Welcome to Conmo documentation!

Release v1.0.1.

What is Conmo?

Conmo is a framework developed in Python whose main objective is to facilitate the execution and comparison of different experiments mainly related to Anomaly Detection and Condition Monitoring. These experiments consist of a series of concatenated stages forming a pipeline architecture, i.e. the output of one stage is the input of the next one. This framework aims to provide a way to standarize machine learning experiments, thus being able to reconstruct result tables of scientific papers.

User Guide

• Quickstart Guide
  • Requirements
  • Installation
  • Overview
  • Running an experiment

Examples

• Examples
  • NASA TurboFan Degradation
  • Batteries Degradation
  • Server Machine Dataset with PCAMahalanobis

API Reference

• API Reference
  • conmo.experiments
  • conmo.datasets
  • conmo.splitters
  • conmo.preprocesses
  • conmo.algorithms
  • conmo.metrics

Development Guide

• Development guide
  • Possibilities of Conmo
  • Add a new dataset
  • Add a new algorithm
  • Add a new splitter
  • Add a new preprocess
  • Add a new metric
  • CSV dataset import example
  • Coding conventions

Known Issues & Limitations

• Known Issues & Limitations

Frequently Asked Questions

• Frequently Asked Questions
  • How can I contribute to Conmo?
  • I don’t have a great knowledge of programming, can I still use Conmo?

Release Notes

• Release Notes
  • Conmo 1.0.1
  • Conmo 1.0.0