def save(self, output, deps=None):
if not deps:
deps = tuple()
self._meta = generate_meta(self.NAME, ast2vec.__version__, *deps)
write_model(self._meta, {
"embeddings": self.embeddings,
"tokens": merge_strings(self.tokens)
}, output)
def test_write(self):
meta = generate_meta("test", (1, 0, 3))
with tempfile.NamedTemporaryFile() as tmp:
write_model(meta, {"xxx": 100500}, tmp.name)
with asdf.open(tmp.name) as f:
self.assertEqual(f.tree["meta"]["model"], "test")
self.assertEqual(f.tree["xxx"], 100500)
self.assertEqual(oct(os.stat(tmp.name).st_mode)[-3:], "666")
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:
deps = tuple()
self._meta = generate_meta(self.NAME, ast2vec.__version__, *deps)
tokens = self.tokens()
freqs = numpy.array([self._df[t] for t in tokens], dtype=numpy.float32)
write_model(self._meta, {
"docs": self.docs,
"tokens": merge_strings(tokens),
"freqs": freqs
}, 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:
"""
Serializes the model on disk.
:param output: path to the file.
:param deps: the list of dependencies.
:return: None
"""
if not deps:
deps = tuple()
self._meta = generate_meta(self.NAME, ast2vec.__version__, *deps)
write_model(self._meta, self._to_dict(), 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:
deps = tuple()
self._meta = generate_meta(self.NAME, 0, *deps)
tokens = self.tokens()
freqs = numpy.array([self._df[t] for t in tokens], dtype=numpy.float32)
if tokens:
write_model(self._meta, {
"docs": self.docs,
"tokens": merge_strings(tokens),
"freqs": freqs
}, output)
else:
self._log.warning("Did not write %s because the model is empty",
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 save(self, output, deps=None):
if not deps:
deps = tuple()
self._meta = generate_meta(self.NAME, ast2vec.__version__, *deps)
write_model(self._meta, self._to_dict_to_save(), output)