[BUG] Using precomputed KNN with more neighbors than n_neighbors drops trustworthiness score for UMAP #5717
Description
Describe the bug
cuML UMAP's implementation allows to use precomputed graphs. We can precompute the KNN graph with more neighbors (k_neighbors > n_neighbors), and then choose to experiment with different values of n_neighbors in cuML UMAP while doing grid search or similar. This is something that is advertised in the APIs too. Choosing a value of n_neighbors less than k_neighbors results in a catastrophic drop in the trustworthiness metric.
Steps/Code to reproduce bug
A minimal working example with synthetic dataset
import numpy as np
import pandas as pd
from cuml.manifold.umap import UMAP as cuUMAP
from sklearn.datasets import make_blobs
from sklearn.manifold import trustworthiness
from sklearn.neighbors import NearestNeighbors
def fit_umap_and_get_trust(model, data, n_neighbors):
embedding = model.fit_transform(data)
trust = trustworthiness(data, embedding, n_neighbors=n_neighbors)
return trust
def generate_data():
data, _ = make_blobs(n_samples=2000, n_features=10, centers=5, random_state=0)
data = data.astype(np.float32)
return data
def generate_sklearn_knn_graph(data, n_neighbors):
nn = NearestNeighbors(n_neighbors=n_neighbors, algorithm="brute")
nn.fit(data)
# we keep query vector same as data
adjacency_matrix = nn.kneighbors_graph(data, mode="distance")
return adjacency_matrix
def main():
n_neighbors = 8
data = generate_data()
# from sklearn
knn_graph = generate_sklearn_knn_graph(data, n_neighbors)
model = cuUMAP(n_neighbors=n_neighbors, precomputed_knn=knn_graph, min_dist=0.0)
sklearn_trust = fit_umap_and_get_trust(model, data, n_neighbors)
# from sklearn, but neighbors > n_neighbors
more_neighbors = 1
knn_graph = generate_sklearn_knn_graph(data, n_neighbors + more_neighbors)
model = cuUMAP(n_neighbors=n_neighbors, precomputed_knn=knn_graph, min_dist=0.0)
sklearn_moreNN_trust = fit_umap_and_get_trust(model, data, n_neighbors)
# from scratch
model = cuUMAP(n_neighbors=n_neighbors, min_dist=0.0, metric="euclidean")
nograph_trust = fit_umap_and_get_trust(model, data, n_neighbors)
print("sklearn trust: ", sklearn_trust)
print("sklearn trust with more neighbors: ", sklearn_moreNN_trust)
print("trust without graph: ", nograph_trust)
if __name__ == "__main__":
main()Expected behavior
As long as NN neighbors > n_neighbors, trustworthiness should be invariant under numerical precision.
Environment details (please complete the following information):
- Environment location: [GCP VertexAI notebook VM]
- Linux Distro/Architecture: [5.10.0-26-cloud-amd64 [REVIEW] - Adding example docstrings for cuML - PCA, TSVD, and DBSCAN algos #1 SMP Debian 5.10.197-1 (2023-09-29) x86_64 GNU/Linux]
- GPU Model/Driver: [Tesla T4 and driver 525.105.17]
- CUDA: [11.8 checked with
nvcc --version] - Method of cuDF & cuML install: [conda]
conda list output
# packages in environment at /opt/conda/envs/rapids-23.10:
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orc 1.9.0 h52d3b3c_2 conda-forge
overrides 7.4.0 pyhd8ed1ab_0 conda-forge
packaging 23.2 pyhd8ed1ab_0 conda-forge
pandas 1.5.3 py310h9b08913_1 conda-forge
pandas-gbq 0.20.0 pypi_0 pypi
pandocfilters 1.5.0 pyhd8ed1ab_0 conda-forge
panel 1.3.4 pyhd8ed1ab_0 conda-forge
param 2.0.1 pyhca7485f_0 conda-forge
parso 0.8.3 pyhd8ed1ab_0 conda-forge
partd 1.4.1 pyhd8ed1ab_0 conda-forge
pathspec 0.12.1 pypi_0 pypi
pcre2 10.42 hcad00b1_0 conda-forge
pexpect 4.8.0 pyh1a96a4e_2 conda-forge
pickleshare 0.7.5 py_1003 conda-forge
pillow 10.1.0 py310h01dd4db_0 conda-forge
pip 23.3.1 pyhd8ed1ab_0 conda-forge
pixman 0.42.2 h59595ed_0 conda-forge
pkgutil-resolve-name 1.3.10 pyhd8ed1ab_1 conda-forge
platformdirs 4.1.0 pyhd8ed1ab_0 conda-forge
poppler 23.11.0 h590f24d_0 conda-forge
poppler-data 0.4.12 hd8ed1ab_0 conda-forge
postgresql 16.1 h8972f4a_0 conda-forge
proj 9.3.0 h1d62c97_2 conda-forge
prometheus_client 0.19.0 pyhd8ed1ab_0 conda-forge
prompt-toolkit 3.0.41 pyha770c72_0 conda-forge
proto-plus 1.23.0 pypi_0 pypi
protobuf 4.23.4 py310h620c231_3 conda-forge
psutil 5.9.5 py310h2372a71_1 conda-forge
pthread-stubs 0.4 h36c2ea0_1001 conda-forge
ptxcompiler 0.8.1 py310h70a93da_2 conda-forge
ptyprocess 0.7.0 pyhd3deb0d_0 conda-forge
pure_eval 0.2.2 pyhd8ed1ab_0 conda-forge
py-xgboost 1.7.6 rapidsai_py310h1395376_2310 rapidsai
pyarrow 12.0.1 py310hf9e7431_14_cpu conda-forge
pyasn1 0.5.1 pypi_0 pypi
pyasn1-modules 0.3.0 pypi_0 pypi
pycparser 2.21 pyhd8ed1ab_0 conda-forge
pyct 0.5.0 pyhd8ed1ab_0 conda-forge
pydata-google-auth 1.8.2 pypi_0 pypi
pyee 8.1.0 pyhd8ed1ab_0 conda-forge
pygments 2.17.2 pyhd8ed1ab_0 conda-forge
pylibcugraph 23.10.00 cuda11_py310_231011_gc3dc860d_0 rapidsai
pylibraft 23.10.00 cuda11_py310_231011_gafdddfb3_0 rapidsai
pynndescent 0.5.11 pypi_0 pypi
pynvml 11.4.1 pyhd8ed1ab_0 conda-forge
pyparsing 3.1.1 pyhd8ed1ab_0 conda-forge
pyppeteer 1.0.2 pyhd8ed1ab_0 conda-forge
pyproj 3.6.1 py310h32c33b7_4 conda-forge
pysocks 1.7.1 pyha2e5f31_6 conda-forge
python 3.10.13 hd12c33a_0_cpython conda-forge
python-confluent-kafka 1.9.2 py310h5764c6d_2 conda-forge
python-dateutil 2.8.2 pyhd8ed1ab_0 conda-forge
python-fastjsonschema 2.19.0 pyhd8ed1ab_0 conda-forge
python-json-logger 2.0.7 pyhd8ed1ab_0 conda-forge
python_abi 3.10 4_cp310 conda-forge
pytz 2023.3.post1 pyhd8ed1ab_0 conda-forge
pyviz_comms 3.0.0 pyhd8ed1ab_0 conda-forge
pywavelets 1.4.1 py310h1f7b6fc_1 conda-forge
pyyaml 6.0.1 py310h2372a71_1 conda-forge
pyzmq 25.1.2 py310h795f18f_0 conda-forge
raft-dask 23.10.00 cuda11_py310_231011_gafdddfb3_0 rapidsai
rapids 23.10.00 cuda11_py310_231011_gd3958fe_0 rapidsai
rapids-xgboost 23.10.00 cuda11_py310_231011_gd3958fe_0 rapidsai
rav1e 0.6.6 he8a937b_2 conda-forge
rdma-core 28.9 h59595ed_1 conda-forge
re2 2023.03.02 h8c504da_0 conda-forge
readline 8.2 h8228510_1 conda-forge
referencing 0.32.0 pyhd8ed1ab_0 conda-forge
requests 2.31.0 pyhd8ed1ab_0 conda-forge
requests-oauthlib 1.3.1 pypi_0 pypi
rfc3339-validator 0.1.4 pyhd8ed1ab_0 conda-forge
rfc3986-validator 0.1.1 pyh9f0ad1d_0 conda-forge
rich 13.7.0 pyhd8ed1ab_0 conda-forge
rmm 23.10.00 cuda11_py310_231011_gf8ac6f8e_0 rapidsai
rpds-py 0.13.2 py310hcb5633a_0 conda-forge
rsa 4.9 pypi_0 pypi
rtree 1.1.0 py310hbdcdc62_0 conda-forge
s2n 1.3.54 h06160fa_0 conda-forge
scikit-image 0.21.0 py310hc6cd4ac_0 conda-forge
scikit-learn 1.3.2 py310h1fdf081_2 conda-forge
scipy 1.11.4 py310hb13e2d6_0 conda-forge
send2trash 1.8.2 pyh41d4057_0 conda-forge
setuptools 68.2.2 pyhd8ed1ab_0 conda-forge
shapely 2.0.2 py310hc3e127f_1 conda-forge
simpervisor 1.0.0 pyhd8ed1ab_0 conda-forge
six 1.16.0 pyh6c4a22f_0 conda-forge
snappy 1.1.10 h9fff704_0 conda-forge
sniffio 1.3.0 pyhd8ed1ab_0 conda-forge
sortedcontainers 2.4.0 pyhd8ed1ab_0 conda-forge
soupsieve 2.5 pyhd8ed1ab_1 conda-forge
sparse 0.14.0 pypi_0 pypi
spdlog 1.11.0 h9b3ece8_1 conda-forge
sqlite 3.44.2 h2c6b66d_0 conda-forge
stack_data 0.6.2 pyhd8ed1ab_0 conda-forge
streamz 0.6.4 pyh6c4a22f_0 conda-forge
svt-av1 1.7.0 h59595ed_0 conda-forge
sympy 1.12 pypi_0 pypi
tblib 2.0.0 pyhd8ed1ab_0 conda-forge
tensorboard 2.15.1 pypi_0 pypi
tensorboard-data-server 0.7.2 pypi_0 pypi
tensorflow 2.15.0.post1 pypi_0 pypi
tensorflow-estimator 2.15.0 pypi_0 pypi
tensorflow-io-gcs-filesystem 0.35.0 pypi_0 pypi
termcolor 2.4.0 pypi_0 pypi
terminado 0.18.0 pyh0d859eb_0 conda-forge
threadpoolctl 3.2.0 pyha21a80b_0 conda-forge
tifffile 2023.9.26 pyhd8ed1ab_0 conda-forge
tiledb 2.16.3 h8c794c1_3 conda-forge
tinycss2 1.2.1 pyhd8ed1ab_0 conda-forge
tk 8.6.13 noxft_h4845f30_101 conda-forge
tomli 2.0.1 pypi_0 pypi
toolz 0.12.0 pyhd8ed1ab_0 conda-forge
torch 2.1.2 pypi_0 pypi
tornado 6.3.3 py310h2372a71_1 conda-forge
tqdm 4.66.1 pyhd8ed1ab_0 conda-forge
traitlets 5.14.0 pyhd8ed1ab_0 conda-forge
treelite 3.9.1 py310h4a6579d_0 conda-forge
treelite-runtime 3.9.1 pypi_0 pypi
triton 2.1.0 pypi_0 pypi
types-python-dateutil 2.8.19.14 pyhd8ed1ab_0 conda-forge
typing-extensions 4.8.0 hd8ed1ab_0 conda-forge
typing_extensions 4.8.0 pyha770c72_0 conda-forge
typing_utils 0.1.0 pyhd8ed1ab_0 conda-forge
tzcode 2023c h0b41bf4_0 conda-forge
tzdata 2023c h71feb2d_0 conda-forge
uc-micro-py 1.0.1 pyhd8ed1ab_0 conda-forge
ucx 1.14.1 h64cca9d_5 conda-forge
ucx-proc 1.0.0 gpu rapidsai
ucx-py 0.34.00 py310_231011_g17dceab_0 rapidsai
umap-learn 0.5.5 pypi_0 pypi
unicodedata2 15.1.0 py310h2372a71_0 conda-forge
uri-template 1.3.0 pyhd8ed1ab_0 conda-forge
uriparser 0.9.7 hcb278e6_1 conda-forge
urllib3 1.26.18 pyhd8ed1ab_0 conda-forge
wcwidth 0.2.12 pyhd8ed1ab_0 conda-forge
webcolors 1.13 pyhd8ed1ab_0 conda-forge
webencodings 0.5.1 pyhd8ed1ab_2 conda-forge
websocket-client 1.7.0 pyhd8ed1ab_0 conda-forge
websockets 10.4 py310h5764c6d_1 conda-forge
werkzeug 3.0.1 pypi_0 pypi
wheel 0.42.0 pyhd8ed1ab_0 conda-forge
wrapt 1.14.1 pypi_0 pypi
xarray 2023.11.0 pyhd8ed1ab_0 conda-forge
xerces-c 3.2.4 hac6953d_3 conda-forge
xgboost 1.7.6 rapidsai_py310h1395376_2310 rapidsai
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
xorg-libice 1.1.1 hd590300_0 conda-forge
xorg-libsm 1.2.4 h7391055_0 conda-forge
xorg-libx11 1.8.7 h8ee46fc_0 conda-forge
xorg-libxau 1.0.11 hd590300_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h0b41bf4_2 conda-forge
xorg-libxrender 0.9.11 hd590300_0 conda-forge
xorg-renderproto 0.11.1 h7f98852_1002 conda-forge
xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xyzservices 2023.10.1 pyhd8ed1ab_0 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
yarl 1.9.3 py310h2372a71_0 conda-forge
zeromq 4.3.5 h59595ed_0 conda-forge
zfp 1.0.0 h59595ed_4 conda-forge
zict 3.0.0 pyhd8ed1ab_0 conda-forge
zipp 3.17.0 pyhd8ed1ab_0 conda-forge
zlib 1.2.13 hd590300_5 conda-forge
zlib-ng 2.0.7 h0b41bf4_0 conda-forge
zstd 1.5.5 hfc55251_0 conda-forge
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