Hi !
This is a cool one, Machine Learning .Net (ML.Net) now support ranking scenario in AutoML. As an Machine Learning aficionado, this is amazing. I can now process my problems with AutoML, and then learn the specifics of the best produced models.
Note: a couple of weeks ago, someone asked a question around ranking scenarios. My knowledge is low here, so I kindly shared a couple of starting points. With AutoML now supporting ranking scenarios, the response is completely different!
So I picked up the sample for the current version (1.5.1), the sample for standard ranking scenarios from ML.Net and a data source based on a public datasets provided by Microsoft originally provided Microsoft Bing (see references); and I created this sample
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using System;
using System. Linq;
using System. Net. Http. Headers;
using Microsoft. ML;
using Microsoft. ML. AutoML;
using Microsoft. ML. Data;
namespace ConsoleApp1
{
public class Program
{
static void Main ( string [ ] args )
{
Console. WriteLine ( " Start …" ) ;
Run( ) ;
Console. WriteLine ( " End" ) ;
}
private static string TrainDataPath = @"data\train.txt" ;
private static string TestDataPath = @"data\test.txt" ;
private static string ModelPath = @"Model.zip" ;
private static string LabelColumnName = " Label" ;
private static string GroupColumnName = " GroupId" ;
private static uint ExperimentTime = 600 ;
public static void Run ( )
{
var mlContext = new MLContext( ) ;
// STEP 1: Load data
var trainDataView = mlContext. Data. LoadFromTextFile < SearchData > ( TrainDataPath, hasHeader: false , separatorChar: '\t ' ) ;
var testDataView = mlContext. Data. LoadFromTextFile < SearchData > ( TestDataPath, hasHeader: false , separatorChar: '\t ' ) ;
// STEP 2: Run AutoML experiment
Console. WriteLine ( $" Running AutoML recommendation experiment for { ExperimentTime} seconds… " ) ;
var experimentResult = mlContext. Auto ( )
. CreateRankingExperiment ( new RankingExperimentSettings( ) { MaxExperimentTimeInSeconds = ExperimentTime } )
. Execute ( trainDataView, testDataView,
new ColumnInformation( )
{
LabelColumnName = LabelColumnName ,
GroupIdColumnName = GroupColumnName
} ) ;
// STEP 3: Print metric from best model
var bestRun = experimentResult. BestRun;
Console. WriteLine ( $" ===================================================== " ) ;
Console. WriteLine ( $" Total models produced: { experimentResult. RunDetails. Count ( ) } " ) ;
var i = 0 ;
foreach ( var experimentResultRunDetail in experimentResult. RunDetails)
{
i ++ ;
Console. WriteLine ( $" { i} – TrainerName: { experimentResultRunDetail. TrainerName} " ) ;
Console. WriteLine ( $" Runtime In Seconds: { experimentResultRunDetail. RuntimeInSeconds} " ) ;
Console. WriteLine ( $" " ) ;
//PrintMetrics(experimentResultRunDetail.ValidationMetrics);
}
Console. WriteLine ( $" " ) ;
Console. WriteLine ( $" ===================================================== " ) ;
Console. WriteLine ( $" Best model's trainer: { bestRun. TrainerName} " ) ;
// STEP 5: Evaluate test data
var testDataViewWithBestScore = bestRun. Model. Transform ( testDataView) ;
var testMetrics = mlContext. Ranking. Evaluate ( testDataViewWithBestScore, labelColumnName: LabelColumnName) ;
Console. WriteLine ( $" Metrics of best model on test data — " ) ;
PrintMetrics( testMetrics) ;
// STEP 6: Save the best model for later deployment and inferencing
mlContext. Model. Save ( bestRun. Model, trainDataView. Schema, ModelPath) ;
// STEP 7: Create prediction engine from the best trained model
var predictionEngine = mlContext. Model. CreatePredictionEngine < SearchData , SearchDataPrediction > ( bestRun. Model) ;
// STEP 8: Initialize a new test, and get the prediction
var testPage = new SearchData
{
GroupId = " 1" ,
Features = 9 ,
Label = 1
} ;
var prediction = predictionEngine. Predict ( testPage) ;
Console. WriteLine ( $" Predicted rating for: { prediction. Prediction} " ) ;
// New Page
testPage = new SearchData
{
GroupId = " 2" ,
Features = 2 ,
Label = 9
} ;
prediction = predictionEngine. Predict ( testPage) ;
Console. WriteLine ( $" Predicted: { prediction. Prediction} " ) ;
Console. WriteLine ( " Press any key to continue…" ) ;
Console. ReadKey ( ) ;
}
private static void PrintMetrics ( RankingMetrics metrics )
{
if ( metrics is null )
{
Console. WriteLine ( $" No metrics " ) ;
return ;
}
var ndcg = metrics. NormalizedDiscountedCumulativeGains. Aggregate ( " " , ( current , p ) => current + p + " – " ) ;
var dcg = metrics. DiscountedCumulativeGains. Aggregate ( " " , ( current , p ) => current + p + " – " ) ;
Console. WriteLine ( $" Normalized Discounted Cumulative Gains: { ndcg} " ) ;
Console. WriteLine ( $" Discounted Cumulative Gains: { dcg} " ) ;
}
}
class SearchData
{
[ LoadColumn( 0 ) ]
public string GroupId ;
[ LoadColumn( 1 ) ]
public float Features ;
[ LoadColumn( 2 ) ]
public float Label ;
}
class SearchDataPrediction
{
[ ColumnName( " PredictedLabel" ) ]
public float Prediction ;
public float Score { get ; set ; }
}
}
The sample run for 10 minutes and evaluates 33 models.
The test file is 266MB and the train data file is 799 MBs.
At the end, the best trainer is [FastTreeRanking].
The output is also very clear about the tested models and the best one. (I trimmed this to make it clearer).
Start ...
Running AutoML recommendation experiment for 600 seconds...
=====================================================
Total models produced: 33
1 - TrainerName: LightGbmRanking
Runtime In Seconds: 10.6167636
2 - TrainerName: FastTreeRanking
Runtime In Seconds: 11.1055165
3 - TrainerName: FastTreeRanking
Runtime In Seconds: 35.0196598
4 - TrainerName: FastTreeRanking
Runtime In Seconds: 6.0401781
...
=====================================================
Best model's trainer: FastTreeRanking
Press any key to continue...
Super cool feature !
References
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El Bruno 
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