Dataset for the challenge at the 2nd MODE workshop on differentiable programming 2022
Authors/Creators
- 1. INFN, Padua
- 2. Université catholique de Louvain
- 3. CERN
- 4. Lebanese University
- 5. Technical University of Munich
Description
Data is in HDF5 format (with LZF compression). For specifics and details, please see https://github.com/GilesStrong/mode_diffprog_22_challenge
The training file contains two datasets:
- `'x0'`: a set of voxelwise X0 predictions (float32)
- `'targs'`: a set of voxelwise classes (int):
- 0 = soil
- 1 = wall
The format of the datasets is a rank-4 array, with dimensions corresponding to (samples, z position, x position, y position).
All passive volumes are of the same size: 10x10x10 m, with cubic voxels of size 1x1x1 m, i.e. every passive volume contains 1000 voxels.
The arrays are ordered such that zeroth z layer is the bottom layer of the passive volume, and the ninth layer is the top layer.
It can be read using e.g. the code below:
with h5py.File('train.h5', 'r') as f:
inputs = h5['x0'][()]
targets = h5['targs'][()]
The test file only contains the X0 inputs:
with h5py.File('test.h5', 'r') as h5:
inputs = h5['x0'][()]
The private testing sample also contains targets. The private and public splits can be recovered using:
from sklearn.model_selection import train_test_split
pub, pri = train_test_split(targets, test_size=25000, random_state=3452, shuffle=True)
Files
Additional details
Related works
- Is documented by
- Software: 10.5281/zenodo.6947862 (DOI)