def process_speaker(db):
TEST_FOLD = 2
#db = connect_to_database()
#db.add_son_manipulator(TransformToBinary())
#db = NeoEngine('/data/neo4j')
#Impostor ratio is ratio of impostor records in training
#and testing population. For ratio=N, subject is 1/(N+1),
#impostor N/(N+1) of population
IMPOSTOR_RATIO = 3
LIMIT = 30
#Set up queue
ctx = zmq.Context()
q = ctx.socket(zmq.PULL)
q.connect("tcp://127.0.0.1:5555")
outQ = ctx.socket(zmq.PUSH)
outQ.connect("tcp://127.0.0.1:5556")
while True:
speaker_name = q.recv_json()
print 'Received %s' % speaker_name
#time.sleep(random.randint(1,10))
#Find all SVs for current subject
#print 'Speaker Name: %s' % speaker.name
#print 'Count:', db.sv.find({'speaker_name': speaker.name}).count()
#cursor_subject = Concurrent_cursor(SV.objects(speaker_name=speaker.name))
sv_subject = stack_SVs(db.get('sv', {'speaker_name': speaker_name}),
limit=LIMIT)
num_subject = np.size(sv_subject, 0)
print num_subject
if num_subject < 20:
continue
#Get random SVs from rest of database for test population
#cursor_impostor = db.sv.find({'speaker_name': {'$ne': speaker['name']}})
sv_impostor = stack_SVs_random(db, speaker_name,
num_subject * IMPOSTOR_RATIO)
num_impostor = np.size(sv_impostor, 0)
print 'Subject: %i, Impostor: %i' % (num_subject, num_impostor)
#generate total dataset of observations X with class labels y
X = np.vstack((sv_subject, sv_impostor))
y = np.array([1] * num_subject + [0] * num_impostor)
#Pick random assortment from each set to form training observations
#Switch ensures that smaller number always used for training
if TEST_FOLD < 3:
train, test = iter(StratifiedKFold(y, TEST_FOLD)).next()
else:
test, train = iter(StratifiedKFold(y, TEST_FOLD)).next()
#Perform crossvalidated SVM training
#print type(X), type(y)
#print np.shape(X[train]), np.shape(y[train])
clf = train_svm_crossvalidated(X[train], y[train])
#print type(clf)
#clf_rec = {'classifier': SVMModelField(clf), 'speaker_name': speaker.name}
#db.svm.insert(clf_rec, safe=True)
#Collect classification statistics
accuracy = test_svm_accuracy(X[test], y[test], clf)
num_subject_test = np.sum(y[test])
num_impostor_test = len(y[test]) - num_subject_test
print 'Accuracy: %f' % (float(accuracy['correct_subject']) /
float(num_subject_test))
#print 'Sub: %i/%i Imp: %i/%i' % (accuracy['correct_subject'], num_subject_test, accuracy['correct_impostor'], num_impostor_test)
#print 'False Neg: %i False Pos: %i' % (accuracy['false_neg'], accuracy['false_pos'])
msg = {
'speaker_name': speaker_name,
'accuracy': accuracy,
'num_subject': num_subject,
'num_subject_test': num_subject_test,
'num_impostor_test': num_impostor_test
}
outQ.send_pyobj(msg)
def process_speaker(db):
TEST_FOLD = 2
#db = connect_to_database()
#db.add_son_manipulator(TransformToBinary())
#db = NeoEngine('/data/neo4j')
#Impostor ratio is ratio of impostor records in training
#and testing population. For ratio=N, subject is 1/(N+1),
#impostor N/(N+1) of population
IMPOSTOR_RATIO = 3
LIMIT = 30
#Set up queue
ctx = zmq.Context()
q = ctx.socket(zmq.PULL)
q.connect("tcp://127.0.0.1:5555")
outQ = ctx.socket(zmq.PUSH)
outQ.connect("tcp://127.0.0.1:5556")
while True:
speaker_name = q.recv_json()
print 'Received %s' % speaker_name
#time.sleep(random.randint(1,10))
#Find all SVs for current subject
#print 'Speaker Name: %s' % speaker.name
#print 'Count:', db.sv.find({'speaker_name': speaker.name}).count()
#cursor_subject = Concurrent_cursor(SV.objects(speaker_name=speaker.name))
sv_subject = stack_SVs(db.get('sv', {'speaker_name': speaker_name}),limit=LIMIT)
num_subject = np.size(sv_subject,0)
print num_subject
if num_subject < 20:
continue
#Get random SVs from rest of database for test population
#cursor_impostor = db.sv.find({'speaker_name': {'$ne': speaker['name']}})
sv_impostor = stack_SVs_random(db, speaker_name, num_subject*IMPOSTOR_RATIO)
num_impostor = np.size(sv_impostor,0)
print 'Subject: %i, Impostor: %i' % (num_subject, num_impostor)
#generate total dataset of observations X with class labels y
X = np.vstack((sv_subject, sv_impostor))
y = np.array([1] * num_subject + [0] * num_impostor)
#Pick random assortment from each set to form training observations
#Switch ensures that smaller number always used for training
if TEST_FOLD < 3:
train, test = iter(StratifiedKFold(y, TEST_FOLD)).next()
else:
test, train = iter(StratifiedKFold(y, TEST_FOLD)).next()
#Perform crossvalidated SVM training
#print type(X), type(y)
#print np.shape(X[train]), np.shape(y[train])
clf = train_svm_crossvalidated(X[train], y[train])
#print type(clf)
#clf_rec = {'classifier': SVMModelField(clf), 'speaker_name': speaker.name}
#db.svm.insert(clf_rec, safe=True)
#Collect classification statistics
accuracy = test_svm_accuracy(X[test], y[test], clf)
num_subject_test = np.sum(y[test])
num_impostor_test = len(y[test]) - num_subject_test
print 'Accuracy: %f' % (float(accuracy['correct_subject'])/float(num_subject_test))
#print 'Sub: %i/%i Imp: %i/%i' % (accuracy['correct_subject'], num_subject_test, accuracy['correct_impostor'], num_impostor_test)
#print 'False Neg: %i False Pos: %i' % (accuracy['false_neg'], accuracy['false_pos'])
msg = {'speaker_name': speaker_name, 'accuracy': accuracy, 'num_subject': num_subject, 'num_subject_test': num_subject_test, 'num_impostor_test': num_impostor_test}
outQ.send_pyobj(msg)
def test_accuracy_concurrent(worker, concurrency):
TEST_FOLD = 2
RUN_MAX = False
iter_num = 0
db = connect_to_database()
db.add_son_manipulator(TransformToBinary())
#take this out later when testing complete
fid = open('/home/ubuntu/project/backend-search/src/spkrec/utils/hist'+str(worker)+'.csv', 'wb')
csv_writer = csv.writer(fid)
if worker == 0:
csv_writer.writerow(['name', 'num speaker SVs', 'test subjects', 'test impostors', 'correct subject', 'correct impostor', 'false neg', 'false pos'])
cursor = Concurrent_cursor(Speaker.objects())
cursor.set_concurrency(concurrency)
cursor.set_worker(worker)
for speaker in Speaker.objects():
#Impostor ratio is ratio of impostor records in training
#and testing population. For ratio=N, subject is 1/(N+1),
#impostor N/(N+1) of population
IMPOSTOR_RATIO = 3
#Find all SVs for current subject
print 'Speaker Name: %s' % speaker.name
#print 'Count:', db.sv.find({'speaker_name': speaker.name}).count()
#cursor_subject = Concurrent_cursor(SV.objects(speaker_name=speaker.name))
sv_subject = stack_SVs(db.sv.find({'speaker_name': speaker.name}))
num_subject = np.size(sv_subject,0)
#csv_writer.writerow([speaker.name, num_subject])
#print num_subject
if num_subject < 20:
continue
#Get random SVs from rest of database for test population
#cursor_impostor = db.sv.find({'speaker_name': {'$ne': speaker['name']}})
sv_impostor = stack_SVs_random(db, speaker.name, num_subject*IMPOSTOR_RATIO)
num_impostor = np.size(sv_impostor,0)
print 'Subject: %i, Impostor: %i' % (num_subject, num_impostor)
#generate total dataset of observations X with class labels y
X = np.vstack((sv_subject, sv_impostor))
y = np.array([1] * num_subject + [0] * num_impostor)
#Pick random assortment from each set to form training observations
#Switch ensures that smaller number always used for training
if TEST_FOLD < 3:
train, test = iter(StratifiedKFold(y, TEST_FOLD)).next()
else:
test, train = iter(StratifiedKFold(y, TEST_FOLD)).next()
#print train
#Perform crossvalidated SVM training
#print type(X), type(y)
#print np.shape(X[train]), np.shape(y[train])
clf = train_svm_crossvalidated(X[train], y[train])
#print type(clf)
#clf_rec = {'classifier': SVMModelField(clf), 'speaker_name': speaker.name}
#db.svm.insert(clf_rec, safe=True)
#Collect classification statistics
accuracy = test_svm_accuracy(X[test], y[test], clf)
num_subject_test = np.sum(y[test])
num_impostor_test = len(y[test]) - num_subject_test
print 'Accuracy: %f' % (float(accuracy['correct_subject'])/float(num_subject_test))
print 'Sub: %i/%i Imp: %i/%i' % (accuracy['correct_subject'], num_subject_test, accuracy['correct_impostor'], num_impostor_test)
print 'False Neg: %i False Pos: %i' % (accuracy['false_neg'], accuracy['false_pos'])
csv_writer.writerow([speaker.name, num_subject, num_subject_test, num_impostor_test, accuracy['correct_subject'], accuracy['correct_impostor'], accuracy['false_neg'], accuracy['false_pos']])
iter_num = iter_num + 1
#if RUN_MAX and iter_num >= RUN_MAX:
# print "I'm breaking"
# break
#print num_subject, num_impostor
fid.close()
print "Complete"
