class TestDataAugmentation(unittest.TestCase):
def setUp(self):
self.called = 0
self.value = 1.0
self.nn = MLPR(
layers=[L("Linear")],
n_iter=1,
batch_size=2,
mutator=self._mutate_fn)
def _mutate_fn(self, sample):
self.called += 1
sample[sample == 0.0] = self.value
def test_TestCalledOK(self):
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,4))
self.nn._fit(a_in, a_out)
assert_equals(a_in.shape[0], self.called)
def test_DataIsUsed(self):
self.value = float("nan")
a_in, a_out = numpy.zeros((8,16)), numpy.zeros((8,4))
assert_raises(RuntimeError, self.nn._fit, a_in, a_out)
class TestDataAugmentation(unittest.TestCase):
def setUp(self):
self.called = 0
self.value = 1.0
self.nn = MLPR(layers=[L("Linear")],
n_iter=1,
batch_size=1,
callback={'on_batch_start': self._mutate_fn})
def _mutate_fn(self, Xb, **_):
self.called += 1
Xb[Xb == 0.0] = self.value
def test_TestCalledOK(self):
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, 4))
self.nn._fit(a_in, a_out)
assert_equals(a_in.shape[0], self.called)
def test_DataIsUsed(self):
self.value = float("nan")
a_in, a_out = numpy.zeros((8, 16)), numpy.zeros((8, 4))
assert_raises(RuntimeError, self.nn._fit, a_in, a_out)
