logml.data.transformers.normalization

Classes

`BaseNormalizationTransformer`(**kwargs)	Provides normalization functionality.
`Log1pStandardNormalizationTransformer`(**kwargs)	Log1p transformation + standard normalization.
`LogStandardNormalizationTransformer`(**kwargs)	Log transformation + standard normalization.
`MaxAbsNormalizationTransformer`(**kwargs)	MaxAbs normalization.
`MinMaxNegNormalizationTransformer`(**kwargs)	MinMax normalization (result range is [-1; -1]).
`MinMaxNormalizationTransformer`(**kwargs)	MinMax normalization.
`StandardNormalizationTransformer`(**kwargs)	Standard normalization.

class logml.data.transformers.normalization.BaseNormalizationTransformer(**kwargs)

Bases: logml.data.base.BaseTransformer

Provides normalization functionality.

LABEL = None

NORMALIZER = None

CONFIG_CLASS: alias of logml.data.config.BaseTransformerParams

fit(dataframe: pandas.core.frame.DataFrame, dataset_metadata: Optional[logml.data.metadata.DatasetMetadata] = None, **kwargs): Fits required scallers.

transform(dataframe: pandas.core.frame.DataFrame) → pandas.core.frame.DataFrame: Applies required scalers.

update_transform_log(change: logml.data.utils.DataTransformLogItem): Add custom data to the log.

params: BaseTransformerParams

global_params: Dict

metadata_cfg: ModelingTaskSpec

affected_columns_: List[str]

class logml.data.transformers.normalization.StandardNormalizationTransformer(**kwargs)

Bases: logml.data.transformers.normalization.BaseNormalizationTransformer

Standard normalization.

LABEL = 'standard'

params: BaseTransformerParams

global_params: Dict

metadata_cfg: ModelingTaskSpec

affected_columns_: List[str]

class logml.data.transformers.normalization.MaxAbsNormalizationTransformer(**kwargs)

Bases: logml.data.transformers.normalization.BaseNormalizationTransformer

MaxAbs normalization.

LABEL = 'maxabs'

params: BaseTransformerParams

global_params: Dict

metadata_cfg: ModelingTaskSpec

affected_columns_: List[str]

class logml.data.transformers.normalization.MinMaxNormalizationTransformer(**kwargs)

Bases: logml.data.transformers.normalization.BaseNormalizationTransformer

MinMax normalization.

LABEL = 'minmax'

params: BaseTransformerParams

global_params: Dict

metadata_cfg: ModelingTaskSpec

affected_columns_: List[str]

class logml.data.transformers.normalization.MinMaxNegNormalizationTransformer(**kwargs)

Bases: logml.data.transformers.normalization.BaseNormalizationTransformer

MinMax normalization (result range is [-1; -1]).

LABEL = 'minmax_neg'

params: BaseTransformerParams

global_params: Dict

metadata_cfg: ModelingTaskSpec

affected_columns_: List[str]

class logml.data.transformers.normalization.LogStandardNormalizationTransformer(**kwargs)

Bases: logml.data.transformers.normalization.BaseNormalizationTransformer

Log transformation + standard normalization.

LABEL = 'log_standard'

params: BaseTransformerParams

global_params: Dict

metadata_cfg: ModelingTaskSpec

affected_columns_: List[str]

class logml.data.transformers.normalization.Log1pStandardNormalizationTransformer(**kwargs)

Bases: logml.data.transformers.normalization.BaseNormalizationTransformer

Log1p transformation + standard normalization.

LABEL = 'log1p_standard'

params: BaseTransformerParams

global_params: Dict

metadata_cfg: ModelingTaskSpec

affected_columns_: List[str]