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.

Args:

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.

Attributes:

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.

Args:

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.

Attributes:

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]) → MinMaxScaler¶

Compute min and max values of the dataset.

Validates the transformer arguments and derives the scaling factors and ranges 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:

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]¶

Scale features according to feature_range.

Args:

dataset: Input dataset.

Returns:

Output dataset.