Reformatting BinaryClassification samples to width 85

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptron.cs

@@ -10,35 +10,43 @@ public static class AveragedPerceptron
     {
         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.BinaryClassification.Trainers.AveragedPerceptron();
+            var pipeline = mlContext.BinaryClassification.Trainers
+                .AveragedPerceptron();
 
             // 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();
 
             // Print 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: True, Prediction: True
@@ -48,7 +56,9 @@ public static void Example()
             //   Label: False, Prediction: False
 
             // Evaluate the overall metrics.
-            var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(transformedTestData);
+            var metrics = mlContext.BinaryClassification
+                .EvaluateNonCalibrated(transformedTestData);
+
             PrintMetrics(metrics);
 
             // Expected output:
@@ -71,7 +81,9 @@ public static void Example()
             //   Precision ||   0.7402 |   0.7061 |
         }
 
-        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);
             float randomFloat() => (float)random.NextDouble();
@@ -82,13 +94,18 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    // For data points with false label, the feature values are slightly increased by adding a constant.
-                    Features = Enumerable.Repeat(label, 50).Select(x => x ? randomFloat() : randomFloat() + 0.1f).ToArray()
+                    // For data points with false label, the feature values are
+                    // slightly increased by adding a constant.
+                    Features = Enumerable.Repeat(label, 50)
+                        .Select(x => x ? randomFloat() : randomFloat() +
+                        0.1f).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 bool Label { get; set; }
@@ -111,11 +128,16 @@ private static void PrintMetrics(BinaryClassificationMetrics metrics)
             Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
             Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
             Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
-            Console.WriteLine($"Negative Precision: {metrics.NegativePrecision:F2}");
+            Console.WriteLine($"Negative Precision: " + 
+                $"{metrics.NegativePrecision:F2}");
+
             Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
-            Console.WriteLine($"Positive Precision: {metrics.PositivePrecision:F2}");
+            Console.WriteLine($"Positive Precision: " +
+                $"{metrics.PositivePrecision:F2}");
+
             Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptronWithOptions.cs

@@ -11,15 +11,17 @@ public static class AveragedPerceptronWithOptions
     {
         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.
@@ -33,23 +35,29 @@ public static void Example()
             };
 
             // Define the trainer.
-            var pipeline = mlContext.BinaryClassification.Trainers.AveragedPerceptron(options);
+            var pipeline = mlContext.BinaryClassification.Trainers
+                .AveragedPerceptron(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();
 
             // Print 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: True, Prediction: True
@@ -59,7 +67,9 @@ public static void Example()
             //   Label: False, Prediction: False
 
             // Evaluate the overall metrics.
-            var metrics = mlContext.BinaryClassification.EvaluateNonCalibrated(transformedTestData);
+            var metrics = mlContext.BinaryClassification
+                .EvaluateNonCalibrated(transformedTestData);
+
             PrintMetrics(metrics);
 
             // Expected output:
@@ -82,7 +92,9 @@ public static void Example()
             //   Precision ||   0.7402 |   0.7061 |
         }
 
-        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);
             float randomFloat() => (float)random.NextDouble();
@@ -93,13 +105,18 @@ private static IEnumerable<DataPoint> GenerateRandomDataPoints(int count, int se
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    // For data points with false label, the feature values are slightly increased by adding a constant.
-                    Features = Enumerable.Repeat(label, 50).Select(x => x ? randomFloat() : randomFloat() + 0.1f).ToArray()
+                    // For data points with false label, the feature values are
+                    // slightly increased by adding a constant.
+                    Features = Enumerable.Repeat(label, 50)
+                        .Select(x => x ? randomFloat() : randomFloat() +
+                        0.1f).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 bool Label { get; set; }
@@ -122,11 +139,16 @@ private static void PrintMetrics(BinaryClassificationMetrics metrics)
             Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
             Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
             Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
-            Console.WriteLine($"Negative Precision: {metrics.NegativePrecision:F2}");
+            Console.WriteLine($"Negative Precision: " + 
+                $"{metrics.NegativePrecision:F2}");
+
             Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
-            Console.WriteLine($"Positive Precision: {metrics.PositivePrecision:F2}");
+            Console.WriteLine($"Positive Precision: " +
+                $"{metrics.PositivePrecision:F2}");
+
             Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
 }
+

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/BinaryClassification.ttinclude

@@ -13,63 +13,79 @@ namespace Samples.Dynamic.Trainers.BinaryClassification
     {<#=Comments#>
         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);
 <# if (CacheData) { #>
 
-            // ML.NET doesn't cache data set by default. Therefore, if one reads a data set from a file and accesses it many times,
-            // it can be slow due to expensive featurization and disk operations. When the considered data can fit into memory,
-            // a solution is to cache the data in memory. Caching is especially helpful when working with iterative algorithms 
+            // ML.NET doesn't cache data set by default. Therefore, if one reads a
+            // data set from a file and accesses it many times, it can be slow due
+            // to expensive featurization and disk operations. When the considered
+            // data can fit into memory, a solution is to cache the data in memory.
+            // Caching is especially helpful when working with iterative algorithms 
             // which needs many data passes.
             trainingData = mlContext.Data.Cache(trainingData);
 <# } #>
 
 <# if (TrainerOptions == null) { #>
             // Define the trainer.
-            var pipeline = mlContext.BinaryClassification.Trainers.<#=Trainer#>();
+            var pipeline = mlContext.BinaryClassification.Trainers
+                .<#=Trainer#>();
 <# } else { #>
             // Define trainer options.
             var options = new <#=TrainerOptions#>;
 
             // Define the trainer.
-            var pipeline = mlContext.BinaryClassification.Trainers.<#=Trainer#>(options);
+            var pipeline = mlContext.BinaryClassification.Trainers
+                .<#=Trainer#>(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();
 
             // Print 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}");
 
             <#=ExpectedOutputPerInstance#>
-            <# string Evaluator = IsCalibrated ? "Evaluate" : "EvaluateNonCalibrated"; #>
+            <# string Evaluator = IsCalibrated ? "Evaluate" :
+                "EvaluateNonCalibrated"; #>
 
             // Evaluate the overall metrics.
-            var metrics = mlContext.BinaryClassification.<#=Evaluator#>(transformedTestData);
+            var metrics = mlContext.BinaryClassification
+                .<#=Evaluator#>(transformedTestData);
+
             PrintMetrics(metrics);
 
             <#=ExpectedOutput#>
         }
 
-        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);
             float randomFloat() => (float)random.NextDouble();
@@ -80,13 +96,18 @@ namespace Samples.Dynamic.Trainers.BinaryClassification
                 {
                     Label = label,
                     // Create random features that are correlated with the label.
-                    // For data points with false label, the feature values are slightly increased by adding a constant.
-                    Features = Enumerable.Repeat(label, 50).Select(x => x ? randomFloat() : randomFloat() + <#=DataSepValue#>).ToArray()
+                    // For data points with false label, the feature values are
+                    // slightly increased by adding a constant.
+                    Features = Enumerable.Repeat(label, 50)
+                        .Select(x => x ? randomFloat() : randomFloat() +
+                        <#=DataSepValue#>).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 bool Label { get; set; }
@@ -109,11 +130,15 @@ namespace Samples.Dynamic.Trainers.BinaryClassification
             Console.WriteLine($"Accuracy: {metrics.Accuracy:F2}");
             Console.WriteLine($"AUC: {metrics.AreaUnderRocCurve:F2}");
             Console.WriteLine($"F1 Score: {metrics.F1Score:F2}");
-            Console.WriteLine($"Negative Precision: {metrics.NegativePrecision:F2}");
+            Console.WriteLine($"Negative Precision: " + 
+                $"{metrics.NegativePrecision:F2}");
+
             Console.WriteLine($"Negative Recall: {metrics.NegativeRecall:F2}");
-            Console.WriteLine($"Positive Precision: {metrics.PositivePrecision:F2}");
+            Console.WriteLine($"Positive Precision: " +
+                $"{metrics.PositivePrecision:F2}");
+
             Console.WriteLine($"Positive Recall: {metrics.PositiveRecall:F2}\n");
             Console.WriteLine(metrics.ConfusionMatrix.GetFormattedConfusionTable());
         }
     }
-}
+}