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.s2i seq2seq lstm outlier detector Jan 8, 2019
data
images
models
CoreSeq2SeqLSTM.py
OutlierSeq2SeqLSTM.py
README.md
__init__.py
doc.md
model.py
requirements.txt
seq2seq_lstm.ipynb
train.py
utils.py

README.md

Sequence-to-Sequence LSTM (seq2seq-LSTM) Outlier Detector

Description

Anomaly or outlier detection has many applications, ranging from preventing credit card fraud to detecting computer network intrusions.

The implemented seq2seq outlier detector aims to predict anomalies in a sequence of input features. The model can be trained in an unsupervised or semi-supervised way, which is helpful since labeled training data is often scarce. The outlier detector predicts whether the input features represent normal behaviour or not, dependent on a threshold level set by the user.

Implementation

The architecture of the seq2seq model is defined in model.py and it is trained by running the train.py script. The OutlierSeq2SeqLSTM class loads a pre-trained model and makes predictions on new data.

A detailed explanation of the implementation and usage of the seq2seq model as an outlier detector can be found in the seq2seq documentation.

Running on Seldon

An end-to-end example running a seq2seq outlier detector on GCP or Minikube using Seldon to identify anomalies in ECGs is available here.

Docker images to use the generic Mahalanobis outlier detector as a model or transformer can be found on Docker Hub:

A model docker image specific for the demo is also available: