[ENH] Distance module n_jobs support

[chrisholder]

Reference Issues/PRs

#1886 #1797

What does this implement/fix? Explain your changes.

This PR adds parallelism to the distance module. Specifically to pairwise distance computations.

To do this the n_job parameter can now be specified to any pairwise distance (aside mpdist which was excluded due to complexity) and it will use that many threads.

This PR just updates the distance module. In a follow up PR I will update all our models that use pairwise_distance and n_jobs to make use of this.

Does your contribution introduce a new dependency? If yes, which one?

Any other comments?

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[baraline]

LGTM! The only thing that would be missing from the PR to fix the issue is to include n_jobs in the distance params in our KNeighborsTimeSeriesClassifier and we would be good to go

[MatthewMiddlehurst]

Would be nice to benchmark and see if this actually speeds it up.

[MatthewMiddlehurst]

I feel like this should set it back to the original value after?

Maybe its possible to use a decorator in these cases similar to https://github.com/aeon-toolkit/aeon/blob/v1.0.0/aeon/testing/utils/output_suppression.py

[baraline]

Can confirm that this does indeed provide some speed ups :

import numpy as np
from aeon.distances import euclidean_pairwise_distance

X = np.random.rand(1000, 1000)
Y = np.random.rand(1000, 1000)
euclidean_pairwise_distance(X, Y)

%timeit euclidean_pairwise_distance(X, Y)
%timeit euclidean_pairwise_distance(X, Y, n_jobs=-1)

199 ms ± 7.05 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)
82.7 ms ± 1.19 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

Seconds Matthew's comment to revert the number of threads back to previously setted value (using get_num_threads).

[chrisholder]

With distance measures like Euclidean I found unless you have a huge number of very long time series threading is almost never going to be faster just due to the cost of spawning a thread. This is why I added the warning as I can't really see a scenario where running pairwise euclidean distance thread will ever achieve a meaningful speed up.

However, for something like DTW I get huge speed ups:

    X = make_example_3d_numpy(10000, 1, 100, return_y=False, random_state=42)
    start = time.time()
    pairwise_distance(X, method="dtw", n_jobs=1)
    print(f"1 Job Time: {time.time() - start}")

    start = time.time()
    pairwise_distance(X, method="dtw", n_jobs=14)
    print(f"14 Job Time: {time.time() - start}")

Time: 1620.196809053421
Time: 255.28647780418396

[chrisholder]

I also decided to add the threading with a custom distance function.

[chrisholder]

I followed your suggestion @MatthewMiddlehurst and defined a decorator to handle setting and resetting threading.