Do you happen to have numba installed? Our FisherJenks implementation should be accelerated using that... we'd definitely invite a rigorous performance comparison there 😄

Also, the *Sampled variants are useful in extremely large data, since a random subsample of the target data can often yield the same results.

Do either of these work for your use case?

@ljwolf shall we maybe add a warning here

Ah, I didn't notice there's numba support. I just gave it a try and compared it with jenkspy. I noticed that you're just simply using jit without type specification. So I replaced @jit with @njit("f8[:](f8[:], u2, b1)") and compared the three versions with the following data:

import jenkspy
from mapclassify.classifiers import _fisher_jenks_means
import numpy as np

data = np.random.random(12000) * 5000
data = data.astype("f8")

Here are the results.

I ran the same test using an array with 120,000 elements instead of 12,000. Here's the results:

BTW, I ran these tests on my iMac with 8 cores and 16 G RAM.

That is interesting!

In any case, I would rather not add jenskpy as a dependency here. We don't know how maintained it is and it may be a hassle to install. They offer wheels only for Windows and only up to Python 3.8. I assume that this could cause a lot of friction for users.

Right, it makes sense.

You can shave off a few more seconds by adding cache to njit like so @njit("f8[:](f8[:], u2, b1)", cache=True). Feel free to close or if interested, I can modify the code to add the signatures and submit a PR.

I can modify the code to add the signatures and submit a PR

That would be very welcome! Thanks!

I would definitely not integrate jenkspy in your environment. Your library works perfectly in my tests, but jenkspy have some problematic outputs. I don't trust to outputs of jenkspy.