# Bayesian Histogram-based Anomaly Detection (BHAD)
Python implementation of the BHAD algorithm as presented in [Vosseler, A. (2023): BHAD: Explainable anomaly detection using Bayesian histograms](https://www.researchgate.net/publication/364265660_BHAD_Fast_unsupervised_anomaly_detection_using_Bayesian_histograms). The ***bhad* package** follows Scikit-learn's standard API for [outlier detection](https://scikit-learn.org/stable/modules/outlier_detection.html).
<!--- The *bhad* package has been presented on *PyCon DE & PyData Berlin 2023*, you can watch the presentation [here](https://vimeo.com/user/171811262/folder/15825490). -->
## Installation
```bash
pip install bhad
```
## Usage
1.) Preprocess the input data: discretize continuous features and conduct Bayesian model selection (optionally).
2.) Train the model using discrete data.
For convenience these two steps can be wrapped up via a scikit-learn pipeline (optionally).
```python
from bhad.model import BHAD
from bhad.utils import Discretize
from sklearn.pipeline import Pipeline
num_cols = [....] # names of numeric features
cat_cols = [....] # categorical features
pipe = Pipeline(steps=[
('discrete', Discretize(nbins = None)),
('model', BHAD(contamination = 0.01, num_features = num_cols, cat_features = cat_cols))
])
```
For a given dataset get binary model decisons:
```python
y_pred = pipe.fit_predict(X = dataset)
```
Get global model explanation as well as for individual observations:
```python
from bhad.explainer import Explainer
local_expl = Explainer(pipe.named_steps['model'], pipe.named_steps['discrete']).fit()
local_expl.get_explanation(nof_feat_expl = 5, append = False) # individual explanations
local_expl.global_feat_imp # global explanation
```
A detailed toy example using synthetic data for anomaly detection can be found [here](https://github.com/AVoss84/bhad/blob/main/src/notebooks/Toy_Example.ipynb) and an example using the Titanic dataset illustrating model explanability can be found [here](https://github.com/AVoss84/bhad/blob/main/src/notebooks/Titanic_Example.ipynb).
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"description": "# Bayesian Histogram-based Anomaly Detection (BHAD)\n\nPython implementation of the BHAD algorithm as presented in [Vosseler, A. (2023): BHAD: Explainable anomaly detection using Bayesian histograms](https://www.researchgate.net/publication/364265660_BHAD_Fast_unsupervised_anomaly_detection_using_Bayesian_histograms). The ***bhad* package** follows Scikit-learn's standard API for [outlier detection](https://scikit-learn.org/stable/modules/outlier_detection.html). \n<!--- The *bhad* package has been presented on *PyCon DE & PyData Berlin 2023*, you can watch the presentation [here](https://vimeo.com/user/171811262/folder/15825490). --> \n\n## Installation\n\n```bash\npip install bhad\n```\n\n## Usage\n\n1.) Preprocess the input data: discretize continuous features and conduct Bayesian model selection (optionally).\n\n2.) Train the model using discrete data.\n\nFor convenience these two steps can be wrapped up via a scikit-learn pipeline (optionally). \n\n```python\nfrom bhad.model import BHAD\nfrom bhad.utils import Discretize\nfrom sklearn.pipeline import Pipeline\n\nnum_cols = [....] # names of numeric features\ncat_cols = [....] # categorical features\n\npipe = Pipeline(steps=[\n ('discrete', Discretize(nbins = None)), \n ('model', BHAD(contamination = 0.01, num_features = num_cols, cat_features = cat_cols))\n])\n```\n\nFor a given dataset get binary model decisons:\n\n```python\ny_pred = pipe.fit_predict(X = dataset) \n```\n\nGet global model explanation as well as for individual observations:\n\n```python\nfrom bhad.explainer import Explainer\n\nlocal_expl = Explainer(pipe.named_steps['model'], pipe.named_steps['discrete']).fit()\n\nlocal_expl.get_explanation(nof_feat_expl = 5, append = False) # individual explanations\n\nlocal_expl.global_feat_imp # global explanation\n```\n\nA detailed toy example using synthetic data for anomaly detection can be found [here](https://github.com/AVoss84/bhad/blob/main/src/notebooks/Toy_Example.ipynb) and an example using the Titanic dataset illustrating model explanability can be found [here](https://github.com/AVoss84/bhad/blob/main/src/notebooks/Titanic_Example.ipynb).\n",
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