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Downsample in-memory splitter #11
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(lgtm) |
| @@ -255,17 +256,18 @@ case class Trainer[K: Ordering, V, T: Monoid]( | |||
| for ( | |||
| (treeIndex, tree) <- treeMap; | |||
| i <- 1.to(sampler.timesInTrainingSet(instance.id, instance.timestamp, treeIndex)).toList; | |||
| leaf <- tree.leafFor(instance.features).toList if stopper.canSplit(leaf.target) && !stopper.shouldSplitDistributed(leaf.target) && stopper.shouldSplitLocally(leaf.target) | |||
| leaf <- tree.leafFor(instance.features).toList if stopper.shouldSplit(leaf.target) && (r.nextDouble < stopper.samplingRateToSplitLocally(leaf.target)) | |||
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I believe you have id's for rows (or at least optionally do). Could you use them to seed your random selection for consistency?
avibryant
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Dec 20, 2014
Downsample in-memory splitter
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See #9 for more info
@snoble @RyW90