[query] lower poisson regression #12793
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My apologies. I made several changes to lowered logistic regression as well. All the generalized linear model methods share the same fit result. I abstracted this into one datatype at the top of `statgen.py`: `numerical_regression_fit_dtype`. --- You'll notice I moved the cases such that we check for convergence *before* checking if we are at the maximum iteration. It seemed to me that: - `max_iter == 0` means do not even attempt to fit. - `max_iter == 1` means take one gradient step, if you've converged, then return successfully, otherwise fail. - etc. The `main` branch currently always fails if you set `max_iter == 1`, even if the first step lands on the true maximum likelihood fit. I substantially refactored logistic regression. There were dead code paths (e.g. the covariates array is known to be non-empty). I also found all the function currying and comingling of fitting and testing really confusing. To be fair, the Scala code does this (and its really confusing). I think the current structure is easier to follow: 1. Fit the null model. 2. If wald, assume the beta for the genotypes is zero and use the rest of the parameters from the null model fit to compute the score (i.e. the gradient of the likelihood). Recall calculus: gradient near zero => value near the maximum. Return: this is the test. 3. Otherwise, fit the full model starting at the null fit parameters. 4. Test the "goodness" of this new & full fit. --- Poisson regression is similar but with a different likelihood function and gradient thereof. Notice that I `key_cols_by()` to indicate to Hail that the order of the cols is irrelevant (the result is a locus-keyed table after all). This is necessary at least until hail-is#12753 merges. I think it's generally a good idea though: it indicates to Hail that the ordering of the columns is irrelevant, which is potentially useful information for the optimizer! --- Both logistic and Poisson regression can benefit from BLAS3 by running at least the score test for multiple variants at once. --- I'll attach an image in the comments, but I spend ~6 seconds compiling this trivial model and ~140ms testing it. ```python3 import hail as hl mt = hl.utils.range_matrix_table(1, 3) mt = mt.annotate_entries(x=hl.literal([1, 3, 10, 5])) ht = hl.poisson_regression_rows( 'wald', y=hl.literal([0, 1, 1, 0])[mt.col_idx], x=mt.x[mt.col_idx], covariates=[1], max_iterations=2) ht.collect() ``` I grabbed some [sample code from scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PoissonRegressor.html) for Poisson regression (doing a score test rather than a wald test) and timed it. It takes ~8ms. So we're 3 orders of magnitude including the compiler, and ~1.2 orders of magnitude off without the compiler. Digging in a bit: - ~65ms for class loading. - ~15ms for region allocation. - ~20ms various little spots. Leaving about 40ms strictly executing generated code That's about 5x which is starting to feel reasonable.
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patrick-schultz
previously requested changes
Mar 16, 2023
hail/python/hail/methods/statgen.py
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| def nd_exp(hl_nd): | ||
| return hl_nd.map(lambda x: hl.exp(x)) |
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hl.exp(hl_nd) works
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ah nice, I also simplified sigmoid to sigmoid = hl.expit.
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Also, inspired by this, I fixed hl.log to do the right thing for ndarrays.
hail/python/hail/methods/statgen.py
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| def test(yvec, null_fit): | ||
| if test == 'score': | ||
| return logistic_score_test(covs_and_x, y, null_fit) |
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Should this be yvec?
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eagle eyes, yes it absolutely should be. Luckily this was caught by the tests as a mismatched source objects error.
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With the binding changes I can't even find the regression code. We're totally swamped by the compiler. After the JIT is warm, the compiler is ~3s. The regression code is probably equally as fast as scikit learn on this tiny example. |
added 2 commits
March 20, 2023 19:22
patrick-schultz
approved these changes
Mar 21, 2023
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cc @tpoterba
My apologies. I made several changes to lowered logistic regression as well.
All the generalized linear model methods share the same fit result. I abstracted this into one datatype at the top of
statgen.py:numerical_regression_fit_dtype.You'll notice I moved the cases such that we check for convergence before checking if we are at the maximum iteration. It seemed to me that:
max_iter == 0means do not even attempt to fit.max_iter == 1means take one gradient step, if you've converged, then return successfully, otherwise fail.mainbranch currently always fails if you setmax_iter == 1, even if the first step lands on the true maximum likelihood fit.I substantially refactored logistic regression. There were dead code paths (e.g. the covariates array is known to be non-empty). I also found all the function currying and comingling of fitting and testing really confusing. To be fair, the Scala code does this (and its really confusing). I think the current structure is easier to follow:
Poisson regression is similar but with a different likelihood function and gradient thereof. Notice that I
key_cols_by()to indicate to Hail that the order of the cols is irrelevant (the result is a locus-keyed table after all). This is necessary at least until #12753 merges. I think it's generally a good idea though: it indicates to Hail that the ordering of the columns is irrelevant, which is potentially useful information for the optimizer!Both logistic and Poisson regression can benefit from BLAS3 by running at least the score test for multiple variants at once.
I'll attach an image in the comments, but I spend ~6 seconds compiling this trivial model and ~140ms testing it.
I grabbed some sample code from
scikit-learn for Poisson regression (doing a score test rather than a wald test) and timed it. It takes ~8ms. So we're 3 orders of magnitude including the compiler, and ~1.2 orders of magnitude off without the compiler. Digging in a bit: