Dataset for the challenge at the 2nd MODE workshop on differentiable programming 2022
DOI10.5281/zenodo.7050560Zenodo7050560MaRDI QIDQ6720588
Dataset published at Zenodo repository.
Anna Bordignon, Max Lamparth, Federico Nardi, Tommaso Dorigo, Oleg Savchenko, Maxime Lagrange, Giles Chatham Strong, Haitham Zaraket, Andrea Giammanco, Jan Kieseler, Federica Fanzango, Pietro Vischia
Publication date: 20 July 2022
Copyright license: Creative Commons Attribution 4.0 International
Data is in HDF5 format (with LZF compression). For specifics and details, please seehttps://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)
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