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https://github.com/coelhosilva/flight-ad
flight-ad is a Python package for anomaly detection in the aviation domain built on top of scikit-learn.
https://github.com/coelhosilva/flight-ad
anomaly-detection data-science fdm flight-data flight-data-analysis flight-data-monitoring machine-learning python scikit-learn
Last synced: 2 days ago
JSON representation
flight-ad is a Python package for anomaly detection in the aviation domain built on top of scikit-learn.
- Host: GitHub
- URL: https://github.com/coelhosilva/flight-ad
- Owner: coelhosilva
- License: mit
- Created: 2021-06-16T03:26:57.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-05-15T21:00:45.000Z (over 2 years ago)
- Last Synced: 2024-09-04T11:18:51.041Z (23 days ago)
- Topics: anomaly-detection, data-science, fdm, flight-data, flight-data-analysis, flight-data-monitoring, machine-learning, python, scikit-learn
- Language: Python
- Homepage:
- Size: 84 KB
- Stars: 6
- Watchers: 0
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# flight-ad
[](https://www.codacy.com/gh/coelhosilva/flight-ad/dashboard?utm_source=github.com&utm_medium=referral&utm_content=coelhosilva/flight-ad&utm_campaign=Badge_Grade)
[](https://badge.fury.io/py/flight-ad)
`flight-ad` is a Python package for anomaly detection in the aviation domain built on top of scikit-learn.
It provides:
- An implementation of an anomaly detection pipeline;
- A DataBinder object for loading and transforming the data within the pipeline on the fly;
- A DataWrangler object for building a data wrangling pipeline;
- A StatisticalLearner object for binding scikit-learn's pipelines and integrating them on the anomaly detection workflow;
- Visualization tools for assessing potential anomalies;
- Reporting tools for analyzing results;
- Sample airplane sensor data, repackaged from NASA's DASHlink for the purpose of evaluating and advancing data mining capabilities that can be used to promote aviation safety;
- Adaptations of machine learning algorithms, such as a DBSCAN implementation that calculates the hyperparameter epsilon from the input data.
## Installation
The easiest way to install `flight-ad` is using pip from your virtual environment.
From PyPI:
`pip install flight-ad`
Or directly from GitHub:
`pip install git+https://github.com/coelhosilva/flight-ad.git`
## Examples
This is a sample usage of the package for constructing an anomaly detection pipeline. Beware that the sample dataset
may take up roughly 1 GB in disk space.
```python
from flight_ad.datasets import load_dashlink_bindings
from flight_ad.utils.data import DataBinder
from flight_ad.wrangling import DataWrangler
from wrangling_functions import preprocess, change_col, resample, select
from flight_ad.transformations import reshape_df_interspersed
from sklearn.preprocessing import StandardScaler
from sklearn.decomposition import PCA
from flight_ad.cluster import DBSCAN
from flight_ad.learn import FunctionTransformer
from flight_ad.learn import StatisticalLearner
from flight_ad.pipeline import AnomalyDetectionPipeline
from flight_ad.report import clustering_info, silhouette
# Binder
data_bindings = load_dashlink_bindings(download=True)
binder = DataBinder(data_bindings)
# Wrangler
wrangling_steps = [
('preprocess_flight', preprocess),
('resample_dataframe', resample),
('change_col', change_col),
('select_col', select)
]
wrangler = DataWrangler(wrangling_steps, memorize='change_col')
# Learner
learning_steps = {
'preprocessing': [
('reshaper', FunctionTransformer(reshape_df_interspersed)),
('scaler', StandardScaler()),
('pca', PCA())
],
'training': [
('dbscan', DBSCAN())
]
}
learner = StatisticalLearner(learning_steps, record='pca')
# Pipeline
ad_pipeline = AnomalyDetectionPipeline(binder, wrangler, learner)
ad_pipeline.fit()
# Results
labels, n_clusters, n_noise = clustering_info(learner.pipeline['dbscan'])
avg_silhouette, _ = silhouette(learner.partial_data['pca'], labels)
```
## Package structure
TBD.
## Dependencies
`flight-ad` requires:
- Python (>=3.6)
- NumPy
- pandas
- scikit-learn
- matplotlib
- tqdm
## Contributions
We welcome and encourage new contributors to help test `flight-ad` and add new functionality. Any input, feedback,
bug report or contribution is welcome.
If one wishes to contact the author, they may do so by emailing [email protected].
## Citation
If you use `flight-ad` in a scientific publication, we would appreciate citations.
BibTex: TBD.
Citation string: TBD.