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Quickstart - LightGBM in Dotnet

note

Make sure you have followed the guidance in .NET installation before jumping into this example.

Classification with LightGBMClassifier

Install NuGet packages by running following command:

dotnet add package Microsoft.Spark --version 2.1.1
dotnet add package SynapseML.Lightgbm --version 1.0.2
dotnet add package SynapseML.Core --version 1.0.2

Use the following code in your main program file:

using System;
using System.Collections.Generic;
using Synapse.ML.Lightgbm;
using Synapse.ML.Featurize;
using Microsoft.Spark.Sql;
using Microsoft.Spark.Sql.Types;

namespace SynapseMLApp
{
class Program
{
static void Main(string[] args)
{
// Create Spark session
SparkSession spark =
SparkSession
.Builder()
.AppName("LightGBMExample")
.GetOrCreate();

// Load Data
DataFrame df = spark.Read()
.Option("inferSchema", true)
.Parquet("wasbs://publicwasb@mmlspark.blob.core.windows.net/AdultCensusIncome.parquet")
.Limit(2000);

var featureColumns = new string[] {"age", "workclass", "fnlwgt", "education", "education-num",
"marital-status", "occupation", "relationship", "race", "sex", "capital-gain",
"capital-loss", "hours-per-week", "native-country"};

// Transform features
var featurize = new Featurize()
.SetOutputCol("features")
.SetInputCols(featureColumns)
.SetOneHotEncodeCategoricals(true)
.SetNumFeatures(14);

var dfTrans = featurize
.Fit(df)
.Transform(df)
.WithColumn("label", Functions.When(Functions.Col("income").Contains("<"), 0.0).Otherwise(1.0));

DataFrame[] dfs = dfTrans.RandomSplit(new double[] {0.75, 0.25}, 123);
var trainDf = dfs[0];
var testDf = dfs[1];

// Create LightGBMClassifier
var lightGBMClassifier = new LightGBMClassifier()
.SetFeaturesCol("features")
.SetRawPredictionCol("rawPrediction")
.SetObjective("binary")
.SetNumLeaves(30)
.SetNumIterations(200)
.SetLabelCol("label")
.SetLeafPredictionCol("leafPrediction")
.SetFeaturesShapCol("featuresShap");

// Fit the model
var lightGBMClassificationModel = lightGBMClassifier.Fit(trainDf);

// Apply transformation and displayresults
lightGBMClassificationModel.Transform(testDf).Show(50);

// Stop Spark session
spark.Stop();
}
}
}

Run dotnet build to build the project. Then navigate to build output directory, and run following command:

spark-submit --class org.apache.spark.deploy.dotnet.DotnetRunner --packages com.microsoft.azure:synapseml_2.12:1.0.2,org.apache.hadoop:hadoop-azure:3.3.1 --master local microsoft-spark-3-2_2.12-2.1.1.jar dotnet SynapseMLApp.dll
note

Here we added two packages: synapseml_2.12 for SynapseML's scala source, and hadoop-azure to support reading files from ADLS.

Expected output:

+---+---------+------+-------------+-------------+--------------+------------------+---------------+-------------------+-------+------------+------------+--------------+--------------+------+--------------------+-----+--------------------+--------------------+----------+--------------------+--------------------+
|age|workclass|fnlwgt| education|education-num|marital-status| occupation| relationship| race| sex|capital-gain|capital-loss|hours-per-week|native-country|income| features|label| rawPrediction| probability|prediction| leafPrediction| featuresShap|
+---+---------+------+-------------+-------------+--------------+------------------+---------------+-------------------+-------+------------+------------+--------------+--------------+------+--------------------+-----+--------------------+--------------------+----------+--------------------+--------------------+
| 17| ?|634226| 10th| 6| Never-married| ?| Own-child| White| Female| 0| 0| 17.0| United-States| <=50K|(61,[7,9,11,15,20...| 0.0|[9.37122343731523...|[0.99991486808581...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.0560742274706...|
| 17| Private| 73145| 9th| 5| Never-married| Craft-repair| Own-child| White| Female| 0| 0| 16.0| United-States| <=50K|(61,[7,9,11,15,17...| 0.0|[12.7512760001880...|[0.99999710138899...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1657810433238...|
| 17| Private|150106| 10th| 6| Never-married| Sales| Own-child| White| Female| 0| 0| 20.0| United-States| <=50K|(61,[5,9,11,15,17...| 0.0|[12.7676985938038...|[0.99999714860282...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1276877355292...|
| 17| Private|151141| 11th| 7| Never-married| Handlers-cleaners| Own-child| White| Male| 0| 0| 15.0| United-States| <=50K|(61,[8,9,11,15,17...| 0.0|[12.1656242513070...|[0.99999479363924...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1279828578119...|
| 17| Private|327127| 11th| 7| Never-married| Transport-moving| Own-child| White| Male| 0| 0| 20.0| United-States| <=50K|(61,[1,9,11,15,17...| 0.0|[12.9962776686392...|[0.99999773124636...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1164691543415...|
| 18| ?|171088| Some-college| 10| Never-married| ?| Own-child| White| Female| 0| 0| 40.0| United-States| <=50K|(61,[7,9,11,15,20...| 0.0|[12.9400428266629...|[0.99999760000817...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1554829578661...|
| 18| Private|115839| 12th| 8| Never-married| Adm-clerical| Not-in-family| White| Female| 0| 0| 30.0| United-States| <=50K|(61,[0,9,11,15,17...| 0.0|[11.8393032168619...|[0.99999278472630...| 0.0|[0.0,0.0,0.0,0.0,...|[0.44080835709189...|
| 18| Private|133055| HS-grad| 9| Never-married| Other-service| Own-child| White| Female| 0| 0| 30.0| United-States| <=50K|(61,[3,9,11,15,17...| 0.0|[11.5747235180479...|[0.99999059936124...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1415862541824...|
| 18| Private|169745| 7th-8th| 4| Never-married| Other-service| Own-child| White| Female| 0| 0| 40.0| United-States| <=50K|(61,[3,9,11,15,17...| 0.0|[11.8316427733613...|[0.99999272924226...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1527378526573...|
| 18| Private|177648| HS-grad| 9| Never-married| Sales| Own-child| White| Female| 0| 0| 25.0| United-States| <=50K|(61,[5,9,11,15,17...| 0.0|[10.0820248199174...|[0.99995817710510...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1151843103241...|
| 18| Private|188241| 11th| 7| Never-married| Other-service| Own-child| White| Male| 0| 0| 16.0| United-States| <=50K|(61,[3,9,11,15,17...| 0.0|[10.4049945509280...|[0.99996972005153...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1356854966291...|
| 18| Private|200603| HS-grad| 9| Never-married| Adm-clerical| Other-relative| White| Female| 0| 0| 30.0| United-States| <=50K|(61,[0,9,11,15,17...| 0.0|[12.1354343020828...|[0.99999463406365...| 0.0|[0.0,0.0,0.0,0.0,...|[0.53241098695335...|
| 18| Private|210026| 10th| 6| Never-married| Other-service| Other-relative| White| Female| 0| 0| 40.0| United-States| <=50K|(61,[3,9,11,15,17...| 0.0|[12.3692360082180...|[0.99999575275599...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1275208795564...|
| 18| Private|447882| Some-college| 10| Never-married| Adm-clerical| Not-in-family| White| Female| 0| 0| 20.0| United-States| <=50K|(61,[0,9,11,15,17...| 0.0|[10.2514945786032...|[0.99996469655062...| 0.0|[0.0,0.0,0.0,0.0,...|[0.36497782752201...|
| 19| ?|242001| Some-college| 10| Never-married| ?| Own-child| White| Female| 0| 0| 40.0| United-States| <=50K|(61,[7,9,11,15,20...| 0.0|[13.9439986622060...|[0.99999912057674...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1265631737386...|
| 19| Private| 63814| Some-college| 10| Never-married| Adm-clerical| Not-in-family| White| Female| 0| 0| 18.0| United-States| <=50K|(61,[0,9,11,15,17...| 0.0|[10.2057742895673...|[0.99996304506073...| 0.0|[0.0,0.0,0.0,0.0,...|[0.77645146059597...|
| 19| Private| 83930| HS-grad| 9| Never-married| Other-service| Own-child| White| Female| 0| 0| 20.0| United-States| <=50K|(61,[3,9,11,15,17...| 0.0|[10.4771335467356...|[0.99997182742919...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1625827100973...|
| 19| Private| 86150| 11th| 7| Never-married| Sales| Own-child| Asian-Pac-Islander| Female| 0| 0| 19.0| Philippines| <=50K|(61,[5,9,14,15,17...| 0.0|[12.0241839747799...|[0.99999400263272...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1532111483051...|
| 19| Private|189574| HS-grad| 9| Never-married| Other-service| Not-in-family| White| Female| 0| 0| 30.0| United-States| <=50K|(61,[3,9,11,15,17...| 0.0|[9.53742673004733...|[0.99992790305091...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.0988907054317...|
| 19| Private|219742| Some-college| 10| Never-married| Other-service| Own-child| White| Female| 0| 0| 15.0| United-States| <=50K|(61,[3,9,11,15,17...| 0.0|[12.8625329757574...|[0.99999740658642...| 0.0|[0.0,0.0,0.0,0.0,...|[-0.1922327651359...|
+---+---------+------+-------------+-------------+--------------+------------------+---------------+-------------------+-------+------------+------------+--------------+--------------+------+--------------------+-----+--------------------+--------------------+----------+--------------------+--------------------+