def main():
FORMAT = '%(asctime)s %(levelname)s %(message)s'
logging.basicConfig(format=FORMAT)
logging.getLogger().setLevel(logging.INFO)
args = parse_args()
lang_map = {i: fn for i, fn in enumerate(sorted(listdir(args.lang_map)))}
if args.train.endswith('.mtx'):
mtx = mmread(args.train).todense()
t_mtx = mmread(args.test).todense()
else:
with open(args.train) as stream:
mtx = np.loadtxt(stream, np.float64)
with open(args.test) as stream:
t_mtx = np.loadtxt(stream, np.float64)
labels = np.ravel(mtx[:, 0])
test_labels = t_mtx[:, 0]
test_mtx = t_mtx[:, 1:]
if args.scale:
train = scale(mtx[:, 1:], with_mean=False)
else:
train = mtx[:, 1:]
kwargs = {}
for a in args.params:
k, v = a.split('=')
try:
v = int(v)
except:
pass
kwargs[k] = v
r = Representation(args.encoder, args.classifier, **kwargs)
r.encode(train)
logging.info('Matrix encoded')
r.train_classifier(labels)
logging.info('Model trained')
acc = 0
N = 0
for vec_ in test_mtx:
vec = np.ravel(vec_)
cl = r.classify_vector(vec, with_probs=args.with_probs)
try:
lab = test_labels[N, 0]
except IndexError:
lab = test_labels[N]
N += 1
if args.with_probs:
guess = max(enumerate(cl[0, :]), key=lambda x: x[1])[0]
print('{0}\t{1}\t{2}'.format('\t'.join(map(str, cl[0, :])), lang_map[guess], lang_map[int(lab)]))
else:
try:
guess = int(cl[0, 0])
except IndexError:
guess = int(cl + 0.5)
print('{0}\t{1}'.format(lang_map[guess], lang_map[int(lab)]))
if int(guess) == int(lab):
acc += 1
def main():
r = Representation('pca', 'naive_bayes', dimension=3)
raw_mtx = numpy.array([[1, 1, 1, 0, 0, 0], [1, 0, 1, 0, 0, 0], [1, 1, 1, 0, 0, 0], [0, 0, 1, 1, 1, 0], [0, 0, 1, 1, 0, 0], [0, 0, 1, 1, 1, 0]])
r.encode(raw_mtx)
r.train_classifier([0, 0, 0, 1, 1, 1])
print r.classify_vector([1, 2, 1, 0, 1, 0])