Metadata-Version: 2.1
Name: emmv
Version: 0.0.0
Summary: Metrics for unsupervised anomaly detection models
Home-page: https://gitlab.com/chris.oleary/emmv
Author: Christian O'Leary
Author-email: christian.oleary@cit.ie
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: pandas
Requires-Dist: setuptools
Requires-Dist: scikit-learn

# EMMV

Implementation of EM/MV metrics based on N. Goix et al.

This is a means of evaluating anomaly detection models without anomaly labels

## Installation

```shell
pip install emmv
```

## Example Use

```python
from emmv import emmv_scores

test_scores = emmv_scores(model, features)
```

- Where 'model' is your **trained** scikit-learn model
- Where 'features' is a 2D dataframe of features (the *X* matrix)

Example resulting object:

```json
{ 
    "em": 0.77586,
    "mv": 0.25367
}
```

## Interpreting scores

- The best model should have the **highest** Excess Mass score
- The best model should have the **lowest** Mass Volume score
- Probably easiest to just use one of the metrics


