h2o

This is what I tried. Depends on what does user put into the function I want to add String or Double to new Chunk. package org.apache.spark.h2o.utils import water.fvec.{NewChunk, Frame, Chunk} import water._ import water.parser.ValueString class ReplaceNa[T >: Any](a: T) extends MRTask{ override def map(c: Chunk, nc: NewChunk): Unit = { for (row <- 0 until c.len()) { a match{ case s: ValueString if(c.isNA(row)) => nc.addStr(s) case d: Double if(c.isNA(row)) => nc.addNum(d) } } } } But I got error error: value outputFrame is not a member of Nothing pred.add(new ReplaceNa(3).doAll(1, pred.vec(4)

I am currently running h2o 's DRF algorithm an a 3-node EC2 cluster (the h2o server spans across all 3 nodes). My data set has 1m rows and 41 columns (40 predictors and 1 response). I use the R bindings to control the cluster and the RF call is as follows model=h2o.randomForest(x=x, y=y, ignore_const_cols=TRUE, training_frame=train_data, seed=1234, mtries=7, ntrees=2000, max_depth=15, min_rows=50, stopping_rounds=3, stopping_metric="MSE", stopping_tolerance=2e-5) For the 3-node cluster (c4.8xlarge, enhanced networking turned on), this takes about 240sec; the CPU utilization is between 10-20%;

Please consider the following basic reproducible example: library(h2o) h2o.init() data("iris") iris.hex = as.h2o(iris, "iris.hex") mod = h2o.glm(y = "Sepal.Length", x = setdiff(colnames(iris), "Sepal.Length"), training_frame = iris.hex, nfolds = 2, seed = 100, lambda_search = T, early_stopping = F, family = "gamma", nlambdas = 100) When I run the above, I expect that h2o will iterate over 100 different values of lambda. However, running length(mod@allparameters$lambda) will show that only 79 values of lambda were actually tested. These 79 values are the first 79 values in the sequence:

I'm trying to implement the FBeta_Score() of the MLmetrics R package : FBeta_Score <- function(y_true, y_pred, positive = NULL, beta = 1) { Confusion_DF <- ConfusionDF(y_pred, y_true) if (is.null(positive) == TRUE) positive <- as.character(Confusion_DF[1,1]) Precision <- Precision(y_true, y_pred, positive) Recall <- Recall(y_true, y_pred, positive) Fbeta_Score <- (1 + beta^2) * (Precision * Recall) / (beta^2 * Precision + Recall) return(Fbeta_Score) } in the H2O distributed random forest model and I want to optimize it during the training phase using the custom_metric_func option. The help

AUTONICS E40S-6-3000-3-T-24 Autonics -E40S6-3000-3-T-24 VEM B21R 100LX 4MLEN TPM40/5083/3KW VEM B21R 180LA/22KW BUCHI V-700 pump jokab safty EDEN 20-046-00 Eva jokab safty EDEN 20-046-06 Adam M12 Jokab Safety 2TLA020046R0600 old: 20-046-06 Dr. Dietrich Muller GmbH Thermigrease TG 20032 HONEYWELL STG74L-E1G000-1-0-AHS-11S-A-10A0 Honeywell GmbH STG74L GAUGE IL LRL -14.7 URL 500 PSI STG74L-E1G000-1-0-AHD-11S-A-10A0-00-0000 HONEYWELL STD720-E1AC4AS-1-0-AHS-11S-A-10A0 Honeywell GmbHSTD720 DP LRL -400 URL 400 H2O STD720-E1AC4AS-1-0-AHD-11S-A-10A0-00-0000 HONEYWELL STG77L-E1G000-1-0-AHS-11S-A-10A0

Looking for an efficient way to plot trees in rstudio, H2O's Flow or in local html page from h2o's RF and GBM models similar to the one in the image in link below. Specifically, how do you plot trees for the objects, (fitted models) rf1 and gbm2 produced by code below perhaps by parsing h2o.download_pojo(rf1) or h2o.download_pojo(gbm1)? # # The following two commands remove any previously installed H2O packages for R. # if ("package:h2o" %in% search()) { detach("package:h2o", unload=TRUE) } # if ("h2o" %in% rownames(installed.packages())) { remove.packages("h2o") } # # Next, we download

I am trying to get a sparse matrix into H2O and I was wondering whether that was possible. Suppose we have the following: test <- Matrix(c(1,0,0,1,1,1,1,0,1), nrow = 3, sparse = TRUE) and assuming my local H2O is localH2O , I can't seem to do the following: as.h2o(test) It gives the error: cannot coerce class "structure("dgCMatrix", package = "Matrix")" to a data.frame . That seems to be pretty logical, however assuming that test is so big that I can't transform it into a dataframe, how am I suppose to load this into H2O? Using a sparse matrix representation it is only 500MB or so. How can I