111 lines
3.4 KiB
Python
111 lines
3.4 KiB
Python
'''
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Thomas Matlak, Avi Vajpeyi, Avery Rapson
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CS 310 Final Project
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Given textfiles with the musical notes in int format, this creates a pickle of
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the attributes and classes for all the musical data stored in the text files
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(each text file is for one class).
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The data is stored as frequencies of each note on a keyboard, and the class label
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is stored in 'one hot' format. 10 pre cent of data present set aside as testing data.
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Usage:
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python createMusicalFeaturesets.py
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OUTPUT: notesData.pickle
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A pickle with the attributes and classes for music data
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pickle data continas: train_attribute ,train_class, test_attribute, test_class
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NOTE: Need to update the follwoing depending on usage of script
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ROOT_DIR = root/directrory/where/text/reside
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DataFile = ["emotion1.txt","emotion2.txt"...])
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'''
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from mido import MidiFile, MidiTrack, Message
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import mido
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import random
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import pickle
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from collections import Counter
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import numpy as np
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import os
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'''
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Assume we have the following as our 'LEXICON'
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unique word list : [chair, table, spoon, television]
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Assume this is our current sample data:
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String: I pulled my chair up to the table
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Create a training vector that holds the count of each lexicon word:
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training vector : [1, 1, 0, 0]
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(since chair table are in string, but spoon TV arnt)
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Do this for all strings
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'''
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ROOT_DIR = "TrainingData/"
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DataFile = ["NegExamples/sadSongs.txt","PosExamples/happySongs.txt"]
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pianoSize = 128 # notes 0 - 127
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# this also defines our lexicon
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# larger dataset, more memory gets used up MemoryError
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def sample_handling(sample, classification):
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featureset = []
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'''
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featureset =
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[
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[[0 1 0 0 1 0 0 ...], [1, 0]]
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[[0 1 0 0 1 1 1 ...], [0, 1]]
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....
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]
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so the first list is the array of matches with the lexicon
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the second is which classification the features falls into (yes or no)
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'''
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with open(sample,'r') as f:
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contents = f.readlines()
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for l in contents:
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notes = np.fromstring(l, dtype=int, sep=' ')
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noteCount = np.zeros(pianoSize)
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for note in notes:
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noteCount[note] += 1
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noteCount = list(noteCount)
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featureset.append([noteCount, classification])
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return featureset
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def create_feature_sets_and_labels(DataFile,test_size = 0.1):
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features = []
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features += sample_handling(ROOT_DIR+DataFile[0],[0,1])# neg
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features += sample_handling(ROOT_DIR+DataFile[1],[1,0]) # pos
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random.shuffle(features)
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'''
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does tf.argmax([output]) == tf.argmax([expectations]) will look like:
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tf.argmax([55454, 342324]) == tf.argmax([1,0])
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'''
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features = np.array(features)
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testing_size = int(test_size*len(features))
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train_x = list(features[:,0][:-testing_size]) #[[5,8],[7,9]] --> [:,0] does [5,7] (all of the 0 elememts) ie the labels in this case
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train_y = list(features[:,1][:-testing_size])
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test_x = list(features[:,0][-testing_size:])
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test_y = list(features[:,1][-testing_size:])
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return train_x,train_y,test_x,test_y
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if __name__ == '__main__':
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train_x,train_y,test_x,test_y = create_feature_sets_and_labels(DataFile)
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with open('notesData.pickle','wb') as f:
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pickle.dump([train_x,train_y,test_x,test_y],f) # dump data as a list, into a file
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# this saves the lexicon for pos and neg words
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# every inputted value is converted to a lexicon saving this info
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# a lot of memory!
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