# Machine Learming benchmarks

Aiming to thouroughly benchmark and compare ML algorithms (with current focus on HTM), be designing specialized synthetic datasets that stress a single feature and can be well evaluated and understood.

For users being able to decide where each algorithm has its strong/weak-spots and decide in application for real-world problems.

This can also work as a benchmark to evaluate development impact in changes to the algorithms.

Goals of this project

This repository should be a collection of

• datasets
• real-world
• synthetic
• papers
• algorithm implementations
• (initially) focus on HTM from NuPIC, gladly include any other algorithms/results.
• results
• as CSV, image, ...
• collection of ideas in the Issues

Current state

• Anomalies classified in independent, significant categories -> anomaly categorization
• create synthetic benchmars/datasets for the anomaly categories:
• point anomalies
• interval/section anomalies
• "higher" trend (low freq. modulation) anomalies
• additional
• HTM benchmars on the above datasets
• Discussion of the results & suggested improvements
• Comparison with other well known ML approaches

Research interest

The topics of research interest are classified in Issue's labels, and are related to: NuPIC, dataset creation, novel research ideas, cognitive modeling, and so on...

How do I use/work with this repo?

• You can browse CSV and images in data/datasets/*/results/ folders.
• To modify or regenerate new datasets, go to datasets/generatingScripts/MAIN.m, you'll need Matlab/R for that.
• To rerun (HTM/NuPIC) models, python opf/anomaly_benchmark.py NuPIC has to be installed.
• For visualizing data, use plotResutls/plotDatasets.m or interactive tool nupic.visualizations, which can be run online

Results

• Evaluation results on benchmarks are located in results/ subfolders under respective paths and presented in form of CSV and image data.
• See Hypotheses for ongoing results.
• Ideas & findings from our work:

• Importance of noise/"non-integer f/fs sampling problem" for ease of learning and quality of abstraction in (artificial) benchmarks.
• NuPIC: AdaptiveScalar encoder is NOT suitable for streaming data, use RDSE instead.
• How to compute optimal resulution for RDSE
• NuPIC: Boosting implementation is causing artificial disturbances in the predictions (and can be improved, or should be turned off)
• NuPIC:
• good on point anomaly
• good on interval anomaly

Open-research & hypothesis

Warning: anything here may, or may not be true. It is under evaluation. We are raising the topics here to get your focus on the current issues and possible findings.

• NuPIC: AnomalyLikelihood implementation is inferior to the "raw" Anomaly, esp. with combination of noisy data that distort the internal distribution model.
• Impact of parameter optimization (swarming) on NuPIC's performance
• NuPIC: failing to abstract on trend data?

## Sources

1. Hierarchical Temporal Memory, Numenta. Available at: http://numenta.org/resources/HTM_CorticalLearningAlgorithms.pdf

2. Hawkins, Jeff (2004). On Intelligence, Times Books. ISBN 0805074562.

3. Uhl, Christian (1999). Analysis of Neurophysiological Brain Functioning, Springer. ISBN 978-3-642-64219-7

4. The Sicence of Anomaly Detection, Numenta. Available at: http://numenta.com/assets/pdf/whitepapers/Numenta%20White%20Paper%20-%20Science%20of%20Anomaly%20Detection.pdf

5. Schmidhuber, Jürgen (2014). Deep Learning in Neural Networks: An Overview, The Swiss AI Lab IDSIA. Available at: http://arxiv.org/pdf/1404.7828v4.pdf

6. Twitter, Anomaly Detection. Available at: https://blog.twitter.com/2015/introducing-practical-and-robust-anomaly-detection-in-a-time-series

7. Skyline, Anomaly Detection. Available at: https://github.com/etsy/skyline

8. Yahoo, Time Series Anomaly Detection. Available at: http://yahoolabs.tumblr.com/post/114590420346/a-benchmark-dataset-for-time-series-anomaly

Related sources

• Numenta Anomaly Benchmark/NAB is a collection of streaming, real-time, real-world datasets and benchmarks.

Acknowledgement