Class LabelEncoder (1.29.0)

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.