def save(self, output, deps=None):
if not deps:
deps = tuple()
self._meta = generate_meta(self.NAME, ast2vec.__version__, *deps)
write_model(
self._meta, {
"tokens": merge_strings(self.tokens),
"matrix": disassemble_sparse_matrix(self.matrix)
}, output)
def save(self, output, deps=None):
if not deps or len(deps) < 2:
raise ValueError(
"You must specify DocumentFrequencies and Id2Vec dependencies "
"to save NBOW.")
self._meta = generate_meta(self.NAME, ast2vec.__version__, *deps)
write_model(
self._meta, {
"repos": merge_strings(self._repos),
"matrix": disassemble_sparse_matrix(self._matrix)
}, output)
def save(self, output, deps: Union[None, list] = None) -> None:
if not deps:
deps = self.meta["dependencies"]
self._meta = generate_meta(self.NAME, ast2vec.__version__, *deps)
write_model(
self._meta, {
"tokens":
merge_strings(self.tokens),
"topics":
merge_strings(self.topics)
if self.topics is not None else False,
"matrix":
disassemble_sparse_matrix(self.matrix)
}, output)
def save(self, output, deps=None):
if not deps:
raise ValueError(
"You must specify DocumentFrequencies dependency to save BOW.")
self._meta = generate_meta(self.NAME, ast2vec.__version__, *deps)
if self.tokens:
write_model(
self._meta, {
"repos": merge_strings(self._repos),
"matrix": disassemble_sparse_matrix(self.matrix),
"tokens": merge_strings(self.tokens)
}, output)
else:
self._log.warning("Did not write %s because the model is empty",
output)
def test_disassemble_sparse_matrix_empty(self):
arr = numpy.zeros((10, 10), dtype=numpy.float32)
mat = csr_matrix(arr)
dis = disassemble_sparse_matrix(mat)
self.assertIsInstance(dis, dict)
self.assertIn("shape", dis)
self.assertIn("format", dis)
self.assertIn("data", dis)
self.assertEqual(dis["shape"], arr.shape)
self.assertEqual(dis["format"], "csr")
self.assertIsInstance(dis["data"], list)
self.assertEqual(len(dis["data"]), 3)
self.assertEqual(dis["data"][0].size, 0)
self.assertEqual(dis["data"][1].size, 0)
self.assertTrue((dis["data"][2] == 0).all())
def test_disassemble_sparse_matrix(self):
arr = numpy.zeros((10, 10), dtype=numpy.float32)
numpy.random.seed(0)
arr[numpy.random.randint(0, 10, (50, 2))] = 1
mat = csr_matrix(arr)
dis = disassemble_sparse_matrix(mat)
self.assertIsInstance(dis, dict)
self.assertIn("shape", dis)
self.assertIn("format", dis)
self.assertIn("data", dis)
self.assertEqual(dis["shape"], arr.shape)
self.assertEqual(dis["format"], "csr")
self.assertIsInstance(dis["data"], (tuple, list))
self.assertEqual(len(dis["data"]), 3)
self.assertTrue((dis["data"][0] == mat.data).all())
self.assertTrue((dis["data"][1] == mat.indices).all())
self.assertTrue((dis["data"][2] == mat.indptr).all())
def _generate_tree(self):
return {
"tokens": merge_strings(self.tokens),
"matrix": disassemble_sparse_matrix(self.matrix)
}
