Breaking the PFI examples into different files in a subfolder.

Author: Rogan Carr

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

@@ -0,0 +1,88 @@
+using Microsoft.ML.Runtime.Data;
+using Microsoft.ML.Runtime.Learners;
+using Microsoft.ML.Trainers.HalLearners;
+using System;
+using System.Linq;
+
+namespace Microsoft.ML.Samples.Dynamic.PermutationFeatureImportance
+{
+    public class PfiHelper
+    {
+        public static IDataView GetHousingRegressionIDataView(MLContext mlContext, out string labelName, out string[] featureNames, bool binaryPrediction = false)
+        {
+            // 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();
+
+            // Read the data as an IDataView.
+            // First, we define the reader: specify the data columns and where to find them in the text file.
+            // The data file is composed of rows of data, with each row having 11 numerical columns
+            // separated by whitespace.
+            var reader = mlContext.Data.CreateTextReader(
+                columns: new[]
+                    {
+                        // Read the first column (indexed by 0) in the data file as an R4 (float)
+                        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),
+                    },
+                hasHeader: true
+            );
+
+            // Read the data
+            var data = reader.Read(dataFile);
+            var labelColumn = "MedianHomeValue";
+
+            if (binaryPrediction)
+            {
+                labelColumn = nameof(BinaryOutputRow.AboveAverage);
+                data = mlContext.Transforms.CustomMappingTransformer(GreaterThanAverage, null).Transform(data);
+                data = mlContext.Transforms.DropColumns("MedianHomeValue").Fit(data).Transform(data);
+            }
+
+            labelName = labelColumn;
+            featureNames = data.Schema.AsEnumerable()
+                .Select(column => column.Name) // Get the column names
+                .Where(name => name != labelColumn) // Drop the Label
+                .ToArray();
+
+            return data;
+        }
+
+        // Define a class for all the input columns that we intend to consume.
+        private class ContinuousInputRow
+        {
+            public float MedianHomeValue { get; set; }
+        }
+
+        // Define a class for all output columns that we intend to produce.
+        private class BinaryOutputRow
+        {
+            public bool AboveAverage { get; set; }
+        }
+
+        // Define an Action to apply a custom mapping from one object to the other
+        private readonly static Action<ContinuousInputRow, BinaryOutputRow> GreaterThanAverage = (input, output) 
+            => output.AboveAverage = input.MedianHomeValue > 22.6;
+
+        public static float[] GetLinearModelWeights(OlsLinearRegressionModelParameters linearModel)
+        {
+            return linearModel.Weights.ToArray();
+        }
+
+        public static float[] GetLinearModelWeights(LinearBinaryModelParameters linearModel)
+        {
+            return linearModel.Weights.ToArray();
+        }
+    }
+}