snowflake.ml.modeling.preprocessing.MaxAbsScaler¶

class snowflake.ml.modeling.preprocessing.MaxAbsScaler(*, input_cols: Optional[Union[str, Iterable[str]]] = None, output_cols: Optional[Union[str, Iterable[str]]] = None, passthrough_cols: Optional[Union[str, Iterable[str]]] = None, drop_input_cols: Optional[bool] = False)¶

Bases: BaseTransformer

Scale each feature by its maximum absolute value.

This transformer scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.

Values must be of float type. Each feature is scaled and transformed individually such that the maximal absolute value of each feature in the dataset is 1.0. This scaler does not shift or center the data, preserving sparsity.

For more details on what this transformer does, see sklearn.preprocessing.MaxAbsScaler.

Args:

input_cols: Optional[Union[str, List[str]]], default=None

The name(s) of one or more columns in a DataFrame containing a feature to be scaled.

output_cols: Optional[Union[str, List[str]]], default=None

The name(s) of one or more columns in a DataFrame in which results will be stored. The number of columns specified must match the number of input columns.

passthrough_cols: Optional[Union[str, List[str]]], default=None

A string or a list of strings indicating column names to be excluded from any operations (such as train, transform, or inference). These specified column(s) will remain untouched throughout the process. This option is helpful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during training or inference.

drop_input_cols: Optional[bool], default=False

Remove input columns from output if set True. False by default.

Attributes:

scale_: Dict[str, float]

dict {column_name: value} or None. Per-feature relative scaling factor.

max_abs_: Dict[str, float]

dict {column_name: value} or None. Per-feature maximum absolute value.

Methods

fit(dataset)	Compute the maximum absolute value to be used for later scaling.
get_input_cols()	Input columns getter.
get_label_cols()	Label column getter.
get_output_cols()	Output columns getter.
get_params([deep])	Get parameters for this transformer.
get_passthrough_cols()	Passthrough columns getter.
get_sample_weight_col()	Sample weight column getter.
get_sklearn_args([default_sklearn_obj, ...])	Get sklearn keyword arguments.
set_drop_input_cols([drop_input_cols])
set_input_cols(input_cols)	Input columns setter.
set_label_cols(label_cols)	Label column setter.
set_output_cols(output_cols)	Output columns setter.
set_params(**params)	Set the parameters of this transformer.
set_passthrough_cols(passthrough_cols)	Passthrough columns setter.
set_sample_weight_col(sample_weight_col)	Sample weight column setter.
to_lightgbm()
to_sklearn()
to_xgboost()
transform(dataset)	Scale the data.