more reversion to previous code

topepo · Aug 8, 2022 · f7f1ab0 · f7f1ab0
1 parent f8d4749
commit f7f1ab0
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Showing 3 changed files with 17 additions and 31 deletions.
diff --git a/models/files/ranger.R b/models/files/ranger.R
@@ -14,8 +14,7 @@ modelInfo <- list(label = "Random Forest",
                                           class = c("numeric", "character", "numeric"),
                                           label = c("#Randomly Selected Predictors",
                                                     "Splitting Rule",
-                                                    "Minimal Node Size"
-                                                    )),
+                                                    "Minimal Node Size")),
                   grid = function(x, y, len = NULL, search = "grid") {
                     if(search == "grid") {
                       srule <-
@@ -24,9 +23,9 @@ modelInfo <- list(label = "Random Forest",
                       else
                         "variance"
                       out <- expand.grid(mtry =
-                                          caret::var_seq(p = ncol(x),
-                                                         classification = is.factor(y),
-                                                         len = len),
+                                           caret::var_seq(p = ncol(x),
+                                                          classification = is.factor(y),
+                                                          len = len),
                                          min.node.size = ifelse( is.factor(y), 1, 5),
                                          splitrule = c(srule, "extratrees"))
                     } else {
@@ -38,8 +37,7 @@ modelInfo <- list(label = "Random Forest",
                         data.frame(
                           min.node.size= sample(1:(min(20,nrow(x))), size = len, replace = TRUE),
                           mtry = sample(1:ncol(x), size = len, replace = TRUE),
-                          splitrule = sample(srules, size = len, replace = TRUE),
-                          num.trees = 500
+                          splitrule = sample(srules, size = len, replace = TRUE)
                         )
                     }
                   },
@@ -52,7 +50,6 @@ modelInfo <- list(label = "Random Forest",
                                             mtry = min(param$mtry, ncol(x)),
                                             min.node.size = param$min.node.size,
                                             splitrule = as.character(param$splitrule),
-                                            num.trees = param$num.trees,
                                             write.forest = TRUE,
                                             probability = classProbs,
                                             case.weights = wts,
@@ -63,7 +60,6 @@ modelInfo <- list(label = "Random Forest",
                                             mtry = min(param$mtry, ncol(x)),
                                             min.node.size = param$min.node.size,
                                             splitrule = as.character(param$splitrule),
-                                            num.trees = param$num.trees,
                                             write.forest = TRUE,
                                             probability = classProbs,
                                             ...)

diff --git a/models/files/rf.R b/models/files/rf.R
@@ -2,28 +2,20 @@ modelInfo <- list(label = "Random Forest",
                   library = "randomForest",
                   loop = NULL,
                   type = c("Classification", "Regression"),
-                  parameters = data.frame(parameter = c("mtry", "nodesize"),
-                                          class = c("numeric", "numeric"),
-                                          label = c("#Randomly Selected Predictors",
-                                                    "Minimum Node Size")),
+                  parameters = data.frame(parameter = "mtry",
+                                          class = "numeric",
+                                          label = "#Randomly Selected Predictors"),
                   grid = function(x, y, len = NULL, search = "grid") {
                     if(search == "grid") {
-                      out <- expand.grid(mtry = caret::var_seq(p = ncol(x),
+                      out <- data.frame(mtry = caret::var_seq(p = ncol(x),
                                                               classification = is.factor(y),
-                                                              len = len),
-                                         nodesize = ifelse( is.factor(y), 1, 5)
-                      )
+                                                              len = len))
                     } else {
-                      out <- data.frame(mtry = unique(sample(1:ncol(x), size = len, replace = TRUE)),
-                                        nodesize = sample(1:(min(20,nrow(x))), size = len, replace = TRUE)
-                      )
+                      out <- data.frame(mtry = unique(sample(1:ncol(x), size = len, replace = TRUE)))
                     }
                   },
                   fit = function(x, y, wts, param, lev, last, classProbs, ...)
-                    randomForest::randomForest(x, y,
-                                               mtry = min(param$mtry, ncol(x)),
-                                               nodesize = param$nodesize,
-                                               ...),
+                    randomForest::randomForest(x, y, mtry = param$mtry, ...),
                   predict = function(modelFit, newdata, submodels = NULL)
                     if(!is.null(newdata)) predict(modelFit, newdata) else predict(modelFit),
                   prob = function(modelFit, newdata, submodels = NULL)
@@ -41,20 +33,17 @@ modelInfo <- list(label = "Random Forest",
                     varImp <- randomForest::importance(object, ...)
                     if(object$type == "regression") {
                       if("%IncMSE" %in% colnames(varImp)) {
-                        varImp <- as.data.frame(varImp[,"%IncMSE", drop = FALSE])
-                        colnames(varImp) <- "Overall"
+                        varImp <- data.frame(Overall = varImp[,"%IncMSE"])
                       } else {
-                        varImp <- as.data.frame(varImp[,1, drop = FALSE])
-                        colnames(varImp) <- "Overall"
+                        varImp <- data.frame(Overall = varImp[,1])
                       }
                     }
                     else {
                       retainNames <- levels(object$y)
                       if(all(retainNames %in% colnames(varImp))) {
-                        varImp <- varImp[, retainNames, drop = FALSE]
+                        varImp <- varImp[, retainNames]
                       } else {
-                        varImp <- as.data.frame(varImp[,1, drop = FALSE])
-                        colnames(varImp) <- "Overall"
+                        varImp <- data.frame(Overall = varImp[,1])
                       }
                     }
 
@@ -76,3 +65,4 @@ modelInfo <- list(label = "Random Forest",
                     names(out) <- if(x$type == "regression") c("RMSE", "Rsquared") else c("Accuracy", "Kappa")
                     out
                   })
+
diff --git a/pkg/caret/inst/models/models.RData b/pkg/caret/inst/models/models.RData