PoissonInclusion

Randomly selects features with an inclusion trial.

When a new feature is encountered, it is selected with probability p. The number of times a feature needs to beseen before it is added to the model follows a geometric distribution with expected value 1 / p. This feature selection method is meant to be used when you have a very large amount of sparse features.

Parameters

• p

  Type → float

  Probability of including a feature the first time it is encountered.

• seed

  Type → int | None

  Default → None

  Random seed value used for reproducibility.

Examples

from river import datasets
from river import feature_selection
from river import stream

selector = feature_selection.PoissonInclusion(p=0.1, seed=42)

dataset = iter(datasets.TrumpApproval())

feature_names = next(dataset)[0].keys()
n = 0

while True:
    x, y = next(dataset)
    xt = selector.transform_one(x)
    if xt.keys() == feature_names:
        break
    n += 1

n

12

Methods

learn_one

Update with a set of features x.

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.

Parameters

• x — 'dict'

Returns

Transformer: self

transform_one

Transform a set of features x.

Parameters

• x — 'dict'

Returns

dict: The transformed values.

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1. McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D. and Chikkerur, S., 2013, August. Ad click prediction: a view from the trenches. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 1222-1230) ↩