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.

Args:

input_cols: Optional[Union[str, List[str]]]

The name of a column in a DataFrame to be encoded. May be a string or a list containing one string.

output_cols: Optional[Union[str, List[str]]]

The name of a column in a DataFrame where the results will be stored. May be a string or a list containing one string.

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.

Args:

input_cols: Optional[Union[str, List[str]]]

The name of a column in a DataFrame to be encoded. May be a string or a list containing one string.

output_cols: Optional[Union[str, List[str]]]

The name of a column in a DataFrame where the results will be stored. May be a string or a list containing one string.

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.

Attributes:

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]) → LabelEncoder¶

Fit label encoder with label column in dataset.

Validates the transformer arguments and derives the list of classes (distinct values) from the data, making this list available as an attribute of the transformer instance (see Attributes). Returns the transformer instance.

Args:

dataset: Input dataset.

Returns:

self

Raises:

SnowflakeMLException: If length of input_cols is not 1 or length of output_cols is greater than 1.

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]¶

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

Args:

dataset: Input dataset.

Returns:

Output dataset.