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Update Code Review authored by Jean-Rene Cudell's avatar Jean-Rene Cudell
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......@@ -104,12 +104,9 @@ The ML model predictions are compared to posterior overlap statistic results als
| Scripts | Short description | Status | git hash | Comment | final sign-off |
|---------|-------------------|--------|----------|---------|----------------|
| [train_densenets_qts.py](https://git.ligo.org/srashti.goyal/lensid/-/blob/master/package/lensid/train_test/train_densenets_qts.py) | Train densenet with qtransform for a given detector. Eg: `python train_densenets_qts.py -lensed_df ~/strong-lensing-ml/data/dataframes/train/lensed.csv -unlensed_df ~/strong-lensing-ml/data/dataframes/train/unlensed_half.csv -odir dense_out/cit/ -epochs 10 -data_dir ~/alice_data_lensid/qts/train/`. Note: requires `tensorflow-gpu` to load CUDA libraries. | --OK-jrc | -------- | | ---------------- |
| [train_crossvalidate_test_XGB_qts.py](https://git.ligo.org/srashti.goyal/lensid/-/blob/master/package/lensid/train_test/train_crossvalidate_test_XGB_qts.py) | Trains, cross-validate and compare to BLU "XGBoost with QTs" model. Requires dataframe that already has the input features calculated from the Qtransform images and trained DenseNets. `python train_crossvalidate_test_XGB_qts.py -help` | OK-jrc\
\------ | -------- | jrc: the values of the parameters of XGBoost could be documented. | -------------- |
| [train_crossvalidate_test_XGB_sky.py](https://git.ligo.org/srashti.goyal/lensid/-/blob/master/package/lensid/train_test/train_crossvalidate_test_XGB_sky.py) | Train, cross-validates and compare to BLU "XGBoost with Skymaps" model. Requires dataframe that already has the input features calculated from the Bayestar/PE skymaps. `python train_crossvalidate_test_XGB_sky.py -help` | OK-jrc\
\------ | -------- | | -------------- |
| [test_combined_ML_results.py](https://git.ligo.org/srashti.goyal/lensid/-/blob/master/package/lensid/train_test/test_combined_ML_results.py) | Test and compare to BLU overall ML model. Requires dataframes that already has the ML predictions calculated from the qts and skymaps. `python test_combined_ML_results.py -help` | OK-jrc\
\------ | -------- | | -------------- |
| [train_crossvalidate_test_XGB_qts.py](https://git.ligo.org/srashti.goyal/lensid/-/blob/master/package/lensid/train_test/train_crossvalidate_test_XGB_qts.py) | Trains, cross-validate and compare to BLU "XGBoost with QTs" model. Requires dataframe that already has the input features calculated from the Qtransform images and trained DenseNets. `python train_crossvalidate_test_XGB_qts.py -help` | OK-jrc\\ | jrc: the values of the parameters of XGBoost could be documented. | | |
| [train_crossvalidate_test_XGB_sky.py](https://git.ligo.org/srashti.goyal/lensid/-/blob/master/package/lensid/train_test/train_crossvalidate_test_XGB_sky.py) | Train, cross-validates and compare to BLU "XGBoost with Skymaps" model. Requires dataframe that already has the input features calculated from the Bayestar/PE skymaps. `python train_crossvalidate_test_XGB_sky.py -help` | OK-jrc | -------------- | | |
| [test_combined_ML_results.py](https://git.ligo.org/srashti.goyal/lensid/-/blob/master/package/lensid/train_test/test_combined_ML_results.py) | Test and compare to BLU overall ML model. Requires dataframes that already has the ML predictions calculated from the qts and skymaps. `python test_combined_ML_results.py -help` | OK-jrc | -------------- | | |
| [ml_predict_workflow.py](https://git.ligo.org/srashti.goyal/lensid/-/blob/master/package/lensid/ml_predict_workflow.py), [config_O3_events.yaml](https://git.ligo.org/srashti.goyal/lensid-ml-o3/-/blob/master/config_O3_events.yaml) | Script for computing ML predictions for a given event pairs in dataframe, their skymaps, Qtransforms and the trained ML models. Optionally computes the False Positive Probabilities given the background. It requires config file as input. Eg: `lensid_make_predictions -config /home/srashti.goyal/lensid-ml-O3/config_O3_events.yaml` Note: change `odir` in **config** file. | -------- | ---------- | --------- | ---------------- |
## Investigations: Injection parameters, features statistics, PSDs etc.
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