Class LabelEncoder (1.29.0)

Version latest

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LabelEncoder(
    min_frequency: typing.Optional[int] = None,
    max_categories: typing.Optional[int] = None,
)

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

This transformer should be used to encode target values, i.e. y, and not the input X.

Parameters
Name	Description
min_frequency	Optional[int], default None Specifies the minimum frequency below which a category will be considered infrequent. Default None. int: categories with a smaller cardinality will be considered infrequent as ßindex 0.
max_categories	Optional[int], default None Specifies an upper limit to the number of output features for each input feature when considering infrequent categories. If there are infrequent categories, max_categories includes the category representing the infrequent categories along with the frequent categories. Default None. Set limit to 1,000,000.

Methods

__repr__

__repr__()

Print the estimator's constructor with all non-default parameter values.

fit

fit(
    y: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ]
) -> bigframes.ml.preprocessing.LabelEncoder

Fit label encoder.

Parameter
Name	Description
y	bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series The DataFrame or Series with training data.

Returns
Type	Description
LabelEncoder	Fitted encoder.

fit_transform

fit_transform(
    y: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ]
) -> bigframes.dataframe.DataFrame

API documentation for fit_transform method.

get_params

get_params(deep: bool = True) -> typing.Dict[str, typing.Any]

Get parameters for this estimator.

Parameter
Name	Description
deep	bool, default True Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
Type	Description
Dictionary	A dictionary of parameter names mapped to their values.

to_gbq

to_gbq(model_name: str, replace: bool = False) -> bigframes.ml.base._T

Save the transformer as a BigQuery model.

Parameters
Name	Description
model_name	str The name of the model.
replace	bool, default False Determine whether to replace if the model already exists. Default to False.

transform

transform(
    y: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ]
) -> bigframes.dataframe.DataFrame

Transform y using label encoding.

Parameter
Name	Description
y	bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series The DataFrame or Series to be transformed.

Returns
Type	Description
bigframes.dataframe.DataFrame	The result is an array-like of values.