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());
  }
  }
-}
+}

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());
  }
  }
-}
+}

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());
  }
  }
-}
+}

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());
  }
  }
-}
+}

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());
  }
  }
-}
+}

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());
  }
  }
-}
+}

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());
  }
  }
-}
+}

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());
  }
  }
-}
+}