static void Main(string[] args)
{
// Specify which files to use.
var projectDir = Directory.GetParent(Directory.GetCurrentDirectory()).Parent.Parent.FullName;
var pathFiles = Directory.EnumerateFiles(projectDir + @"\OrderByMostSimilarDocumentExample\Samples").ToList();
// Hyper parameters.
// This option prevent overfitting on missing words.
var replaceMissingValueWithRandomValue = false;
var strategy = ValueStrategy.Presence;
var minVectorElements = 25;
var freqMin = 5;
var minWordCount = 1;
var maxWordCount = 3;
var minGroupOfWordsLength = 1;
var minWordLength = 1;
var firstWordMinLength = 1;
var lastWordMinLength = 1;
var maxComposition = 50;
var badWords = File.ReadLines(projectDir + @"\DocumentClusteringExample\stop-words-english.txt")
.Where(m => !string.IsNullOrWhiteSpace(m))
.ToArray();
var badPatternList = new string[]
{
};
// Files -> List of expressions (Our dictionary based on files)
var expressions = ExtractExpressionFromTextFiles.ExtractExpressions(
pathFiles,
new ExtractExpressionFromTextFilesOption
{
BadPatternList = badPatternList,
BadWords = badWords,
FirstWordMinLength = firstWordMinLength,
LastWordMinLength = lastWordMinLength,
MaxExpressionComposition = maxComposition,
MaxWordCount = maxWordCount,
MinGroupOfWordsLength = minGroupOfWordsLength,
MinWordCount = minWordCount,
MinWordFrequency = freqMin,
MinWordLength = minWordLength
});
Console.WriteLine("Expressions: " + expressions.Count);
// Files -> Vectors
var expressionVectorOption = new TextFileToExpressionVectorOption
{
MinVectorElements = minVectorElements,
BadPatternList = badPatternList,
MaxWordCount = maxWordCount,
MinWordCount = minWordCount,
Strategy = strategy,
ReplaceMissingValueWithRandomValue = replaceMissingValueWithRandomValue
};
List <Tuple <string, double[]> > filesToVector = new List <Tuple <string, double[]> >();
foreach (var pathFile in pathFiles)
{
filesToVector.Add(
new Tuple <string, double[]>(
pathFile,
TextFileToExpressionVector.GenerateExpressionVector(
expressions,
pathFile,
expressionVectorOption)
)
);
}
var vectors = filesToVector
.Select(m => m.Item2)
.ToList();
Console.WriteLine("vectors count: " + vectors.Count);
// Remove non-representative vectors
for (int i = 0; i < vectors.Count; i++)
{
var vector = vectors[i];
if (vector.Sum() < minVectorElements)
{
vectors.RemoveAt(i);
pathFiles.RemoveAt(i);
i--;
}
}
Console.WriteLine("vectors count (after removing non-representative vectors): " + vectors.Count);
var listFileAndVector = new List <FileAndVector>();
for (int i = 0; i < vectors.Count; i++)
{
var path = pathFiles[i];
var vector = vectors[i];
listFileAndVector.Add(new FileAndVector {
Path = path, Vector = vector
});
}
var distanceFunc = new CustomDistance();
Shuffle(listFileAndVector);
for (int i = 0; i < listFileAndVector.Count; i++)
{
var element = listFileAndVector[i];
var orderedList = listFileAndVector.OrderByDescending(m => distanceFunc.ComputeDistance(element.Vector, m.Vector));
var pathA = Path.GetFileNameWithoutExtension(element.Path);
pathA = string.Join("", pathA.Take(70));
var catA = pathA.Split('-')[0].Trim();
int countSameCat = 0;
Console.WriteLine("\n\n\n# " + pathA + "\n");
foreach (var item in orderedList.Skip(1).Take(5))
{
var pathB = Path.GetFileNameWithoutExtension(item.Path);
pathB = string.Join("", pathB.Take(70));
var catB = pathB.Split('-')[0].Trim();
double score = distanceFunc.ComputeDistance(element.Vector, item.Vector);
if (catA == catB)
{
countSameCat++;
}
Console.WriteLine(" - " + pathB + " " + string.Format("{0:#.##}", score));
}
Console.WriteLine("\nSame category: " + countSameCat);
Console.ReadLine();
}
Console.WriteLine("Press any key to continue...");
Console.ReadLine();
}
static void Main(string[] args)
{
// Specify which files to use.
var projectDir = Directory.GetParent(Directory.GetCurrentDirectory()).Parent.Parent.FullName;
var pathFiles = Directory.EnumerateFiles(projectDir + @"\DocumentClusteringExample\Samples").ToList();
// Hyper parameters.
// This option prevent overfitting on missing words.
var replaceMissingValueWithRandomValue = false;
var usePCA = false;
var numberOfOutputPCA = 100;
var distanceFunction = new PearsonCorrelation();
var strategy = ValueStrategy.Freq;
var minVectorElements = 2;
var freqMin = 2;
var minWordCount = 1;
var maxWordCount = 3;
var minGroupOfWordsLength = 3;
var minWordLength = 1;
var firstWordMinLength = 1;
var lastWordMinLength = 1;
var maxComposition = int.MaxValue;
var badWords = File.ReadLines(projectDir + @"\DocumentClusteringExample\stop-words-english.txt")
.Where(m => !string.IsNullOrWhiteSpace(m))
.ToArray();
var badPatternList = new string[]
{
};
// Files -> List of expressions (Our dictionary based on files)
var expressions = ExtractExpressionFromTextFiles.ExtractExpressions(
pathFiles,
new ExtractExpressionFromTextFilesOption
{
BadPatternList = badPatternList,
BadWords = badWords,
FirstWordMinLength = firstWordMinLength,
LastWordMinLength = lastWordMinLength,
MaxExpressionComposition = maxComposition,
MaxWordCount = maxWordCount,
MinGroupOfWordsLength = minGroupOfWordsLength,
MinWordCount = minWordCount,
MinWordFrequency = freqMin,
MinWordLength = minWordLength
});
Console.WriteLine("Expressions: " + expressions.Count);
// Files -> Vectors
var expressionVectorOption = new TextFileToExpressionVectorOption
{
MinVectorElements = minVectorElements,
BadPatternList = badPatternList,
MaxWordCount = maxWordCount,
MinWordCount = minWordCount,
Strategy = strategy,
ReplaceMissingValueWithRandomValue = replaceMissingValueWithRandomValue
};
List <Tuple <string, double[]> > filesToVector = new List <Tuple <string, double[]> >();
foreach (var pathFile in pathFiles)
{
filesToVector.Add(
new Tuple <string, double[]>(
pathFile,
TextFileToExpressionVector.GenerateExpressionVector(
expressions,
pathFile,
expressionVectorOption)
)
);
}
var vectors = filesToVector
.Select(m => m.Item2)
.ToList();
Console.WriteLine("vectors count: " + vectors.Count);
// Remove non-representative vectors
for (int i = 0; i < vectors.Count; i++)
{
var vector = vectors[i];
if (vector.Sum() < minVectorElements)
{
vectors.RemoveAt(i);
pathFiles.RemoveAt(i);
i--;
}
}
Console.WriteLine("vectors count (after removing non-representative vectors): " + vectors.Count);
// Reduce the vector size with PCA.
if (usePCA)
{
Console.WriteLine("Reducing vector size with PCA");
Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();
PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis();
pca.NumberOfOutputs = numberOfOutputPCA;
var trainingVector = vectors.ToArray();
Shuffle(trainingVector);
trainingVector = trainingVector.Take(600).ToArray();
var pcaResult = pca.Learn(trainingVector);
var reducedVectorsWithPCA = pcaResult.Transform(vectors.ToArray());
stopwatch.Stop();
Console.WriteLine("PCA duration: " + stopwatch.Elapsed.ToString());
vectors = reducedVectorsWithPCA.ToList();
}
// Run HDBSCAN algo.
Console.WriteLine("HDBSCAN starting...");
var contraintsList = new List <HdbscanConstraint>();
if (usePCA)
{
for (int i = 1; i < numberOfOutputPCA; i++)
{
contraintsList.Add(new HdbscanConstraint(i - 1, i, HdbscanConstraintType.CannotLink));
}
}
var watch = Stopwatch.StartNew();
var result = HdbscanRunner.Run(new HdbscanParameters
{
DataSet = vectors.ToArray(),
MinPoints = 5,
MinClusterSize = 5,
DistanceFunction = distanceFunction,
Constraints = contraintsList,
UseMultipleThread = true
});
watch.Stop();
Console.WriteLine("HDBSCAN done " + watch.Elapsed);
// Read results.
var labels = result.Labels;
int n = labels.Max();
Console.WriteLine("\n\n");
int clusterId = 0;
for (int iCluster = 1; iCluster <= n; iCluster++)
{
Dictionary <string, int> categories = new Dictionary <string, int>();
bool anyFound = false;
for (int i = 0; i < labels.Length; i++)
{
if (labels[i] == iCluster)
{
var fileName = Path.GetFileNameWithoutExtension(pathFiles[i]);
var category = fileName.Split('-')[0].Trim();
if (categories.ContainsKey(category))
{
var count = categories[category];
categories.Remove(category);
categories.Add(category, count + 1);
}
else
{
categories.Add(category, 1);
}
anyFound = true;
}
}
if (anyFound)
{
clusterId++;
Console.WriteLine("Cluster #" + clusterId);
Console.WriteLine();
foreach (var category in categories)
{
Console.WriteLine(category.Key + ": " + category.Value);
}
Console.ReadLine();
}
}
Console.WriteLine("Press any key to continue...");
Console.ReadLine();
}