snowflake.ml.modeling.preprocessing.LabelEncoder¶

class snowflake.ml.modeling.preprocessing.LabelEncoder(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

Encodes target labels with values between 0 and n_classes-1.

In other words, each class (i.e., distinct numeric or string) is assigned an integer value, starting with zero. LabelEncoder is a specialization of OrdinalEncoder for 1-dimensional data.

For more details on what this transformer does, see sklearn.preprocessing.LabelEncoder.

Parameters:

• input_cols – Optional[Union[str, List[str]]] The name of a column or a list containing one column name to be encoded in the input DataFrame. There must be exactly one input column specified before fit. This argument is optional for API consistency.

• output_cols – Optional[Union[str, List[str]]] The name of a column or a list containing one column name where the results will be stored. There must be exactly one output column specified before trainsform. This argument is optional for API consistency.

• passthrough_cols – Optional[Union[str, List[str]]] 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.

Encode target labels with integers between 0 and n_classes-1.

Parameters:

• input_cols – Optional[Union[str, List[str]]] The name of a column or a list containing one column name to be encoded in the input DataFrame. There must be exactly one input column specified before fit. This argument is optional for API consistency.

• output_cols – Optional[Union[str, List[str]]] The name of a column or a list containing one column name where the results will be stored. There must be exactly one output column specified before transform. This argument is optional for API consistency.

• passthrough_cols – Optional[Union[str, List[str]]] 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 – Optional[bool], default=False Remove input columns from output if set True. False by default.

classes_¶

Optional[type_utils.LiteralNDArrayType] A np.ndarray that holds the label for each class. Attributes are valid only after fit() has been called.

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]¶

Use fit result to transform snowpark dataframe or pandas dataframe. The original dataset with the transform result column added will be returned.

Parameters:

dataset – Input dataset.

Returns:

Output dataset.