HEA ML Project

Installation

Docker

@todo

Linux

Using conda/mamba:

mamba env create -f environment.yml
conda activate tf3

Using pip: @todo

# Install virtualenv
sudo apt install virtualenv

# Create a new environment
virtualenv hea_ml

# Activate environment
source hea_ml/bin/activate

# Install dependencies
pip install autopep8 astropy matplotlib numpy pandas scipy

Usage

Sample input parameter space

Create a CSV containing vectors of inputs.

python dataset/prepare_inputs.py -s <count> -o <path_to_csv>

Generate Dataset

Create a config.json using a template under config_files/dataset/template.json. Update the file to use the <path_to_csv> of the previous step. Then run:

python dataset/generate_dataset.py -c config.json

Tuning

• Single run: Use a config file under config_files/spectrum/tune.

  python ml/tune.py -c <config>

• Single type run: Choose a type among the following {dnn, rnn, gru, lstm}. This will run all config files under config_files/spectrum/tune/<type>.

  ./train.sh -m tune -t <type>

Training

• Single run: Use a config file under config_files/spectrum/train.

  python ml/train.py -c `<config>`

• Single type run: Choose a type among the following {dnn, rnn, gru, lstm}. This will run all config files under config_files/spectrum/train/<type>.

  ./train.sh -m train -t <type>

Removal

Linux

conda deactivate
rm -rf hea_ml

Contributing

License