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)