Timings (How Fast Is It?)¶
In the previous section we have demonstrated that Genie generates quality partitions. Now the crucial question is: does it do it quickly?
Genie will be compared against Kmeans from scikitlearn [PVG+11] version 0.23.1 (sklearn.cluster.KMeans) for different number of threads (by default it uses all available resources; note that the number of restarts, n_init, defaults to 10) and hierarchical agglomerative algorithms with the centroid, median, and Ward linkage implemented in the fastcluster package [Mul13].
Genie, just like the single linkage, is based on a minimum spanning tree (MST) of the
pairwise distance graph of an input point set.
Given the MST (the slow part), Genie itself has \(O(n \sqrt{n})\) time
and \(O(n)\) memory complexity.
Generally, our parallelised implementation of a Jarník (Prim/Dijkstra)like
method [Ols95] will be called to compute an MST, which takes \(O(d n^2)\) time.
However, mlpack.emst [CEL+18] provides a very fast
alternative in the case of Euclidean spaces of (very) low dimensionality,
see [MRG10] and the mlpack_enabled parameter, which is automatically used
for datasets with up to \(d=6\) features.
Moreover, in the approximate method (exact = False
), we apply
the Kruskal algorithm on the nearneighbour graph determined
by nmslib [NBMN19]. Albeit this only gives some sort of a spanning forest,
such a data structure turns out to be very suitable for our clustering task.
All timings will be performed on a PC running GNU/Linux 5.4.040generic #44Ubuntu SMP x86_64 kernel with an Intel(R) Core(TM) i79750H CPU @ 2.60GHz (12M cache, 6 cores, 12 threads) and total memory of 16,242,084 kB.
Large Datasets¶
Let’s study the algorithm’s run times for some of the “larger” datasets (70,000105,600 observations, see section on benchmark results for discussion) from the Benchmark Suite for Clustering Algorithms — Version 1 [GC20]. 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.
Here are the results (in seconds) if 6 threads are requested (except for fastcluster which is not parallelised). For Kmeans, the timings are listed as a function of the number of clusters to detect, for the other hierarchical methods the runtimes are almost identical irrespective of the partitions’ cardinality.
dataset 
n 
d 
method 
10 
100 
1000 

mnist/digits 
70000 
719 
Genie_0.3 
412.72 

Genie_0.3_approx 
42.77 

fastcluster_centroid 
4170.98 

fastcluster_median 
3927.93 

fastcluster_ward 
4114.05 

sklearn_kmeans 
26.3 
217.62 
1691.68 

mnist/fashion 
70000 
784 
Genie_0.3 
445.81 

Genie_0.3_approx 
38.02 

fastcluster_centroid 
4486.32 

fastcluster_median 
4384.62 

fastcluster_ward 
4757.32 

sklearn_kmeans 
24.9 
225.04 
1745.88 

sipu/worms_2 
105600 
2 
Genie_0.3 
0.57 

Genie_0.3_approx 
3.67 

fastcluster_centroid 
66.3 

fastcluster_median 
64.11 

fastcluster_ward 
60.92 

sklearn_kmeans 
0.86 
10.96 
111.9 

sipu/worms_64 
105000 
64 
Genie_0.3 
76.7 

Genie_0.3_approx 
8.26 

fastcluster_centroid 
4945.91 

fastcluster_median 
2854.27 

fastcluster_ward 
778.18 

sklearn_kmeans 
3.35 
37.89 
357.84 
Of course, the Kmeans algorithm is the fastest. However, its performance degrades as K increases. Hence, it might not be a good choice for the socalled extreme clustering (compare [KMKM17]) problems. Most importantly, the approximate version of Genie (based on nmslib) is only slightly slower. The exact variant is extremely performant in Euclidean spaces of low dimensionality (thanks to mlpack) and overall at least 10 times more efficient than the other hierarchical algorithms in this study.
Timings as a Function of n and d¶
In order to study the runtimes as a function dataset size and dimensionality, let’s consider a series of synthetic benchmarks, each with two Gaussian blobs of size n/2 (with i.i.d. coordinates), in a ddimensional space.
Here are the medians of 310 timings (depending on the dataset size), in seconds, on 6 threads:
method 
d 
10000 
50000 
100000 
500000 
1000000 

Genie_0.3_approx 
2 
0.17 
0.98 
2.12 
14.93 
33.79 
5 
0.2 
1.3 
2.87 
22.75 
54.66 

10 
0.25 
1.69 
3.84 
36.18 
92.03 

25 
0.29 
1.95 
5.46 
62.25 
158.27 

50 
0.36 
3.15 
8.15 
81.95 
202.08 

100 
0.48 
4.6 
12.6 
113.37 
266.64 

Genie_0.3_mlpack 
2 
0.04 
0.26 
0.55 
3.03 
6.58 
5 
0.28 
1.96 
4.46 
28.4 
62.75 

10 
3.08 
35.54 
92.87 
794.71 
2014.59 

Genie_0.3_nomlpack 
2 
0.16 
2.52 
9.87 
267.76 
1657.86 
5 
0.14 
2.62 
11.4 
421.46 
2997.11 

10 
0.15 
3.21 
12.74 
719.33 
4388.26 

25 
0.28 
6.51 
26.65 
1627.9 
7708.23 

50 
0.47 
11.97 
54.52 
2175.3 
11346.3 

100 
1 
26.07 
132.47 
4408.07 
16021.8 
By default, mlpack_enabled is "auto"
, which translates
to True
if the requested metric is Euclidean, Python package mlpack is available,
and d is not greater than 6.
The effect of the curse of dimensionality is clearly visible – clustering
in very lowdimensional Euclidean spaces is extremely fast.
On the other hand, the approximate version of Genie can easily cluster
very large datasets. Only the system’s memory limits might become a problem then.
Timings as a Function of the Number of Threads¶
Recall that the timings are done on a PC with 6 physical cores.
Genie turns out to be nicely parallelisable — as evidenced on
the mnist/digits
dataset:
Summary¶
The approximate (exact = False
) version of Genie is much faster
than the original one. At the same time, it is still
highly compatible with it
(at least at higher levels of the cluster hierarchy). Therefore, we
can safely recommend its use in large problem instances.
Most importantly, its performance is not much worse than the Kmeans method
with small K. Once a complete cluster hierarchy is determined,
partitioning of any cardinality can be extracted in less than 0.34 s on a 1M dataset.
Still, even the exact Genie is amongst the fastest clustering algorithms in the pool.
On top of that, we are also allowed to change our mind about the gini_threshold parameter once the clustering is has been determined. The MST is stored for further reference and is not recomputed unless needed. Here are the timings for a first run of the algorithm:
import time, genieclust, numpy as np
X = np.loadtxt("worms_2.data.gz", ndmin=2)
g = genieclust.Genie(n_clusters=2, gini_threshold=0.3)
t0 = time.time()
g.fit(X)
print("time elapsed  first run: %.3f" % (time.time()t0))
time elapsed  first run: 0.601
Changing some parameters and rerunning the cluster search:
g.set_params(n_clusters=10)
g.set_params(gini_threshold=0.1)
t0 = time.time()
g.fit(X)
print("time elapsed  consecutive run: %.3f" % (time.time()t0))
time elapsed  consecutive run: 0.025