Anomaly detection with superexperts under delayed feedback

The visualisation of results from the paper is available here and the analysis of losses and classification metrics is available here. To run the project locally follow the installation instructions below.

Installation

Install anaconda or miniconda https://docs.conda.io/en/latest/miniconda.html.

Clone the repository (note that the flag --recursive is important as the repository contains the submodule NAB):

git clone --recursive https://github.com/alan-turing-institute/anomaly_with_experts.git anomaly_with_experts
cd anomaly_with_experts

Create a conda environment:

conda create -n anomaly_with_experts python=3.7

Activate the environment:

conda activate anomaly_with_experts

Use the package manager pip to install the requirements:

pip install -r requirements.txt

If you do not have Jupyter Notebook installed:

pip install notebook

To launch it:

jupyter notebook

After that, you should first run calculate_predictions.ipynb which calculates the predictions of Fixed-share and Variable-share on NAB and outputs the results. Then you can run results_analysis.ipynb to analyse the losses and classification metrics and results_plots.ipynb to visualise the plots from the paper. The main functions of the implementation are available in folder anomaly_delays.