snowflake.ml.modeling.preprocessing.MaxAbsScalerΒΆ
- class snowflake.ml.modeling.preprocessing.MaxAbsScaler(*, 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)ΒΆ
Bases:
BaseTransformer
Scale each feature by its maximum absolute value.
This transformer scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.
Values must be of float type. Each feature is scaled and transformed individually such that the maximal absolute value of each feature in the dataset is 1.0. This scaler does not shift or center the data, preserving sparsity.
For more details on what this transformer does, see sklearn.preprocessing.MaxAbsScaler.
- Args:
- 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 each feature by its maximum absolute value.
This transformer scales and translates each feature individually such that the maximal absolute value of each feature in the training set will be 1.0. It does not shift/center the data, and thus does not destroy any sparsity.
- Args:
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:
Methods
- fit(dataset: Union[DataFrame, DataFrame]) MaxAbsScaler ΒΆ
Compute the maximum absolute value to be used for later scaling.
Validates the transformer arguments and derives the scaling factors and maximum absolute values from the data, making dictionaries of both available as attributes of the transformer instance (see Attributes).
Returns the transformer instance.
- Args:
dataset: Input dataset.
- Returns:
Return self as fitted scaler.
- get_input_cols() List[str] ΒΆ
Input columns getter.
- Returns:
Input columns.
- get_label_cols() List[str] ΒΆ
Label column getter.
- Returns:
Label column(s).
- get_output_cols() List[str] ΒΆ
Output columns getter.
- Returns:
Output columns.
- get_params(deep: bool = True) Dict[str, Any] ΒΆ
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] ΒΆ
Passthrough columns getter.
- Returns:
Passthrough column(s).
- get_sample_weight_col() Optional[str] ΒΆ
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] ΒΆ
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 ΒΆ
- set_input_cols(input_cols: Optional[Union[str, Iterable[str]]]) Base ΒΆ
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 ΒΆ
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 ΒΆ
Output columns setter.
- Args:
output_cols: A single output column or multiple output columns.
- Returns:
self
- set_params(**params: Dict[str, Any]) None ΒΆ
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 itβs 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 ΒΆ
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 ΒΆ
Sample weight column setter.
- Args:
sample_weight_col: A single column that represents sample weight.
- Returns:
self
- to_lightgbm() Any ΒΆ
- to_sklearn() Any ΒΆ
- to_xgboost() Any ΒΆ
- transform(dataset: Union[DataFrame, DataFrame]) Union[DataFrame, DataFrame] ΒΆ
Scale the data.
- Args:
dataset: Input dataset.
- Returns:
Output dataset.