dataset-homophones.py
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	• Jsalsman
	Sep 8 2019, 1:29 AM

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dataset-homophones.py
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	#!/usr/bin/env python3

	import dataset
	from sqlalchemy.types import *
	from glob import glob
	from datetime import datetime
	from os import system
	from string import ascii_lowercase, digits
	from autocorrect import spell
	from itertools import product
	from sklearn.svm import SVC
	from sklearn.model_selection import cross_val_score, KFold
	from numpy import asarray

	connect_string = 'postgresql://jsalsman@/sctest?host=/var/opt/gitlab/postgresql'

	def create_database(dbname='sctest', clobber=False):

	db = dataset.connect(connect_string)

	t = db['users'] # makes primary key autoincrementing integer 'id'
	if clobber: t.drop()
	t.create_column('email', String(length=100))
	t.create_index('email')
	t.create_column('name', String(length=100))
	t.create_index('name')
	t.create_column('registered', DateTime)
	t.create_column('telephone', String(length=30))
	t.create_column('passhash', String(length=144)) # 512 bits as hex plus salt
	t.create_column('utterances', ARRAY(Integer)) # foreign keys: utterances/id
	t.create_column('native', String(length=20)) # native language
	t.create_column('learning', String(length=20)) # goal language
	t.create_column('currentlevel', Float(precision=1))
	t.create_column('goallevel', Float(precision=1))
	t.create_column('goalmonths', Float(precision=1))
	# TODO: billing/payments/methods
	t.create_column('categories', ARRAY(String(length=15)))
	t.create_column('disabled', Boolean)
	t.create_column('students', ARRAY(Integer)) # foreign keys: users/id
	t.create_column('teachers', ARRAY(Integer)) # foreign keys: users/id
	t.create_column('parents', ARRAY(Integer)) # foreign keys: users/id
	t.create_column('schools', ARRAY(Integer)) # foreign keys: schools/id
	t.create_column('about', Text)

	t = db['words']
	if clobber: t.drop()
	t.create_column('spelling', String(length=30))
	t.create_index('spelling')
	t.create_column('phonemes', ARRAY(String(length=3))) # CMUBET, no dipthongs
	t.create_column('homographs', ARRAY(Integer)) # f.keys: words/id TEMP_UNUSED
	t.create_column('homophones', ARRAY(Integer)) # f.keys: words/id TEMP_UNUSED
	t.create_column('speechrank', Integer) # nth most common spoken word TEMP_UNUSED
	t.create_column('speechpart', String(length=1)) # see below TEMP_UNUSED TODO
	#
	# TEMPORARY LITERAL HOMOPHONES:
	t.create_column('homops', ARRAY(Text))
	#
	# speechpart codes:
	#
	# q: quantifier
	# n: noun
	# v: verb
	# x: negative
	# w: adverb
	# m: adjective
	# o: pronoun
	# s: possessive
	# p: preposition
	# c: conjunction
	# a: article

	t = db['prompts']
	if clobber: t.drop()
	t.create_column('display', Text)
	t.create_index('display')
	t.create_column('language', String(length=20))
	t.create_column('words', ARRAY(Integer)) # foreign keys: words/id
	t.create_column('utterances', ARRAY(Integer)) # foreign keys: utterances/id
	t.create_column('mediatype', Text)
	t.create_column('media', LargeBinary)
	t.create_column('level', Float(precision=1))
	t.create_column('phonemes', ARRAY(String(length=3))) # CMUBET, no dipthongs
	t.create_column('freeform', Boolean)

	t = db['utterances']
	if clobber: t.drop()
	t.create_column('promptid', Integer) # foreign key: prompts/id
	t.create_index('promptid')
	t.create_column('userid', Integer) # foreign key: users/id
	t.create_index('userid')
	t.create_column('pcm', LargeBinary)
	t.create_column('mp3', LargeBinary)
	t.create_column('wav', LargeBinary)
	t.create_column('exemplar', Boolean)
	t.create_column('adult', Boolean)
	t.create_column('male', Boolean)
	t.create_column('phonemes', ARRAY(String(length=3))) # CMUBET, no dipthongs
	t.create_column('features', ARRAY(Float(precision=3))) # ten per phoneme +1
	t.create_column('transcriptions', ARRAY(Integer)) # fk.s: transcriptions/id
	t.create_column('alignment', ARRAY(Float(precision=3)))
	t.create_column('worstphonemeposn', Integer)
	t.create_column('worstdiphoneposn', Integer)
	t.create_column('worstphoneme', String(length=3))
	t.create_column('worstdiphone', String(length=6))
	t.create_column('score', Float(precision=2))
	t.create_column('at', DateTime)
	t.create_column('whose', String(length=100))

	t = db['transcriptions']
	if clobber: t.drop()
	t.create_column('utteranceid', Integer) # foreign key: utterances/id
	t.create_column('promptid', Integer) # foreign key: prompts/id
	t.create_index('promptid')
	t.create_column('transcription', Text)
	t.create_column('intelligible', Boolean)
	t.create_column('userid', Integer) # foreign key: users/id
	t.create_column('transcribed', DateTime)
	t.create_column('source', String(length=1)) # (m)turk (p)eer (s)urvey (a)d (e)xemplar
	t.create_column('mturkuid', String(length=30)) #was length=14 until 21 seen
	t.create_column('mturkhit', String(length=35)) #was length=30

	t = db['log']
	if clobber: t.drop()
	t.create_column('userid', Integer) # foreign key: users/id
	t.create_index('userid')
	t.create_column('at', DateTime)
	t.create_index('at')
	t.create_column('event', String(length=20))
	t.create_index('event')
	t.create_column('address', String(length=300)) # IPv4 or v6["="domain name]
	t.create_index('address')
	t.create_column('details', Text)
	t.create_column('mediatype', Text)
	t.create_column('media', LargeBinary)

	t = db['choices'] # no index except 'id'
	if clobber: t.drop()
	t.create_column('question', Text)
	t.create_column('responses', ARRAY(Integer)) # foreign keys: responses/id
	t.create_column('activity', Text) #e.g. CMS assignment URL (w/type prefix?)
	t.create_column('mediatype', Text)
	t.create_column('media', LargeBinary)
	t.create_column('proofread', Boolean)
	t.create_column('disabled', Boolean)
	t.create_column('level', Float(precision=1))

	t = db['responses']
	if clobber: t.drop()
	t.create_column('choiceid', Integer) # foreign key: choices/id
	t.create_column('promptid', Integer) # foreign key: prompts/id
	t.create_column('resultid', Integer) # foreign key: choices/id
	t.create_column('terminal', Boolean)
	t.create_column('activity', Text) # e.g. assignment URL (with type prefix?)
	t.create_column('action', Text) # javascript?

	t = db['lessons']
	if clobber: t.drop()
	t.create_column('name', Text)
	t.create_column('choices', ARRAY(Integer)) # foreign keys: choices/id
	t.create_column('level', Float(precision=1))

	t = db['topics']
	if clobber: t.drop()
	t.create_column('name', Text)
	t.create_index('name')
	t.create_column('lessons', ARRAY(Integer)) # foreign keys: lessons/id
	t.create_column('level', Float(precision=1))
	t.create_index('level')

	t = db['schools']
	if clobber: t.drop()
	t.create_column('name', String(length=100))
	t.create_index('name')
	t.create_column('students', ARRAY(Integer)) # foreign keys: users/id
	t.create_column('teachers', ARRAY(Integer)) # foreign keys: users/id
	t.create_column('admins', ARRAY(Integer)) # foreign keys: users/id
	t.create_column('telephone', String(length=30))
	t.create_column('about', Text)


	def load_database(dbname='sctest', clobber=False):
	db = dataset.connect(connect_string)
	wt = db['words']
	pt = db['prompts']
	ut = db['utterances']
	tt = db['transcriptions']
	if clobber:
	print('deleting words, prompt, utterance, and transcription data...')
	wt.delete()
	pt.delete()
	ut.delete()
	tt.delete()
	print('...data deleted')
	pris = {}
	for pr in sorted(glob('../database/db/p??????.txt')):
	with open(pr, 'r') as f:
	ps = f.read().strip().replace(' .','.').replace(' ,',','
	).replace(' ?','?').replace(' !','!')
	pri = pr.replace('../database/db/p','').replace('.txt','')
	pris[pri] = ps
	ws = ps.replace(',','').replace('-',' ').replace('.',''
	).replace('!','').replace('?','').lower().split()
	wids = []
	for wd in ws:
	w = wt.find_one(spelling=wd)
	if w is None:
	wid = wt.insert(dict(spelling=wd))
	print('word:', wd, 'id:', wid)
	else:
	wid = w['id']
	wids.append(wid)
	pid = pt.insert(dict(display=ps, words=wids, language='en',
	freeform=False)) # gets utterances= below
	print('prompt:', ps, ws, 'id:', pid)
	uids = []
	for us in sorted(glob('../database/*-mp3s/p'+pri+'s??????.mp3')):
	with open(us, 'rb') as f:
	mp3 = f.read()
	uid = ut.insert(dict(promptid=pid, exemplar=False, adult=False,
	mp3=mp3, at=datetime(2017,5,1), whose=us.split('/')[-1]))
	# gets transcriptions=[id,...] below;
	print (us, 'len:', len(mp3), 'id:', uid)
	uids.append(uid)
	tids = []
	for ts in sorted(glob(us.replace('.mp3', 'n*.txt'
	).replace('shorter-mp3s', 'db').replace('longer-mp3s', 'db'))):
	with open(ts, 'r') as f:
	tf = f.readlines()
	tsn = tf[0].strip()
	mturkuid = tf[2].strip().split()[1]
	mturkhit = tf[3].strip().split()[1]
	if tsn.lower() == 'english': # skip defective transcriptions
	continue
	tid = tt.insert(dict(utteranceid=uid, promptid=pid,
	transcription=tsn, transcribed=datetime(2017,9,1),
	source='m', mturkuid=mturkuid, mturkhit=mturkhit))
	print('transcription id', tid, 'was:', tsn)
	tids.append(tid)
	ut.update(dict(id=uid, transcriptions=tids), ['id'])
	print(len(tids), 'transcriptions')
	pt.update(dict(id=pid, utterances=uids), ['id'])
	print(len(uids), 'utterances')
	print(len(pris), 'prompts:', pris)


	def load_homophones_and_phonemes(spelling_schema=True):
	db = dataset.connect(connect_string)
	wt = db['words']
	if spelling_schema:
	wt.create_column('homops', ARRAY(Text))
	pt = db['prompts']
	ut = db['utterances']
	with open('words.txt', 'r') as f:
	for l in f:
	if ' also ' in l:
	g = l.strip().split('#')[0].split('also')[0].split()
	h = l.strip().split('#')[0].split('also')[1].strip().split(', ')
	else:
	g = l.strip().split('#')[0].split()
	h = []
	w = g[0]
	p = g[1:]
	# insert phoneme string in words
	wt.update(dict(spelling=w, phonemes=p, homops=h), ['spelling'])
	print('word:', w, p, 'homophones:', h)
	# loop over prompts constructing phonemes from words' phonemes
	for p in pt:
	ps = []
	for w in p['words']:
	for pn in wt.find_one(id=w)['phonemes']:
	ps.append(pn)
	pt.update(dict(id=p['id'], phonemes=ps), ['id'])
	print('prompt', p['id'], p['display'], ps)
	# loop over utterances copying phonemes from prompts
	for u in ut:
	p = pt.find_one(id=u['promptid'])
	ut.update(dict(id=u['id'], phonemes=p['phonemes']), ['id'])
	print('utterance', u['id'], p['display'], p['phonemes'])


	def load_exemplars():
	db = dataset.connect(connect_string)
	pt = db['prompts']
	ut = db['utterances']
	wt = db['words']
	for e in sorted(glob('../database/exemplars//.wav')):
	w = e.split('/')[-1].replace('.wav','').split('-')[0]
	n = e.split('/')[-1].replace('.wav','').split('-')[1]
	with open(e, 'rb') as f:
	wav = f.read()
	wd = wt.find_one(spelling=w) # l/c only
	p = pt.find_one(words='{' + str(wd['id']) + '}') # mixed case display
	pid = p['id']
	ps = p['phonemes']
	put = p['utterances']
	if ut.find_one(promptid=pid, whose=n) is None:
	uid = ut.insert(dict(promptid=pid, phonemes=ps, wav=wav,
	exemplar=True, adult=True, at=datetime(2017,11,1), whose=n))
	if uid not in put:
	pt.update(dict(id=pid, utterances=put + [uid]), ['id'])
	print('exemplar', w, 'by', n)
	else:
	print('exemplar', w, 'by', n, 'already extant')

	def load_exemplar_transcripts():
	db = dataset.connect(connect_string)
	pt = db['prompts']
	ut = db['utterances']
	tt = db['transcriptions']
	for u in ut.find(exemplar=True, order_by='id'):
	p = pt.find_one(id=u['promptid'])
	tid = tt.insert(dict(utteranceid=u['id'], promptid=u['promptid'],
	intelligible=True, transcription=p['display'],
	transcribed=datetime(2017,11,1), source='e'))
	ut.update(dict(id=u['id'], transcriptions=[tid]))
	put = p['utterances']
	pt.update(dict(id=u['promptid'], utterances=sorted(list(
	set(put + [u['id']])))), ['id'])


	def load_fvta10():
	db = dataset.connect(connect_string)
	ut = db['utterances']
	for u in ut:
	if u['features'] is not None: # skip if done
	continue
	uid = u['id']
	ps = u['phonemes']
	mp3 = u['mp3']
	if mp3 is None:
	wav = u['wav']
	with open('featex.wav', 'wb') as f:
	f.write(wav)
	else:
	with open('featex.mp3', 'wb') as f:
	f.write(mp3)
	system('mpg123 -qw featex.wav featex.mp3 ; rm -f featex.mp3')
	system('sox -q featex.wav -r16k -ts16 -c1 featex.raw norm fade t 0.05 0')
	system('rm -f featex.wav ; ./ps/featex/featex-vta10 ' + ' '.join(ps)
	+ ' > featex.out 2> /dev/null ; rm -f featex.raw')
	with open('featex.out', 'r') as f:
	o = f.read().split()
	system('rm -f featex.out')
	if len(o) > 4:
	v = [float(n) for n in o]
	ut.update(dict(id=uid, features=v), ['id'])
	print('utterance', uid, v)
	else:
	print('utterance', uid, 'failed')


	def align_exemplars():
	db = dataset.connect(connect_string)
	ut = db['utterances']
	for u in ut.find(exemplar=True):
	if u['alignment'] is not None: # skip if done
	continue
	uid = u['id']
	ps = u['phonemes']
	wav = u['wav']
	a = []
	with open('featex.wav', 'wb') as f:
	f.write(wav)
	system('sox -q featex.wav -r16k -ts16 -c1 featex.raw norm fade t 0.05 0')
	system('rm -f featex.wav ; ./ps/featex/featex-vta10 ' + ' '.join(ps)
	+ ' 2> featex.out > /dev/null ; rm -f featex.raw')
	with open('featex.out', 'r') as f:
	for l in f:
	if 'featex-vta10: phoneme ' in l:
	o = l.strip().replace(':', '').replace('s', '').replace(
	',', '').split()
	a.append(o[4]) # start
	a.append(o[6]) # duration
	system('rm -f featex.out')
	if len(o) > 4:
	v = [float(n) for n in a]
	ut.update(dict(id=uid, alignment=v), ['id'])
	print('exemplar', uid, v)
	else:
	print('exemplar', uid, 'failed')


	def load_intelligibility():
	db = dataset.connect(connect_string)
	tt = db['transcriptions']
	pt = db['prompts']
	wt = db['words']
	okchars = ascii_lowercase + digits + " '"
	for t in tt.find(exemplar=False, order_by='id'):
	p = pt.find_one(id=t['promptid'])
	wl = p['words']
	ws = [] # two layer list of spellings plus homographs
	match = False
	for w in wl:
	tw = wt.find_one(id=w)
	ws.append([tw['spelling']] + tw['homops']) # lowercase already
	ts = ''.join([c.lower() for c in t['transcription'] # keep only lower-
	if c.lower() in okchars]).strip() + ' ' # case, spaces & apostrophes
	tc = '' # autocorrect-ed alternative
	for w in ts.split():
	tc += spell(w) + ' ' # spelling correction
	# compare permutations.startswith(word(s)), appending a space to each
	for ps in [' '.join(s) + ' ' for s in product(*ws)]: # itertools.product
	if (tc.startswith(ps) or tc.startswith(ps.replace(' ', '') + ' ')
	or (tc.replace(' ', '') + ' ').startswith(ps) or
	ts.startswith(ps) or ts.startswith(ps.replace(' ', '') +' ')
	or (ts.replace(' ', '') + ' ').startswith(ps)):
	match = True
	break
	tt.update(dict(id=t['id'], intelligible=match), ['id'])
	print('transcription ', ts, 'is', match, 'for:', p['display'])


	def measure_models():
	db = dataset.connect(connect_string)
	pt = db['prompts']
	tt = db['transcriptions']
	ut = db['utterances']
	s = 0.0; se = 0.0; sv = 0.0; n = 0; ne = 0; nv = 0
	for p in pt.all(order_by='display'):
	if p['utterances'] in [None, []]:
	continue # duplicate audio utterances got merged leaving []s
	pd = p['display']
	np = len(p['phonemes'])
	nu = len(p['utterances'])
	X = []; y = []
	for u in ut.find(promptid=p['id'], order_by='whose'):
	if u['transcriptions'] in [None, []] or u['features'] in [None, []]:
	continue
	for t in tt.find(utteranceid=u['id'], order_by='transcription'):
	X.append(u['features'])
	if t['intelligible']:
	y.append(1.0)
	else:
	y.append(0.0)
	if len(X) < 4:
	continue
	# negative examples of other prompts features of the same length
	for fl in db.query('select distinct on (promptid) features from '
	+ '(select * from (select distinct on (features) features, '
	+ 'promptid from utterances where promptid <> :pid and '
	+ 'cardinality(phonemes) = :pl) as o order by random()) as t '
	+ 'limit 15', pid=p['id'], pl=np): # 30 bombs w/"NaN" TODO
	if fl['features'] is not None: # some were missing, not sure why
	X.append(fl['features'])
	y.append(0.0)
	m = SVC(gamma='auto') # was C=7, class_weight='balanced'
	cvs = cross_val_score(m, X, y, cv=KFold(len(X)), n_jobs=-1)
	s1 = cvs.mean()
	cvp = cross_val_predict(m, X, y, cv=KFold(len(X)), n_jobs=-1)
	print('Prompt:', pd, np, 'phonemes,', nu, 'utterances,',
	len(y), 'transcriptions, %.1f%% intelligible,'
	% (sum(y) * 100 / len(y)), '%.1f%% accurate'
	% (s1 * 100), end='')
	s += s1; n += 1
	if len(set(cvp)) == 1:
	print (' VOID', end='')
	else:
	sv += s1; nv += 1
	if ut.find_one(promptid=p['id'], exemplar=True) is not None:
	se += s1; ne += 1
	print(' with exemplar')
	else:
	print('')
	print('Mean leave-one-out cross-validated accuracy: %.1f%%'
	% (s * 100 / n), 'for', n, 'prompts')
	print('Mean leave-one-out cross-validated accuracy: %.1f%%'
	% (se * 100 / ne), 'for', ne, 'with exemplars.')
	print('Mean leave-one-out cross-validated accuracy: %.1f%%'
	% (sv * 100 / nv), 'for', nv, 'non-void.')

	# vs. 73.8% before autocorrect up to 74.1% after, non-cross
	#
	### before exemplar transcriptions:
	# Mean 5-fold cross-validated accuracy: 53.2 ± 25.8% for 690 prompts
	# Mean 5-fold cross-validated accuracy: 62.3 ± 31.1% for 40 with exemplars.
	### after exemplar transcriptions:
	# Mean 5-fold cross-validated accuracy: 53.3 ± 25.6% for 690 prompts
	# Mean 5-fold cross-validated accuracy: 63.7 ± 27.7% for 40 with exemplars.
	#
	### leave-one-out:
	# Mean leave-one-out cross-validated accuracy: 65.8 ± 93.0% for 694 prompts
	# Mean leave-one-out cross-validated accuracy: 73.3 ± 87.0% for 40 with exemplars.
	#
	### negative examples:
	# Mean leave-one-out cross-validated accuracy: 69.5 ± 89.6% for 694 prompts
	# Mean leave-one-out cross-validated accuracy: 80.2 ± 77.7% for 40 with exemplars.
	#
	### vs DNNs:
	# SVC: 69.2, DNN: 74.3; with exemplars: 80.0 85.4
	#
	### SVC(C=7)
	# Mean leave-one-out cross-validated accuracy: 72.2 ± 87.9% for 694 prompts
	# Mean leave-one-out cross-validated accuracy: 79.1 ± 80.4% for 40 with exemplars.
	#
	### negative examples
	# 10 71.0, 20 73.5, 30 75.2, 40 76.8, 50 77.8
	# with exemplars: 10 77.6, 20 79.9, 30 82.4, 40 84.0, 50 84.8

	## regular SVC() with void detection:
	# Mean leave-one-out cross-validated accuracy: 71.7% for 694 prompts
	# Mean leave-one-out cross-validated accuracy: 83.1% for 40 with exemplars.
	# Mean leave-one-out cross-validated accuracy: 70.3% for 549 non-void.
	# jsalsman@gitlab-db-vm:~/src$ grep -c VOID\ with measurelog4.txt
	# 8
	# jsalsman@gitlab-db-vm:~/src$ grep -c VOID measurelog4.txt
	# 145



	model = {}
	def train_models():
	global model
	db = dataset.connect(connect_string)
	pt = db['prompts']
	tt = db['transcriptions']
	ut = db['utterances']
	for p in pt.all(order_by='display'):
	if p['utterances'] in [None, []]:
	continue # duplicate audio utterances got merged leaving []s
	pd = p['display']
	np = len(p['phonemes'])
	nu = len(p['utterances'])
	X = []; y = []
	for u in ut.find(promptid=p['id'], order_by='whose'):
	if u['transcriptions'] in [None, []] or u['features'] in [None, []]:
	continue
	for t in tt.find(utteranceid=u['id'], order_by='transcription'):
	X.append(u['features'])
	if t['intelligible']:
	y.append(1.0)
	else:
	y.append(0.0)
	if len(X) < 4:
	continue
	# negative examples
	for fl in db.query('select distinct on (promptid) features from '
	+ '(select * from (select distinct on (features) features, '
	+ 'promptid from utterances where promptid <> :pid and '
	+ 'cardinality(phonemes) = :pl) as o order by random()) as t '
	+ 'limit 15', pid=p['id'], pl=np): # TODO: 10? 20?
	if fl['features'] is not None: # some were missing, not sure why
	X.append(fl['features'])
	y.append(0.0)
	m = SVC(C=7, gamma='scale', class_weight='balanced', probability=True
	).fit(X, y) # and class_weight; weight exemplars
	if ut.find_one(promptid=p['id'], exemplar=True) is not None:
	ex = True
	else:
	ex = False
	model[p['display']] = [m, X, y, len(p['phonemes']), ex]
	print(len(model), 'trained ', p['display'], ' with',
	len(p['phonemes']), 'phonemes and', len(y), 'transcriptions',
	'with' if ex else 'without', 'exemplars')
	#for k, v in model.items():


	# def diphone_remix()

	# user input

	# login

	# learner status

	# logout?

	# sequencing


	# multiple choices


	## print('replacing schema...')
	## create_database(clobber=True)
	## print('...schema replaced')
	##
	## print('loading database...')
	## load_database(clobber=True)
	## print('...database loaded')
	##
	## print('loading homophones and phonemes...')
	## load_homophones_and_phonemes()
	## print('...homophones and phonemes loaded')
	##
	## print('loading exemplars...')
	## load_exemplars()
	## load_exemplar_transcripts()
	## print('...exemplars loaded')
	##
	## print('extracting features...')
	## load_fvta10()
	## print('...features extracted')
	##
	## print('aligning exemplars...')
	## align_exemplars()
	## print('...exemplars aligned')
	##
	## print('determining intelligibility...')
	## load_intelligibility()
	## print('...transcriptions processed')
	##
	print('measuring models...')
	measure_models()
	##
	#print('training models...')
	#train_models()

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