Metadata-Version: 2.3
Name: evaluation-lumo
Version: 0.1.9
Summary: evaluation_lumo is a package for evaluating the LUMO damage detection system.
Author-email: Yacine Bel-Hadj <yacine.bel-hadj@vub.be>
Maintainer-email: Yacine Bel-Hadj <yacine.bel-hadj@vub.be>
Requires-Python: >=3.9.0
Description-Content-Type: text/markdown
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Environment :: Console
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Information Technology
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Topic :: Scientific/Engineering
Requires-Dist: pandas>2
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Requires-Dist: sphinx ; extra == "docs"
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Project-URL: Repository, https://github.com/YacineBelHadj/evaluation_lumo
Provides-Extra: ci
Provides-Extra: docs
Provides-Extra: test

# LUMO Damage Detection Evaluation Package

This package provides a standardized framework for evaluating damage detection and localization strategies using the LUMO dataset. Users can input timestamps alongside their corresponding anomaly indices, and the package computes various performance scores for each damage case, promoting consistency in damage detection evaluation.

## Features

- **Standardized Evaluation Metrics**: Calculates TPR and FPR at a threshold set such as FPR for training data is 1%.
The training dataset should be only the first month of data.
- **Damage Case Analysis**: Provides detailed performance evaluations for each specific damage scenario within the LUMO dataset.

## Installation

To install the package, run:

```bash
pip install evaluation_lumo
```

## Usage

To use the package, import the `evaluation_lumo.evaluation` module and call the `compute_tr_by_events` function, `compute_mean_variation` function, or `compute_mad` function.

```python
from evaluation_lumo.evaluation import compute_tr_by_events, compute_mean_variation, compute_mad

# Example usage

date_index = pd.date_range(start='2021-08-01', ends="2022-08-01", freq='10T')
associated_damage_index = np.random.random(len(date_index))
compute_tr_by_events(date_index, associated_damage_index)
compute_mean_variation(date_index, associated_damage_index)
compute_mad(date_index, associated_damage_index)
```

