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.
Probability of including a feature the first time it is encountered.
seed (int) – defaults to
Random seed value used for reproducibility.
>>> 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).keys() >>> n = 0 >>> while True: ... x, y = next(dataset) ... xt = selector.transform_one(x) ... if xt.keys() == feature_names: ... break ... n += 1 >>> n 12
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.
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) ↩