Code and data for the paper "Coherent Hierarchical Multi-label Classification Networks".
In order to evaluate the model for a single seed run:
python main.py --dataset <dataset_name> --seed <seed_num> --device <device_num>
Example:
python main.py --dataset cellcycle_FUN --seed 0 --device 0
Note: the parameter passed to "dataset" must end with: '_FUN', '_GO', or '_others'.
If you want to execute the model for 10 seeds you can modify the script main_script.sh and execute it.
The results will be written in the folder results/ in the file <dataset_name>.csv.
If you want to execute again the hyperparameters search you can modify the script script.shaccording to your necessity and execute it.
The code was run on a Titan Xp with 12GB memory. A description of the environment used and its dependencies is given in c-hmcnn_enc.yml.
By running the script main_script.sh we obtain the following results (average over the 10 runs):
| Dataset | Result |
|---|---|
| Cellcycle_FUN | 0.255 |
| Derisi_FUN | 0.195 |
| Eisen_FUN | 0.306 |
| Expr_FUN | 0.302 |
| Gasch1_FUN | 0.286 |
| Gasch2_FUN | 0.258 |
| Seq_FUN | 0.292 |
| Spo_FUN | 0.215 |
| Cellcycle_GO | 0.413 |
| Derisi_GO | 0.370 |
| Eisen_GO | 0.455 |
| Expr_GO | 0.447 |
| Gasch1_GO | 0.436 |
| Gasch2_GO | 0.414 |
| Seq_GO | 0.446 |
| Spo_GO | 0.382 |
| Diatoms_others | 0.758 |
| Enron_others | 0.756 |
| Imclef07a_others | 0.956 |
| Imclef07d_others | 0.927 |
@inproceedings{giunchiglia2020neurips,
title = {Coherent Hierarchical Multi-label Classification Networks},
author = {Eleonora Giunchiglia and
Thomas Lukasiewicz},
booktitle = {34th Conference on Neural Information Processing Systems (NeurIPS 2020)},
address = {Vancouver, Canada},
month = {December},
year = {2020}
}
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