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import numpy as np
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import pandas as pd
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from sys import path
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path.append('..')
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from feature_extraction.batch_feature_extractor import batchExtract
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from preprocessing.data_preprocessing import standardization
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from training.model_training import simpleTrain
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musicFeatures = batchExtract('../../dataset/music_wav/', '../feature_extraction/music_features/', 22050)
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musicFeatures = musicFeatures.assign(target=0)
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speechFeatures = batchExtract('../../dataset/speech_wav/', '../feature_extraction/speech_features/', 22050)
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speechFeatures = speechFeatures.assign(target=1)
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dataset = pd.concat([musicFeatures, speechFeatures])
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target = dataset.pop('target').values
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dataset = pd.DataFrame(standardization(dataset), columns = dataset.columns.values)
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wholeAccuracy = simpleTrain(dataset, target, 'svm')
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print('Accuracy using whole dataset = ' + str(wholeAccuracy))
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damages = np.zeros(dataset.columns.values.size)
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for index, key in enumerate(dataset.columns.values):
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acc = simpleTrain(dataset.drop(key, axis=1), target, 'svm')
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damages[index] = 100*(wholeAccuracy-acc)
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print('Accuracy without ' + key + '\t= ' + str(acc) +
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',\tdamage\t= ' + "%.2f" % damages[index] + '%')
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