tempor.methods.preprocessing.scaling.temporal.plugin_ts_minmax_scaler module¶

MinMax scaling for the temporal data.

class tempor.methods.preprocessing.scaling.temporal.plugin_ts_minmax_scaler.TimeSeriesMinMaxScalerParams(feature_range: tuple[int, int] = (0, 1), clip: bool = False)[source]¶

Bases: object

Initialization parameters for TimeSeriesMinMaxScaler.

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.temporal.plugin_ts_minmax_scaler.TimeSeriesMinMaxScaler(**params: Any)[source]¶

Bases: BaseScaler

MinMax scaling for the time-series data.

Transform the temporal 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.

The time series data will be represented as a multi-index (sample_idx, time_idx) dataframe of features, and the scaling will be applied to this dataframe.

Parameters:¶

**params : Any

Parameters and defaults as defined in TimeSeriesMinMaxScalerParams.

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.temporal.ts_minmax_scaler")
>>>
>>> # Train:
>>> model.fit(dataset)
TimeSeriesMinMaxScaler(...)
>>>
>>> # Scale:
>>> scaled = model.transform(dataset)

ParamsDefinition¶

alias of TimeSeriesMinMaxScalerParams

params : TimeSeriesMinMaxScalerParams¶

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

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

name : ClassVar[plugin_typing.PluginName] = 'ts_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.