def fit(self, data: dict) -> None:
super().fit(data)
data_lang = data.get('language')
if data_lang != self.lang: # pragma: no cover
self._logger.warning(
'Training language "%s" and interpreter language "%s" do not match,'\
'things could go badly',
data_lang, self.lang)
self._logger.info('Fitting using "snips v%s"', __version__)
checksum = compute_checksum(data)
cached_checksum = None
# Try to load the used checksum
if self.cache_directory:
cached_checksum_path = os.path.join(self.cache_directory,
'trained.checksum')
cached_checksum = read_file(cached_checksum_path,
ignore_errors=True)
if not cached_checksum:
self._logger.debug('Checksum file not found')
if checksum == cached_checksum:
self.load_from_cache()
else:
config = None
try:
self._logger.info(
'Importing default configuration for language "%s"',
self.lang)
config = getattr(snips_confs, 'CONFIG_%s' % self.lang.upper())
except AttributeError:
self._logger.warning(
'Could not import default configuration, it will use the generic one instead'
)
resource_pkg_name = self._check_and_install_resources_package()
self._engine = SnipsNLUEngine(
config, resources=load_resources(resource_pkg_name))
self._engine.fit(data)
if self.cache_directory: # pragma: no cover
self._logger.info('Persisting trained engine to "%s"',
self.cache_directory)
# Make sure the cache directory has been cleaned out
rmtree(self.cache_directory, ignore_errors=True)
self._engine.persist(self.cache_directory)
with open(cached_checksum_path, mode='w') as file:
file.write(checksum)
self._configure()
def test_it_should_not_raise_exception_when_errors_ignored(self):
expect(read_file('something', ignore_errors=True)).to.be.none