Remove native model from Isolation Forest estimators - #8493
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Match how random forest handles things by using treelite and nvforest only. There is no need for a native model. This means pickling will work and less code to maintain.
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/ok to test 6c6a777 |
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This PR removes the native model (
self._model) which currently prevents a fitted model from being pickeld (and used again after unpickling).IsolationForestis the only forest estimator in cuML that keeps a native C++ model alive in Python.fitallocates anIsolationForestModel<T>and stores it on the estimator asself._model. This means we can currently not pickle anIsolationForestand we need a lot of additional code in isolation_forest.pyx`.The new design matches what we already do for random forest (c.f. #7249).
predictnow returnsint64instead ofint32. The old implementation wrote into anint32device buffer because that is what the C++ signature took. The new one iscp.where(scores < offset_, -1, 1), which yieldsint64. This matchessklearn.ensemble.IsolationForest.predict.fit_treeliteneeds to hand backc(n)because Python applies the2^(-E[h(x)] / c(n))transform. The alternative is to drop the parameter and recomputec(n)in Python frommax_samples_. I prefer thefit_treeliteway because we don't end up duplicating code.