# 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 | <ul><li>Processing Configuration</li> <li>Raw Data</li></ul> | <ul><li>Preprocessed Data (.p, .p.gz, .json, .npy, ...)</li> <li>Data Statistics (stats.json)</li> <li>Preprocessing Config (config.json)</li></ul> | DataFolder -> DataManager
Training/Fitting | <ul><li>Preprocessed Data</li> <li>Hyperparameters</li></ul> | <ul><li>Model checkpoints</li> <li>Hyperparameter configs</li></ul> | ModelManager -> RunManager
Evaluation | <ul><li>Trained model</li><li>Evaluation Config</li></ul> | <ul><li>Evaluation Config</li> <li>Evaluation Results</li></ul> | RunManager -> EvaluationManager
Manual Analysis | Any model/data | Plots, statistics, images | AnalysisFolder -> AnalysisManager
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"description": "# Experiment Library and Setup (ELIAS)\n\n## 1. Main functionalities\n\n### `Config` class\nIntuitive dataclass extension that addresses issues commonly encountered in research projects. \nA `Config` has the following features:\n - Easily **persistable** to a file (JSON or YAML)\n - Stored configs are **human-readable and editable**\n - Special support for storing **enums** and **inheritance structures**\n - **Backward compatibility** to allow loading older persisted configs\n\nTODO: Overview image of benefits of using Config over regular dataclass\n\nPhilosophy for stored config file types:\n - Everything should be human-readable\n - JSON for configs that may be viewed often (statistics, preprocessing/training configs, evaluation results)\n - YAML for configs that have to be edited (run specifications)\n\n### Experiment Workflow Utilities\nTo speed up the experimentation process with utility classes the `elias` library assumes the following workflow:\n \nStage | Input | Output | Utility\n---|---|---|---\nData Preprocessing | <ul><li>Processing Configuration</li> <li>Raw Data</li></ul> | <ul><li>Preprocessed Data (.p, .p.gz, .json, .npy, ...)</li> <li>Data Statistics (stats.json)</li> <li>Preprocessing Config (config.json)</li></ul> | DataFolder -> DataManager\nTraining/Fitting | <ul><li>Preprocessed Data</li> <li>Hyperparameters</li></ul> | <ul><li>Model checkpoints</li> <li>Hyperparameter configs</li></ul> | ModelManager -> RunManager\nEvaluation | <ul><li>Trained model</li><li>Evaluation Config</li></ul> | <ul><li>Evaluation Config</li> <li>Evaluation Results</li></ul> | RunManager -> EvaluationManager\nManual Analysis | Any model/data | Plots, statistics, images | AnalysisFolder -> AnalysisManager\n\n\n",
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