[BUG] Error out when using 'cosine' distance metrics (ufunc 'correct_alternative_cosine' did not contain a loop with signature matching types)

Hi,

I am actually using umap, but i know it is using pynndescent under the hood. When I am running umap with > 10k rows, I get following errors:

numpy.core._exceptions.UFuncTypeError: ufunc 'correct_alternative_cosine' did not contain a loop with signature matching types <class 'numpy.dtype[float32]'> -> None

This is the minimal reproducible codes

import numpy as np
import umap

print(100, umap.UMAP(metric="cosine").fit(np.random.random([100,10])).embedding_.shape)
print(1000, umap.UMAP(metric="cosine").fit(np.random.random([1000,10])).embedding_.shape)
print(10000, umap.UMAP(metric="cosine").fit(np.random.random([10000,10])).embedding_.shape)

This is the environment:

python 3.8.2

colorama      0.4.4  Cross-platform colored terminal text.
joblib        1.1.0  Lightweight pipelining with Python functions
llvmlite      0.34.0 lightweight wrapper around basic LLVM functionality
numba         0.51.2 compiling Python code using LLVM
numpy         1.22.0 NumPy is the fundamental package for array computing with Python.
pynndescent   0.5.5  Nearest Neighbor Descent
scikit-learn  1.0.2  A set of python modules for machine learning and data mining
scipy         1.6.1  SciPy: Scientific Library for Python
threadpoolctl 3.0.0  threadpoolctl
tqdm          4.62.3 Fast, Extensible Progress Meter
umap-learn    0.5.2  Uniform Manifold Approximation and Projection

This did not happen in the prev version of my application. I suspect might be due to the new numpy version. However, because i am also using hdbscan, it does not work with any numpy version except 1.22.0.

[jjsnlee]

Another option (not having to mess around in an install env) is to do the following somewhere in your own code:

import pynndescent
pynn_dist_fns_fda = pynndescent.distances.fast_distance_alternatives
pynn_dist_fns_fda["cosine"]["correction"] = correct_alternative_cosine
pynn_dist_fns_fda["dot"]["correction"] = correct_alternative_cosine

[Kydlaw]

Hello,

I just took the same bullet.

Environment:

• Python 3.9.7
• Numpy 1.22.0
• Numba 0.53.0
• Pynndescent 0.5.5
• Umap 0.5.2

I used a higher version of Numpy as a fix to https://github.com/scikit-learn-contrib/hdbscan/issues/457.

Having that one fixed, I stumbled on this issue. So I tried the fix you suggested in:

[…] In umap/distances.py there is a function definition:

@numba.vectorize(fastmath=True)
def correct_alternative_cosine(d):
    return 1.0 - pow(2.0, -d)

If you change that to

@numba.njit(fastmath=True)
def correct_alternative_cosine(ds):
    result = np.empty_like(ds)
    for i in range(ds.shape[0]):
        result[i] = 1.0 - np.power(2.0, ds[i])
    return result

[…] you can just make this edit in your installed copy of umap in site-packages and have it work.

This change works for me. However, small correction here: the distance definition is not in umap/distances.py but in pynndescent/distances.py.

So, if you are using venv, in .venv/lib/pythonX.X/site-packages/pynndescent/distances.py apply the changes suggested.