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Experiment Library and Setup (ELIAS)

1. Main functionalities

Config class

Intuitive dataclass extension that addresses issues commonly encountered in research projects. A Config has the following features:

  • Easily persistable to a file (JSON or YAML)
  • Stored configs are human-readable and editable
  • Special support for storing enums and inheritance structures
  • Backward compatibility to allow loading older persisted configs

TODO: Overview image of benefits of using Config over regular dataclass

Philosophy for stored config file types:

  • Everything should be human-readable
  • JSON for configs that may be viewed often (statistics, preprocessing/training configs, evaluation results)
  • YAML for configs that have to be edited (run specifications)

Experiment Workflow Utilities

To speed up the experimentation process with utility classes the elias library assumes the following workflow:

Stage Input Output Utility
Data Preprocessing
  • Processing Configuration
  • Raw Data
  • Preprocessed Data (.p, .p.gz, .json, .npy, ...)
  • Data Statistics (stats.json)
  • Preprocessing Config (config.json)
DataFolder -> DataManager
Training/Fitting
  • Preprocessed Data
  • Hyperparameters
  • Model checkpoints
  • Hyperparameter configs
ModelManager -> RunManager
Evaluation
  • Trained model
  • Evaluation Config
  • Evaluation Config
  • Evaluation Results
RunManager -> EvaluationManager
Manual Analysis Any model/data Plots, statistics, images AnalysisFolder -> AnalysisManager

Metadata

Release files for elias 0.4.19

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for elias 0.4.19
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elias-0.4.19.tar.gz 47.3 kB Details

Built distribution (wheel)

Table of built distributions (wheels) for elias 0.4.19
File Interpreter ABI Platform
elias-0.4.19-py3-none-any.whl Python 3 none any Details

Total release size: 104.3 kB

Release files / elias-0.4.19.tar.gz

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