public void Initialize()
{
Lexicon = new Dictionary <string, int>()
{
{ "hejsa", 1 },
{ "dig", 1 },
{ "nedern", -1 },
{ "sq", -1 },
{ "godt", 1 }
};
Trigger = new Dictionary <string, List <IModifier> >()
{
{ "sq", new List <IModifier>()
{
new Mult(2f, 1)
} }
};
PositiveSentence = "hejsa med digsa.";
NegativeSentence = "nedern er det.";
NeutralSentence = "et sort hul.";
OneWordLookahead = "det var meget godt.";
TwoWordLookahead = "det var ikke så godt.";
VendingSentence = "hun har hovedet under armen.";
var setting = new Setting();
Tokenizer = new Tokenizer(setting);
RuleBased = new RuleBasedClassifier(Lexicon, setting, Trigger);
}
} = src_path.Substring(0, (src_path.Length) - 4); // USE THIS IF RUNNING FROM VISUAL STUDIO!
//public static string src_dir { get; } = Path.GetDirectoryName(Assembly.GetExecutingAssembly().Location); // USE THIS IF RUNNING FROM EXE!
static void Main(string[] args)
{
var setting = JsonHandler.DeserializeSettingsFromFile(src_dir + "/Data/settings.json");
//var setting = new Setting(threaded: true);
string method = null;
string filename = null;
Parser.Default.ParseArguments <Options>(args)
.WithParsed <Options>(o =>
{
switch (o.Classifier)
{
case "svm":
Console.WriteLine("Support Vector Machine on: " + o.InputFile);
method = o.Classifier;
break;
case "rb":
Console.WriteLine("Rule Based Classifier on: " + o.InputFile);
method = o.Classifier;
break;
case "rf":
Console.WriteLine("Random Forest on: " + o.InputFile);
method = o.Classifier;
break;
case "lstm":
Console.WriteLine("LSTM on: " + o.InputFile);
method = o.Classifier;
break;
case "random":
Console.WriteLine("Random Values on : " + o.InputFile);
method = o.Classifier;
break;
case "bl":
Console.WriteLine("Baseline Classifier on: " + o.InputFile);
method = o.Classifier;
break;
case "mml":
Console.WriteLine("Microsoft ML on: " + o.InputFile);
method = o.Classifier;
break;
case "data":
Console.WriteLine("Creating DataSet");
method = o.Classifier;
break;
default:
Console.WriteLine("Please provide a valid classifier");
System.Environment.Exit(1);
break;
}
filename = o.InputFile;
});
var watch = Stopwatch.StartNew();
var tokenizer = new Tokenizer(setting);
//var filename = "/Data/all_data.csv";
int serverProcessId = -1;
var testData = src_dir + "/Classifiers/TrainingData/test_data.csv";
List <Result> threadedResult = new List <Result>();
List <Result> comparissons = new List <Result>();
Interfaces.IEvaluator evalr = new BaselineClassifer();
switch (method)
{
case "prediction":
case "nb":
case "svr":
case "svm":
case "lstm":
case "rf":
Console.WriteLine("Runnning clasifier: " + method);
setting.threaded = false; //program cannot run threaded if connected to server (yet)
serverProcessId = InitServer(method); // runs the PythonServer with argument parsed
evalr = new PythonClassifier(Connect());
break;
case "rb":
Console.WriteLine("Runnning clasifier: " + method);
var lex = CSVReader.ReadLexicon(src_dir + "\\Data\\AgreedLexicon.csv"); // lex should be created like this first and then parsed into a pipe, so that we can evaluate on the fly (like we're tokenizing)
var tlex = CSVReader.ReadTriggerLex(src_dir + "\\Data\\triggerwords.csv");
evalr = new RuleBasedClassifier(lex, setting, tlex);
break;
case "random":
evalr = new RandomClassifier();
break;
case "bl":
evalr = new BaselineClassifer();
break;
case "mml":
evalr = new SentimentClassifier();
break;
case "data":
var list = new List <(string, string)>();
foreach (var s in CSVReader.ReadComparrisonsYield(src_dir + "\\" + filename))
{
list.Add(s);
}
double count = list.Count * 0.1;
FileWriter.CreateRandomTrainingTest(list, (int)count, (int)(list.Count - count));
FileWriter.flushPrinter(setting);
Environment.Exit(0);
break;
}
if (setting.compare)
{
Console.WriteLine("Comparing...");
comparissons = ConvertTestData(CSVReader.ReadComparrisonsYield, evalr, src_dir + filename).ToList();
var results = Pipe_AccuracyTest(comparissons, evalr).ToList();
var formatted = from result in results select result.ToString();
addToOutput(formatted);
Console.WriteLine(Environment.NewLine + Analyzer.MatrixWithNeutralToString(Analyzer.GetConfusion(results)));
Console.WriteLine(Analyzer.GetAccuracy(results) + "% Accuracy");
}
else
{
Console.WriteLine("Predicting...");
//Procedual evaluation
if (!setting.threaded)
{
var results = ProcedualSentenceLevelPipe(CSVReader.ReadCommentsYield, filename, evalr).ToList();
var formatted = from result in results select result.Value.ToString();
addToOutput(formatted);
}
//threaded evaluation
if (setting.threaded)
{
var result = ThreadedSentenceLevelPipe(CSVReader.ReadCommentsYield, filename, evalr).ToList();
var formatted = from val in result select val.Value.ToString();
addToOutput(formatted);
}
}
FileWriter.flushPrinter(setting);
watch.Stop();
var elapsed = watch.ElapsedMilliseconds;
Console.WriteLine("Completed in: " + elapsed + "Ms with " + method);
Console.WriteLine("Go to PATH/out/your_time_stamp for the output");
if (serverProcessId != -1)
{
try
{
Process.GetProcessById(serverProcessId).Kill();
}
catch (SocketException)
{
// succesfully closed
}
}
Console.ReadKey();
Environment.Exit(0);
}
