work in progress

Author: Rogan Carr

test/Microsoft.ML.Functional.Tests/IntrospectiveTraining.cs

@@ -0,0 +1,116 @@
+// Licensed to the .NET Foundation under one or more agreements.
+// The .NET Foundation licenses this file to you under the MIT license.
+// See the LICENSE file in the project root for more information.
+
+using System;
+using System.Collections.Generic;
+using Microsoft.ML.Data;
+using Microsoft.ML.Functional.Tests.Datasets;
+using Microsoft.ML.RunTests;
+using Microsoft.ML.TestFramework;
+using Microsoft.ML.Transforms;
+using Xunit;
+using Xunit.Abstractions;
+
+namespace Microsoft.ML.Functional.Tests
+{
+    public class IntrospectiveTraining : BaseTestClass
+    {
+        public IntrospectiveTraining(ITestOutputHelper output): base(output)
+        {
+        }
+
+        /// <summary>
+        /// Introspective Training: Map hashed values back to the original value.
+        /// </summary>
+        [Fact]
+        public void InspectSlotNamesForReversibleHash()
+        {
+            var mlContext = new MLContext(seed: 1, conc: 1);
+
+            // Load the Adult dataset.
+            var data = mlContext.Data.LoadFromTextFile<Adult>(GetDataPath(TestDatasets.adult.trainFilename),
+                hasHeader: TestDatasets.adult.fileHasHeader,
+                separatorChar: TestDatasets.adult.fileSeparator);
+
+            // Create the learning pipeline.
+            var pipeline = mlContext.Transforms.Concatenate("NumericalFeatures", Adult.NumericalFeatures)
+                .Append(mlContext.Transforms.Concatenate("CategoricalFeatures", Adult.CategoricalFeatures))
+                .Append(mlContext.Transforms.Categorical.OneHotHashEncoding("CategoricalFeatures", hashBits: 8, // get collisions!
+                    invertHash: -1, outputKind: OneHotEncodingTransformer.OutputKind.Bag));
+
+            // Train the model.
+            var model = pipeline.Fit(data);
+
+            // Transform the data.
+            var transformedData = model.Transform(data);
+
+            // Verify that the slotnames cane be used to backtrack by confirming that 
+            //  all unique values in the input data are in the output data slot names.
+            // First get a list of the unique values.
+            VBuffer<ReadOnlyMemory<char>> categoricalSlotNames = new VBuffer<ReadOnlyMemory<char>>();
+            transformedData.Schema["CategoricalFeatures"].GetSlotNames(ref categoricalSlotNames);
+            var uniqueValues = new HashSet<string>();
+            foreach (var slotName in categoricalSlotNames.GetValues())
+            {
+                var slotNameString = slotName.ToString();
+                if (slotNameString.StartsWith('{'))
+                {
+                    // Values look like this: {3:Exec-managerial,2:Widowed}.
+                    slotNameString = slotNameString.Substring(1, slotNameString.Length - 2);
+                    foreach (var name in slotNameString.Split(','))
+                        uniqueValues.Add(name);
+                }
+                else
+                    uniqueValues.Add(slotNameString);
+            }
+
+            // Now validate that all values in the dataset are there
+            var transformedRows = mlContext.Data.CreateEnumerable<Adult>(data, false);
+            foreach (var row in transformedRows)
+            {
+                for (int i = 0; i < Adult.CategoricalFeatures.Length; i++)
+                {
+                    // Fetch the categorical value.
+                    string value = (string) row.GetType().GetProperty(Adult.CategoricalFeatures[i]).GetValue(row, null);
+                    Assert.Contains($"{i}:{value}", uniqueValues);
+                }
+            }
+
+            float x = (float)double.MinValue;
+            Output.WriteLine($"{x}");
+        }
+
+        //private void BooYa()
+        //{
+        //    // Create the learning pipeline
+        //    var nestedPipeline = mlContext.Transforms.Concatenate("NumericalFeatures", Adult.NumericalFeatures)
+        //        .Append(mlContext.Transforms.Concatenate("CategoricalFeatures", Adult.CategoricalFeatures))
+        //        .Append(mlContext.Transforms.Categorical.OneHotHashEncoding("CategoricalFeatures",
+        //            invertHash: 2, outputKind: OneHotEncodingTransformer.OutputKind.Bag)
+        //        .Append(mlContext.Transforms.Concatenate("Features", "NumericalFeatures", "CategoricalFeatures"))
+        //        .Append(mlContext.BinaryClassification.Trainers.LogisticRegression()));
+
+        //    // Train the model.
+        //    var nestedModel = nestedPipeline.Fit(data);
+        //    var nestedPredictor = nestedModel.LastTransformer.LastTransformer;
+        //    var nestedTransformedData = nestedModel.Transform(data);
+
+        //    Assert.Equal(predictor.Model.SubModel.Bias, nestedPredictor.Model.SubModel.Bias);
+        //        int nFeatures = predictor.Model.SubModel.Weights.Count;
+        //        for (int i = 0; i<nFeatures; i++ )
+        //            Assert.Equal(predictor.Model.SubModel.Weights[i], nestedPredictor.Model.SubModel.Weights[i]);
+
+        //        var transformedRows = mlContext.Data.CreateEnumerable<BinaryPrediction>(transformedData, false).ToArray();
+        //    var nestedTransformedRows = mlContext.Data.CreateEnumerable<BinaryPrediction>(nestedTransformedData, false).ToArray();
+        //        for (int i = 0; i<transformedRows.Length; i++)
+        //            Assert.Equal(transformedRows[i].Score, nestedTransformedRows[i].Score);
+        //}
+
+        //private class BinaryPrediction
+        //{
+        //    public float Score { get; set; }
+        //    public float Probability { get; set; }
+        //}
+    }
+}

test/Microsoft.ML.TestFramework/Datasets.cs

@@ -276,6 +276,8 @@ public static class TestDatasets
             name = "Census",
             trainFilename = "adult.tiny.with-schema.txt",
             testFilename = "adult.tiny.with-schema.txt",
+            fileHasHeader = true,
+            fileSeparator = '\t',
             loaderSettings = "loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8}",
             mamlExtraSettings = new[] { "xf=Cat{col=Cat}", "xf=Concat{col=Features:Num,Cat}" },
             extraSettings = @"/inst Text{header+ sep=, label=14 handler=Categorical{cols=5-9,1,13,3}}",