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@@ -65,12 +65,11 @@ class PipelineSelector(ABC):
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cost_func: Union[Callable, str],
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greater_is_better: bool,
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trials_path: str,
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- backup_trials_freq: Optional[int] = None,
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+ backup_trials_freq: int = None,
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cross_val_averaging_func: Callable = np.mean,
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- additional_metrics: Optional[Dict[str, Callable]] = None,
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- additional_averaging_funcs:
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- Optional[Dict[str, Callable]] = None,
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- strategy_name: Optional[str] = None,
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+ additional_metrics: Dict[str, Callable] = None,
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+ additional_averaging_funcs: Dict[str, Callable] = None,
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+ strategy_name: str = None,
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stdout_log_level: Literal["INFO", "WARNING", "ERROR"]
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= "INFO"):
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"""
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@@ -302,13 +301,13 @@ class PipelineSelector(ABC):
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self._logger.loger_and_raise_error(err)
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def attach_data(self, X_train: Union[pd.DataFrame, np.ndarray],
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- y_train: Optional[pd.DataFrame, pd.Series, np.ndarray]
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+ y_train: Union[pd.DataFrame, pd.Series, np.ndarray]
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= None,
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- X_val: Optional[pd.DataFrame, np.ndarray]
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+ X_val: Union[pd.DataFrame, np.ndarray]
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= None,
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- y_val: Optional[pd.DataFrame, pd.Series, np.ndarray]
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+ y_val: Union[pd.DataFrame, pd.Series, np.ndarray]
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= None,
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- cv: Optional[Iterable[Tuple[List[int], List[int]]]]
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+ cv: Union[Iterable[Tuple[List[int], List[int]]]]
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= None) -> None:
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'''
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:param array X_train: data on which
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@@ -468,7 +467,7 @@ class PipelineSelector(ABC):
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= functools.partial(
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pd.DataFrame.to_excel,
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**{"path_or_buf": "result.csv"}),
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- kwargs: Optional[dict] = None) -> None:
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+ kwargs: dict = None) -> None:
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"""
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When the score calculated by _objective function improves,
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the default summary is updated with information about the
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