Paper link. Created by Uzu Lim, Harald Oberhauser, and Vidit Nanda
HADES is a fast singularity detection algorithm. Singularities are points in data where the Manifold Hypothesis fails, such as cusps and self-intersections. HADES does not use topological methods, and instead works by (1) Locally applying dimensionality reduction and then (2) Performing a kernel goodness-of-fit test. HADES stands for Hypothesis-testing Algorithm for Detection and Exploration of Singularities.
To install, clone the repository and use Poetry:
$ poetry install
Given a Numpy array X where each row represents a data point, run the following:
from hades import judge
verdict = judge(X)Below is a convenient starting point for generating sample data, detecting singularities, and plotting them:
from hades import judge
from hades.misc import plot, plot_filt
from hades.gen import two_circles
X = two_circles(5000, noise=0.01)
verdict = judge(X)
plot(X, c=verdict['score'], show=True)
plot_filt(X, verdict['label'], show=True)The following are hyperparameters used by hades :
-
r: radius -
k: k nearest neighbors -
t: threshold for PCA ($0 < t < 1$ ) -
a: kernel parameter ($0 < a < 1$ )
(only one of r and k are used at each time, so that only 3 hyperparameters are relevant in each run)
The following are 3 modes of performing hyperparameter search. The default run is the fully automatic search, only over the radius parameter.
# Mode 1. Fully automatic search
verdict = judge(X, search_auto=['r', 't'],
search_res={'r': 5, 't': 3})
# Mode 2. Search over a specified grid of hyperparameters
verdict = judge(X, search_range={'r': (0.05, 0.15), 't': (0.7, 0.9)},
search_res={'r': 5, 't': 3})
# Mode 3. Search over a specified list of hyperparameters
verdict = judge(X, search_list = [{'a': 0.1, 'k': 50, 't': 0.9}, {'a': 0.5, 'k': 50, 't': 0.9}, {'a': 0.9, 'k': 50, 't': 0.9}])There are Jupyter notebooks in the notebooks folder that reproduce computational experiments in the paper.