public void CreatePredictionModel()
{
// Define a feature contribution calculator for all the features.
// Don't normalize the contributions.
// https://docs.microsoft.com/en-us/dotnet/api/microsoft.ml.transforms.featurecontributioncalculatingestimator?view=ml-dotnet
// "Does this estimator need to look at the data to train its parameters? No"
var regressionData = _regressionModel.Transform(MLContext.Data.TakeRows(_transformedData, 1));
var featureContributionCalculator = MLContext.Transforms
.CalculateFeatureContribution(_regressionModel, normalize: false) // Estimator
.Fit(regressionData); // Transformer
// Create the full transformer chain.
var scoringPipeline = _transformationModel
.Append(_regressionModel)
.Append(featureContributionCalculator);
// Create the prediction engine.
_predictionEngine = MLContext.Model.CreatePredictionEngine <FeatureContributionData, FeatureContributionPrediction>(scoringPipeline);
}
public (double RSquaredX, double RSquaredY) Train(IList <Record> data)
{
mlContext = new MLContext(seed: 0);
var train = data.Select(r => new RegressionRecord(r));
dataView = mlContext.Data.LoadFromEnumerable(train);
labelX = nameof(RegressionRecord.DisplayX);
var pipelineX = mlContext.Regression.Trainers.FastTree(labelX, FEATURE_COLUMN_NAME);
estimatorX = pipelineX.Fit(dataView);
labelY = nameof(RegressionRecord.DisplayY);
var pipelineY = mlContext.Regression.Trainers.FastTree(labelY, FEATURE_COLUMN_NAME);
estimatorY = pipelineY.Fit(dataView);
GenPredictors();
var transX = estimatorX.Transform(dataView);
var evalX = mlContext.Regression.Evaluate(transX, labelX);
var transY = estimatorY.Transform(dataView);
var evalY = mlContext.Regression.Evaluate(transY, labelY);
return(evalX.RSquared, evalY.RSquared);
}