aswi2021tri-musketyri-gitlab/Server/ServerApp/Predictor/PredictionController.cs @ 60a60164

//
// Author: Roman Kalivoda
//

using System;
using System.Collections.Generic;
using ServerApp.Connection.XMLProtocolHandler;
using ServerApp.Parser.Parsers;

namespace ServerApp.Predictor
{
    /// <summary>
    /// Implentation of the <c>IPredicitionController</c> interface.
    /// </summary>
    class PredictionController : IPredictionController
    {
        /// <summary>
        /// A dictionary for storing trained predictors.
        /// </summary>
        private Dictionary<string, int> buildingsToAreas;

        private List<IPredictor> predictors;

        /// <summary>
        /// A reference to a data parser.
        /// </summary>
        private IDataParser dataParser;

        /// <summary>
        /// A feature extractor instance.
        /// </summary>
        private FeatureExtractor featureExtractor;

        /// <summary>
        /// Instantiates new prediction controller.
        /// </summary>
        /// <param name="dataParser">A data parser used to get training data.</param>
        public PredictionController(IDataParser dataParser)
        {
            this.dataParser = dataParser;
            this.predictors = new List<IPredictor>();
            this.buildingsToAreas = new Dictionary<string, int>();
            this.featureExtractor = new FeatureExtractor(this.dataParser, buildingsToAreas);

            // fill predictors with all available locationKeys
            // TODO Currently all locations use the same predictor. Try dividing locations into subareas with separate predictors.
            var locationKeys = TagInfo.buildings;
            foreach (string key in locationKeys)
            {
                buildingsToAreas.Add(key, 0);
            }
            IPredictor predictor = new NaiveBayesClassifier();
            predictors.Add(predictor);
        }
        public List<string> GetPredictors()
        {
            return new List<string>(buildingsToAreas.Keys);
        }

        public void Load(string locationKey = null, string path = null)
        {
            if (locationKey is null)
            {
                throw new NotImplementedException();
            }
            else
            {
                throw new NotImplementedException();
            }
        }

        public Response Predict(Request request)
        {
            throw new NotImplementedException();
        }


        public void Train(string locationKey = null)
        {
            if (locationKey is null)
            // train all predictors
            {
                // TODO A single predictor is used for all areas, so training is done only once now.
                for (int i = 0; i < this.predictors.Count; i++)
                {
                    // train on all available data
                    // TODO the train/test split is used just temporarily for demonstration.
                    List<ModelInput> data = featureExtractor.PrepareTrainingInput(i, DateTime.MinValue, DateTime.MaxValue);
                    List<ModelInput> trainingData = data.GetRange(index: 0, count: 500);
                    List<ModelInput> testData = data.GetRange(index: 500, count: 94);
                    Console.WriteLine("Training predictor with {0} samples.", trainingData.Count);
                    this.predictors[i].Fit(trainingData);

                    Console.WriteLine("Evaluating predictor with {0} samples.", testData.Count);
                    this.predictors[i].Evaluate(testData);
                }
            }
            else
            // train specified predictor only
            {
                throw new NotImplementedException();
            }
        }


    }
}