Class KBinsDiscretizer (1.15.0)

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], 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.

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]
) -> 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.