Suggested changes to progress bar (dotnet#335)

srsaggam · Dmitry-A · commit 3cacc459127d · 2019-08-22T14:40:33.000-07:00
* progressbar

* added progressbar and refactoring

* reverted

* revert sign assembly

* added headers and removed exception rethrow

* bug fixes and updates to UI

* added friendly name printing for metric

* formatting
diff --git a/src/mlnet/CodeGenerator/CodeGenerationHelper.cs b/src/mlnet/CodeGenerator/CodeGenerationHelper.cs
@@ -86,8 +86,9 @@ public void GenerateCode()
                 {
                     ForegroundColor = ConsoleColor.Yellow,
                     ForegroundColorDone = ConsoleColor.DarkGreen,
-                    BackgroundColor = ConsoleColor.DarkGray,
-                    BackgroundCharacter = '\u2593'
+                    BackgroundColor = ConsoleColor.Gray,
+                    ProgressCharacter = '\u2593',
+                    BackgroundCharacter = '─',
                 };
                 var wait = TimeSpan.FromSeconds(settings.MaxExplorationTime);
                 using (var pbar = new FixedDurationBar(wait, "", options))
@@ -114,9 +115,10 @@ public void GenerateCode()
 
                     if (t.IsCompleted == false)
                     {
-                        logger.Log(LogLevel.Info, "Waiting for the last iteration to complete ...");
+                        string originalMessage = pbar.Message;
+                        pbar.Message = " Waiting for the last iteration to complete ...";
+                        t.Wait();
                     }
-                    t.Wait();
                 }
 
             }
diff --git a/src/mlnet/Utilities/ConsolePrinter.cs b/src/mlnet/Utilities/ConsolePrinter.cs
@@ -34,25 +34,25 @@ internal static void PrintMetrics(int iteration, string trainerName, RegressionM
 
         internal static void PrintBinaryClassificationMetricsHeader(LogLevel logLevel)
         {
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             logger.Log(logLevel, $"{Strings.MetricsForBinaryClassModels}");
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             logger.Log(logLevel, $"{" ",-4} {"Trainer",-35} {"Accuracy",9} {"AUC",8} {"AUPRC",8} {"F1-score",9} {"Best",8} {"Duration",9}");
         }
 
         internal static void PrintMulticlassClassificationMetricsHeader(LogLevel logLevel)
         {
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             logger.Log(logLevel, $"{Strings.MetricsForMulticlassModels}");
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             logger.Log(logLevel, $"{" ",-4} {"Trainer",-35} {"AccuracyMicro",14} {"AccuracyMacro",14} {"Best",14} {"Duration",9}");
         }
 
         internal static void PrintRegressionMetricsHeader(LogLevel logLevel)
         {
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             logger.Log(logLevel, $"{Strings.MetricsForRegressionModels}");
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             logger.Log(logLevel, $"{" ",-4} {"Trainer",-35} {"R2-Score",9} {"LossFn",12} {"Absolute-loss",15} {"Squared-loss",15} {"RMS-loss",12} {"Best",12} {"Duration",9}");
         }
 
@@ -70,61 +70,61 @@ internal static void PrintTopNHeader(int count)
 
         internal static void ExperimentResultsHeader(LogLevel logLevel, string mltask, string datasetName, string labelName, string time, int numModelsExplored)
         {
-            logger.Log(logLevel, $"===============================Experiment Results===================================");
-            logger.Log(logLevel, $"------------------------------------------------------------------------------------");
+            logger.Log(logLevel, $"==============================================Experiment Results==================================================");
+            logger.Log(logLevel, $"------------------------------------------------------------------------------------------------------------------");
             logger.Log(logLevel, $"{"ML Task",-7} : {mltask,-20}");
             logger.Log(logLevel, $"{"Dataset",-7}: {datasetName,-25}");
             logger.Log(logLevel, $"{"Label",-6} : {labelName,-25}");
             logger.Log(logLevel, $"{"Exploration time",-20} : {time} Secs");
             logger.Log(logLevel, $"{"Total number of models explored",-30} : {numModelsExplored}");
-            logger.Log(logLevel, $"------------------------------------------------------------------------------------");
+            logger.Log(logLevel, $"------------------------------------------------------------------------------------------------------------------");
         }
         internal static void PrintIterationSummary(IEnumerable<RunResult<BinaryClassificationMetrics>> results, BinaryClassificationMetric optimizationMetric, int count)
         {
             var metricsAgent = new BinaryMetricsAgent(optimizationMetric);
             var topNResults = RunResultUtil.GetTopNRunResults(results, metricsAgent, count);
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             logger.Log(LogLevel.Info, $"Top {topNResults?.Count()} models explored                                          ");
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             PrintBinaryClassificationMetricsHeader(LogLevel.Info);
             int i = 0;
             foreach (var result in topNResults)
             {
                 PrintMetrics(++i, result.TrainerName, result.ValidationMetrics, metricsAgent.GetScore(result.ValidationMetrics), result.RuntimeInSeconds, LogLevel.Info);
             }
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
         }
 
         internal static void PrintIterationSummary(IEnumerable<RunResult<RegressionMetrics>> results, RegressionMetric optimizationMetric, int count)
         {
             var metricsAgent = new RegressionMetricsAgent(optimizationMetric);
             var topNResults = RunResultUtil.GetTopNRunResults(results, metricsAgent, count);
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             logger.Log(LogLevel.Info, $"Top {topNResults?.Count()} models explored                                          ");
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             PrintRegressionMetricsHeader(LogLevel.Info);
             int i = 0;
             foreach (var result in topNResults)
             {
                 PrintMetrics(++i, result.TrainerName, result.ValidationMetrics, metricsAgent.GetScore(result.ValidationMetrics), result.RuntimeInSeconds, LogLevel.Info);
             }
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
         }
 
         internal static void PrintIterationSummary(IEnumerable<RunResult<MultiClassClassifierMetrics>> results, MulticlassClassificationMetric optimizationMetric, int count)
         {
             var metricsAgent = new MultiMetricsAgent(optimizationMetric);
             var topNResults = RunResultUtil.GetTopNRunResults(results, metricsAgent, count);
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             logger.Log(LogLevel.Info, $"Top {topNResults?.Count()} models explored                                          ");
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
             PrintMulticlassClassificationMetricsHeader(LogLevel.Info);
             int i = 0;
             foreach (var result in topNResults)
             {
                 PrintMetrics(++i, result.TrainerName, result.ValidationMetrics, metricsAgent.GetScore(result.ValidationMetrics), result.RuntimeInSeconds, LogLevel.Info);
             }
-            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");
+            logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");
         }
     }
 }
diff --git a/src/mlnet/Utilities/ProgressHandlers.cs b/src/mlnet/Utilities/ProgressHandlers.cs
@@ -47,7 +47,11 @@ private void UpdateBestResult(RunResult<RegressionMetrics> iterationResult)
                 if (MetricComparator(GetScore(iterationResult), GetScore(bestResult), isMaximizing) > 0)
                 {
                     bestResult = iterationResult;
-                    progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult)} , Best Algorithm : {iterationResult.TrainerName}";
+                    progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {bestResult.TrainerName}";
+                }
+                else
+                {
+                    progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {iterationResult.TrainerName}";
                 }
             }
         }
@@ -82,7 +86,11 @@ private void UpdateBestResult(RunResult<BinaryClassificationMetrics> iterationRe
                 if (MetricComparator(GetScore(iterationResult), GetScore(bestResult), isMaximizing) > 0)
                 {
                     bestResult = iterationResult;
-                    progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult)} , Best Algorithm : {iterationResult.TrainerName}";
+                    progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {bestResult.TrainerName}";
+                }
+                else
+                {
+                    progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {iterationResult.TrainerName}";
                 }
             }
         }
@@ -117,7 +125,11 @@ private void UpdateBestResult(RunResult<MultiClassClassifierMetrics> iterationRe
                 if (MetricComparator(GetScore(iterationResult), GetScore(bestResult), isMaximizing) > 0)
                 {
                     bestResult = iterationResult;
-                    progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult)} , Best Algorithm : {iterationResult.TrainerName}";
+                    progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {bestResult.TrainerName}";
+                }
+                else
+                {
+                    progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {iterationResult.TrainerName}";
                 }
             }
         }

Original file line number	Diff line number	Diff line change
`@@ -86,8 +86,9 @@ public void GenerateCode()`
`86`	`86`	`{`
`87`	`87`	`ForegroundColor = ConsoleColor.Yellow,`
`88`	`88`	`ForegroundColorDone = ConsoleColor.DarkGreen,`
`89`		`- BackgroundColor = ConsoleColor.DarkGray,`
`90`		`- BackgroundCharacter = '\u2593'`
	`89`	`+ BackgroundColor = ConsoleColor.Gray,`
	`90`	`+ ProgressCharacter = '\u2593',`
	`91`	`+ BackgroundCharacter = '─',`
`91`	`92`	`};`
`92`	`93`	`var wait = TimeSpan.FromSeconds(settings.MaxExplorationTime);`
`93`	`94`	`using (var pbar = new FixedDurationBar(wait, "", options))`
`@@ -114,9 +115,10 @@ public void GenerateCode()`
`114`	`115`
`115`	`116`	`if (t.IsCompleted == false)`
`116`	`117`	`{`
`117`		`- logger.Log(LogLevel.Info, "Waiting for the last iteration to complete ...");`
	`118`	`+ string originalMessage = pbar.Message;`
	`119`	`+ pbar.Message = " Waiting for the last iteration to complete ...";`
	`120`	`+ t.Wait();`
`118`	`121`	`}`
`119`		`- t.Wait();`
`120`	`122`	`}`
`121`	`123`
`122`	`124`	`}`
Original file line number	Diff line number	Diff line change
`@@ -34,25 +34,25 @@ internal static void PrintMetrics(int iteration, string trainerName, RegressionM`
`34`	`34`
`35`	`35`	`internal static void PrintBinaryClassificationMetricsHeader(LogLevel logLevel)`
`36`	`36`	`{`
`37`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`37`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`38`	`38`	`logger.Log(logLevel, $"{Strings.MetricsForBinaryClassModels}");`
`39`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`39`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`40`	`40`	`logger.Log(logLevel, $"{" ",-4} {"Trainer",-35} {"Accuracy",9} {"AUC",8} {"AUPRC",8} {"F1-score",9} {"Best",8} {"Duration",9}");`
`41`	`41`	`}`
`42`	`42`
`43`	`43`	`internal static void PrintMulticlassClassificationMetricsHeader(LogLevel logLevel)`
`44`	`44`	`{`
`45`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`45`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`46`	`46`	`logger.Log(logLevel, $"{Strings.MetricsForMulticlassModels}");`
`47`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`47`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`48`	`48`	`logger.Log(logLevel, $"{" ",-4} {"Trainer",-35} {"AccuracyMicro",14} {"AccuracyMacro",14} {"Best",14} {"Duration",9}");`
`49`	`49`	`}`
`50`	`50`
`51`	`51`	`internal static void PrintRegressionMetricsHeader(LogLevel logLevel)`
`52`	`52`	`{`
`53`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`53`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`54`	`54`	`logger.Log(logLevel, $"{Strings.MetricsForRegressionModels}");`
`55`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`55`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`56`	`56`	`logger.Log(logLevel, $"{" ",-4} {"Trainer",-35} {"R2-Score",9} {"LossFn",12} {"Absolute-loss",15} {"Squared-loss",15} {"RMS-loss",12} {"Best",12} {"Duration",9}");`
`57`	`57`	`}`
`58`	`58`
`@@ -70,61 +70,61 @@ internal static void PrintTopNHeader(int count)`
`70`	`70`
`71`	`71`	`internal static void ExperimentResultsHeader(LogLevel logLevel, string mltask, string datasetName, string labelName, string time, int numModelsExplored)`
`72`	`72`	`{`
`73`		`- logger.Log(logLevel, $"===============================Experiment Results===================================");`
`74`		`- logger.Log(logLevel, $"------------------------------------------------------------------------------------");`
	`73`	`+ logger.Log(logLevel, $"==============================================Experiment Results==================================================");`
	`74`	`+ logger.Log(logLevel, $"------------------------------------------------------------------------------------------------------------------");`
`75`	`75`	`logger.Log(logLevel, $"{"ML Task",-7} : {mltask,-20}");`
`76`	`76`	`logger.Log(logLevel, $"{"Dataset",-7}: {datasetName,-25}");`
`77`	`77`	`logger.Log(logLevel, $"{"Label",-6} : {labelName,-25}");`
`78`	`78`	`logger.Log(logLevel, $"{"Exploration time",-20} : {time} Secs");`
`79`	`79`	`logger.Log(logLevel, $"{"Total number of models explored",-30} : {numModelsExplored}");`
`80`		`- logger.Log(logLevel, $"------------------------------------------------------------------------------------");`
	`80`	`+ logger.Log(logLevel, $"------------------------------------------------------------------------------------------------------------------");`
`81`	`81`	`}`
`82`	`82`	`internal static void PrintIterationSummary(IEnumerable<RunResult<BinaryClassificationMetrics>> results, BinaryClassificationMetric optimizationMetric, int count)`
`83`	`83`	`{`
`84`	`84`	`var metricsAgent = new BinaryMetricsAgent(optimizationMetric);`
`85`	`85`	`var topNResults = RunResultUtil.GetTopNRunResults(results, metricsAgent, count);`
`86`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`86`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`87`	`87`	`logger.Log(LogLevel.Info, $"Top {topNResults?.Count()} models explored ");`
`88`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`88`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`89`	`89`	`PrintBinaryClassificationMetricsHeader(LogLevel.Info);`
`90`	`90`	`int i = 0;`
`91`	`91`	`foreach (var result in topNResults)`
`92`	`92`	`{`
`93`	`93`	`PrintMetrics(++i, result.TrainerName, result.ValidationMetrics, metricsAgent.GetScore(result.ValidationMetrics), result.RuntimeInSeconds, LogLevel.Info);`
`94`	`94`	`}`
`95`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`95`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`96`	`96`	`}`
`97`	`97`
`98`	`98`	`internal static void PrintIterationSummary(IEnumerable<RunResult<RegressionMetrics>> results, RegressionMetric optimizationMetric, int count)`
`99`	`99`	`{`
`100`	`100`	`var metricsAgent = new RegressionMetricsAgent(optimizationMetric);`
`101`	`101`	`var topNResults = RunResultUtil.GetTopNRunResults(results, metricsAgent, count);`
`102`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`102`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`103`	`103`	`logger.Log(LogLevel.Info, $"Top {topNResults?.Count()} models explored ");`
`104`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`104`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`105`	`105`	`PrintRegressionMetricsHeader(LogLevel.Info);`
`106`	`106`	`int i = 0;`
`107`	`107`	`foreach (var result in topNResults)`
`108`	`108`	`{`
`109`	`109`	`PrintMetrics(++i, result.TrainerName, result.ValidationMetrics, metricsAgent.GetScore(result.ValidationMetrics), result.RuntimeInSeconds, LogLevel.Info);`
`110`	`110`	`}`
`111`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`111`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`112`	`112`	`}`
`113`	`113`
`114`	`114`	`internal static void PrintIterationSummary(IEnumerable<RunResult<MultiClassClassifierMetrics>> results, MulticlassClassificationMetric optimizationMetric, int count)`
`115`	`115`	`{`
`116`	`116`	`var metricsAgent = new MultiMetricsAgent(optimizationMetric);`
`117`	`117`	`var topNResults = RunResultUtil.GetTopNRunResults(results, metricsAgent, count);`
`118`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`118`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`119`	`119`	`logger.Log(LogLevel.Info, $"Top {topNResults?.Count()} models explored ");`
`120`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`120`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`121`	`121`	`PrintMulticlassClassificationMetricsHeader(LogLevel.Info);`
`122`	`122`	`int i = 0;`
`123`	`123`	`foreach (var result in topNResults)`
`124`	`124`	`{`
`125`	`125`	`PrintMetrics(++i, result.TrainerName, result.ValidationMetrics, metricsAgent.GetScore(result.ValidationMetrics), result.RuntimeInSeconds, LogLevel.Info);`
`126`	`126`	`}`
`127`		`- logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------");`
	`127`	`+ logger.Log(LogLevel.Info, $"------------------------------------------------------------------------------------------------------------------");`
`128`	`128`	`}`
`129`	`129`	`}`
`130`	`130`	`}`
Original file line number	Diff line number	Diff line change
`@@ -47,7 +47,11 @@ private void UpdateBestResult(RunResult<RegressionMetrics> iterationResult)`
`47`	`47`	`if (MetricComparator(GetScore(iterationResult), GetScore(bestResult), isMaximizing) > 0)`
`48`	`48`	`{`
`49`	`49`	`bestResult = iterationResult;`
`50`		`- progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult)} , Best Algorithm : {iterationResult.TrainerName}";`
	`50`	`+ progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {bestResult.TrainerName}";`
	`51`	`+ }`
	`52`	`+ else`
	`53`	`+ {`
	`54`	`+ progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {iterationResult.TrainerName}";`
`51`	`55`	`}`
`52`	`56`	`}`
`53`	`57`	`}`
`@@ -82,7 +86,11 @@ private void UpdateBestResult(RunResult<BinaryClassificationMetrics> iterationRe`
`82`	`86`	`if (MetricComparator(GetScore(iterationResult), GetScore(bestResult), isMaximizing) > 0)`
`83`	`87`	`{`
`84`	`88`	`bestResult = iterationResult;`
`85`		`- progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult)} , Best Algorithm : {iterationResult.TrainerName}";`
	`89`	`+ progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {bestResult.TrainerName}";`
	`90`	`+ }`
	`91`	`+ else`
	`92`	`+ {`
	`93`	`+ progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {iterationResult.TrainerName}";`
`86`	`94`	`}`
`87`	`95`	`}`
`88`	`96`	`}`
`@@ -117,7 +125,11 @@ private void UpdateBestResult(RunResult<MultiClassClassifierMetrics> iterationRe`
`117`	`125`	`if (MetricComparator(GetScore(iterationResult), GetScore(bestResult), isMaximizing) > 0)`
`118`	`126`	`{`
`119`	`127`	`bestResult = iterationResult;`
`120`		`- progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult)} , Best Algorithm : {iterationResult.TrainerName}";`
	`128`	`+ progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {bestResult.TrainerName}";`
	`129`	`+ }`
	`130`	`+ else`
	`131`	`+ {`
	`132`	`+ progressBar.Message = $"Best {this.optimizationMetric} : {GetScore(bestResult):F2} , Best Algorithm : {bestResult.TrainerName}, Last Algorithm : {iterationResult.TrainerName}";`
`121`	`133`	`}`
`122`	`134`	`}`
`123`	`135`	`}`