def n_fold_cv(n, samples, labels):
(sample_groups, sample_labels) = split_groups(n, samples, labels)
p = {}
r = {}
sz = {}
for i in range(n):
train_samples = [
x for j in range(n) if j != i for x in sample_groups[j]
]
train_labels = [
x for j in range(n) if j != i for x in sample_labels[j]
]
test_samples = sample_groups[i]
test_labels = sample_labels[i]
algs = ml.run(train_samples, train_labels, test_samples, test_labels)
for alg in algs:
key = alg.__class__.__name__
if key not in p:
p[key] = 0.0
r[key] = 0.0
p[key] += alg.P()
r[key] += alg.R()
fmt = "{0:30}{1:<20}{2:<20}"
print "N-fold Cross Validation:\n----------------------------------------"
print fmt.format("Algo", "P", "R")
for key in p.keys():
print fmt.format(key, p[key] / n, r[key] / n)
def run_test(train_samples, train_labels, test_samples, test_labels):
algs = ml.run(train_samples, train_labels, test_samples, test_labels)
print ""
fmt = "{0:30}{1:<20}{2:<20}"
print "Test set results:\n----------------------------------------"
print fmt.format("Algo", "P", "R")
for alg in algs:
key = alg.__class__.__name__
print fmt.format(key, alg.P(), alg.R())
def generete_embeddings(code):
# printAst(*parse(code))
parser_res = parse(code)
emb = ml.run(parser_res)
return emb
def test(request):
animal = ml.run()
print(animal)
return HttpResponse(animal)
type=int,
help='print frequency')
parser.add_argument('--output-dir',
default='data/coco/checkpoints',
help='path where to save')
parser.add_argument('--resume', default='', help='resume from checkpoint')
parser.add_argument('--aspect-ratio-group-factor', default=0, type=int)
parser.add_argument(
"--test-only",
dest="test_only",
help="Only test the model",
action="store_true",
)
parser.add_argument(
"--pretrained",
dest="pretrained",
help="Use pre-trained models from the modelzoo",
action="store_true",
)
# distributed training parameters
#parser.add_argument('--world-size', default=2, type=int, help='number of distributed processes')
#parser.add_argument('--dist-url', default='env://', help='url used to set up distributed training')
args = parser.parse_args()
if args.output_dir:
utils.mkdir(args.output_dir)
ml.run(main, args, name='train')