Reformatting MulticlassClassification samples to width 85

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

@@ -10,39 +10,50 @@ public static class LbfgsMaximumEntropy
     {
         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.
+            // 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.
             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.
+            // Convert the list of data points to an IDataView object, which is
+	    // 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(nameof(DataPoint.Label))
+                    mlContext.Transforms.Conversion
+		        .MapValueToKey(nameof(DataPoint.Label))
+
                     // Apply LbfgsMaximumEntropy multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy());
+                    .Append(mlContext.MulticlassClassification.Trainers
+	                .LbfgsMaximumEntropy());
+
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+            // Create testing data. Use different random seed to make it different
+	    // from training data.
+            var testData = mlContext.Data
+                .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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+	        reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+	            $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -52,7 +63,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
 
             // Expected output:
@@ -72,8 +85,11 @@ public static void Example()
             //  Precision ||0.9308 |0.9593 |0.8580 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, 
+	    int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -85,13 +101,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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.
-                    Features = Enumerable.Repeat(label, 20).Select(x => randomFloat() + label * 0.2f).ToArray()
+                    // The feature values are slightly increased by adding a
+		    // constant multiple of label.
+                    Features = Enumerable.Repeat(label, 20)
+	                .Select(x => randomFloat() + label * 0.2f).ToArray()
+
                 };
             }
         }
 
-        // Example with label and 20 feature values. A data set is a collection of such examples.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -114,7 +134,9 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }

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

@@ -8,7 +8,8 @@ string TrainerOptions = null;
 string OptionsInclude = "";
 string Comments = "";
 bool CacheData = false;
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1

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

@@ -11,15 +11,17 @@ public static class LbfgsMaximumEntropyWithOptions
     {
         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.
+            // 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.
             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.
+            // Convert the list of data points to an IDataView object, which is
+	    // consumable by ML.NET API.
             var trainingData = mlContext.Data.LoadFromEnumerable(dataPoints);
 
             // Define trainer options.
@@ -32,27 +34,33 @@ public static void Example()
 
             // Define the trainer.
             var pipeline = 
-			        // Convert the string labels into key types.
+	            // Convert the string labels into key types.
                     mlContext.Transforms.Conversion.MapValueToKey("Label")
                     // Apply LbfgsMaximumEntropy multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.LbfgsMaximumEntropy(options));
+                    .Append(mlContext.MulticlassClassification.Trainers
+	                .LbfgsMaximumEntropy(options));
 
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+            // Create testing data. Use different random seed to make it different
+	    // from training data.
+            var testData = mlContext.Data
+                .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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+	        reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+                    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -62,7 +70,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+                .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
 
             // Expected output:
@@ -82,8 +92,11 @@ public static void Example()
             //   Precision ||0.9304 |0.9593 |0.8529 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -95,13 +108,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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.
-                    Features = Enumerable.Repeat(label, 20).Select(x => randomFloat() + label * 0.2f).ToArray()
+                    // The feature values are slightly increased by adding a
+		    // constant multiple of label.
+                    Features = Enumerable.Repeat(label, 20)
+		        .Select(x => randomFloat() + label * 0.2f).ToArray()
+
                 };
             }
         }
 
-        // Example with label and 20 feature values. A data set is a collection of such examples.
+        // Example with label and 20 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -124,7 +141,9 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+                $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }

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

@@ -12,7 +12,9 @@ string TrainerOptions = @"LbfgsMaximumEntropyMulticlassTrainer.Options
 
 string OptionsInclude = "using Microsoft.ML.Trainers;";
 string Comments = "";
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
+
 bool CacheData = false;
 
 string ExpectedOutputPerInstance = @"// Expected output:

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

@@ -8,43 +8,55 @@ namespace Samples.Dynamic.Trainers.MulticlassClassification
 {
     public static class LightGbm
     {
-        // This example requires installation of additional NuGet package
-        // <a href="https://www.nuget.org/packages/Microsoft.ML.FastTree/">Microsoft.ML.FastTree</a>.
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/
         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.
+            // 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.
             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.
+            // Convert the list of data points to an IDataView object, which is
+	    // 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(nameof(DataPoint.Label))
+                    mlContext.Transforms.Conversion
+		        .MapValueToKey(nameof(DataPoint.Label))
+
                     // Apply LightGbm multiclass trainer.
-                    .Append(mlContext.MulticlassClassification.Trainers.LightGbm());
+                    .Append(mlContext.MulticlassClassification.Trainers
+			.LightGbm());
+
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
 
-            // Create testing data. Use different random seed to make it different from training data.
-            var testData = mlContext.Data.LoadFromEnumerable(GenerateRandomDataPoints(500, seed: 123));
+            // Create testing data. Use different random seed to make it different
+	    // from training data.
+            var testData = mlContext.Data
+	        .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();
+            var predictions = mlContext.Data
+                .CreateEnumerable<Prediction>(transformedTestData,
+		reuseRowObject: false).ToList();
 
             // Look at 5 predictions
             foreach (var p in predictions.Take(5))
-                Console.WriteLine($"Label: {p.Label}, Prediction: {p.PredictedLabel}");
+                Console.WriteLine($"Label: {p.Label}, " + 
+		    $"Prediction: {p.PredictedLabel}");
 
             // Expected output:
             //   Label: 1, Prediction: 1
@@ -54,7 +66,9 @@ public static void Example()
             //   Label: 3, Prediction: 3
 
             // Evaluate the overall metrics
-            var metrics = mlContext.MulticlassClassification.Evaluate(transformedTestData);
+            var metrics = mlContext.MulticlassClassification
+	        .Evaluate(transformedTestData);
+
             PrintMetrics(metrics);
 
             // Expected output:
@@ -74,8 +88,11 @@ public static void Example()
             //   Precision ||0.9936 |1.0000 |0.9701 |
         }
 
-        // Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        // Generates random uniform doubles in [-0.5, 0.5)
+        // range with labels 1, 2 or 3.
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+	    int seed=0)
+
         {
             var random = new Random(seed);
             float randomFloat() => (float)(random.NextDouble() - 0.5);
@@ -87,13 +104,17 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     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.
-                    Features = Enumerable.Repeat(label, 20).Select(x => randomFloat() + label * 0.2f).ToArray()
+                    // The feature values are slightly increased by adding a
+		    // constant multiple of label.
+                    Features = Enumerable.Repeat(label, 20)
+		        .Select(x => randomFloat() + label * 0.2f).ToArray()
+
                 };
             }
         }
 
-        // Example with label and 20 feature values. A data set is a collection of such examples.
+        // Example with label and 20 feature values. A data set is a collection of
+	// such examples.
         private class DataPoint
         {
             public uint Label { get; set; }
@@ -116,7 +137,9 @@ public static void PrintMetrics(MulticlassClassificationMetrics metrics)
             Console.WriteLine($"Micro Accuracy: {metrics.MicroAccuracy:F2}");
             Console.WriteLine($"Macro Accuracy: {metrics.MacroAccuracy:F2}");
             Console.WriteLine($"Log Loss: {metrics.LogLoss:F2}");
-            Console.WriteLine($"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+            Console.WriteLine(
+	        $"Log Loss Reduction: {metrics.LogLossReduction:F2}\n");
+
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }

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

@@ -7,10 +7,13 @@ string TrainerOptions = null;
 
 string OptionsInclude = "";
 string Comments = @"
-        // This example requires installation of additional NuGet package
-        // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>.";
+        // This example requires installation of additional NuGet package for 
+        // Microsoft.ML.FastTree at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/";
+
 bool CacheData = false;
-string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5) range with labels 1, 2 or 3.";
+string DataGenerationComments= "// Generates random uniform doubles in [-0.5, 0.5)"
+    + "\n        // range with labels 1, 2 or 3.";
 
 string ExpectedOutputPerInstance = @"// Expected output:
             //   Label: 1, Prediction: 1