This PR follows the previous pr (#93). I took the time to carefully examine my code and notice a few bugs concerning the generative model performance:
The grid search algorithm wasn't selecting the best model. It was selecting a model at random. Fixed that and then I realized I was using the wrong column to estimate model performance.
The indices for the GiG and possibly CbG relation had an added label function that shouldn't be in there.
After fixing the above bugs I have corrected the gen model's auroc and aupr point plot grid:
Lastly, this means that the spike I was observing within the GiG and CbG relations was just a bug.. Anyway for this review take a look at the updated figures and notebooks and the train_model_helpher.py file. The other files have quick one liner changes.
After this PR, there will be another one that just contains the updated data files. Didn't want to risk this PR getting too large.
This PR follows the previous pr (#93). I took the time to carefully examine my code and notice a few bugs concerning the generative model performance:
After fixing the above bugs I have corrected the gen model's auroc and aupr point plot grid:
Lastly, this means that the spike I was observing within the GiG and CbG relations was just a bug.. Anyway for this review take a look at the updated figures and notebooks and the
train_model_helpher.py
file. The other files have quick one liner changes.After this PR, there will be another one that just contains the updated data files. Didn't want to risk this PR getting too large.