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KryeKuzhinieri / predicting-driver-stress-using-deep-learning

Predicting driver stress levels using Physionet's SRAD (drivedb) dataset with methods such as LSTMs, RNNs, CNNs
MIT License
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Result Verification #2

Closed karandeep8 closed 1 year ago

karandeep8 commented 1 year ago

Sir, for the e_models.py

I have run this file using the following the code if name == "main": main( feature_selection="rf", # Choose one: "lda", "pca", "autoencoder", "rf" classifier="loop", # Choose one: "rf", "rnn", "loop" undersample=True, # True or False shuffle=True, # True or False path=DATASET_PATH, # Provide the path to your dataset CSV file ) Can you confirm that, if I am running the code in the proper way or not?

After running this code
I got the following results (Accuracy on the Testing Data)
lda + rf -> 54.76
lda + RNN -> 53.99
pca + rf -> 54.0575
pca + rnn -> 54.760
autoencoder + rf -> 50.86
autoencoder + rnn -> 52.65
rf + rf -> 70.95
rf + rnn -> 59.52

Can you please confirm these results?

And I still have to run the remaining combinations.
So for the remaining combinations, please tell me their accuracy too. 
It would be a great help.
KryeKuzhinieri commented 1 year ago

To get a full list of results you should check my paper which can be found in the readme file (Table 6.1). For a full list of parameters check tables 6.3-6.5. I would suggest you to read the methodology section of the paper for full reproducibility.

For easier access, here is the link to the paper: https://www.dropbox.com/s/dp3hm900j2x88j0/full_thesis_with_approvals.pdf?dl=0

karandeep8 commented 1 year ago

Thank you, sir I will look into that.