Autopopulus

Autopopulus is a framework that enables the design, profiling, evaluation, and selection of various autoencoder architectures for efficient imputation on large datasets.

Imputation

To use Autopopulus for imputation, we provide the AEImputer class, following on the fit()/transform() paradigm from sklearn. AEImputer wraps an inner AEDitto object, a Pytorch Lightning LightningModule that implements a flexible autoencoder. The AEImputer expects data in the form of a CommonDataModule object, which at minimum requires a user define and pass a datasetloader. Users may define their own dataset class that extends the AbstractDatasetLoader class, or they may use the SimpleDatasetLoader for convenience. Each dataset loader requires the data (in the form of a pandas Dataframe), the label data (in the form of a pandas Series) or the name of the label column, a list of continuous columns, a list of categorical columns, and one-hot encoding prefixes. Optionally, if the data are already split into train, val, and test, SimpleDatasetLoader allows to indicate which column assigns each sample to a split.

Both CommonDataModule and AEImputer can be partially initialized using command-line arguments for convenience. For more examples, explore autopopulus/task_logic/ae_imputation.py. First initialize a CommonDataModule object for your dataset and call setup() on it. After initializing AEImputer with the settings you like, to train the imputer call aeimputer_object.fit(data_object). If you choose to instead tune, call aeimputer_object.tune(*args), refer to the function signature for more. To impute with the imputer call aeimputer_object.transform(dataset), where the dataset is expected to be a numpy array or a pandas dataframe.

Comparison and Model Selection

Autopopulus consists of a 3-step pipeline for experimental workflow:

1. Imputation via impute.py.
2. Imputation evaluation via evaluate.py, which relies on the impute.py step upstream.
3. Downstream predictive evaluation via predict.py, which relies on the impute.py step upstream.

To run one-off experiments, you may create scripts that passes in command-line args, or optionally define an options.yml file at the root of the project. Additionally, we have scripts in the dev/experiment_running/ directory to help execute runs, particularly the RunManager class and experiment_track_all_imputers.py.

Autopopulus currently supports 3 experiment trackers: guildai/Tensorboard, Aim, and MLflow. To choose your experiment tracker, change the LOGGER_TYPE global variable in autopopulus/utils.log_utils.py. In future versions we will make this parameter of the AEImputer class. By default we use MLflow and will automatically serialize the trained imputer model and the imputed data for all data splits.

Citation

Autopopulus was initially introduced in our earlier paper:

@inproceedings{zamanzadehAutopopulusNovelFramework2021a,
  title = {Autopopulus: {{A Novel Framework}} for {{Autoencoder Imputation}} on {{Large Clinical Datasets}}},
  shorttitle = {Autopopulus},
  booktitle = {2021 43rd {{Annual International Conference}} of the {{IEEE Engineering}} in {{Medicine}} \& {{Biology Society}} ({{EMBC}})},
  author = {Zamanzadeh, Davina J. and Petousis, Panayiotis and Davis, Tyler A. and Nicholas, Susanne B. and Norris, Keith C. and Tuttle, Katherine R. and Bui, Alex A. T. and Sarrafzadeh, Majid},
  year = {2021},
  month = nov,
  pages = {2303--2309},
  issn = {2694-0604},
}