Class KBinsDiscretizer (1.24.0)

Version 1.24.0

• 1.25.0 (latest)
• 1.24.0
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0
• 1.15.0
• 1.14.0
• 1.13.0
• 1.12.0
• 1.11.1
• 1.10.0
• 1.9.0
• 1.8.0
• 1.7.0
• 1.6.0
• 1.5.0
• 1.4.0
• 1.3.0
• 1.2.0
• 1.1.0
• 1.0.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.1
• 0.19.2
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.1
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.0
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0

KBinsDiscretizer(
    n_bins: int = 5, strategy: typing.Literal["uniform", "quantile"] = "quantile"
)

Bin continuous data into intervals.

Parameters
Name	Description
n_bins	int, default 5 The number of bins to produce. Raises ValueError if n_bins < 2.
strategy	{'uniform', 'quantile'}, default='quantile' Strategy used to define the widths of the bins. 'uniform': All bins in each feature have identical widths. 'quantile': All bins in each feature have the same number of points.

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.KBinsDiscretizer

Fit the estimator.

Parameters
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series or pandas.core.frame.DataFrame or pandas.core.series.Series The Dataframe or Series with training data.
y	default None Ignored.

Returns
Type	Description
KBinsDiscretizer	Fitted scaler.

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

Fit to data, then transform it.

Parameters
Name	Description
X	bigframes.dataframe.DataFrame or bigframes.series.Series Series or DataFrame of shape (n_samples, n_features). Input samples.
y	bigframes.dataframe.DataFrame or bigframes.series.Series Series or DataFrame of shape (n_samples,) or (n_samples, n_outputs). Default None. Target values (None for unsupervised transformations).

Returns
Type	Description
bigframes.dataframe.DataFrame	DataFrame of shape (n_samples, n_features_new). Transformed DataFrame.

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

Discretize the data.

Parameter
Name	Description
X	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	Transformed result.