Reformatted Regression samples to width 85

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForest.cs

@@ -8,37 +8,46 @@ namespace Samples.Dynamic.Trainers.Regression
 {
     public static class FastForestRegression
     {
-        // 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 found 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 = mlContext.Regression.Trainers.FastForest(labelColumnName: nameof(DataPoint.Label), featureColumnName: nameof(DataPoint.Features));
+            var pipeline = mlContext.Regression.Trainers.FastForest(
+                labelColumnName: nameof(DataPoint.Label),
+                featureColumnName: nameof(DataPoint.Features));
 
             // 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(5, seed: 123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(
+                GenerateRandomDataPoints(5, 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 for the Label, side by side with the actual Label for comparison.
+            // Look at 5 predictions for the Label, side by side with the actual
+            // Label for comparison.
             foreach (var p in predictions)
                 Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
 
@@ -60,7 +69,8 @@ public static void Example()
             //   RSquared: 0.96 (closer to 1 is better. The worest case is 0)
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             for (int i = 0; i < count; i++)
@@ -70,12 +80,14 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    Features = Enumerable.Repeat(label, 50).Select(x => x + (float)random.NextDouble()).ToArray()
+                    Features = Enumerable.Repeat(label, 50).Select(
+                        x => x + (float)random.NextDouble()).ToArray()
                 };
             }
         }
 
-        // Example with label and 50 feature values. A data set is a collection of such examples.
+        // Example with label and 50 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public float Label { get; set; }
@@ -95,10 +107,12 @@ private class Prediction
         // Print some evaluation metrics to regression problems.
         private static void PrintMetrics(RegressionMetrics metrics)
         {
-            Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
-            Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
-            Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
-            Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
+            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
+            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
+            Console.WriteLine(
+                "Root Mean Squared Error: " + metrics.RootMeanSquaredError);
+
+            Console.WriteLine("RSquared: " + metrics.RSquared);
         }
     }
 }

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForest.tt

@@ -1,12 +1,16 @@
 <#@ include file="RegressionSamplesTemplate.ttinclude"#>
 
 <#+
-string ClassHeader = @"// This example requires installation of additional NuGet package
-        // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>. ";
+string ClassHeader = @"// This example requires installation of additional NuGet
+        // package for Microsoft.ML.FastTree found at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/";
 
 string ClassName="FastForestRegression";
 string ExtraUsing = null;
-string Trainer = @"FastForest(labelColumnName: nameof(DataPoint.Label), featureColumnName: nameof(DataPoint.Features))";
+string Trainer = @"FastForest(
+                labelColumnName: nameof(DataPoint.Label),
+                featureColumnName: nameof(DataPoint.Features))";
+
 string TrainerOptions = null;
 
 string ExpectedOutputPerInstance= @"// Expected output:

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForestWithOptions.cs

@@ -9,19 +9,22 @@ namespace Samples.Dynamic.Trainers.Regression
 {
     public static class FastForestWithOptionsRegression
     {
-        // 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 found 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 trainer options.
@@ -38,21 +41,26 @@ public static void Example()
             };
 
             // Define the trainer.
-            var pipeline = mlContext.Regression.Trainers.FastForest(options);
+            var pipeline =
+                mlContext.Regression.Trainers.FastForest(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(5, seed: 123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(
+                GenerateRandomDataPoints(5, 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 for the Label, side by side with the actual Label for comparison.
+            // Look at 5 predictions for the Label, side by side with the actual
+            // Label for comparison.
             foreach (var p in predictions)
                 Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
 
@@ -74,7 +82,8 @@ public static void Example()
             //   RSquared: 0.95 (closer to 1 is better. The worest case is 0)
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             for (int i = 0; i < count; i++)
@@ -84,12 +93,14 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    Features = Enumerable.Repeat(label, 50).Select(x => x + (float)random.NextDouble()).ToArray()
+                    Features = Enumerable.Repeat(label, 50).Select(
+                        x => x + (float)random.NextDouble()).ToArray()
                 };
             }
         }
 
-        // Example with label and 50 feature values. A data set is a collection of such examples.
+        // Example with label and 50 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public float Label { get; set; }
@@ -109,10 +120,12 @@ private class Prediction
         // Print some evaluation metrics to regression problems.
         private static void PrintMetrics(RegressionMetrics metrics)
         {
-            Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
-            Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
-            Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
-            Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
+            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
+            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
+            Console.WriteLine(
+                "Root Mean Squared Error: " + metrics.RootMeanSquaredError);
+
+            Console.WriteLine("RSquared: " + metrics.RSquared);
         }
     }
 }

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastForestWithOptions.tt

@@ -1,8 +1,9 @@
 <#@ include file="RegressionSamplesTemplate.ttinclude"#>
 
 <#+
-string ClassHeader = @"// This example requires installation of additional NuGet package
-        // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>. ";
+string ClassHeader = @"// This example requires installation of additional NuGet
+        // package for Microsoft.ML.FastTree found at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/";
 
 string ClassName="FastForestWithOptionsRegression";
 string ExtraUsing = "using Microsoft.ML.Trainers.FastTree;";

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTree.cs

@@ -8,37 +8,46 @@ namespace Samples.Dynamic.Trainers.Regression
 {
     public static class FastTreeRegression
     {
-        // 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 found 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 = mlContext.Regression.Trainers.FastTree(labelColumnName: nameof(DataPoint.Label), featureColumnName: nameof(DataPoint.Features));
+            var pipeline = mlContext.Regression.Trainers.FastForest(
+                labelColumnName: nameof(DataPoint.Label),
+                featureColumnName: nameof(DataPoint.Features));
 
             // 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(5, seed: 123));
+            // Create testing data. Use different random seed to make it different
+            // from training data.
+            var testData = mlContext.Data.LoadFromEnumerable(
+                GenerateRandomDataPoints(5, 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 for the Label, side by side with the actual Label for comparison.
+            // Look at 5 predictions for the Label, side by side with the actual
+            // Label for comparison.
             foreach (var p in predictions)
                 Console.WriteLine($"Label: {p.Label:F3}, Prediction: {p.Score:F3}");
 
@@ -60,7 +69,8 @@ public static void Example()
             //   RSquared: 0.99 (closer to 1 is better. The worest case is 0)
         }
 
-        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int seed=0)
+        private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count,
+            int seed=0)
         {
             var random = new Random(seed);
             for (int i = 0; i < count; i++)
@@ -70,12 +80,14 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    Features = Enumerable.Repeat(label, 50).Select(x => x + (float)random.NextDouble()).ToArray()
+                    Features = Enumerable.Repeat(label, 50).Select(
+                        x => x + (float)random.NextDouble()).ToArray()
                 };
             }
         }
 
-        // Example with label and 50 feature values. A data set is a collection of such examples.
+        // Example with label and 50 feature values. A data set is a collection of
+        // such examples.
         private class DataPoint
         {
             public float Label { get; set; }
@@ -95,10 +107,12 @@ private class Prediction
         // Print some evaluation metrics to regression problems.
         private static void PrintMetrics(RegressionMetrics metrics)
         {
-            Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
-            Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
-            Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
-            Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
+            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
+            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
+            Console.WriteLine(
+                "Root Mean Squared Error: " + metrics.RootMeanSquaredError);
+
+            Console.WriteLine("RSquared: " + metrics.RSquared);
         }
     }
 }

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Regression/FastTree.tt

@@ -1,12 +1,15 @@
 <#@ include file="RegressionSamplesTemplate.ttinclude"#>
 
 <#+
-string ClassHeader = @"// This example requires installation of additional NuGet package
-        // <a href=""https://www.nuget.org/packages/Microsoft.ML.FastTree/"">Microsoft.ML.FastTree</a>. ";
+string ClassHeader = @"// This example requires installation of additional NuGet
+        // package for Microsoft.ML.FastTree found at
+        // https://www.nuget.org/packages/Microsoft.ML.FastTree/";
 
 string ClassName="FastTreeRegression";
 string ExtraUsing = null;
-string Trainer = @"FastTree(labelColumnName: nameof(DataPoint.Label), featureColumnName: nameof(DataPoint.Features))";
+string Trainer = @"FastForest(
+                labelColumnName: nameof(DataPoint.Label),
+                featureColumnName: nameof(DataPoint.Features))";
 string TrainerOptions = null;
 
 string ExpectedOutputPerInstance= @"// Expected output: