astronet

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astronet is a package to classify Astrophysical transients using Deep Learning methods

WARNING

Expect this to be "unstable" with frequent changes to the API. See below for details on The Road to v1.0.0

If you are interested in contributing to this package, please review CONTRIBUTING.md

Citation

If you find the software here useful, please consider citing this work.

latex @software{Allam_Jr_astronet_Multivariate_Time-Series_2022, author = {Allam, Jr., Tarek}, month = {6}, title = {{astronet: Multivariate Time-Series Classification of Astrophysical Transients using Deep Learning}}, url = {https://github.com/tallamjr/astronet}, year = {2022} }

astronet.tinho [https://arxiv.org/pdf/2303.08951.pdf]

astronet.t2 [https://arxiv.org/pdf/2105.06178.pdf]

astronet.atx

MTS Benchmark Results

Results can be found in ./results. Where results are -9999, the run was unstable and needs to be trained again.

Accuracy

| | t2 | atx | cnn | encoder | fcn | mcdcnn | mcnn | mlp | resnet | tlenet | twiesn | |:----------------------|-------:|------:|-------:|----------:|-------:|---------:|-------:|------:|---------:|---------:|---------:| | ArabicDigits | 97.32 | 98.50 | 95.77 | 98.07 | 99.42 | 95.88 | 10.00 | 96.91 | 99.55 | 10.00 | 85.28 | | AUSLAN | 92.91 | 87.09 | 72.55 | 93.84 | 97.54 | 85.38 | 1.05 | 93.26 | 97.40 | 1.05 | 72.41 | | CharacterTrajectories | 94.57 | 97.97 | 96.00 | 97.06 | 98.98 | 93.82 | 5.36 | 96.90 | 99.04 | 6.68 | 92.04 | | CMUsubject16 | 100.00 | 93.10 | 97.59 | 98.28 | 100.00 | 51.38 | 53.10 | 60.00 | 99.66 | 51.03 | 89.31 | | ECG | 84.00 | 76.00 | 84.10 | 87.20 | 87.20 | 50.00 | 67.00 | 74.80 | 86.70 | 67.00 | 73.70 | | JapaneseVowels | 97.30 | 97.03 | 95.65 | 97.57 | 99.30 | 94.43 | 9.24 | 97.57 | 99.16 | 23.78 | 96.54 | | KickvsPunch | 90.00 | 70.00 | 62.00 | 61.00 | 54.00 | 56.00 | 54.00 | 61.00 | 51.00 | 50.00 | 67.00 | | Libras | 82.78 | 74.44 | 63.72 | 78.33 | 96.39 | 65.06 | 6.67 | 78.00 | 95.44 | 6.67 | 79.44 | | NetFlow | 86.14 | 77.90 | 88.95 | 77.70 | 89.06 | 62.96 | 77.90 | 55.04 | 62.72 | 72.32 | 94.49 | | UWave | 84.53 | 90.95 | 85.88 | 90.76 | 93.43 | 84.50 | 12.50 | 90.06 | 92.59 | 12.51 | 75.44 | | Wafer | 89.40 | 89.40 | 94.81 | 98.56 | 98.24 | 65.76 | 89.40 | 89.40 | 98.85 | 89.40 | 94.90 | | WalkvsRun | 100.00 | 75.00 | 100.00 | 100.00 | 100.00 | 45.00 | 75.00 | 70.00 | 100.00 | 60.00 | 94.38 |

Precision

| | t2 | atx | cnn | encoder | fcn | mcdcnn | mcnn | mlp | resnet | tlenet | twiesn | |:----------------------|-----------:|-----------:|-------:|----------:|-------:|---------:|-------:|------:|---------:|---------:|---------:| | ArabicDigits | 96.79 | 98.51 | 95.84 | 98.10 | 99.43 | 95.95 | 1.00 | 96.97 | 99.56 | 1.00 | 86.16 | | AUSLAN | 86.19 | 88.46 | 76.12 | 94.72 | 97.92 | 87.87 | 0.01 | 94.41 | 97.79 | 0.01 | 75.00 | | CharacterTrajectories | 87.14 | 97.84 | 96.18 | 97.11 | 98.86 | 93.86 | 0.27 | 96.98 | 98.91 | 0.33 | 92.94 | | CMUsubject16 | 27.59 | 93.03 | 97.50 | 98.23 | 100.00 | 30.60 | 26.55 | 39.46 | 99.71 | 25.52 | 89.59 | | ECG | 77.39 | 41.33 | 81.87 | 85.55 | 85.31 | 25.00 | 33.50 | 65.05 | 84.91 | 33.50 | 70.96 | | JapaneseVowels | 96.09 | 96.84 | 95.56 | 97.33 | 99.14 | 94.22 | 1.03 | 97.33 | 99.00 | 2.64 | 96.75 | | KickvsPunch | 79.17 | 69.05 | 68.19 | 62.39 | 52.12 | 28.00 | 27.00 | 58.21 | 55.19 | 25.00 | 67.98 | | Libras | 84.32 | 74.77 | 64.15 | 79.12 | 96.69 | 67.17 | 0.44 | 79.66 | 95.84 | 0.44 | 81.62 | | NetFlow | 80.58 | 38.95 | 84.61 | 42.78 | 85.77 | 45.80 | 38.95 | 34.93 | 69.33 | 36.16 | 94.19 | | UWave | -999900.00 | 90.46 | 86.19 | 90.99 | 93.42 | 85.05 | 1.56 | 90.70 | 92.59 | 1.56 | 77.38 | | Wafer | -999900.00 | -999900.00 | 87.89 | 98.27 | 96.09 | 32.88 | 44.70 | 44.70 | 97.95 | 44.70 | 97.20 | | WalkvsRun | 37.50 | 37.50 | 100.00 | 100.00 | 100.00 | 22.50 | 37.50 | 35.00 | 100.00 | 30.00 | 93.05 |

Recall

| | t2 | atx | cnn | encoder | fcn | mcdcnn | mcnn | mlp | resnet | tlenet | twiesn | |:----------------------|-----------:|-----------:|-------:|----------:|-------:|---------:|-------:|------:|---------:|---------:|---------:| | ArabicDigits | 96.77 | 98.50 | 95.77 | 98.07 | 99.42 | 95.88 | 10.00 | 96.91 | 99.55 | 10.00 | 85.28 | | AUSLAN | 84.63 | 87.09 | 72.55 | 93.84 | 97.54 | 85.38 | 1.05 | 93.26 | 97.40 | 1.05 | 72.41 | | CharacterTrajectories | 86.63 | 97.69 | 95.66 | 96.77 | 98.86 | 93.48 | 5.00 | 96.62 | 98.91 | 5.00 | 91.44 | | CMUsubject16 | 50.00 | 93.03 | 97.81 | 98.37 | 100.00 | 50.31 | 50.00 | 58.13 | 99.62 | 50.00 | 89.23 | | ECG | 77.39 | 49.23 | 83.14 | 85.60 | 86.53 | 50.00 | 50.00 | 72.27 | 85.15 | 50.00 | 66.53 | | JapaneseVowels | 95.70 | 96.96 | 96.21 | 97.89 | 99.28 | 94.26 | 11.11 | 97.71 | 99.23 | 11.11 | 97.21 | | KickvsPunch | 79.17 | 66.67 | 65.83 | 62.50 | 55.00 | 50.00 | 50.00 | 61.25 | 55.00 | 50.00 | 68.33 | | Libras | 82.78 | 73.33 | 63.72 | 78.33 | 96.39 | 65.06 | 6.67 | 78.00 | 95.44 | 6.67 | 79.44 | | NetFlow | 77.45 | 50.00 | 82.59 | 50.41 | 81.05 | 50.21 | 50.00 | 50.77 | 66.20 | 50.00 | 89.49 | | UWave | -999900.00 | 90.25 | 85.88 | 90.76 | 93.43 | 84.50 | 12.50 | 90.06 | 92.59 | 12.50 | 75.44 | | Wafer | -999900.00 | -999900.00 | 83.41 | 94.05 | 94.56 | 50.00 | 50.00 | 50.00 | 95.97 | 50.00 | 75.99 | | WalkvsRun | 50.00 | 50.00 | 100.00 | 100.00 | 100.00 | 50.00 | 50.00 | 50.00 | 100.00 | 50.00 | 95.42 |

Tests

See astronet/tests/README.md for more details

Note: some tests require large data files

If a new plot is created, it should be visually inspected and a new baseline generated.

Run from top-level directory (where this README.md file is):

bash $ unset CI; pytest --mpl-generate-path=astronet/tests/reg/baseline --mpl-hash-library=baseline/arm64-hashlib.json --mpl-results-always astronet/tests/reg/test_plots.py

The Road to v1.0.0

The idea of astronet is not really to be a library, but to be more of a repository for the code developed during my PhD and my thesis.

Having said that, it would be nice to have astronet be more "stable" and to have extra features that would allow someone else to pick it up and use with minimal frustrations.

Therefore, the plan is to get to v1.0.0 at some point, but I will not be prioritizing this. Anyone interested should follow this meta-issue where I will log the progress and put placeholder issues to be addressed in order for v1.0.0 to be "ready".

The main aspects will be a reduce the cost of the data processing pipeline such that it can be done lazily and locally for PLAsTiCC at least, and in the future for ELAsTiCC dataset. Once this is done, much of the rest of the updates will be cosmetic and to ensure usability of the codebase.