aligned comments

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/LogLossPerClass.cs

@@ -30,7 +30,8 @@ public static void Example()
                         .MapValueToKey(nameof(DataPoint.Label))
 
                     // Apply a multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.LightGbm());
+                        .Append(mlContext.MulticlassClassification.Trainers
+                        .LightGbm());
 
             // Train the model.
             var model = pipeline.Fit(trainingData);

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.cs

@@ -11,55 +11,54 @@ public static class NaiveBayes
         // Naive Bayes classifier is based on Bayes' theorem. 
         // It assumes independence among the presence of features in a class even
         // though they may be dependent on each other. It is  a multi-class trainer
-    // that accepts binary feature values of type float, i.e., feature values
-    // are either true or false. Specifically a feature value greater than zero
-    // is treated as true, zero or less is treated as false.
+        // that accepts binary feature values of type float, i.e., feature values
+        // are either true or false. Specifically a feature value greater than zero
+        // is treated as true, zero or less is treated as false.
         public static void Example()
         {
             // Create a new 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. Setting the seed to a fixed number
-        // in this example to make outputs deterministic.
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
             // Convert the list of data points to an IDataView object, which is
-        // consumable by ML.NET API.
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
             var pipeline =
-                    // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion
+            // Convert the string labels into key types.
+                mlContext.Transforms.Conversion
                 .MapValueToKey(nameof(DataPoint.Label))
-
-                    // Apply NaiveBayes multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers
+            // Apply NaiveBayes multiclass trainer.
+                .Append(mlContext.MulticlassClassification.Trainers
                 .NaiveBayes());
 
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
             // Create testing data. Use different random seed to make it different
-        // from training data.
+            // from training data.
             var testData = mlContext.Data
-            .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Convert IDataView object to a list.
             var predictions = mlContext.Data
-            .CreateEnumerable<Prediction>(transformedTestData,
-        reuseRowObject: false).ToList();
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
                 Console.WriteLine($"Label: {p.Label}, " + 
-            $"Prediction: {p.PredictedLabel}");
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -70,7 +69,7 @@ public static void Example()
 
             // Evaluate the overall metrics
             var metrics = mlContext.MulticlassClassification
-            .Evaluate(transformedTestData);
+                .Evaluate(transformedTestData);
 
             PrintMetrics(metrics);
 
@@ -93,8 +92,8 @@ public static void Example()
 
 
         // Generates random uniform doubles in [-0.5, 0.5) range with labels
-    // 1, 2 or 3. For NaiveBayes values greater than zero are treated as true,
-    // zero or less are treated as false.
+        // 1, 2 or 3. For NaiveBayes values greater than zero are treated as true,
+        // zero or less are treated as false.
         private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
             int seed=0)
 
@@ -110,7 +109,7 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
                     Label = (uint)label,
                     // Create random features that are correlated with the label.
                     // The feature values are slightly increased by adding a
-            // constant multiple of label.
+                    // constant multiple of label.
                     Features = Enumerable.Repeat(label, 20)
                         .Select(x => randomFloat() + label * 0.2f).ToArray()
 
@@ -119,7 +118,7 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
         }
 
         // Example with label and 20 feature values. A data set is a collection of
-    // such examples.
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -143,7 +142,7 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
             Console.WriteLine(
-            $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
 
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/NaiveBayes.tt

@@ -11,14 +11,14 @@ string Comments= @"
         // Naive Bayes classifier is based on Bayes' theorem. 
         // It assumes independence among the presence of features in a class even
         // though they may be dependent on each other. It is  a multi-class trainer
-    // that accepts binary feature values of type float, i.e., feature values
-    // are either true or false. Specifically a feature value greater than zero
-    // is treated as true, zero or less is treated as false.";
+        // that accepts binary feature values of type float, i.e., feature values
+        // are either true or false. Specifically a feature value greater than zero
+        // is treated as true, zero or less is treated as false.";
 
 string DataGenerationComments= @"
         // Generates random uniform doubles in [-0.5, 0.5) range with labels
-    // 1, 2 or 3. For NaiveBayes values greater than zero are treated as true,
-    // zero or less are treated as false.";
+        // 1, 2 or 3. For NaiveBayes values greater than zero are treated as true,
+        // zero or less are treated as false.";
 
 string ExpectedOutputPerInstance= @"// Expected output:
             //   Label: 1, Prediction: 1

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/OneVersusAll.cs

@@ -11,49 +11,49 @@ public static class OneVersusAll
         public static void Example()
         {
             // Create a new 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. Setting the seed to a fixed number
-        // in this example to make outputs deterministic.
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
             // Convert the list of data points to an IDataView object, which is
-        // consumable by ML.NET API.
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
             var pipeline =
-                    // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey("Label")
-                    // Apply OneVersusAll multiclass meta trainer on top of
+            // Convert the string labels into key types.
+                mlContext.Transforms.Conversion.MapValueToKey("Label")
+            // Apply OneVersusAll multiclass meta trainer on top of
             // binary trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers
+                .Append(mlContext.MulticlassClassification.Trainers
                 .OneVersusAll(
-            mlContext.BinaryClassification.Trainers.SdcaLogisticRegression()));
+                mlContext.BinaryClassification.Trainers.SdcaLogisticRegression()));
 
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
             // Create testing data. Use different random seed to make it different
-        // from training data.
+            // from training data.
             var testData = mlContext.Data
-            .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Convert IDataView object to a list.
             var predictions = mlContext.Data
-            .CreateEnumerable<Prediction>(transformedTestData,
-        reuseRowObject: false).ToList();
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
                 Console.WriteLine($"Label: {p.Label}, " + 
-            $"Prediction: {p.PredictedLabel}");
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -64,7 +64,7 @@ public static void Example()
 
             // Evaluate the overall metrics
             var metrics = mlContext.MulticlassClassification
-            .Evaluate(transformedTestData);
+                .Evaluate(transformedTestData);
 
             PrintMetrics(metrics);
 
@@ -102,7 +102,7 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
                     Label = (uint)label,
                     // Create random features that are correlated with the label.
                     // The feature values are slightly increased by adding a
-            // constant multiple of label.
+                    // constant multiple of label.
                     Features = Enumerable.Repeat(label, 20)
                         .Select(x => randomFloat() + label * 0.2f).ToArray()
 
@@ -111,7 +111,7 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
         }
 
         // Example with label and 20 feature values. A data set is a collection of
-    // such examples.
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -135,7 +135,7 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
             Console.WriteLine(
-            $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
 
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/MulticlassClassification/PairwiseCoupling.cs

@@ -11,49 +11,49 @@ public static class PairwiseCoupling
         public static void Example()
         {
             // Create a new 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. Setting the seed to a fixed number
-        // in this example to make outputs deterministic.
+            // exception tracking and logging, as a catalog of available operations
+            // and as the source of randomness. Setting the seed to a fixed number
+            // in this example to make outputs deterministic.
             var mlContext = new MLContext(seed: 0);
 
             // Create a list of training data points.
             var dataPoints = GenerateRandomDataPoints(1000);
 
             // Convert the list of data points to an IDataView object, which is
-        // consumable by ML.NET API.
+            // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define the trainer.
             var pipeline =
-                    // Convert the string labels into key types.
-                    mlContext.Transforms.Conversion.MapValueToKey("Label")
-                    // Apply PairwiseCoupling multiclass meta trainer on top of
+            // Convert the string labels into key types.
+                mlContext.Transforms.Conversion.MapValueToKey("Label")
+            // Apply PairwiseCoupling multiclass meta trainer on top of
             // binary trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers
+                .Append(mlContext.MulticlassClassification.Trainers
                 .PairwiseCoupling(
-            mlContext.BinaryClassification.Trainers.SdcaLogisticRegression()));
+                mlContext.BinaryClassification.Trainers.SdcaLogisticRegression()));
 
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
             // Create testing data. Use different random seed to make it different
-        // from training data.
+            // from training data.
             var testData = mlContext.Data
-            .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+                .LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
 
             // Run the model on test data set.
             var transformedTestData = model.Transform(testData);
 
             // Convert IDataView object to a list.
             var predictions = mlContext.Data
-            .CreateEnumerable<Prediction>(transformedTestData,
-            reuseRowObject: false).ToList();
+                .CreateEnumerable<Prediction>(transformedTestData,
+                reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
                 Console.WriteLine($"Label: {p.Label}, " + 
-            $"Prediction: {p.PredictedLabel}");
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -64,7 +64,7 @@ public static void Example()
 
             // Evaluate the overall metrics
             var metrics = mlContext.MulticlassClassification
-            .Evaluate(transformedTestData);
+                .Evaluate(transformedTestData);
 
             PrintMetrics(metrics);
 
@@ -102,9 +102,9 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
                     Label = (uint)label,
                     // Create random features that are correlated with the label.
                     // The feature values are slightly increased by adding a
-            // constant multiple of label.
+                    // constant multiple of label.
                     Features = Enumerable.Repeat(label, 20)
-                .Select(x => randomFloat() + label * 0.2f).ToArray()
+                        .Select(x => randomFloat() + label * 0.2f).ToArray()
 
                 };
             }
@@ -135,9 +135,10 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
             Console.WriteLine(
-            $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
 
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+