List of all modules

Package Structure

Below we show how the software package is structured. While this software is still in heavy development, the overall design idea is to include related modules within a common sub-directory. To this end, multiple sub-directories were created which include python modules with common utilities covering each of the following functionalities: (1) data preparation , (2) model architecture , (3) sensitivity analysis , (4) surrogate modeling . A separate sub-directory ( utils ) contains modules that can be used on top of program, e.g. for initializing distributed workers or logger, or extract results from log files.

hyppo
├── __init__.py
├── config.py
├── datasets
│   ├── __init__.py
│   ├── cifar10.py
│   ├── custom.py
│   ├── fake.py
│   ├── generic.py
│   ├── loaders.py
│   ├── network.py
│   ├── plots.py
│   └── utils.py
├── dnnmodels
│   ├── __init__.py
│   ├── plots.py
│   ├── pytorch
│   │   ├── __init__.py
│   │   ├── cnn.py
│   │   ├── lstm.py
│   │   ├── mlp.py
│   │   └── utils.py
│   └── tensorflow
│       ├── __init__.py
│       ├── cnn.py
│       ├── gcn.py
│       ├── lstm.py
│       ├── mlp.py
│       ├── rnn.py
│       └── utils.py
├── evaluation.py
├── hyperparams.py
├── obj_fct.py
├── plots.py
├── sensitivity
│   └── __init__.py
├── surrogate
│   ├── ComputeRBF.py
│   ├── InitialRBFMatrices.py
│   ├── __init__.py
│   ├── gp_opt.py
│   ├── phi.py
│   ├── rbf_opt.py
│   └── utility.py
├── train.py
├── update.py
└── utils
    ├── __init__.py
    ├── distributed.py
    ├── estimator.py
    ├── extract.py
    ├── logging.py
    ├── parallel.py
    └── sampling.py

Methods & Classes

config.load_config ([config_file, debug, ...])	Load configuration file and embed relevant parameters and data in dictionary.
evaluation.evaluation (config)	Execute initial evaluations in parallel.
evaluation.single_evaluation (ii, samples, ...)
hyperparams.get_hyperprms (trainer[, x_sc, ...])	This function handles the recording and/or loading of the compelte hyperparameter set ready-to-use for training.
hyperparams.set_hyperparams (random_set, ...)	This function merges into a single dictionary the hyperparameters that will be evaluated with the one that will be fixed.
train.train_evaluation (x_sc, samples, ...[, ...])	Execute training according to requested trainer mode.
datasets.cifar10.get_data ([library, data_path])	Loading CIFAR10 dataset .
dnnmodels.pytorch.mlp.PytorchMLP ([data, prms])	PyTorch class for multi-layer perceptron model.