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99 lines
3.3 KiB
99 lines
3.3 KiB
from os import listdir
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from os.path import isfile, join
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import numpy as np
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import json
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class bcolors:
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BLUE = '\033[94m'
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GREEN = '\033[92m'
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YELLOW = '\033[93m'
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RED = '\033[91m'
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ENDC = '\033[0m'
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def arrayFromJSON(JSONPath):
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with open(JSONPath) as jsonFile:
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rawJSON = json.load(jsonFile)
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keys = np.array([])
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values = np.array([])
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for featureKey, featureValues in rawJSON.items():
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if keys.size == 0 or values.size == 0:
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keys = np.array(featureKey)
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values = np.array(featureValues)
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else:
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keys = np.append(keys, (np.array(featureKey)))
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values = np.vstack((values, np.array(featureValues)))
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values = np.transpose(values)
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return keys, values
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def createSingleFeaturesArray(musicJSONsPath, speechJSONsPath):
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print(bcolors.YELLOW + 'Creating single features array' + bcolors.ENDC)
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dataset = np.array([])
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featureKeys = np.array([])
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# Reads the extracted features for the music class
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featuresFiles = [file for file in listdir(musicJSONsPath) if isfile(join(musicJSONsPath, file))]
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for file in featuresFiles:
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if dataset.size == 0:
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# Gets feature arrays
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featureKeys, musicFeatures = arrayFromJSON(musicJSONsPath + file)
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# Initializes dataset array
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dataset = np.copy(musicFeatures)
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else:
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# Gets feature arrays
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musicFeatures = arrayFromJSON(musicJSONsPath + file)[1]
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dataset = np.vstack((dataset, musicFeatures))
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# Initializes target array (0 for music)
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target = np.zeros((dataset.shape[0]), dtype=int)
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# Reads the extracted features for the speech class
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featuresFiles = [file for file in listdir(speechJSONsPath) if isfile(join(speechJSONsPath, file))]
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for file in featuresFiles:
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# Gets feature arrays
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speechFeatures = arrayFromJSON(speechJSONsPath + file)[1]
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dataset = np.vstack((dataset, speechFeatures))
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# Appends the new class to the target array (1 for speech)
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target = np.hstack((target, np.ones((dataset.shape[0] - target.size), dtype=int)))
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return dataset, target, featureKeys
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# Details about this part can be found in the link bellow:
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# https://scikit-learn.org/stable/modules/preprocessing.html#preprocessing
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def standardization(dataset):
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from sklearn import preprocessing
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print(bcolors.YELLOW + 'Running standardization' + bcolors.ENDC)
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# Standardization
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scaledDataset = preprocessing.scale(dataset)
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return scaledDataset
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# Details about this part can be found in the link bellow:
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# https://scikit-learn.org/stable/modules/generated/sklearn.decomposition.PCA.html#sklearn.decomposition.PCA
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def PCA(dataset):
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from sklearn.decomposition import PCA
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print(bcolors.YELLOW + 'Running PCA' + bcolors.ENDC)
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pca = PCA(n_components=15, svd_solver='full', whiten = True)
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transformedDataset = pca.fit(dataset).transform(dataset)
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# TODO: change the return value after the values of the parameters are decided
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# and the feature selection is complete
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return transformedDataset
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# Prints a nice message to let the user know the module was imported
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print(bcolors.BLUE + 'feature_preprocessing loaded' + bcolors.ENDC)
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# Enables executing the module as a standalone script
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if __name__ == "__main__":
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import sys
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dataset, target, featureKeys = createSingleFeaturesArray(sys.argv[1], sys.argv[2])
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newDataset = PCA(standardization(dataset))
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print(bcolors.GREEN + 'Saving results to files' + bcolors.ENDC)
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np.save('dataset.npy', newDataset)
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np.save('target.npy', target)
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np.save('featureKeys.npy', featureKeys)
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