snowflake.ml.modeling.preprocessing.MinMaxScaler

class snowflake.ml.modeling.preprocessing.MinMaxScaler(*, feature_range: Tuple[float, float] = (0, 1), clip: 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

Transforms features by scaling each feature to a given range, by default between zero and one.

Values must be of float type. Each feature is scaled and translated independently.

For more details on what this transformer does, see sklearn.preprocessing.MinMaxScaler (https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.MinMaxScaler.html).

Parameters:

  • feature_range – Tuple[float, float], default=(0, 1) Desired range of transformed data (default is 0 to 1).

  • clip – bool, default=False Whether to clip transformed values of held-out data to the specified feature range (default is True).

  • 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. Each specified input column is scaled independently and stored in the corresponding output column.

  • 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.

min_

Dict[str, float] dict {column_name: value} or None. Per-feature adjustment for minimum.

scale_

Dict[str, float] dict {column_name: value} or None. Per-feature relative scaling factor.

data_min_

Dict[str, float] dict {column_name: value} or None. Per-feature minimum seen in the data.

data_max_

Dict[str, float] dict {column_name: value} or None. Per-feature maximum seen in the data.

data_range_

Dict[str, float] dict {column_name: value} or None. Per-feature range seen in the data as a (min, max) tuple.

Transform features by scaling each feature to a given range.

Parameters:

  • feature_range – Desired range of transformed data.

  • clip – Set to True to clip transformed values of held-out data to provided feature range.

  • 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.

min_

Per feature adjustment for minimum.

scale_

Per feature relative scaling of the data.

data_min_

Per feature minimum seen in the data.

data_max_

Per feature maximum seen in the data.

data_range_

Per feature range (data_max_ - data_min_) seen in the data.

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]

Scale features according to feature_range.

Parameters:

dataset – Input dataset.

Returns:

Output dataset.

语言: 中文