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.

Parameters:

  • 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 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.

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

Standardize features by removing the mean and scaling to unit variance.

Parameters:

  • with_mean – If True, center the data before scaling. This does not work (and will 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_std – If True, scale the data to unit variance (or equivalently, unit standard deviation).

  • 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.

scale_

dict {column_name: value} or None Per feature relative scaling of the data to achieve zero mean and unit variance. If a variance is zero, we can’t achieve unit variance, and the data is left as-is, giving a scaling factor of 1. scale_ is equal to None when with_std=False.

mean_

dict {column_name: value} or None The mean value for each feature in the training set. Equal to None when with_mean=False.

var_

dict {column_name: value} or None The variance for each feature in the training set. Used to compute scale_. Equal to None when with_std=False.

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 the snowflake-ml 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: Any) → None

Set the parameters of this transformer.

The method works on simple transformers as well as on sklearn compatible pipelines with nested objects, once the transformer has been fit. Nested objects 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]

Perform standardization by centering and scaling.

Parameters:

dataset – Input dataset.

Returns:

Output dataset.

Return type:

transformed_dataset

Language: English