Release Version 1.0.0 · Center-for-Health-Data-Science/bulkDGD

[v1.0.0] - 2024-07-07

The train() method was added to the 'core.model.DGDModel' class to train the DGD model.
The dgd_train executable was added to train the DGD model using the command line.
A new type of configuration file containing the options to train the DGD model is available. An example can be found in the newly created bulkDGD/ioutil/configs/training directory inside the package. This file, along with the other configuration files, is installed with the package.
A new example of a configuration file (model_untrained.yaml) containing the options to set up the DGD model is available in the 'bulkDGD/ioutil/configs/model' directory for when the model needs to be set up before training.
The documentation now includes a new tutorial on how to train the DGD model (Tutorial 3).
The load_loss() and save_loss() functions were introduced in the new bulkDGD.ioutil.lossio module to load and save CSV files containing the losses reported during the training procedure.

The configuration file used to find the representations for new samples now has a new format. Please take a look at the documentation for a detailed explanation of the new format. The format is not backward compatible.

The _get_data_loader() method has been introduced into the core.model.DGDModel class to create data loaders.
There is now only one internal method in the core.model.DGDModel class responsible for optimizing one or multiple representations for a set of samples, _optimize_rep(). The _get_representations_one_opt() and the _get_representations_two_opt() methods have been updated accordingly.
New sanity checks have been introduced when loading configurations (ioutil.configio module).
A new _get_final_dataframes_train method has been introduced to create the data frames produced by the new train() method in the core.model.DGDModel class.