def _load_local(self, path: str) -> Dataset:
builder = DatasetBuilder()
data, description = self._read(path)
for mod, snrs in description.items():
for snr in snrs:
for iq in data[(mod, snr)]:
builder.add(iq=iq, Modulation=mod, SNR=snr)
return builder.build()
def _create_dataset(cls, extras: dict = {}):
keys = ["SNR", "Modulation"] + list(extras.keys())
db = DatasetBuilder(n=cls.NSAMPLES, keys=keys)
iq = np.zeros((2, cls.NSAMPLES))
for mod in cls.MODS:
for snr in cls.SNRS:
for _ in range(cls.NEXAMPLES):
db.add(iq, SNR=snr, Modulation=mod, **extras)
return db.build()
def test_isbalanced(self):
"""Verify that the Dataset can correctly detect imbalance.
"""
db = DatasetBuilder(n=TestDataset.NSAMPLES, keys=["SNR", "Modulation"])
iq = np.zeros((2, TestDataset.NSAMPLES))
for i, mod in enumerate(TestDataset.MODS):
for snr in TestDataset.SNRS:
for _ in range(i * TestDataset.NEXAMPLES):
db.add(iq, SNR=snr, Modulation=mod)
baddata = db.build()
self.assertTrue(TestDataset._data.is_balanced(label="Modulation"))
self.assertTrue(TestDataset._data.is_balanced(label="SNR"))
self.assertFalse(baddata.is_balanced(label="Modulation"))
self.assertTrue(baddata.is_balanced(label="SNR"))
def test_equality(self):
"""Verify that the dataset.__eq__ method works
"""
db = DatasetBuilder(n=TestDataset.NSAMPLES, keys=["SNR", "Modulation"])
iq = np.zeros((2, TestDataset.NSAMPLES))
for i, mod in enumerate(TestDataset.MODS):
for snr in TestDataset.SNRS:
for _ in range(i * TestDataset.NEXAMPLES):
db.add(iq, SNR=snr, Modulation=mod)
baddata = db.build()
copieddata = deepcopy(TestDataset._data)
# By definition, a dataset must be equivalent to itself
self.assertEqual(TestDataset._data, TestDataset._data)
# Verify that equality works in both directions
self.assertEqual(TestDataset._data, copieddata)
self.assertEqual(copieddata, TestDataset._data)
# Verify that inequality works in both directions
self.assertNotEqual(TestDataset._data, baddata)
self.assertNotEqual(baddata, TestDataset._data)
def test_IncorrectShape(self):
"""Verify that the assertion on IQ shape works
"""
# Verify that simply not providing anything close to IQ is failed
iq = np.zeros(1024)
db = DatasetBuilder()
with self.assertRaises(ValueError):
db.add(iq)
# Verify that IQ length must match defaults
iq = np.zeros((2, 1024))
db = DatasetBuilder(n=128)
with self.assertRaises(ValueError):
db.add(iq)
# Verify that IQ length must match prior rows
iq = np.zeros((2, 1024))
db = DatasetBuilder()
db.add(iq)
iq = np.zeros((2, 2048))
with self.assertRaises(ValueError):
db.add(iq)
def test_MissingRequiredMeta(self):
"""Verify that the assertion on missing meta data works
"""
# Verify that the additions must match the constructor provided keys
iq = np.zeros((2, 1024))
db = DatasetBuilder(keys=["Foo", "Bar"])
with self.assertRaises(ValueError):
db.add(iq)
# Verify that the defaults can make up for missing keys
iq = np.zeros((2, 1024))
db = DatasetBuilder(keys=["Foo", "Bar"], defaults={"Foo": 1})
db.add(iq, Bar=3)
db.add(iq, Foo=5, Bar=3)
with self.assertRaises(ValueError):
db.add(iq, Foo=2)
# Verify that the required keys are picked up from prior rows
iq = np.zeros((2, 1024))
db = DatasetBuilder()
db.add(iq, Foo=1, Bar=3)
with self.assertRaises(ValueError):
db.add(iq, Foo=2)
def test_CorrectBuilding(self):
"""Verify that a Dataset is properly created when given valid args.
"""
db = DatasetBuilder()
iq = np.zeros((2, 1024))
db.add(iq, label=1)
db.add(iq, label=2)
db.add(iq, label=2)
db.add(iq, label=3)
db.add(iq, label=3)
db.add(iq, label=3)
db.add(iq, label=4)
db.add(iq, label=4)
db.add(iq, label=4)
db.add(iq, label=4)
dataset = db.build()
self.assertEqual(len(dataset), 10)
self.assertEqual(
dataset.get_examples_per_class(label="label"), {1: 1, 2: 2, 3: 3, 4: 4}
)