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.

Args:

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 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. For dense output, the column names specified are used as base names for the columns created for each category.

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:

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.

Args:

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.

Attributes:

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]) → RobustScaler¶

Compute center, scale and quantile values of the dataset.

Args:

dataset: Input dataset.

Returns:

Return self as fitted scaler.

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.

Args:

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.

Args:

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.

Args:

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.

Args:

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.

Args:

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.

Args:

**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.

Args:

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.

Args:

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.

Args:

dataset: Input dataset.

Returns:

Output dataset.

Attributes

center_¶

scale_¶