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Artem Malykh commented on IGNITE-10144: --------------------------------------- According to series of quick experiments, approach used now {code:java} upstream.sequential().flatMap(en -> Stream.generate(() -> en).limit(poisson.sample())) {code} outperforms following approaches: 1. Using hash code for determinism instead of "sequential" and then using parallel {code:java} PoissonWithUnderlyingRandomAccess p = new PoissonWithUnderlyingRandomAccess( new Well19937c(123L), 0.3, PoissonDistribution.DEFAULT_EPSILON, PoissonDistribution.DEFAULT_MAX_ITERATIONS upstream .flatMap(en -> { p.getRand().setSeed(1234L + en.hashCode()); return Stream.generate(() -> en).limit(p.sample()); }).parallel().count();{code} 2. Zipping upstream with indexes stream and then using index for determinism. > Optimize bagging upstream transformer > ------------------------------------- > > Key: IGNITE-10144 > URL: https://issues.apache.org/jira/browse/IGNITE-10144 > Project: Ignite > Issue Type: Improvement > Components: ml > Reporter: Artem Malykh > Assignee: Artem Malykh > Priority: Minor > Fix For: 2.8 > > > For now BaggingUpstreamTransformer makes upstream sequential to make > transformation deterministic. Maybe we should do it other way, for example > use mapping of the form (entryIdx, en) -> Stream.generate(() -> en).limit(new > PoissonDistribution(Well19937c(entryIdx + seed), ...).sample()) -- This message was sent by Atlassian JIRA (v7.6.3#76005)