def load_data_c(self, config): """ Load the data for your ML pipeline. Will be fed into `train`. :param config: object constructed with all the relevant arguments for `load_data` :type config: ```Union[dict, Config, Any]``` :return: a call to .load_data with the config as params :rtype: ```Union[Tuple[tf.data.Dataset, tf.data.Dataset], Tuple[np.ndarray, np.ndarray]]``` """ return self.load_data(**to_d(config))
def train_c(self, config): """ Run the training loop for your ML pipeline. :param config: object constructed with all the relevant arguments for `train` :type config: ```Union[dict, Config, Any]``` :return: a call to .train with the config as params :rtype: ```train``` """ return self.train(**to_d(config))
def load_model_c(self, config): """ Load the model. Takes a model object, or a pipeline that downloads & configures before returning a model object. :param config: object constructed with all the relevant arguments for `load_model` :type config: ```Union[dict, Config, Any]``` :return: a call to .load_model with the config as params :rtype: ```load_model``` """ return self.load_model(**to_d(config))
def test_to_d(self) -> None: """ Tests whether `to_d` creates the right dictionary """ self.assertDictEqual(to_d({}), {}) self.assertListEqual(*map( sorted, ( to_d(ml_params.utils).keys(), (ml_params.utils.__all__ + [ "deepcopy", "environ", "getmembers", "parse_to_argv_gen", "partial", "path", "version_info", "itemgetter", ]), ), ))
def test_properties(self) -> None: """ Tests whether `BaseTrainer` has the right properties """ self.assertListEqual( sorted(to_d(BaseTrainer).keys()), [ "data", "load_data", "load_data_c", "load_model", "load_model_c", "model", "train", "train_c", ], )