install this package as a python module with pip via:
pip install git+https://github.com/erikbuh/erum_data_data.git
# or just git clone & 'pip install .' in this folder
The essential function so far is the "load" function to load the training and testing datasets. The datasets features "X" are returned as a list of numpy arrays. The labels are returend directly as a numpy array.
from erum_data_data import Spinodal # or any other dataset (see below)
# loading training data into RAM (downloads dataset first time)
X_train, y_train = Spinodal.load('train', path='./datasets')
# loading test data into RAM (downloads dataset first time)
X_test, y_test = Spinodal.load('test', path = './datasets')Here a subfolder ./datasets is created. The datasets take up a total disk space of about 2.4 GB. For loading the training datasets a free RAM of at at least 5 GB is necessary (depending on the dataset).
Included datasets at the moment with the tags:
1: TopTagging, 2: Spinodal, 3: EOSL, 4: Airshower, 5: Belle
An description of the datasets can be printed via the function:
Spinodal.print_description()Show all available datasets:
import erum_data_data as edd
for dataset in edd.Dataset.datasets_register:
print(dataset.name)Some example plots can be found in the notebooks in the example folder.
In the folder template_model_implamentation multiple model implementations can be found. Each can be imported in the main.py script and run on the specified datasets. If you'd like to contribute a model, please submit a model in the form outlined in template.py with a pull request.
Loading the data as a Graph (with features and an adjacency matrix):
from erum_data_data import Spinodal # or any other dataset
x_train, y_train = Spinodal.load_graph('train', path = './datasets')x_train is dictionary with the contents features and adj_matrix
The original datasets can be found here:
- Top Tagging at the LHC link, Publication: 1902.09914
- Spinodal or not? link, Publication: 1906:06562
- EOSL or EOSQ link, Publication: 1910.11530
- Cosmic Airshower link missing
- SmartBKG dataset (Belle II - generated events passing downstream selection) link