recent changes in np.maximum.accumulate ?
I'm trying to track down test failures of statsmodels against recent master dev versions of numpy and scipy. The core computation is the following in one set of tests that fail pvals_corrected_raw = pvals * np.arange(ntests, 0, -1) pvals_corrected = np.maximum.accumulate(pvals_corrected_raw) this numpy version numpy-1.19.0.dev0%2B20200214184618_1f9ab28-cp38-cp38-manylinux2010_x86_64.whl is in the test run with failures (the first time statsmodel master failed) the previous version in the test runs didn't have these failures numpy-1.19.0.dev0%2B20200212232857_af0dfce-cp38-cp38-manylinux1_x86_64.whl I'm right now just fishing for candidates for the failures. And I'm not running any dev versions on my computer. Were there any recent changes that affect np.maximum.accumulate? Josef
On Tue, 2020-02-18 at 10:14 -0500, josef.pktd@gmail.com wrote:
Hmmm, the two git hashes indicate few changes between the two versions (mainly unicode related). However, recently there was also the addition of AVX-512F loops to maximum, so that seems like the most reasonable candidate (although I am unsure it changed exactly between those versions, it is also more complex maybe due to needing a machine that supports the instructions). Some details about the input could be nice. But if this is all that is as input, it sounds like it should be a contiguous array? I guess it might include subnormal numbers or NaN? Can you open an issue with some of those details if you have them? - Sebastian
On Tue, Feb 18, 2020 at 10:43 AM Sebastian Berg <sebastian@sipsolutions.net> wrote:
The test failures are on a Travis machine https://travis-ci.org/statsmodels/statsmodels/jobs/650430129 I can extract the numbers and examples from the unit test on my Windows computer. But, if it's machine dependent, then that might not be enough. The main reason why maximum.accumulate might be the problem is that in some tests we don't get monotonically increasing values, e.g. E x: array([0.012, 0.02 , 0.024, 0.024, 0.02 , 0.012]) E y: array([0.012, 0.02 , 0.024, 0.024, 0.024, 0.024]) first row is computed, second row is expected Josef
On Tue, 2020-02-18 at 10:14 -0500, josef.pktd@gmail.com wrote:
Hmmm, the two git hashes indicate few changes between the two versions (mainly unicode related). However, recently there was also the addition of AVX-512F loops to maximum, so that seems like the most reasonable candidate (although I am unsure it changed exactly between those versions, it is also more complex maybe due to needing a machine that supports the instructions). Some details about the input could be nice. But if this is all that is as input, it sounds like it should be a contiguous array? I guess it might include subnormal numbers or NaN? Can you open an issue with some of those details if you have them? - Sebastian
On Tue, Feb 18, 2020 at 10:43 AM Sebastian Berg <sebastian@sipsolutions.net> wrote:
The test failures are on a Travis machine https://travis-ci.org/statsmodels/statsmodels/jobs/650430129 I can extract the numbers and examples from the unit test on my Windows computer. But, if it's machine dependent, then that might not be enough. The main reason why maximum.accumulate might be the problem is that in some tests we don't get monotonically increasing values, e.g. E x: array([0.012, 0.02 , 0.024, 0.024, 0.02 , 0.012]) E y: array([0.012, 0.02 , 0.024, 0.024, 0.024, 0.024]) first row is computed, second row is expected Josef
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Sebastian Berg