/Google-Brain-Ventilator

Primary LanguagePythonMIT LicenseMIT

Part of 1st place solution (LSTM CNN Transformer Encoder) of Google-Brain-Ventilator competition

Competition website: https://www.kaggle.com/c/ventilator-pressure-prediction/overview
Our solution write-up: https://www.kaggle.com/c/ventilator-pressure-prediction/discussion/285256

Features

Features I use include a few lag and diff features, which are basically previous values of u_in and differences between current u_in and previous u_in. R and C are one-hot encoded with combinations of R and C one-hot encoded as well. Additionally, cumulative u_in integrated over time is also calculated (area_true). For more details, see add_features in Functions.py.

Architecture

My deep learning architecture is a combination of LSTM, 1D convolution, and transformers. LSTM is necessary to model this data because of target pressure's heavy dependence on previous time points. Convolution in conjunction with transformers is a good combination to model global dependencies while making up for transformers' inability to capture local interactions.

Since I'm using a series of many different modules, the network becomes quite deep. Eventually, I ran into some issues with gradient propagation since nn.LSTM does not have residual connection. Therefore I created a new module called ResidualLSTM, which adds a Feedforward Network (FFN) and connects the input to the LSTM with the output after FFN with a residual connection. Below is a simplified visualization of the architecture (Nl is the number of ResidualLSTM blocks and Nt is the number of convolution+transformer blocks).

Packages you need

  1. Pytorch
  2. Ranger optimizer: https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer
  3. Sklearn
  4. tqdm

To run

  1. run.sh is used to run training. The only argument you need to change in run.sh is the --path argument. Change it to where you have train.csv and test.csv
  2. calculate_cv.py calculates cv and outputs in cv.txt
  3. predict.sh to make predictions, generate prediction file, and save 10-fold predictions. Similar to 1., change --path to where you have train.csv, test.csv, and sample_submission.csv

files

  1. Network.py has the architecture
  2. Dataset.py has the dataset object
  3. Functions.py has some functions i use (mainly add_features)
  4. Logger.py is the custom csv logger i use to log train/val loss and metrics