address small things

Author: Ivan Matantsev

src/Microsoft.ML.Data/TrainCatalog.cs

@@ -43,7 +43,7 @@ public struct TrainTestData
             /// </summary>
             /// <param name="trainSet">Training set.</param>
             /// <param name="testSet">Testing set.</param>
-            public TrainTestData(IDataView trainSet, IDataView testSet)
+            internal TrainTestData(IDataView trainSet, IDataView testSet)
             {
                 TrainSet = trainSet;
                 TestSet = testSet;
@@ -98,7 +98,7 @@ protected internal struct CrossValidationResult
             /// </summary>
             public readonly ITransformer Model;
             /// <summary>
-            /// <see cref="IDataView"/> for scored fold.
+            /// Scored test set with <see cref="Model"/> for this fold.
             /// </summary>
             public readonly IDataView Scores;
             /// <summary>
@@ -113,27 +113,30 @@ public CrossValidationResult(ITransformer model, IDataView scores, int fold)
                 Fold = fold;
             }
         }
-
+        /// <summary>
+        /// Results of running crossvalidation.
+        /// </summary>
+        /// <typeparam name="T">Type of metric class</typeparam>
         public class CrossValidationResult<T> where T : class
         {
             /// <summary>
-            /// Metrics for cross validation fold.
+            /// Metrics for this cross validation fold.
             /// </summary>
             public readonly T Metrics;
             /// <summary>
             /// Model trained during cross validation fold.
             /// </summary>
             public readonly ITransformer Model;
             /// <summary>
-            /// <see cref="IDataView"/> for scored fold.
+            /// Scored test set with <see cref="Model"/> for this fold.
             /// </summary>
             public readonly IDataView Scores;
             /// <summary>
             /// Fold number.
             /// </summary>
             public readonly int Fold;
 
-            public CrossValidationResult(ITransformer model, T metrics, IDataView scores, int fold)
+            internal CrossValidationResult(ITransformer model, T metrics, IDataView scores, int fold)
             {
                 Model = model;
                 Metrics = metrics;
@@ -341,13 +344,14 @@ public BinaryClassificationMetrics EvaluateNonCalibrated(IDataView data, string
         /// If the <paramref name="stratificationColumn"/> is not provided, the random numbers generated to create it, will use this seed as value.
         /// And if it is not provided, the default value will be used.</param>
         /// <returns>Per-fold results: metrics, models, scored datasets.</returns>
-        public (BinaryClassificationMetrics metrics, ITransformer model, IDataView scoredTestData)[] CrossValidateNonCalibrated(
+        public CrossValidationResult<BinaryClassificationMetrics>[] CrossValidateNonCalibrated(
             IDataView data, IEstimator<ITransformer> estimator, int numFolds = 5, string labelColumn = DefaultColumnNames.Label,
             string stratificationColumn = null, uint? seed = null)
         {
             Host.CheckNonEmpty(labelColumn, nameof(labelColumn));
             var result = CrossValidateTrain(data, estimator, numFolds, stratificationColumn, seed);
-            return result.Select(x => (EvaluateNonCalibrated(x.Scores, labelColumn), x.Model, x.Scores)).ToArray();
+            return result.Select(x => new CrossValidationResult<BinaryClassificationMetrics>(x.Model,
+                EvaluateNonCalibrated(x.Scores, labelColumn), x.Scores, x.Fold)).ToArray();
         }
 
         /// <summary>

src/Microsoft.ML.StaticPipe/TrainingStaticExtensions.cs

@@ -221,9 +221,9 @@ public static (BinaryClassificationMetrics metrics, Transformer<TInShape, TOutSh
             var results = catalog.CrossValidateNonCalibrated(data.AsDynamic, estimator.AsDynamic, numFolds, labelName, stratName, seed);
 
             return results.Select(x => (
-                    x.metrics,
-                    new Transformer<TInShape, TOutShape, TTransformer>(env, (TTransformer)x.model, data.Shape, estimator.Shape),
-                    new DataView<TOutShape>(env, x.scoredTestData, estimator.Shape)))
+                    x.Metrics,
+                    new Transformer<TInShape, TOutShape, TTransformer>(env, (TTransformer)x.Model, data.Shape, estimator.Shape),
+                    new DataView<TOutShape>(env, x.Scores, estimator.Shape)))
                 .ToArray();
         }