documentation

src/Microsoft.ML.Transforms/Dracula/CountTableBuilder.cs

@@ -12,6 +12,10 @@ internal interface ICountTableBuilderFactory : IComponentFactory<CountTableBuild
     {
     }
 
+    /// <summary>
+    /// Builds a table that provides counts to the <see cref="CountTargetEncodingTransformer"/>
+    /// by going over the training data.
+    /// </summary>
     public abstract class CountTableBuilderBase
     {
         private protected CountTableBuilderBase()
@@ -20,9 +24,20 @@ private protected CountTableBuilderBase()
 
         internal abstract InternalCountTableBuilderBase GetInternalBuilder(long labelCardinality);
 
+        /// <summary>
+        /// Create a builder that creates the count table using the count-min sketch structure, which has a smaller memory footprint,
+        /// at the expense of some possible overcounting due to collisions.
+        /// </summary>
+        /// <param name="depth">The depth of the count-min sketch table.</param>
+        /// <param name="width">The width of the count-min sketch table.</param>
         public static CountTableBuilderBase CreateCMCountTableBuilder(int depth = 4, int width = 1 << 23)
             => new CMCountTableBuilder(depth, width);
 
+        /// <summary>
+        /// Create a builder that creates the count table by building a dictionary containing the exact count of each
+        /// categorical feature value.
+        /// </summary>
+        /// <param name="garbageThreshold">The garbage threshold (counts below or equal to the threshold are assigned to the garbage bin).</param>
         public static CountTableBuilderBase CreateDictionaryCountTableBuilder(float garbageThreshold = 0)
             => new DictCountTableBuilder(garbageThreshold);
     }

src/Microsoft.ML.Transforms/Dracula/CountTableTransformer.cs

@@ -169,7 +169,7 @@ public CountTableTransformer Fit(IDataView input)
 
             var multiCountTable = multiBuilder.CreateMultiCountTable();
 
-            var featurizer = new DraculaFeaturizer(_host, _columns.Select(col => col.PriorCoefficient).ToArray(), _columns.Select(col => col.LaplaceScale).ToArray(), labelCardinality, multiCountTable);
+            var featurizer = new CountTargetEncodingFeaturizer(_host, _columns.Select(col => col.PriorCoefficient).ToArray(), _columns.Select(col => col.LaplaceScale).ToArray(), labelCardinality, multiCountTable);
 
             return new CountTableTransformer(_host, featurizer, labelClassNames,
                 _columns.Select(col => col.Seed).ToArray(), _columns.Select(col => (col.Name, col.InputColumnName)).ToArray());
@@ -417,13 +417,13 @@ internal static class Defaults
             public const bool SharedTable = false;
         }
 
-        internal readonly DraculaFeaturizer Featurizer;
+        internal readonly CountTargetEncodingFeaturizer Featurizer;
         private readonly string[] _labelClassNames;
 
         internal int[] Seeds { get; }
 
         internal const string Summary = "Transforms the categorical column into the set of features: count of each label class, "
-            + "log-odds for each label class, back-off indicator. The input columns must be keys. This is a part of the Dracula transform.";
+            + "log-odds for each label class, back-off indicator. The input columns must be keys.";
 
         internal const string LoaderSignature = "CountTableTransform";
         internal const string UserName = "Count Table Transform";
@@ -438,7 +438,7 @@ private static VersionInfo GetVersionInfo()
                 loaderAssemblyName: typeof(CountTableTransformer).Assembly.FullName);
         }
 
-        internal CountTableTransformer(IHostEnvironment env, DraculaFeaturizer featurizer, string[] labelClassNames,
+        internal CountTableTransformer(IHostEnvironment env, CountTargetEncodingFeaturizer featurizer, string[] labelClassNames,
             int[] seeds, (string outputColumnName, string inputColumnName)[] columns)
             : base(Contracts.CheckRef(env, nameof(env)).Register(nameof(CountTableTransformer)), columns)
         {
@@ -551,7 +551,7 @@ private CountTableTransformer(IHost host, ModelLoadContext ctx)
             }
 
             Seeds = ctx.Reader.ReadIntArray(ColumnPairs.Length);
-            ctx.LoadModel<DraculaFeaturizer, SignatureLoadModel>(host, out Featurizer, "DraculaFeaturizer");
+            ctx.LoadModel<CountTargetEncodingFeaturizer, SignatureLoadModel>(host, out Featurizer, "Featurizer");
         }
 
         private protected override void SaveModel(ModelSaveContext ctx)
@@ -582,7 +582,7 @@ private protected override void SaveModel(ModelSaveContext ctx)
             }
 
             ctx.Writer.WriteIntsNoCount(Seeds);
-            ctx.SaveModel(Featurizer, "DraculaFeaturizer");
+            ctx.SaveModel(Featurizer, "Featurizer");
         }
 
         private protected override IRowMapper MakeRowMapper(DataViewSchema schema) => new Mapper(this, schema);

src/Microsoft.ML.Transforms/Dracula/DraculaTransform.cs → src/Microsoft.ML.Transforms/Dracula/CountTargetEncodingTransformer.cs

@@ -24,6 +24,30 @@
 
 namespace Microsoft.ML.Transforms
 {
+    /// <summary>
+    /// Transforms a categorical column into a set of features that includes the count of each label class,
+    /// the log-odds for each label class and the back-off indicator.
+    /// </summary>
+    /// <remarks>
+    /// <format type="text/markdown"><![CDATA[
+    /// ###  Estimator Characteristics
+    /// |  |  |
+    /// | -- | -- |
+    /// | Does this estimator need to look at the data to train its parameters? | Yes |
+    /// | Input column data type | Any |
+    /// | Output column data type | Vector of <xref:System.Single>. |
+    ///
+    /// The resulting <xref:Microsoft.ML.Transforms.CountTargetEncodingTransformer> creates a new column, named as specified in the output column name parameters,
+    /// containing three parts: the count of each label class, the log-odds for each label class and the back-off indicator.
+    ///
+    /// Check the See Also section for links to usage examples.
+    /// ]]>
+    /// </format>
+    /// </remarks>
+    /// <seealso cref="CountTargetEncodingCatalog.CountTargetEncode(TransformsCatalog, InputOutputColumnPair[], CountTargetEncodingTransformer, string)" />
+    /// <seealso cref="CountTargetEncodingCatalog.CountTargetEncode(TransformsCatalog, InputOutputColumnPair[], string, CountTableBuilderBase, float, float, bool, int, bool, uint)" />
+    /// <seealso cref="CountTargetEncodingCatalog.CountTargetEncode(TransformsCatalog, string, CountTargetEncodingTransformer, string, string)"/>
+    /// <seealso cref="CountTargetEncodingCatalog.CountTargetEncode(TransformsCatalog, string, string, string, CountTableBuilderBase, float, float, int, bool, uint)"/>
     public class CountTargetEncodingEstimator : IEstimator<CountTargetEncodingTransformer>
     {
         /// <summary>
@@ -344,6 +368,9 @@ private SchemaShape CreateHashJoinOutputSchema(SchemaShape inputSchema)
         }
     }
 
+    /// <summary>
+    /// <see cref="ITransformer"/> resulting from fitting a <see cref="LpNormNormalizingEstimator"/> or <see cref="CountTargetEncodingEstimator"/>.
+    /// </summary>
     public sealed class CountTargetEncodingTransformer : ITransformer
     {
         private readonly IHost _host;
@@ -353,8 +380,8 @@ public sealed class CountTargetEncodingTransformer : ITransformer
 
         internal const string Summary = "Transforms the categorical column into the set of features: count of each label class, "
             + "log-odds for each label class, back-off indicator. The columns can be of arbitrary type.";
-        internal const string LoaderSignature = "DraculaTransform";
-        internal const string UserName = "Dracula Transform";
+        internal const string LoaderSignature = "CountTargetEncode";
+        internal const string UserName = "Count Target Encoding Transform";
 
         bool ITransformer.IsRowToRowMapper => true;
 

src/Microsoft.ML.Transforms/Dracula/Featurizer.cs

@@ -11,12 +11,12 @@
 using Microsoft.ML.Runtime;
 using Microsoft.ML.Transforms;
 
-[assembly: LoadableClass(typeof(DraculaFeaturizer), null, typeof(SignatureLoadModel),
-    "Dracula Featurizer", DraculaFeaturizer.RegistrationName)]
+[assembly: LoadableClass(typeof(CountTargetEncodingFeaturizer), null, typeof(SignatureLoadModel),
+    "Count Target Encoding Featurizer", CountTargetEncodingFeaturizer.RegistrationName)]
 
 namespace Microsoft.ML.Transforms
 {
-    internal sealed class DraculaFeaturizer : ICanSaveModel
+    internal sealed class CountTargetEncodingFeaturizer : ICanSaveModel
     {
         private readonly IHost _host;
         private readonly int _labelBinCount;
@@ -27,7 +27,7 @@ internal sealed class DraculaFeaturizer : ICanSaveModel
         public float[] PriorCoef { get; }
         public float[] LaplaceScale { get; }
 
-        internal const string RegistrationName = "DraculaFeaturizer";
+        internal const string RegistrationName = "CountTargetEncodingFeaturizer";
         private static VersionInfo GetVersionInfo()
         {
             return new VersionInfo(
@@ -36,14 +36,14 @@ private static VersionInfo GetVersionInfo()
                 verReadableCur: 0x00010001,
                 verWeCanReadBack: 0x00010001,
                 loaderSignature: RegistrationName,
-                loaderAssemblyName: typeof(DraculaFeaturizer).Assembly.FullName);
+                loaderAssemblyName: typeof(CountTargetEncodingFeaturizer).Assembly.FullName);
         }
 
         public int ColCount => _countTables.ColCount;
 
         public ReadOnlySpan<int> SlotCount => _countTables.SlotCount;
 
-        public DraculaFeaturizer(IHostEnvironment env, float[] priorCoef, float[] laplaceScale, long labelBinCount, MultiCountTableBase countTable)
+        public CountTargetEncodingFeaturizer(IHostEnvironment env, float[] priorCoef, float[] laplaceScale, long labelBinCount, MultiCountTableBase countTable)
         {
             Contracts.CheckValue(env, nameof(env));
             _host = env.Register(RegistrationName);
@@ -61,7 +61,7 @@ public DraculaFeaturizer(IHostEnvironment env, float[] priorCoef, float[] laplac
             _countTables = countTable;
         }
 
-        public DraculaFeaturizer(IHostEnvironment env, ModelLoadContext ctx)
+        public CountTargetEncodingFeaturizer(IHostEnvironment env, ModelLoadContext ctx)
         {
             Contracts.CheckValue(env, nameof(env));
             _host = env.Register(RegistrationName);

test/Microsoft.ML.Core.Tests/UnitTests/TestEntryPoints.cs

@@ -4697,10 +4697,10 @@ public void EntryPointHashJoinCountTable()
         }
 
         [Fact]
-        public void EntryPointDracula()
+        public void EntryPointCountTargetEncoding()
         {
             var dataPath = GetDataPath("breast-cancer.txt");
-            var countsModel = DeleteOutputPath("Dracula-trained-counts.zip");
+            var countsModel = DeleteOutputPath("cte-trained-counts.zip");
 
             var data = ML.Data.LoadFromTextFile(dataPath, new[]
                 {
@@ -4711,7 +4711,6 @@ public void EntryPointDracula()
             var transformer = estimator.Fit(data);
             ML.Model.Save(transformer, data.Schema, countsModel);
 
-            //var countsFile = GetDataPath(@"Dracula/ext-count-table.tsv");
             TestEntryPointPipelineRoutine(dataPath, "col=Text:TX:1-9 col=OneText:TX:1 col=Label:0",
                 new[]
                 {

test/Microsoft.ML.TestFramework/DataPipe/TestDataPipe.cs

@@ -1327,7 +1327,7 @@ public void SavePipeCountTableShared()
         }
 
         [Fact]
-        public void SavePipeDracula()
+        public void SavePipeCountTargetEncoding()
         {
             TestCore(null, false,
                 new[] {
@@ -1340,7 +1340,7 @@ public void SavePipeDracula()
         }
 
         [Fact]
-        public void SavePipeDraculaKeyLabel()
+        public void SavePipeCountTargetEncodingKeyLabel()
         {
             TestCore(null, false,
                 new[] {
@@ -1354,12 +1354,11 @@ public void SavePipeDraculaKeyLabel()
         }
 
         [Fact]
-        public void SavePipeDraculaExternalCounts()
+        public void SavePipeCountTargetEncodingLoadModel()
         {
-            //var countsFile = GetDataPath("Dracula", "ext-count-table.tsv");
             var inputData = GetDataPath("breast-cancer.txt");
-            var initialCountsModel = DeleteOutputPath("Dracula", "initialCounts.zip");
-            var outputData = DeleteOutputPath("Dracula", "countsData.txt");
+            var initialCountsModel = DeleteOutputPath("CTE", "initialCounts.zip");
+            var outputData = DeleteOutputPath("CTE", "countsData.txt");
             var loaderArg = "loader=Text{col=Text:TX:1-9 col=OneText:TX:1 col=Label:0}";
             MainForTest($"SaveData data={inputData} {loaderArg} xf=Dracula{{lab=Label col={{name=DT src=Text customSlotMap=0,1;2,3,4,5}} table = Dict}} out={initialCountsModel} dout={outputData}");
 

test/Microsoft.ML.TestFramework/TestCommandBase.cs

@@ -1760,7 +1760,7 @@ public void CommandSaveData()
             Done();
         }
 
-        [TestCategory(Cat), TestCategory("Dracula")]
+        [TestCategory(Cat), TestCategory("CountTargetEncoding")]
         [Fact(Skip = "Need CoreTLC specific baseline update")]
         public void CommandDraculaInfer()
         {