MikeYeh/ XGBPaclet

This paclet utilizes XGBoost algorithm in Wolfram Language

Contributed by: Mike Yeh

In this paclet, we provide Wolfram Language functions for implementing XGBoost python functions, e.g., xgb.DMatrix(), xgb.train(), and predict(). So far we have XgbTrain[] to implement xgb.train() and XgbModelPredict[] to perform model prediction. More functions will be added later.

Installation Instructions

To install this paclet in your Wolfram Language environment, evaluate this code:
PacletInstall["MikeYeh/XGBPaclet"]

To load the code after installation, evaluate this code:
Needs["MikeYeh`XGBPaclet`"]

Details

The paclet now is in test version().

Paclet Guide

Examples

Basic Examples (6)

Create a XGBoost Python session

A XGBoost python session is recommended before using XgbTrain[]. The code demonstrates the minimum packages needed to be installed before creating a python session:

In[1]:=

$ResourceFunction[ ResourceObject[<|"Name" -> "RegisterEnvironment", "UUID" -> "54433ce2-bf45-4364-959d-a88f5444a26c", "ResourceType" -> "Function", "Description" -> "Registers a new environment with the given Python package dependencies", "ResourceLocations" -> { CloudObject[ "https://www.wolframcloud.com/obj/arnoudbuzing/Resources/544/54433ce2-bf45-4364-959d-a88f5444a26c"]}, "Version" -> None, "Categories" -> {"External Interfaces & Connections", "Programming Utilities"}, "Compatibility" -> <|"WolframLanguageVersionRequired" -> "13.0+", "OperatingSystem" -> {"Windows", "MacOSX", "Unix"}, "Features" -> {}, "EvaluationEnvironment" -> {"Session", "WebEvaluation", "BatchJob", "Script", "WebAPI", "Subkernel", "Scheduled"}, "CloudSupport" -> True|>, "ContributorInformation" -> <|"ContributedBy" -> "Arnoud Buzing"|>, "DefinitionNotebook" -> CloudObject[ "https://www.wolframcloud.com/obj/arnoudbuzing/DeployedResources/Function/RegisterEnvironment/RegisterEnvironment-DefinitionNotebook.nb"], "Documentation" -> <|"Usage" -> {<|"Usage" -> BoxData[ RowBox[{ StyleBox[ RowBox[{ StyleBox["ResourceFunction", "ResourceFunctionSymbol"], "[", StyleBox["\"RegisterEnvironment\"", "ResourceFunctionName"], "]"}], "ResourceFunctionHandle"], "[", RowBox[{ StyleBox["name", "TI"], "->", StyleBox["dependencies", "TI"]}], "]"}]], "Description" -> TextData[{"registers environment ", Append[ Cell[ BoxData[ StyleBox["name", "TI"]], "InlineFormula", FontFamily -> "Source Sans Pro"], FontFamily -> "Source Sans Pro"], " using the listed ", Append[ Cell[ BoxData[ StyleBox["dependencies", "TI"]], "InlineFormula", FontFamily -> "Source Sans Pro"], FontFamily -> "Source Sans Pro"], "."}]|>}|>, "DocumentationLink" -> URL[ "https://www.wolframcloud.com/obj/arnoudbuzing/DeployedResources/Function/RegisterEnvironment"], "ExampleNotebook" -> CloudObject[ "https://www.wolframcloud.com/obj/arnoudbuzing/DeployedResources/Function/RegisterEnvironment/RegisterEnvironment-ExampleNotebook.nb"], "ExampleNotebookData" -> Automatic, "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/arnoudbuzing/Resources/544/54433ce2-bf45-4364-959d-a88f5444a26c/download/DefinitionData"], "PageHeaderClickToCopy" -> "ResourceObject[CloudObject[\"https://www.wolframcloud.com/obj/arnoudbuzing/DeployedResources/Function/RegisterEnvironment\"]]", "ShortName" -> "RegisterEnvironment", "SymbolName" -> "FunctionRepository`$54433ce2bf454364959da88f5444a26c`RegisterEnvironment"|>]][ "xgb" -> {"xgboost", "scikit-learn==1.5.2", "matplotlib" }];$

To create a XGBoost python session:

In[2]:=

$session = ResourceFunction[ ResourceObject[<|"Name" -> "StartEnvironment", "UUID" -> "eb4c6583-7ac8-404d-bd8d-e917e0ccef8b", "ResourceType" -> "Function", "Description" -> "Starts, or gives, the specified Python environment session", "ResourceLocations" -> { CloudObject[ "https://www.wolframcloud.com/obj/arnoudbuzing/Resources/eb4/eb4c6583-7ac8-404d-bd8d-e917e0ccef8b"]}, "Version" -> None, "Categories" -> {"External Interfaces & Connections", "Programming Utilities"}, "Compatibility" -> <|"WolframLanguageVersionRequired" -> "13.0+", "OperatingSystem" -> {"Windows", "MacOSX", "Unix"}, "Features" -> {}, "EvaluationEnvironment" -> {"Session", "WebEvaluation", "BatchJob", "Script", "WebAPI", "Subkernel", "Scheduled"}, "CloudSupport" -> True|>, "ContributorInformation" -> <|"ContributedBy" -> "Arnoud Buzing"|>, "DefinitionNotebook" -> CloudObject[ "https://www.wolframcloud.com/obj/arnoudbuzing/DeployedResources/Function/StartEnvironment/StartEnvironment-DefinitionNotebook.nb"], "Documentation" -> <|"Usage" -> {<|"Usage" -> BoxData[ RowBox[{ StyleBox[ RowBox[{ StyleBox["ResourceFunction", "ResourceFunctionSymbol"], "[", StyleBox["\"StartEnvironment\"", "ResourceFunctionName"], "]"}], "ResourceFunctionHandle"], "[", "name", "]"}]], "Description" -> "starts or gives the specified Python environment session."|>}|>, "DocumentationLink" -> URL[ "https://www.wolframcloud.com/obj/arnoudbuzing/DeployedResources/Function/StartEnvironment"], "ExampleNotebook" -> CloudObject[ "https://www.wolframcloud.com/obj/arnoudbuzing/DeployedResources/Function/StartEnvironment/StartEnvironment-ExampleNotebook.nb"], "ExampleNotebookData" -> Automatic, "FunctionLocation" -> CloudObject[ "https://www.wolframcloud.com/obj/arnoudbuzing/Resources/eb4/eb4c6583-7ac8-404d-bd8d-e917e0ccef8b/download/DefinitionData"], "PageHeaderClickToCopy" -> "ResourceObject[CloudObject[\"https://www.wolframcloud.com/obj/arnoudbuzing/DeployedResources/Function/StartEnvironment\"]]", "ShortName" -> "StartEnvironment", "SymbolName" -> "FunctionRepository`$eb4c65837ac8404dbd8de917e0ccef8b`StartEnvironment"|>]]["xgb"];$

Within the session our function will automatically import xgboost and other packages.

Prepare dataset for the following examples

Create binary classification training set and validation set for the following examples:

In[3]:=

binary`traindata = RandomReal[{-10, 10}, {100, 3}];
binary`testdata = RandomReal[{-10, 10}, {100, 3}];
binary`trainlabel = RandomChoice[{"a", "b"}, {100}];
binary`testlabel = RandomChoice[{"a", "b"}, {100}];
binary`trainset = {binary`traindata -> binary`trainlabel};
binary`testset = {binary`testdata -> binary`testlabel};

Use XgbTrain[] to train a model

UUse XgbTrain[] to train a model with binary`trainset and XGBoost python session, and store the trained model in the output session:

In[4]:=

$session = InterpretationBox[FrameBox[TagBox[TooltipBox[PaneBox[GridBox[List[List[GraphicsBox[List[Thickness[0.0025`], List[FaceForm[List[RGBColor[0.9607843137254902`, 0.5058823529411764`, 0.19607843137254902`], Opacity[1.`]]], FilledCurveBox[List[List[List[0, 2, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0]], List[List[0, 2, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0]], List[List[0, 2, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0]], List[List[0, 2, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3]]], List[List[List[205.`, 22.863691329956055`], List[205.`, 212.31669425964355`], List[246.01799774169922`, 235.99870109558105`], List[369.0710144042969`, 307.0436840057373`], List[369.0710144042969`, 117.59068870544434`], List[205.`, 22.863691329956055`]], List[List[30.928985595703125`, 307.0436840057373`], List[153.98200225830078`, 235.99870109558105`], List[195.`, 212.31669425964355`], List[195.`, 22.863691329956055`], List[30.928985595703125`, 117.59068870544434`], List[30.928985595703125`, 307.0436840057373`]], List[List[200.`, 410.42970085144043`], List[364.0710144042969`, 315.7036876678467`], List[241.01799774169922`, 244.65868949890137`], List[200.`, 220.97669792175293`], List[158.98200225830078`, 244.65868949890137`], List[35.928985595703125`, 315.7036876678467`], List[200.`, 410.42970085144043`]], List[List[376.5710144042969`, 320.03370475769043`], List[202.5`, 420.53370475769043`], List[200.95300006866455`, 421.42667961120605`], List[199.04699993133545`, 421.42667961120605`], List[197.5`, 420.53370475769043`], List[23.428985595703125`, 320.03370475769043`], List[21.882003784179688`, 319.1406993865967`], List[20.928985595703125`, 317.4896984100342`], List[20.928985595703125`, 315.7036876678467`], List[20.928985595703125`, 114.70369529724121`], List[20.928985595703125`, 112.91769218444824`], List[21.882003784179688`, 111.26669120788574`], List[23.428985595703125`, 110.37369346618652`], List[197.5`, 9.87369155883789`], List[198.27300024032593`, 9.426692008972168`], List[199.13700008392334`, 9.203690528869629`], List[200.`, 9.203690528869629`], List[200.86299991607666`, 9.203690528869629`], List[201.72699999809265`, 9.426692008972168`], List[202.5`, 9.87369155883789`], List[376.5710144042969`, 110.37369346618652`], List[378.1179962158203`, 111.26669120788574`], List[379.0710144042969`, 112.91769218444824`], List[379.0710144042969`, 114.70369529724121`], List[379.0710144042969`, 315.7036876678467`], List[379.0710144042969`, 317.4896984100342`], List[378.1179962158203`, 319.1406993865967`], List[376.5710144042969`, 320.03370475769043`]]]]], List[FaceForm[List[RGBColor[0.5529411764705883`, 0.6745098039215687`, 0.8117647058823529`], Opacity[1.`]]], FilledCurveBox[List[List[List[0, 2, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 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List[63.928985595703125`, 315.4997024536133`], List[200.`, 394.0606994628906`]]]]]], List[Rule[BaselinePosition, Scaled[0.15`]], Rule[ImageSize, 10], Rule[ImageSize, 15]]], StyleBox[RowBox[List["XgbTrain", " "]], Rule[ShowAutoStyles, False], Rule[ShowStringCharacters, False], Rule[FontSize, Times[0.9`, Inherited]], Rule[FontColor, GrayLevel[0.1`]]]]], Rule[GridBoxSpacings, List[Rule["Columns", List[List[0.25`]]]]]], Rule[Alignment, List[Left, Baseline]], Rule[BaselinePosition, Baseline], Rule[FrameMargins, List[List[3, 0], List[0, 0]]], Rule[BaseStyle, List[Rule[LineSpacing, List[0, 0]], Rule[LineBreakWithin, False]]]], RowBox[List["PacletSymbol", "[", RowBox[List["\"MikeYeh/XGBPaclet\"", ",", "\"MikeYeh`XGBPaclet`XgbTrain\""]], "]"]], Rule[TooltipStyle, List[Rule[ShowAutoStyles, True], Rule[ShowStringCharacters, True]]]], Function[Annotation[Slot[1], Style[Defer[PacletSymbol["MikeYeh/XGBPaclet", "MikeYeh`XGBPaclet`XgbTrain"]], Rule[ShowStringCharacters, True]], "Tooltip"]]], Rule[Background, RGBColor[0.968`, 0.976`, 0.984`]], Rule[BaselinePosition, Baseline], Rule[DefaultBaseStyle, List[]], Rule[FrameMargins, List[List[0, 0], List[1, 1]]], Rule[FrameStyle, RGBColor[0.831`, 0.847`, 0.85`]], Rule[RoundingRadius, 4]], PacletSymbol["MikeYeh/XGBPaclet", "MikeYeh`XGBPaclet`XgbTrain"], Rule[Selectable, False], Rule[SelectWithContents, True], Rule[BoxID, "PacletSymbolBox"]][session, binary`trainset];$

Store all information of the trained model in the XGBTrainResultsObject[]:

In[5]:=

$Allresult = InterpretationBox[FrameBox[TagBox[TooltipBox[PaneBox[GridBox[List[List[GraphicsBox[List[Thickness[0.0025`], List[FaceForm[List[RGBColor[0.9607843137254902`, 0.5058823529411764`, 0.19607843137254902`], Opacity[1.`]]], FilledCurveBox[List[List[List[0, 2, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0]], List[List[0, 2, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0]], List[List[0, 2, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0]], List[List[0, 2, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3]]], List[List[List[205.`, 22.863691329956055`], List[205.`, 212.31669425964355`], List[246.01799774169922`, 235.99870109558105`], List[369.0710144042969`, 307.0436840057373`], List[369.0710144042969`, 117.59068870544434`], List[205.`, 22.863691329956055`]], List[List[30.928985595703125`, 307.0436840057373`], List[153.98200225830078`, 235.99870109558105`], List[195.`, 212.31669425964355`], List[195.`, 22.863691329956055`], List[30.928985595703125`, 117.59068870544434`], List[30.928985595703125`, 307.0436840057373`]], List[List[200.`, 410.42970085144043`], List[364.0710144042969`, 315.7036876678467`], List[241.01799774169922`, 244.65868949890137`], List[200.`, 220.97669792175293`], List[158.98200225830078`, 244.65868949890137`], List[35.928985595703125`, 315.7036876678467`], List[200.`, 410.42970085144043`]], List[List[376.5710144042969`, 320.03370475769043`], List[202.5`, 420.53370475769043`], List[200.95300006866455`, 421.42667961120605`], List[199.04699993133545`, 421.42667961120605`], List[197.5`, 420.53370475769043`], List[23.428985595703125`, 320.03370475769043`], List[21.882003784179688`, 319.1406993865967`], List[20.928985595703125`, 317.4896984100342`], List[20.928985595703125`, 315.7036876678467`], List[20.928985595703125`, 114.70369529724121`], 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Rule[Background, RGBColor[0.968`, 0.976`, 0.984`]], Rule[BaselinePosition, Baseline], Rule[DefaultBaseStyle, List[]], Rule[FrameMargins, List[List[0, 0], List[1, 1]]], Rule[FrameStyle, RGBColor[0.831`, 0.847`, 0.85`]], Rule[RoundingRadius, 4]], PacletSymbol["MikeYeh/XGBPaclet", "MikeYeh`XGBPaclet`XgbTrain"], Rule[Selectable, False], Rule[SelectWithContents, True], Rule[BoxID, "PacletSymbolBox"]][session, binary`trainset, All]$

Out[5]=

Use XgbMeasurement[] for the prediction.

XgbMeasurement[] can evaluate the model in the session and make a prediction on given data:

In[6]:=

$result = InterpretationBox[FrameBox[TagBox[TooltipBox[PaneBox[GridBox[List[List[GraphicsBox[List[Thickness[0.0025`], List[FaceForm[List[RGBColor[0.9607843137254902`, 0.5058823529411764`, 0.19607843137254902`], Opacity[1.`]]], FilledCurveBox[List[List[List[0, 2, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0]], List[List[0, 2, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0]], List[List[0, 2, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0], List[0, 1, 0]], List[List[0, 2, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3], List[0, 1, 0], List[1, 3, 3]]], List[List[List[205.`, 22.863691329956055`], List[205.`, 212.31669425964355`], List[246.01799774169922`, 235.99870109558105`], List[369.0710144042969`, 307.0436840057373`], List[369.0710144042969`, 117.59068870544434`], List[205.`, 22.863691329956055`]], List[List[30.928985595703125`, 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"Tooltip"]]], Rule[Background, RGBColor[0.968`, 0.976`, 0.984`]], Rule[BaselinePosition, Baseline], Rule[DefaultBaseStyle, List[]], Rule[FrameMargins, List[List[0, 0], List[1, 1]]], Rule[FrameStyle, RGBColor[0.831`, 0.847`, 0.85`]], Rule[RoundingRadius, 4]], PacletSymbol["MikeYeh/XGBPaclet", "MikeYeh`XGBPaclet`XgbMeasurement"], Rule[Selectable, False], Rule[SelectWithContents, True], Rule[BoxID, "PacletSymbolBox"]][session, binary`trainset]; result["prediction"]$

Out[7]=

Scope (2)

Change XGBoost training runs "numBoostRound" to be 2:
In[8]:=
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XGBTrainResultsObject

XgbTrain with All as input parameter will return XGBTrainResultsObject[], which stores all training information:

In[9]:=

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