Fixed #1112 Migrate to New NumPy Random Number Generator (RNG) API by seanlaw · Pull Request #1147 · stumpy-dev/stumpy

seanlaw · 2026-07-07T15:36:05Z

See issue #1112

This is a second attempt that follows failed PR #1131

Pull Request Checklist

Below is a simple checklist but please do not hesitate to ask for assistance!

To Do List

Search for "np.random" in all stumpy/*.py and tests/test_*py files
Replace np.random.rand(x) with rng.RNG.random(x)
Replace np.random.rand(x*y).reshape(x,y) with rng.RNG.random(size=(x, y))
Replace np.random.uniform(x, y, [z]) with rng.RNG.uniform(x, y, size=z)
Replace np.random.permutation([x, y, z]) with rng.RNG.permutation([x, y, z])
Replace np.random.randint(x, y, z) with rng.RNG.integers(x, y, z)
Replace np.random.choice([x, y], l, replace=True) with rng.RNG.choice([x, y], l, replace=True)
Replace np.random.normal(size=x) with rng.RNG.normal(size=x)
Replace all np.random from docstrings, README, tutorials, etc
Fix tests/test_non_normalized.py
Search for"seed" in all stumpy/*.py and tests/test_*py files
Run unit tests on Google Colab

gitnotebooks · 2026-07-07T15:36:09Z

Review these changes at https://app.gitnotebooks.com/stumpy-dev/stumpy/pull/1147

seanlaw · 2026-07-07T15:40:50Z

@NimaSarajpoor I've created a newstumpy/rng.py module that will manage our random number usage and tested the numpy.random migration on test_core.py as an initial trial run. Would you mind taking a look and providing any feedback before I do the same thing on the other tests?

~~I will be adding a tests/test_rng.py next~~

NimaSarajpoor · 2026-07-08T02:59:58Z

@seanlaw Please allow me some time to take a look. Will provide an update in a couple of days.

seanlaw · 2026-07-09T13:55:11Z

This function in tests/test_scrump.py is going to need more attention in order to migrate:

stumpy/tests/test_scrump.py

Lines 963 to 1065 in 86145c2

    
           def test_prescrump_self_join_KNN_no_overlap(): 
        
               # This test is particularly designed to raise error in a rare case described 
        
               # as follows: Let's denote `I[i]` as the array with length `k` that contains 
        
               # the start indices of the best-so-far top-k nearest neighbors of `subseq i`, 
        
               # (`S_i`). Also, we denote `P[i]` as their corresponding distances sorted in 
        
               # ascending order. Let's denote `d` as the distance between `S_i` and `S_j`. P[i] 
        
               # and I[i] must be updated if (1) `j` is not in I[i] and (2) `d` < P[i,-1]. 
        
               # Regarding the former condition, one needs to check the whole array I[i]. Checking 
        
               # the array I[i, :idx], where `idx = np.searchsorted(P[i], 'd', side='right')` is 
        
               # not completely correct and that is due to imprecision in numerical calculation. 
        
               # It may happen that `j` is not in `I[i, :idx]`, but it is in fact at `I[i, idx]` 
        
               # (or any other position in array I[i]). And, its corresponding distance, i.e 
        
               # P[i, idx], is d + 1e-5, for instance. In theory, this should be exactly `d`. 
        
               #  However, due to imprecision, we may calculated a slightly different value 
        
               # for such distance in one of previous iterations in function prescrump. This 
        
               #  test results in error if someone tries to change the performant code of prescrump 
        
               # function and check `I[i, :idx]` rather than the full array `I[i]`. 
        
               T = np.array( 
        
                   [ 
        
                       -916.64703784, 
        
                       -327.42056679, 
        
                       379.19386284, 
        
                       -281.80427628, 
        
                       -189.85401773, 
        
                       -38.69610569, 
        
                       187.89889345, 
        
                       578.65862523, 
        
                       528.09687811, 
        
                       -667.42973795, 
        
                       -285.27749324, 
        
                       -211.28930925, 
        
                       -703.93802657, 
        
                       -820.53780562, 
        
                       -955.91174663, 
        
                       383.65471851, 
        
                       932.08809422, 
        
                       -563.57569746, 
        
                       784.0546579, 
        
                       -343.14886064, 
        
                       -612.72329848, 
        
                       -270.09273091, 
        
                       -448.39346549, 
        
                       578.03202014, 
        
                       867.15436674, 
        
                       -783.55167049, 
        
                       -494.78062922, 
        
                       -311.18567747, 
        
                       522.70052256, 
        
                       933.45474094, 
        
                       192.34822368, 
        
                       -162.11374908, 
        
                       -612.95359279, 
        
                       -449.62297051, 
        
                       -351.79138459, 
        
                       -77.70189101, 
        
                       -439.46519487, 
        
                       -660.48431174, 
        
                       548.69362177, 
        
                       485.36004744, 
        
                       -535.3566627, 
        
                       -568.0955257, 
        
                       755.26647273, 
        
                       736.1079588, 
        
                       -597.65672557, 
        
                       379.3299783, 
        
                       731.38211912, 
        
                       247.34827447, 
        
                       545.41888454, 
        
                       644.94300763, 
        
                       20.99042666, 
        
                       788.19859515, 
        
                       -898.24325898, 
        
                       -929.47841134, 
        
                       -738.45875181, 
        
                       66.01030291, 
        
                       512.945841, 
        
                       -44.07720164, 
        
                       302.97141464, 
        
                       -696.95271302, 
        
                       662.98385163, 
        
                       -712.3807531, 
        
                       -43.62688539, 
        
                       74.16927482, 
        
                   ] 
        
               ) 
        
               # test_cases: dict() with `key: value` pair, where key is `(m, k)`, and value 
        
               # is a list of random `seeds` 
        
               test_cases = { 
        
                   (3, 2): [4279, 9133, 8190], 
        
                   (3, 5): [1267, 4016, 4046], 
        
                   (5, 2): [6327, 4926, 3712], 
        
                   (5, 5): [3032, 3032, 8117], 
        
               } 
        
               for (m, k), specified_seeds in test_cases.items(): 
        
                   zone = int(np.ceil(m / 4)) 
        
                   for seed in specified_seeds: 
        
                       np.random.seed(seed) 
        
                       ref_P, ref_I = naive.prescrump(T, m, T, s=1, exclusion_zone=zone, k=k) 
        
                       comp_P, comp_I = prescrump(T, m, s=1, k=k) 
        
                       npt.assert_almost_equal(ref_P, comp_P) 
        
                       npt.assert_almost_equal(ref_I, comp_I)

Update

We cannot easily recreate this test and, since it's such a low probability event, I've decided to remove the test altogether (open for discussion!)

seanlaw · 2026-07-09T14:00:33Z

The function get_central_motif (and also get_aamp_central_motif) in naive.py is missing coverage in some occasions. However, when the state is set to:

RNG.bit_generator.state = {
    "bit_generator": "PCG64",
    "state": {
        "state": 62474265484091438068208639126596717761,
        "inc": 18021712563375961045953296450069431849
    },
    "has_uint32": 0,
    "uinteger": 0
}

This allows it to be covered (likely by tests/test_ostinato.py)

Update

This was resolved by setting an RNG state that passes through the missing coverage code

seanlaw · 2026-07-09T14:01:44Z

tests/test_precision.py has a lot of seeds set and causing missing coverage

Update
Resolved by hardcoding input array T

seanlaw · 2026-07-10T11:23:59Z

The _iac function in stumpy/floss.py sets a random seed AND the scipy.stats.rv_histogram uses a random number generator:

stumpy/stumpy/floss.py

Lines 87 to 106 in 86145c2

    
           np.random.seed(seed) 
        
           I = np.random.randint(0, width, size=width, dtype=np.int64) 
        
           if bidirectional is False:  # Idealized 1-dimensional matrix profile index 
        
               I[:-1] = width 
        
               for i in range(width - 1): 
        
                   I[i] = np.random.randint(i + 1, width, dtype=np.int64) 
        
           target_AC = _nnmark(I) 
        
           params = np.empty((n_iter, 2), dtype=np.float64) 
        
           for i in range(n_iter): 
        
               hist_dist = scipy.stats.rv_histogram( 
        
                   (target_AC, np.append(np.arange(width), width)) 
        
               ) 
        
               data = hist_dist.rvs(size=n_samples) 
        
               a, b, c, d = scipy.stats.beta.fit(data, floc=0, fscale=width) 
        
               params[i, 0] = a 
        
               params[i, 1] = b

Update

Luckily, the rvs method inside of scipy.stats.rv_histogram supports a random_state parameter and you can simply pass in your own random_state=rng and this resolves the issue of reproducibility

seanlaw · 2026-07-10T19:24:27Z

@NimaSarajpoor I believe that I've fully completed the migration. Looking forward to your review and apologies in advance for needing to change so many files!

seanlaw · 2026-07-11T00:52:31Z

So, I just learned that the new random number generator does NOT guarantee the same results across different NumPy versions and so we are having some issues. In order to guarantee that we get the same results, we must continue using the old "legacy random number generator" (i.e., np.random.seed()

seanlaw · 2026-07-11T01:05:30Z

I think I have a clearer plan now. Will close this and try again!

NimaSarajpoor · 2026-07-11T01:15:33Z

@seanlaw

So, I just learned that the new random number generator does NOT guarantee the same results across different NumPy versions and so we are having some issues.

Is there any part in this PR that can help me understand where this created / can create an issue in STUMPY? Was checking the runs in Actions but couldn't find anything. It seems that the unit tests were completed successfully.

seanlaw · 2026-07-11T01:32:37Z

Is there any part in this PR that can help me understand where this created / can create an issue in STUMPY? Was checking the runs in Actions but couldn't find anything. It seems that the unit tests were completed successfully.

So, all of the unit tests were passing on my machine, which brought me great joy. However, while all of the unit tests passed in Github Actions, one of the coverage tests failed:

https://github.com/stumpy-dev/stumpy/actions/runs/29119219021/job/86450147736

This was very surprising because the same tests passed locally on my machine and the coverage displayed 100%. Since, I could easily reproduce the random state on my laptop but I couldn't reproduce the missing coverage, I did some research and learned about the issue with the new RNG.

NimaSarajpoor · 2026-07-11T02:33:23Z

Got it. Thanks!

Initial migration conversion - test w/ core.py

40877d2

seanlaw added 2 commits July 7, 2026 13:58

Added tests for rng.py module

1eaca96

Minor change

3f9e69c

seanlaw mentioned this pull request Jul 8, 2026

refactor: migrate to new NumPy RNG API #1148

Closed

Completed first draft of numpy migration

a810207

Updated tutorials to use new RNG

c57c4df

seanlaw closed this Jul 11, 2026

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