How to get feature importance from a regression model?

References:

https://devblogs.microsoft.com/premier-developer/permutation-implementation-with-ml-net/

https://docs.microsoft.com/en-us/dotnet/machine-learning/how-to-guides/explain-machine-learning-model-permutation-feature-importance-ml-net

The code below is largely copied from the Taxi sample. I am trying to get relative importance of my inputs. I referred to the pages shown above but I am obviously doing something wrong. I have ten inputs to the model but the `PermutationFeatureImportance` method returns 16 elements. Also - my second attempt - my own `PrintFeatureImportance` method winds up with the same problem which results in an "index out of bounds" error.

public class RegressionModelService
{
private readonly DataService dataService;

public RegressionModelService(DataService dataService) => this.dataService = dataService;

public ITransformer BuildTrainEvaluateAndSaveModel()
{
// STEP 1: Common data loading configuration
MLContext mlContext = new MLContext(seed: 0);
Console.WriteLine($"Start data load at {DateTime.Now.ToLongTimeString()}.");
var sw = Stopwatch.StartNew();
IDataView trainingDataView = mlContext.Data.LoadFromEnumerable(dataService.GetChunk(0, Program.SetSize));
IDataView testDataView = mlContext.Data.LoadFromEnumerable(dataService.GetChunk(Program.SetSize, Program.SetSize));
sw.Stop();
string elapsed = sw.Elapsed.TotalMinutes.ToString();
Console.WriteLine($"Data load completed in {elapsed} seconds at {DateTime.Now.ToLongTimeString()}.");

string[] featureColumnNames = new[] {
nameof(BatteryHistory.LastReadVoltage),
nameof(BatteryHistory.HistoricVoltage1),
nameof(BatteryHistory.HistoricVoltage2),
nameof(BatteryHistory.CurrentTemperature),
nameof(BatteryHistory.LastReadTemperature),
nameof(BatteryHistory.HistoricTemperature1),
nameof(BatteryHistory.HistoricTemperature2),
"CD_COMM_FW_VER_Encoded",
"CD_COMM_HW_VER_Encoded",
nameof(BatteryHistory.ModuleAgeInDays)
};

// STEP 2: Common data process configuration with pipeline data transformations
var pipeline = mlContext.Transforms
.CopyColumns("Label", nameof(BatteryHistory.CurrentVoltage))
.Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "CD_COMM_FW_VER_Encoded", inputColumnName: nameof(BatteryHistory.CD_COMM_FW_VER)))
.Append(mlContext.Transforms.Categorical.OneHotEncoding(outputColumnName: "CD_COMM_HW_VER_Encoded", inputColumnName: nameof(BatteryHistory.CD_COMM_HW_VER)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(BatteryHistory.LastReadVoltage)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(BatteryHistory.HistoricVoltage1)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(BatteryHistory.HistoricVoltage2)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(BatteryHistory.LastReadVoltage)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(BatteryHistory.CurrentTemperature)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(BatteryHistory.LastReadTemperature)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(BatteryHistory.HistoricTemperature1)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(BatteryHistory.HistoricTemperature2)))
.Append(mlContext.Transforms.NormalizeMeanVariance(outputColumnName: nameof(BatteryHistory.ModuleAgeInDays)))
.Append(mlContext.Transforms.Concatenate("Features", featureColumnNames));

// (OPTIONAL) Peek data (such as 5 records) in training DataView after applying the ProcessPipeline's transformations into "Features"
ConsoleHelper.PeekDataViewInConsole(mlContext, trainingDataView, pipeline, 5);
ConsoleHelper.PeekVectorColumnDataInConsole(mlContext, "Features", trainingDataView, pipeline, 5);

// STEP 3: Set the training algorithm, then create and config the modelBuilder - Selected Trainer (SDCA Regression algorithm)
var trainer = mlContext.Regression.Trainers.Sdca(labelColumnName: "Label", featureColumnName: "Features");
var trainingPipeline = pipeline.Append(trainer);

// STEP 4: Train the model fitting to the DataSet
//The pipeline is trained on the dataset that has been loaded and transformed.
Console.WriteLine("=============== Training the model ===============");
var trainedModel = trainingPipeline.Fit(trainingDataView);

// STEP 5: Evaluate the model and show accuracy stats
Console.WriteLine("===== Evaluating Model's accuracy with Test data =====");

IDataView predictions = trainedModel.Transform(testDataView);
var metrics = mlContext.Regression.Evaluate(predictions, labelColumnName: "Label", scoreColumnName: "Score");

ConsoleHelper.PrintRegressionMetrics(trainer.ToString(), metrics);

// STEP 6: Save/persist the trained model to a .ZIP file
mlContext.Model.Save(trainedModel, trainingDataView.Schema, Program.ModelPath);

Console.WriteLine("The model is saved to {0}", Program.ModelPath);

// Feature Importance -------------------

// ====================================== PROBLEM HERE ===========================================================
var permutationMetrics = mlContext.Regression.PermutationFeatureImportance(trainedModel.LastTransformer, predictions, permutationCount: 100); // 16 elements

// Trying a different way, same problem as previous line
PrintFeatureImportance(mlContext, predictions, featureColumnNames);

// Feature Importance -------------------

return trainedModel;

}

private static void PrintFeatureImportance(MLContext mlContext, IDataView data, string[] featureColumnNames)
{

// 2. Define estimator with data pre-processing steps
IEstimator dataPrepEstimator =
mlContext.Transforms.Concatenate("Features", featureColumnNames)
.Append(mlContext.Transforms.NormalizeMinMax("Features"));

// 3. Create transformer using the data pre-processing estimator
ITransformer dataPrepTransformer = dataPrepEstimator.Fit(data);

// 4. Pre-process the training data
IDataView preprocessedTrainData = dataPrepTransformer.Transform(data);

// 5. Define Stochastic Dual Coordinate Ascent machine learning estimator
var sdcaEstimator = mlContext.Regression.Trainers.Sdca();

// 6. Train machine learning model
var sdcaModel = sdcaEstimator.Fit(preprocessedTrainData);

ImmutableArray permutationFeatureImportance =
mlContext.Regression.PermutationFeatureImportance(sdcaModel, preprocessedTrainData, permutationCount: featureColumnNames.Count());

var featureImportanceMetrics = permutationFeatureImportance.Select((metric, index) => new { index, metric.RSquared }).OrderByDescending(x => Math.Abs(x.RSquared.Mean));

Console.Clear();
Console.WriteLine("Feature Importance");

foreach (var feature in featureImportanceMetrics)
{
Console.WriteLine($"{featureColumnNames[feature.index],-20}|\t{feature.RSquared.Mean:F6}"); // Index out of bounds error here
}
Console.WriteLine("Press any key...");
Console.ReadKey();
}
}