Revize 4977ce53

Re #8832 Refactoring

Refactored existing classes to comply with the coding conventions

.gitignore

@@ +1 @@
+################################################################################
+# This .gitignore file was automatically created by Microsoft(R) Visual Studio.
+################################################################################
+
+/Server/ServerApp/data/auto

Server/ServerApp/Predictor/IPredictor.cs

@@ -1 @@
-using System;
+//
+// Author: Roman Kalivoda
+//
+
+using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
@@ -6 +10 @@
 using Microsoft.ML;
 using Microsoft.ML.Data;
 
+
 namespace ServerApp.Predictor
 {
+    /// <summary>
+    /// A predictor interface.
+    /// </summary>
     interface IPredictor
     {
-
+        /// <summary>
+        /// Trains the predictor with the given training data input.
+        /// </summary>
+        /// <param name="trainInput">A collection of <c>ModelInput</c> instances. The objects contain both feature vector inputs and corresponding labels.</param>
         void Fit(IEnumerable<ModelInput> trainInput);
 
-        String[] Predict(IEnumerable<ModelInput> input);
+        /// <summary>
+        /// Predicts classes to the given feature vectors.
+        /// </summary>
+        /// <param name="input">A collection of model feature vectors in <c>ModelInput</c> instances.</param>
+        /// <returns>A collection of <c>PredictionResult</c> instances.</returns>
+        IDataView Predict(IEnumerable<ModelInput> input);
     }
 }

Server/ServerApp/Predictor/ModelInput.cs

@@ -1 @@
-using Microsoft.ML.Data;
+//
+// Author: Roman Kalivoda
+//
+
+using Microsoft.ML.Data;
 
 namespace ServerApp.Predictor
 {
+
+    /// <summary>
+    /// A container of input features and label for the ML.NET classification model.
+    /// </summary>
     public class ModelInput
     {
+        /// <summary>
+        /// A label of this training input.
+        /// </summary>
         [ColumnName("Label"), LoadColumn(0)]
         public string Label { get; set; }
 
-
+        /// <summary>
+        /// Temperature at the site.
+        /// </summary>
         [ColumnName("temp"), LoadColumn(1)]
         public float Temp { get; set; }
 
@@ -17 +30 @@
         //[ColumnName("time"), LoadColumn(3)]
         //public string Col3 { get; set; }
     }
-}
+}

Server/ServerApp/Predictor/ModelOutput.cs

@@ -1 @@
-using System;
+//
+// Author: Roman Kalivoda
+//
+
+using System;
 using Microsoft.ML.Data;
 
 namespace ServerApp.Predictor
 {
+    /// <summary>
+    /// An output class of the ML.NET classifier model.
+    /// </summary>
     public class ModelOutput
     {
+        /// <summary>
+        /// A predicted class.
+        /// </summary>
         [ColumnName("prediction")]
         public String Prediction { get; set; }
+
+        /// <summary>
+        /// The score of prediction probability into individual classes.
+        /// </summary>
         public float[] Score { get; set; }
 
     }

Server/ServerApp/Predictor/NaiveBayesClassifier.cs

@@ -1 @@
-using System;
+//
+// Author: Roman Kalivoda
+//
+
+using System;
 using System.Collections.Generic;
 using System.Linq;
 using System.Text;
@@ -9 +13 @@
 
 namespace ServerApp.Predictor
 {
+    /// <summary>
+    /// Implementation of the naive Bayes classifier in ML.NET.
+    /// </summary>
     class NaiveBayesClassifier : IPredictor
     {
+        /// <summary>
+        /// Context of the ML.NET framework.
+        /// </summary>
         private MLContext mlContext;
 
+        /// <summary>
+        /// Model instance
+        /// </summary>
         private ITransformer model;
 
+        /// <summary>
+        /// Instantiates new <c>MLContext</c>.
+        /// </summary>
         public NaiveBayesClassifier()
         {
             mlContext = new MLContext();
 
         }
 
+        /// <summary>
+        /// Extracts list of feature vectors from parsed info objects.
+        /// </summary>
+        /// <param name="weatherInfos">List of weather info objects.</param>
+        /// <param name="activityInfos">List of info objects about activities at the site.</param>
+        /// <returns>A list of feature vectors for model training.</returns>
         public IEnumerable<ModelInput> ExtractModelInput(List<WeatherInfo> weatherInfos, List<ActivityInfo> activityInfos)
         {
             return weatherInfos.Select(e => new ModelInput(){
@@ -29 +51 @@
             }).ToList();
         }
 
-        public void Fit(IEnumerable<ModelInput> trainingData)
+        public void Fit(IEnumerable<ModelInput> trainInput)
         {
-            IDataView trainingDataView = mlContext.Data.LoadFromEnumerable(trainingData);
+            IDataView trainingDataView = mlContext.Data.LoadFromEnumerable(trainInput);
             var dataProcessPipeline = mlContext.Transforms.Conversion.MapValueToKey(nameof(ModelInput.Label))
                 .Append(mlContext.Transforms.Concatenate("Features", new[] { "temp" })
                 .Append(mlContext.Transforms.NormalizeMinMax("Features", "Features")));
@@ -43 +65 @@
 
         }
 
-        public String[] Predict(IEnumerable<ModelInput> input)
+        public IDataView Predict(IEnumerable<ModelInput> input)
         {
             var data = mlContext.Data.LoadFromEnumerable(input);
             IDataView result = model.Transform(data);
-            String[] prediction = result.GetColumn<String>("prediction").ToArray();
-
-            return prediction;
+            return result;
         }
     }
 }