Add Ranking AutoML Sample (dotnet#852)

* Initial add of project * Update ranking sample * Get sample working * Updates based on feedback * Add refitting on validation and test data sets * Update console headers * Iteration print improvements * Correct validationData * Printing NDCG@1,3,10 & DCG@10 * Printing NDCG@1,3,10 & DCG@10 * Add readme * Update based on feedback * Use new DcgTruncation property * Update to latest AutoML package * Review feedback * Wording for 1st refit step * Update to include original label in output Co-authored-by: Justin Ormont <[email protected]>
ylu3 · Jan 1, 2021 · 1c804f5 · 1c804f5
1 parent 2990008
commit 1c804f5
Show file tree

Hide file tree

Showing 8 changed files with 559 additions and 7 deletions.
diff --git a/samples/csharp/common/AutoML/ConsoleHelper.cs b/samples/csharp/common/AutoML/ConsoleHelper.cs
@@ -44,17 +44,26 @@ public static void PrintBinaryClassificationMetrics(string name, BinaryClassific
         public static void PrintMulticlassClassificationMetrics(string name, MulticlassClassificationMetrics metrics)
         {
             Console.WriteLine($"************************************************************");
-            Console.WriteLine($"*    Metrics for {name} multi-class classification model   ");
+            Console.WriteLine($"*    Metrics for {name} multi-class classification model    ");
             Console.WriteLine($"*-----------------------------------------------------------");
-            Console.WriteLine($"    MacroAccuracy = {metrics.MacroAccuracy:0.####}, a value between 0 and 1, the closer to 1, the better");
-            Console.WriteLine($"    MicroAccuracy = {metrics.MicroAccuracy:0.####}, a value between 0 and 1, the closer to 1, the better");
+            Console.WriteLine($"    MacroAccuracy = {metrics.MacroAccuracy:0.####}, a value from 0 and 1, where closer to 1.0 is better");
+            Console.WriteLine($"    MicroAccuracy = {metrics.MicroAccuracy:0.####}, a value from 0 and 1, where closer to 1.0 is better");
             Console.WriteLine($"    LogLoss = {metrics.LogLoss:0.####}, the closer to 0, the better");
             Console.WriteLine($"    LogLoss for class 1 = {metrics.PerClassLogLoss[0]:0.####}, the closer to 0, the better");
             Console.WriteLine($"    LogLoss for class 2 = {metrics.PerClassLogLoss[1]:0.####}, the closer to 0, the better");
             Console.WriteLine($"    LogLoss for class 3 = {metrics.PerClassLogLoss[2]:0.####}, the closer to 0, the better");
             Console.WriteLine($"************************************************************");
         }
 
+        public static void PrintRankingMetrics(string name, RankingMetrics metrics, uint optimizationMetricTruncationLevel)
+        {
+            Console.WriteLine($"************************************************************");
+            Console.WriteLine($"*             Metrics for {name} ranking model              ");
+            Console.WriteLine($"*-----------------------------------------------------------");
+            Console.WriteLine($"    Normalized Discounted Cumulative Gain (NDCG@{optimizationMetricTruncationLevel}) = {metrics?.NormalizedDiscountedCumulativeGains?[(int)optimizationMetricTruncationLevel - 1] ?? double.NaN:0.####}, a value from 0 and 1, where closer to 1.0 is better");
+            Console.WriteLine($"    Discounted Cumulative Gain (DCG@{optimizationMetricTruncationLevel}) = {metrics?.DiscountedCumulativeGains?[(int)optimizationMetricTruncationLevel - 1] ?? double.NaN:0.####}");
+        }
+
         public static void ShowDataViewInConsole(MLContext mlContext, IDataView dataView, int numberOfRows = 4)
         {
             string msg = string.Format("Show data in DataView: Showing {0} rows with the columns", numberOfRows.ToString());
@@ -89,6 +98,11 @@ internal static void PrintIterationMetrics(int iteration, string trainerName, Re
             CreateRow($"{iteration,-4} {trainerName,-35} {metrics?.RSquared ?? double.NaN,8:F4} {metrics?.MeanAbsoluteError ?? double.NaN,13:F2} {metrics?.MeanSquaredError ?? double.NaN,12:F2} {metrics?.RootMeanSquaredError ?? double.NaN,8:F2} {runtimeInSeconds.Value,9:F1}", Width);
         }
 
+        internal static void PrintIterationMetrics(int iteration, string trainerName, RankingMetrics metrics, double? runtimeInSeconds)
+        {
+            CreateRow($"{iteration,-4} {trainerName,-15} {metrics?.NormalizedDiscountedCumulativeGains[0] ?? double.NaN,9:F4} {metrics?.NormalizedDiscountedCumulativeGains[2] ?? double.NaN,9:F4} {metrics?.NormalizedDiscountedCumulativeGains[9] ?? double.NaN,9:F4} {metrics?.DiscountedCumulativeGains[9] ?? double.NaN,9:F4} {runtimeInSeconds.Value,9:F1}", Width);
+        }
+
         internal static void PrintIterationException(Exception ex)
         {
             Console.WriteLine($"Exception during AutoML iteration: {ex}");
@@ -109,6 +123,11 @@ internal static void PrintRegressionMetricsHeader()
             CreateRow($"{"",-4} {"Trainer",-35} {"RSquared",8} {"Absolute-loss",13} {"Squared-loss",12} {"RMS-loss",8} {"Duration",9}", Width);
         }
 
+        internal static void PrintRankingMetricsHeader()
+        {
+            CreateRow($"{"",-4} {"Trainer",-15} {"NDCG@1",9} {"NDCG@3",9} {"NDCG@10",9} {"DCG@10",9} {"Duration",9}", Width);
+        }
+
         private static void CreateRow(string message, int width)
         {
             Console.WriteLine("|" + message.PadRight(width - 2) + "|");
@@ -239,10 +258,10 @@ private void AppendTableRow(ICollection<string[]> tableRows,
 
                 tableRows.Add(new[]
                 {
-                columnName,
-                GetColumnDataType(columnName),
-                columnPurpose
-            });
+                    columnName,
+                    GetColumnDataType(columnName),
+                    columnPurpose
+                });
             }
 
             private void AppendTableRows(ICollection<string[]> tableRows,

diff --git a/samples/csharp/common/AutoML/ProgressHandlers.cs b/samples/csharp/common/AutoML/ProgressHandlers.cs
@@ -81,4 +81,27 @@ public void Report(RunDetail<RegressionMetrics> iterationResult)
             }
         }
     }
+
+    public class RankingExperimentProgressHandler : IProgress<RunDetail<RankingMetrics>>
+    {
+        private int _iterationIndex;
+
+        public void Report(RunDetail<RankingMetrics> iterationResult)
+        {
+            if (_iterationIndex++ == 0)
+            {
+                ConsoleHelper.PrintRankingMetricsHeader();
+            }
+
+            if (iterationResult.Exception != null)
+            {
+                ConsoleHelper.PrintIterationException(iterationResult.Exception);
+            }
+            else
+            {
+                ConsoleHelper.PrintIterationMetrics(_iterationIndex, iterationResult.TrainerName,
+                    iterationResult.ValidationMetrics, iterationResult.RuntimeInSeconds);
+            }
+        }
+    }
 }