WTTE-RNN a framework for churn and time to event prediction

Overview

WTTE-RNN

Build Status

Weibull Time To Event Recurrent Neural Network

A less hacky machine-learning framework for churn- and time to event prediction. Forecasting problems as diverse as server monitoring to earthquake- and churn-prediction can be posed as the problem of predicting the time to an event. WTTE-RNN is an algorithm and a philosophy about how this should be done.

Installation

Python

Check out README for Python package.

If this seems like overkill, the basic implementation can be found inlined as a jupyter notebook

Ideas and Basics

You have data consisting of many time-series of events and want to use historic data to predict the time to the next event (TTE). If you haven't observed the last event yet we've only observed a minimum bound of the TTE to train on. This results in what's called censored data (in red):

Censored data

Instead of predicting the TTE itself the trick is to let your machine learning model output the parameters of a distribution. This could be anything but we like the Weibull distribution because it's awesome. The machine learning algorithm could be anything gradient-based but we like RNNs because they are awesome too.

example WTTE-RNN architecture

The next step is to train the algo of choice with a special log-loss that can work with censored data. The intuition behind it is that we want to assign high probability at the next event or low probability where there wasn't any events (for censored data):

WTTE-RNN prediction over a timeline

What we get is a pretty neat prediction about the distribution of the TTE in each step (here for a single event):

WTTE-RNN prediction

A neat sideresult is that the predicted params is a 2-d embedding that can be used to visualize and group predictions about how soon (alpha) and how sure (beta). Here by stacking timelines of predicted alpha (left) and beta (right):

WTTE-RNN alphabeta.png

Warnings

There's alot of mathematical theory basically justifying us to use this nice loss function in certain situations:

loss-equation

So for censored data it only rewards pushing the distribution up, beyond the point of censoring. To get this to work you need the censoring mechanism to be independent from your feature data. If your features contains information about the point of censoring your algorithm will learn to cheat by predicting far away based on probability of censoring instead of tte. A type of overfitting/artifact learning. Global features can have this effect if not properly treated.

Status and Roadmap

The project is under development. The goal is to create a forkable and easily deployable model framework. WTTE is the algorithm but the whole project aims to be more. It's a visual philosophy and an opinionated idea about how churn-monitoring and reporting can be made beautiful and easy.

Pull-requests, recommendations, comments and contributions very welcome.

What's in the repository

  • Transformations
    • Data pipeline transformations (pandas.DataFrame of expected format to numpy)
    • Time to event and censoring indicator calculations
  • Weibull functions (cdf, pdf, quantile, mean etc)
  • Objective functions:
    • Tensorflow
    • Keras (Tensorflow + Theano)
  • Keras helpers
    • Weibull output layers
    • Loss functions
    • Callbacks
  • ~~ Lots of example-implementations ~~

Licensing

  • MIT license

Citation

@MastersThesis{martinsson:Thesis:2016,
    author = {Egil Martinsson},
    title  = {{WTTE-RNN : Weibull Time To Event Recurrent Neural Network}},
    school = {Chalmers University Of Technology},
    year   = {2016},
}

Contributing

Contributions/PR/Comments etc are very welcome! Post an issue if you have any questions and feel free to reach out to egil.martinsson[at]gmail.com.

Contributors (by order of commit)

  • Egil Martinsson
  • Dayne Batten (made the first keras-implementation)
  • Clay Kim
  • Jannik Hoffjann
  • Daniel Klevebring
  • Jeongkyu Shin
  • Joongi Kim
  • Jonghyun Park
Owner
Egil Martinsson
Egil Martinsson
Self-Supervised Speech Pre-training and Representation Learning Toolkit.

What's New Sep 2021: We host a challenge in AAAI workshop: The 2nd Self-supervised Learning for Audio and Speech Processing! See SUPERB official site

s3prl 1.6k Jan 08, 2023
Deformable DETR is an efficient and fast-converging end-to-end object detector.

Deformable DETR: Deformable Transformers for End-to-End Object Detection.

2k Jan 05, 2023
Depth image based mouse cursor visual haptic

Depth image based mouse cursor visual haptic How to run it. Install pyqt5. Install python modules pip install Pillow pip install numpy For illustrati

Xiong Jie 17 Dec 20, 2022
Graph parsing approach to structured sentiment analysis.

Fine-grained Sentiment Analysis as Dependency Graph Parsing This repository contains the code and datasets described in following paper: Fine-grained

Jeremy Barnes 36 Dec 12, 2022
Hydra Lightning Template for Structured Configs

Hydra Lightning Template for Structured Configs Template for creating projects with pytorch-lightning and hydra. How to use this template? Create your

Model-driven Machine Learning 4 Jul 19, 2022
CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation

CSKG: The CommonSense Knowledge Graph CSKG is a commonsense knowledge graph that combines seven popular sources into a consolidated representation: AT

USC ISI I2 85 Dec 12, 2022
A collection of Google research projects related to Federated Learning and Federated Analytics.

Federated Research Federated Research is a collection of research projects related to Federated Learning and Federated Analytics. Federated learning i

Google Research 483 Jan 05, 2023
CNN Based Meta-Learning for Noisy Image Classification and Template Matching

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

Kumar Manas 2 Dec 09, 2021
Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning

Manifold-SCA Research Artifact of USENIX Security 2022 Paper: Automated Side Channel Analysis of Media Software with Manifold Learning The repo is org

Yuanyuan Yuan 172 Dec 29, 2022
Another pytorch implementation of FCN (Fully Convolutional Networks)

FCN-pytorch-easiest Trying to be the easiest FCN pytorch implementation and just in a get and use fashion Here I use a handbag semantic segmentation f

Y. Dong 158 Dec 21, 2022
NeurIPS 2021, self-supervised 6D pose on category level

SE(3)-eSCOPE video | paper | website Leveraging SE(3) Equivariance for Self-Supervised Category-Level Object Pose Estimation Xiaolong Li, Yijia Weng,

Xiaolong 63 Nov 22, 2022
ImageNet Adversarial Image Evaluation

ImageNet Adversarial Image Evaluation This repository contains the code and some materials used in the experimental work presented in the following pa

Utku Ozbulak 11 Dec 26, 2022
GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily

GBK-GNN: Gated Bi-Kernel Graph Neural Networks for Modeling Both Homophily and Heterophily Abstract Graph Neural Networks (GNNs) are widely used on a

10 Dec 20, 2022
Convert game ISO and archives to CD CHD for emulation on Linux.

tochd Convert game ISO and archives to CD CHD for emulation. Author: Tuncay D. Source: https://github.com/thingsiplay/tochd Releases: https://github.c

Tuncay 20 Jan 02, 2023
A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets

HOW TO USE THIS PROJECT A Review of Deep Learning Techniques for Markerless Human Motion on Synthetic Datasets Based on DeepLabCut toolbox, we run wit

1 Jan 10, 2022
A simple Python library for stochastic graphical ecological models

What is Viridicle? Viridicle is a library for simulating stochastic graphical ecological models. It implements the continuous time models described in

Theorem Engine 0 Dec 04, 2021
PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation

StyleSpeech - PyTorch Implementation PyTorch Implementation of Meta-StyleSpeech : Multi-Speaker Adaptive Text-to-Speech Generation. Status (2021.06.13

Keon Lee 140 Dec 21, 2022
It's like Shape Editor in Maya but works with skeletons (transforms).

Skeleposer What is Skeleposer? Briefly, it's like Shape Editor in Maya, but works with transforms and joints. It can be used to make complex facial ri

Alexander Zagoruyko 1 Nov 11, 2022
Implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

PRP Introduction This is the implementation of our paper "Video Playback Rate Perception for Self-supervised Spatio-Temporal Representation Learning".

yuanyao366 39 Dec 29, 2022
ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

ALBERT ***************New March 28, 2020 *************** Add a colab tutorial to run fine-tuning for GLUE datasets. ***************New January 7, 2020

Google Research 3k Jan 01, 2023