snowflake.ml.modeling.preprocessing.StandardScaler¶

class snowflake.ml.modeling.preprocessing.StandardScaler(*, with_mean: bool = True, with_std: bool = True, 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

Standardizes features by removing the mean and scaling to unit variance. Values must be of float type.

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

Args:

with_mean: bool, default=True

If True, center the data before scaling.

with_std: bool, default=True

If True, scale the data unit variance (i.e. unit standard deviation).

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_: Optional[Dict[str, float]] = {}

Dictionary mapping input column names to relative scaling factor to achieve zero mean and unit variance. If a variance is zero, unit variance could not be achieved, and the data is left as-is, giving a scaling factor of 1. None if with_std is False.

mean_: Optional[Dict[str, float]] = {}

Dictionary mapping input column name to the mean value for that feature. None if with_mean is False.

var_: Optional[Dict[str, float]] = {}

Dictionary mapping input column name to the variance for that feature. Used to compute scale_. None if with_std is False

Methods

fit(dataset)	Compute mean and std values of the dataset.
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)	Perform standardization by centering and scaling.