ray.data.preprocessors.CustomKBinsDiscretizer#

class ray.data.preprocessors.CustomKBinsDiscretizer(columns: List[str], bins: Union[Iterable[float], pandas.core.indexes.interval.IntervalIndex, Dict[str, Union[Iterable[float], pandas.core.indexes.interval.IntervalIndex]]], *, right: bool = True, include_lowest: bool = False, duplicates: str = 'raise', dtypes: Optional[Dict[str, Union[pandas.core.dtypes.dtypes.CategoricalDtype, Type[numpy.integer]]]] = None)[source]#

Bases: ray.data.preprocessors.discretizer._AbstractKBinsDiscretizer

Bin values into discrete intervals using custom bin edges.

Columns must contain numerical values.

Examples

Use CustomKBinsDiscretizer to bin continuous features.

>>> import pandas as pd
>>> import ray
>>> from ray.data.preprocessors import CustomKBinsDiscretizer
>>> df = pd.DataFrame({
...     "value_1": [0.2, 1.4, 2.5, 6.2, 9.7, 2.1],
...     "value_2": [10, 15, 13, 12, 23, 25],
... })
>>> ds = ray.data.from_pandas(df)
>>> discretizer = CustomKBinsDiscretizer(
...     columns=["value_1", "value_2"],
...     bins=[0, 1, 4, 10, 25]
... )
>>> discretizer.transform(ds).to_pandas()
   value_1  value_2
0        0        2
1        1        3
2        1        3
3        2        3
4        2        3
5        1        3

You can also specify different bin edges per column.

>>> discretizer = CustomKBinsDiscretizer(
...     columns=["value_1", "value_2"],
...     bins={"value_1": [0, 1, 4], "value_2": [0, 18, 35, 70]},
... )
>>> discretizer.transform(ds).to_pandas()
   value_1  value_2
0      0.0        0
1      1.0        0
2      1.0        0
3      NaN        0
4      NaN        1
5      1.0        1

Parameters

columns – The columns to discretize.
bins – Defines custom bin edges. Can be an iterable of numbers, a pd.IntervalIndex, or a dict mapping columns to either of them. Note that pd.IntervalIndex for bins must be non-overlapping.
right – Indicates whether bins include the rightmost edge.
include_lowest – Indicates whether the first interval should be left-inclusive.
duplicates – Can be either ‘raise’ or ‘drop’. If bin edges are not unique, raise ValueError or drop non-uniques.
dtypes – An optional dictionary that maps columns to pd.CategoricalDtype objects or np.integer types. If you don’t include a column in dtypes or specify it as an integer dtype, the outputted column will consist of ordered integers corresponding to bins. If you use a pd.CategoricalDtype, the outputted column will be a pd.CategoricalDtype with the categories being mapped to bins. You can use pd.CategoricalDtype(categories, ordered=True) to preserve information about bin order.

Ray 2.3.0

ray.data.preprocessors.CustomKBinsDiscretizer

ray.data.preprocessors.CustomKBinsDiscretizer#