Our void-and-cluster method (https://arxiv.org/abs/1907.05073) was designed to perform optimally stratified sampling of spatiotemporal scattered data in 2D and 3D. This repository contains an easy to use and to extend prototype implementation in python.
This implementation is not restricted to spatiotemporal data. However, since the sampling strategy is based on kernel density estimation, the curse of dimensionality will be problematic in higher dimensions. One goal of this prototype is to explore stratification in higher dimensions.
This is a simple example of a two-dimensional point set from which we sample a subset using vc sample:
The original dataset
10% of samples
Samples should be maximally pairwise distant, whilst still respecting the density of data points. I.e. dense regions in the original data are sampled more often.
Extending this sampling method to higher dimensions is problematic, due to the curse of dimensionality. More specifically, the kernel density estimation does not scale with the dimensionality. However, in a lot of cases, our data is actually lower dimensional, but is embedded in a higher dimensional space.
For example, this S-curve is a 2D manifold (think of a deformed rectangle) that lies in a 3D space:
To sample such a dataset, we only have to change the density estimation. By specifying the density on the S-curve and by not computing distances in 3D, the void and cluster algorithm can be directly applied without changes. Note that this reduces the dimensionality of the density estimation.
The difficult part is now finding such a density estimation method on this embedded manifold. Here, we use an estimate based on the UMAP dimensionality reduction technique and then sample accordingly:
10% of stratified samples on the S-curve
This project is still under development. For setting it up for development, pyscaffold is used, to simplify python packaging.
Clone the repo:
`
git clone [email protected]:TobiasRp/vc_sample.git && cd vc_sample
`
Optionally, create and initialize conda or another virtual python env. This is not included here. Then, install pyscaffold.
`
pip install pyscaffold
`
Lastly, install vc_sample as an editable package including its dependencies:
`
pip install -e .
`
Now, vc_sample can be imported and used as a python package.
Example notebooks can be found in the notebooks folder. Start with notebooks/sampling_examples.