Applies a transformer within different groups.
This transformer allows you to split your data into groups and apply a transformer within each group. This happens in a streaming manner, which means that the groups are discovered online. A separate copy of the provided transformer is made whenever a new group appears. The groups are defined according to one or more keys.
by (Union[Hashable, List[Hashable]])
The field on which to group the data. This can either by a single value, or a list of values.
Return a fresh estimator with the same parameters.
The clone has the same parameters but has not been updated with any data. This works by looking at the parameters from the class signature. Each parameter is either - recursively cloned if it's a River classes. - deep-copied via
copy.deepcopy if not. If the calling object is stochastic (i.e. it accepts a seed parameter) and has not been seeded, then the clone will not be idempotent. Indeed, this method's purpose if simply to return a new instance with the same input parameters.
Update with a set of features
A lot of transformers don't actually have to do anything during the
learn_one step because they are stateless. For this reason the default behavior of this function is to do nothing. Transformers that however do something during the
learn_one can override this method.
- x (dict)
Transform a set of features
- x (dict)
dict: The transformed values.