exalate-issue-sync[bot]
commented
1 year ago Wendy commented: Thank you Erin. Should be a simple fix.
Closed exalate-issue-sync[bot] closed 1 year ago
exalate-issue-sync[bot]
commented
1 year ago Wendy commented: Thank you Erin. Should be a simple fix.
h2o-ops
commented
1 year ago JIRA Issue Details
Jira Issue: PUBDEV-8267 Assignee: Wendy Reporter: Erin LeDell State: Resolved Fix Version: 3.34.0.1 Attachments: N/A Development PRs: Available
h2o-ops
commented
1 year ago Linked PRs from JIRA
This should just overwrite the previous model & R object, rather than give an NPE.
{code:java}library(h2o)
> Warning: package 'h2o' was built under R version 4.0.5
>
> ----------------------------------------------------------------------
>
> Your next step is to start H2O:
> > h2o.init()
>
> For H2O package documentation, ask for help:
> > ??h2o
>
> After starting H2O, you can use the Web UI at http://localhost:54321
> For more information visit https://docs.h2o.ai
>
> ----------------------------------------------------------------------
>
> Attaching package: 'h2o'
> The following objects are masked from 'package:stats':
>
> cor, sd, var
> The following objects are masked from 'package:base':
>
> %*%, %in%, &&, ||, apply, as.factor, as.numeric, colnames,
> colnames<-, ifelse, is.character, is.factor, is.numeric, log,
> log10, log1p, log2, round, signif, trunc
h2o.init()
> Connection successful!
>
> R is connected to the H2O cluster:
> H2O cluster uptime: 19 minutes 25 seconds
> H2O cluster timezone: America/Chicago
> H2O data parsing timezone: UTC
> H2O cluster version: 3.32.1.3
> H2O cluster version age: 2 months and 21 days
> H2O cluster name: H2O_started_from_R_E014307_zqv421
> H2O cluster total nodes: 1
> H2O cluster total memory: 26.08 GB
> H2O cluster total cores: 20
> H2O cluster allowed cores: 20
> H2O cluster healthy: TRUE
> H2O Connection ip: localhost
> H2O Connection port: 54321
> H2O Connection proxy: NA
> H2O Internal Security: FALSE
> H2O API Extensions: Amazon S3, Algos, AutoML, Core V3, TargetEncoder, Core V4
> R Version: R version 4.0.2 (2020-06-22)
create frame knots
knots1 <- c(-1.99905699, -0.98143075, 0.02599159, 1.00770987, 1.99942290) frame_Knots1 <- as.h2o(knots1)
> | | | 0% | |======================================================================| 100%
import the dataset
h2o_data <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/glm_test/multinomial_10_classes_10_cols_10000_Rows_train.csv")
> | | | 0% | |==================== | 29% | |================================================================== | 94% | |======================================================================| 100%
h2o_data[["C1"]] <- as.factor(h2o_data[["C1"]])
build the GAM model
gam_model <- h2o.gam(x = "C6", y = "C1", training_frame = h2o_data, family = 'multinomial', gam_columns = "C6", scale = 1, num_knots = 5, knot_ids = h2o.keyof(frame_Knots1))
> | | | 0% | |======================================================================| 100%
error
gam_model <- h2o.gam(x = "C6", y = "C1", training_frame = h2o_data, family = 'multinomial', gam_columns = "C6", scale = 1, num_knots = 5, knot_ids = h2o.keyof(frame_Knots1))
> | | | 0%
>
> java.lang.NullPointerException
>
> java.lang.NullPointerException
> at water.Scope.track(Scope.java:94)
> at hex.gam.GAM.generateKnotsFromKeys(GAM.java:153)
> at hex.gam.GAM.validateGamParameters(GAM.java:268)
> at hex.gam.GAM.init(GAM.java:216)
> at hex.gam.GAM$GAMDriver.computeImpl(GAM.java:664)
> at hex.ModelBuilder$Driver.compute2(ModelBuilder.java:246)
> at water.H2O$H2OCountedCompleter.compute(H2O.java:1637)
> at jsr166y.CountedCompleter.exec(CountedCompleter.java:468)
> at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)
> at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974)
> at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477)
> at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)
> Error: java.lang.NullPointerException{code}