Revize 76072df0

Re #8597 Implementation of AbstractClassificationPredictor, SdcaMEClassifier

Server/ServerApp/Predictor/NaiveBayesClassifier.cs

@@ -13 +13 @@
     /// <summary>
     /// Implementation of the naive Bayes classifier in ML.NET.
     /// </summary>
-    class NaiveBayesClassifier : IPredictor
+    class NaiveBayesClassifier : AbstractClassificationPredictor
     {
         private static readonly ILog _log = LogManager.GetLogger(typeof(NaiveBayesClassifier));
 
-        /// <summary>
-        /// Context of the ML.NET framework.
-        /// </summary>
-        private MLContext _mlContext;
-
-        /// <summary>
-        /// Model instance
-        /// </summary>
-        private ITransformer _trainedModel;
-
-        private PredictionEngine<ModelInput, ModelOutput> _predictionEngine;
-
-        IDataView _trainingDataView;
-
         /// <summary>
         /// Instantiates new <c>MLContext</c>.
         /// </summary>
         public NaiveBayesClassifier()
         {
-            _mlContext = new MLContext();
+            this._mlContext = new MLContext();
         }
 
         public NaiveBayesClassifier(string filename) : this()
@@ -47 +33 @@
             this._predictionEngine = _mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(this._trainedModel);
         }
 
-        public void Save(string filename)
-        {
-            if (this._trainingDataView is null)
-            {
-                throw new NullReferenceException("DataView is not set.");
-            }
-            if( this._trainedModel is null)
-            {
-                throw new NullReferenceException("Trained model instance does not exist. This predictor has not been trained yet.");
-            }
-            if(filename is null)
-            {
-                throw new ArgumentNullException(nameof(filename));
-            }
-            this._mlContext.Model.Save(this._trainedModel, this._trainingDataView.Schema, filename);
-        }
-
-        public void Fit(IEnumerable<ModelInput> trainInput)
+        public override void Fit(IEnumerable<ModelInput> trainInput)
         {
             this._trainingDataView = _mlContext.Data.LoadFromEnumerable(trainInput);
             var pipeline = _mlContext.Transforms.Conversion.MapValueToKey(nameof(ModelInput.Label))
@@ -82 +51 @@
             _log.Info($"Selected the model #{cvResults.OrderByDescending(fold => fold.Metrics.MicroAccuracy).Select(fold => fold.Fold).First()} as the best.");
             this._predictionEngine = _mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(this._trainedModel);
 
-        }
-
-        public string Predict(ModelInput input)
-        {
-            _log.Debug($"Predicting for input: {input}");
-            return this._predictionEngine.Predict(input).PredictedLabel;
-        }
-
-        public void Evaluate(IEnumerable<ModelInput> modelInputs)
-        {
-            var testDataView = this._mlContext.Data.LoadFromEnumerable(modelInputs);
-            var data = _trainedModel.Transform(testDataView);
-            var testMetrics = _mlContext.MulticlassClassification.Evaluate(data);
-
-            Console.WriteLine($"*************************************************************************************************************");
-            Console.WriteLine($"*       Metrics for Multi-class Classification model - Test Data     ");
-            Console.WriteLine($"*------------------------------------------------------------------------------------------------------------");
-            Console.WriteLine($"*       MicroAccuracy:    {testMetrics.MicroAccuracy:0.###}");
-            Console.WriteLine($"*       MacroAccuracy:    {testMetrics.MacroAccuracy:0.###}");
-            Console.WriteLine($"*       LogLoss:          {testMetrics.LogLoss:#.###}");
-            Console.WriteLine($"*       LogLossReduction: {testMetrics.LogLossReduction:#.###}");
-            Console.WriteLine($"*       Confusion Matrix: {testMetrics.ConfusionMatrix.GetFormattedConfusionTable()}");
-            Console.WriteLine($"*************************************************************************************************************");
-        }
+        }    
     }
 }