Follow coding conventions (dotnet#672)

Author: Youssef1313

samples/csharp/common/Compress.cs

@@ -17,11 +17,11 @@ public static void ExtractGZip(string gzipFileName, string targetDir)
             // Use a 4K buffer. Any larger is a waste.    
             byte[] dataBuffer = new byte[4096];
 
-            using (System.IO.Stream fs = new FileStream(gzipFileName, FileMode.Open, FileAccess.Read))
+            using (Stream fs = new FileStream(gzipFileName, FileMode.Open, FileAccess.Read))
             {
-                using (GZipInputStream gzipStream = new GZipInputStream(fs))
+                using (var gzipStream = new GZipInputStream(fs))
                 {
-                    // Change this to your needs
+                    // Change this to your needs.
                     string fnOut = Path.Combine(targetDir, Path.GetFileNameWithoutExtension(gzipFileName));
 
                     using (FileStream fsOut = File.Create(fnOut))
@@ -37,7 +37,7 @@ public static void UnZip(String gzArchiveName, String destFolder)
             var flag = gzArchiveName.Split(Path.DirectorySeparatorChar).Last().Split('.').First() + ".bin";
             if (File.Exists(Path.Combine(destFolder, flag))) return;
 
-            Console.WriteLine($"Extracting.");
+            Console.WriteLine("Extracting.");
             var task = Task.Run(() =>
             {
                 ZipFile.ExtractToDirectory(gzArchiveName, destFolder);
@@ -59,7 +59,7 @@ public static void ExtractTGZ(String gzArchiveName, String destFolder)
             var flag = gzArchiveName.Split(Path.DirectorySeparatorChar).Last().Split('.').First() + ".bin";
             if (File.Exists(Path.Combine(destFolder, flag))) return;
 
-            Console.WriteLine($"Extracting.");
+            Console.WriteLine("Extracting.");
             var task = Task.Run(() =>
             {
                 using (var inStream = File.OpenRead(gzArchiveName))

samples/csharp/end-to-end-apps/AnomalyDetection-Sales/SpikeDetectionE2EApp/SpikeDetection.ModelTrainer/Program.cs

@@ -22,21 +22,21 @@ internal static class Program
         private static MLContext mlContext;
         static void Main()
         {
-            // Create MLContext to be shared across the model creation workflow objects 
+            // Create MLContext to be shared across the model creation workflow objects.
             mlContext = new MLContext();
 
-            // Assign the Number of records in dataset file to constant variable
+            // Assign the Number of records in dataset file to constant variable.
             const int size = 36;
 
-            //Load the data into IDataView.
-            //This dataset is used for detecting spikes or changes not for training.
+            // Load the data into IDataView.
+            // This dataset is used for detecting spikes or changes not for training.
             IDataView dataView = mlContext.Data.LoadFromTextFile<ProductSalesData>(path: DatasetPath, hasHeader: true, separatorChar: ',');
 
-            // Detect temporary changes (spikes) in the pattern
+            // Detect temporary changes (spikes) in the pattern.
             ITransformer trainedSpikeModel = DetectSpike(size, dataView);
 
-            // Detect persistent change in the pattern
-            ITransformer trainedChangePointModel =  DetectChangepoint(size, dataView);
+            // Detect persistent change in the pattern.
+            ITransformer trainedChangePointModel = DetectChangepoint(size, dataView);
 
             SaveModel(mlContext, trainedSpikeModel, SpikeModelPath, dataView);
             SaveModel(mlContext, trainedChangePointModel, ChangePointModelPath, dataView);
@@ -49,17 +49,17 @@ private static ITransformer DetectSpike(int size, IDataView dataView)
         {
             Console.WriteLine("===============Detect temporary changes in pattern===============");
 
-            //STEP 1: Create Esimator   
+            // STEP 1: Create Esimator.
             var estimator = mlContext.Transforms.DetectIidSpike(outputColumnName: nameof(ProductSalesPrediction.Prediction), inputColumnName: nameof(ProductSalesData.numSales), confidence: 95, pvalueHistoryLength: size / 4);
 
-            //STEP 2:The Transformed Model.
-            //In IID Spike detection, we don't need to do training, we just need to do transformation. 
-            //As you are not training the model, there is no need to load IDataView with real data, you just need schema of data.
-            //So create empty data view and pass to Fit() method. 
+            // STEP 2:The Transformed Model.
+            // In IID Spike detection, we don't need to do training, we just need to do transformation. 
+            // As you are not training the model, there is no need to load IDataView with real data, you just need schema of data.
+            // So create empty data view and pass to Fit() method. 
             ITransformer tansformedModel = estimator.Fit(CreateEmptyDataView());
 
-            //STEP 3: Use/test model
-            //Apply data transformation to create predictions.
+            // STEP 3: Use/test model.
+            // Apply data transformation to create predictions.
             IDataView transformedData = tansformedModel.Transform(dataView);
             var predictions = mlContext.Data.CreateEnumerable<ProductSalesPrediction>(transformedData, reuseRowObject: false);
 
@@ -82,17 +82,17 @@ private static ITransformer DetectChangepoint(int size, IDataView dataView)
         {
             Console.WriteLine("===============Detect Persistent changes in pattern===============");
 
-            //STEP 1: Setup transformations using DetectIidChangePoint
+            // STEP 1: Setup transformations using DetectIidChangePoint.
             var estimator = mlContext.Transforms.DetectIidChangePoint(outputColumnName: nameof(ProductSalesPrediction.Prediction), inputColumnName: nameof(ProductSalesData.numSales), confidence: 95, changeHistoryLength: size / 4);
 
-            //STEP 2:The Transformed Model.
-            //In IID Change point detection, we don't need need to do training, we just need to do transformation. 
-            //As you are not training the model, there is no need to load IDataView with real data, you just need schema of data.
-            //So create empty data view and pass to Fit() method.  
+            // STEP 2:The Transformed Model.
+            // In IID Change point detection, we don't need need to do training, we just need to do transformation. 
+            // As you are not training the model, there is no need to load IDataView with real data, you just need schema of data.
+            // So create empty data view and pass to Fit() method.  
             ITransformer tansformedModel = estimator.Fit(CreateEmptyDataView());
 
-            //STEP 3: Use/test model
-            //Apply data transformation to create predictions.
+            // STEP 3: Use/test model.
+            // Apply data transformation to create predictions.
             IDataView transformedData = tansformedModel.Transform(dataView);
             var predictions = mlContext.Data.CreateEnumerable<ProductSalesPrediction>(transformedData, reuseRowObject: false);
 
@@ -119,12 +119,12 @@ private static void SaveModel(MLContext mlcontext, ITransformer trainedModel, st
             Console.WriteLine("=============== Saving model ===============");
             mlcontext.Model.Save(trainedModel,dataView.Schema, modelPath);
 
-            Console.WriteLine("The model is saved to {0}", modelPath);
+            Console.WriteLine($"The model is saved to {modelPath}");
         }
 
         public static string GetAbsolutePath(string relativePath)
         {
-            FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
+            var _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
             string assemblyFolderPath = _dataRoot.Directory.FullName;
 
             string fullPath = Path.Combine(assemblyFolderPath, relativePath);

samples/csharp/end-to-end-apps/AnomalyDetection-Sales/SpikeDetectionE2EApp/SpikeDetection.WinForms/Form1.cs

@@ -29,43 +29,43 @@ public Form1()
             InitializeComponent();
         }
 
-        // Find file button
+        // Find file button.
         private void button1_Click(object sender, EventArgs e)
         {
-            // Open File Explorer
+            // Open File Explorer.
             DialogResult result = openFileExplorer.ShowDialog();
 
-            // Set text in file path textbox to file path from file explorer
+            // Set text in file path textbox to file path from file explorer.
             if (result == DialogResult.OK)
             {
                 filePathTextbox.Text = openFileExplorer.FileName;
             }
         }
 
-        // Go button
+        // Go button.
         private void button2_Click(object sender, EventArgs e)
         {
-            // Set filepath from text from filepath textbox
+            // Set filepath from text from filepath textbox.
             filePath = filePathTextbox.Text;
 
-            // Checkk if file exists
+            // Check if file exists.
             if (File.Exists(filePath))
             {
                 dict = new Dictionary<int, Tuple<string, string>>();
 
                 if (filePath != "")
                 {
-                    // Reset text in anomaly textbox
+                    // Reset text in anomaly textbox.
                     anomalyText.Text = "";
 
-                    // Display preview of dataset and graph
+                    // Display preview of dataset and graph.
                     displayDataTableAndGraph();
 
-                    // Load a trained model to detect anomalies and then mark them on the graph
+                    // Load a trained model to detect anomalies and then mark them on the graph.
                     detectAnomalies();
 
                 }
-                // If file path textbox is empty, prompt user to input file path
+                // If file path textbox is empty, prompt user to input file path.
                 else
                 {
                     MessageBox.Show("Please input file path.");
@@ -97,7 +97,7 @@ private void displayDataTableAndGraph()
 
             foreach (string line in dataset.Skip(1))
             {
-                // Add next row of data
+                // Add next row of data.
                 dataCol = commaSeparatedRadio.Checked ? line.Split(',') : line.Split('\t');
                 dataTable.Rows.Add(dataCol);
 
@@ -107,17 +107,17 @@ private void displayDataTableAndGraph()
                 a++;
             }
 
-            // Set data view preview source
+            // Set data view preview source.
             dataGridView1.DataSource = dataTable;
 
-            // Update y axis min and max values
-            double yMax = Convert.ToDouble(dataTable.Compute("max([" + yAxis + "])", string.Empty));
-            double yMin = Convert.ToDouble(dataTable.Compute("min([" + yAxis + "])", string.Empty));
+            // Update y axis min and max values.
+            double yMax = Convert.ToDouble(dataTable.Compute($"max([{yAxis}])", string.Empty));
+            double yMin = Convert.ToDouble(dataTable.Compute($"min([{yAxis}])", string.Empty));
 
-            // Set graph source
+            // Set graph source.
             graph.DataSource = dataTable;
 
-            // Set graph options
+            // Set graph options.
             graph.Series["Series1"].ChartType = SeriesChartType.Line;
 
             graph.Series["Series1"].XValueMember = xAxis;
@@ -139,14 +139,14 @@ private void displayDataTableAndGraph()
 
         private void detectAnomalies()
         {
-            // Create MLContext to be shared across the model creation workflow objects 
+            // Create MLContext to be shared across the model creation workflow objects.
             var mlcontext = new MLContext();
 
             // STEP 1: Load the data into IDataView.
             IDataView dataView = mlcontext.Data.LoadFromTextFile<ProductSalesData>(path: filePath, hasHeader: true, separatorChar: commaSeparatedRadio.Checked ? ',' : '\t');
 
-            // Step 2: Load & use model
-            // Note -- The model is trained with the product-sales dataset in a separate console app (see AnomalyDetectionConsoleApp)            
+            // Step 2: Load & use model.
+            // Note -- The model is trained with the product-sales dataset in a separate console app (see AnomalyDetectionConsoleApp).
             if (spikeDet.Checked)
             {
                 if (File.Exists(spikeModelPath))
@@ -174,7 +174,7 @@ private void detectAnomalies()
 
         public static string GetAbsolutePath(string relativePath)
         {
-            FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
+            var _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
             string assemblyFolderPath = _dataRoot.Directory.FullName;
 
             string fullPath = Path.Combine(assemblyFolderPath, relativePath);
@@ -186,37 +186,37 @@ private void loadAndUseModel(MLContext mlcontext, IDataView dataView, String mod
         {
             ITransformer tansformedModel = mlcontext.Model.Load(modelPath, out var modelInputSchema);
 
-            // Step 3: Apply data transformation to create predictions
+            // Step 3: Apply data transformation to create predictions.
             IDataView transformedData = tansformedModel.Transform(dataView);
             var predictions = mlcontext.Data.CreateEnumerable<ProductSalesPrediction>(transformedData, reuseRowObject: false);
 
-            // Index key for dictionary (date, sales)
+            // Index key for dictionary (date, sales).
             int a = 0;
 
             foreach (var prediction in predictions)
             {
-                // Check if anomaly is predicted (indicated by an alert)
+                // Check if anomaly is predicted (indicated by an alert).
                 if (prediction.Prediction[0] == 1)
                 {
-                    // Get the date (year-month) where spike is detected
+                    // Get the date (year-month) where spike is detected.
                     var xAxisDate = dict[a].Item1;
-                    // Get the number of sales which was detected to be a spike
+                    // Get the number of sales which was detected to be a spike.
                     var yAxisSalesNum = dict[a].Item2;
 
                     // Add anomaly points to graph
-                    // and set point/marker options
+                    // and set point/marker options.
                     graph.Series["Series1"].Points[a].SetValueXY(a, yAxisSalesNum);
                     graph.Series["Series1"].Points[a].MarkerStyle = MarkerStyle.Star4;
                     graph.Series["Series1"].Points[a].MarkerSize = 10;
                     graph.Series["Series1"].Points[a].MarkerColor = color;
 
                     // Print out anomalies as text for user &
-                    // change color of text accordingly
+                    // change color of text accordingly.
                     string text = type + " detected in " + xAxisDate + ": " + yAxisSalesNum + "\n";
                     anomalyText.SelectionColor = color;
                     anomalyText.AppendText(text);
 
-                    // Change row color in table where anomalies occur
+                    // Change row color in table where anomalies occur.
                     DataGridViewRow row = dataGridView1.Rows[a];
                     row.DefaultCellStyle.BackColor = color;
                     row.DefaultCellStyle.ForeColor = Color.White;

samples/csharp/end-to-end-apps/DeepLearning_ImageClassification_TensorFlow/TensorFlowImageClassification/Controllers/ImageClassificationController.cs

@@ -25,13 +25,13 @@ public class ImageClassificationController : ControllerBase
 
         public ImageClassificationController(PredictionEnginePool<ImageInputData, ImageLabelPredictions> predictionEnginePool, IConfiguration configuration, ILogger<ImageClassificationController> logger) //When using DI/IoC
         {
-            // Get the ML Model Engine injected, for scoring
+            // Get the ML Model Engine injected, for scoring.
             _predictionEnginePool = predictionEnginePool;
 
             Configuration = configuration;
             _labelsFilePath = GetAbsolutePath(Configuration["MLModel:LabelsFilePath"]);
 
-            //Get other injected dependencies
+            // Get other injected dependencies.
             _logger = logger;
         }
 
@@ -44,37 +44,37 @@ public async Task<IActionResult> ClassifyImage(IFormFile imageFile)
             if (imageFile.Length == 0)
                 return BadRequest();
 
-            MemoryStream imageMemoryStream = new MemoryStream();
+            var imageMemoryStream = new MemoryStream();
             await imageFile.CopyToAsync(imageMemoryStream);
 
-            //Check that the image is valid
+            // Check that the image is valid.
             byte[] imageData = imageMemoryStream.ToArray();
             if (!imageData.IsValidImage())
                 return StatusCode(StatusCodes.Status415UnsupportedMediaType);
 
-            //Convert to Image
+            // Convert to Image.
             Image image = Image.FromStream(imageMemoryStream);
 
-            //Convert to Bitmap
+            // Convert to Bitmap.
             Bitmap bitmapImage = (Bitmap)image;
 
-            _logger.LogInformation($"Start processing image...");
+            _logger.LogInformation("Start processing image...");
 
-            //Measure execution time
+            // Measure execution time.
             var watch = System.Diagnostics.Stopwatch.StartNew();
 
-            //Set the specific image data into the ImageInputData type used in the DataView
-            ImageInputData imageInputData = new ImageInputData { Image = bitmapImage };
+            // Set the specific image data into the ImageInputData type used in the DataView.
+            var imageInputData = new ImageInputData { Image = bitmapImage };
 
-            //Predict code for provided image
+            // Predict code for provided image.
             ImageLabelPredictions imageLabelPredictions = _predictionEnginePool.Predict(imageInputData);
 
-            //Stop measuring time
+            // Stop measuring time.
             watch.Stop();
             var elapsedMs = watch.ElapsedMilliseconds;
             _logger.LogInformation($"Image processed in {elapsedMs} miliseconds");
 
-            //Predict the image's label (The one with highest probability)
+            // Predict the image's label (The one with highest probability).
             ImagePredictedLabelWithProbability imageBestLabelPrediction
                                 = FindBestLabelWithProbability(imageLabelPredictions, imageInputData);
 
@@ -83,12 +83,12 @@ ImagePredictedLabelWithProbability imageBestLabelPrediction
 
         private ImagePredictedLabelWithProbability FindBestLabelWithProbability(ImageLabelPredictions imageLabelPredictions, ImageInputData imageInputData)
         {
-            //Read TF model's labels (labels.txt) to classify the image across those labels
+            // Read TF model's labels (labels.txt) to classify the image across those labels.
             var labels = ReadLabels(_labelsFilePath);
 
             float[] probabilities = imageLabelPredictions.PredictedLabels;
 
-            //Set a single label as predicted or even none if probabilities were lower than 70%
+            // Set a single label as predicted or even none if probabilities were lower than 70%.
             var imageBestLabelPrediction = new ImagePredictedLabelWithProbability()
             {
                 ImageId = imageInputData.GetHashCode().ToString(), //This ID is not really needed, it could come from the application itself, etc.
@@ -117,7 +117,7 @@ private string[] ReadLabels(string labelsLocation)
 
         public static string GetAbsolutePath(string relativePath)
         {
-            FileInfo _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
+            var _dataRoot = new FileInfo(typeof(Program).Assembly.Location);
             string assemblyFolderPath = _dataRoot.Directory.FullName;
 
             string fullPath = Path.Combine(assemblyFolderPath, relativePath);