def rand_assert(self, iters, size, conf):
for i in range(iters):
should = torch.rand(size, dtype=conf.data_type)
should = conf.to(should)
ctx = SparseTensorFeatureContext.instance('some_feature_id',
should, conf)
self.assertTensorEquals(should, conf.to(ctx.to_tensor(conf)))
def test_sparse(self):
conf = self.conf
should = [
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 1.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 1.50, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 10.50, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[2.50, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 1.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 0.00],
[0.00, 0.00, 0.00, 0.00, 0.00, 0.00, 13.20, 0.00, 0.00, 0.00]
]
tarr = torch.tensor(should)
ctx = SparseTensorFeatureContext.instance('afeattype', tarr, conf)
should = conf.singleton(should, dtype=tarr.dtype)
dense = ctx.to_tensor(conf)
self.assertTensorEquals(should, dense)
def test_3d_int_mat(self):
should = torch.randint(0, 5, (2, 7, 11))
ctx = SparseTensorFeatureContext.instance('afeattype', should,
self.conf)
for m in ctx.sparse_arr:
self.assertTrue(isinstance(m, csr_matrix))
dense = ctx.to_tensor(self.conf)
self.assertTensorEquals(should, dense)
self.assertEqual(should.shape, dense.shape)
def encode(self, doc: Union[Tuple[FeatureDocument], FeatureDocument]) -> \
FeatureContext:
ctx: TensorFeatureContext
if isinstance(doc, (tuple, list)):
self._assert_doc(doc)
docs = doc
comb_doc = FeatureDocument.combine_documents(docs)
n_toks = self.manager.get_token_length(comb_doc)
arrs = tuple(
map(lambda d: self._encode_doc(d.combine_sentences(), n_toks),
docs))
arr = torch.cat(arrs, dim=0)
arr = arr.unsqueeze(-1)
ctx = SparseTensorFeatureContext.instance(self.feature_id, arr,
self.torch_config)
else:
ctx = super().encode(doc)
return ctx
def _encode(self, doc: FeatureDocument) -> FeatureContext:
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'encoding doc: {doc}')
sent_arrs = []
for sent in doc.sents:
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'encoding sentence: {sent}')
tok_arrs = []
for fvec in self.manager.spacy_vectorizers.values():
cnts: Tensor = self.get_feature_counts(sent, fvec)
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'encoding with {fvec}')
tok_arrs.append(cnts)
sent_arrs.append(torch.cat(tok_arrs))
arr = torch.stack(sent_arrs)
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'encoded shape: {arr.shape}')
return SparseTensorFeatureContext.instance(self.feature_id, arr,
self.torch_config)
def _encode(self, doc: FeatureDocument) -> FeatureContext:
"""Encode tokens found in the container by aggregating the spaCy vectorizers
output.
"""
arr = self.torch_config.zeros(self._get_shape_for_document(doc))
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'type array shape: {arr.shape}')
sent: FeatureSentence
for six, sent in enumerate(doc.sents):
col_start = 0
for fvec in self.manager.spacy_vectorizers.values():
col_end = col_start + fvec.shape[1]
self._populate_feature_vectors(sent, six, fvec, arr, col_start,
col_end)
col_start = col_end
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'encoded array shape: {arr.shape}')
return SparseTensorFeatureContext.instance(self.feature_id, arr,
self.torch_config)
def _encode(self, doc: FeatureDocument) -> FeatureContext:
slen = len(doc)
tlen = self.manager.get_token_length(doc)
attr = self.feature_attribute
arr = self.torch_config.zeros((slen, tlen, self.shape[2]))
doc_val = getattr(doc, attr) if self.level == 'document' else None
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'vectorizing: {attr} for token length: {tlen} ' +
f'in to {arr.shape}')
for six, sent in enumerate(doc.sents):
if self.level == 'document':
feats = [doc_val] * len(sent)
elif self.level == 'sentenece':
sent_val = getattr(sent, attr)
feats = [sent_val] * len(sent)
elif self.level == 'token':
feats = tuple(map(lambda s: getattr(s, attr), sent))
else:
raise VectorizerError(f'Unknown doc level: {self.level}')
self._encode_cats(feats, arr[six])
if logger.isEnabledFor(logging.DEBUG):
logger.debug(f'vectorized: {len(doc)} sents in to {arr.shape}')
return SparseTensorFeatureContext.instance(self.feature_id, arr,
self.torch_config)
def _encode(self, doc: FeatureDocument) -> FeatureContext:
n_toks = self.manager.get_token_length(doc)
arr = self._encode_doc(doc, n_toks)
arr = arr.unsqueeze(-1)
return SparseTensorFeatureContext.instance(self.feature_id, arr,
self.torch_config)