Using Driverless AI

211 Pages·2017·10.21 MB·English

Using Driverless AI
Release 1.4.0
H2O.ai
Oct 30, 2018

RELEASE NOTES
1 H2ODriverlessAIReleaseNotes 3
1.1 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.2 Roadmap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.3 ChangeLog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
2 WhyDriverlessAI? 19
3 KeyFeatures 21
3.1 FlexibilityofDataandDeployment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.2 NVIDIAGPUAcceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.3 DataVisualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.4 AutomaticFeatureEngineering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.5 MachineLearningInterpretability(MLI) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
3.6 TimeSeries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.7 NLPwithTensorFlow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
3.8 AutomaticScoringPipelines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4 InstallingandUpgradingDriverlessAI 23
4.1 LinuxDockerImages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
4.2 LinuxRPMs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
4.3 LinuxDEBs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
4.4 LinuxTARSH . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
4.5 LinuxintheCloud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
4.6 MacOSX . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.7 Windows10Pro . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.8 IBMPower . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
5 LaunchingDriverlessAI 101
5.1 Messages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
6 TheDatasetsPage 103
6.1 AddingDatasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
6.2 DatasetDetails . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
6.3 VisualizingDatasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107
7 Experiments 111
7.1 BeforeYouBegin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
7.2 NewExperiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
7.3 ExperimentGraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
7.4 CompletedExperiment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
7.5 ViewingExperiments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
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8 InterpretingaModel 147
8.1 IntrepretthisModelButton-Non-Time-Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
8.2 IntrepretthisModelButton-Time-Series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
8.3 ModelInterpretationonDriverlessAIModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
8.4 ModelInterpretationonExternalModels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
8.5 UnderstandingtheModelInterpretationPage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
8.6 GeneralConsiderations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
9 ViewingExplanations 167
10 ScoreonAnotherDataset 171
11 TransformAnotherDataset 173
12 ThePythonandMOJOScoringPipelines 175
12.1 WhichPipelineShouldIUse? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
12.2 DriverlessAIStandalonePythonScoringPipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
12.3 DriverlessAIMLIStandalonePythonScoringPackage . . . . . . . . . . . . . . . . . . . . . . . . 184
12.4 DriverlessAIMOJOScoringPipeline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
13 What’sHappeninginDriverlessAI? 197
14 DriverlessAITransformations 199
14.1 AvailableTransformers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
14.2 ExampleTransformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
15 InternalValidationTechnique 205
16 TimeSeriesinDriverlessAI 207
16.1 UnderstandingTimeSeries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207
16.2 TimeSeriesConstraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
16.3 TimeSeriesUseCase: SalesForecasting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
17 NLPinDriverlessAI 215
17.1 ATypicalNLPExample: SentimentAnalysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215
18 Usingtheconfig.tomlFile 219
18.1 DockerImageUsers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219
18.2 NativeInstallUsers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
18.3 SampleConfig.tomlFile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
19 SettingEnvironmentVariables 237
19.1 SettingVariablesinDockerImages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
19.2 SettingVariablesinNativeInstalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 237
20 EnablingDataConnectors 239
20.1 UsingDataConnectorswiththeDockerImage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239
20.2 UsingDataConnectorswithNativeInstalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 250
21 ConfiguringAuthentication 265
21.1 EnablingAuthenticationinDockerImages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
21.2 EnablingAuthenticationinNativeInstalls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265
21.3 LDAPAuthenticationExample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 266
21.4 PAMAuthenticationExample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270
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22 EnablingNotifications 273
22.1 ScriptInterfaces . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
22.2 Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 273
23 DriverlessAILogs 277
23.1 AccessingDriverlessAILogs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 277
23.2 SendingLogstoH2O . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
24 FAQ 283
24.1 General . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283
24.2 Installation/Upgrade/Authentication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 284
24.3 Data/Experiments/Predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287
24.4 TimeSeries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 293
25 AppendixA:ThePythonClient 297
25.1 InstallingthePythonClient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297
25.2 CreditCardDemo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298
25.3 TimeSeriesAnalysisonaDriverlessAIModel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 318
25.4 DriverlessAINLPDemo-AirlineSentimentDataset . . . . . . . . . . . . . . . . . . . . . . . . . 328
26 References 331
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iv
UsingDriverlessAI,Release1.4.0
H2ODriverlessAIisanartificialintelligence(AI)platformforautomaticmachinelearning. DriverlessAIautomates
someofthemostdifficultdatascienceandmachinelearningworkflowssuchasfeatureengineering,modelvalidation,
modeltuning,modelselectionandmodeldeployment. Itaimstoachievehighestpredictiveaccuracy,comparableto
expertdatascientists,butinmuchshortertimethankstoend-to-endautomation. DriverlessAIalsooffersautomatic
visualizationsandmachinelearninginterpretability(MLI).Especiallyinregulatedindustries,modeltransparencyand
explanation are just as important as predictive performance. Modeling pipelines (feature engineering and models)
are exported (in full fidelity, without approximations) both as Python modules and as pure Java standalone scoring
artifacts.
DriverlessAIrunsoncommodityhardware.Itwasalsospecificallydesignedtotakeadvantageofgraphicalprocessing
units (GPUs), including multi-GPU workstations and servers such as IBM’s Power9-GPU AC922 server and the
NVIDIADGX-1fororder-of-magnitudefastertraining.
ThisdocumentdescribeshowtoinstallanduseDriverlessAI.FormoreinformationaboutDriverlessAI,pleasesee
https://www.h2o.ai/products/h2o-driverless-ai/.
For a third-party review, please see https://www.infoworld.com/article/3236048/machine-learning/
review-h2oai-automates-machine-learning.html.
HaveQuestions?
If you have questions about using Driverless AI, post them on Stack Overflow using the driverless-ai tag at http:
//stackoverflow.com/questions/tagged/driverless-ai.
RELEASENOTES 1
UsingDriverlessAI,Release1.4.0
2 RELEASENOTES
CHAPTER
ONE
H2O DRIVERLESS AI RELEASE NOTES
H2ODriverlessAIisahigh-performance,GPU-enabled,client-serverapplicationfortherapiddevelopmentandde-
ploymentofstate-of-the-artpredictiveanalyticsmodels. Itreadstabulardatafromvarioussourcesandautomatesdata
visualization,grand-masterlevelautomaticfeatureengineering,modelvalidation(overfittingandleakageprevention),
model parameter tuning, model interpretability and model deployment. H2O Driverless AI is currently targeting
commonregression,binomialclassification,andmultinomialclassificationapplicationsincludingloss-given-default,
probability of default, customer churn, campaign response, fraud detection, anti-money-laundering, and predictive
assetmaintenancemodels. Italsohandlestime-seriesproblemsforindividualorgroupedtime-seriessuchasweekly
salespredictionsperstoreanddepartment,withtime-causalfeatureengineeringandvalidationschemes. Theability
tomodelunstructureddataiscomingsoon.
High-levelcapabilities:
• Client/serverapplicationforrapidexperimentationanddeploymentofstate-of-the-artsupervisedmachinelearn-
ingmodels
• Automaticallycreatesmachinelearningmodelingpipelinesforhighestpredictiveaccuracy
• Automaticallycreatesstand-alonescoringpipelineforin-processscoringorclient/serverscoringviahttportcp
protocols,inPythonandJava(low-latencyscoring).
• PythonAPIorGUI(JavaAPIcomingsoon)
• Multi-GPUandmulti-CPUsupportforpowerfulworkstationsandNVidiaDGXsupercomputers
• MachineLearningmodelinterpretationmodulewithglobalandlocalmodelinterpretation
• AutomaticVisualizationmodule
• Multi-usersupport
• Backwardcompatibility
Problemtypessupported:
• Regression(continuoustargetvariable,forage,income,houseprice,lossprediction,time-seriesforecasting)
• Binaryclassification(0/1or“N”/”Y”,forfraudprediction,churnprediction,failureprediction,etc.)
• Multinomialclassification(0/1/2/3or“A”/”B”/”C”/”D”forcategoricaltargetvariables,forpredictionofmem-
bershiptype,next-action,productrecommendation,etc.)
Datatypessupported:
• Tabularstructureddata,rowsareobservations,columnsarefields/features/variables
• i.i.d. (identicallyandindependentlydistributed)data
• Numeric,categoricalandtextualfields
• Missingvaluesareallowed
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UsingDriverlessAI,Release1.4.0
• Time-seriesdatawithasingletime-series(timeflowsacrosstheentiredataset,notperblockofdata)
• Groupedtime-series(e.g.,salesperstoreperdepartmentperweek,allinonefile,with3columnsforstore,dept,
week)
• Time-series problems with a gap between training and testing (i.e., the time to deploy), and a known forecast
horizon(afterwhichmodelhastoberetrained)
DatatypesNOT supported:
• Image/video/audio
Datasourcessupported:
• Localfilesystem
• Networkfilesystem
• S3(Amazon)
• Googlecloudstorage
• Googlebigquery
• Hadoop(HDFS)
• Minio
• Snowflake
Fileformatssupported:
• Plaintextformatsofcolumnardata(.csv,.tsv,.txt)
• Compressedarchives(.zip,.gz,.bz2)
• Excel
• Parquet
• Feather
• Pythondatatable
4 Chapter1. H2ODriverlessAIReleaseNotes