tempor.methods.preprocessing.scaling.static.plugin_static_minmax_scaler module¶

MinMax scaling for the static data.

class tempor.methods.preprocessing.scaling.static.plugin_static_minmax_scaler.StaticMinMaxScalerParams(feature_range: tuple[int, int] = (0, 1), clip: bool = False)[source]¶

Bases: object

Initialization parameters for StaticMinMaxScaler.

feature_range : tuple[int, int] = (0, 1)¶

Desired range of transformed data. See sklearn.preprocessing.MinMaxScaler.

clip : bool = False¶

Set to True to clip transformed values of held-out data to provided feature_range. See sklearn.preprocessing.MinMaxScaler.

class tempor.methods.preprocessing.scaling.static.plugin_static_minmax_scaler.StaticMinMaxScaler(**params: Any)[source]¶

Bases: BaseScaler

MinMax scaling for the static data.

Transform the static features by scaling each feature to a given range. This estimator scales and translates each feature individually such that it is in the given range on the training set, e.g. between zero and one.

Parameters:¶

**params : Any

Parameters and defaults as defined in StaticMinMaxScalerParams.

Example

>>> from tempor import plugin_loader
>>>
>>> dataset = plugin_loader.get("prediction.one_off.sine", plugin_type="datasource").load()
>>>
>>> # Load the model:
>>> model = plugin_loader.get("preprocessing.scaling.static.static_minmax_scaler")
>>>
>>> # Train:
>>> model.fit(dataset)
StaticMinMaxScaler(...)
>>>
>>> # Scale:
>>> scaled = model.transform(dataset)

ParamsDefinition¶

alias of StaticMinMaxScalerParams

params : StaticMinMaxScalerParams¶

category : ClassVar[plugin_typing.PluginCategory] = 'preprocessing.scaling.static'¶

Plugin category, such as 'prediction.one_off.classification'. Must be set by the plugin class using @register_plugin.

name : ClassVar[plugin_typing.PluginName] = 'static_minmax_scaler'¶

Plugin name, such as 'my_nn_classifier'. Must be set by the plugin class using @register_plugin.

plugin_type : ClassVar[plugin_typing.PluginTypeArg] = 'method'¶

Plugin type, such as 'method'. May be optionally set by the plugin class using @register_plugin, else will set the default plugin type.

static hyperparameter_space(*args: Any, **kwargs: Any) → list[Params][source]¶

The hyperparameter search domain, used for tuning.

Can provide variadics *args and **kwargs, these will be received from sample_hyperparameters.