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
Standardize features by removing the mean and scaling to unit variance.
- Args:
- 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.
- Attributes:
- 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
whenwith_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
whenwith_std=False
.
Methods
- fit(dataset: Union[DataFrame, DataFrame]) StandardScaler ¶
Compute mean and std values of the dataset.
- Args:
dataset: Input dataset.
- Returns:
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] ¶
Perform standardization by centering and scaling.
- Args:
dataset: Input dataset.
- Returns:
transformed_dataset: Output dataset.