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)