yongsukyee / uncertain_blackholemass

Uncertainty quantification of black hole mass estimation

Uncertainty Quantification of the Black Hole Mass Estimation

Predicting virial black hole masses using neural network model and quantifying their uncertainties.

This repository is to accompany the paper Uncertainty Quantification of the Virial Black Hole Mass with Conformal Prediction.

Setup and Requirement

git clone https://github.com/yongsukyee/uncertain_blackholemass.git && cd uncertain_blackholemass
pip install -r requirement.txt

Pipeline

0. Get data

   • Setup configs:
     • config/config.yaml: Main config file
     • config/config_getdata.yaml: Additional configs to specify the data directory
   • To run: $ python get_data.py
   • See Dataset on the catalogue and data used.
1. Feature extractor using fully-connected neural network model

   • Setup configs: config/config.yaml
   • To run: $ python run_featureextractor.py
   • Generated output files:
     • datasplitidx.pkl: dict of data split indices {'train' train_idx, 'test': test_idx}
     • yscaler.pkl: sklearn.preprocessing scaler
     • model.pth: Trained PyTorch model
     • loss.pkl: DataFrame of 'train' and 'test' losses
     • features.pkl: DataFrame of features, 'objid', 'label', 'scaled_label', 'output'
2. Uncertainty quantification for regression using MAPIE

   • Setup configs:
     • config/config.yaml: Main config file
     • config/config_runuqregressor.yaml: Additional configs to select saved log directory from run_featureextractor.py
   • To run: $ python run_uqregressor.py
   • Generated output files:
     • estimator_optim.pkl: Optimized GradientBoostingRegressor model for normal regression
     • estimatorq_optim.pkl: Optimized GradientBoostingRegressor model for quantile regression
     • mapieuq_<STRATEGY>.pkl: Fitted MapieRegressor for specified strategy
     • mapieuq_pred.pkl: y_pred, y_pis, sorted {'target', 'pred', 'lower', 'upper', 'pierr_metric'}
     • mapieuq_picp_alpha.pkl: DataFrame of prediction interval coverage probability (PICP) for different uncertainty quantification methods with alpha as index
     • mapieuq_mpiw_alpha.pkl: DataFrame of mean prediction interval width (MPIW) for different uncertainty quantification methods with alpha as index

Dataset

Dataset from Sloan Digital Sky Survey (SDSS) Data Release 16 quasar properties are publicly available for:

• Catalogue: http://quasar.astro.illinois.edu/paper_data/DR16Q/
• Spectra: http://quasar.astro.illinois.edu/paper_data/DR16Q/fits/

Example Exploratory Notebooks

• notebooks/inspectdata.ipynb: Inspect data from SDSS
• notebooks/featureextraction.ipynb: Examine outputs of feature extraction
• notebooks/uncertaintyquantification.ipynb: Analyse various uncertainty quantification methods for the black hole mass estimation

Reproducible Results

The reproducible_output/ directory contains the generated logs and output files that are used for the analysis in our paper. For a list of output files, see Pipeline.

Citation

If you find this repository useful, please cite the paper:

TODO: insert citation

References and Resources

• Taquet, Vianney, V. Blot, Thomas Morzadec, Louis Lacombe and Nicolas J.-B. Brunel. “MAPIE: an open-source library for distribution-free uncertainty quantification.” ArXiv abs/2207.12274 (2022). [Paper | Code]
• Romano, Yaniv, Evan Patterson and Emmanuel J. Candès. “Conformalized Quantile Regression.” Neural Information Processing Systems (2019). [Paper | Code]
• Angelopoulos, Anastasios Nikolas and Stephen Bates. “A Gentle Introduction to Conformal Prediction and Distribution-Free Uncertainty Quantification.” ArXiv abs/2107.07511 (2021). [Paper | Code]