Reformatting Featurization of Transform and Misc files in Transform to Width 85 (dotnet#3950)

* transform/treefeaturization formatted to 85 char

* transform files not in folder formatted to 35 char

Author: Rayan-Krishnan

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyONNXModelWithInMemoryImages.cs

@@ -15,44 +15,61 @@ public static void Example()
             // Download the squeeznet image model from ONNX model zoo, version 1.2
             // https://github.com/onnx/models/tree/master/squeezenet or use
             // Microsoft.ML.Onnx.TestModels nuget.
-            // It's a multiclass classifier. It consumes an input "data_0" and produces
-            // an output "softmaxout_1".
+            // It's a multiclass classifier. It consumes an input "data_0" and
+            // produces an output "softmaxout_1".
             var modelPath = @"squeezenet\00000001\model.onnx";
 
             // Create ML pipeline to score the data using OnnxScoringEstimator
             var mlContext = new MLContext();
 
-            // Create in-memory data points. Its Image/Scores field is the input/output of the used ONNX model.
+            // Create in-memory data points. Its Image/Scores field is the
+            // input /output of the used ONNX model.
             var dataPoints = new ImageDataPoint[]
             {
                 new ImageDataPoint(Color.Red),
                 new ImageDataPoint(Color.Green)
             };
 
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var dataView = mlContext.Data.LoadFromEnumerable(dataPoints);
 
-            // Create a ML.NET pipeline which contains two steps. First, ExtractPixle is used to convert the 224x224 image to a 3x224x224 float tensor.
-            // Then the float tensor is fed into a ONNX model with an input called "data_0" and an output called "softmaxout_1". Note that "data_0" and
-            // "softmaxout_1" are model input and output names stored in the used ONNX model file. Users may need to inspect their own models to
-            // get the right input and output column names.
-            var pipeline = mlContext.Transforms.ExtractPixels("data_0", "Image")                   // Map column "Image" to column "data_0"
-                .Append(mlContext.Transforms.ApplyOnnxModel("softmaxout_1", "data_0", modelPath)); // Map column "data_0" to column "softmaxout_1"
+            // Create a ML.NET pipeline which contains two steps. First,
+            // ExtractPixle is used to convert the 224x224 image to a 3x224x224
+            // float tensor. Then the float tensor is fed into a ONNX model with an
+            // input called "data_0" and an output called "softmaxout_1". Note that
+            // "data_0" and "softmaxout_1" are model input and output names stored
+            // in the used ONNX model file. Users may need to inspect their own
+            // models to get the right input and output column names.
+            // Map column "Image" to column "data_0"
+            // Map column "data_0" to column "softmaxout_1"
+            var pipeline = mlContext.Transforms.ExtractPixels("data_0", "Image")
+                .Append(mlContext.Transforms.ApplyOnnxModel("softmaxout_1",
+                "data_0", modelPath)); 
+
             var model = pipeline.Fit(dataView);
             var onnx = model.Transform(dataView);
 
-            // Convert IDataView back to IEnumerable<ImageDataPoint> so that user can inspect the output, column "softmaxout_1", of the ONNX transform.
-            // Note that Column "softmaxout_1" would be stored in ImageDataPont.Scores because the added attributed [ColumnName("softmaxout_1")]
-            // tells that ImageDataPont.Scores is equivalent to column "softmaxout_1".
-            var transformedDataPoints = mlContext.Data.CreateEnumerable<ImageDataPoint>(onnx, false).ToList();
+            // Convert IDataView back to IEnumerable<ImageDataPoint> so that user
+            // can inspect the output, column "softmaxout_1", of the ONNX transform.
+            // Note that Column "softmaxout_1" would be stored in ImageDataPont
+            //.Scores because the added attributed [ColumnName("softmaxout_1")]
+            // tells that ImageDataPont.Scores is equivalent to column
+            // "softmaxout_1".
+            var transformedDataPoints = mlContext.Data.CreateEnumerable<
+                ImageDataPoint>(onnx, false).ToList();
 
-            // The scores are probabilities of all possible classes, so they should all be positive. 
+            // The scores are probabilities of all possible classes, so they should
+            // all be positive. 
             foreach (var dataPoint in transformedDataPoints)
             {
                 var firstClassProb = dataPoint.Scores.First();
                 var lastClassProb = dataPoint.Scores.Last();
-                Console.WriteLine($"The probability of being the first class is {firstClassProb * 100}%.");
-                Console.WriteLine($"The probability of being the last class is {lastClassProb * 100}%.");
+                Console.WriteLine("The probability of being the first class is " + 
+                    (firstClassProb * 100) + "%.");
+
+                Console.WriteLine($"The probability of being the last class is " +
+                    (lastClassProb * 100) + "%.");
             }
 
             // Expected output:
@@ -62,7 +79,8 @@ public static void Example()
             //  The probability of being the last class is 0.394428%.
         }
 
-        // This class is used in Example() to describe data points which will be consumed by ML.NET pipeline.
+        // This class is used in Example() to describe data points which will be
+        // consumed by ML.NET pipeline.
         private class ImageDataPoint
         {
             // Height of Image.
@@ -75,9 +93,9 @@ private class ImageDataPoint
             [ImageType(height, width)]
             public Bitmap Image { get; set; }
 
-            // Expected output of ONNX model. It contains probabilities of all classes.
-            // Note that the ColumnName below should match the output name in the used
-            // ONNX model file.
+            // Expected output of ONNX model. It contains probabilities of all
+            // classes. Note that the ColumnName below should match the output name
+            // in the used ONNX model file.
             [ColumnName("softmaxout_1")]
             public float[] Scores { get; set; }
 

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApplyOnnxModel.cs

@@ -19,22 +19,25 @@ public static void Example()
 
             // Generate sample test data.
             var samples = GetTensorData();
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
             // Create the pipeline to score using provided onnx model.
             var pipeline = mlContext.Transforms.ApplyOnnxModel(modelPath);
             // Fit the pipeline and get the transformed values
             var transformedValues = pipeline.Fit(data).Transform(data);
             // Retrieve model scores into Prediction class
-            var predictions = mlContext.Data.CreateEnumerable<Prediction>(transformedValues, reuseRowObject: false);
+            var predictions = mlContext.Data.CreateEnumerable<Prediction>(
+                transformedValues, reuseRowObject: false);
 
             // Iterate rows
             foreach (var prediction in predictions)
             {
                 int numClasses = 0;
                 foreach (var classScore in prediction.softmaxout_1.Take(3))
                 {
-                    Console.WriteLine($"Class #{numClasses++} score = {classScore}");
+                    Console.WriteLine("Class #" + numClasses++ + " score = " +
+                        classScore);
                 }
                 Console.WriteLine(new string('-', 10));
             }
@@ -65,9 +68,14 @@ public class TensorData
         public static TensorData[] GetTensorData()
         {
             // This can be any numerical data. Assume image pixel values.
-            var image1 = Enumerable.Range(0, inputSize).Select(x => (float)x / inputSize).ToArray();
-            var image2 = Enumerable.Range(0, inputSize).Select(x => (float)(x + 10000) / inputSize).ToArray();
-            return new TensorData[] { new TensorData() { data_0 = image1 }, new TensorData() { data_0 = image2 } };
+            var image1 = Enumerable.Range(0, inputSize).Select(x => (float)x /
+                inputSize).ToArray();
+
+            var image2 = Enumerable.Range(0, inputSize).Select(x => (float)(x +
+                10000) / inputSize).ToArray();
+
+            return new TensorData[] { new TensorData() { data_0 = image1 }, new
+                TensorData() { data_0 = image2 } };
         }
 
         // Class to contain the output values from the transformation.

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/ApproximatedKernelMap.cs

@@ -9,11 +9,12 @@ namespace Samples.Dynamic
 {
     public static class ApproximatedKernelMap
     {
-        // Transform feature vector to another non-linear space. See https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf.
+        // Transform feature vector to another non-linear space. See
+        // https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf.
         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();
             var samples = new List<DataPoint>()
             {
@@ -22,19 +23,26 @@ public static void Example()
                 new DataPoint(){ Features = new float[7] {-1, 1, 0,-1,-1, 0,-1} },
                 new DataPoint(){ Features = new float[7] { 0,-1, 0, 1, 0,-1,-1} }
             };
-            // Convert training data to IDataView, the general data type used in ML.NET.
+            // Convert training data to IDataView, the general data type used in
+            // ML.NET.
             var data = mlContext.Data.LoadFromEnumerable(samples);
-            // ApproximatedKernel map takes data and maps it's to a random low-dimensional space.
-            var approximation = mlContext.Transforms.ApproximatedKernelMap("Features", rank: 4, generator: new GaussianKernel(gamma: 0.7f), seed: 1);
+            // ApproximatedKernel map takes data and maps it's to a random
+            // low -dimensional space.
+            var approximation = mlContext.Transforms.ApproximatedKernelMap(
+                "Features", rank: 4, generator: new GaussianKernel(gamma: 0.7f),
+                seed: 1);
 
-            // Now we can transform the data and look at the output to confirm the behavior of the estimator.
-            // This operation doesn't actually evaluate data until we read the data below.
+            // Now we can transform the data and look at the output to confirm the
+            // behavior of the estimator. This operation doesn't actually evaluate
+            // data until we read the data below.
             var tansformer = approximation.Fit(data);
             var transformedData = tansformer.Transform(data);
 
             var column = transformedData.GetColumn<float[]>("Features").ToArray();
             foreach (var row in column)
-                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString("f4"))));
+                Console.WriteLine(string.Join(", ", row.Select(x => x.ToString(
+                    "f4"))));
+
             // Expected output:
             // -0.0119, 0.5867, 0.4942,  0.7041
             //  0.4720, 0.5639, 0.4346,  0.2671

docs/samples/Microsoft.ML.Samples/Dynamic/Transforms/CalculateFeatureContribution.cs

@@ -9,7 +9,8 @@ public static class CalculateFeatureContribution
     {
         public static void Example()
         {
-            // Create a new context for ML.NET operations. It can be used for exception tracking and logging, 
+            // 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.
             var mlContext = new MLContext(seed: 1);
 
@@ -19,7 +20,8 @@ public static void Example()
             // Convert training data to IDataView.
             var data = mlContext.Data.LoadFromEnumerable(samples);
 
-            // Create a pipeline to concatenate the features into a feature vector and normalize it.
+            // Create a pipeline to concatenate the features into a feature vector
+            // and normalize it.
             var transformPipeline = mlContext.Transforms.Concatenate("Features",
                     new string[] { nameof(Data.Feature1), nameof(Data.Feature2) })
                 .Append(mlContext.Transforms.NormalizeMeanVariance("Features"));
@@ -37,28 +39,45 @@ public static void Example()
             var linearModel = linearTrainer.Fit(transformedData);
             // Print the model parameters.
             Console.WriteLine($"Linear Model Parameters");
-            Console.WriteLine($"Bias: {linearModel.Model.Bias} Feature1: {linearModel.Model.Weights[0]} Feature2: {linearModel.Model.Weights[1]}");
+            Console.WriteLine("Bias: " + linearModel.Model.Bias+ " Feature1: " +
+                linearModel.Model.Weights[0] + " Feature2: " +linearModel.Model
+                .Weights[1]);
 
-            // Define a feature contribution calculator for all the features, and don't normalize the contributions.
-            // These are "trivial estimators" and they don't need to fit to the data, so we can feed a subset.
-            var simpleScoredDataset = linearModel.Transform(mlContext.Data.TakeRows(transformedData, 1));
-            var linearFeatureContributionCalculator = mlContext.Transforms.CalculateFeatureContribution(linearModel, normalize: false).Fit(simpleScoredDataset);
+            // Define a feature contribution calculator for all the features, and
+            // don't normalize the contributions.These are "trivial estimators" and
+            // they don't need to fit to the data, so we can feed a subset.
+            var simpleScoredDataset = linearModel.Transform(mlContext.Data
+                .TakeRows(transformedData, 1));
+
+            var linearFeatureContributionCalculator = mlContext.Transforms
+                .CalculateFeatureContribution(linearModel, normalize: false).Fit(
+                simpleScoredDataset);
 
             // Create a transformer chain to describe the entire pipeline.
-            var scoringPipeline = transformer.Append(linearModel).Append(linearFeatureContributionCalculator);
+            var scoringPipeline = transformer.Append(linearModel).Append(
+                linearFeatureContributionCalculator);
 
-            // Create the prediction engine to get the features extracted from the text.
-            var predictionEngine = mlContext.Model.CreatePredictionEngine<Data, ScoredData>(scoringPipeline);
+            // Create the prediction engine to get the features extracted from the
+            // text.
+            var predictionEngine = mlContext.Model.CreatePredictionEngine<Data,
+                ScoredData>(scoringPipeline);
 
             // Convert the text into numeric features.
             var prediction = predictionEngine.Predict(samples.First());
 
             // Write out the prediction, with contributions.
-            // Note that for the linear model, the feature contributions for a feature in an example is the feature-weight*feature-value.
+            // Note that for the linear model, the feature contributions for a
+            // feature in an example is the feature-weight*feature-value.
             // The total prediction is thus the bias plus the feature contributions.
-            Console.WriteLine($"Label: {prediction.Label} Prediction: {prediction.Score}");
-            Console.WriteLine($"Feature1: {prediction.Features[0]} Feature2: {prediction.Features[1]}");
-            Console.WriteLine($"Feature Contributions: {prediction.FeatureContributions[0]} {prediction.FeatureContributions[1]}");
+            Console.WriteLine("Label: " + prediction.Label + " Prediction: " +
+                prediction.Score);
+
+            Console.WriteLine("Feature1: " + prediction.Features[0] + 
+                " Feature2: " + prediction.Features[1]);
+
+            Console.WriteLine("Feature Contributions: " + prediction
+                .FeatureContributions[0] + " " + prediction
+                .FeatureContributions[1]);
 
             // Expected output:
             //  Linear Model Parameters
@@ -107,7 +126,9 @@ private static IEnumerable<Data> GenerateData(int nExamples = 10000,
                 };
 
                 // Create a noisy label.
-                data.Label = (float)(bias + weight1 * data.Feature1 + weight2 * data.Feature2 + rng.NextDouble() - 0.5);
+                data.Label = (float)(bias + weight1 * data.Feature1 + weight2 *
+                    data.Feature2 + rng.NextDouble() - 0.5);
+
                 yield return data;
             }
         }