Class OneHotEncoder (0.16.0)

OneHotEncoder(
    drop: typing.Optional[typing.Literal["most_frequent"]] = None,
    min_frequency: typing.Optional[int] = None,
    max_categories: typing.Optional[int] = None,
)

Encode categorical features as a one-hot format.

The input to this transformer should be an array-like of integers or strings, denoting the values taken on by categorical (discrete) features. The features are encoded using a one-hot (aka 'one-of-K' or 'dummy') encoding scheme.

Note that this method deviates from Scikit-Learn; instead of producing sparse binary columns, the encoding is a single column of STRUCT<index INT64, value DOUBLE>.

Examples:

Given a dataset with two features, we let the encoder find the unique
values per feature and transform the data to a binary one-hot encoding.

.. code-block::

    from bigframes.ml.preprocessing import OneHotEncoder
    import bigframes.pandas as bpd

    enc = OneHotEncoder()
    X = bpd.DataFrame({"a": ["Male", "Female", "Female"], "b": ["1", "3", "2"]})
    enc.fit(X)
    print(enc.transform(bpd.DataFrame({"a": ["Female", "Male"], "b": ["1", "4"]})))

Parameters

NameDescription
drop Optional[Literal["most_frequent"]], default None

Specifies a methodology to use to drop one of the categories per feature. This is useful in situations where perfectly collinear features cause problems, such as when feeding the resulting data into an unregularized linear regression model. However, dropping one category breaks the symmetry of the original representation and can therefore induce a bias in downstream models, for instance for penalized linear classification or regression models. Default None: retain all the categories. "most_frequent": Drop the most frequent category found in the string expression. Selecting this value causes the function to use dummy encoding.

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.

Methods

__repr__

__repr__()

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

fit

fit(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series], y=None
) -> bigframes.ml.preprocessing.OneHotEncoder

Fit OneHotEncoder to X.

Parameters
NameDescription
X bigframes.dataframe.DataFrame or bigframes.series.Series

The DataFrame or Series with training data.

y default None

Ignored.

Returns
TypeDescription
OneHotEncoderFitted encoder.

fit_transform

fit_transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
    ] = None,
) -> 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
NameDescription
deep bool, default True

Default True. If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns
TypeDescription
DictionaryA dictionary of parameter names mapped to their values.

transform

transform(
    X: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]
) -> bigframes.dataframe.DataFrame

Transform X using one-hot encoding.

Parameter
NameDescription
X bigframes.dataframe.DataFrame or bigframes.series.Series

The DataFrame or Series to be transformed.

Returns
TypeDescription
bigframes.dataframe.DataFrameThe result is categorized as index: number, value: number. Where index is the position of the dict that seeing the category, and value is 0 or 1.