aswi2021tri-musketyri-gitlab/Server/ServerApp/Predictor/NaiveBayesClassifier.cs @ 22211075

//
// Author: Roman Kalivoda
//

using System;
using System.Collections.Generic;
using System.Linq;
using Microsoft.ML;

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 _trainedModel;

        private PredictionEngine<ModelInput, ModelOutput> _predictionEngine;

        IDataView _trainingDataView;

        /// <summary>
        /// Instantiates new <c>MLContext</c>.
        /// </summary>
        public NaiveBayesClassifier()
        {
            _mlContext = new MLContext();
        }

        public void Fit(IEnumerable<ModelInput> trainInput)
        {
            this._trainingDataView = _mlContext.Data.LoadFromEnumerable(trainInput);
            var pipeline = _mlContext.Transforms.Conversion.MapValueToKey(nameof(ModelInput.Label))
                .Append(_mlContext.Transforms.Concatenate("Features", new[] { "Temp" }))
                .Append(_mlContext.Transforms.NormalizeMinMax("Features", "Features"))
                .AppendCacheCheckpoint(_mlContext)
                .Append(_mlContext.MulticlassClassification.Trainers.NaiveBayes())
                .Append(_mlContext.Transforms.Conversion.MapKeyToValue("PredictedLabel")); ;

            this._trainedModel = pipeline.Fit(this._trainingDataView);
            this._predictionEngine = _mlContext.Model.CreatePredictionEngine<ModelInput, ModelOutput>(this._trainedModel);

        }

        public string Predict(ModelInput input)
        {
            return this._predictionEngine.Predict(input).Prediction;
        }

        public void Evaluate(IEnumerable<ModelInput> modelInputs)
        {
            var testDataView = this._mlContext.Data.LoadFromEnumerable(modelInputs);
            var testMetrics = _mlContext.MulticlassClassification.Evaluate(_trainedModel.Transform(testDataView));

            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($"*       LogLossReduction: {testMetrics.LogLossReduction:#.###}");
            Console.WriteLine($"*************************************************************************************************************");
        }
    }
}