The Baseline Solution can be found on our GitHub here
Inside this repo you will find the baseline solution for the Ariel Data Challenge. To run the script you will need access to the training and test data, both of which can be found on the website (after registration). There are two ways to run the baseline:
python run_baseline.py --training PATH/TO/TRAININGDATA/ --test PATH/TO/TESTDATA
We trained a neural network to perform a supervised multi-target regression task. The architecture of the network is modified from the CNN network as described in Yip et al.
At test time we performed Monte Carlo Dropout to provide a mutlivariate distribution for each test example.
We have inlcuded the algorithm we used to compute the metric score in the jupyter notebook. Note that in order to compute the final score, you must have TauREx3 installed in your python environment, with the appropriate linelist, which can be found here. For installation procedure of TauREx3, please see here
For more information about the metric please see here.
There are different direction to take from here on, let us summarise the shortcoming of this model:
The data preprocessing is quite simplitic, we list the limitations below:
Tracedata.hdf5
), which are the GT for this competition.