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MSDA - An open source, low-code time-series multi-sensor data analysis, unsupervised feature selection, deep unsupervised anomaly detection & explainable time-series predictor library in Python.
Readme
Prototype for unsupervised feature selection and/or unsupervised deep convolutional neural network & lstm autoencoders based real-time anomaly detection from high-dimensional heterogeneous/homogeneous time series multi-sensor data. Prototype of Explainable AI for the built time-series predictor.
Intuitive representation of the unsupervised feature selection is as shown below.
Intuitive representation of the unsupervised real-time point anomalies detection is as shown below.
From local explanations to global understanding with explainable AI for trees - motivation from here - https://www.nature.com/articles/s42256-019-0138-9
, Image credits - https://github.com/slundberg/shap
MSDA is an open source low-code
Multi-Sensor Data Analysis library in Python that aims to reduce the hypothesis to insights cycle time in a time-series multi-sensor data analysis & experiments. It enables users to perform end-to-end proof-of-concept experiments quickly and efficiently. The module identifies events in the multidimensional time series by capturing the variation and trend to establish relationship aimed towards identifying the correlated features helping in feature selection from raw sensor signals. Also, it provides a provision to precisely detect the anomalies in real-time streaming data an unsupervised deep convolutional neural network & also a lstm autoencoders based detectors are designed to run on GPU/CPU. Finally, a game theoretic approach is used to explain the output of the built anomaly detector model.
The package includes:-
MSDA is simple
, easy to use
and low-code
.
** Deep Convolutional Neural Network **
inspiration from this IEEE paper - https://ieeexplore.ieee.org/document/8581424
** LSTM Autoencoder **
inspiration from here - https://arxiv.org/pdf/1809.10717.pdf
The easiest way to install msda is using pip.
pip install msda
$ git clone https://github.com/ajayarunachalam/msda
$ cd msda
$ python setup.py install
!pip install msda
Follow the rest as demonstrated in the demo example for Unsupervised Feature Selection [here] -- https://github.com/ajayarunachalam/msda/blob/main/demo.ipynb
Follow the rest as demonstrated in the demo example for Unsupervised Deep Anomaly Detectors & Time series predictor as Explainable AI [here] -- https://github.com/ajayarunachalam/msda/blob/main/demo1_v1.ipynb
Most of the dependencies are installed automatically. But, if not installed when you install MSDA, then these dependencies must be installed as shown below.
pip install pandas
pip install numpy
pip install matplotlib
pip install datetime
pip install statistics
pip install torch
pip install seaborn
pip install sklearn
pip install scipy
pip install shap
pip install keras
pip install ipywidgets
Installation is only supported on 64-bit version of Python. Tested on numpy version '1.18.3', pandas <= '1.0.5', torch == 1.4.0, keras == 2.0.8, seaborn = '0.9.0', shap = '0.39.0', ipywidgets == 7.5.1
MSDA is an open source library that anybody can use. In our view, the ideal target audience of MSDA is:
Copyright 2021 Ajay Arunachalam ajay.arunachalam08@gmail.com
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. © 2021 GitHub, Inc.
FAQs
MSDA - An open source, low-code time-series multi-sensor data analysis, unsupervised feature selection, deep unsupervised anomaly detection & explainable time-series predictor library in Python.
We found that msda demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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