Benchmarks — Approximate Method#
In one of the previous sections, we have demonstrated that the approximate version
of the Genie algorithm (genieclust.Genie(exact=False, ...)), i.e.,
one which relies on nmslib’s [29] approximate nearest neighbour search,
is much faster than the exact one on large, high-dimensional datasets.
In particular, we have noted that clustering of 1 million points
in a 100d Euclidean space takes less than 5 minutes on a laptop.
As fast does not necessarily mean meaningful (tl;dr spoiler alert: in our case, it does),
let’s again consider all the datasets
from the Benchmark Suite for Clustering Algorithms (Version 1.0)
[12]
(except the h2mg and g2mg batteries). Features with variance of 0 were
removed, datasets were centred at 0 and scaled so that they have total
variance of 1. Tiny bit of Gaussian noise was added to each observation.
Clustering is performed with respect to the Euclidean distance.
On each benchmark dataset (“small” and “large” altogether)
we have fired 10 runs of the approximate Genie method (exact=False)
and computed the adjusted Rand (AR) indices to quantify the similarity between the predicted
outputs and the reference ones.
We’ve computed the differences between each of the 10 AR indices
and the AR index for the exact method. Here is the complete list of datasets
and gini_thresholds where this discrepancy is seen at least 2 digits of precision:
dataset |
gini_threshold |
count |
mean |
std |
min |
25% |
50% |
75% |
max |
|---|---|---|---|---|---|---|---|---|---|
sipu/birch2 |
0.7 |
10 |
-0.01 |
0.01 |
-0.02 |
-0.02 |
-0.01 |
-0.01 |
0 |
1 |
10 |
-0.35 |
0.18 |
-0.44 |
-0.44 |
-0.43 |
-0.43 |
0 |
|
sipu/worms_64 |
0.1 |
10 |
-0.03 |
0.01 |
-0.06 |
-0.03 |
-0.02 |
-0.02 |
-0.02 |
0.3 |
10 |
0.02 |
0.01 |
-0.01 |
0.02 |
0.03 |
0.03 |
0.03 |
|
0.5 |
10 |
0.23 |
0.08 |
0.11 |
0.16 |
0.25 |
0.29 |
0.34 |
|
wut/trajectories |
0.1 |
10 |
-0 |
0.02 |
-0.05 |
0 |
0 |
0 |
0 |
0.3 |
10 |
-0 |
0.02 |
-0.05 |
0 |
0 |
0 |
0 |
|
0.5 |
10 |
-0 |
0.02 |
-0.05 |
0 |
0 |
0 |
0 |
|
0.7 |
10 |
-0 |
0.02 |
-0.05 |
0 |
0 |
0 |
0 |
|
1 |
10 |
-0.1 |
0.32 |
-1 |
0 |
0 |
0 |
0 |
The only noteworthy difference is for the sipu/birch2 dataset
where we observe that the approximate method generates worse results
(although recall that gini_threshold of 1 corresponds to the single linkage method).
Interestingly, for sipu/worms_64, the in-exact algorithm with gini_threshold
of 0.5 yields a much better outcome than the original one.
Here are the descriptive statistics for the AR indices across all the datasets (for the approximate method we chose the median AR in each of the 10 runs):
method |
count |
mean |
std |
min |
25% |
50% |
75% |
max |
|---|---|---|---|---|---|---|---|---|
Genie_0.1 |
79 |
0.728 |
0.307 |
0 |
0.516 |
0.844 |
1 |
1 |
Genie_0.1_approx |
79 |
0.728 |
0.307 |
0 |
0.516 |
0.844 |
1 |
1 |
Genie_0.3 |
79 |
0.755 |
0.292 |
0 |
0.555 |
0.9 |
1 |
1 |
Genie_0.3_approx |
79 |
0.755 |
0.292 |
0 |
0.568 |
0.9 |
1 |
1 |
Genie_0.5 |
79 |
0.731 |
0.332 |
0 |
0.531 |
0.844 |
1 |
1 |
Genie_0.5_approx |
79 |
0.734 |
0.326 |
0 |
0.531 |
0.844 |
1 |
1 |
Genie_0.7 |
79 |
0.624 |
0.376 |
0 |
0.264 |
0.719 |
1 |
1 |
Genie_0.7_approx |
79 |
0.624 |
0.376 |
0 |
0.264 |
0.719 |
1 |
1 |
Genie_1.0 |
79 |
0.415 |
0.447 |
0 |
0 |
0.174 |
1 |
1 |
Genie_1.0_approx |
79 |
0.409 |
0.45 |
0 |
0 |
0.148 |
1 |
1 |
For the recommended ranges of the gini_threshold parameter,
i.e., between 0.1 and 0.5, we see that the approximate version of Genie
behaves similarly to the original one.