getModel fixes for kmeans

panzelon · Dec 17, 2014 · bf6fba1 · bf6fba1
1 parent 46efdc5
commit bf6fba1
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Showing 3 changed files with 13 additions and 9 deletions.
diff --git a/R/h2o-package/R/Algorithms.R b/R/h2o-package/R/Algorithms.R
@@ -535,11 +535,11 @@ h2o.kmeans <- function(data, centers, cols = '', key = "", iter.max = 10, normal
   params$centers = res$parameters$k
   params$iter.max = res$parameters$max_iter
   result$params = params
-  
-  result$cluster = .h2o.exec2(clusters_key, h2o = data@h2o, clusters_key)
-  feat = res$'_names'
-  result$centers = t(matrix(unlist(res$centers), ncol = res$parameters$k))
-  dimnames(result$centers) = list(seq(1,res$parameters$k), feat)
+
+  result$cluster <- if( .check.exists(data@h2o, clusters_key) ) NULL else .h2o.exec2(clusters_key, h2o = data@h2o, clusters_key)
+  feat <- res$'_names'
+  result$centers <- t(matrix(unlist(res$centers), ncol = res$parameters$k))
+  dimnames(result$centers) <- list(seq(1,res$parameters$k), feat)
   #result$totss <- res$total_SS
   result$withinss <- res$within_cluster_variances ## FIXME: sum of squares != variances (bad name of the latter)
   result$tot.withinss <- res$total_within_SS

diff --git a/R/h2o-package/R/Classes.R b/R/h2o-package/R/Classes.R
@@ -49,7 +49,7 @@ setClass("H2OGLMModelList", representation(models="list", best_model="numeric",
 # setMethod("initialize", "H2OParsedData", function(.Object, h2o = new("H2OClient"), key = "") {
 #   .Object@h2o = h2o
 #   .Object@key = key
-#   .Object@env = new.env()
+#   .Object@env = new.env()ASTSS
 #
 #   assign("h2o", .Object@h2o, envir = .Object@env)
 #   assign("key", .Object@key, envir = .Object@env)
@@ -270,11 +270,10 @@ setMethod("show", "H2OKMeansModel", function(object) {
     print(object@data@h2o)
     cat("Parsed Data Key:", object@data@key, "\n\n")
     cat("K-Means Model Key:", object@key)
-
     model = object@model
     cat("\n\nK-means clustering with", length(model$size), "clusters of sizes "); cat(model$size, sep=", ")
     cat("\n\nCluster means:\n"); print(model$centers)
-    cat("\nClustering vector:\n"); print(summary(model$cluster))
+    if (!is.null(model$cluster)) { cat("\nClustering vector:\n"); print(summary(model$cluster)) }
     cat("\nWithin cluster sum of squares by cluster:\n"); print(model$withinss)
 #    cat("(between_SS / total_SS = ", round(100*sum(model$betweenss)/model$totss, 1), "%)\n")
     cat("\nAvailable components:\n\n"); print(names(model))

diff --git a/R/h2o-package/R/Internal.R b/R/h2o-package/R/Internal.R
@@ -889,6 +889,11 @@ function(h2o, key) {
   .fetchJSON(h2o, key)
 }
 
+.check.exists <- function(h2o, key) {
+  keys <- as.data.frame(h2o.ls(h2o))[,1]
+  key %in% keys
+}
+
 #'
 #' Fetch the model from the key
 h2o.getModel <- function(h2o, key) {
@@ -926,7 +931,7 @@ h2o.getModel <- function(h2o, key) {
   dest_key   <- key #params$destination_key
 
   train_fr   <- new("H2OParsedData", key = "NA")
-  if(!is.null(response$"_dataKey")) train_fr <- h2o.getFrame(h2o, response$"_dataKey")
+  if(!is.null(response$"_dataKey") && .check.exists(h2o, response$"_dataKey")) train_fr <- h2o.getFrame(h2o, response$"_dataKey")
   params$importance <- !is.null(params$varimp)
   if(!is.null(params$family) && model.type == "gbm_model") {
     if(params$classification == "false") {params$distribution <- "gaussian"