Skip to main content

evaluation_lumo is a package for evaluating the LUMO damage detection system.

Project description

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 moth of data
  • Damage Case Analysis: Provides detailed performance evaluations for each specific damage scenario within the LUMO dataset.

Installation

To install the package, run:

pip install evaluation_lumo

## Usage

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

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

# 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)

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

evaluation_lumo-0.1.5.tar.gz (7.9 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

evaluation_lumo-0.1.5-py3-none-any.whl (6.8 kB view details)

Uploaded Python 3

File details

Details for the file evaluation_lumo-0.1.5.tar.gz.

File metadata

  • Download URL: evaluation_lumo-0.1.5.tar.gz
  • Upload date:
  • Size: 7.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.0.1 CPython/3.8.20

File hashes

Hashes for evaluation_lumo-0.1.5.tar.gz
Algorithm Hash digest
SHA256 e17fb70f57f06365c00aa785606ace4acca1feea0d6b4a64f1ddb042a093bc66
MD5 58305d6b3dea436ae5a3bc9502af8251
BLAKE2b-256 6e759b2f4f61f656fd4fe844916627e57ac97e6157fd792a0463004f1f649048

See more details on using hashes here.

File details

Details for the file evaluation_lumo-0.1.5-py3-none-any.whl.

File metadata

File hashes

Hashes for evaluation_lumo-0.1.5-py3-none-any.whl
Algorithm Hash digest
SHA256 00404c26c88a7d126a6639e5b53055c422b56f17658e4325111f004ba3eeb800
MD5 f71035b8e34aa578a2b22cb3d020da45
BLAKE2b-256 266bbec82fb5db6835f53eadbd8fb6d1e2121cf29cfcdc9cfb0c2c3e6ccb5e75

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page