From ccf0ecf3efc5d7c82ffdf2f5966393e7fa03686d Mon Sep 17 00:00:00 2001 From: Apostolof Date: Sat, 5 Jan 2019 21:49:57 +0200 Subject: [PATCH] Migrate structures to pandas DataFrame, Complete visualization, Minor fixes and improvements --- .../batch_feature_extractor.py | 22 ++++--- .../feature_extraction/feature_extractor.py | 4 ++ classifier/pipeline.py | 22 +++---- classifier/preprocessing/__init__.py | 0 .../preprocessing/data_preprocessing.py | 17 +++--- classifier/preprocessing/dataset.npy | Bin 3287168 -> 0 bytes classifier/preprocessing/dataset.pkl | Bin 0 -> 6137009 bytes classifier/preprocessing/featureKeys.npy | Bin 776 -> 0 bytes classifier/preprocessing/target.npy | Bin 219264 -> 0 bytes classifier/test/accuracyWithoutFeature.py | 55 +++++------------- classifier/training/__init__.py | 0 classifier/visualization/output/figure_1.png | Bin 0 -> 334347 bytes classifier/visualization/output/figure_10.png | Bin 0 -> 433018 bytes classifier/visualization/output/figure_11.png | Bin 0 -> 364816 bytes classifier/visualization/output/figure_12.png | Bin 0 -> 389647 bytes classifier/visualization/output/figure_13.png | Bin 0 -> 360657 bytes classifier/visualization/output/figure_14.png | Bin 0 -> 356084 bytes classifier/visualization/output/figure_15.png | Bin 0 -> 498444 bytes classifier/visualization/output/figure_2.png | Bin 0 -> 327455 bytes classifier/visualization/output/figure_3.png | Bin 0 -> 346894 bytes classifier/visualization/output/figure_4.png | Bin 0 -> 321728 bytes classifier/visualization/output/figure_5.png | Bin 0 -> 342131 bytes classifier/visualization/output/figure_6.png | Bin 0 -> 302834 bytes classifier/visualization/output/figure_7.png | Bin 0 -> 444318 bytes classifier/visualization/output/figure_8.png | Bin 0 -> 473382 bytes classifier/visualization/output/figure_9.png | Bin 0 -> 440002 bytes classifier/visualization/visualization.py | 35 ++++++++--- 27 files changed, 77 insertions(+), 78 deletions(-) create mode 100644 classifier/preprocessing/__init__.py delete mode 100644 classifier/preprocessing/dataset.npy create mode 100644 classifier/preprocessing/dataset.pkl delete mode 100644 classifier/preprocessing/featureKeys.npy delete mode 100644 classifier/preprocessing/target.npy create mode 100644 classifier/training/__init__.py create mode 100644 classifier/visualization/output/figure_1.png create mode 100644 classifier/visualization/output/figure_10.png create mode 100644 classifier/visualization/output/figure_11.png create mode 100644 classifier/visualization/output/figure_12.png create mode 100644 classifier/visualization/output/figure_13.png create mode 100644 classifier/visualization/output/figure_14.png create mode 100644 classifier/visualization/output/figure_15.png create mode 100644 classifier/visualization/output/figure_2.png create mode 100644 classifier/visualization/output/figure_3.png create mode 100644 classifier/visualization/output/figure_4.png create mode 100644 classifier/visualization/output/figure_5.png create mode 100644 classifier/visualization/output/figure_6.png create mode 100644 classifier/visualization/output/figure_7.png create mode 100644 classifier/visualization/output/figure_8.png create mode 100644 classifier/visualization/output/figure_9.png diff --git a/classifier/feature_extraction/batch_feature_extractor.py b/classifier/feature_extraction/batch_feature_extractor.py index 6acbe84..8d089de 100644 --- a/classifier/feature_extraction/batch_feature_extractor.py +++ b/classifier/feature_extraction/batch_feature_extractor.py @@ -1,7 +1,8 @@ from os import listdir from os.path import isfile, join import multiprocessing as mp -from .feature_extractor import extractFeatures +import pandas as pd +from feature_extractor import extractFeatures class bcolors: BLUE = '\033[94m' @@ -13,18 +14,21 @@ class bcolors: def batchExtract(audioFilesPath, featureFilesPath, sampleRate): audioFiles = [file for file in listdir(audioFilesPath) if isfile(join(audioFilesPath, file))] - # Without multithreading - # for file in audioFiles: - # extractFeatures(audioFilesPath + file, - # featureFilesPath + file[0:file.rfind('.')] + '.json', int(sampleRate)) + dataframesList = [None]*len(audioFiles) - pool = mp.Pool(processes = 8) - for file in audioFiles: - pool.apply(extractFeatures,args=(audioFilesPath + file, - featureFilesPath + file[0:file.rfind('.')] + '.json',int(sampleRate))) + pool = mp.Pool() + for process, file in enumerate(audioFiles): + dataframesList[process] = pool.apply_async(extractFeatures,args=(audioFilesPath + file, + featureFilesPath + file[0:file.rfind('.')] + '.json',int(sampleRate))).get() + pool.close() + pool.join() + + joinedDataset = pd.concat(dataframesList) print('Batch feature extraction finished successfully.') + return joinedDataset + # Prints a nice message to let the user know the module was imported print(bcolors.BLUE + 'batch_feature_extractor loaded' + bcolors.ENDC) diff --git a/classifier/feature_extraction/feature_extractor.py b/classifier/feature_extraction/feature_extractor.py index 34cc6b9..2fd1a82 100644 --- a/classifier/feature_extraction/feature_extractor.py +++ b/classifier/feature_extraction/feature_extractor.py @@ -1,4 +1,6 @@ import essentia +import pandas as pd +import numpy as np from essentia.standard import (MonoLoader, Windowing, Spectrum, MFCC, ZeroCrossingRate, SpectralCentroidTime, RollOff, Flux, Envelope, FlatnessSFX, LogAttackTime, StrongDecay, FlatnessDB, HFC, @@ -95,6 +97,8 @@ def extractFeatures(audio, outputPath, sampleRate): YamlOutput(filename = outputPath, format = 'json', writeVersion = False)(pool) + return pd.DataFrame(np.array([pool[i] for i in pool.descriptorNames()]).T, columns = pool.descriptorNames()) + def _4HzModulation(melEnergies, frameEnergy, sampleRate): from scipy.signal import butter, sosfilt, sosfreqz nyquist = 0.5 * sampleRate diff --git a/classifier/pipeline.py b/classifier/pipeline.py index f291d77..82150d7 100644 --- a/classifier/pipeline.py +++ b/classifier/pipeline.py @@ -1,24 +1,26 @@ import numpy as np +import pandas as pd from feature_extraction.feature_extractor import extractFeatures from feature_extraction.batch_feature_extractor import batchExtract -from preprocessing.data_preprocessing import arrayFromJSON, createSingleFeaturesArray, standardization, PCA +from preprocessing.data_preprocessing import arrayFromJSON, standardization, PCA from training.model_training import simpleTrain, kFCrossValid -batchExtract('../dataset/music_wav/', 'feature_extraction/music_features/', 22050) -batchExtract('../dataset/speech_wav/', 'feature_extraction/speech_features/', 22050) +musicFeatures = batchExtract('../dataset/music_wav/', 'feature_extraction/music_features/', 22050) +musicFeatures = musicFeatures.assign(target=0) +speechFeatures = batchExtract('../dataset/speech_wav/', 'feature_extraction/speech_features/', 22050) +speechFeatures = speechFeatures.assign(target=1) -dataset, target, featureKeys = createSingleFeaturesArray( - 'feature_extraction/music_features/', - 'feature_extraction/speech_features/') +dataset = pd.concat([musicFeatures, speechFeatures]) +target = dataset.pop('target').values dataset = standardization(dataset) # dataset = PCA(dataset) + print('Simple train accuracy achieved = ' + str(simpleTrain(dataset, target))) kFCrossValid(dataset, target, model = 'svm') clf = kFCrossValid(dataset, target, model = 'rndForest') -extractFeatures('compined.wav', 'featuresStream/tmp.json', 22050) -values = arrayFromJSON('featuresStream/tmp.json')[1] -values = standardization(values) -audioClass = clf.predict(values) +features = extractFeatures('compined.wav', 'tmp.json', 22050) +features = standardization(features) +audioClass = clf.predict(features) print(audioClass) \ No newline at end of file diff --git a/classifier/preprocessing/__init__.py b/classifier/preprocessing/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/classifier/preprocessing/data_preprocessing.py b/classifier/preprocessing/data_preprocessing.py index 8c21610..cbd6344 100644 --- a/classifier/preprocessing/data_preprocessing.py +++ b/classifier/preprocessing/data_preprocessing.py @@ -1,6 +1,7 @@ from os import listdir from os.path import isfile, join import numpy as np +import pandas as pd import json class bcolors: @@ -77,12 +78,10 @@ def PCA(dataset): from sklearn.decomposition import PCA print(bcolors.YELLOW + 'Running PCA' + bcolors.ENDC) - pca = PCA(n_components=15, svd_solver='full', whiten = True) + pca = PCA(n_components=10, svd_solver='full', whiten = True) transformedDataset = pca.fit(dataset).transform(dataset) - # TODO: change the return value after the values of the parameters are decided - # and the feature selection is complete - return transformedDataset + return pca, transformedDataset # Prints a nice message to let the user know the module was imported print(bcolors.BLUE + 'feature_preprocessing loaded' + bcolors.ENDC) @@ -91,9 +90,9 @@ print(bcolors.BLUE + 'feature_preprocessing loaded' + bcolors.ENDC) if __name__ == "__main__": import sys dataset, target, featureKeys = createSingleFeaturesArray(sys.argv[1], sys.argv[2]) - newDataset = PCA(standardization(dataset)) + scaledDataset = standardization(dataset) - print(bcolors.GREEN + 'Saving results to files' + bcolors.ENDC) - np.save('dataset.npy', newDataset) - np.save('target.npy', target) - np.save('featureKeys.npy', featureKeys) \ No newline at end of file + print(bcolors.GREEN + 'Saving scaled results to file' + bcolors.ENDC) + datasetFrame = pd.DataFrame(scaledDataset, columns = featureKeys) + datasetFrame = datasetFrame.assign(target=target) + datasetFrame.to_pickle("./dataset.pkl") \ No newline at end of file diff --git a/classifier/preprocessing/dataset.npy b/classifier/preprocessing/dataset.npy deleted file mode 100644 index e10dde5b8f01013ce378ba696e4e85b949cb04d2..0000000000000000000000000000000000000000 GIT binary patch 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