Added support for RankingMetrics with CrossValSummaryRunner (#5386)

* Added support for RankingMetrics with CrossValSummaryRunner

* Addressed reviews

* Addressed reviews

* Edited naming of baselines metrics

* Addressed reviews

Author: mstfbl

src/Microsoft.ML.AutoML/Experiment/Runners/CrossValSummaryRunner.cs

@@ -144,9 +144,31 @@ private static TMetrics GetAverageMetrics(IEnumerable<TMetrics> metrics, TMetric
                 return result as TMetrics;
             }
 
+            if (typeof(TMetrics) == typeof(RankingMetrics))
+            {
+                var newMetrics = metrics.Select(x => x as RankingMetrics);
+                Contracts.Assert(newMetrics != null);
+
+                var result = new RankingMetrics(
+                    dcg: GetAverageOfNonNaNScoresInNestedEnumerable(newMetrics.Select(x => x.DiscountedCumulativeGains)),
+                    ndcg: GetAverageOfNonNaNScoresInNestedEnumerable(newMetrics.Select(x => x.NormalizedDiscountedCumulativeGains)));
+                return result as TMetrics;
+            }
+
             throw new NotImplementedException($"Metric {typeof(TMetrics)} not implemented");
         }
 
+        private static double[] GetAverageOfNonNaNScoresInNestedEnumerable(IEnumerable<IEnumerable<double>> results)
+        {
+            double[] arr = new double[results.ElementAt(0).Count()];
+            for (int i = 0; i < arr.Length; i++)
+            {
+                Contracts.Assert(arr.Length == results.ElementAt(i).Count());
+                arr[i] = GetAverageOfNonNaNScores(results.Select(x => x.ElementAt(i)));
+            }
+            return arr;
+        }
+
         private static double GetAverageOfNonNaNScores(IEnumerable<double> results)
         {
             var newResults = results.Where(r => !double.IsNaN(r));

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

@@ -63,7 +63,7 @@ internal DataOperationsCatalog(IHostEnvironment env)
         /// results.
         /// </summary>
         /// <typeparam name="TRow">The user-defined item type.</typeparam>
-        /// <param name="data">The enumerable data containing type <typeparamref name="TRow"/> to convert to an<see cref="IDataView"/>.</param>
+        /// <param name="data">The enumerable data containing type <typeparamref name="TRow"/> to convert to a <see cref="IDataView"/>.</param>
         /// <param name="schemaDefinition">The optional schema definition of the data view to create. If <c>null</c>,
         /// the schema definition is inferred from <typeparamref name="TRow"/>.</param>
         /// <returns>The constructed <see cref="IDataView"/>.</returns>

test/Microsoft.ML.AutoML.Tests/AutoFitTests.cs

@@ -164,8 +164,8 @@ public void AutoFitRankingTest()
                 RunDetail<RankingMetrics> bestRun = experimentResults[i].BestRun;
                 Assert.True(experimentResults[i].RunDetails.Count() > 0);
                 Assert.NotNull(bestRun.ValidationMetrics);
-                Assert.True(experimentResults[i].RunDetails.Max(i => i.ValidationMetrics.NormalizedDiscountedCumulativeGains.Max() > .4));
-                Assert.True(experimentResults[i].RunDetails.Max(i => i.ValidationMetrics.DiscountedCumulativeGains.Max() > 20));
+                Assert.True(bestRun.ValidationMetrics.NormalizedDiscountedCumulativeGains.Last() > 0.4);
+                Assert.True(bestRun.ValidationMetrics.DiscountedCumulativeGains.Last() > 20);
                 var outputSchema = bestRun.Model.GetOutputSchema(trainDataView.Schema);
                 var expectedOutputNames = new string[] { labelColumnName, groupIdColumnName, groupIdColumnName, featuresColumnVectorNameA, featuresColumnVectorNameB,
                 "Features", scoreColumnName };
@@ -187,6 +187,9 @@ public void AutoFitRankingCVTest()
             var reader = new TextLoader(mlContext, GetLoaderArgsRank(labelColumnName, groupIdColumnName,
                 featuresColumnVectorNameA, featuresColumnVectorNameB));
             var trainDataView = reader.Load(DatasetUtil.GetMLSRDataset());
+            // Take less than 1500 rows of data to satisfy CrossValSummaryRunner's
+            // limit.
+            trainDataView = mlContext.Data.TakeRows(trainDataView, 1499);
 
             var experiment = mlContext.Auto()
                 .CreateRankingExperiment(5);
@@ -208,8 +211,8 @@ public void AutoFitRankingCVTest()
                 while (enumerator.MoveNext())
                 {
                     var model = enumerator.Current;
-                    Assert.True(model.ValidationMetrics.NormalizedDiscountedCumulativeGains.Max() > .4);
-                    Assert.True(model.ValidationMetrics.DiscountedCumulativeGains.Max() > 19);
+                    Assert.True(model.ValidationMetrics.NormalizedDiscountedCumulativeGains.Max() > 0.31);
+                    Assert.True(model.ValidationMetrics.DiscountedCumulativeGains.Max() > 15);
                 }
             }
         }