Reformatting Test, Projection and TimeSeries of Transform to Width 85 (dotnet#3947)

* transform/text and projection formatted to 85 char

* transforms/timeseries formatted to 85 char

* minor tab and spacing fixes

Author: Rayan-Krishnan

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhiten.cs

@@ -9,11 +9,12 @@ namespace Samples.Dynamic
     public sealed class VectorWhiten
     {
 
-        /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
+        /// This example requires installation of additional nuget package 
+        /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var ml = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
@@ -32,20 +33,29 @@ public static void Example()
             // 6   7   8   9   0   1   2   3   4   5
 
             // A small printing utility.
-            Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
+            Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
+                column) =>
             {
-                Console.WriteLine($"{colName} column obtained post-transformation.");
+                Console.WriteLine($"{colName} column obtained " +
+                    $"post-transformation.");
+
                 foreach (var row in column)
-                    Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
+                    Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
+                        x.ToString("f3")))+" ");
             };
 
             // A pipeline to project Features column into white noise vector.
-            var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features),
-                kind: Microsoft.ML.Transforms.WhiteningKind.ZeroPhaseComponentAnalysis);
+            var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
+                SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
+                .WhiteningKind.ZeroPhaseComponentAnalysis);
+
             // The transformed (projected) data.
-            var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
+            var transformedData = whiteningPipeline.Fit(trainData).Transform(
+                trainData);
+
             // Getting the data of the newly created column, so we can preview it.
-            var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
+            var whitening = transformedData.GetColumn<VBuffer<float>>(
+                transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 
             printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);
 
@@ -68,11 +78,16 @@ private class SampleVectorOfNumbersData
         /// <summary>
         /// Returns a few rows of the infertility dataset.
         /// </summary>
-        private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
+        private static IEnumerable<SampleVectorOfNumbersData>
+            GetVectorOfNumbersData()
         {
             var data = new List<SampleVectorOfNumbersData>();
-            data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
-            data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
+            data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
+                1, 2, 3, 4, 5, 6, 7, 8, 9 } });
+
+            data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
+                2, 3, 4, 5, 6, 7, 8, 9, 0 } });
+
             data.Add(new SampleVectorOfNumbersData
             {
                 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Projection/VectorWhitenWithOptions.cs

@@ -8,11 +8,12 @@ namespace Samples.Dynamic
 {
     public sealed class VectorWhitenWithOptions
     {
-        /// This example requires installation of additional nuget package <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
+        /// This example requires installation of additional nuget package
+        /// <a href="https://www.nuget.org/packages/Microsoft.ML.Mkl.Components/">Microsoft.ML.Mkl.Components</a>.
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var ml = new MLContext();
 
             // Get a small dataset as an IEnumerable and convert it to an IDataView.
@@ -31,20 +32,30 @@ public static void Example()
             // 6   7   8   9   0   1   2   3   4   5
 
             // A small printing utility.
-            Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName, column) =>
+            Action<string, IEnumerable<VBuffer<float>>> printHelper = (colName,
+                column) =>
             {
-                Console.WriteLine($"{colName} column obtained post-transformation.");
+                Console.WriteLine($"{colName} column obtained" +
+                    $"post-transformation.");
+
                 foreach (var row in column)
-                    Console.WriteLine($"{string.Join(" ", row.DenseValues().Select(x => x.ToString("f3")))} ");
+                    Console.WriteLine(string.Join(" ", row.DenseValues().Select(x =>
+                        x.ToString("f3")))+" ");
             };
 
 
             // A pipeline to project Features column into white noise vector.
-            var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms.WhiteningKind.PrincipalComponentAnalysis, rank: 4);
+            var whiteningPipeline = ml.Transforms.VectorWhiten(nameof(
+                SampleVectorOfNumbersData.Features), kind: Microsoft.ML.Transforms
+                .WhiteningKind.PrincipalComponentAnalysis, rank: 4);
+
             // The transformed (projected) data.
-            var transformedData = whiteningPipeline.Fit(trainData).Transform(trainData);
+            var transformedData = whiteningPipeline.Fit(trainData).Transform(
+                trainData);
+
             // Getting the data of the newly created column, so we can preview it.
-            var whitening = transformedData.GetColumn<VBuffer<float>>(transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
+            var whitening = transformedData.GetColumn<VBuffer<float>>(
+                transformedData.Schema[nameof(SampleVectorOfNumbersData.Features)]);
 
             printHelper(nameof(SampleVectorOfNumbersData.Features), whitening);
 
@@ -66,11 +77,16 @@ private class SampleVectorOfNumbersData
         /// <summary>
         /// Returns a few rows of the infertility dataset.
         /// </summary>
-        private static IEnumerable<SampleVectorOfNumbersData> GetVectorOfNumbersData()
+        private static IEnumerable<SampleVectorOfNumbersData> 
+            GetVectorOfNumbersData()
         {
             var data = new List<SampleVectorOfNumbersData>();
-            data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 } });
-            data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1, 2, 3, 4, 5, 6, 7, 8, 9, 0 } });
+            data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 0,
+                1, 2, 3, 4, 5, 6, 7, 8, 9 } });
+
+            data.Add(new SampleVectorOfNumbersData { Features = new float[10] { 1,
+                2, 3, 4, 5, 6, 7, 8, 9, 0 } });
+
             data.Add(new SampleVectorOfNumbersData
             {
                 Features = new float[10] { 2, 3, 4, 5, 6, 7, 8, 9, 0, 1 }

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyCustomWordEmbedding.cs

@@ -9,12 +9,13 @@ public static class ApplyCustomWordEmbedding
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
-            // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
-            // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
+            // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
+            // not require training data as the estimator ('WordEmbeddingEstimator')
+            // created by 'ApplyWordEmbedding' API is not a trainable estimator.
             // The empty list is only needed to pass input schema to the pipeline.
             var emptySamples = new List<TextData>();
 
@@ -33,25 +34,33 @@ public static void Example()
                 file.WriteLine("buy 0 0 20");
             }
 
-            // A pipeline for converting text into a 9-dimension word embedding vector using the custom word embedding model.
-            // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
-            // Tokens in 'custommodel.txt' model are represented as 3-dimension vector.
-            // Therefore, the output is of 9-dimension [min, avg, max].
+            // A pipeline for converting text into a 9-dimension word embedding
+            // vector using the custom word embedding model. The 
+            // 'ApplyWordEmbedding' computes the minimum, average and maximum values
+            // for each token's embedding vector. Tokens in 'custommodel.txt' model
+            // are represented as 3-dimension vector. Therefore, the output is of
+            // 9 -dimension [min, avg, max].
             //
             // The 'ApplyWordEmbedding' API requires vector of text as input.
-            // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
+            // The pipeline first normalizes and tokenizes text then applies word
+            // embedding transformation.
             var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
-                .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
-                .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", pathToCustomModel, "Tokens"));
+                .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
+                    "Text"))
+                .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
+                    pathToCustomModel, "Tokens"));
 
             // Fit to data.
             var textTransformer = textPipeline.Fit(emptyDataView);
 
-            // Create the prediction engine to get the embedding vector from the input text/string.
-            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
+            // Create the prediction engine to get the embedding vector from the
+            // input text/string.
+            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
+                TransformedTextData>(textTransformer);
 
             // Call the prediction API to convert the text into embedding vector.
-            var data = new TextData() { Text = "This is a great product. I would like to buy it again."  };
+            var data = new TextData() { Text = "This is a great product. I would " +
+                "like to buy it again."  };
             var prediction = predictionEngine.Predict(data);
 
             // Print the length of the embedding vector.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/Text/ApplyWordEmbedding.cs

@@ -9,38 +9,48 @@ public static class ApplyWordEmbedding
     {
         public static void Example()
         {
-            // Create a new ML context, for ML.NET operations. It can be used for exception tracking and logging, 
-            // as well as the source of randomness.
+            // Create a new ML context, for ML.NET operations. It can be used for
+            // exception tracking and logging, as well as the source of randomness.
             var mlContext = new MLContext();
 
-            // Create an empty list as the dataset. The 'ApplyWordEmbedding' does not require training data as
-            // the estimator ('WordEmbeddingEstimator') created by 'ApplyWordEmbedding' API is not a trainable estimator.
+            // Create an empty list as the dataset. The 'ApplyWordEmbedding' does
+            // not require training data as the estimator ('WordEmbeddingEstimator')
+            // created by 'ApplyWordEmbedding' API is not a trainable estimator.
             // The empty list is only needed to pass input schema to the pipeline.
             var emptySamples = new List<TextData>();
 
             // Convert sample list to an empty IDataView.
             var emptyDataView = mlContext.Data.LoadFromEnumerable(emptySamples);
 
-            // A pipeline for converting text into a 150-dimension embedding vector using pretrained 'SentimentSpecificWordEmbedding' model.
-            // The 'ApplyWordEmbedding' computes the minimum, average and maximum values for each token's embedding vector.
-            // Tokens in 'SentimentSpecificWordEmbedding' model are represented as 50-dimension vector.
-            // Therefore, the output is of 150-dimension [min, avg, max].
+            // A pipeline for converting text into a 150-dimension embedding vector
+            // using pretrained 'SentimentSpecificWordEmbedding' model. The
+            // 'ApplyWordEmbedding' computes the minimum, average and maximum values
+            // for each token's embedding vector. Tokens in 
+            // 'SentimentSpecificWordEmbedding' model are represented as
+            // 50 -dimension vector. Therefore, the output is of 150-dimension [min,
+            // avg, max].
             //
             // The 'ApplyWordEmbedding' API requires vector of text as input.
-            // The pipeline first normalizes and tokenizes text then applies word embedding transformation.
+            // The pipeline first normalizes and tokenizes text then applies word
+            // embedding transformation.
             var textPipeline = mlContext.Transforms.Text.NormalizeText("Text")
-                .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens", "Text"))
-                .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features", "Tokens",
-                    WordEmbeddingEstimator.PretrainedModelKind.SentimentSpecificWordEmbedding));
+                .Append(mlContext.Transforms.Text.TokenizeIntoWords("Tokens",
+                    "Text"))
+                .Append(mlContext.Transforms.Text.ApplyWordEmbedding("Features",
+                    "Tokens", WordEmbeddingEstimator.PretrainedModelKind
+                    .SentimentSpecificWordEmbedding));
 
             // Fit to data.
             var textTransformer = textPipeline.Fit(emptyDataView);
 
-            // Create the prediction engine to get the embedding vector from the input text/string.
-            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData, TransformedTextData>(textTransformer);
+            // Create the prediction engine to get the embedding vector from the
+            // input text/string.
+            var predictionEngine = mlContext.Model.CreatePredictionEngine<TextData,
+                TransformedTextData>(textTransformer);
 
             // Call the prediction API to convert the text into embedding vector.
-            var data = new TextData() { Text = "This is a great product. I would like to buy it again."  };
+            var data = new TextData() { Text = "This is a great product. I would " +
+                "like to buy it again."  };
             var prediction = predictionEngine.Predict(data);
 
             // Print the length of the embedding vector.