featurebyte.Feature.cd.get_relative_frequency¶

key: Union[StrictInt, StrictFloat, StrictStr, bool, Feature]

) -> Feature

Description¶

Computes the relative frequency of a specific key in the Cross Aggregate feature. The key may either be a lookup feature or a scalar value.

Parameters¶

key: Union[StrictInt, StrictFloat, StrictStr, bool, Feature]
Key to lookup the value for.

Returns¶

Feature
A new Feature object.

Examples¶

Create a new feature by computing the relative frequency for a particular key:

>>> counts = catalog.get_feature("CustomerProductGroupCounts_7d")
>>> new_feature = counts.cd.get_relative_frequency("Chips et Tortillas")
>>> new_feature.name = "Chips et Tortillas Relative Frequency"

Preview the features:

>>> features = fb.FeatureGroup([counts, new_feature])
>>> df = features.preview(pd.DataFrame([{"POINT_IN_TIME": "2022-04-15 10:00:00", "GROCERYCUSTOMERGUID": "2f4c1578-29d6-44b7-83da-7c5bfb981fa0"}]))

Dictionary feature:

>>> df["CustomerProductGroupCounts_7d"].iloc[0]
'{"Chips et Tortillas":1,"Colas, Thés glacés et Sodas":3,"Crèmes et Chantilly":1,"Pains":1,"Œufs":1}'

New feature:

>>> df["Chips et Tortillas Relative Frequency"].iloc[0]
0.14285714285714302