Hello Isaak, There is a paper from the same authors as iforest but for streaming data: http://ijcai.org/Proceedings/11/Papers/254.pdf
For now it is not cited enough (24) to satisfy the sklearn requirements. Waiting for more citations, this could be a nice addition to sklearn-contrib. Otherwise, we could imagine extending iforest to streaming data by building new trees when data come (and removing the oldest ones), prediction still being based on the average depth of the forest. I'm not sure this heuristic could be merged on scikit-learn, since it is not based on well-cited papers. In the same time, it is a natural and simple extension of iforest to streaming data... Any opinion on it? Nicolas 2016-05-26 13:32 GMT+02:00 Arthur Mensch <arthur.men...@inria.fr>: > Hi Isaac, > > You may have a look at MiniBatchKMeans and MiniBatchDictionaryLearning > that both proposes this API. At the moment, you should fit a single mini > batch to the estimator using partial_fit, and update the inner attributes > accordingly. During the first partial_fit, you should take care of various > memory allocation that are needed by the estimator. > > Please fill free to create a pull request whenever you think your code is > ready for review. > > Good luck! > Le 26 mai 2016 13:14, <donkey-ho...@cryptolab.net> a écrit : > >> hello scikit-learn devs, >> >> After following the work on IsolationForest so far and testing on a >> real-world problem here we've found this model to be very promising for >> anomaly detection. However, at present, IsolationForest only fits data in >> batch even while it may be well suited to incremental on-line learning >> since one could subsample recent history and older estimators can be >> dropped progressively. >> >> I'd like to contribute this feature, but being new to ML and scikit-learn >> I'm curious how I should start making a quick & dirty version to see how >> this may work. Are there other good examples where one could see the >> difference between .fit and .partial_fit in other models? >> >> thanks >> isaak y. >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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