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Update FeatureClassifier integration timescale and add secular timescale feature #39

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merged 142 commits into from
Jan 24, 2025

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Ethadhani
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@Ethadhani Ethadhani commented Oct 21, 2024

Made multiple changes that only affect FeatureClassifier

Changes are fully implemented in commit 5c4e548, changes include:

  • Incorporated integrating to secular timescale from Yang & Tamayo 2024 as opposed to 1e4 orbits
  • Added secular time scale and Tsec feature for the model
  • Changed implementation of how features are generated
  • Ensured model compatibility with new version of REBOUND
  • Re-trained model with cleaned data and tested impact of duplications/mislabeling in original dataset
  • Removed systems that go unstable in short integration from testing dataset
  • Updated saved model
  • New FeatureClassifier should have a higher AUC and a decreased FPR

Change comparisons, tests, and backwards compatibility

In paper_plots

  • testUnstable.ipynb demonstrates change in AUC calculation to not include systems that go unstable in short integration
  • cleanVnonClean.ipynb compares model trained with clean data verses not clean data.
  • oldReboundNewRebound.ipynb compares old rebound verses new rebound
  • compareTsec.ipynb in paper_plots folder contains Tsec integration, feature ablation study, and comparisons to old integration and features

commit 2a79a42 contains additional info and comparisons of:

  • Old REBOUND to new REBOUND with training/testing
  • Model trained with clean verses dirty data
  • Initial Tsec tests

commit 9468319 contains comparisons for old feature generation implementation verses new implementation.

commit 086be72 contains all updates except for Tsec related integration and features, this version is avalable for backwards compatibility.

Data filtration/clean data generation is done in accordance to, and with the data from, 10.5281/zenodo.14327471

Ethadhani added 30 commits July 10, 2024 22:25
…arison. This new method allows more flexibility for future advancements/new features without impacting the deep regressor, while decreasing computation time.
…dentical features, proper documentation to come
…e and full examples that the old feature generation method and new feature generation method yield the exact same results and are compatible to each other
…od, also isolated featureclassifier from other models such that featureclassifier.py, ClassifierSeries.py, and features.py operate together and independently from the rest of the spock system to allow changes to be made
… make a copy of simulation if using new rebound, this is because old rebound does not support sim.copy()
…n. Was supposed to state, fixed pool function to generate training data and removed redundant file, made it so that the training data pool runs through jupyter notebook
…fically accounted for rebound.Collision and rebound.Ejected
Ethadhani and others added 29 commits November 27, 2024 15:56
fixed depreciating warning by updating used XGBoost version, this also decreases training time and increases performance by marginal amount. (was using 1.7, now 2.1)
…trios, this does not effect any tests since all training/testing is done on three planet case
Included mass of star in Tsec equation so that calculations work in abstracted case, this does not effect any tests since all testing systems have a star of mass 1, this also fixes unit analysis
@dtamayo dtamayo merged commit 52bf05b into dtamayo:master Jan 24, 2025
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2 participants