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added semi-supervised fearture learning
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@mkholghi
mkholghi committed Jan 4, 2019
1 parent 0d64531 commit 73b2b5f
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Showing 3 changed files with 410 additions and 28 deletions.
133 changes: 122 additions & 11 deletions src/AnalysisPrograms/MahnooshSandpit.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -30,12 +30,12 @@ public void Execute(Arguments arguments)
{
LoggedConsole.WriteLine("feature learning process...");

var inputDir = @"M:\Postdoc\Liz\Least Bittern\"; //@"D:\Mahnoosh\Liz\"; //@"C:\Users\kholghim\Mahnoosh\Liz\"; // @"C:\Users\kholghim\Mahnoosh\UnsupervisedFeatureLearning\"; //
var inputDir = @"D:\Mahnoosh\Liz\Least_Bittern\"; // @"M:\Postdoc\Liz\Least Bittern\"; //@"D:\Mahnoosh\Liz\"; //@"C:\Users\kholghim\Mahnoosh\Liz\"; // @"C:\Users\kholghim\Mahnoosh\UnsupervisedFeatureLearning\"; //
var resultDir = Path.Combine(inputDir, "FeatureLearning");
var inputPath = Path.Combine(inputDir, "TrainSet\\one_min_recordings-samples"); //TrainSet //PatchSamplingSegments //PatchSampling
var trainSetPath = Path.Combine(inputDir, "TrainSet\\one_min_recordings-samples"); //TrainSet
var inputPath = Path.Combine(inputDir, "TrainSet\\one_min_recordings"); //TrainSet //PatchSamplingSegments //PatchSampling
var trainSetPath = Path.Combine(inputDir, "TrainSet\\train_data"); //TrainSet
// var testSetPath = Path.Combine(inputDir, "TestSet");
var configPath = @"M:\Postdoc\Liz\Least Bittern\FeatureLearningConfig.yml"; //@"D:\Mahnoosh\Liz\AnalysisConfigFiles\FeatureLearningConfig.yml"; //@"C:\Users\kholghim\Mahnoosh\Liz\FeatureLearningConfig.yml"; //@"C:\Work\GitHub\audio-analysis\src\AnalysisConfigFiles\FeatureLearningConfig.yml"; //
var configPath = @"D:\Mahnoosh\Liz\Least_Bittern\FeatureLearningConfig.yml"; //@"M:\Postdoc\Liz\Least Bittern\FeatureLearningConfig.yml"; //@"D:\Mahnoosh\Liz\AnalysisConfigFiles\FeatureLearningConfig.yml"; //@"C:\Users\kholghim\Mahnoosh\Liz\FeatureLearningConfig.yml"; //@"C:\Work\GitHub\audio-analysis\src\AnalysisConfigFiles\FeatureLearningConfig.yml"; //
// var outputMelImagePath = Path.Combine(resultDir, "MelScaleSpectrogram.png");
// var outputNormMelImagePath = Path.Combine(resultDir, "NormalizedMelScaleSpectrogram.png");
// var outputNoiseReducedMelImagePath = Path.Combine(resultDir, "NoiseReducedMelSpectrogram.png");
Expand Down Expand Up @@ -134,7 +134,6 @@ public void Execute(Arguments arguments)
// extracting features
FeatureExtraction.UnsupervisedFeatureExtraction(configuration, allBandsCentroids, trainSetPath, resultDir);
LoggedConsole.WriteLine("Done...");

}

[Command(
Expand All @@ -144,23 +143,135 @@ public class Arguments : SubCommandBase
{
public override Task<int> Execute(CommandLineApplication app)
{
var instance = new MahnooshSandpit();
instance.Execute(this);
//var instance = new MahnooshSandpit();
//instance.Execute(this);
//GenerateSpectrograms();
//ExtractClusteringFeatures();
ExtractClusteringFeatures();

return this.Ok();
}
}

// output is the cluster centroids that obtained from an semi-supervised feature learning approach.
public static void BuildSemisupervisedClusters()
{
LoggedConsole.WriteLine("semi-supervised clustering process...");
var inputDir = @"M:\Postdoc\Liz\Least Bittern\"; //@"D:\Mahnoosh\Liz\Least_Bittern\"; // @"D:\Mahnoosh\Liz\"; //@"C:\Users\kholghim\Mahnoosh\Liz\"; // @"C:\Users\kholghim\Mahnoosh\UnsupervisedFeatureLearning\"; //
var resultDir = Path.Combine(inputDir, "SemisupervisedClusters");
var inputPath = Path.Combine(inputDir, "TrainSet\\one_min_recordings"); //PatchSamplingSegments
// the infoFile contains the info about the frames of interest for supervised feature learning.
var frameInfoFilePath = @"M:\Postdoc\Liz\Least Bittern\manual-cluster\PositiveFrames.csv";//Path.Combine(inputDir, "TrainSet\\train_data");
var configPath = @"M:\Postdoc\Liz\Least Bittern\FeatureLearningConfig.yml"; // @"D:\Mahnoosh\Liz\Least_Bittern\FeatureLearningConfig.yml";

var configFile = configPath.ToFileInfo();

if (configFile == null)
{
throw new FileNotFoundException("No config file argument provided");
}
else if (!configFile.Exists)
{
throw new ArgumentException($"Config file {configFile.FullName} not found");
}

var configuration = ConfigFile.Deserialize<FeatureLearningSettings>(configFile);
int patchWidth =
(configuration.MaxFreqBin - configuration.MinFreqBin + 1) / configuration.NumFreqBand;

var frameInfoFile = frameInfoFilePath.ToFileInfo();

if (frameInfoFile == null)
{
throw new FileNotFoundException("No information file argument provided");
}
else if (!frameInfoFile.Exists)
{
throw new ArgumentException($"Info file {frameInfoFile.FullName} not found");
}

string[,] frameInfo = Csv.ReadMatrixFromCsv<string>(frameInfoFile, TwoDimensionalArray.None);

var clusteringOutputList = FeatureLearning.SemisupervisedFeatureLearning(configuration, inputPath, frameInfo);

List<double[][]> allBandsCentroids = new List<double[][]>();
for (int i = 0; i < clusteringOutputList.Count; i++)
{
var clusteringOutput = clusteringOutputList[i];

// writing centroids to a csv file
// note that Csv.WriteToCsv can't write data types like dictionary<int, double[]> (problems with arrays)
// I converted the dictionary values to a matrix and used the Csv.WriteMatrixToCsv
// it might be a better way to do this
string pathToClusterCsvFile = Path.Combine(resultDir, "ClusterCentroids" + i.ToString() + ".csv");
var clusterCentroids = clusteringOutput.ClusterIdCentroid.Values.ToArray();
Csv.WriteMatrixToCsv(pathToClusterCsvFile.ToFileInfo(), clusterCentroids.ToMatrix());
//Csv.WriteToCsv(pathToClusterCsvFile.ToFileInfo(), clusterCentroids);

// sorting clusters based on size and output it to a csv file
Dictionary<int, double> clusterIdSize = clusteringOutput.ClusterIdSize;
int[] sortOrder = KmeansClustering.SortClustersBasedOnSize(clusterIdSize);

// Write cluster ID and size to a CSV file
string pathToClusterSizeCsvFile = Path.Combine(resultDir, "ClusterSize" + i.ToString() + ".csv");
Csv.WriteToCsv(pathToClusterSizeCsvFile.ToFileInfo(), clusterIdSize);

// Draw cluster image directly from clustering output
List<KeyValuePair<int, double[]>> list = clusteringOutput.ClusterIdCentroid.ToList();
double[][] centroids = new double[list.Count][];

for (int j = 0; j < list.Count; j++)
{
centroids[j] = list[j].Value;
}

allBandsCentroids.Add(centroids);
//allClusteringOutput.Add(clusteringOutput.Clusters);

List<double[,]> allCentroids = new List<double[,]>();
for (int k = 0; k < centroids.Length; k++)
{
// convert each centroid to a matrix in order of cluster ID
// double[,] cent = PatchSampling.ArrayToMatrixByColumn(centroids[i], patchWidth, patchHeight);
// OR: in order of cluster size
double[,] cent = MatrixTools.ArrayToMatrixByColumn(centroids[sortOrder[k]], patchWidth, configuration.PatchHeight);

// normalize each centroid
double[,] normCent = DataTools.normalise(cent);

// add a row of zero to each centroid
double[,] cent2 = PatchSampling.AddRow(normCent);

allCentroids.Add(cent2);
}

// concatenate all centroids
double[,] mergedCentroidMatrix = PatchSampling.ListOf2DArrayToOne2DArray(allCentroids);

// Draw clusters
// int gridInterval = 1000;
// var freqScale = new FrequencyScale(FreqScaleType.Mel, nyquist, frameSize, finalBinCount, gridInterval);

var clusterImage = ImageTools.DrawMatrixWithoutNormalisation(mergedCentroidMatrix);
clusterImage.RotateFlip(RotateFlipType.Rotate270FlipNone);
// clusterImage.Save(outputClusterImagePath, ImageFormat.Bmp);

var outputClusteringImage = Path.Combine(resultDir, "ClustersWithGrid" + i.ToString() + ".bmp");
// Image bmp = ImageTools.ReadImage2Bitmap(filename);
// FrequencyScale.DrawFrequencyLinesOnImage((Bitmap)clusterImage, freqScale, includeLabels: false);
clusterImage.Save(outputClusteringImage);
}

LoggedConsole.WriteLine("Done...");
}

public static void ExtractClusteringFeatures()
{
LoggedConsole.WriteLine("feature extraction process...");
var inputDir = @"D:\Mahnoosh\Liz\"; //@"M:\Postdoc\Liz\"; //@"C:\Users\kholghim\Mahnoosh\UnsupervisedFeatureLearning\"; //@"M:\Postdoc\Liz\"; //
var inputDir = @"D:\Mahnoosh\Liz\Least_Bittern\"; // @"D:\Mahnoosh\Liz\"; //@"M:\Postdoc\Liz\"; //@"C:\Users\kholghim\Mahnoosh\UnsupervisedFeatureLearning\"; //@"M:\Postdoc\Liz\"; //
var resultDir = Path.Combine(inputDir, "FeatureLearning");
//var trainSetPath = Path.Combine(inputDir, "TrainSet");
var testSetPath = Path.Combine(inputDir, "TestSet");
var configPath = @"D:\Mahnoosh\Liz\AnalysisConfigFiles\FeatureLearningConfig.yml"; //@"C:\Users\kholghim\Mahnoosh\Liz\FeatureLearningConfig.yml"; //@"C:\Work\GitHub\audio-analysis\src\AnalysisConfigFiles\FeatureLearningConfig.yml"; //
var testSetPath = Path.Combine(inputDir, "TestSet\\one_min_recordings");
var configPath = @"D:\Mahnoosh\Liz\Least_Bittern\FeatureLearningConfig.yml"; //@"D:\Mahnoosh\Liz\AnalysisConfigFiles\FeatureLearningConfig.yml"; //@"C:\Users\kholghim\Mahnoosh\Liz\FeatureLearningConfig.yml"; //@"C:\Work\GitHub\audio-analysis\src\AnalysisConfigFiles\FeatureLearningConfig.yml"; //
var centroidsPath = Path.Combine(resultDir, "ClusterCentroids0.csv");

var configFile = configPath.ToFileInfo();
Expand Down
16 changes: 8 additions & 8 deletions src/AudioAnalysisTools/DSP/FeatureExtraction.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -518,7 +518,7 @@ public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config,
// creating the header for CSV file
List<string> header = new List<string>();
header.Add("file name");

/*
for (int j = 0; j < allMins.ToArray()[i][0].GetLength(1); j++)
{
header.Add("min" + j.ToString());
Expand All @@ -533,17 +533,17 @@ public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config,
{
header.Add("max" + j.ToString());
}

*/
for (int j = 0; j < allStds.ToArray()[i][0].GetLength(1); j++)
{
header.Add("std" + j.ToString());
}

/*
for (int j = 0; j < allSkewness.ToArray()[i][0].GetLength(1); j++)
{
header.Add("skewness" + j.ToString());
}

*/
var csv = new StringBuilder();
string content = string.Empty;
foreach (var entry in header.ToArray())
Expand All @@ -565,11 +565,11 @@ public static void UnsupervisedFeatureExtraction(FeatureLearningSettings config,
{
List<double[]> featureList = new List<double[]>
{
allMins.ToArray()[i][j].ToJagged()[k],
allMeans.ToArray()[i][j].ToJagged()[k],
allMaxs.ToArray()[i][j].ToJagged()[k],
//allMins.ToArray()[i][j].ToJagged()[k],
//allMeans.ToArray()[i][j].ToJagged()[k],
//allMaxs.ToArray()[i][j].ToJagged()[k],
allStds.ToArray()[i][j].ToJagged()[k],
allSkewness.ToArray()[i][j].ToJagged()[k],
//allSkewness.ToArray()[i][j].ToJagged()[k],
};
double[] featureVector = DataTools.ConcatenateVectors(featureList);
featureVectors.Add(featureVector);
Expand Down
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