def test_ndr_over_sample(self):
config = {"label1": 100, "label2": 100, "label3": 100}
# train : 300, val : 300, test : 300
np.random.seed(1234)
source = self._generate_classification_dataset(config, 10)
result = ndr.NDR(source,
working_subset='train',
num_cut=10,
over_sample='random',
seed=12145)
self.assertEqual(10, len(result.get_subset("train")))
self.assertEqual(290, len(result.get_subset("duplicated")))
self.assertEqual(300, len(result.get_subset("val")))
self.assertEqual(300, len(result.get_subset("test")))
# Check source
self.assertEqual(300, len(source.get_subset("train")))
self.assertEqual(300, len(source.get_subset("val")))
self.assertEqual(300, len(source.get_subset("test")))
result = ndr.NDR(source,
working_subset='train',
num_cut=10,
over_sample='similarity',
seed=12145)
self.assertEqual(10, len(result.get_subset("train")))
self.assertEqual(290, len(result.get_subset("duplicated")))
self.assertEqual(300, len(result.get_subset("val")))
self.assertEqual(300, len(result.get_subset("test")))
# Check source
self.assertEqual(300, len(source.get_subset("train")))
self.assertEqual(300, len(source.get_subset("val")))
self.assertEqual(300, len(source.get_subset("test")))
def test_ndr_gradient_fails_on_invalid_parameters(self):
source = self._generate_dataset({'label1': 5}, 10)
with self.assertRaisesRegex(ValueError, "Invalid block_shape"):
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
block_shape=(3, 6, 6),
algorithm='gradient')
len(result)
with self.assertRaisesRegex(ValueError,
"block_shape should be positive"):
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
block_shape=(-1, 0),
algorithm='gradient')
len(result)
with self.assertRaisesRegex(ValueError,
"sim_threshold should be large than 0"):
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
sim_threshold=0,
block_shape=(8, 8),
algorithm='gradient')
len(result)
with self.assertRaisesRegex(
ValueError, "hash_dim should be smaller than feature shape"):
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
hash_dim=1024,
block_shape=(8, 8),
algorithm='gradient')
len(result)
with self.assertRaisesRegex(ValueError, "hash_dim should be positive"):
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
hash_dim=-5,
block_shape=(8, 8),
algorithm='gradient')
len(result)
def test_ndr_seed(self):
config = {"label1": 100, "label2": 100, "label3": 100}
# train : 300, val : 300, test : 300
np.random.seed(1234)
source = self._generate_classification_dataset(config, 10)
result1 = ndr.NDR(source, working_subset="train", seed=12345)
result2 = ndr.NDR(source, working_subset="train", seed=12345)
result3 = ndr.NDR(source, working_subset="train", seed=12)
self.assertEqual(tuple(result1.get_subset("train")),
tuple(result2.get_subset("train")))
self.assertNotEqual(tuple(result1.get_subset("train")),
tuple(result3.get_subset("train")))
self.assertNotEqual(tuple(result2.get_subset("train")),
tuple(result3.get_subset("train")))
def test_ndr_without_cut(self):
config = {"label1": 100, "label2": 100, "label3": 100}
# train : 300, val : 300, test : 300
np.random.seed(1234)
source = self._generate_classification_dataset(config, 10)
result = ndr.NDR(source, working_subset='train', seed=12145)
self.assertEqual(2, len(result.get_subset("train")))
self.assertEqual(298, len(result.get_subset("duplicated")))
self.assertEqual(300, len(result.get_subset("val")))
self.assertEqual(300, len(result.get_subset("test")))
# Check source
self.assertEqual(300, len(source.get_subset("train")))
self.assertEqual(300, len(source.get_subset("val")))
self.assertEqual(300, len(source.get_subset("test")))
def test_ndr_with_error(self):
config = {"label1": 100, "label2": 100, "label3": 100}
# train : 300, val : 300, test : 300
np.random.seed(1234)
with self.assertRaisesRegex(ValueError, "Invalid working_subset name"):
source = self._generate_classification_dataset(config, 3)
subset = "no_such_subset"
result = ndr.NDR(source, working_subset=subset)
len(result)
with self.assertRaisesRegex(ValueError,
"working_subset == duplicated_subset"):
source = self._generate_classification_dataset(config, 3)
result = ndr.NDR(source,
working_subset="train",
duplicated_subset="train")
len(result)
with self.assertRaisesRegex(ValueError, "Invalid algorithm name"):
source = self._generate_classification_dataset(config, 3)
algorithm = "no_such_algo"
result = ndr.NDR(source,
working_subset="train",
algorithm=algorithm)
len(result)
with self.assertRaisesRegex(
ValueError,
"The number of images is smaller than the cut you want"):
source = self._generate_classification_dataset(config, 3)
result = ndr.NDR(source, working_subset='train', num_cut=10000)
len(result)
with self.assertRaisesRegex(ValueError, "Invalid over_sample"):
source = self._generate_classification_dataset(config, 10)
sampling = "no_such_sampling"
result = ndr.NDR(source,
working_subset='train',
num_cut=100,
seed=12145,
over_sample=sampling)
len(result)
with self.assertRaisesRegex(ValueError, "Invalid under_sample"):
source = self._generate_classification_dataset(config, 10)
sampling = "no_such_sampling"
result = ndr.NDR(source,
working_subset='train',
num_cut=1,
seed=12145,
under_sample=sampling)
len(result)
def test_ndr_gradient_can_use_block(self):
config = {"label1": 100, "label2": 100, "label3": 100}
# train : 300, val : 300, test : 300
np.random.seed(1234)
source = self._generate_dataset(config, 10)
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
block_shape=(8, 8),
seed=12145)
self.assertEqual(1, len(result.get_subset("train")))
self.assertEqual(299, len(result.get_subset("duplicated")))
self.assertEqual(300, len(result.get_subset("val")))
self.assertEqual(300, len(result.get_subset("test")))
# Check source
self.assertEqual(300, len(source.get_subset("train")))
self.assertEqual(300, len(source.get_subset("val")))
self.assertEqual(300, len(source.get_subset("test")))
def test_ndr_can_use_undersample_inverse(self):
config = {"label1": 100, "label2": 100, "label3": 100}
# train : 300, val : 300, test : 300
np.random.seed(1234)
source = self._generate_dataset(config, 10)
result = ndr.NDR(source,
working_subset='train',
num_cut=1,
under_sample='inverse',
seed=12145)
self.assertEqual(1, len(result.get_subset("train")))
self.assertEqual(299, len(result.get_subset("duplicated")))
self.assertEqual(300, len(result.get_subset("val")))
self.assertEqual(300, len(result.get_subset("test")))
# Check source
self.assertEqual(300, len(source.get_subset("train")))
self.assertEqual(300, len(source.get_subset("val")))
self.assertEqual(300, len(source.get_subset("test")))
def test_ndr_gradient_specific(self):
config = {"label1": 100, "label2": 100, "label3": 100}
# train : 300, val : 300, test : 300
np.random.seed(1234)
source = self._generate_classification_dataset(config, 10)
with self.assertRaisesRegex(ValueError, "Invalid block_shape"):
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
block_shape=(3, 6, 6),
seed=12145)
len(result)
with self.assertRaisesRegex(ValueError,
"block_shape should be positive"):
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
block_shape=(-1, 0),
seed=12145)
len(result)
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
block_shape=(8, 8),
seed=12145)
self.assertEqual(1, len(result.get_subset("train")))
self.assertEqual(299, len(result.get_subset("duplicated")))
self.assertEqual(300, len(result.get_subset("val")))
self.assertEqual(300, len(result.get_subset("test")))
# Check source
self.assertEqual(300, len(source.get_subset("train")))
self.assertEqual(300, len(source.get_subset("val")))
self.assertEqual(300, len(source.get_subset("test")))
with self.assertRaisesRegex(
ValueError, "hash_dim should be smaller than feature shape"):
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
hash_dim=1024,
block_shape=(8, 8),
seed=12145)
len(result)
with self.assertRaisesRegex(ValueError, "hash_dim should be positive"):
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
hash_dim=-5,
block_shape=(8, 8),
seed=12145)
len(result)
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
hash_dim=16,
seed=12145)
self.assertEqual(2, len(result.get_subset("train")))
self.assertEqual(298, len(result.get_subset("duplicated")))
self.assertEqual(300, len(result.get_subset("val")))
self.assertEqual(300, len(result.get_subset("test")))
# Check source
self.assertEqual(300, len(source.get_subset("train")))
self.assertEqual(300, len(source.get_subset("val")))
self.assertEqual(300, len(source.get_subset("test")))
with self.assertRaisesRegex(ValueError,
"sim_threshold should be large than 0"):
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
sim_threshold=0,
block_shape=(8, 8),
seed=12145)
len(result)
result = ndr.NDR(source,
working_subset='train',
over_sample='random',
sim_threshold=0.7,
seed=12145)
self.assertEqual(2, len(result.get_subset("train")))
self.assertEqual(298, len(result.get_subset("duplicated")))
self.assertEqual(300, len(result.get_subset("val")))
self.assertEqual(300, len(result.get_subset("test")))
# Check source
self.assertEqual(300, len(source.get_subset("train")))
self.assertEqual(300, len(source.get_subset("val")))
self.assertEqual(300, len(source.get_subset("test")))
