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Tutorials and training material for the H2O Machine Learning Platform

R 0.44% Java 2.94% HTML 11.48% Python 0.17% Shell 0.01% Scala 0.04% Jupyter Notebook 82.28% C 0.01% Cuda 0.04% Makefile 0.01% Dockerfile 0.02% CSS 0.03% JavaScript 2.56%

h2o tutorial machine-learning data-science deep-learning python r

h2o-tutorials's Introduction

H2O Tutorials

This document contains tutorials and training materials for H2O-3. If you find any problems with the tutorial code, please open an issue in this repository.

For general H2O questions, please post those to Stack Overflow using the "h2o" tag or join the H2O Stream Google Group for questions that don't fit into the Stack Overflow format.

Finding tutorial material in Github

There are a number of tutorials on all sorts of topics in this repo. To help you get started, here are some of the most useful topics in both R and Python.

R Tutorials

Intro to H2O in R
H2O Grid Search & Model Selection in R
H2O Deep Learning in R
H2O Stacked Ensembles in R
H2O AutoML in R
LatinR 2019 H2O Tutorial (broad overview of all the above topics)

Python Tutorials

Most current material

Tutorials in the master branch are intended to work with the lastest stable version of H2O.

	URL
Training material	https://github.com/h2oai/h2o-tutorials/blob/master/SUMMARY.md
Latest stable H2O release	http://h2o.ai/download

Historical events

Tutorial versions in named branches are snapshotted for specific events. Scripts should work unchanged for the version of H2O used at that time.

H2O World 2017 Training

	URL
Training material	https://github.com/h2oai/h2o-tutorials/tree/master/h2o-world-2017/README.md
Wheeler-2 H2O release	http://h2o-release.s3.amazonaws.com/h2o/rel-wheeler/2/index.html

H2O World 2015 Training

	URL
Training material	https://github.com/h2oai/h2o-tutorials/blob/h2o-world-2015-training/SUMMARY.md
Tibshirani-3 H2O release	http://h2o-release.s3.amazonaws.com/h2o/rel-tibshirani/3/index.html

Requirements:

For most tutorials using Python you can install dependent modules to your environment by running the following commands.

# As current user
pip install -r requirements.txt

# As root user
sudo -E pip install -r requirements.txt

Note: If you are behind a corporate proxy you may need to set environment variables for https_proxy accordingly.

# If you are behind a corporate proxy
export https_proxy=https://<user>:<password>@<proxy_server>:<proxy_port>

# As current user
pip install -r requirements.txt

# If you are behind a corporate proxy
export https_proxy=https://<user>:<password>@<proxy_server>:<proxy_port>

# As root user
sudo -E pip install -r requirements.txt

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h2o-tutorials's Issues

Unable to Compile and Run jettyRunWar (Step 1)

Hi I'm working on a project for my capstone project, and would like some additional support on running your script:

h2o-tutorials/tutorials/building-a-smarter-application/BuildingASmarterApplication.pdf

https://github.com/h2oai/app-consumer-loan

../gradle-2.8/bin/gradle jettyRunWar in Step 1 does not execute. I have the Master folder downloaded from the github link provided. Any chance the directories have been renamed?

Apache Drill UDF: Problem with h2o-genmodel

Hello there,
I am trying to write a UDF for Apache Drill which wraps a h2o MOJO but I'm running into a problem. When I copy h2o-genmodel.jar to the Drill path where you put jars, I get the following errors when I attempt to execute a query, regardless of whether the query has anything to do with h2o:

2018-05-28 22:48:17,563 ERROR [24f3408d-9cd2-f5ee-6bf6-30274b289811:frag:0:0] record.AbstractSingleRecordBatch: Failure during query
org.apache.drill.exec.exception.SchemaChangeException: Failure while attempting to load generated class
	at org.apache.drill.exec.physical.impl.svremover.RemovingRecordBatch.getGenerated2Copier(RemovingRecordBatch.java:232)
	at org.apache.drill.exec.physical.impl.svremover.RemovingRecordBatch.setupNewSchema(RemovingRecordBatch.java:70)
	at org.apache.drill.exec.record.AbstractSingleRecordBatch.innerNext(AbstractSingleRecordBatch.java:82)
	at org.apache.drill.exec.physical.impl.svremover.RemovingRecordBatch.innerNext(RemovingRecordBatch.java:93)
	at org.apache.drill.exec.record.AbstractRecordBatch.next(AbstractRecordBatch.java:164)
	at org.apache.drill.exec.record.AbstractRecordBatch.next(AbstractRecordBatch.java:119)
	at org.apache.drill.exec.record.AbstractRecordBatch.next(AbstractRecordBatch.java:109)
	at org.apache.drill.exec.record.AbstractSingleRecordBatch.innerNext(AbstractSingleRecordBatch.java:51)
	at org.apache.drill.exec.physical.impl.project.ProjectRecordBatch.innerNext(ProjectRecordBatch.java:134)
	at org.apache.drill.exec.record.AbstractRecordBatch.next(AbstractRecordBatch.java:164)
	at org.apache.drill.exec.physical.impl.BaseRootExec.next(BaseRootExec.java:105)
	at org.apache.drill.exec.physical.impl.ScreenCreator$ScreenRoot.innerNext(ScreenCreator.java:79)
	at org.apache.drill.exec.physical.impl.BaseRootExec.next(BaseRootExec.java:95)
	at org.apache.drill.exec.work.fragment.FragmentExecutor$1.run(FragmentExecutor.java:234)
	at org.apache.drill.exec.work.fragment.FragmentExecutor$1.run(FragmentExecutor.java:227)
	at java.security.AccessController.doPrivileged(Native Method)
	at javax.security.auth.Subject.doAs(Subject.java:422)
	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
	at org.apache.drill.exec.work.fragment.FragmentExecutor.run(FragmentExecutor.java:227)
	at org.apache.drill.common.SelfCleaningRunnable.run(SelfCleaningRunnable.java:38)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.drill.exec.exception.ClassTransformationException: com.google.common.util.concurrent.UncheckedExecutionException: java.lang.IllegalStateException: Problem found with implementationClasses
	at org.apache.drill.exec.compile.CodeCompiler.createInstances(CodeCompiler.java:197)
	at org.apache.drill.exec.compile.CodeCompiler.createInstance(CodeCompiler.java:163)
	at org.apache.drill.exec.ops.BaseFragmentContext.getImplementationClass(BaseFragmentContext.java:60)
	at org.apache.drill.exec.physical.impl.svremover.RemovingRecordBatch.getGenerated2Copier(RemovingRecordBatch.java:224)
	... 22 more
Caused by: com.google.common.util.concurrent.UncheckedExecutionException: java.lang.IllegalStateException: Problem found with implementationClasses
	at com.google.common.cache.LocalCache$Segment.get(LocalCache.java:2203)
	at com.google.common.cache.LocalCache.get(LocalCache.java:3937)
	at com.google.common.cache.LocalCache.getOrLoad(LocalCache.java:3941)
	at com.google.common.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4824)
	at org.apache.drill.exec.compile.CodeCompiler.createInstances(CodeCompiler.java:186)
	... 25 more
Caused by: java.lang.IllegalStateException: Problem found with implementationClasses
	at org.apache.drill.exec.compile.ClassTransformer.getImplementationClass(ClassTransformer.java:252)
	at org.apache.drill.exec.compile.ClassTransformer.getImplementationClass(ClassTransformer.java:229)
	at org.apache.drill.exec.compile.CodeCompiler$CodeGenCompiler.compile(CodeCompiler.java:79)
	at org.apache.drill.exec.compile.CodeCompiler.makeClass(CodeCompiler.java:229)
	at org.apache.drill.exec.compile.CodeCompiler.access$300(CodeCompiler.java:41)
	at org.apache.drill.exec.compile.CodeCompiler$Loader.load(CodeCompiler.java:212)
	at org.apache.drill.exec.compile.CodeCompiler$Loader.load(CodeCompiler.java:209)
	at com.google.common.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3527)
	at com.google.common.cache.LocalCache$Segment.loadSync(LocalCache.java:2319)
	at com.google.common.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2282)
	at com.google.common.cache.LocalCache$Segment.get(LocalCache.java:2197)
	... 29 more
2018-05-28 22:48:17,564 ERROR [24f3408d-9cd2-f5ee-6bf6-30274b289811:frag:0:0] fragment.FragmentExecutor: SYSTEM ERROR: IllegalStateException: Problem found with implementationClasses

Fragment 0:0

[Error Id: 81d76aa7-51bb-4a7d-88fc-465d8de5734f on charles-mbp.fios-router.home:31010]
org.apache.drill.common.exceptions.UserException: SYSTEM ERROR: IllegalStateException: Problem found with implementationClasses

Fragment 0:0

[Error Id: 81d76aa7-51bb-4a7d-88fc-465d8de5734f on charles-mbp.fios-router.home:31010]
	at org.apache.drill.common.exceptions.UserException$Builder.build(UserException.java:586)
	at org.apache.drill.exec.work.fragment.FragmentExecutor.sendFinalState(FragmentExecutor.java:298)
	at org.apache.drill.exec.work.fragment.FragmentExecutor.cleanup(FragmentExecutor.java:160)
	at org.apache.drill.exec.work.fragment.FragmentExecutor.run(FragmentExecutor.java:267)
	at org.apache.drill.common.SelfCleaningRunnable.run(SelfCleaningRunnable.java:38)
	at java.util.concurrent.ThreadPoolExecutor.runWorker(ThreadPoolExecutor.java:1142)
	at java.util.concurrent.ThreadPoolExecutor$Worker.run(ThreadPoolExecutor.java:617)
	at java.lang.Thread.run(Thread.java:745)
Caused by: org.apache.drill.exec.exception.SchemaChangeException: Failure while attempting to load generated class
	at org.apache.drill.exec.physical.impl.svremover.RemovingRecordBatch.getGenerated2Copier(RemovingRecordBatch.java:232)
	at org.apache.drill.exec.physical.impl.svremover.RemovingRecordBatch.setupNewSchema(RemovingRecordBatch.java:70)
	at org.apache.drill.exec.record.AbstractSingleRecordBatch.innerNext(AbstractSingleRecordBatch.java:82)
	at org.apache.drill.exec.physical.impl.svremover.RemovingRecordBatch.innerNext(RemovingRecordBatch.java:93)
	at org.apache.drill.exec.record.AbstractRecordBatch.next(AbstractRecordBatch.java:164)
	at org.apache.drill.exec.record.AbstractRecordBatch.next(AbstractRecordBatch.java:119)
	at org.apache.drill.exec.record.AbstractRecordBatch.next(AbstractRecordBatch.java:109)
	at org.apache.drill.exec.record.AbstractSingleRecordBatch.innerNext(AbstractSingleRecordBatch.java:51)
	at org.apache.drill.exec.physical.impl.project.ProjectRecordBatch.innerNext(ProjectRecordBatch.java:134)
	at org.apache.drill.exec.record.AbstractRecordBatch.next(AbstractRecordBatch.java:164)
	at org.apache.drill.exec.physical.impl.BaseRootExec.next(BaseRootExec.java:105)
	at org.apache.drill.exec.physical.impl.ScreenCreator$ScreenRoot.innerNext(ScreenCreator.java:79)
	at org.apache.drill.exec.physical.impl.BaseRootExec.next(BaseRootExec.java:95)
	at org.apache.drill.exec.work.fragment.FragmentExecutor$1.run(FragmentExecutor.java:234)
	at org.apache.drill.exec.work.fragment.FragmentExecutor$1.run(FragmentExecutor.java:227)
	at java.security.AccessController.doPrivileged(Native Method)
	at javax.security.auth.Subject.doAs(Subject.java:422)
	at org.apache.hadoop.security.UserGroupInformation.doAs(UserGroupInformation.java:1657)
	at org.apache.drill.exec.work.fragment.FragmentExecutor.run(FragmentExecutor.java:227)
	... 4 more
Caused by: org.apache.drill.exec.exception.ClassTransformationException: com.google.common.util.concurrent.UncheckedExecutionException: java.lang.IllegalStateException: Problem found with implementationClasses
	at org.apache.drill.exec.compile.CodeCompiler.createInstances(CodeCompiler.java:197)
	at org.apache.drill.exec.compile.CodeCompiler.createInstance(CodeCompiler.java:163)
	at org.apache.drill.exec.ops.BaseFragmentContext.getImplementationClass(BaseFragmentContext.java:60)
	at org.apache.drill.exec.physical.impl.svremover.RemovingRecordBatch.getGenerated2Copier(RemovingRecordBatch.java:224)
	... 22 more
Caused by: com.google.common.util.concurrent.UncheckedExecutionException: java.lang.IllegalStateException: Problem found with implementationClasses
	at com.google.common.cache.LocalCache$Segment.get(LocalCache.java:2203)
	at com.google.common.cache.LocalCache.get(LocalCache.java:3937)
	at com.google.common.cache.LocalCache.getOrLoad(LocalCache.java:3941)
	at com.google.common.cache.LocalCache$LocalLoadingCache.get(LocalCache.java:4824)
	at org.apache.drill.exec.compile.CodeCompiler.createInstances(CodeCompiler.java:186)
	... 25 more
Caused by: java.lang.IllegalStateException: Problem found with implementationClasses
	at org.apache.drill.exec.compile.ClassTransformer.getImplementationClass(ClassTransformer.java:252)
	at org.apache.drill.exec.compile.ClassTransformer.getImplementationClass(ClassTransformer.java:229)
	at org.apache.drill.exec.compile.CodeCompiler$CodeGenCompiler.compile(CodeCompiler.java:79)
	at org.apache.drill.exec.compile.CodeCompiler.makeClass(CodeCompiler.java:229)
	at org.apache.drill.exec.compile.CodeCompiler.access$300(CodeCompiler.java:41)
	at org.apache.drill.exec.compile.CodeCompiler$Loader.load(CodeCompiler.java:212)
	at org.apache.drill.exec.compile.CodeCompiler$Loader.load(CodeCompiler.java:209)
	at com.google.common.cache.LocalCache$LoadingValueReference.loadFuture(LocalCache.java:3527)
	at com.google.common.cache.LocalCache$Segment.loadSync(LocalCache.java:2319)
	at com.google.common.cache.LocalCache$Segment.lockedGetOrLoad(LocalCache.java:2282)
	at com.google.common.cache.LocalCache$Segment.get(LocalCache.java:2197)
	... 29 more
Error: SYSTEM ERROR: IllegalStateException: Problem found with implementationClasses

Fragment 0:0

[Error Id: 81d76aa7-51bb-4a7d-88fc-465d8de5734f on charles-mbp.fios-router.home:31010] (state=,code=0)
java.sql.SQLException: SYSTEM ERROR: IllegalStateException: Problem found with implementationClasses

Fragment 0:0

[Error Id: 81d76aa7-51bb-4a7d-88fc-465d8de5734f on charles-mbp.fios-router.home:31010]
	at org.apache.drill.jdbc.impl.DrillCursor.nextRowInternally(DrillCursor.java:489)
	at org.apache.drill.jdbc.impl.DrillCursor.loadInitialSchema(DrillCursor.java:561)
	at org.apache.drill.jdbc.impl.DrillResultSetImpl.execute(DrillResultSetImpl.java:1895)
	at org.apache.drill.jdbc.impl.DrillResultSetImpl.execute(DrillResultSetImpl.java:61)
	at org.apache.calcite.avatica.AvaticaConnection$1.execute(AvaticaConnection.java:473)
	at org.apache.drill.jdbc.impl.DrillMetaImpl.prepareAndExecute(DrillMetaImpl.java:1100)
	at org.apache.calcite.avatica.AvaticaConnection.prepareAndExecuteInternal(AvaticaConnection.java:477)
	at org.apache.drill.jdbc.impl.DrillConnectionImpl.prepareAndExecuteInternal(DrillConnectionImpl.java:191)
	at org.apache.calcite.avatica.AvaticaStatement.executeInternal(AvaticaStatement.java:110)
	at org.apache.calcite.avatica.AvaticaStatement.execute(AvaticaStatement.java:121)
	at org.apache.drill.jdbc.impl.DrillStatementImpl.execute(DrillStatementImpl.java:101)
	at sqlline.Commands.execute(Commands.java:841)
	at sqlline.Commands.sql(Commands.java:751)
	at sqlline.SqlLine.dispatch(SqlLine.java:746)
	at sqlline.SqlLine.begin(SqlLine.java:621)
	at sqlline.SqlLine.start(SqlLine.java:375)
	at sqlline.SqlLine.main(SqlLine.java:268)
Caused by: org.apache.drill.common.exceptions.UserRemoteException: SYSTEM ERROR: IllegalStateException: Problem found with implementationClasses

Fragment 0:0

[Error Id: 81d76aa7-51bb-4a7d-88fc-465d8de5734f on charles-mbp.fios-router.home:31010]
	at org.apache.drill.exec.rpc.user.QueryResultHandler.resultArrived(QueryResultHandler.java:123)
	at org.apache.drill.exec.rpc.user.UserClient.handle(UserClient.java:468)
	at org.apache.drill.exec.rpc.user.UserClient.handle(UserClient.java:102)
	at org.apache.drill.exec.rpc.RpcBus$InboundHandler.decode(RpcBus.java:274)
	at org.apache.drill.exec.rpc.RpcBus$InboundHandler.decode(RpcBus.java:244)
	at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:88)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:356)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:342)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:335)
	at io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:287)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:356)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:342)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:335)
	at io.netty.handler.codec.MessageToMessageDecoder.channelRead(MessageToMessageDecoder.java:102)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:356)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:342)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:335)
	at io.netty.handler.codec.ByteToMessageDecoder.fireChannelRead(ByteToMessageDecoder.java:312)
	at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:286)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:356)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:342)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:335)
	at io.netty.channel.ChannelInboundHandlerAdapter.channelRead(ChannelInboundHandlerAdapter.java:86)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:356)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:342)
	at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:335)
	at io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1294)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:356)
	at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:342)
	at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:911)
	at io.netty.channel.nio.AbstractNioByteChannel$NioByteUnsafe.read(AbstractNioByteChannel.java:131)
	at io.netty.channel.nio.NioEventLoop.processSelectedKey(NioEventLoop.java:645)
	at io.netty.channel.nio.NioEventLoop.processSelectedKeysOptimized(NioEventLoop.java:580)
	at io.netty.channel.nio.NioEventLoop.processSelectedKeys(NioEventLoop.java:497)
	at io.netty.channel.nio.NioEventLoop.run(NioEventLoop.java:459)
	at io.netty.util.concurrent.SingleThreadEventExecutor$2.run(SingleThreadEventExecutor.java:131)
	at java.lang.Thread.run(Thread.java:745)

It seems that some library is conflicting with h2o-genmodel in Drill. Any suggestions?

multithread training and reporting errors.

On the same port H2O, two training tasks are launched, and the first one will start reporting errors.
Server error java.lang.IllegalArgumentException: Error: Job is missing Request: GET /3/Jobs/$03017f000001c20bffffffff$_8e3b4719b2f288d17446c7fa2c164cc3

the error occurred in the Linux operating system, and windows was not recovered.thanks.

Video link

On the README for "h2o-tutorials/tutorials/streaming/storm/" at the end you put "Here's a short video showing what it looks like all together." But the link to the vieo is dead. Little issue but I think the video can bring a lot of information.

h2o.glm build regression with intercept only (Null Model)

Hi All,

I was wondering how to build h2o.glm regression with intercept only (Null Model)

The equivalent in lm would be m0 = lm(y~1, data = data)

Thanks!

What is TargetEncoder?

Hello!
I have a question:
What is TargetEncoder in this line:

from h2o.targetencoder import TargetEncoder

?
I can't import it and can find no code for it.

Fail to run H2o cluster in Kubernetes with Flannel

We try to run distributed H2o cluster in Kubernetes. It works in single-node Kubernetes cluster. But it fails to connect each node by broadcasting because Kubernetes and Flannel may change the source and destination IPs. We have try with flatfile.txt or not and it doesn't work.

Is there any way to run H2o cluster in Kubernetes cluster? If someone has deployed, can you provide the test YAML for this?

Error using h2o.randomForest

With this syntax:

data=h2o.uploadFile("RIC2Simone.csv", header = TRUE,sep=";",
destination_frame = "data")
data[,1] = as.factor(data[,1])

splits <- h2o.splitFrame(data = data,
ratios = c(0.50, 0.25), #partition data into 50%, 25%, 25% chunks
seed = 1) #setting a seed will guarantee reproducibility
train <- h2o.assign(splits[[1]], "train.hex")
valid <- h2o.assign(splits[[2]], "valid.hex")
test <- h2o.assign(splits[[3]],"test.hex")

response <- "Stimolo"

predictors<-setdiff(names(data),response)

m1 <- h2o.deeplearning(
model_id="dl_model_first",
training_frame=train,
validation_frame=valid,
x=predictors,
y=response,
hidden=c(32,32,32),
epochs=1000000,
score_validation_samples=10000,
stopping_rounds=2,
stopping_metric="misclassification",
stopping_tolerance=0.01,
nfolds=5,
fold_assignment="Stratified",
variable_importances=T
)

I got the following error message:

Illegal argument(s) for DRF model: DRF_model_R_1487947076967_1. Details: ERRR on field: _train: Training data must have at least 2 features (incl. response).

grid-search-model-selection.ipynb content broken

h2o-tutorials/h2o-open-tour-2016/chicago/grid-search-model-selection.ipynb The jupyter notebook cannot be rendered by Github, nor could it be opened with local jupyter after been downloaded.

training_data.csv in storm streaming tutorial a 'Numbers' binary file

The training_data.csv is not a csv file (can't head like the tutorial README suggests and running the example.R script errors when the file is attempted to be imported).

A little investigation shows that it is a binary format belonging to Number (Mac spreadsheet software). Opening and exporting as a csv file resolves the problems.

python version

would like to see a python version of h2o stacking

What kind of model is XRT?

Looking at the AutoML documentation, I see a model called XRT. What kind of model is this? What does this acronym stand for?

How to transpose a h2o dataframe in R?

my csv file has features in rows and samples in columns. is there a way to transpose the dataframe in h2o R after reading it using importFile ?

H2OValueError: Unknown parameter keep_cross_validation_models = True

Running through the Python example code given here:
http://docs.h2o.ai/h2o/latest-stable/h2o-docs/data-science/algo-params/keep_cross_validation_models.html

and I get a "H2OValueError: Unknown parameter keep_cross_validation_models = True"

My h2o version is 3.18.0.2, h2o-py version is 3.18.0.2

Error in deeplearning.Rmd

The following line generates an error (Line 78):
plotC( "DL", h2o.deeplearning(1:2,3, spiral,epochs=1e3))

The error is:

Error in h2o.deeplearning(1:2, 3, spiral, epochs = 1000) : argument 'training_frame' is missing, with no default

I found the following edit removes the error:

plotC( "DL", h2o.deeplearning(1:2,3, training_frame = spiral,epochs=1e3))

Is this the right thing to do?

update h2o.shutdown()

When I learn h2o-tutorials/tutorials/gbm-randomforest/GBM_RandomForest_Example.py
I receive a warning:

line 322: h2o.shutdown(prompt=False)

[WARNING] in train_h2o.py line 58:
    >>> h2o.shutdown(prompt=False)
        ^^^^ Deprecated, use ``h2o.cluster().shutdown()``.
H2O session _sid_9e69 closed.

Automl tutorial error

While trying to run automl_binary_classification_product_backorders.ipynb, the last line in the first Ensemble Exploration section:

metalearner = h2o.get_model(aml.leader.metalearner()['name'])

raises:

AttributeError: type object 'H2OGradientBoostingEstimator' has no attribute 'metalearner'

can help users choose parameters for a certain model ?

H2O AutoMl can help users choose the model, but can help users choose parameters for a certain model?

H2O on a Multi-Node LSF Batch Submission System?

Hello,

I work in the Crowther Lab at ETH-Zürich, and we're starting to use H2O to crunch massive ecological datasets (1.2+ million rows with 75+ covariates) that we've collected. In terms of computing, ETH uses an LSF system to manage computational resources of the university clusters. When recently submitting some of the large models, we realized that H2O wasn't using all of the nodes that were assigned to the job. From the university cluster support staff: "it appears that the [script] can not use multiple nodes at the same time... I suggest you check your program's documentation to see whether it is possible to run it with distributed memory, so it can use more lower-memory nodes".

When diagnosing the issue, I found the article on how to use multiple nodes on a SLURM system and another article on how to use H2O on multi-node clusters.

Essentially, I'm unsure as to whether these types of approaches would work with an LSF system as the nodes that are used for each job are only assigned after a full program script has been submitted via Bash using batch submission functions. In other words, I don't know that it's possible to access the IP addresses of the connected nodes before the program has been submitted to the cluster.

Has anyone else had any experience with H2O on LSF based clusters? Have I missed a critical or obvious step somewhere that would allow H2O to access/distribute the memory across all of the nodes?

Thanks so much!

Cheers,
Devin Routh

glm_h2oworld_demo error

In the above tutorial, when we get to this point
glm_binom_v1.train(covtype_X, covtype_y, training_frame=train_b, validation_frame=valid_b), we get the following error

---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-80-629452a19acf> in <module>()
----> 1 glm_binom_v1.train(covtype_X, covtype_y, training_frame=train_b, validation_frame=valid_b)

C:\Users\srahee01\AppData\Local\Continuum\anaconda3\lib\site-packages\h2o\estimators\estimator_base.py in train(self, x, y, training_frame, offset_column, fold_column, weights_column, validation_frame, max_runtime_secs, ignored_columns, model_id, verbose)
    235             return
    236 
--> 237         model.poll(verbose_model_scoring_history=verbose)
    238         model_json = h2o.api("GET /%d/Models/%s" % (rest_ver, model.dest_key))["models"][0]
    239         self._resolve_model(model.dest_key, model_json)

C:\Users\srahee01\AppData\Local\Continuum\anaconda3\lib\site-packages\h2o\job.py in poll(self, verbose_model_scoring_history)
     75             if (isinstance(self.job, dict)) and ("stacktrace" in list(self.job)):
     76                 raise EnvironmentError("Job with key {} failed with an exception: {}\nstacktrace: "
---> 77                                        "\n{}".format(self.job_key, self.exception, self.job["stacktrace"]))
     78             else:
     79                 raise EnvironmentError("Job with key %s failed with an exception: %s" % (self.job_key, self.exception))

OSError: Job with key $03017f00000132d4ffffffff$_9865f9eac98add358d8c3e0068c24322 failed with an exception: java.lang.AssertionError: x out of bounds, expected <0,1> range, got NaN
stacktrace: 
java.lang.AssertionError: x out of bounds, expected <0,1> range, got NaN
	at hex.glm.GLMModel$GLMWeightsFun.link(GLMModel.java:547)
	at hex.glm.GLM.getNullBeta(GLM.java:358)
	at hex.glm.GLM.init(GLM.java:504)
	at hex.glm.GLM$GLMDriver.computeImpl(GLM.java:1200)
	at hex.ModelBuilder$Driver.compute2(ModelBuilder.java:218)
	at hex.glm.GLM$GLMDriver.compute2(GLM.java:571)
	at water.H2O$H2OCountedCompleter.compute(H2O.java:1395)
	at jsr166y.CountedCompleter.exec(CountedCompleter.java:468)
	at jsr166y.ForkJoinTask.doExec(ForkJoinTask.java:263)
	at jsr166y.ForkJoinPool$WorkQueue.runTask(ForkJoinPool.java:974)
	at jsr166y.ForkJoinPool.runWorker(ForkJoinPool.java:1477)
	at jsr166y.ForkJoinWorkerThread.run(ForkJoinWorkerThread.java:104)

h2o R Package: Cheatsheet

I would like to propose that a new cheatsheet is created for h2o R Package, understood as a concept summary for using h2o with R:

https://cran.r-project.org/web/packages/h2o/index.html

The template can be found in the following webpage:
https://www.rstudio.com/resources/cheatsheets/how-to-contribute-a-cheatsheet/

Then, the cheatsheet could be distributed through RStudio Cheatsheet webpage, in the "Contributed Cheatsheets" Section:
https://www.rstudio.com/resources/cheatsheets/

naive bayes on a more realistic example?

Hello the h20 team. Thanks for this wonderful package!

I was simply wondering if there is a tutorial somewhere that shows how we can use h20 to perform a full naive bayes text classification model.

I see a small example here, but in my dataset I have many documents (think headlines of articles), so the matrix representation of the bag-of-word processing would be (in a regular R session) a sparse matrix or a document term matrix.

Can h20 manage/create that? or somehow h2o can only work with a dataframe with a small number of dummies for each selected word already created by the user?

Thanks!

AUC showing different values on the same data.

Version information has been attached as a a text file.
version_info.txt

Code snippet is attached below:

issue_auc.txt

Let me know whether I am missing something obvious here.

GBM fundamentals fail

GBM need low learning rates, not high ones. This means it takes a long time (aka high number of tree-augments to get down in error). It is the Achilles-heel of the algorithm.

Check out slide 25 (and the rest of them) here:
http://www.slideshare.net/DataRobot/gradient-boosted-regression-trees-in-scikitlearn

Check out slide 26 here:
http://www.slideshare.net/0xdata/gbm-27891077

H2O Cluster Shut Down without Notice

I am a h2o R version user and I have a question regarding the h2o local cluster. I setup the cluster by execute the command in r,

h2o.init()

However, the cluster will be turned off automatically when I do not use it for a few hours. For example, I run my model during the night, but when I come back to my office in the morning to check on my model. It says,

Error in h2o.getConnection() : No active connection to an H2O cluster. Did you run h2o.init() ?

Is there a way to fix or work around it ?

how to display the ROC curve with h2o in python?

how to display the ROC curve with h2o in python?
plot(h2o.performance(dlmodel)) this is for R. When i used "plot(mymodel.model_performance(train=True))" or "plot(mymodel.auc(train=True))" it raised a error:
TypeError: 'module' object is not callable

Can not load GBM_RandomForest_Example.ipynb in browser

Can't load this in browser
https://github.com/h2oai/h2o-tutorials/blob/master/tutorials/gbm-randomforest/GBM_RandomForest_Example.ipynb

error with h2o.importFile

df <- h2o.importFile(path = normalizePath("../data/covtype.full.csv"))
WARNING: ParseError at file nfs:\C:\Users\Utilisateur\Documents\R\Machine_Learning\data\covtype.full.csv at byte offset 10305; error = 'Unmatched quote char "'
WARNING: ParseError at file nfs:\C:\Users\Utilisateur\Documents\R\Machine_Learning\data\covtype.full.csv at byte offset 20872; error = 'Unmatched quote char "'
WARNING: ParseError at file nfs:\C:\Users\Utilisateur\Documents\R\Machine_Learning\data\covtype.full.csv at byte offset 24788; error = 'Unmatched quote char "'
WARNING: ParseError at file nfs:\C:\Users\Utilisateur\Documents\R\Machine_Learning\data\covtype.full.csv at byte offset 32650; error = 'Unmatched quote char "'
| | 0%

DistributedException from /127.0.0.1:54321, caused by java.lang.ArrayIndexOutOfBoundsException: 3

Error: DistributedException from /127.0.0.1:54321, caused by java.lang.ArrayIndexOutOfBoundsException: 3

Thanks for your help

unable to deep copy a h20 dataframe

H2O does not seem to have a function to deep copy dataframe yet.

df_orig = h2o.import_file(path = "titanic.csv")
print df_orig[0:3,'age']

df_new = df_orig
df_new[0:3,'age'] = 2
print df_orig[0:3,'age']


age
--
29
0.9167
2


age
--
2
2
2

Hive UDF example: Hadoop installed via Homebrew

For the pom.xml file in the Hive UDF tutorial (I'm using 3.6.0.8), you provide repo links to the HDP repos by default and Cloudera is commented out. I'm using plain vanilla Hadoop installed on OSX via Homebrew.

Do you have the link(s) I should use? Trying the mvn compile step leads to the following:

Randys-MacBook-Pro:hive_udf_template randyzwitch$ mvn compile
[INFO] Scanning for projects...
[WARNING] 
[WARNING] Some problems were encountered while building the effective model for ai.h2o.hive.udf:ScoreData:jar:1.0-SNAPSHOT
[WARNING] 'dependencies.dependency.systemPath' for water:h2o-genmodel:jar should not point at files within the project directory, ${project.basedir}/localjars/h2o-genmodel.jar will be unresolvable by dependent projects @ line 37, column 37
[WARNING] 
[WARNING] It is highly recommended to fix these problems because they threaten the stability of your build.
[WARNING] 
[WARNING] For this reason, future Maven versions might no longer support building such malformed projects.
[WARNING] 
[INFO]                                                                         
[INFO] ------------------------------------------------------------------------
[INFO] Building ScoreData 1.0-SNAPSHOT
[INFO] ------------------------------------------------------------------------
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[INFO] Compiling 2 source files to /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/target/classes
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[ERROR] COMPILATION ERROR : 
[INFO] -------------------------------------------------------------
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[24,7] error: duplicate class: GBMModel
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[181,0] error: duplicate class: NamesHolder_GBMModel
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[204,0] error: duplicate class: GBMModel_ColInfo_1
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[223,0] error: duplicate class: GBMModel_ColInfo_2
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[258,0] error: duplicate class: GBMModel_ColInfo_3
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[274,0] error: duplicate class: GBMModel_ColInfo_4
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[551,0] error: duplicate class: GBMModel_ColInfo_5
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[580,0] error: duplicate class: GBMModel_ColInfo_6
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[600,0] error: duplicate class: GBMModel_ColInfo_7
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[613,0] error: duplicate class: GBMModel_ColInfo_9
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[839,0] error: duplicate class: GBMModel_Forest_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[844,0] error: duplicate class: GBMModel_Tree_0_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[908,0] error: duplicate class: GBMModel_Forest_1
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[913,0] error: duplicate class: GBMModel_Tree_1_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[989,0] error: duplicate class: GBMModel_Forest_2
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[994,0] error: duplicate class: GBMModel_Tree_2_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1078,0] error: duplicate class: GBMModel_Forest_3
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1083,0] error: duplicate class: GBMModel_Tree_3_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1165,0] error: duplicate class: GBMModel_Forest_4
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1170,0] error: duplicate class: GBMModel_Tree_4_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1252,0] error: duplicate class: GBMModel_Forest_5
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1257,0] error: duplicate class: GBMModel_Tree_5_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1341,0] error: duplicate class: GBMModel_Forest_6
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1346,0] error: duplicate class: GBMModel_Tree_6_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1434,0] error: duplicate class: GBMModel_Forest_7
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1439,0] error: duplicate class: GBMModel_Tree_7_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1523,0] error: duplicate class: GBMModel_Forest_8
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1528,0] error: duplicate class: GBMModel_Tree_8_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1618,0] error: duplicate class: GBMModel_Forest_9
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1623,0] error: duplicate class: GBMModel_Tree_9_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1715,0] error: duplicate class: GBMModel_Forest_10
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1720,0] error: duplicate class: GBMModel_Tree_10_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1814,0] error: duplicate class: GBMModel_Forest_11
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1819,0] error: duplicate class: GBMModel_Tree_11_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1909,0] error: duplicate class: GBMModel_Forest_12
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1914,0] error: duplicate class: GBMModel_Tree_12_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2012,0] error: duplicate class: GBMModel_Forest_13
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2017,0] error: duplicate class: GBMModel_Tree_13_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2105,0] error: duplicate class: GBMModel_Forest_14
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2110,0] error: duplicate class: GBMModel_Tree_14_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2204,0] error: duplicate class: GBMModel_Forest_15
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2209,0] error: duplicate class: GBMModel_Tree_15_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2301,0] error: duplicate class: GBMModel_Forest_16
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2306,0] error: duplicate class: GBMModel_Tree_16_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2404,0] error: duplicate class: GBMModel_Forest_17
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2409,0] error: duplicate class: GBMModel_Tree_17_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2501,0] error: duplicate class: GBMModel_Forest_18
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2506,0] error: duplicate class: GBMModel_Tree_18_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2600,0] error: duplicate class: GBMModel_Forest_19
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2605,0] error: duplicate class: GBMModel_Tree_19_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2699,0] error: duplicate class: GBMModel_Forest_20
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2704,0] error: duplicate class: GBMModel_Tree_20_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2798,0] error: duplicate class: GBMModel_Forest_21
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2803,0] error: duplicate class: GBMModel_Tree_21_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2895,0] error: duplicate class: GBMModel_Forest_22
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2900,0] error: duplicate class: GBMModel_Tree_22_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2990,0] error: duplicate class: GBMModel_Forest_23
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2995,0] error: duplicate class: GBMModel_Tree_23_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3087,0] error: duplicate class: GBMModel_Forest_24
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3092,0] error: duplicate class: GBMModel_Tree_24_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3170,0] error: duplicate class: GBMModel_Forest_25
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3175,0] error: duplicate class: GBMModel_Tree_25_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3273,0] error: duplicate class: GBMModel_Forest_26
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3278,0] error: duplicate class: GBMModel_Tree_26_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3374,0] error: duplicate class: GBMModel_Forest_27
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3379,0] error: duplicate class: GBMModel_Tree_27_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3463,0] error: duplicate class: GBMModel_Forest_28
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3468,0] error: duplicate class: GBMModel_Tree_28_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3556,0] error: duplicate class: GBMModel_Forest_29
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3561,0] error: duplicate class: GBMModel_Tree_29_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3653,0] error: duplicate class: GBMModel_Forest_30
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3658,0] error: duplicate class: GBMModel_Tree_30_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3752,0] error: duplicate class: GBMModel_Forest_31
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3757,0] error: duplicate class: GBMModel_Tree_31_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3855,0] error: duplicate class: GBMModel_Forest_32
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3860,0] error: duplicate class: GBMModel_Tree_32_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3954,0] error: duplicate class: GBMModel_Forest_33
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3959,0] error: duplicate class: GBMModel_Tree_33_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4043,0] error: duplicate class: GBMModel_Forest_34
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4048,0] error: duplicate class: GBMModel_Tree_34_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4136,0] error: duplicate class: GBMModel_Forest_35
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4141,0] error: duplicate class: GBMModel_Tree_35_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4231,0] error: duplicate class: GBMModel_Forest_36
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4236,0] error: duplicate class: GBMModel_Tree_36_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4334,0] error: duplicate class: GBMModel_Forest_37
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4339,0] error: duplicate class: GBMModel_Tree_37_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4435,0] error: duplicate class: GBMModel_Forest_38
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4440,0] error: duplicate class: GBMModel_Tree_38_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4532,0] error: duplicate class: GBMModel_Forest_39
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4537,0] error: duplicate class: GBMModel_Tree_39_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4593,0] error: duplicate class: GBMModel_Forest_40
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4598,0] error: duplicate class: GBMModel_Tree_40_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4694,0] error: duplicate class: GBMModel_Forest_41
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4699,0] error: duplicate class: GBMModel_Tree_41_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4781,0] error: duplicate class: GBMModel_Forest_42
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4786,0] error: duplicate class: GBMModel_Tree_42_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4882,0] error: duplicate class: GBMModel_Forest_43
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4887,0] error: duplicate class: GBMModel_Tree_43_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4947,0] error: duplicate class: GBMModel_Forest_44
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4952,0] error: duplicate class: GBMModel_Tree_44_class_0
[INFO] 100 errors 
[INFO] -------------------------------------------------------------
[INFO] ------------------------------------------------------------------------
[INFO] BUILD FAILURE
[INFO] ------------------------------------------------------------------------
[INFO] Total time: 01:17 min
[INFO] Finished at: 2015-11-26T21:30:29-05:00
[INFO] Final Memory: 26M/245M
[INFO] ------------------------------------------------------------------------
[ERROR] Failed to execute goal org.apache.maven.plugins:maven-compiler-plugin:2.3.2:compile (default-compile) on project ScoreData: Compilation failure: Compilation failure:
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[24,7] error: duplicate class: GBMModel
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[181,0] error: duplicate class: NamesHolder_GBMModel
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[204,0] error: duplicate class: GBMModel_ColInfo_1
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[223,0] error: duplicate class: GBMModel_ColInfo_2
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[258,0] error: duplicate class: GBMModel_ColInfo_3
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[274,0] error: duplicate class: GBMModel_ColInfo_4
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[551,0] error: duplicate class: GBMModel_ColInfo_5
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[580,0] error: duplicate class: GBMModel_ColInfo_6
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[600,0] error: duplicate class: GBMModel_ColInfo_7
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[613,0] error: duplicate class: GBMModel_ColInfo_9
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[839,0] error: duplicate class: GBMModel_Forest_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[844,0] error: duplicate class: GBMModel_Tree_0_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[908,0] error: duplicate class: GBMModel_Forest_1
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[913,0] error: duplicate class: GBMModel_Tree_1_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[989,0] error: duplicate class: GBMModel_Forest_2
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[994,0] error: duplicate class: GBMModel_Tree_2_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1078,0] error: duplicate class: GBMModel_Forest_3
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1083,0] error: duplicate class: GBMModel_Tree_3_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1165,0] error: duplicate class: GBMModel_Forest_4
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1170,0] error: duplicate class: GBMModel_Tree_4_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1252,0] error: duplicate class: GBMModel_Forest_5
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1257,0] error: duplicate class: GBMModel_Tree_5_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1341,0] error: duplicate class: GBMModel_Forest_6
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1346,0] error: duplicate class: GBMModel_Tree_6_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1434,0] error: duplicate class: GBMModel_Forest_7
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1439,0] error: duplicate class: GBMModel_Tree_7_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1523,0] error: duplicate class: GBMModel_Forest_8
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1528,0] error: duplicate class: GBMModel_Tree_8_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1618,0] error: duplicate class: GBMModel_Forest_9
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1623,0] error: duplicate class: GBMModel_Tree_9_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1715,0] error: duplicate class: GBMModel_Forest_10
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1720,0] error: duplicate class: GBMModel_Tree_10_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1814,0] error: duplicate class: GBMModel_Forest_11
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1819,0] error: duplicate class: GBMModel_Tree_11_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1909,0] error: duplicate class: GBMModel_Forest_12
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[1914,0] error: duplicate class: GBMModel_Tree_12_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2012,0] error: duplicate class: GBMModel_Forest_13
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2017,0] error: duplicate class: GBMModel_Tree_13_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2105,0] error: duplicate class: GBMModel_Forest_14
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2110,0] error: duplicate class: GBMModel_Tree_14_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2204,0] error: duplicate class: GBMModel_Forest_15
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2209,0] error: duplicate class: GBMModel_Tree_15_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2301,0] error: duplicate class: GBMModel_Forest_16
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2306,0] error: duplicate class: GBMModel_Tree_16_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2404,0] error: duplicate class: GBMModel_Forest_17
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2409,0] error: duplicate class: GBMModel_Tree_17_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2501,0] error: duplicate class: GBMModel_Forest_18
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2506,0] error: duplicate class: GBMModel_Tree_18_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2600,0] error: duplicate class: GBMModel_Forest_19
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2605,0] error: duplicate class: GBMModel_Tree_19_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2699,0] error: duplicate class: GBMModel_Forest_20
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2704,0] error: duplicate class: GBMModel_Tree_20_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2798,0] error: duplicate class: GBMModel_Forest_21
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2803,0] error: duplicate class: GBMModel_Tree_21_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2895,0] error: duplicate class: GBMModel_Forest_22
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2900,0] error: duplicate class: GBMModel_Tree_22_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2990,0] error: duplicate class: GBMModel_Forest_23
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[2995,0] error: duplicate class: GBMModel_Tree_23_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3087,0] error: duplicate class: GBMModel_Forest_24
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3092,0] error: duplicate class: GBMModel_Tree_24_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3170,0] error: duplicate class: GBMModel_Forest_25
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3175,0] error: duplicate class: GBMModel_Tree_25_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3273,0] error: duplicate class: GBMModel_Forest_26
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3278,0] error: duplicate class: GBMModel_Tree_26_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3374,0] error: duplicate class: GBMModel_Forest_27
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3379,0] error: duplicate class: GBMModel_Tree_27_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3463,0] error: duplicate class: GBMModel_Forest_28
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3468,0] error: duplicate class: GBMModel_Tree_28_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3556,0] error: duplicate class: GBMModel_Forest_29
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3561,0] error: duplicate class: GBMModel_Tree_29_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3653,0] error: duplicate class: GBMModel_Forest_30
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3658,0] error: duplicate class: GBMModel_Tree_30_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3752,0] error: duplicate class: GBMModel_Forest_31
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3757,0] error: duplicate class: GBMModel_Tree_31_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3855,0] error: duplicate class: GBMModel_Forest_32
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3860,0] error: duplicate class: GBMModel_Tree_32_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3954,0] error: duplicate class: GBMModel_Forest_33
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[3959,0] error: duplicate class: GBMModel_Tree_33_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4043,0] error: duplicate class: GBMModel_Forest_34
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4048,0] error: duplicate class: GBMModel_Tree_34_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4136,0] error: duplicate class: GBMModel_Forest_35
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4141,0] error: duplicate class: GBMModel_Tree_35_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4231,0] error: duplicate class: GBMModel_Forest_36
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4236,0] error: duplicate class: GBMModel_Tree_36_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4334,0] error: duplicate class: GBMModel_Forest_37
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4339,0] error: duplicate class: GBMModel_Tree_37_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4435,0] error: duplicate class: GBMModel_Forest_38
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4440,0] error: duplicate class: GBMModel_Tree_38_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4532,0] error: duplicate class: GBMModel_Forest_39
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4537,0] error: duplicate class: GBMModel_Tree_39_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4593,0] error: duplicate class: GBMModel_Forest_40
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4598,0] error: duplicate class: GBMModel_Tree_40_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4694,0] error: duplicate class: GBMModel_Forest_41
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4699,0] error: duplicate class: GBMModel_Tree_41_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4781,0] error: duplicate class: GBMModel_Forest_42
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4786,0] error: duplicate class: GBMModel_Tree_42_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4882,0] error: duplicate class: GBMModel_Forest_43
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4887,0] error: duplicate class: GBMModel_Tree_43_class_0
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4947,0] error: duplicate class: GBMModel_Forest_44
[ERROR] /Users/randyzwitch/h2o-world-2015-training/tutorials/hive_udf_template/src/main/java/ai/h2o/hive/udf/GBMModel.java:[4952,0] error: duplicate class: GBMModel_Tree_44_class_0
[ERROR] -> [Help 1]
[ERROR] 
[ERROR] To see the full stack trace of the errors, re-run Maven with the -e switch.
[ERROR] Re-run Maven using the -X switch to enable full debug logging.
[ERROR] 
[ERROR] For more information about the errors and possible solutions, please read the following articles:
[ERROR] [Help 1] http://cwiki.apache.org/confluence/display/MAVEN/MojoFailureException

GridSearch for Ordinal Regression fails

Used dataset (http://h2o-public-test-data.s3.amazonaws.com/bigdata/laptop/glm_ordinal_logit/ordinal_multinomial_training_set.csv) from tutorial to configure grid search

hyper_parameters = {'alpha': [0.01,0.3,0.5],
'lambda': [1e-5,1e-6,1e-7,1e-8]}
gs = H2OGridSearch(H2OGeneralizedLinearEstimator(family='ordinal'),
grid_id='ordinal_grid',
hyper_params=hyper_parameters)
gs.train(x=list(range(0,10)), y="C11", training_frame=train)
gs.show()

glm Grid Build progress: |████████████████████████████████████100%

NotImplementedError Traceback (most recent call last)
in ()
4 grid_id='ordinal_grid',
5 hyper_params=hyper_parameters)
----> 6 gs.train(x=list(range(0,10)), y="C11", training_frame=train)
7 gs.show()

/apps/anaconda3/lib/python3.6/site-packages/h2o/grid/grid_search.py in train(self, x, y, training_frame, offset_column, fold_column, weights_column, validation_frame, **params)
206 x = list(xset)
207 parms["x"] = x
--> 208 self.build_model(parms)
209
210

/apps/anaconda3/lib/python3.6/site-packages/h2o/grid/grid_search.py in build_model(self, algo_params)
221 if is_auto_encoder and y is not None: raise ValueError("y should not be specified for autoencoder.")
222 if not is_unsupervised and y is None: raise ValueError("Missing response")
--> 223 self._model_build(x, y, training_frame, validation_frame, algo_params)
224
225

/apps/anaconda3/lib/python3.6/site-packages/h2o/grid/grid_search.py in _model_build(self, x, y, tframe, vframe, kwargs)
277 first_model_json = h2o.api("GET /%d/Models/%s" %
278 (rest_ver or 3, grid_json['model_ids'][0]['name']))['models'][0]
--> 279 self._resolve_grid(grid.dest_key, grid_json, first_model_json)
280 else:
281 if len(failure_messages_stacks)>0:

/apps/anaconda3/lib/python3.6/site-packages/h2o/grid/grid_search.py in _resolve_grid(self, grid_id, grid_json, first_model_json)
286
287 def _resolve_grid(self, grid_id, grid_json, first_model_json):
--> 288 model_class = H2OGridSearch._metrics_class(first_model_json)
289 m = model_class()
290 m._id = grid_id

/apps/anaconda3/lib/python3.6/site-packages/h2o/grid/grid_search.py in _metrics_class(model_json)
756 model_class = H2ODimReductionGridSearch
757 else:
--> 758 raise NotImplementedError(model_type)
759 return model_class
760

NotImplementedError: Ordinal

How to distinguish between classifier and regressor in the h2o models?

H2O provides H20GradientBoostingEstimator which does not provide any field for specifying whether it is a classifier or regressor. Also the predict method returns.only real values and thus no labels when predicted on a classification based dataset. So how do we distinguish or specify whether the model is a classifier or regressor in all the h2o models?

Intro H2o tutorial not works

In tutorial h2o-tutorials/h2o-open-tour-2016/chicago/intro-to-h2o.R:
When I run data <- h2o.importFile(loan_csv), it would not import the data, instead, it returns:
https://raw.githubusercontent.com/h2oai/app-consumer-loan/master/data/loan.csv failed to importError in h2o.importFolder(path, pattern = "", destination_frame = destination_frame, : all files failed to import

I am using a MAC machine and R 3.5.0., and I am open to open that link. However, it would just not let me import that data, any help will be extremely appreciated!

h2o.predict issue related to !!!StackedEnsemble!!! h2o version: 3.23.0.4602

set.seed(23)
aml<-h2o.automl(y=outcome, x= features,
                training_frame = train,
                #validation_frame = test,
                leaderboard_frame = test,seed=3,#max_runtime_secs = 120,
                #exclude_algos = c("StackedEnsemble"))#,
                max_models = 2)
                
testing=read_csv('../input/test.csv') %>% select(-ID_code)

light_test2 = h2o.predict(aml@leader,testing %>% as.h2o()) %>% as.data.frame()%>% .$p1

H2O VERSION 3.23.0.4602

Loading required package: lubridate

Loading required package: methods


Attaching package: ‘lubridate’


The following object is masked from ‘package:base’:

    date


Loading required package: PerformanceAnalytics

Loading required package: xts

Loading required package: zoo


Attaching package: ‘zoo’

The following objects are masked from ‘package:base’:

    as.Date, as.Date.numeric



Attaching package: ‘PerformanceAnalytics’


The following object is masked from ‘package:graphics’:

    legend


Loading required package: quantmod

Loading required package: TTR

Version 0.4-0 included new data defaults. See ?getSymbols.

Loading required package: tidyverse

── Attaching packages ─────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.1.0.9000     ✔ purrr   0.3.1     
✔ tibble  2.0.1          ✔ dplyr   0.8.0.1   
✔ tidyr   0.8.3          ✔ stringr 1.4.0     
✔ readr   1.3.1          ✔ forcats 0.4.0     

── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ lubridate::as.difftime() masks base::as.difftime()
✖ lubridate::date()        masks base::date()
✖ dplyr::filter()          masks stats::filter()
✖ dplyr::first()           masks xts::first()
✖ lubridate::intersect()   masks base::intersect()
✖ dplyr::lag()             masks stats::lag()
✖ dplyr::last()            masks xts::last()
✖ lubridate::setdiff()     masks base::setdiff()
✖ lubridate::union()       masks base::union()

Loading required package: R6


Attaching package: ‘lightgbm’


The following object is masked from ‘package:dplyr’:

    slice


Type 'citation("pROC")' for a citation.


Attaching package: ‘pROC’

The following objects are masked from ‘package:stats’:

    cov, smooth, var


Parsed with column specification:
cols(
  .default = col_double(),
  ID_code = col_character()
)
See spec(...) for full column specifications.

Loading required package: lattice


Attaching package: ‘caret’


The following object is masked from ‘package:purrr’:

    lift


Warning message:
replacing previous import ‘ggplot2::empty’ by ‘plyr::empty’ when loading ‘caret’ 

numeric(0)

# A tibble: 2 x 15
  Fold01 Fold02 Fold03 Fold04 Fold05 Fold06 Fold07 Fold08 Fold09 Fold10 Fold11
   <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
1  0.896  0.892  0.899  0.900  0.900  0.900 0.903   0.898 0.901  0.906   0.895
2  0.104  0.108  0.101  0.100  0.100  0.100 0.0972  0.102 0.0991 0.0943  0.105
# … with 4 more variables: Fold12 <dbl>, Fold13 <dbl>, Fold14 <dbl>,
#   Fold15 <dbl>

Loading required package: mlr

Loading required package: ParamHelpers


Attaching package: ‘ParamHelpers’

The following object is masked from ‘package:quantmod’:

    getDefaults



Attaching package: ‘mlr’

The following object is masked from ‘package:caret’:

    train


Loading required package: foreach


Attaching package: ‘foreach’


The following objects are masked from ‘package:purrr’:

    accumulate, when


Loading required package: doParallel

Loading required package: iterators

Loading required package: parallel


----------------------------------------------------------------------

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 http://docs.h2o.ai

----------------------------------------------------------------------



Attaching package: ‘h2o’


The following object is masked from ‘package:pROC’:

    var

The following objects are masked from ‘package:lubridate’:

    day, hour, month, week, year

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 is not running yet, starting it now...


Note:  In case of errors look at the following log files:
    /tmp/RtmpvEQjuP/h2o_UnknownUser_started_from_r.out
    /tmp/RtmpvEQjuP/h2o_UnknownUser_started_from_r.err


openjdk version "1.8.0_141"
OpenJDK Runtime Environment (build 1.8.0_141-8u141-b15-1~deb9u1-b15)

OpenJDK 64-Bit Server VM (build 25.141-b15, mixed mode)



Starting H2O JVM and connecting: 
.
.
 Connection successful!


R is connected to the H2O cluster: 

    H2O cluster uptime:         2 seconds 222 milliseconds 
    H2O cluster timezone:       Etc/UTC 
    H2O data parsing timezone:  UTC 
    H2O cluster version:        3.23.0.4602 
    H2O cluster version age:    14 days, 4 hours and 48 minutes  
    H2O cluster name:           H2O_started_from_R_root_zqv228 
    H2O cluster total nodes:    1 
    H2O cluster total memory:   5.67 GB 
    H2O cluster total cores:    4 
    H2O cluster allowed cores:  4 
    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, XGBoost, Algos, AutoML, Core V3, Core V4 
    R Version:                  R version 3.4.2 (2017-09-28) 



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                                             model_id       auc   logloss
1 StackedEnsemble_BestOfFamily_AutoML_20190325_112022 0.8197364 0.5185754
2    StackedEnsemble_AllModels_AutoML_20190325_112022 0.8197364 0.5185754
3                        XRT_1_AutoML_20190325_112022 0.8041693 0.5892604
4                        DRF_1_AutoML_20190325_112022 0.7969176 0.5914487
  mean_per_class_error      rmse       mse
1            0.2743024 0.4163262 0.1733275
2            0.2743024 0.4163262 0.1733275
3            0.2817554 0.4473749 0.2001443
4            0.2936830 0.4486491 0.2012860
$model_id

[1] "StackedEnsemble_BestOfFamily_AutoML_20190325_112022"

$training_frame
[1] "automl_training_RTMP_sid_92e1_3"

$base_models
$base_models[[1]]
$base_models[[1]]$`__meta`
$base_models[[1]]$`__meta`$schema_version
[1] 3

$base_models[[1]]$`__meta`$schema_name
[1] "ModelKeyV3"

$base_models[[1]]$`__meta`$schema_type
[1] "Key<Model>"


$base_models[[1]]$name
[1] "XRT_1_AutoML_20190325_112022"

$base_models[[1]]$type
[1] "Key<Model>"

$base_models[[1]]$URL
[1] "/3/Models/XRT_1_AutoML_20190325_112022"


$base_models[[2]]
$base_models[[2]]$`__meta`
$base_models[[2]]$`__meta`$schema_version
[1] 3

$base_models[[2]]$`__meta`$schema_name
[1] "ModelKeyV3"

$base_models[[2]]$`__meta`$schema_type
[1] "Key<Model>"


$base_models[[2]]$name
[1] "DRF_1_AutoML_20190325_112022"

$base_models[[2]]$type
[1] "Key<Model>"

$base_models[[2]]$URL
[1] "/3/Models/DRF_1_AutoML_20190325_112022"



$metalearner_nfolds
[1] 5

$seed
[1] 3

$keep_levelone_frame
[1] TRUE

$x
  [1]
 "var_0"   "var_1"   "var_2"   "var_3"   "var_4"   "var_5"   "var_6"  
  [8] "var_7"   "var_8"   "var_9"   "var_10"  "var_11"  "var_12"  "var_13" 
 [15] "var_14"  "var_15"  "var_16"  "var_17"  "var_18"  "var_19"  "var_20" 
 [22] "var_21"  "var_22"  "var_23"  "var_24"  "var_25"  "var_26"  "var_27" 
 [29] "var_28"  "var_29"  "var_30"  "var_31"  "var_32"  "var_33"  "var_34" 
 [36] "var_35"  "var_36"  "var_37"  "var_38"  "var_39"  "var_40"  "var_41" 
 [43] "var_42"  "var_43"  "var_44"  "var_45"  "var_46"  "var_47"  "var_48" 
 [50] "var_49"  "var_50"  "var_51"  "var_52"  "var_53"  "var_54"  "var_55" 
 [57] "var_56"  "var_57"  "var_58"  "var_59"  "var_60"  "var_61"  "var_62" 
 [64] "var_63"  "var_64"  "var_65"  "var_66"  "var_67"  "var_68"  "var_69" 
 [71] "var_70"  "var_71"  "var_72"  "var_73"  "var_74"  "var_75"  "var_76" 
 [78] "var_77"  "var_78"  "var_79"  "var_80"  "var_81"  "var_82"  "var_83" 
 [85] "var_84"  "var_85"  "var_86"  "var_87"  "var_88"  "var_89"  "var_90" 
 [92] "var_91"  "var_92"  "var_93"  "var_94"  "var_95"  "var_96"  "var_97" 
 [99] "var_98"  "var_99"  "var_100" "var_101" "var_102" "var_103" "var_104"
[106] "var_105" "var_106" "var_107" "var_108" "var_109" "var_110" "var_111"
[113] "var_112" "var_113" "var_114" "var_115" "var_116" "var_117" "var_118"
[120] "var_119" "var_120" "var_121" "var_122" "var_123" "var_124" "var_125"
[127] "var_126" "var_127" "var_128" "var_129" "var_130" "var_131" "var_132"
[134] "var_133" "var_134" "var_135" "var_136" "var_137" "var_138" "var_139"
[141] "var_140" "var_141" "var_142" "var_143" "var_144" "var_145" "var_146"
[148] "var_147" "var_148" "var_149" "var_150" "var_151" "var_152" "var_153"
[155] "var_154" "var_155" "var_156" "var_157" "var_158" "var_159" "var_160"
[162] "var_161" "var_162" "var_163" "var_164" "var_165" "var_166" "var_167"
[169] "var_168" "var_169" "var_170" "var_171" "var_172" "var_173" "var_174"
[176] "var_175" "var_176" "var_177" "var_178" "var_179" "var_180" "var_181"
[183] "var_182" "var_183" "var_184" "var_185" "var_186" "var_187" "var_188"
[190] "var_189" "var_190" "var_191" "var_192" "var_193" "var_194" "var_195"
[197] "var_196" "var_197" "var_198" "var_199"

$y
[1] "target"


[1] "/kaggle/working/StackedEnsemble_BestOfFamily_AutoML_20190325_112022"

# A tibble: 4 x 6
  model_id                            auc logloss mean_per_class_er…  rmse   mse
  <chr>                             <dbl>   <dbl>              <dbl> <dbl> <dbl>
1 StackedEnsemble_BestOfFamily_Aut… 0.820   0.519              0.274 0.416 0.173
2 StackedEnsemble_AllModels_AutoML… 0.820   0.519              0.274 0.416 0.173
3 XRT_1_AutoML_20190325_112022      0.804   0.589              0.282 0.447 0.200
4 DRF_1_AutoML_20190325_112022      0.797   0.591              0.294 0.449 0.201

Parsed with column specification:
cols(
  .default = col_double(),
  ID_code = col_character()
)
See spec(...) for full column specifications.


  |                                                                            
  |                                                                      |   0%

  |                                                                            
  |======================================================================| 100%


  |                                                                            
  |                                                                      |   0%


java.lang.IllegalArgumentException: Actual column must be integer class labels!

java.lang.IllegalArgumentException: Actual column must be integer class labels!
	at hex.GainsLift.init(GainsLift.java:51)
	at hex.GainsLift.exec(GainsLift.java:124)
	at hex.glm.GLMMetricBuilder.makeModelMetrics(GLMMetricBuilder.java:217)
	at hex.glm.GLMModel.predictScoreImpl(GLMModel.java:1456)
	at hex.Model.score(Model.java:1381)
	at hex.ensemble.StackedEnsembleModel.predictScoreImpl(StackedEnsembleModel.java:150)
	at hex.Model.score(Model.java:1381)
	at water.api.ModelMetricsHandler$1.compute2(ModelMetricsHandler.java:374)
	at water.H2O$H2OCountedCompleter.compute(H2O.java:1386)
	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.IllegalArgumentException: Actual column must be integer class labels!
Execution halted

I INSTALLED H2O VERSION 3.22.1.6 AND NO ERROR

Incorrect number of arguments - hive_udf_pojo_template

Hello,

Thank you for providing a template to create a hive udf based on a h2o pojo.
I'm having some difficulties to get this working. When building the jar file, based on my own model, I get an error -> Arguments length mismatch: Incorrect number of arguments.

I followed these steps:

Create model in H2O
Export model as POJO
Copy "modelname.java" to src/main/java/ai/h2o/hive/udf/
Copy place h2o-genmodel.jar into localjars
Place "package ai.h2o.hive.udf;" to the top of modelname.java
Update pom.xml with the version numbers of hadoop and hive
Change the line with "GBMModel p = new GBMModel();" with "modelname p = new modelname();" (This is what I thought should be necessary, but not sure of it.
execute mvn clean
execute mvn compile
Copy Snapshot jar to HDFS together with h2o-genmodel.jar
ADD JAR h2o-genmodel.jar;
ADD JAR ScoreData-1.0-SNAPSHOT.jar;
CREATE TEMPORARY FUNCTION scoredata AS 'ai.h2o.hive.udf.ScoreDataUDF';

When I now execute query statement: select scoredata(c2,c3,c4,c5,c6) from my_dataset limit 10; then I will receive:
Error while compiling statement: FAILED: SemanticException [Error 10015]: Line 1:7 Arguments length mismatch 'c6': Incorrect number of arguments. scoredata() requires: [AGEP, COW, SCHL, MAR, INDP, RELP, RAC1P, SEX, WKHP, POBP, LOG_CAPGAIN, LOG_CAPLOSS], in the listed order. Received 5 arguments.

The strange thing is that in the template there is not any reference to the example case, but in above error message it is. I'm no java expert, so I'm certainly do something wrong but can't figure out what.

Help will be very much appreciated.

Thanks.

Confusion matrix shown as part of h2o randomForest model output looks incorrect.

Version info :
version_info.txt

Code snippet :

confusion_matrix_issue.txt

Relevant data :

train.csv.txt

Let me know if you need more information.

If you specify the following parameters, h2O will throw an error. h2o version 3.24

That was tested on this dataset: https://www.kaggle.com/c/santander-customer-transaction-prediction

xgb = h2o.xgboost(x = features,
                  y = outcome,
                  training_frame = train_,
                  validation_frame = test_,
                  booster = 'gblinear',
                  grow_policy = 'lossguide',
                  max_bins = 128,tree_method='hist',
                  eta = 0.199,gamma = 0.961,max_depth = 6,colsample_bytree = 0.997,
                  colsample_bylevel = 0.64,reg_lambda = 0.017,reg_alpha =0.002,
                  subsample=0.687)

|                                                                                             |   0%Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = urlSuffix,  : 
  Unexpected CURL error: Failed to connect to localhost port 54321: Connection refused
Error during wrapup: H2O connection has been severed. Cannot connect to instance at http://localhost:54321/
Failed to connect to localhost port 54321: Connection refused
Error during wrapup: H2O connection has been severed. Cannot connect to instance at http://localhost:54321/
Failed to connect to localhost port 54321: Connection refused

I tried the same parametrs with xgboost package but there was no such error

[tutorial Request] : any tutorials to train h20 deep neural network with audio features (mfcc's)

can h20.ai be used to work with audio features like mfcc's/plp's/rmse/ etc?
I want to train a h2o neural network using audio features. Please point me to tutorials.

Can't install cause url from jolbox.com

500 internal server error from:
https://jolbox.com/bonecp/downloads/maven/org/apache/hadoop/hadoop-common/0.22.0-SNAPSHOT/hadoop-common-0.22.0-SNAPSHOT.pom

For tutorial: hive_udf_mojo_template
U_U

Additional information about feature engineering in H2O or Time-series

Hi there,
I am learning about the platform to use in our project to create a predictive model like sales forecast...
Anyone can help me to answer some question about it:
1/ How does H2O create new features in feature engineering?
2/ What are the primitives (min, max, mean, mode, ... or some special primitives to solve some problem in time-series)?
3/ What is the maximum/minimum value of prediction length in the feature Time-series in H20? From your case study about sale forecasting, the maximum value of prediction length is 39 weeks. Can H2O do with the different interval time in the data like minutes, ..?
4/ For performance, can we have set the number of cores CPU to use in the training process? Is it supported parallel?

Thank you.

Cannot able to fetch specific number of rows using 'h2o.import_file'

Hello there!

I am trying to fetch some large dataset(which has more than 50Million rows). Using Pandas I can able to fetch limited number of rows using below code.

train_df = pd.read_csv('train.csv', nrows = 15_000_000)

I am looking for the same parameters in H2O import_file but I cannot able to find it.

train_df = h2o.import_file("train.csv", destination_frame=“train_df")

Also, if you give me a tip for these dtype, skiprows parameters then it will helps me more.

Thanks in advance!

-Anand Selvadurai

Link in Intro to R outdated

The link http://www.h2o.ai/download/h2o/r in the Intro to R

# The latest version is always here: http://www.h2o.ai/download/h2o/r

leads to version 3.10.0.10 instead of the latest version.

The autoML leaderboard is null(and I used the IDE is spyder)

When I use the H2OAutoML, the code like below:
aml = H2OAutoML(max_runtime_secs = 30)
aml.train(x = x, y = y,
training_frame = train,
leaderboard_frame = test)

View the AutoML Leaderboard

lb = aml.leaderboard
lb

but run the lb is null. need your help ,thanks !

model loading error due to version conflict

I updated my h2o to the newest version and then tried to load the pre-trained model by typing:

randomforest = h2o.loadModel('randomforest')

However, it shows:

Error in .h2o.doSafeREST(h2oRestApiVersion = h2oRestApiVersion, urlSuffix = page,  : 
ERROR MESSAGE:
Found version 3.10.5.3, but running version 3.14.0.7

Does that mean I need to retrained all the model I built before ? That's extremely inconvenient.

What is differnet btw h20ai word2vec vs glove/genism word2vec implementation ?

Fail to start H2o cluster in docker container

Now we try to use H2o to construct training cluster. It is easy to use by running java -jar ./h2o.jar and we can setup the cluster with simple flatfile.txt which contain multiple ip and ports.

But we found that it is impossible to setup the h2o cluster within docker containers. Although we can start multiple containers to run java -jar ./h2o.jar and add the prepared flatfile.txt, the h2o process will try to bind local(container's eth0) ip which is different from the one in flatfile.txt. We can java -jar ./h2o.jar -ip $ip to set the one which is in flatfile.txt but h2o instance is not able to run without this "external" ip.

how to use gradient boosting on textual data?

Hello there!

First of all, congrats for this amazing package. I am starting to use h2o on my Spark cluster and I think this is just amazing.

I am trying to implement a very simple ML learning problem, where I use text to predict some outcome. In R, some basic example would be:

import some text data and outcome

> dataframe <- data_frame(id = c(1,2,3,4),
+                         text = c("this is a this", "this is another",'hello','what???'),
+                         value = c(200,400,120,300),
+                         output = c('win', 'lose','win','lose'))
> dataframe
# A tibble: 4 x 4
     id            text value output
  <dbl>           <chr> <dbl>  <chr>
1     1  this is a this   200    win
2     2 this is another   400   lose
3     3           hello   120    win
4     4         what???   300   lose

Use text2vec to get a sparse matrix representation of my text

> dtm_train
4 x 6 sparse Matrix of class "dgCMatrix"
  hello another what??? a is this
1     .       .       . 1  1    2
2     .       1       . .  1    1
3     1       .       . .  .    .
4     .       .       1 .  .    .

train the algo (for instance, using caret)

mymodel <- train(x=dtm_train, y =dataframe$output, method="xgbTree")

My problem is: I see how to import data into h20 using spark_read_csv, rsparkling and as_h2o_frame. However, for points 2. and 3. above I am completely lost.

Can someone please give me some hints or tell me if this approach is even possible with h2o?

Many thanks!!

grid-search-model-selection.ipynb issues in the Random Grid section

Hi
Learning to use h2o in Python (3.6) as part of a self training effort. I got as far as the Random Grid section but the script crashes around the "Train and validate a random grid of GBMs' part' .I am copying the complete error message below. I am just wondering if this script is geared towards the latest h2o version - 3.15.etc while I am running 3.14.03?

gbm_grid2 = H2OGridSearch(model=H2OGradientBoostingEstimator,
                          grid_id='gbm_grid2',
                          hyper_params=gbm_params2,
                          search_criteria=search_criteria2)
gbm_grid2.train(x=x, y=y, 
                training_frame=train, 
                validation_frame=valid, 
                ntrees=100,
                seed=1)

H2OServerError                            Traceback (most recent call last)
<ipython-input-24-575508382194> in <module>()
      7                 validation_frame=valid,
      8                 ntrees=100,
----> 9                 seed=1)

~/anaconda3/lib/python3.6/site-packages/h2o/grid/grid_search.py in train(self, x, y, training_frame, offset_column, fold_column, weights_column, validation_frame, **params)
    187                     raise ValueError('y must be a single column reference')
    188             self._estimator_type = "classifier" if tframe[y].isfactor() else "regressor"
--> 189         self.build_model(parms)
    190 
    191 

~/anaconda3/lib/python3.6/site-packages/h2o/grid/grid_search.py in build_model(self, algo_params)
    202         if is_auto_encoder and y is not None: raise ValueError("y should not be specified for autoencoder.")
    203         if not is_unsupervised and y is None: raise ValueError("Missing response")
--> 204         self._model_build(x, y, training_frame, validation_frame, algo_params)
    205 
    206 

~/anaconda3/lib/python3.6/site-packages/h2o/grid/grid_search.py in _model_build(self, x, y, tframe, vframe, kwargs)
    224         rest_ver = kwargs.pop("_rest_version") if "_rest_version" in kwargs else None
    225 
--> 226         grid = H2OJob(h2o.api("POST /99/Grid/%s" % algo, data=kwargs), job_type=(algo + " Grid Build"))
    227 
    228         if self._future:

~/anaconda3/lib/python3.6/site-packages/h2o/h2o.py in api(endpoint, data, json, filename, save_to)
    101     # type checks are performed in H2OConnection class
    102     _check_connection()
--> 103     return h2oconn.request(endpoint, data=data, json=json, filename=filename, save_to=save_to)
    104 
    105 

~/anaconda3/lib/python3.6/site-packages/h2o/backend/connection.py in request(self, endpoint, data, json, filename, save_to)
    400                                     auth=self._auth, verify=self._verify_ssl_cert, proxies=self._proxies)
    401             self._log_end_transaction(start_time, resp)
--> 402             return self._process_response(resp, save_to)
    403 
    404         except (requests.exceptions.ConnectionError, requests.exceptions.HTTPError) as e:

~/anaconda3/lib/python3.6/site-packages/h2o/backend/connection.py in _process_response(response, save_to)
    728         # Note that it is possible to receive valid H2OErrorV3 object in this case, however it merely means the server
    729         # did not provide the correct status code.
--> 730         raise H2OServerError("HTTP %d %s:\n%r" % (status_code, response.reason, data))
    731 
    732 

H2OServerError: HTTP 500 Server Error:
Server error com.google.gson.JsonSyntaxException:
  Error: com.google.gson.stream.MalformedJsonException: Expected ':' at line 1 column 103 path $.
  Request: None

I am running h2o 3.14.03 on two Ubuntu 16.04 computers, Python 3.6 and while I am a h2o newbie, im rather experience in Linux, Python, tensorflow, pytorch, etc. Any hints would be appreciated.

Thanks in advance

Clive

How to contribute code?

Hi,

I wrote an external program to call to H2ORandomForestEstimator.train() in Python. My program should show the progress when training but there is no method to get the progress.

I saw the source code and thought about to add a "callback" parameter to train(). How can I contribute code? There is no "issue" column for this project in github. Directly pull request?

GBM models in h2o

I am new to h2o. I ran a simulation study where I want to recover a simple tree. I have two predictors: x1 and x2 where x2 is categorical with 26 levels (a, b, ..., z). A simple tree is as follows:

If x1 > 0.5 or x2 is from one of the following characters ['w','a','n','g','s','h','e'], the I will assign y a yes, otherwise no.

I am trying to use GBM models to recover this tree. The code is as follows:

`
####################### Start of code #########################
import pandas as pd
import numpy as np
import random
import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
h2o.init()

random.seed(7)
x1 = [random.gauss(mu = 0, sigma=2) for a in range(52)]
x2 = np.repeat(list(string.ascii_lowercase),2)
y = ['Yes' if x1[a] > 0.5 or x2[a] in ['w','a','n','g','s','h','e'] else 'No' for a in range(52)]

h2o_model = H2OGradientBoostingEstimator(ntrees=5, max_depth=3, seed=10, sample_rate=1, distribution='bernoulli')
X = h2o.H2OFrame(pd.DataFrame({'x1':x1,'x2':x2, 'y':y}))
X['y'] = X['y'].asfactor()
h2o_model.train(y='y', training_frame = X)

h2o_model.predict(h2o.H2OFrame(pd.DataFrame({'x1':x1,'x2':x2})))
#################### End of code ##############################
`

However, the output of the last line is below (shows only the first a bunch of rows):

predict No Yes
0 No 0.374969 0.625031
1 Yes 0.283827 0.716173
2 No 0.693166 0.306834
3 No 0.693166 0.306834
4 No 0.650580 0.349420
5 No 0.650580 0.349420
6 Yes 0.286945 0.713055
7 Yes 0.283955 0.716045
8 Yes 0.286945 0.713055
9 Yes 0.283955 0.716045
10 Yes 0.328816 0.671184
11 No 0.598123 0.401877
12 No 0.374969 0.625031
13 Yes 0.283827 0.716173
14 Yes 0.286945 0.713055
15 Yes 0.286945 0.713055
16 No 0.651392 0.348608
...

It looks like that for the 0th and the 12th line, the prediction is not in line with the probabilities. For the first line, the probability of getting 'Yes' is more than that of getting a 'No'. Why the prediction is 'No' for this example? There are in total 6 rows like them.

Anyone has any idea about why? Thanks very much.

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