public Sentiment(SentimentConfiguration configuration, ILanguageProcessor languageProcessor)
{
_configuration = configuration ?? throw new ArgumentNullException(nameof(configuration));
_languageProcessor = languageProcessor ?? throw new ArgumentNullException(nameof(languageProcessor));
AddLanguage(configuration.DefaultLanguage);
}
public void Start(string mainFolder, string outputDir, ISmoothingTechnique smoothing, int nGramSize, ILanguageProcessor processor)
{
var resultSet = new ResultSet();
var bayesClassifier = new BayesTextClassifier();
var docReader = new ReadDocumentFromTiraFile();
var categories = new List<TextSource>();
Console.WriteLine("Scanning...");
dynamic jsonConfig;
using (StreamReader sr = new StreamReader(Path.Combine(mainFolder, "meta-file.json")))
{
jsonConfig = JsonConvert.DeserializeObject(sr.ReadToEnd());
}
var unknownFolder = (string)jsonConfig.folder;
var encodingString = (string)jsonConfig.encoding;
var cultureString = (string)jsonConfig.language;
CultureInfo ci = null;
switch (cultureString)
{
case "EN":
ci = new CultureInfo("en-US");
break;
default:
throw new ApplicationException("culture not found");
}
Encoding encoding = null;
switch (encodingString)
{
case "UTF8":
encoding = Encoding.UTF8;
break;
case "ASCII":
encoding = Encoding.ASCII;
break;
default:
throw new ApplicationException("encoding not found");
}
foreach(var item in jsonConfig["candidate-authors"])
{
var authorName = (string)item["author-name"];
var docs = new DirectoryInfo(Path.Combine(mainFolder, authorName)).GetFiles();
var dataSource = new List<string>();
foreach (var doc in docs)
{
try
{
dataSource.Add(docReader.ReadDocumentText(doc.FullName, encoding, ci));
}
catch
{
Console.WriteLine("Document {0} unreadable", doc.FullName);
}
}
categories.Add(processor.Process(dataSource, authorName));
}
int n = nGramSize;
Console.WriteLine("Scanned {1} documents in {0} categories", categories.Count, categories.Select(el => el.Documents.Count).Aggregate((el1, el2) => el1 + el2));
var allInOne = new TextSource();
allInOne.Documents.AddRange(categories.SelectMany(el => el.Documents));
Console.WriteLine("Building hash tables ..", n);
Parallel.ForEach(categories, category =>
{
for (int i = 1; i <= n; i++)
{
category.BuildSegmentTable(i);
Console.WriteLine("hashed {0} with n={1}", category.Name, i);
}
});
allInOne.SetNGramCache(NGramCache.Aggregate(categories.Select(el => el.GetNGramCache())));
Console.WriteLine("aggregated hashing");
Console.WriteLine("Getting smoothing ready ..");
smoothing.Init(allInOne, nGramSize);
var categoriesToTest = new Dictionary<TextSource, CategoryProbabilityDistribution>();
foreach (var cat in categories)
{
categoriesToTest[cat] = new CategoryProbabilityDistribution(cat, smoothing, n);
}
Console.WriteLine("Start classifying ..");
int totalProgress = jsonConfig["unknown-texts"].Count * categoriesToTest.Count;
int progress = 0;
System.Timers.Timer t = new System.Timers.Timer(5000);
t.Elapsed += (sender, eventArgs) =>
{
Console.Title = "Task is Running. Progress: " + Math.Round((((double)progress / (double)totalProgress) * 100.0), 2).ToString() + "%";
};
t.AutoReset = true;
t.Start();
foreach (var item in jsonConfig["unknown-texts"])
{
TextSource topCategory = null;
var maxProb = 0.0;
var textName = (string)item["unknown-text"];
var probs = new List<double>();
Parallel.ForEach(categoriesToTest, catDist =>
{
var docText = new string[] { docReader.ReadDocumentText(Path.Combine(mainFolder, unknownFolder, textName), encoding, ci) };
var docSource = processor.Process(docText, "unknown").Documents.First();
double p = bayesClassifier.P_c(catDist.Value, docSource, n, (double)catDist.Key.Documents.Count / (double)allInOne.Documents.Count);
lock (probs)
{
probs.Add(p);
if (topCategory == null || p > maxProb)
{
topCategory = catDist.Key;
maxProb = p;
}
}
Interlocked.Increment(ref progress);
});
// getting the score
probs.Remove(maxProb);
double pre_score = 0.0;
double max_sub_score = 0.0;
foreach (var p in probs)
{
var subScore = Math.Abs((maxProb - p) / maxProb) * Math.Pow(Math.E, 3); // normalized difference
var eSubScore = Math.Exp(-subScore);
pre_score += eSubScore;
if (eSubScore > max_sub_score)
max_sub_score = eSubScore;
}
double score = Math.Round(1.0 - (0.5 * (pre_score / probs.Count) + 0.5 * max_sub_score), 2);
Console.WriteLine("Classified {0} as author {1} with score {2}", textName, topCategory.Name, score);
resultSet.answers.Add(new Result(textName, topCategory.Name, score));
Console.WriteLine("writing data to file ...");
string data = JsonConvert.SerializeObject(resultSet, Formatting.Indented);
using (StreamWriter sw = new StreamWriter(Path.Combine(outputDir, "answers.json"), false))
{
sw.Write(data);
sw.Flush();
}
}
}
public Sentiment(ILanguageProcessor languageProcessor) : this(_defaultConfiguration, languageProcessor)
{
}
public void Start(ILanguageProcessor processor, ISmoothingTechnique smoothing)
{
var baseDirectory = AppDomain.CurrentDomain.BaseDirectory;
var bayesClassifier = new BayesTextClassifier();
var docReader = new ReadDocumentFromXmlFile();
var docPath = Path.Combine(baseDirectory, "data");
var authors = new DirectoryInfo(docPath).GetDirectories();
var categories = new List<TextSource>();
// Prepare data
foreach (var item in authors)
{
var docs = item.GetFiles();
var dataSource = new List<string>();
foreach (var doc in docs)
{
try
{
dataSource.Add(docReader.ReadDocumentText(doc.FullName, Encoding.GetEncoding(1253), new CultureInfo("el-GR")));
}
catch
{
Console.WriteLine("Document {0} unreadable", doc.FullName);
}
}
categories.Add(processor.Process(dataSource, item.Name));
}
Console.WriteLine("Scanned {1} documents in {0} categories", categories.Count, categories.Select(el => el.Documents.Count).Aggregate((el1, el2) => el1 + el2));
var testPath = Path.Combine(baseDirectory, "test");
var testAuthors = new DirectoryInfo(testPath).GetDirectories();
var allInOne = new TextSource();
allInOne.Documents.AddRange(categories.SelectMany(el => el.Documents));
// choose n from 1 to 4
for (int n = 1; n <= 4; n++)
{
Console.WriteLine("-----PREPARE for n = {0}", n);
Console.WriteLine("Building hash tables ..", n);
Parallel.ForEach(categories, category =>
{
category.BuildSegmentTable(n);
});
allInOne.SetNGramCache(NGramCache.Aggregate(categories.Select(el => el.GetNGramCache())));
Console.WriteLine("Getting smoothing ready ..");
smoothing.Init(allInOne, n);
var categoriesToTest = new Dictionary<TextSource, CategoryProbabilityDistribution>();
foreach(var cat in categories)
{
categoriesToTest[cat] = new CategoryProbabilityDistribution(cat, smoothing, n);
}
int rightClassified = 0;
int wrongClassified = 0;
Console.WriteLine("-----Algorithm starts now");
foreach (var testAuthor in testAuthors)
{
foreach (var testDocument in testAuthor.GetFiles())
{
TextSource topCategory = null;
var maxProb = 0.0;
Parallel.ForEach(categoriesToTest, catDist =>
{
var docText = new[] { docReader.ReadDocumentText(testDocument.FullName, Encoding.GetEncoding(1253), new CultureInfo("el-GR")) };
var docSource = processor.Process(docText, testAuthor.Name).Documents.First();
double p = bayesClassifier.P_c(catDist.Value, docSource, n, 1.0 / (double)categories.Count);
if (topCategory == null || p > maxProb)
{
topCategory = catDist.Key;
maxProb = p;
}
});
Console.WriteLine("Classified {0} as author {1} - {2}", testDocument.Name, topCategory.Name, topCategory.Name == testAuthor.Name ? "correct" : "incorrect");
if (topCategory.Name == testAuthor.Name) rightClassified++;
else wrongClassified++;
}
}
Console.WriteLine("-----SUMMARY");
Console.WriteLine("Success rate for n={0} is {1}\n", n, (double)rightClassified / ((double)rightClassified + (double)wrongClassified));
}
}
public void Start(string mainFolder, string outputDir, ISmoothingTechnique smoothing, int nGramSize, ILanguageProcessor processor)
{
var resultSet = new ResultSet();
var bayesClassifier = new BayesTextClassifier();
var docReader = new ReadDocumentFromTiraFile();
var categories = new List <TextSource>();
Console.WriteLine("Scanning...");
dynamic jsonConfig;
using (StreamReader sr = new StreamReader(Path.Combine(mainFolder, "meta-file.json")))
{
jsonConfig = JsonConvert.DeserializeObject(sr.ReadToEnd());
}
var unknownFolder = (string)jsonConfig.folder;
var encodingString = (string)jsonConfig.encoding;
var cultureString = (string)jsonConfig.language;
CultureInfo ci = null;
switch (cultureString)
{
case "EN":
ci = new CultureInfo("en-US");
break;
default:
throw new ApplicationException("culture not found");
}
Encoding encoding = null;
switch (encodingString)
{
case "UTF8":
encoding = Encoding.UTF8;
break;
case "ASCII":
encoding = Encoding.ASCII;
break;
default:
throw new ApplicationException("encoding not found");
}
foreach (var item in jsonConfig["candidate-authors"])
{
var authorName = (string)item["author-name"];
var docs = new DirectoryInfo(Path.Combine(mainFolder, authorName)).GetFiles();
var dataSource = new List <string>();
foreach (var doc in docs)
{
try
{
dataSource.Add(docReader.ReadDocumentText(doc.FullName, encoding, ci));
}
catch
{
Console.WriteLine("Document {0} unreadable", doc.FullName);
}
}
categories.Add(processor.Process(dataSource, authorName));
}
int n = nGramSize;
Console.WriteLine("Scanned {1} documents in {0} categories", categories.Count, categories.Select(el => el.Documents.Count).Aggregate((el1, el2) => el1 + el2));
var allInOne = new TextSource();
allInOne.Documents.AddRange(categories.SelectMany(el => el.Documents));
Console.WriteLine("Building hash tables ..", n);
Parallel.ForEach(categories, category =>
{
for (int i = 1; i <= n; i++)
{
category.BuildSegmentTable(i);
Console.WriteLine("hashed {0} with n={1}", category.Name, i);
}
});
allInOne.SetNGramCache(NGramCache.Aggregate(categories.Select(el => el.GetNGramCache())));
Console.WriteLine("aggregated hashing");
Console.WriteLine("Getting smoothing ready ..");
smoothing.Init(allInOne, nGramSize);
var categoriesToTest = new Dictionary <TextSource, CategoryProbabilityDistribution>();
foreach (var cat in categories)
{
categoriesToTest[cat] = new CategoryProbabilityDistribution(cat, smoothing, n);
}
Console.WriteLine("Start classifying ..");
int totalProgress = jsonConfig["unknown-texts"].Count * categoriesToTest.Count;
int progress = 0;
System.Timers.Timer t = new System.Timers.Timer(5000);
t.Elapsed += (sender, eventArgs) =>
{
Console.Title = "Task is Running. Progress: " + Math.Round((((double)progress / (double)totalProgress) * 100.0), 2).ToString() + "%";
};
t.AutoReset = true;
t.Start();
foreach (var item in jsonConfig["unknown-texts"])
{
TextSource topCategory = null;
var maxProb = 0.0;
var textName = (string)item["unknown-text"];
var probs = new List <double>();
Parallel.ForEach(categoriesToTest, catDist =>
{
var docText = new string[] { docReader.ReadDocumentText(Path.Combine(mainFolder, unknownFolder, textName), encoding, ci) };
var docSource = processor.Process(docText, "unknown").Documents.First();
double p = bayesClassifier.P_c(catDist.Value, docSource, n, (double)catDist.Key.Documents.Count / (double)allInOne.Documents.Count);
lock (probs)
{
probs.Add(p);
if (topCategory == null || p > maxProb)
{
topCategory = catDist.Key;
maxProb = p;
}
}
Interlocked.Increment(ref progress);
});
// getting the score
probs.Remove(maxProb);
double pre_score = 0.0;
double max_sub_score = 0.0;
foreach (var p in probs)
{
var subScore = Math.Abs((maxProb - p) / maxProb) * Math.Pow(Math.E, 3); // normalized difference
var eSubScore = Math.Exp(-subScore);
pre_score += eSubScore;
if (eSubScore > max_sub_score)
{
max_sub_score = eSubScore;
}
}
double score = Math.Round(1.0 - (0.5 * (pre_score / probs.Count) + 0.5 * max_sub_score), 2);
Console.WriteLine("Classified {0} as author {1} with score {2}", textName, topCategory.Name, score);
resultSet.answers.Add(new Result(textName, topCategory.Name, score));
Console.WriteLine("writing data to file ...");
string data = JsonConvert.SerializeObject(resultSet, Formatting.Indented);
using (StreamWriter sw = new StreamWriter(Path.Combine(outputDir, "answers.json"), false))
{
sw.Write(data);
sw.Flush();
}
}
}
public void LoadAndRunExperiment(string[] args)
{
Console.WriteLine("---------------------- Prepare for execution [Reading config]");
CalculationConstants.SmoothingEpsilon = double.Parse(ConfigurationManager.AppSettings["smoothingEpsilon"]);
Console.WriteLine("smoothEpsilon: {0}", CalculationConstants.SmoothingEpsilon);
var smoothing = ConfigurationManager.AppSettings["smoothing"];
ISmoothingTechnique sm = null;
if (smoothing == "absolute")
{
sm = new AbsoluteSmoothing();
Console.WriteLine("smoothing: absolute smoothing");
}
else if (smoothing == "linear")
{
sm = new LinearSmoothing();
Console.WriteLine("smoothing: linear smoothing");
}
if (ConfigurationManager.AppSettings.AllKeys.Contains("preInitSmoothingParameter"))
{
var param = double.Parse(ConfigurationManager.AppSettings["preInitSmoothingParameter"]);
sm.Init(param);
Console.WriteLine("smoothing: pre-init with param {0}", param);
}
else
{
Console.WriteLine("smoothing: no pre-init, getting parameters from data");
}
var processor = ConfigurationManager.AppSettings["processor"];
ILanguageProcessor lp = null;
if (processor == "word")
{
lp = new WordLevelProcessor();
Console.WriteLine("processor: word level processor");
}
else if (processor == "char")
{
lp = new CharacterLevelProcessor();
Console.WriteLine("processor: character level processor");
}
var experiment = ConfigurationManager.AppSettings["experiment"];
if (experiment == "tira")
{
var nGramSize = int.Parse(ConfigurationManager.AppSettings["nGramSize"]);
Console.WriteLine("n-gram size: {0}", nGramSize);
if (args.Any() && args[0] == "test")
{
// this is the test case
Console.WriteLine("setting: test case");
Console.WriteLine("---------------------- Starting execution [TIRA Experiment]");
new TiraExperiment().Start(ConfigurationManager.AppSettings["tiraTestFolder"], ConfigurationManager.AppSettings["tiraTestFolder"], sm, nGramSize, lp);
}
else if (!args.Any())
{
Console.WriteLine("\n\nyou must specify a directory!");
}
else if (!Directory.Exists(args[0]))
{
Console.WriteLine("\n\nDirectory {0} not found!", args[0]);
}
else if (!Directory.Exists(args[1]))
{
Console.WriteLine("\n\nDirectory {0} not found!", args[1]);
}
else
{
Console.WriteLine("setting: real run on folder: ", Path.GetDirectoryName(args[0]));
Console.WriteLine("Starting execution ---------------------- [TIRA Experiment]");
new TiraExperiment().Start(args[0], args[1], sm, nGramSize, lp);
}
}
else if (experiment == "expOne")
{
Console.WriteLine("---------------------- Starting execution [GREEK Authors]");
new ExperimentOne().Start(lp, sm);
}
}
public void Start(ILanguageProcessor processor, ISmoothingTechnique smoothing)
{
var baseDirectory = AppDomain.CurrentDomain.BaseDirectory;
var bayesClassifier = new BayesTextClassifier();
var docReader = new ReadDocumentFromXmlFile();
var docPath = Path.Combine(baseDirectory, "data");
var authors = new DirectoryInfo(docPath).GetDirectories();
var categories = new List <TextSource>();
// Prepare data
foreach (var item in authors)
{
var docs = item.GetFiles();
var dataSource = new List <string>();
foreach (var doc in docs)
{
try
{
dataSource.Add(docReader.ReadDocumentText(doc.FullName, Encoding.GetEncoding(1253), new CultureInfo("el-GR")));
}
catch
{
Console.WriteLine("Document {0} unreadable", doc.FullName);
}
}
categories.Add(processor.Process(dataSource, item.Name));
}
Console.WriteLine("Scanned {1} documents in {0} categories", categories.Count, categories.Select(el => el.Documents.Count).Aggregate((el1, el2) => el1 + el2));
var testPath = Path.Combine(baseDirectory, "test");
var testAuthors = new DirectoryInfo(testPath).GetDirectories();
var allInOne = new TextSource();
allInOne.Documents.AddRange(categories.SelectMany(el => el.Documents));
// choose n from 1 to 4
for (int n = 1; n <= 4; n++)
{
Console.WriteLine("-----PREPARE for n = {0}", n);
Console.WriteLine("Building hash tables ..", n);
Parallel.ForEach(categories, category =>
{
category.BuildSegmentTable(n);
});
allInOne.SetNGramCache(NGramCache.Aggregate(categories.Select(el => el.GetNGramCache())));
Console.WriteLine("Getting smoothing ready ..");
smoothing.Init(allInOne, n);
var categoriesToTest = new Dictionary <TextSource, CategoryProbabilityDistribution>();
foreach (var cat in categories)
{
categoriesToTest[cat] = new CategoryProbabilityDistribution(cat, smoothing, n);
}
int rightClassified = 0;
int wrongClassified = 0;
Console.WriteLine("-----Algorithm starts now");
foreach (var testAuthor in testAuthors)
{
foreach (var testDocument in testAuthor.GetFiles())
{
TextSource topCategory = null;
var maxProb = 0.0;
Parallel.ForEach(categoriesToTest, catDist =>
{
var docText = new[] { docReader.ReadDocumentText(testDocument.FullName, Encoding.GetEncoding(1253), new CultureInfo("el-GR")) };
var docSource = processor.Process(docText, testAuthor.Name).Documents.First();
double p = bayesClassifier.P_c(catDist.Value, docSource, n, 1.0 / (double)categories.Count);
if (topCategory == null || p > maxProb)
{
topCategory = catDist.Key;
maxProb = p;
}
});
Console.WriteLine("Classified {0} as author {1} - {2}", testDocument.Name, topCategory.Name, topCategory.Name == testAuthor.Name ? "correct" : "incorrect");
if (topCategory.Name == testAuthor.Name)
{
rightClassified++;
}
else
{
wrongClassified++;
}
}
}
Console.WriteLine("-----SUMMARY");
Console.WriteLine("Success rate for n={0} is {1}\n", n, (double)rightClassified / ((double)rightClassified + (double)wrongClassified));
}
}
