def test_crit_functions_dict(self): res = make_criterion_functions({ "MASELoss": { "baseline_method": "mean" }, "MSE": {} }) self.assertIsInstance(res, list)
def __init__( self, model_base: str, training_data: str, validation_data: str, test_data: str, params: Dict): self.params = params if "weight_path" in params: params["weight_path"] = get_data(params["weight_path"]) self.model = self.load_model(model_base, params["model_params"], params["weight_path"]) else: self.model = self.load_model(model_base, params["model_params"]) # params["dataset_params"]["forecast_test_len"] = params["inference_params"]["hours_to_forecast"] self.training = self.make_data_load(training_data, params["dataset_params"], "train") self.validation = self.make_data_load(validation_data, params["dataset_params"], "valid") self.test_data = self.make_data_load(test_data, params["dataset_params"], "test") if "GCS" in self.params and self.params["GCS"]: self.gcs_client = get_storage_client() else: self.gcs_client = None self.wandb = self.wandb_init() self.crit = make_criterion_functions(params["metrics"])
def test_crit_functions_list(self): res = make_criterion_functions(["MSE", "RMSE", "MAPE"]) self.assertIsInstance(res, list)