From bd99c5d734bebeda3b0a156b3147f83842e2061f Mon Sep 17 00:00:00 2001 From: Apostolof Date: Wed, 5 Dec 2018 16:30:34 +0200 Subject: [PATCH] Data preprocessing minor fixes --- .../preprocessing/data_preprocessing.py | 92 ++++++++++++------ classifier/preprocessing/dataset.npy | Bin 0 -> 10080384 bytes classifier/preprocessing/featureKeys.npy | Bin 0 -> 1232 bytes classifier/preprocessing/target.npy | Bin 0 -> 219264 bytes 4 files changed, 62 insertions(+), 30 deletions(-) create mode 100644 classifier/preprocessing/dataset.npy create mode 100644 classifier/preprocessing/featureKeys.npy create mode 100644 classifier/preprocessing/target.npy diff --git a/classifier/preprocessing/data_preprocessing.py b/classifier/preprocessing/data_preprocessing.py index 02f1cb4..07acacc 100644 --- a/classifier/preprocessing/data_preprocessing.py +++ b/classifier/preprocessing/data_preprocessing.py @@ -3,7 +3,14 @@ from os.path import isfile, join import numpy as np import json -def arrayFromJSONs(JSONPath): +class bcolors: + BLUE = '\033[94m' + GREEN = '\033[92m' + YELLOW = '\033[93m' + RED = '\033[91m' + ENDC = '\033[0m' + +def arrayFromJSON(JSONPath): with open(JSONPath) as jsonFile: rawJSON = json.load(jsonFile) @@ -21,6 +28,7 @@ def arrayFromJSONs(JSONPath): return keys, values def createSingleFeaturesArray(musicJSONsPath, speechJSONsPath): + print(bcolors.YELLOW + 'Creating single features array' + bcolors.ENDC) dataset = np.array([]) featureKeys = np.array([]) @@ -29,47 +37,47 @@ def createSingleFeaturesArray(musicJSONsPath, speechJSONsPath): for file in featuresFiles: if dataset.size == 0: # Gets feature arrays - featureKeys, musicFeatures = arrayFromJSONs(musicJSONsPath + file) - # Appends the class to the arrays (0 for music, 1 for speech) - musicClass = np.zeros((musicFeatures.shape[0]), dtype=int) - musicFeatures = np.c_[musicFeatures, musicClass] + featureKeys, musicFeatures = arrayFromJSON(musicJSONsPath + file) + # Initializes dataset array dataset = np.copy(musicFeatures) else: # Gets feature arrays - musicFeatures = arrayFromJSONs(musicJSONsPath + file)[1] - # Appends the class to the arrays (0 for music, 1 for speech) - musicFeatures = np.c_[musicFeatures, musicClass] + musicFeatures = arrayFromJSON(musicJSONsPath + file)[1] dataset = np.vstack((dataset, musicFeatures)) + # Initializes target array (0 for music) + target = np.zeros((dataset.shape[0]), dtype=int) + # Reads the extracted features for the speech class featuresFiles = [file for file in listdir(speechJSONsPath) if isfile(join(speechJSONsPath, file))] for file in featuresFiles: # Gets feature arrays - speechFeatures = arrayFromJSONs(speechJSONsPath + file)[1] - # Appends the class to the arrays (0 for music, 1 for speech) - speechClass = np.ones((speechFeatures.shape[0]), dtype=int) - speechFeatures = np.c_[speechFeatures, speechClass] + speechFeatures = arrayFromJSON(speechJSONsPath + file)[1] dataset = np.vstack((dataset, speechFeatures)) - return dataset, featureKeys + # Appends the new class to the target array (1 for speech) + target = np.hstack((target, np.ones((dataset.shape[0] - target.size), dtype=int))) + + return dataset, target, featureKeys # Details about this part can be found in the link bellow: # https://scikit-learn.org/stable/modules/feature_selection.html -def featureSelection(dataset, featureKeys): +def featureSelection(dataset, target, featureKeys): # Selects features based on a variance threshold from sklearn.feature_selection import VarianceThreshold - varianceThreshold = 0.72 + print(bcolors.YELLOW + 'Running variance threshold feature selection' + bcolors.ENDC) + varianceThreshold = 0.1 selector = VarianceThreshold(threshold = (varianceThreshold * (1 - varianceThreshold))) varReducedDataset = selector.fit_transform(dataset) isRetained = selector.get_support() - print('Retaining features:') + print(bcolors.GREEN + 'Retaining features:' + bcolors.ENDC) for index, retain in enumerate(isRetained): if retain and index < featureKeys.size: print(featureKeys[index], end='\t', flush=True) - print('\n\nRemoving features:') + print(bcolors.RED + '\n\nRemoving features:' + bcolors.ENDC) for index, retain in enumerate(isRetained): if not retain and index < featureKeys.size: print(featureKeys[index], end='\t', flush=True) @@ -77,10 +85,30 @@ def featureSelection(dataset, featureKeys): # Selects features based on univariate statistical tests from sklearn.datasets import load_digits - from sklearn.feature_selection import SelectPercentile, mutual_info_regression + from sklearn.feature_selection import SelectPercentile, mutual_info_classif + + print(bcolors.YELLOW + 'Running feature selection based on mutual information' + bcolors.ENDC) + percentileSelector = SelectPercentile(mutual_info_classif, percentile=33) + perReducedDataset = percentileSelector.fit_transform(dataset, target) + isRetained = percentileSelector.get_support() + + print(bcolors.BLUE + 'Scores of features:' + bcolors.ENDC) + for index, score in enumerate(percentileSelector.scores_): + print(featureKeys[index] + ' => ' + str(score), end='\t\t', flush=True) + if index%2: + print('') + print('') + + print(bcolors.GREEN + 'Retaining features:' + bcolors.ENDC) + for index, retain in enumerate(isRetained): + if retain and index < featureKeys.size: + print(featureKeys[index], end='\t', flush=True) - perReducedDataset = SelectPercentile(mutual_info_regression, - percentile=33).fit_transform(dataset[:, :-1], dataset[:, -1]) + print(bcolors.RED + '\n\nRemoving features:' + bcolors.ENDC) + for index, retain in enumerate(isRetained): + if not retain and index < featureKeys.size: + print(featureKeys[index], end='\t', flush=True) + print('\n') # TODO: change the return value after the values of the parameters are decided # and the feature selection is complete @@ -91,13 +119,13 @@ def featureSelection(dataset, featureKeys): def standardization(dataset): from sklearn import preprocessing + print(bcolors.YELLOW + 'Running standardization' + bcolors.ENDC) # Standardization - scaledDataset = preprocessing.scale(dataset[:, :-1]) - scaledDataset = np.c_[scaledDataset, dataset[:, -1]] + scaledDataset = preprocessing.scale(dataset) + print(bcolors.YELLOW + 'Running normalization' + bcolors.ENDC) # Normalization - scaledDataset = preprocessing.normalize(dataset[:, :-1], norm='l2') - scaledDataset = np.c_[scaledDataset, dataset[:, -1]] + normalizedDataset = preprocessing.normalize(dataset, norm='l2') # TODO: change the return value after the values of the parameters are decided # and the feature selection is complete @@ -108,19 +136,23 @@ def standardization(dataset): def PCA(dataset): from sklearn.decomposition import PCA - pca = PCA(n_components=10,svd_solver='full') - transformedDataset = pca.fit(dataset[:, :-1]).transform(dataset[:, :-1]) - transformedDataset = np.c_[transformedDataset, dataset[:, -1]] + print(bcolors.YELLOW + 'Running PCA' + bcolors.ENDC) + pca = PCA(n_components=10, svd_solver='full') + 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 dataset # Prints a nice message to let the user know the module was imported -print('feature_preprocessing loaded') +print(bcolors.BLUE + 'feature_preprocessing loaded' + bcolors.ENDC) # Enables executing the module as a standalone script if __name__ == "__main__": import sys - dataset, featureKeys = createSingleFeaturesArray(sys.argv[1], sys.argv[2]) - PCA(standardization(featureSelection(dataset, featureKeys))) \ No newline at end of file + dataset, target, featureKeys = createSingleFeaturesArray(sys.argv[1], sys.argv[2]) + PCA(standardization(featureSelection(dataset, target, featureKeys))) + print(bcolors.GREEN + 'Saving results to files' + bcolors.ENDC) + np.save('dataset.npy', dataset) + np.save('target.npy', target) + np.save('featureKeys.npy', featureKeys) \ No newline at end of file diff --git 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