = invoke(stats::predict, self$model, newx = newdata,
+      response = invoke(predict, self$model, newx = newdata,
         type = "response", .args = pars)
       list(response = drop(response))
     }

diff --git a/R/LearnerRegrGlmnet.R b/R/LearnerRegrGlmnet.R
@@ -129,7 +129,7 @@ LearnerRegrGlmnet = R6Class("LearnerRegrGlmnet",
         pars$s = self$param_set$default$s
       }
 
-      response = mlr3misc::invoke(stats::predict, self$model,
+      response = mlr3misc::invoke(predict, self$model,
         newx = newdata,
         type = "response", .args = pars)
       list(response = drop(response))

diff --git a/R/LearnerRegrLM.R b/R/LearnerRegrLM.R
@@ -65,10 +65,10 @@ LearnerRegrLM = R6Class("LearnerRegrLM",
       newdata = task$data(cols = task$feature_names)
 
       if (self$predict_type == "response") {
-        response = stats::predict(self$model, newdata = newdata, se.fit = FALSE)
+        response = predict(self$model, newdata = newdata, se.fit = FALSE)
         list(response = response)
       } else {
-        pred = stats::predict(self$model, newdata = newdata, se.fit = TRUE)
+        pred = predict(self$model, newdata = newdata, se.fit = TRUE)
         list(response = pred$fit, se = pred$se.fit)
       }
     }

diff --git a/R/LearnerRegrRanger.R b/R/LearnerRegrRanger.R
@@ -135,7 +135,7 @@ LearnerRegrRanger = R6Class("LearnerRegrRanger",
     .predict = function(task) {
       pars = self$param_set$get_values(tags = "predict")
       newdata = task$data(cols = task$feature_names)
-      preds = mlr3misc::invoke(stats::predict, self$model,
+      preds = mlr3misc::invoke(predict, self$model,
         data = newdata,
         type = self$predict_type, .args = pars)
       list(response = preds$predictions, se = preds$se)

diff --git a/R/LearnerRegrSVM.R b/R/LearnerRegrSVM.R
@@ -72,7 +72,7 @@ LearnerRegrSVM = R6Class("LearnerRegrSVM",
       pars = self$param_set$get_values(tags = "predict")
       newdata = as.matrix(task$data(cols = task$feature_names))
       newdata = newdata[, self$state$feature_names, drop = FALSE]
-      response = invoke(stats::predict, self$model, newdata = newdata, type = "response", .args = pars)
+      response = invoke(predict, self$model, newdata = newdata, type = "response", .args = pars)
       list(response = response)
     }
   )

diff --git a/R/LearnerRegrXgboost.R b/R/LearnerRegrXgboost.R
@@ -192,7 +192,7 @@ LearnerRegrXgboost = R6Class("LearnerRegrXgboost",
       model = self$model
       newdata = data.matrix(task$data(cols = task$feature_names))
       newdata = newdata[, model$feature_names, drop = FALSE]
-      response = invoke(stats::predict, model, newdata = newdata, .args = pars)
+      response = invoke(predict, model, newdata = newdata, .args = pars)
 
       list(response = response)
     }

diff --git a/R/LearnerSurvCVGlmnet.R b/R/LearnerSurvCVGlmnet.R
@@ -124,7 +124,7 @@ LearnerSurvCVGlmnet = R6Class("LearnerSurvCVGlmnet",
         pars$predict.gamma = NULL
       }
 
-      lp = as.numeric(invoke(stats::predict, self$model, newx = newdata, type = "link", .args = pars))
+      lp = as.numeric(invoke(predict, self$model, newx = newdata, type = "link", .args = pars))
       list(lp = lp, crank = lp)
     }
   )

diff --git a/R/LearnerSurvGlmnet.R b/R/LearnerSurvGlmnet.R
@@ -118,7 +118,7 @@ LearnerSurvGlmnet = R6Class("LearnerSurvGlmnet",
         pars$s = self$param_set$default$s
       }
 
-      lp = invoke(stats::predict, self$model, newx = newdata, type = "link", .args = pars)
+      lp = invoke(predict, self$model, newx = newdata, type = "link", .args = pars)
 
       list(crank = lp, lp = lp)
     }

diff --git a/R/LearnerSurvRanger.R b/R/LearnerSurvRanger.R
@@ -115,7 +115,7 @@ LearnerSurvRanger = R6Class("LearnerSurvRanger",
 
     .predict = function(task) {
       newdata = task$data(cols = task$feature_names)
-      fit = stats::predict(object = self$model, data = newdata)
+      fit = predict(object = self$model, data = newdata)
       mlr3proba::.surv_return(times = fit$unique.death.times, surv = fit$survival)
     }
   )

diff --git a/R/LearnerSurvXgboost.R b/R/LearnerSurvXgboost.R
@@ -187,7 +187,7 @@ LearnerSurvXgboost = R6Class("LearnerSurvXgboost",
       model = self$model
       newdata = data.matrix(task$data(cols = task$feature_names))
       newdata = newdata[, model$feature_names, drop = FALSE]
-      lp = log(mlr3misc::invoke(stats::predict, model, newdata = newdata, .args = pars))
+      lp = log(mlr3misc::invoke(predict, model, newdata = newdata, .args = pars))
 
       list(crank = lp, lp = lp)
     }

diff --git a/R/zzz.R b/R/zzz.R
@@ -3,6 +3,7 @@
 #' @import mlr3misc
 #' @importFrom R6 R6Class
 #' @importFrom mlr3 mlr_learners LearnerClassif LearnerRegr
+#' @importFrom stats predict
 #'
 #' @description
 #' More learners are implemented in the [mlr3extralearners package](https://github.com/mlr-org/mlr3extralearners).