Ejemplo n.º 1
0
        public static int Main(string[] args)
        {
            args = new string[]{"todos2"};

            Stream s = File.Open(args[0],FileMode.Open,FileAccess.Read);
            List<string> total = new List<string>(readLines(s));
            s.Close();

            string[] tags = new string[]{"shop","sport","travel","home","reading","work","mlcourse","family","appointment","chore","urgent","school","finance","leisure","friends"};
            metrics.Add(new TruePositivesMetric());
            metrics.Add(new FalsePositivesMetric());
            metrics.Add(new TrueNegativesMetric(tags.Length));
            metrics.Add(new FalseNegativesMetric());
            metrics.Add(new PrecisionMetric());
            metrics.Add(new RecallMetric());
            metrics.Add(new AccuracyMetric());
            metrics.Add(new FMeasureMetric());
            metrics.Add(new HammingLossMetric(tags.Length));

            FileStream fsin = File.Open("innerstats.dat",FileMode.Create,FileAccess.Write);
            FileStream fsou = File.Open("outerstats.dat",FileMode.Create,FileAccess.Write);
            TextWriter twin = new StreamWriter(fsin);
            TextWriter twou = new StreamWriter(fsou);
            twin.Write("#k");
            twou.Write("#k");
            foreach(EvaluationMetric m in metrics) {
                twin.Write("\t{0}",m.Name);
                twou.Write("\t{0}",m.Name);
            }
            twou.WriteLine();
            twin.WriteLine();

            for(int pct = 2; pct < 100; pct++) {

                TRAIN_PERCENTAGE = pct;

                for(int sam = 0; sam < 10; sam++) {

                    vs = new VotingSystem (tags);
                    //vs.AddRecommender(new NeuralNetworkRecommender());
                    //vs.AddRecommender (new CustomRecommender (tags.Length));
                    //vs.AddRecommender (new ID3Recommender(0.7d));
                    //vs.AddRecommender (new NearestNeighbourRecommender());
                    //vs.AddRecommender(new C45Recommender(tags));
                    //vs.AddRecommender(new MLkNNRecommender(tags.Count()));
                    vs.AddRecommender(new VectorClassif(tags.Count()));
                    //vs.AddRecommender(new C45Recommender());

                    Console.WriteLine("train");
                    List<string> test = new List<string>(total);
                    List<string> train = new List<string>();
                    int tp = TRAIN_PERCENTAGE*test.Count/100;
                    Random rand = new Random();
                    for(int i = 0; i < tp; i++) {
                        int k = rand.Next(test.Count);
                        train.Add(test[k]);
                        test.RemoveAt(k);
                    }
                    #region Training
                    foreach(string l in train) {
                        Query(l);
                    }
                    vs.EndTrainingSession();
                    #endregion
                    #region TestInner
                    foreach(string l in train) {
                        Test(l);
                    }
                    twin.Write(pct);
                    //Console.WriteLine("Inner Results:");
                    foreach(EvaluationMetric m in metrics) {
                        //Console.WriteLine("\t{0} = {1}",m.Name,m.Result);
                        twin.Write("\t{0}",m.Result.ToString(nfi));
                        m.Reset();
                    }
                    twin.WriteLine();
                    #endregion
                    #region TestOuter
                    foreach(string l in test) {
                        Test(l);
                    }
                    twou.Write(pct);
                    //Console.WriteLine("Outer Results:");
                    foreach(EvaluationMetric m in metrics) {
                        //Console.WriteLine("\t{0} = {1}",m.Name,m.Result);
                        twou.Write("\t{0}",m.Result.ToString(nfi));
                        m.Reset();
                    }
                    twou.WriteLine();
                    #endregion
                }
                twin.Flush();
                twou.Flush();
            }

            twin.Close();
            twou.Close();
            fsin.Close();
            fsou.Close();

            #region testFree
            string line = Console.ReadLine();
            while(line != "exit") {
                foreach(Tuple<string,double> kvp in vs.TagFiltered(line)) {
                    Console.WriteLine("{0} / {1}",kvp.Item1,kvp.Item2);
                }
                Console.WriteLine("-----------------");
                line = Console.ReadLine();
            }
            #endregion
            return 0;
        }
Ejemplo n.º 2
0
        public static int Main(string[] args)
        {
            //run met "mono MLTag.exe trainfile+testfile ?logfile"
            args = new string[] { "S:\\thesis.txt" };
            metrics.Add(new TruePositivesMetric());
            metrics.Add(new FalsePositivesMetric());
            metrics.Add(new TrueNegativesMetric(tags.Length));
            metrics.Add(new FalseNegativesMetric());
            metrics.Add(new PrecisionMetric());
            metrics.Add(new RecallMetric());
            metrics.Add(new AccuracyMetric());
            metrics.Add(new FMeasureMetric());
            metrics.Add(new HammingLossMetric(tags.Length));
            vs = new VotingSystem(tags);

            //vs.AddRecommender(new NeuralNetworkRecommender());
            //vs.AddRecommender (new CustomRecommender (tags.Length));
            //vs.AddRecommender (new ID3Recommender(0.7d));
            //vs.AddRecommender (new NearestNeighbourRecommender());

            //vs.AddRecommender(new C45Recommender(tags));
            //vs.AddRecommender(new MLkNNRecommender(tags.Count()));
            //vs.AddRecommender(new VectorClassif(tags.Count()));
            vs.AddRecommender(new ConcreteCustomVectorRecommender(tags));

            Console.WriteLine("train");
            Stream s = File.Open(args[0], FileMode.Open, FileAccess.Read);
            List<string> test = new List<string>(readLines(s));
            List<string> train = new List<string>();
            s.Close();
            int tp = TRAIN_PERCENTAGE * test.Count / 100;
            Random rand = new Random();
            for (int i = 0; i < tp; i++) {
                int k = rand.Next(test.Count);
                train.Add(test[k]);
                test.RemoveAt(k);
            }
            #region Training
            foreach (string l in train) {
                Query(l);
            }
            vs.EndTrainingSession();
            #endregion
            #region TestInner
            foreach (string l in train) {
                Test(l);
            }
            Console.WriteLine("Inner Results:");
            foreach (EvaluationMetric m in metrics) {
                Console.WriteLine("\t{0} = {1}", m.Name, m.Result);
                m.Reset();
            }
            #endregion
            #region TestOuter
            foreach (string l in test) {
                Test(l);
            }
            Console.WriteLine("Outer Results:");
            foreach (EvaluationMetric m in metrics) {
                Console.WriteLine("\t{0} = {1}", m.Name, m.Result);
            }
            #endregion
            #region testFree
            string line = Console.ReadLine();
            while (line != "exit") {
                foreach (Tuple<string, double> kvp in vs.TagFiltered(line)) {
                    Console.WriteLine("{0} / {1}", kvp.Item1, kvp.Item2);
                }
                Console.WriteLine("-----------------");
                line = Console.ReadLine();
            }
            #endregion
            return 0;
        }