Class LabelEncoder (1.6.0) 

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]
) -> bigframes.ml.preprocessing.LabelEncoder

Fit label encoder.

Parameter
Name Description
y bigframes.dataframe.DataFrame or bigframes.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]
) -> 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]
) -> bigframes.dataframe.DataFrame

Transform y using label encoding.

Parameter
Name Description
y bigframes.dataframe.DataFrame or bigframes.series.Series

The DataFrame or Series to be transformed.
Returns
Type Description
bigframes.dataframe.DataFrame The result is an array-like of values.