Adding an example for using PFI

docs/samples/Microsoft.ML.Samples/Dynamic/PermutationFeatureImportance.cs

@@ -0,0 +1,97 @@
+using Microsoft.ML.Runtime.Data;
+using Microsoft.ML.Runtime.Learners;
+using System;
+using System.Linq;
+
+namespace Microsoft.ML.Samples.Dynamic
+{
+    public class PFI_RegressionExample
+    {
+        public static void PFI_Regression()
+        {
+            // Download the dataset from github.com/dotnet/machinelearning.
+            // This will create a housing.txt file in the filesystem.
+            // You can open this file to see the data. 
+            string dataFile = SamplesUtils.DatasetUtils.DownloadHousingRegressionDataset();
+
+            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // as a catalog of available operations and as the source of randomness.
+            var mlContext = new MLContext();
+
+            // Step 1: Read the data as an IDataView.
+            // First, we define the reader: specify the data columns and where to find them in the text file.
+            var reader = mlContext.Data.TextReader(new TextLoader.Arguments()
+                {
+                    Separator = "tab",
+                    HasHeader = true,
+                    Column = new[]
+                    {
+                        new TextLoader.Column("MedianHomeValue", DataKind.R4, 0),
+                        new TextLoader.Column("CrimesPerCapita", DataKind.R4, 1),
+                        new TextLoader.Column("PercentResidental", DataKind.R4, 2),
+                        new TextLoader.Column("PercentNonRetail", DataKind.R4, 3),
+                        new TextLoader.Column("CharlesRiver", DataKind.R4, 4),
+                        new TextLoader.Column("NitricOxides", DataKind.R4, 5),
+                        new TextLoader.Column("RoomsPerDwelling", DataKind.R4, 6),
+                        new TextLoader.Column("PercentPre40s", DataKind.R4, 7),
+                        new TextLoader.Column("EmploymentDistance", DataKind.R4, 8),
+                        new TextLoader.Column("HighwayDistance", DataKind.R4, 9),
+                        new TextLoader.Column("TaxRate", DataKind.R4, 10),
+                        new TextLoader.Column("TeacherRatio", DataKind.R4, 11),
+                    }
+                });
+
+            // Read the data
+            var data = reader.Read(dataFile);
+
+            // Step 2: Pipeline
+            // Concatenate the features to create a Feature vector.
+            // Then append a gam regressor, setting the "MedianHomeValue" column as the label of the dataset,
+            // the "Features" column produced by concatenation as the features column.
+            var labelName = "MedianHomeValue";
+            var pipeline = mlContext.Transforms.Concatenate("Features", "CrimesPerCapita", "PercentResidental",
+                    "PercentNonRetail", "CharlesRiver", "NitricOxides", "RoomsPerDwelling", "PercentPre40s",
+                    "EmploymentDistance", "HighwayDistance", "TaxRate", "TeacherRatio")
+                    .Append(mlContext.Regression.Trainers.StochasticDualCoordinateAscent(
+                        labelColumn: labelName, featureColumn: "Features"));
+            var fitPipeline = pipeline.Fit(data);
+
+            // Extract the model from the pipeline
+            var linearPredictor = fitPipeline.LastTransformer;
+            var weights = GetLinearModelWeights(linearPredictor.Model);
+
+            // Compute the permutation metrics using the properly-featurized data.
+            var transformedData = fitPipeline.Transform(data);
+            var permutationMetrics = mlContext.Regression.PermutationFeatureImportance(
+                linearPredictor, transformedData, label: labelName, features: "Features");
+
+            // Now let's look at which features are most important to the model overall
+            // First, we have to prepare the data:
+            // Get the feature names as an IEnumerable
+            var featureNames = data.Schema.GetColumns()
+                .Select(tuple => tuple.column.Name) // Get the column names
+                .Where(name => name != labelName) // Drop the Label
+                .ToArray();
+
+            // Get the feature indices sorted by their impact on R-Squared
+            var sortedIndices = permutationMetrics.Select((metrics, index) => new { index, metrics.RSquared })
+                .OrderByDescending(feature => Math.Abs(feature.RSquared))
+                .Select(feature => feature.index);
+
+            // Print out the permutation results, with the model weights, in order of their impact
+            Console.WriteLine("Feature\tModel Weight\tChange in R-Squared");
+            var rSquared = permutationMetrics.Select(x => x.RSquared).ToArray(); // Fetch r-squared as an array
+            foreach (int i in sortedIndices)
+            {
+                Console.WriteLine("{0}\t{1:0.00}\t{2:G4}", featureNames[i], weights[i], rSquared[i]);
+            }
+        }
+
+        private static float[] GetLinearModelWeights(LinearRegressionPredictor linearModel)
+        {
+            var weights = new VBuffer<float>();
+            linearModel.GetFeatureWeights(ref weights);
+            return weights.GetValues().ToArray();
+        }
+    }
+}