def test_projects_properly(self):
embedder = BagOfWordCountsTokenEmbedder(vocab=self.vocab,
projection_dim=50)
numpy_tensor = np.array([[1, 0], [1, 0], [4, 4]])
inputs = torch.from_numpy(numpy_tensor)
embedder_output = embedder(inputs)
assert embedder_output.shape[1] == 50
def test_projects_properly(self):
params = Params({"projection_dim": 50})
embedder = BagOfWordCountsTokenEmbedder.from_params(self.vocab,
params=params)
numpy_tensor = np.array([[1, 0], [1, 0], [4, 4]])
inputs = torch.from_numpy(numpy_tensor)
embedder_output = embedder(inputs)
assert embedder_output.shape[1] == 50
def test_projects_properly(self):
params = Params({"projection_dim": 50})
embedder = BagOfWordCountsTokenEmbedder.from_params(self.vocab,
params=params)
numpy_tensor = np.array(
[self.vocab.get_token_index(x) for x in ["1", "2", "3"]])
inputs = torch.from_numpy(numpy_tensor).unsqueeze(1)
embedder_output = embedder(inputs)
assert embedder_output.shape[1] == 50
def test_zeros_out_unknown_tokens(self):
embedder = BagOfWordCountsTokenEmbedder(self.vocab, ignore_oov=True)
numpy_tensor = np.array([[1, 5], [2, 0], [4, 4]])
inputs = torch.from_numpy(numpy_tensor)
embedder_output = embedder(inputs)
numpy_tensor = np.array([[0, 0, 0, 0, 0, 1], [0, 0, 1, 0, 0, 0],
[0, 0, 0, 0, 2, 0]])
manual_output = torch.from_numpy(numpy_tensor).float()
assert_almost_equal(embedder_output.data.numpy(),
manual_output.data.numpy())
def test_forward_calculates_bow_properly(self):
embedder = BagOfWordCountsTokenEmbedder(self.vocab)
numpy_tensor = np.array([[2, 0], [3, 0], [4, 4]])
inputs = torch.from_numpy(numpy_tensor)
embedder_output = embedder(inputs)
numpy_tensor = np.array([[0, 0, 1, 0, 0, 0], [0, 0, 0, 1, 0, 0],
[0, 0, 0, 0, 2, 0]])
manual_output = torch.from_numpy(numpy_tensor).float()
assert_almost_equal(embedder_output.data.numpy(),
manual_output.data.numpy())
def test_ignore_oov_should_fail_on_non_padded_vocab(self):
with pytest.raises(ConfigurationError):
BagOfWordCountsTokenEmbedder(self.non_padded_vocab,
ignore_oov=True)