Class KBinsDiscretizer (0.11.0) 

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. Only uniform is supported now.

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.KBinsDiscretizer

Fit the estimator.

Parameters
Name Description
X bigframes.dataframe.DataFrame or bigframes.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],
    y: typing.Optional[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.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.

transform

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

Discretize the data.

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

The DataFrame or Series to be transformed.
Returns
Type Description
bigframes.dataframe.DataFrame Transformed result.