scaling

A module with functions for scaling RUL features.

Scaler = Union[scalers.StandardScaler, scalers.MinMaxScaler, scalers.MaxAbsScaler, scalers.RobustScaler] module-attribute

Supported scalers:

• sklearn.preprocessing.StandardScaler
• sklearn.preprocessing.MinMaxScaler
• sklearn.preprocessing.MaxAbsScaler
• sklearn.preprocessing.RobustScaler

OperationConditionAwareScaler

Bases: BaseEstimator, TransformerMixin

This scaler is an ensemble of multiple base scalers, e.g. [ sklearn.preprocessing.MinMaxScaler][]. It takes an additional operation condition array while fitting and transforming that controls which base scaler is used. The operation condition corresponding to a sample is compared against the boundaries defined during construction of the scaler. If the condition lies between the first set of boundaries, the first base scaler is used, and so forth. If any condition does not fall between any boundaries, an exception will be raised and the boundaries should be adjusted.

n_features_in_ property

Number of expected input features.

__init__(base_scaler, boundaries)

Create a new scaler aware of operation conditions.

Each pair in boundaries represents the lower and upper value of an inclusive interval. For each interval a copy of the base_scaler is maintained. If an operation condition value falls inside an interval, the corresponding scaler is used. The boundaries have to be mutually exclusive.

Parameters:

Name	Type	Description	Default
base_scaler	Scaler	The scaler that should be used for each condition.	required
boundaries	List[Tuple[float, float]]	The pairs that form the inclusive boundaries of each condition.	required

partial_fit(features, operation_conditions)

Fit the base scalers partially.

This function calls partial_fit on each of the base scalers with the samples that fall into the corresponding condition boundaries. If any sample does not fall into one of the boundaries, an exception is raised.

Parameters:

Name	Type	Description	Default
features	ndarray	The feature array to be scaled.	required
operation_conditions	ndarray	The condition values compared against the boundaries.	required

Returns:

Type	Description
OperationConditionAwareScaler	The partially fitted scaler.

transform(features, operation_conditions)

Scale the features with the appropriate condition aware scaler.

This function calls transform on each of the base scalers for the samples that fall into the corresponding condition boundaries. If any sample does not fall into one of the boundaries, an exception is raised.

Parameters:

Name	Type	Description	Default
features	ndarray	The features to be scaled.	required
operation_conditions	ndarray	The condition values compared against the boundaries.	required

Returns:

Type	Description
ndarray	The scaled features.

fit_scaler(features, scaler=None, operation_conditions=None)

Fit a given scaler to the RUL features. If the scaler is omitted, a StandardScaler will be created.

If the scaler is an [OperationConditionAwareScaler][ rul_datasets.reader.scaling.OperationConditionAwareScaler] and operation_conditions are passed, the scaler will be fit aware of operation conditions.

The scaler assumes that the last axis of the features are the channels. Only scalers unaware of operation conditions can be fit with windowed data.

Parameters:

Name	Type	Description	Default
features	List[ndarray]	The RUL features.	required
scaler	Optional[Union[Scaler, OperationConditionAwareScaler]]	The scaler to be fit. Defaults to a StandardScaler.	None
operation_conditions	Optional[List[ndarray]]	The operation conditions for condition aware scaling.	None

Returns:

Type	Description
Union[Scaler, OperationConditionAwareScaler]	The fitted scaler.

load_scaler(save_path)

Load a scaler from disk.

Parameters:

Name	Type	Description	Default
save_path	str	The path the scaler was saved to.	required

Returns:

Type	Description
Scaler	The loaded scaler.

save_scaler(scaler, save_path)

Save a scaler to disk.

Parameters:

Name	Type	Description	Default
scaler	Scaler	The scaler to be saved.	required
save_path	str	The path to save the scaler to.	required

scale_features(features, scaler, operation_conditions=None)

Scale the RUL features with a given scaler.

The features can have a shape of [num_time_steps, channels] or [num_windows, window_size, channels]. The scaler needs to work on the channel dimension. If it was not fit with the right number of channels, a ValueError is thrown.

If the scaler is operation condition aware, the operation_conditions argument needs to be passed. Windowed data cannot be fit this way.

Parameters:

Name	Type	Description	Default
features	List[ndarray]	The RUL features to be scaled.	required
scaler	Union[Scaler, OperationConditionAwareScaler]	The already fitted scaler.	required
operation_conditions	Optional[List[ndarray]]	The operation conditions for condition aware scaling.	None

Returns:

Type	Description
List[ndarray]	The scaled features.