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xypan1232 / iDeepE

inferring RBP binding sites and motifs using local and global CNNs
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Cross validation #2

Open LCHCurtis opened 5 years ago

LCHCurtis commented 5 years ago

Does the ideepe.py implement the cross-validation to fine tune the hyperparameter?

xypan1232 commented 5 years ago

No, we only keep 20% of original training set as validation set, then use grid search to find the best hyper parameters within certain range. You can easily implement it.

LCHCurtis commented 5 years ago

Thank you. I have another question. After I train the model, I run the prediction multiple times with the same dataset, but it always gives different AUCs. Would you please explain why it comes with this? Sorry, I am a beginner in this field.

xypan1232 commented 5 years ago

could I ask how big is the difference? maybe because of random seed number.

LCHCurtis commented 5 years ago

for the ALKBH5 dataset, I run the training once, then using the same model and feed the same testing dataset for multiple times, I got the AUC ranging from 0.67-0.7

xypan1232 commented 5 years ago

Can you add the code np.random.seed(0) torch.manual_seed(0) before run_ideepe(args) in the file to fix the seed and retrain the model and do prediction?

LCHCurtis commented 5 years ago

Problem solved. Thank you so much