H2OAI

1.0.0

H2OAI.psm1

                                $CommandsToExport = @()

function Start-H2O {

        <#

.SYNOPSIS

Start H2O

.DESCRIPTION

Start H2O

.PARAMETER H2oPath

Path of H2o.jar

e.g. c:\h2o\h2o-3.28.0.1\h2o-3.28.0.1\h2o.jar

.INPUTS

H2O Path

c:\h2o\h2o-3.28.0.1\h2o-3.28.0.1\h2o.jar

.OUTPUTS

Nothing.  Starts H2O

.EXAMPLE

Start-H2o c:\h2o\h2o-3.28.0.1\h2o-3.28.0.1\h2o.jar

.LINK

https://blog.darrenjrobinson.com/

#>

    param(

        [Parameter(Mandatory = $true, Position = 0)]

        [String] $H2oPath

    )

    try {

        $out = &"java" -version 2>&1

        $out[0].tostring()

        if ($out[0].tostring() -notlike 'java version "1.8*' ) {

            Write-Error "Incorrect Java Version detected. Found $($out[0].tostring()). Java 1.8 is required."

            break 

        }

        else {

            if (Test-Path $H2oPath) {

                $Global:javaPID = Start-Process "java" "-jar $($H2oPath)" -PassThru

            }

            else {

                Write-Error "$($H2oPath) not found."

                break 

            }

        }

    }

    catch {    

        Write-Error "Java not detected. Ensure Java 1.8 is installed and configured in the environment path."

        break 

    }

}

$CommandsToExport += 'Start-H2O'

function Stop-H2O {

    <#

.SYNOPSIS

    Stop H2O

.DESCRIPTION

    Stop H2O

.PARAMETER processID

    (optional) PID of the java process set when starting H2O. By default this is set to a global variable and passed automatically to Stop-H2O.

.EXAMPLE

    Stop-H2O

    Stop-H2O -processID 12345

.LINK

    https://blog.darrenjrobinson.com/

#>

    param(

        [Parameter(Mandatory = $false, Position = 0)]

        [String] $processID = $Global:javaPID.Id

    )

    try {

        Stop-Process -Id $processID

    }

    catch {

        Write-Error $_

        break 

    }

}

$CommandsToExport += 'Stop-H2O'

function ConvertTo-FormData {

    <#

.SYNOPSIS

    Parse Data into an H2O Dataframe

.DESCRIPTION

    Parse Data into an H2O Dataframe

.PARAMETER InputObject

    PowerShell Object for H2O Dataframe

.INPUTS

    PowerShell Object with values

    source_frames  : 

    parse_type     : 

    separator      : 44

    number_columns : 

    single_quotes  : 

    column_names   : 

    column_types   : 

    check_header   : 

    chunk_size     : 

.OUTPUTS

    Form Data for posting to H2O as a Dataframe

.EXAMPLE

    "Parse the data into a real H2o dataframe"

    $parse_url = $url -f "Parse"

    $parse_body = $ret | Select-Object source_frames, parse_type, separator, number_columns, single_quotes, column_names, column_types, check_header, chunk_size | ConvertTo-FormData

    $parse_body += "&destination_frame=dataSet&delete_on_done=true"

    $ret = Invoke-RestMethod $parse_url -Method Post -Body $parse_body

.LINK

    https://powertoe.wordpress.com/2017/10/23/h2o-machine-learning-with-powershell/

#>

    param(

        [Parameter(ValueFromPipeline = $true)] 

        [PSObject] $InputObject

    )

    Begin {

        $output = ""

    }

    Process {

        foreach ($prop in $InputObject.psobject.properties | Select-Object -expandproperty name) {

            if ($InputObject.($prop).gettype().name -eq "Boolean") {

                if ($InputObject.($prop)) {

                    $output += "$prop=true&"

                }

                else {

                    $output += "$prop=false&"

                }

            } if ($InputObject.($prop).gettype().isarray) {

                if ($InputObject.($prop).name) {

                    $output += "$prop=[{0}]&" -f ($InputObject.($prop).name -join ",")

                }

                else {

                    $output += "$prop=[{0}]&" -f ($InputObject.($prop) -join ",")

                }

            }

            else {

                $output += "$prop=" + $InputObject.($prop) + "&"

            }

        }

    }

    End {

        $output.Remove($output.Length - 1, 1)

    }

}

$CommandsToExport += 'ConvertTo-FormData'

function Wait-H2OJob {

        <#

.SYNOPSIS

    Wait for an H2O Job to complete

.DESCRIPTION

    Wait for an H2O Job to complete

.PARAMETER JobPath

    URL of H2O Job

    e.g. /3/Jobs/$0301c0a8018532d4ffffffff$_ae64f3e55ac939c97697016b66ee4652

.INPUTS

    H2O Job Path

    /3/Jobs/$0301c0a8018532d4ffffffff$_ae64f3e55ac939c97697016b66ee4652

.OUTPUTS

    Nothing.  Sleep loop for job completion 

.EXAMPLE

    Wait-H2OJob $ret.job.key.URL

.LINK

    https://powertoe.wordpress.com/2017/10/23/h2o-machine-learning-with-powershell/

#>

    param(

        [Parameter(Mandatory = $true, Position = 0)]

        [String] $JobPath

    )

    $notdone = $true

    while ($notdone) {

        $status = invoke-restmethod ("http://localhost:54321" + $JobPath) | Select-Object -ExpandProperty jobs | Select-Object -ExpandProperty status

        if ($status -eq "DONE") {

            $notdone = $false

        }

        else {

            Start-Sleep -Milliseconds 1000

        }

    }

}

$CommandsToExport += 'Wait-H2OJob'

function Get-H2OPrediction {

    <#

.SYNOPSIS

    Get an H2O Prediction for a dataset and a sample data request

.DESCRIPTION

    Get an H2O Prediction for a dataset and a sample data request

.PARAMETER url

    H2O URL 

    default: "http://localhost:54321/3/{0}"

.PARAMETER dataset

    H2O Dataset to build model from

.PARAMETER predictData

    Data to build a prediction for

.PARAMETER predictColumn

    Column to provide prediction on

.PARAMETER modelAlgorithm

    Algorithm to build H2O model 

    default: 'glm'

.PARAMETER modelSplit

    Split of Model Dataset between Train and Test

    e.g ".85,.15" 

.EXAMPLE

    Get-H2OPrediction -url "http://localhost:54321/3/{0}" -dataset "c:\Data\v2\dataSet.csv" -predictData = "c:\Data\v2\predictDataSet.csv" -modelAlgorithm = 'glm' -modelSplit = ".85,.15"

.LINK

    http://darrenjrobinson.com/

#>

    [CmdletBinding()]

    param (

        [Parameter(ValueFromPipeline = $true,

            Mandatory = $false)]

        [string]$url = "http://localhost:54321/3/{0}",

        [Parameter(ValueFromPipeline = $true,

            Mandatory = $true)]

        [string]$dataset,

        [Parameter(ValueFromPipeline = $true,

            Mandatory = $true)]

        [string]$predictData,

        [Parameter(ValueFromPipeline = $true,

            Mandatory = $false)]

            [ValidateSet('glm', 'gbm', '"glrm', 'aggregator', 'deeplearning', 'drf', 'isolationforest', 'kmeans', 'naivebayes', 'pca', 'targetencoder', 'word2vec')]

        [string]$modelAlgorithm = "glm",

        [Parameter(ValueFromPipeline = $true,

            Mandatory = $false)]

        [string]$modelSplit = ".85,.15",

        [Parameter(ValueFromPipeline = $true,

            Mandatory = $true)]

        [string]$predictColumn 

    )

    Process {    

        try {         

            $responseTemplate = [pscustomobject][ordered]@{ 

                prediction      = $null  

                modelType       = $null  

                modelConfidence = $null 

            }                 

            $importfiles_url = $url -f "ImportFiles"

            $importfiles_body = "path=$dataset"

            $ret = Invoke-RestMethod $importfiles_url -Method POST -Body $importfiles_body

            # Run parse setup to find out how H2o thinks it should parse the data

            $parsesetup_url = $url -f "ParseSetup"

            $parsesetup_body = 'source_frames=[{0}]' -f $ret.destination_frames[0]

            $ret = $null 

            $ret = Invoke-RestMethod $parsesetup_url -Method Post -Body $parsesetup_body           

            # Parse the data into a real H2o dataframe

            $parse_url = $url -f "Parse"

            $parse_body = $ret | Select-Object source_frames, parse_type, separator, number_columns, single_quotes, column_names, column_types, check_header, chunk_size | ConvertTo-FormData

            $parse_body += "&destination_frame=dataSet&delete_on_done=true"

            $ret = $null 

            $ret = Invoke-RestMethod $parse_url -Method Post -Body $parse_body

            wait-H2oJob $ret.job.key.URL

            # Split the Data into Training and Test DF's

            $splitframe_url = $url -f "SplitFrame"

            $splitframe_body = "dataset=dataSet&ratios=[$($modelSplit)]&destination_frames=[train,validate]"

            $ret = $null 

            $ret = invoke-restmethod $splitframe_url -Method Post -Body $splitframe_body

            wait-H2oJob $ret.key.URL

            # Build a Model

            $model_url = $url -f "ModelBuilders/$($modelAlgorithm)"            

            $model_body = "training_frame=train&validation_frame=validate&response_column=$($predictColumn)&model_id=$($modelAlgorithm)"

            $ret = $null 

            $ret = invoke-restmethod $model_url -Method Post -Body $model_body

            wait-H2oJob $ret.job.key.URL

            # Prediction Quality # Validation Data

            $predict_url = $url -f "Predictions/models/$($modelAlgorithm)/frames/validate"

            $ret = $null 

            $ret = invoke-restmethod $predict_url -method POST -Body "predictions_frame=predicted_validate_data"

            $modelConfidence = $ret.model_metrics | Select-Object -expandproperty MSE

            # Predict New Close Data based off last close data

            $importfiles_url = $url -f "ImportFiles"

            $importfiles_body = "path=$($predictData)"

            $ret = $null 

            $ret = Invoke-RestMethod $importfiles_url -Method POST -Body $importfiles_body

            # Run parse setup to find out how H2o thinks it should parse the data

            $parsesetup_url = $url -f "ParseSetup"

            $parsesetup_body = 'source_frames=[{0}]' -f $ret.destination_frames[0]

            $ret = $null 

            $ret = Invoke-RestMethod $parsesetup_url -Method Post -Body $parsesetup_body

            # Parse the data into a real H2o dataframe"

            $parse_url = $url -f "Parse"

            $parse_body = $ret | Select-Object source_frames, parse_type, separator, number_columns, single_quotes, column_names, column_types, check_header, chunk_size | ConvertTo-FormData

            $parse_body += "&destination_frame=predictme&delete_on_done=true"

            $ret = $null 

            $ret = Invoke-RestMethod $parse_url -Method Post -Body $parse_body

            Wait-H2oJob $ret.job.key.URL

            # Leverage the data model we built earlier to predict against this new data frame

            $predict_url = $url -f "Predictions/models/$($modelAlgorithm)/frames/predictme"

            $ret = $null 

            $ret = invoke-restmethod $predict_url -method POST -Body "predictions_frame=predictme_results"

            $modelType = $ret.model_metrics.model_category 

            $results_url = $url -f "Frames/predictme_results"

            $ret = $null 

            $ret = invoke-restmethod $results_url

            $prediction = $null 

            $prediction = $ret.frames.columns | Select-Object label, data 

            $predictionResult = $responseTemplate.PsObject.Copy()

            # $predictionResult.prediction = $prediction.data[0] 

            $predictionResult.prediction = $prediction

            $predictionResult.modelType = $modelType

            $predictionResult.modelConfidence = $modelConfidence

            return $predictionResult

        }

        catch {

            return "Error returning Prediction. $($_)"

        }    

    }    

}

$CommandsToExport += 'Get-H2OPrediction'

Export-ModuleMember -Function $CommandsToExport

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