Class OneHotEncoder (2.29.0) 

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.

>>> 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)
OneHotEncoder()

>>> print(enc.transform(bpd.DataFrame({"a": ["Female", "Male"], "b": ["1", "4"]})))
                onehotencoded_a               onehotencoded_b
0  [{'index': 1, 'value': 1.0}]  [{'index': 1, 'value': 1.0}]
1  [{'index': 2, 'value': 1.0}]  [{'index': 0, 'value': 1.0}]
<BLANKLINE>
[2 rows x 2 columns]

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,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    y=None,
) -> bigframes.ml.preprocessing.OneHotEncoder

Fit OneHotEncoder to X.

Returns
Type Description
OneHotEncoder Fitted encoder.

fit_transform

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

Transform X using one-hot encoding.

Returns
Type Description
bigframes.dataframe.DataFrame The result is categorized as index: number, value: number, where index is the position of the dict seeing the category, and value is 0 or 1.