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Training and evaluating causal forecasting models for time-series

This repository is a fork from the CausalTransformer repository. The forked repository normalize the data processing pipeline and provide the CausalTransofrmer model architecture.

The project is built with following Python libraries:

  1. Pytorch-Lightning - deep learning models
  2. Hydra - simplified command line arguments management
  3. Encodec - causal convolution

Installations

First one needs to create a Python 3.11 virtual environment and install all the requirements:

pip3 install virtualenv
python3 -m virtualenv -p python3 --always-copy venv
source venv/bin/activate
pip3 install -r requirements.txt

As the causal transformer repository uses lower version of Pytorch-Lightning, we also created an other virtual environment to train the CT model using Python 3.9 and the requirements defined in requirements-ct.txt.

Tasks

There are 5 different scripts each one of them for a specific task (either training, computing metrics or computing RDD dataset). All the script are based on the main config file config/config.yaml. Each task then use a subsection fo the config located in configs/ folder.

RDD Dataset

It is mandatory to first compute the RDD dataset before computing the RDD RMSE. The script associated to this task is compute_rdd_dataset.py. This script uses both the datasetsection fo the config and the rdd section fo the config. The parameters used to reproduce the metrics are located at config/rdd/mimic3_rdd.yaml. You can run the script with python compute_rdd_dataset.py. This should create a dataset at data/processed/rdd_dataset.parquet.

Model training

There are 2 types of TFT models:

  • Baseline model is a plane deep learning architecture
  • Causal model is comopsed of 3 sub models $m_0$/$e_0$/$\Theta$ All the code associated to those models are located at src/models/causal_tft

Training baseline model

The baseline model can be trained using the command python train_tft_baseline.py +model=baseline. The parameters associated to this training are associated to the model's section in the config. The parameters we used to train the model are located at config/model/baseline.yaml. You can train multiple seeds of the model at the same time using python train_tft_baseline.py --multirun +model=baseline exp.seed=10,101,1001,10010,10110. The seeds 10,101,1001,10010,10110 are the one we used for our experiments.

Training causal model

We propose 2 encoding for the causal model. The one_hot model encode the treatments using one hot encoding and the cumulative model uses cumulative sum. Models can respectively trained with the commands python train_tft_causal.py --multirun +model=one_hot exp.seed=10,101,1001,10010,10110 and python train_tft_causal.py --multirun +model=cumulative exp.seed=10,101,1001,10010,10110

Training CT model

In order to compare our model we decided to train a Causal Transformer model aswell. Based on the original repository, you can train a CT model using the command python train_multi.py --multirun +backbone=ct +backbone/ct_hparams/mimic3_real=diastolic_blood_pressure +dataset=mimic3_real exp.seed=10,101,1001,10010,10110. The model checkpoints should be saved under the multirun folder.

Model evaluation

Before computing the metrics, you need to fill the config with the path of the trained model in config/rdd/mimic3_rdd.yaml under the metrics section.

Forecast metrics

You can compute the forecast metrics using the command python compute_metrics.py. This will create a json file under data/processed/forecast_metrics.json. This json file contain a section paper_metrics_per_time_shift with all the final metrics.

RDD metrics

You first need to compute the RDD dataset and fill the config at config/rdd/mimic3_rdd.yaml. Than you can compute the RDD metrics with the command python compute_rdd_rmse.py. This will create a json file under data/processed/rdd_metrics.json. This json file contain a section paper_metrics_per_time_shift with all the final metrics.

Datasets

Before running any task, place MIMIC-III-extract dataset (all_hourly_data.h5) to data/processed/