toad.utils.decorator module¶
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class
toad.utils.decorator.
Decorator
(*args, is_class=False, **kwargs)[source]¶ Bases:
object
base decorater class
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class
toad.utils.decorator.
frame_exclude
(*args, is_class=False, **kwargs)[source]¶ Bases:
toad.utils.decorator.Decorator
decorator for exclude columns
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class
toad.utils.decorator.
select_dtypes
(*args, is_class=False, **kwargs)[source]¶ Bases:
toad.utils.decorator.Decorator
decorator for select frame by dtypes
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class
toad.utils.decorator.
save_to_json
(*args, is_class=False, **kwargs)[source]¶ Bases:
toad.utils.decorator.Decorator
support save result to json file
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class
toad.utils.decorator.
load_from_json
(*args, is_class=False, **kwargs)[source]¶ Bases:
toad.utils.decorator.Decorator
support load data from json file
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class
toad.utils.decorator.
support_dataframe
(*args, is_class=False, **kwargs)[source]¶ Bases:
toad.utils.decorator.Decorator
decorator for supporting dataframe
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class
toad.utils.decorator.
support_numpy
(*args, is_class=False, **kwargs)[source]¶ Bases:
toad.utils.decorator.Decorator
decorator for supporting numpy array to use torch function
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class
toad.utils.decorator.
xgb_loss
(*args, is_class=False, **kwargs)[source]¶ Bases:
toad.utils.decorator.Decorator
decorator for converting function to xgb supported loss function
Parameters: - loss_func (callable) – loss function
- **kwargs – other arguments for loss function except pred and label
Examples:
>>> @xgb_loss(**kwargs) >>> def loss_func(pred, label, **kwargs): >>> ... >>> return loss >>> >>> # or use `xgb_loss` directly >>> xgb_func = xgb_loss(**kwargs)(loss_func) >>> >>> # use in xgb >>> model = xgb.XGBClassifier(objective = xgb_func)