Gradient-boosted regression and classification trees repo for behavioural animal data

This is an implementation of lightGBM and SHAP to predict the probability of an animal making a false alarm, miss, or hit using gradient-boosted regression and classification trees (PLOS Computational Biology, in press, doi: https://doi.org/10.1371/journal.pcbi.1011985). This code assumes you have put your behavioural data into a pandas dataframe where each factor or variable of interest is represented as a column.

To set up:

1. Install python 3.9 (in this repository I used conda)
2. git clone this repository
3. cd into the repository
4. run pip install -r requirements.txt; the main requirements are the SHAP package, pandas, and lightGBM.
5. navigate to the models directory
6. run the selected model, i.e. runfalsealarmmodel.py for the false alarm model

Overview of models in this repository:

1. runfalsealarmmodel.py - classifier to predict whether there will be a false alarm during a catch trial
2. reactiontimemodel.py - regression model, predicts the reaction times of correct target trials
3. reactiontimeforFAandtargmodel.py - regression model, predicts the absolute reaction time relative to the trial start solely based on the words presented in the trial
4. correctrespmissmodel.py - predicts the probability of a miss/correct response during a target trial.
   plotstatsacrossanimals.py - plots general psychophysics metrics

How to use this code

1. Put your data into a pandas dataframe where each column is a variable of interest
2. Change data paths as relevant and column names as relevant
3. Use optuna to fine tune your hyperparameters
4. Run the finalised model