Reformatted Recommendation samples to width 85 (#3941)

* Reformatted Recommendation samples

* Fix for tab spacing errors

* Removed precision for reformatting in Console.WriteLine and fixed KeyType error

Author: sayanshaw24

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.cs

@@ -9,25 +9,32 @@ namespace Samples.Dynamic.Trainers.Recommendation
     public static class MatrixFactorization
     {
 
-        // This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Recommender/">Microsoft.ML.Recommender</a>.
-        // In this example we will create in-memory data and then use it to train 
-        // a matrix factorization model with default parameters. Afterward, quality metrics are reported.
+        // This example requires installation of additional nuget package at
+        // for Microsoft.ML.Recommender at
+        // https://www.nuget.org/packages/Microsoft.ML.Recommender/
+        // In this example we will create in-memory data and then use it to train
+        // a matrix factorization model with default parameters. Afterward, quality
+        // metrics are reported.
         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 = GenerateMatrix();
 
-            // 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.Recommendation().Trainers.MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
-                   nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);
+            var pipeline = mlContext.Recommendation().Trainers.
+                MatrixFactorization(nameof(MatrixElement.Value),
+                nameof(MatrixElement.MatrixColumnIndex),
+                nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1);
 
             // Train the model.
             var model = pipeline.Fit(trainingData);
@@ -36,11 +43,15 @@ public static void Example()
             var transformedData = model.Transform(trainingData);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
+            var predictions = mlContext.Data
+                .CreateEnumerable<MatrixElement>(transformedData,
+                reuseRowObject: false).Take(5).ToList();
 
-            // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
+            // Look at 5 predictions for the Label, side by side with the actual
+            // Label for comparison.
             foreach (var p in predictions)
-                Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
+                Console.WriteLine($"Actual value: {p.Value:F3}," +
+                    $"Predicted score: {p.Score:F3}");
 
             // Expected output:
             //   Actual value: 0.000, Predicted score: 1.234
@@ -50,7 +61,10 @@ public static void Example()
             //   Actual value: 4.000, Predicted score: 2.362
 
             // Evaluate the overall metrics
-            var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
+            var metrics = mlContext.Regression.Evaluate(transformedData,
+                labelColumnName: nameof(MatrixElement.Value),
+                scoreColumnName: nameof(MatrixElement.Score));
+
             PrintMetrics(metrics);
 
             // Expected output:
@@ -60,11 +74,15 @@ public static void Example()
             //   RSquared: 0.61 (closer to 1 is better. The worest case is 0)
         }
 
-        // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
-        // Because in ML.NET key type's minimal value is zero, the first row index is always zero in C# data structure (e.g., MatrixColumnIndex=0
-        // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
-        // starts with 1, their row index 1 would be mapped to the 2nd row in matrix factorization module and their first row may contain no values.
-        // This behavior is also true to column index.
+        // The following variables are used to define the shape of the example
+        // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. Because in 
+        // ML.NET key type's minimal value is zero, the first row index is always
+        // zero in C# data structure (e.g., MatrixColumnIndex=0 and MatrixRowIndex=0
+        // in MatrixElement below specifies the value at the upper-left corner in
+        // the training matrix). If user's row index starts with 1, their row index
+        // 1 would be mapped to the 2nd row in matrix factorization module and their
+        // first row may contain no values. This behavior is also true to column
+        // index.
         private const uint MatrixColumnCount = 60;
         private const uint MatrixRowCount = 100;
 
@@ -74,32 +92,40 @@ private static List<MatrixElement> GenerateMatrix()
             var dataMatrix = new List<MatrixElement>();
             for (uint i = 0; i < MatrixColumnCount; ++i)
                 for (uint j = 0; j < MatrixRowCount; ++j)
-                    dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
+                    dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
+                        MatrixRowIndex = j, Value = (i + j) % 5 });
+
             return dataMatrix;
         }
 
-        // A class used to define a matrix element and capture its prediction result.
+        // A class used to define a matrix element and capture its prediction
+        // result.
         private class MatrixElement
         {
-            // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
+            // Matrix column index. Its allowed range is from 0 to
+            // MatrixColumnCount - 1.
             [KeyType(MatrixColumnCount)]
             public uint MatrixColumnIndex { get; set; }
             // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
             [KeyType(MatrixRowCount)]
             public uint MatrixRowIndex { get; set; }
-            // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
+            // The actual value at the MatrixColumnIndex-th column and the
+            // MatrixRowIndex-th row.
             public float Value { get; set; }
-            // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
+            // The predicted value at the MatrixColumnIndex-th column and the
+            // MatrixRowIndex-th row.
             public float Score { get; set; }
         }
 
         // Print some evaluation metrics to regression problems.
         private static void PrintMetrics(RegressionMetrics metrics)
         {
-            Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
-            Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
-            Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
-            Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
+            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
+            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
+            Console.WriteLine("Root Mean Squared Error: " +
+                metrics.RootMeanSquaredError);
+
+            Console.WriteLine("RSquared: " + metrics.RSquared);
         }
     }
 }

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorization.tt

@@ -2,13 +2,18 @@
 
 <#+
 string ClassHeader = @"
-        // This example requires installation of additional nuget package <a href=""https://www.nuget.org/packages/Microsoft.ML.Recommender/"">Microsoft.ML.Recommender</a>.
-        // In this example we will create in-memory data and then use it to train 
-        // a matrix factorization model with default parameters. Afterward, quality metrics are reported.";
+        // This example requires installation of additional nuget package at
+        // for Microsoft.ML.Recommender at
+        // https://www.nuget.org/packages/Microsoft.ML.Recommender/
+        // In this example we will create in-memory data and then use it to train
+        // a matrix factorization model with default parameters. Afterward, quality
+        // metrics are reported.";
 string ClassName="MatrixFactorization";
 string ExtraUsing = null;
-string Trainer = @"MatrixFactorization(nameof(MatrixElement.Value), nameof(MatrixElement.MatrixColumnIndex),
-                   nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
+string Trainer = @"
+                MatrixFactorization(nameof(MatrixElement.Value),
+                nameof(MatrixElement.MatrixColumnIndex),
+                nameof(MatrixElement.MatrixRowIndex), 10, 0.2, 1)";
 string TrainerOptions = null;
 
 string ExpectedOutputPerInstance= @"// Expected output:
@@ -23,4 +28,4 @@ string ExpectedOutput = @"// Expected output:
             //   Mean Squared Error: 0.79
             //   Root Mean Squared Error: 0.89
             //   RSquared: 0.61 (closer to 1 is better. The worest case is 0)";
-#>
+#>

docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/Recommendation/MatrixFactorizationTemplate.ttinclude

@@ -16,15 +16,17 @@ namespace Samples.Dynamic.Trainers.Recommendation
 <# } #>
         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 = GenerateMatrix();
 
-            // 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 (TrainerOptions == null) { #>
@@ -35,7 +37,8 @@ namespace Samples.Dynamic.Trainers.Recommendation
             var options = new <#=TrainerOptions#>;
 
             // Define the trainer.
-            var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(options);
+            var pipeline = mlContext.Recommendation().Trainers.<#=Trainer#>(
+                options);
 <# } #>
 
             // Train the model.
@@ -45,26 +48,37 @@ namespace Samples.Dynamic.Trainers.Recommendation
             var transformedData = model.Transform(trainingData);
 
             // Convert IDataView object to a list.
-            var predictions = mlContext.Data.CreateEnumerable<MatrixElement>(transformedData, reuseRowObject: false).Take(5).ToList();
+            var predictions = mlContext.Data
+                .CreateEnumerable<MatrixElement>(transformedData,
+                reuseRowObject: false).Take(5).ToList();
 
-            // Look at 5 predictions for the Label, side by side with the actual Label for comparison.
+            // Look at 5 predictions for the Label, side by side with the actual
+            // Label for comparison.
             foreach (var p in predictions)
-                Console.WriteLine($"Actual value: {p.Value:F3}, Predicted score: {p.Score:F3}");
+                Console.WriteLine($"Actual value: {p.Value:F3}," +
+                    $"Predicted score: {p.Score:F3}");
 
             <#=ExpectedOutputPerInstance#>
 
             // Evaluate the overall metrics
-            var metrics = mlContext.Regression.Evaluate(transformedData, labelColumnName: nameof(MatrixElement.Value), scoreColumnName: nameof(MatrixElement.Score));
+            var metrics = mlContext.Regression.Evaluate(transformedData,
+                labelColumnName: nameof(MatrixElement.Value),
+                scoreColumnName: nameof(MatrixElement.Score));
+
             PrintMetrics(metrics);
 
             <#=ExpectedOutput#>
         }
 
-        // The following variables are used to define the shape of the example matrix. Its shape is MatrixRowCount-by-MatrixColumnCount.
-        // Because in ML.NET key type's minimal value is zero, the first row index is always zero in C# data structure (e.g., MatrixColumnIndex=0
-        // and MatrixRowIndex=0 in MatrixElement below specifies the value at the upper-left corner in the training matrix). If user's row index
-        // starts with 1, their row index 1 would be mapped to the 2nd row in matrix factorization module and their first row may contain no values.
-        // This behavior is also true to column index.
+        // The following variables are used to define the shape of the example
+        // matrix. Its shape is MatrixRowCount-by-MatrixColumnCount. Because in 
+        // ML.NET key type's minimal value is zero, the first row index is always
+        // zero in C# data structure (e.g., MatrixColumnIndex=0 and MatrixRowIndex=0
+        // in MatrixElement below specifies the value at the upper-left corner in
+        // the training matrix). If user's row index starts with 1, their row index
+        // 1 would be mapped to the 2nd row in matrix factorization module and their
+        // first row may contain no values. This behavior is also true to column
+        // index.
         private const uint MatrixColumnCount = 60;
         private const uint MatrixRowCount = 100;
 
@@ -74,32 +88,40 @@ namespace Samples.Dynamic.Trainers.Recommendation
             var dataMatrix = new List<MatrixElement>();
             for (uint i = 0; i < MatrixColumnCount; ++i)
                 for (uint j = 0; j < MatrixRowCount; ++j)
-                    dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i, MatrixRowIndex = j, Value = (i + j) % 5 });
+                    dataMatrix.Add(new MatrixElement() { MatrixColumnIndex = i,
+                        MatrixRowIndex = j, Value = (i + j) % 5 });
+
             return dataMatrix;
         }
 
-        // A class used to define a matrix element and capture its prediction result.
+        // A class used to define a matrix element and capture its prediction
+        // result.
         private class MatrixElement
         {
-            // Matrix column index. Its allowed range is from 0 to MatrixColumnCount - 1.
+            // Matrix column index. Its allowed range is from 0 to
+            // MatrixColumnCount - 1.
             [KeyType(MatrixColumnCount)]
             public uint MatrixColumnIndex { get; set; }
             // Matrix row index. Its allowed range is from 0 to MatrixRowCount - 1.
             [KeyType(MatrixRowCount)]
             public uint MatrixRowIndex { get; set; }
-            // The actual value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
+            // The actual value at the MatrixColumnIndex-th column and the
+            // MatrixRowIndex-th row.
             public float Value { get; set; }
-            // The predicted value at the MatrixColumnIndex-th column and the MatrixRowIndex-th row.
+            // The predicted value at the MatrixColumnIndex-th column and the
+            // MatrixRowIndex-th row.
             public float Score { get; set; }
         }
 
         // Print some evaluation metrics to regression problems.
         private static void PrintMetrics(RegressionMetrics metrics)
         {
-            Console.WriteLine($"Mean Absolute Error: {metrics.MeanAbsoluteError:F2}");
-            Console.WriteLine($"Mean Squared Error: {metrics.MeanSquaredError:F2}");
-            Console.WriteLine($"Root Mean Squared Error: {metrics.RootMeanSquaredError:F2}");
-            Console.WriteLine($"RSquared: {metrics.RSquared:F2}");
+            Console.WriteLine("Mean Absolute Error: " + metrics.MeanAbsoluteError);
+            Console.WriteLine("Mean Squared Error: " + metrics.MeanSquaredError);
+            Console.WriteLine("Root Mean Squared Error: " +
+                metrics.RootMeanSquaredError);
+
+            Console.WriteLine("RSquared: " + metrics.RSquared);
         }
     }
 }