bigframes.ml.preprocessing.LabelEncoder#

class bigframes.ml.preprocessing.LabelEncoder(min_frequency: int | None = None, max_categories: int | None = None)[source]#

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:

• 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 index 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.

fit(y: DataFrame | Series | DataFrame | Series) → LabelEncoder[source]#

Fit label encoder.

Parameters:

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:

Fitted encoder.

Return type:

LabelEncoder

transform(y: DataFrame | Series | DataFrame | Series) → DataFrame[source]#

Transform y using label encoding.

Parameters:

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:

The result is an array-like of values.

Return type:

bigframes.dataframe.DataFrame