One name for MulticlassClassification (#2919)

Author: artidoro

docs/samples/Microsoft.ML.Samples/Static/LightGBMMulticlassWithInMemoryData.cs

@@ -9,7 +9,7 @@ namespace Microsoft.ML.Samples.Static
 {
     class LightGBMMulticlassWithInMemoryData
     {
-        public void MultiClassLightGbmStaticPipelineWithInMemoryData()
+        public void MulticlassLightGbmStaticPipelineWithInMemoryData()
         {
             // Create a general 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.

src/Microsoft.ML.Core/Data/AnnotationUtils.cs

@@ -87,7 +87,7 @@ public static class Const
             public static class ScoreColumnKind
             {
                 public const string BinaryClassification = "BinaryClassification";
-                public const string MultiClassClassification = "MultiClassClassification";
+                public const string MulticlassClassification = "MulticlassClassification";
                 public const string Regression = "Regression";
                 public const string Ranking = "Ranking";
                 public const string Clustering = "Clustering";

src/Microsoft.ML.Core/Prediction/IPredictor.cs

@@ -19,7 +19,7 @@ internal enum PredictionKind
         Custom = 1,
 
         BinaryClassification = 2,
-        MultiClassClassification = 3,
+        MulticlassClassification = 3,
         Regression = 4,
         MultiOutputRegression = 5,
         Ranking = 6,

src/Microsoft.ML.Core/Prediction/ITrainer.cs

@@ -19,7 +19,7 @@ namespace Microsoft.ML
     [BestFriend]
     internal delegate void SignatureBinaryClassifierTrainer();
     [BestFriend]
-    internal delegate void SignatureMultiClassClassifierTrainer();
+    internal delegate void SignatureMulticlassClassifierTrainer();
     [BestFriend]
     internal delegate void SignatureRegressorTrainer();
     [BestFriend]

src/Microsoft.ML.Data/DataLoadSave/TransformerChain.cs

@@ -277,7 +277,7 @@ public static TransformerChain<ITransformer> LoadFrom(IHostEnvironment env, Stre
                     if (predictor.PredictionKind == PredictionKind.BinaryClassification)
                         pred = new BinaryPredictionTransformer<IPredictorProducing<float>>(env, predictor as IPredictorProducing<float>, chain.Schema,
                             roles.Where(x => x.Key.Value == RoleMappedSchema.ColumnRole.Feature.Value).First().Value);
-                    else if (predictor.PredictionKind == PredictionKind.MultiClassClassification)
+                    else if (predictor.PredictionKind == PredictionKind.MulticlassClassification)
                         pred = new MulticlassPredictionTransformer<IPredictorProducing<VBuffer<float>>>(env,
                             predictor as IPredictorProducing<VBuffer<float>>, chain.Schema,
                             roles.Where(x => x.Key.Value == RoleMappedSchema.ColumnRole.Feature.Value).First().Value,

src/Microsoft.ML.Data/EntryPoints/InputBuilder.cs

@@ -839,11 +839,11 @@ public static class PipelineSweeperSupportedMetrics
         {
             public static new string ToString() => "SupportedMetric";
             public const string Auc = BinaryClassifierEvaluator.Auc;
-            public const string AccuracyMicro = Data.MultiClassClassifierEvaluator.AccuracyMicro;
-            public const string AccuracyMacro = MultiClassClassifierEvaluator.AccuracyMacro;
+            public const string AccuracyMicro = Data.MulticlassClassificationEvaluator.AccuracyMicro;
+            public const string AccuracyMacro = MulticlassClassificationEvaluator.AccuracyMacro;
             public const string F1 = BinaryClassifierEvaluator.F1;
             public const string AuPrc = BinaryClassifierEvaluator.AuPrc;
-            public const string TopKAccuracy = MultiClassClassifierEvaluator.TopKAccuracy;
+            public const string TopKAccuracy = MulticlassClassificationEvaluator.TopKAccuracy;
             public const string L1 = RegressionLossEvaluatorBase<MultiOutputRegressionEvaluator.Aggregator>.L1;
             public const string L2 = RegressionLossEvaluatorBase<MultiOutputRegressionEvaluator.Aggregator>.L2;
             public const string Rms = RegressionLossEvaluatorBase<MultiOutputRegressionEvaluator.Aggregator>.Rms;

src/Microsoft.ML.Data/Evaluators/EvaluatorUtils.cs

@@ -40,7 +40,7 @@ public static Dictionary<string, Func<IHostEnvironment, IMamlEvaluator>> Instanc
                         var tmp = new Dictionary<string, Func<IHostEnvironment, IMamlEvaluator>>
                         {
                             { AnnotationUtils.Const.ScoreColumnKind.BinaryClassification, env => new BinaryClassifierMamlEvaluator(env, new BinaryClassifierMamlEvaluator.Arguments()) },
-                            { AnnotationUtils.Const.ScoreColumnKind.MultiClassClassification, env => new MultiClassMamlEvaluator(env, new MultiClassMamlEvaluator.Arguments()) },
+                            { AnnotationUtils.Const.ScoreColumnKind.MulticlassClassification, env => new MulticlassClassificationMamlEvaluator(env, new MulticlassClassificationMamlEvaluator.Arguments()) },
                             { AnnotationUtils.Const.ScoreColumnKind.Regression, env => new RegressionMamlEvaluator(env, new RegressionMamlEvaluator.Arguments()) },
                             { AnnotationUtils.Const.ScoreColumnKind.MultiOutputRegression, env => new MultiOutputRegressionMamlEvaluator(env, new MultiOutputRegressionMamlEvaluator.Arguments()) },
                             { AnnotationUtils.Const.ScoreColumnKind.QuantileRegression, env => new QuantileRegressionMamlEvaluator(env, new QuantileRegressionMamlEvaluator.Arguments()) },

src/Microsoft.ML.Data/Evaluators/Metrics/MultiClassClassifierMetrics.cs renamed to src/Microsoft.ML.Data/Evaluators/Metrics/MulticlassClassificationMetrics.cs

@@ -10,9 +10,9 @@
 namespace Microsoft.ML.Data
 {
     /// <summary>
-    /// Evaluation results for multi-class classifiers.
+    /// Evaluation results for multi-class classification trainers.
     /// </summary>
-    public sealed class MultiClassClassifierMetrics
+    public sealed class MulticlassClassificationMetrics
     {
         /// <summary>
         /// Gets the average log-loss of the classifier.
@@ -83,22 +83,22 @@ public sealed class MultiClassClassifierMetrics
         /// </remarks>
         public IReadOnlyList<double> PerClassLogLoss { get; }
 
-        internal MultiClassClassifierMetrics(IExceptionContext ectx, DataViewRow overallResult, int topK)
+        internal MulticlassClassificationMetrics(IExceptionContext ectx, DataViewRow overallResult, int topK)
         {
             double FetchDouble(string name) => RowCursorUtils.Fetch<double>(ectx, overallResult, name);
-            MicroAccuracy = FetchDouble(MultiClassClassifierEvaluator.AccuracyMicro);
-            MacroAccuracy = FetchDouble(MultiClassClassifierEvaluator.AccuracyMacro);
-            LogLoss = FetchDouble(MultiClassClassifierEvaluator.LogLoss);
-            LogLossReduction = FetchDouble(MultiClassClassifierEvaluator.LogLossReduction);
+            MicroAccuracy = FetchDouble(MulticlassClassificationEvaluator.AccuracyMicro);
+            MacroAccuracy = FetchDouble(MulticlassClassificationEvaluator.AccuracyMacro);
+            LogLoss = FetchDouble(MulticlassClassificationEvaluator.LogLoss);
+            LogLossReduction = FetchDouble(MulticlassClassificationEvaluator.LogLossReduction);
             TopK = topK;
             if (topK > 0)
-                TopKAccuracy = FetchDouble(MultiClassClassifierEvaluator.TopKAccuracy);
+                TopKAccuracy = FetchDouble(MulticlassClassificationEvaluator.TopKAccuracy);
 
-            var perClassLogLoss = RowCursorUtils.Fetch<VBuffer<double>>(ectx, overallResult, MultiClassClassifierEvaluator.PerClassLogLoss);
+            var perClassLogLoss = RowCursorUtils.Fetch<VBuffer<double>>(ectx, overallResult, MulticlassClassificationEvaluator.PerClassLogLoss);
             PerClassLogLoss = perClassLogLoss.DenseValues().ToImmutableArray();
         }
 
-        internal MultiClassClassifierMetrics(double accuracyMicro, double accuracyMacro, double logLoss, double logLossReduction,
+        internal MulticlassClassificationMetrics(double accuracyMicro, double accuracyMacro, double logLoss, double logLossReduction,
             int topK, double topKAccuracy, double[] perClassLogLoss)
         {
             MicroAccuracy = accuracyMicro;