snowflake.ml.modeling.preprocessing.RobustScaler

class snowflake.ml.modeling.preprocessing.RobustScaler(*, with_centering: bool = True, with_scaling: bool = True, quantile_range: Tuple[float, float] = (25.0, 75.0), unit_variance: bool = False, 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

Scales features using statistics that are robust to outliers. Values must be of float type.

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

Parameters:
  • with_centering – bool, default=True If True, center the data around zero before scaling.

  • with_scaling – bool, default=True If True, scale the data to interquartile range.

  • quantile_range – Tuple[float, float], default=(25.0, 75.0) tuple like (q_min, q_max), where 0.0 < q_min < q_max < 100.0, default=(25.0, 75.0). Quantile range used to calculate scale_. By default, this is equal to the IQR, i.e., q_min is the first quantile and q_max is the third quantile.

  • unit_variance – bool, default=False If True, scale data so that normally-distributed features have a variance of 1. In general, if the difference between the x-values of q_max and q_min for a standard normal distribution is greater than 1, the dataset is scaled down. If less than 1, the dataset is scaled up.

  • input_cols – Optional[Union[str, List[str]]], default=None The name(s) of one or more columns in the input DataFrame containing feature(s) to be scaled. Input columns must be specified before fit with this argument or after initialization with the set_input_cols method. This argument is optional for API consistency.

  • output_cols – Optional[Union[str, List[str]]], default=None The name(s) to assign output columns in the output DataFrame. The number of columns specified must equal the number of input columns. Output columns must be specified before transform with this argument or after initialization with the set_output_cols method. This argument is optional for API consistency.

  • 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.

center_

Dict[str, float] Dictionary mapping input column name to the median value for that feature.

scale_

Dict[str, float] Dictionary mapping input column name to the (scaled) interquartile range for that feature.

Scale features using statistics that are robust to outliers.

Parameters:
  • with_centering – If True, center the data before scaling. This will cause transform to raise an exception when attempted on sparse matrices, because centering them entails building a dense matrix which in common use cases is likely to be too large to fit in memory.

  • with_scaling – If True, scale the data to interquartile range.

  • quantile_range – tuple (q_min, q_max), 0.0 < q_min < q_max < 100.0, default=(25.0, 75.0) Quantile range used to calculate scale_. By default this is equal to the IQR, i.e., q_min is the first quantile and q_max is the third quantile.

  • unit_variance – If True, scale data so that normally distributed features have a variance of 1. In general, if the difference between the x-values of q_max and q_min for a standard normal distribution is greater than 1, the dataset will be scaled down. If less than 1, the dataset will be scaled up.

  • input_cols – Single or multiple input columns.

  • output_cols – Single or multiple output columns.

  • passthrough_cols – 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 helful in scenarios requiring automatic input_cols inference, but need to avoid using specific columns, like index columns, during in training or inference.

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

center_

dict {column_name: The median value for each feature in the training set}.

scale_

The (scaled) interquartile range for each feature in the training set.

Methods

fit(dataset: Union[DataFrame, DataFrame]) BaseEstimator

Runs universal logics for all fit implementations.

get_input_cols() List[str]

Input columns getter.

Returns:

Input columns.

get_label_cols() List[str]

Label column getter.

Returns:

Label column(s).

get_output_cols() List[str]

Output columns getter.

Returns:

Output columns.

get_params(deep: bool = True) Dict[str, Any]

Get parameters for this transformer.

Parameters:

deep – If True, will return the parameters for this transformer and contained subobjects that are transformers.

Returns:

Parameter names mapped to their values.

get_passthrough_cols() List[str]

Passthrough columns getter.

Returns:

Passthrough column(s).

get_sample_weight_col() Optional[str]

Sample weight column getter.

Returns:

Sample weight column.

get_sklearn_args(default_sklearn_obj: Optional[object] = None, sklearn_initial_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_unused_keywords: Optional[Union[str, Iterable[str]]] = None, snowml_only_keywords: Optional[Union[str, Iterable[str]]] = None, sklearn_added_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_added_kwarg_value_to_version_dict: Optional[Dict[str, Dict[str, str]]] = None, sklearn_deprecated_keyword_to_version_dict: Optional[Dict[str, str]] = None, sklearn_removed_keyword_to_version_dict: Optional[Dict[str, str]] = None) Dict[str, Any]

Get sklearn keyword arguments.

This method enables modifying object parameters for special cases.

Parameters:
  • default_sklearn_obj – Sklearn object used to get default parameter values. Necessary when sklearn_added_keyword_to_version_dict is provided.

  • sklearn_initial_keywords – Initial keywords in sklearn.

  • sklearn_unused_keywords – Sklearn keywords that are unused in snowml.

  • snowml_only_keywords – snowml only keywords not present in sklearn.

  • sklearn_added_keyword_to_version_dict – Added keywords mapped to the sklearn versions in which they were added.

  • sklearn_added_kwarg_value_to_version_dict – Added keyword argument values mapped to the sklearn versions in which they were added.

  • sklearn_deprecated_keyword_to_version_dict – Deprecated keywords mapped to the sklearn versions in which they were deprecated.

  • sklearn_removed_keyword_to_version_dict – Removed keywords mapped to the sklearn versions in which they were removed.

Returns:

Sklearn parameter names mapped to their values.

set_drop_input_cols(drop_input_cols: Optional[bool] = False) None
set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) Base

Input columns setter.

Parameters:

input_cols – A single input column or multiple input columns.

Returns:

self

set_label_cols(label_cols: Optional[Union[str, Iterable[str]]]) Base

Label column setter.

Parameters:

label_cols – A single label column or multiple label columns if multi task learning.

Returns:

self

set_output_cols(output_cols: Optional[Union[str, Iterable[str]]]) Base

Output columns setter.

Parameters:

output_cols – A single output column or multiple output columns.

Returns:

self

set_params(**params: Dict[str, Any]) None

Set the parameters of this transformer.

The method works on simple transformers as well as on nested objects. The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:

**params – Transformer parameter names mapped to their values.

Raises:

SnowflakeMLException – Invalid parameter keys.

set_passthrough_cols(passthrough_cols: Optional[Union[str, Iterable[str]]]) Base

Passthrough columns setter.

Parameters:

passthrough_cols – Column(s) that should not be used or modified by the estimator/transformer. Estimator/Transformer just passthrough these columns without any modifications.

Returns:

self

set_sample_weight_col(sample_weight_col: Optional[str]) Base

Sample weight column setter.

Parameters:

sample_weight_col – A single column that represents sample weight.

Returns:

self

to_lightgbm() Any
to_sklearn() Any
to_xgboost() Any
transform(dataset: Union[DataFrame, DataFrame]) Union[DataFrame, DataFrame]

Center and scale the data.

Parameters:

dataset – Input dataset.

Returns:

Output dataset.

Attributes

center_
scale_