All NaN for an example from document

I tried to run the prime number example in https://umap-learn.readthedocs.io/en/latest/sparse.html. However it got a lot NaN's both for my local pc and google colab.

versions:

Requirement already satisfied: umap-learn[plot] in /root/.local/lib/python3.7/site-packages (0.5.1)
Requirement already satisfied: scipy>=1.0 in /usr/local/lib/python3.7/dist-packages (from umap-learn[plot]) (1.4.1)
Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from umap-learn[plot]) (1.19.5)
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Requirement already satisfied: numba>=0.49 in /usr/local/lib/python3.7/dist-packages (from umap-learn[plot]) (0.51.2)
Requirement already satisfied: scikit-learn>=0.22 in /usr/local/lib/python3.7/dist-packages (from umap-learn[plot]) (0.22.2.post1)
Requirement already satisfied: bokeh; extra == "plot" in /usr/local/lib/python3.7/dist-packages (from umap-learn[plot]) (2.3.2)
Requirement already satisfied: matplotlib; extra == "plot" in /usr/local/lib/python3.7/dist-packages (from umap-learn[plot]) (3.2.2)
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Requirement already satisfied: partd>=0.3.10; extra == "complete" in /root/.local/lib/python3.7/site-packages (from dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (1.2.0)
Requirement already satisfied: toolz>=0.7.3; extra == "complete" in /usr/local/lib/python3.7/dist-packages (from dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (0.11.1)
Requirement already satisfied: fsspec>=0.6.0; extra == "complete" in /root/.local/lib/python3.7/site-packages (from dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (2021.6.0)
Requirement already satisfied: distributed>=2.0; extra == "complete" in /root/.local/lib/python3.7/site-packages (from dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (2021.6.0)
Requirement already satisfied: cloudpickle>=0.2.1; extra == "complete" in /usr/local/lib/python3.7/dist-packages (from dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (1.3.0)
Requirement already satisfied: importlib-metadata; python_version < "3.8" in /usr/local/lib/python3.7/dist-packages (from markdown->panel>=0.8.0->holoviews; extra == "plot"->umap-learn[plot]) (4.5.0)
Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->panel>=0.8.0->holoviews; extra == "plot"->umap-learn[plot]) (2.10)
Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->panel>=0.8.0->holoviews; extra == "plot"->umap-learn[plot]) (2021.5.30)
Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->panel>=0.8.0->holoviews; extra == "plot"->umap-learn[plot]) (3.0.4)
Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->panel>=0.8.0->holoviews; extra == "plot"->umap-learn[plot]) (1.24.3)
Requirement already satisfied: locket in /root/.local/lib/python3.7/site-packages (from partd>=0.3.10; extra == "complete"->dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (0.2.1)
Requirement already satisfied: zict>=0.1.3 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.0; extra == "complete"->dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (2.0.0)
Requirement already satisfied: psutil>=5.0 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.0; extra == "complete"->dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (5.4.8)
Requirement already satisfied: msgpack>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.0; extra == "complete"->dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (1.0.2)
Requirement already satisfied: click>=6.6 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.0; extra == "complete"->dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (7.1.2)
Requirement already satisfied: tblib>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.0; extra == "complete"->dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (1.7.0)
Requirement already satisfied: sortedcontainers!=2.0.0,!=2.0.1 in /usr/local/lib/python3.7/dist-packages (from distributed>=2.0; extra == "complete"->dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (2.4.0)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata; python_version < "3.8"->markdown->panel>=0.8.0->holoviews; extra == "plot"->umap-learn[plot]) (3.4.1)
Requirement already satisfied: heapdict in /usr/local/lib/python3.7/dist-packages (from zict>=0.1.3->distributed>=2.0; extra == "complete"->dask[complete]>=0.18.0->datashader; extra == "plot"->umap-learn[plot]) (1.0.1)

Codes:

import numpy as np
import scipy.sparse
import sympy
import sklearn.datasets
import sklearn.feature_extraction.text
import umap
import umap.plot
import matplotlib.pyplot as plt
%matplotlib inline

primes = list(sympy.primerange(2, 110000))
prime_to_column = {p:i for i, p in enumerate(primes)}

%%time
lil_matrix_rows = []
lil_matrix_data = []
for n in range(100000):
    prime_factors = sympy.primefactors(n)
    lil_matrix_rows.append([prime_to_column[p] for p in prime_factors])
    lil_matrix_data.append([1] * len(prime_factors))

factor_matrix = scipy.sparse.lil_matrix((len(lil_matrix_rows), len(primes)), dtype=np.float32)
factor_matrix.rows = np.array(lil_matrix_rows)
factor_matrix.data = np.array(lil_matrix_data)

mapper = umap.UMAP(metric='cosine', random_state=42, low_memory=True).fit(factor_matrix)

umap.plot.points(mapper, values=np.arange(100000), theme='viridis')

y=mapper.transform(factor_matrix)

Most values in y are NaN's (189874 of 200000).

Start by reproducing the prime-factor example from sparse.html with the reported package versions, then inspect the outputs of fit, plot.points, and transform on factor_matrix. Done means identifying why transform produces NaN values and either correcting the behavior or documenting the supported conditions and expected result.