UPDATE: bug fixes when missing config variables
This commit is contained in:
Ali Zaidi
Родитель
a3fb08063a
Коммит
9d46161353
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@ -1,14 +1,14 @@
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defaults:
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# - data: house_energy.yaml
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- data: house_energy.yaml
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# - data: cartpole_mixed.yaml
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- data: hvac_b1.yaml
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# - data: hvac_b1.yaml
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# - data: quanser_rand.yaml
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- model: xgboost.yaml
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# - model: lightgbm.yaml
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# - model: SVR.yaml
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# - model: torch.yaml
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# - simulator: house_energy_simparam.yaml
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- simulator: house_energy_simparam.yaml
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# - simulator: cartpole_mixed_simparam.yaml
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# - simulator: quanser-log.yaml
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# - simulator: quanser_simparam.yaml
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- simulator: hvac_b1_simparam.yaml
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# - simulator: hvac_b1_simparam.yaml
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@ -60,6 +60,13 @@ def type_conversion(obj, type, minimum, maximum):
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return obj
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# helper function that return None if element is not present in config
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def hydra_read_config_var(cfg: DictConfig, level: str, key_name: str):
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"""Reads the config file and returns the config as a dictionary"""
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return cfg[level][key_name] if key_name in cfg[level] else None
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class Simulator(BaseModel):
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def __init__(
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self,
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@ -194,7 +201,8 @@ class Simulator(BaseModel):
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# if you are using both initial values and exogeneous variables, then
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# make sure to sample a single episode from each and play it through
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if self.initial_values_df is not None:
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if hasattr(self, "initial_values_df"):
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# if self.initial_values_df is not None:
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initial_values_episode = (
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self.initial_values_df["episode"].sample(1).values[0]
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)
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@ -212,8 +220,8 @@ class Simulator(BaseModel):
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# if using exogeneous variables
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# sample from exog df and play it through the episode
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if self.exog_df is not None:
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if self.initial_values_df is not None:
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if hasattr(self, "exog_df"):
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if hasattr(self, "initial_values_df"):
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logger.info(f"Using sampled episode from initial values dataset")
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exog_episode = initial_values_episode
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else:
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@ -410,7 +418,7 @@ class Simulator(BaseModel):
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f"Iteration used as a feature. Iteration #: {self.iteration_counter}"
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)
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if self.exogeneous_variables:
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if hasattr(self, "exogeneous_variables"):
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logger.info(
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f"Updating {self.exogeneous_variables} using next iteration from episode #: {self.exog_ep['episode'].values[0]}"
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)
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@ -632,13 +640,17 @@ def main(cfg: DictConfig):
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else:
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scale_data = cfg["model"]["build_params"]["scale_data"]
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# scale_data = cfg["data"]["scale_data"]
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diff_state = cfg["data"]["diff_state"]
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concatenated_steps = cfg["data"]["concatenated_steps"]
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concatenated_zero_padding = cfg["data"]["concatenated_zero_padding"]
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concatenate_var_length = cfg["data"]["concatenate_length"]
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exogeneous_variables = cfg["data"]["exogeneous_variables"]
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exogeneous_path = cfg["data"]["exogeneous_save_path"]
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initial_values_save_path = cfg["data"]["initial_values_save_path"]
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diff_state = hydra_read_config_var(cfg, "data", "diff_state")
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concatenated_steps = hydra_read_config_var(cfg, "data", "concatenated_steps")
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concatenated_zero_padding = hydra_read_config_var(
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cfg, "data", "concatenated_zero_padding"
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)
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concatenate_var_length = hydra_read_config_var(cfg, "data", "concatenate_length")
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exogeneous_variables = hydra_read_config_var(cfg, "data", "exogeneous_variables")
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exogeneous_save_path = hydra_read_config_var(cfg, "data", "exogeneous_save_path")
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initial_values_save_path = hydra_read_config_var(
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cfg, "data", "initial_values_save_path"
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)
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workspace_setup = cfg["simulator"]["workspace_setup"]
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episode_inits = cfg["simulator"]["episode_inits"]
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@ -646,8 +658,8 @@ def main(cfg: DictConfig):
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input_cols = cfg["data"]["inputs"]
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output_cols = cfg["data"]["outputs"]
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augmented_cols = cfg["data"]["augmented_cols"]
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prep_pipeline = cfg["data"]["preprocess"]
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iteration_col = cfg["data"]["iteration_col"]
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preprocess = hydra_read_config_var(cfg, "data", "preprocess")
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iteration_col = hydra_read_config_var(cfg, "data", "iteration_col")
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iteration_col = iteration_col if iteration_col in input_cols else None
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if type(input_cols) == ListConfig:
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input_cols = list(input_cols)
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@ -703,10 +715,10 @@ def main(cfg: DictConfig):
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concatenated_steps,
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concatenated_zero_padding,
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concatenate_var_length,
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prep_pipeline=prep_pipeline,
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prep_pipeline=preprocess,
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iteration_col=iteration_col,
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exogeneous_variables=exogeneous_variables,
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exogeneous_save_path=exogeneous_path,
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exogeneous_save_path=exogeneous_save_path,
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initial_values_save_path=initial_values_save_path,
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)
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@ -15,31 +15,43 @@ logger = logging.getLogger("datamodeler")
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dir_path = os.path.dirname(os.path.realpath(__file__))
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# helper function that return None if element is not present in config
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def hydra_read_config_var(cfg: DictConfig, level: str, key_name: str):
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"""Reads the config file and returns the config as a dictionary"""
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return cfg[level][key_name] if key_name in cfg[level] else None
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@hydra.main(config_path="conf", config_name="config")
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def main(cfg: DictConfig) -> None:
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logger.info("Configuration: ")
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logger.info(f"\n{OmegaConf.to_yaml(cfg)}")
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# for readability, read common data args into variables
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input_cols = cfg["data"]["inputs"]
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output_cols = cfg["data"]["outputs"]
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augmented_cols = cfg["data"]["augmented_cols"]
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input_cols = hydra_read_config_var(cfg, "data", "inputs")
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output_cols = hydra_read_config_var(cfg, "data", "outputs")
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augmented_cols = hydra_read_config_var(cfg, "data", "augmented_cols")
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iteration_order = cfg["data"]["iteration_order"]
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episode_col = cfg["data"]["episode_col"]
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iteration_col = cfg["data"]["iteration_col"]
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dataset_path = cfg["data"]["path"]
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max_rows = cfg["data"]["max_rows"]
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test_perc = cfg["data"]["test_perc"]
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delta_state = cfg["data"]["diff_state"]
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concatenated_steps = cfg["data"]["concatenated_steps"]
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concatenated_zero_padding = cfg["data"]["concatenated_zero_padding"]
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concatenate_var_length = cfg["data"]["concatenate_length"]
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pipeline = cfg["data"]["preprocess"]
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var_rename = cfg["data"]["var_rename"]
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exogeneous_variables = cfg["data"]["exogeneous_variables"]
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exogeneous_path = cfg["data"]["exogeneous_save_path"]
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initial_values_save_path = cfg["data"]["initial_values_save_path"]
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iteration_order = hydra_read_config_var(cfg, "data", "iteration_order")
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episode_col = hydra_read_config_var(cfg, "data", "episode_col")
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iteration_col = hydra_read_config_var(cfg, "data", "iteration_col")
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dataset_path = hydra_read_config_var(cfg, "data", "path")
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max_rows = hydra_read_config_var(cfg, "data", "max_rows")
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test_perc = hydra_read_config_var(cfg, "data", "test_perc")
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diff_state = hydra_read_config_var(cfg, "data", "diff_state")
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concatenated_steps = hydra_read_config_var(cfg, "data", "concatenated_steps")
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concatenated_zero_padding = hydra_read_config_var(
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cfg, "data", "concatenated_zero_padding"
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)
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concatenate_var_length = hydra_read_config_var(cfg, "data", "concatenate_length")
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preprocess = hydra_read_config_var(cfg, "data", "preprocess")
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var_rename = hydra_read_config_var(cfg, "data", "var_rename")
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exogeneous_variables = hydra_read_config_var(cfg, "data", "exogeneous_variables")
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exogeneous_save_path = hydra_read_config_var(cfg, "data", "exogeneous_save_path")
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initial_values_save_path = hydra_read_config_var(
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cfg, "data", "initial_values_save_path"
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)
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# common model args
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save_path = cfg["model"]["saver"]["filename"]
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@ -99,7 +111,7 @@ def main(cfg: DictConfig) -> None:
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return_ts=False,
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var_rename=var_rename,
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exogeneous_variables=exogeneous_variables,
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exogeneous_path=exogeneous_path,
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exogeneous_path=exogeneous_save_path,
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)
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else:
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X_train, y_train, X_test, y_test = model.load_csv(
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@ -113,14 +125,14 @@ def main(cfg: DictConfig) -> None:
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# drop_nulls: bool = True,
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max_rows=max_rows,
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test_perc=test_perc,
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diff_state=delta_state,
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prep_pipeline=pipeline,
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diff_state=diff_state,
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prep_pipeline=preprocess,
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var_rename=var_rename,
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concatenated_steps=concatenated_steps,
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concatenated_zero_padding=concatenated_zero_padding,
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concatenate_var_length=concatenate_var_length,
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exogeneous_variables=exogeneous_variables,
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exogeneous_save_path=exogeneous_path,
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exogeneous_save_path=exogeneous_save_path,
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initial_values_save_path=initial_values_save_path,
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)
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