static void Main(string[] args)
{
var context = new MLContext();
var data = context.Data.LoadFromTextFile <RankingData>("./ranking.tsv", separatorChar: '\t');
var trainTestSplit = context.Data.TrainTestSplit(data, testFraction: 0.2);
var settings = new RankingExperimentSettings
{
MaxExperimentTimeInSeconds = 300,
OptimizingMetric = RankingMetric.Ndcg,
};
var experiment = context.Auto().CreateRankingExperiment(settings);
var progressHandler = new Progress <RunDetail <RankingMetrics> >(ph =>
{
if (ph.ValidationMetrics != null)
{
Console.WriteLine($"Current trainer - {ph.TrainerName} with nDCG {ph.ValidationMetrics.NormalizedDiscountedCumulativeGains.Average()}");
}
});
var results = experiment.Execute(trainTestSplit.TrainSet, validationData: trainTestSplit.TestSet,
progressHandler: progressHandler);
var bestRun = results.BestRun;
var metrics = bestRun.ValidationMetrics.NormalizedDiscountedCumulativeGains;
Console.WriteLine(Environment.NewLine);
Console.WriteLine($"Best model {bestRun.TrainerName} - with nDCG {metrics.Average()}");
}
static void Main(string[] args)
{
var context = new MLContext();
var data = context.Data.LoadFromTextFile <RankingData>("./ranking.tsv", separatorChar: '\t');
var settings = new RankingExperimentSettings
{
MaxExperimentTimeInSeconds = 300,
OptimizingMetric = RankingMetric.Ndcg
};
var experiment = context.Auto().CreateRankingExperiment(settings);
var results = experiment.Execute(data);
var bestModel = results.BestRun.Model;
}
public void AutoFitRankingTest()
{
string labelColumnName = "Label";
string scoreColumnName = "Score";
string groupIdColumnName = "GroupId";
string featuresColumnVectorNameA = "FeatureVectorA";
string featuresColumnVectorNameB = "FeatureVectorB";
var mlContext = new MLContext(1);
// STEP 1: Load data
var reader = new TextLoader(mlContext, GetLoaderArgsRank(labelColumnName, groupIdColumnName, featuresColumnVectorNameA, featuresColumnVectorNameB));
var trainDataView = reader.Load(new MultiFileSource(DatasetUtil.GetMLSRDataset()));
var testDataView = mlContext.Data.TakeRows(trainDataView, 500);
trainDataView = mlContext.Data.SkipRows(trainDataView, 500);
// STEP 2: Run AutoML experiment
var settings = new RankingExperimentSettings()
{
MaxExperimentTimeInSeconds = 5,
OptimizationMetricTruncationLevel = 3
};
var experiment = mlContext.Auto()
.CreateRankingExperiment(settings);
ExperimentResult <RankingMetrics>[] experimentResults =
{
experiment.Execute(trainDataView, labelColumnName, groupIdColumnName),
experiment.Execute(trainDataView, testDataView),
experiment.Execute(trainDataView, testDataView,
new ColumnInformation()
{
LabelColumnName = labelColumnName,
GroupIdColumnName = groupIdColumnName,
}),
experiment.Execute(trainDataView, testDataView,
new ColumnInformation()
{
LabelColumnName = labelColumnName,
GroupIdColumnName = groupIdColumnName,
SamplingKeyColumnName = groupIdColumnName
})
};
for (int i = 0; i < experimentResults.Length; i++)
{
RunDetail <RankingMetrics> bestRun = experimentResults[i].BestRun;
// The user requested 3, but we always return at least 10.
Assert.Equal(10, bestRun.ValidationMetrics.DiscountedCumulativeGains.Count);
Assert.Equal(10, bestRun.ValidationMetrics.NormalizedDiscountedCumulativeGains.Count);
Assert.True(experimentResults[i].RunDetails.Count() > 0);
Assert.NotNull(bestRun.ValidationMetrics);
Assert.True(bestRun.ValidationMetrics.NormalizedDiscountedCumulativeGains.Last() > 0.4);
Assert.True(bestRun.ValidationMetrics.DiscountedCumulativeGains.Last() > 19);
var outputSchema = bestRun.Model.GetOutputSchema(trainDataView.Schema);
var expectedOutputNames = new string[] { labelColumnName, groupIdColumnName, groupIdColumnName, featuresColumnVectorNameA, featuresColumnVectorNameB,
"Features", scoreColumnName };
foreach (var col in outputSchema)
{
Assert.True(col.Name == expectedOutputNames[col.Index]);
}
}
}
private static (IDataView, IDataView) BuildTrainEvaluateAndSaveModel(MLContext mlContext)
{
// STEP 1: Download and load the data
GetData(InputPath, OutputPath, TrainDatasetPath, TrainDatasetUrl, TestDatasetUrl, TestDatasetPath,
ValidationDatasetUrl, ValidationDatasetPath);
//ColumnInferenceResults columnInference = mlContext.Auto().InferColumns(TrainDatasetPath, labelColumnIndex: 0,
// separatorChar: '\t', hasHeader: true, groupColumns: false, allowSparse: true);
var textLoaderOptions = new TextLoader.Options
{
Separators = new[] { '\t' },
HasHeader = true,
Columns = new[]
{
new TextLoader.Column("Label", DataKind.Single, 0),
new TextLoader.Column("GroupId", DataKind.Int32, 1),
new TextLoader.Column("Features", DataKind.Single, 2, 133),
}
};
TextLoader textLoader = mlContext.Data.CreateTextLoader(textLoaderOptions);
IDataView trainDataView = textLoader.Load(TrainDatasetPath);
IDataView validationDataView = textLoader.Load(ValidationDatasetPath);
IDataView testDataView = textLoader.Load(TestDatasetPath);
// STEP 2: Display first few rows of training data
ConsoleHelper.ShowDataViewInConsole(mlContext, trainDataView);
// STEP 3: Initialize our user-defined progress handler that AutoML will
// invoke after each model it produces and evaluates.
var progressHandler = new RankingExperimentProgressHandler();
// STEP 4: Run AutoML ranking experiment
ConsoleHelper.ConsoleWriteHeader("=============== Running AutoML experiment ===============");
Console.WriteLine($"Running AutoML ranking experiment for {ExperimentTime} seconds...");
var experimentSettings = new RankingExperimentSettings
{
MaxExperimentTimeInSeconds = ExperimentTime,
OptimizingMetric = RankingMetric.Ndcg,
OptimizationMetricTruncationLevel = 10
};
ExperimentResult <RankingMetrics> experimentResult = mlContext.Auto()
.CreateRankingExperiment(experimentSettings)
.Execute(
trainData: trainDataView,
validationData: validationDataView,
progressHandler: progressHandler);
// Print top models found by AutoML
Console.WriteLine("\n===== Evaluating model's NDCG (on validation data) =====");
PrintTopModels(experimentResult, experimentSettings.OptimizationMetricTruncationLevel);
var rankingEvaluatorOptions = new RankingEvaluatorOptions
{
DcgTruncationLevel = Math.Min(10, (int)experimentSettings.OptimizationMetricTruncationLevel * 2)
};
Console.WriteLine("\n===== Evaluating model's NDCG (on test data) =====");
IDataView predictions = experimentResult.BestRun.Model.Transform(testDataView);
var metrics = mlContext.Ranking.Evaluate(predictions, rankingEvaluatorOptions);
ConsoleHelper.PrintRankingMetrics(experimentResult.BestRun.TrainerName, metrics, experimentSettings.OptimizationMetricTruncationLevel);
// STEP 5: Refit the model and get final metrics
// Re-fit best pipeline on train and validation data, to produce
// a model that is trained on as much data as is available while
// still having test data for the final estimate of how well the
// model will do in production.
Console.WriteLine("\n===== Refitting on train+valid and evaluating model's NDCG (on test data) =====");
var trainPlusValidationDataView = textLoader.Load(new MultiFileSource(TrainDatasetPath, ValidationDatasetPath));
var refitModel = experimentResult.BestRun.Estimator.Fit(trainPlusValidationDataView);
IDataView predictionsRefitOnTrainPlusValidation = refitModel.Transform(testDataView);
var metricsRefitOnTrainPlusValidation = mlContext.Ranking.Evaluate(predictionsRefitOnTrainPlusValidation, rankingEvaluatorOptions);
ConsoleHelper.PrintRankingMetrics(experimentResult.BestRun.TrainerName, metricsRefitOnTrainPlusValidation, experimentSettings.OptimizationMetricTruncationLevel);
// STEP 6: Refit the model with all available data
// Re-fit best pipeline again on train, validation, and test data, to
// produce a model that is trained on as much data as is available.
// This is the final model that can be deployed to production.
// No metrics are printed since we no longer have an independent
// scoring dataset.
Console.WriteLine("\n===== Refitting on train+valid+test to get the final model to launch to production =====");
var trainPlusValidationPlusTestDataView = textLoader.Load(new MultiFileSource(TrainDatasetPath, ValidationDatasetPath, TestDatasetPath));
var refitModelOnTrainValidTest = experimentResult.BestRun.Estimator.Fit(trainPlusValidationPlusTestDataView);
// STEP 7: Save/persist the trained model to a .ZIP file
mlContext.Model.Save(refitModelOnTrainValidTest, trainDataView.Schema, ModelPath);
Console.WriteLine("The model is saved to {0}", ModelPath);
return(predictionsRefitOnTrainPlusValidation, testDataView);
}