flight-ad 0.0.1

flight-ad is a Python package for anomaly detection in the aviation domain built on top of scikit-learn.

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.

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.

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', FunctionEstimator(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 [e-mail].

Citation

If you use flight-ad in a scientific publication, we would appreciate citations.

BibTex: TBD.

Citation string: TBD.