quick'n'dirty subsampling

[riastradh-probcomp]

• Initialize Crosscat on single random subset of real data for all models.
• For BQL queries on rows not known to Crosscat, use hypotheticals.
  • No need to manifest insertion into Crosscat in BQL for now.

[riastradh-probcomp]

vkmvkmvkm: In the future, will we want each model to be analyzed on a (potentially) different randomly chosen subset of rows?

[vkmvkmvkmvkm]

Yes. (If it helps, I'm happy to discuss an interface to models and
meta-models that could make this easy and also help decouple BQL & its
primitives from crosscat.)

On Fri, May 29, 2015 at 6:59 PM, riastradh-probcomp <
notifications@github.com> wrote:

vkmvkmvkm: In the future, will we want each model to be analyzed on a
different subset of rows?

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Reply to this email directly or view it on GitHub
#6 (comment)
.

[riastradh-probcomp]

Fixed in 0780f7c.

Selection of rows is not random: it is the first ones in the table. Randomizing it is a separate issue, requires deciding whether to do it nondeterministically or with a fixed seed, and is not what old bayesdb did anyway.

Selection of rows is not per-model: every model shares a common subsampling of rows. Crosscat is not currently set up to allow two models to be trained on two distinct sets of rows.