Menelaus

Background

Menelaus implements algorithms for the purposes of drift detection. Drift detection is a branch of machine learning focused on the detection of unforeseen shifts in data. The relationships between variables in a dataset are rarely static and can be affected by changes in both internal and external factors, e.g. changes in data collection techniques, external protocols, and/or population demographics. Both undetected changes in data and undetected model underperformance pose risks to the users thereof. The aim of this package is to enable monitoring of data and of model performance.

The algorithms contained within this package were identified through a comprehensive literature survey. Menelaus’ aim was to implement drift detection algorithms that cover a range of statistical methodology. Of the algorithms identified, all are able to identify when drift is occurring; some can highlight suspicious regions of the data in which drift is more significant; and others can also provide model retraining recommendations.

Menelaus implements drift detectors for both streaming and batch data. In a streaming setting, data is arriving continuously and is processed one observation at a time. Streaming detectors process the data with each new observation that arrives and are intended for use cases in which instant analytical results are desired. In a batch setting, information is collected over a period of time. Once the predetermined set is “filled”, data is fed into and processed by the drift detection algorithm as a single batch. Within a batch, there is no meaningful ordering of the data with respect to time. Batch algorithms are typically used when it is more important to process large volumes of information simultaneously, where the speed of results after receiving data is of less concern.

Menelaus is named for the Odyssean hero that defeated the shapeshifting Proteus.

Detector List

Menelaus implements the following drift detectors.

Type	Detector	Abbreviation	Streaming	Batch
Change detection	Cumulative Sum Test	CUSUM	x
Change detection	Page-Hinkley	PH	x
Change detection	ADaptive WINdowing	ADWIN	x
Concept drift	Drift Detection Method	DDM	x
Concept drift	Early Drift Detection Method	EDDM	x
Concept drift	Linear Four Rates	LFR	x
Concept drift	Statistical Test of Equal Proportions to Detect concept drift	STEPD	x
Concept drift	Margin Density Drift Detection Method	MD3	x
Data drift	Confidence Distribution Batch Detection	CDBD		x
Data drift	Hellinger Distance Drift Detection Method	HDDDM		x
Data drift	kdq-Tree Detection Method	kdq-Tree	x	x
Data drift	PCA-Based Change Detection	PCA-CD	x
Ensemble	Streaming Ensemble	-	x
Ensemble	Batch Ensemble	-		x

The three main types of detector are described below. More details, including references to the original papers, can be found in the respective module documentation on ReadTheDocs.

Change detectors monitor single variables in the streaming context, and alarm when that variable starts taking on values outside of a pre-defined range.
Concept drift detectors monitor the performance characteristics of a given model, trying to identify shifts in the joint distribution of the data’s feature values and their labels. Note that change detectors can also be applied in this context.
Data drift detectors monitor the distribution of the features; in that sense, they are model-agnostic. Such changes in distribution might be to single variables or to the joint distribution of all the features.
Ensembles are groups of detectors, where each watches the same data, and drift is determined by combining their output. Menelaus implements a framework for wrapping detectors this way.

The detectors may be applied in two settings, as described in the Background section:

Streaming, in which each new observation that arrives is processed separately, as it arrives.
Batch, in which the data has no meaningful ordering with respect to time, and the goal is comparing two datasets as a whole.

Additionally, the library implements a kdq-Tree partitioner, for support of the kdq-Tree Detection Method. This data structure partitions a given feature space, then maintains a count of the number of samples from the given dataset that fall into each section of that partition. More details are given in the respective module.

Installation

Create a virtual environment as desired, then:

# for read-only, install from pypi:
pip install menelaus

# to allow editing, running tests, generating docs, etc.
# First, clone the git repo, then:
cd ./menelaus_clone_folder/
pip install -e .[dev]

Menelaus should work with Python 3.8 or higher.

Getting Started

Each detector implements the API defined by menelaus.detector: notably, they have an update method which allows new data to be passed, and a drift_state attribute which tells the user whether drift has been detected, along with (usually) other attributes specific to the detector class.

Generally, the workflow for using a detector, given some data, is as follows:

from menelaus.concept_drift import ADWINAccuracy
from menelaus.data_drift import KdqTreeStreaming
from menelaus.datasets import fetch_rainfall_data
from menelaus.ensemble import StreamingEnsemble, SimpleMajorityElection


# has feature columns, and a binary response 'rain'
df = fetch_rainfall_data()


# use a concept drift detector (response-only)
detector = ADWINAccuracy()
for i, row in df.iterrows():
    detector.update(X=None, y_true=row['rain'], y_pred=0)
    assert detector.drift_state != "drift", f"Drift detected in row {i}"


# use data drift detector (features-only)
detector = KdqTreeStreaming(window_size=5)
for i, row in df.iterrows():
    detector.update(X=df.loc[[i], df.columns != 'rain'], y_true=None, y_pred=None)
    assert detector.drift_state != "drift", f"Drift detected in row {i}"


# use ensemble detector (detectors + voting function)
ensemble = StreamingEnsemble(
  {
    'a': ADWINAccuracy(),
    'k': KdqTreeStreaming(window_size=5)
  },
  SimpleMajorityElection()
)

for i, row in df.iterrows():
    ensemble.update(X=df.loc[[i], df.columns != 'rain'], y_true=row['rain'], y_pred=0)
    assert ensemble.drift_state != "drift", f"Drift detected in row {i}"

As a concept drift detector, ADWIN requires both a true value (y_true) and a predicted value (y_predicted) at each update step. The data drift detector KdqTreeStreaming only requires the feature values at each step (X). More detailed examples, including code for visualizating drift locations, may be found in the examples directory, as stand-alone python scripts. The examples along with output can also be viewed on the RTD website.

Contributing

Install the library using the [dev] option, as above.

Testing

Unit tests can be run with the command pytest. By default, a coverage report with highlighting will be generated in htmlcov/index.html. These default settings are specified in setup.cfg under [tool:pytest].
Documentation

HTML documentation can be generated at menelaus/docs/build/html/index.html with:
```
cd docs/source
sphinx-build . ../build
```
If the example notebooks for the docs need to be updated, the corresponding python scripts in the examples directory should also be regenerated via:
```
cd docs/source/examples
python convert_notebooks.py
```
Note that this will require the installation of jupyter and nbconvert, which can be added to installation via pip install -e ".[dev, test]".
Formatting:

This project uses black, bandit, and flake8 for code formatting and linting, respectively. To satisfy these requirements when contributing, you may use them as the linter/formatter in your IDE, or manually run the following from the root directory:
```
flake8                 # linting
bandit -r ./menelaus        # security checks
black ./menelaus   # formatting
```

Copyright

Authors: Leigh Nicholl, Thomas Schill, India Lindsay, Anmol Srivastava, Kodie P McNamara, Shashank Jarmale.
©2022 The MITRE Corporation. ALL RIGHTS RESERVED
Approved for Public Release; Distribution Unlimited. Public Release
Case Number 22-0244.

References

Each detector’s documentation contains a reference link to the original paper. The bibliography is here.