Instance-wise Feature Importance in Time (FIT)

Overview

Instance-wise Feature Importance in Time (FIT)

FIT is a framework for explaining time series perdiction models, by assigning feature importance to every observation over time. paper

To run the experiments, you need a trained prediction model that takes in time series data as input, and generates a prediction over time. You also need the training data to train the FIT generator. Below are the instruction for replicating experiments in the paper.

Data preparation

Two different simulated datasets are used in the experiments. The process of creating the data is explained below.

Simulated dataset (State data):

Run the following script to create the data and the ground thruth explanations for the state experiment. You can choose the total number of samples in the dataset as well as the lenght of each recording. The defaults are set to 1000 samples of length 100.

python3 data_generator/state_data.py --signal_len LENGTH_OF_SIGNALS --signal_num TOTAL_NUMBER_OF_SAMPLES

Simulated dataset (Spike data):

python3 data_generator/simulations_threshold_spikes.py 

MIMIC ICU dataset:

You need to have the MIMICIII database running on a server. Run the following scripts to query and preprocess the ICU mortality data (This step might take a few hours)

python3 data_generator/icu_mortality.py --sqluser YOUR_USER --sqlpass YOUR_PASSWORD

Run the following scripts to query and preprocess the ICU mortality data (This step might take a few hours)

python3 data_generator/icu_mortality.py ---sqluser YOUR_USER --sqlpass YOUR_PASSWORD

Running the importance assignment baselines

For running the experiments, you need to train: 1) The black-box predictor model and 2) the conditional generator. You can do this by passing the --train argument. If a model and conditional generator is already trained, skip the '--train' argument. To generate explanations for test samples using any of the baselines and for your required dataset (simulation, simulation_spike, mimic), run the following module.

python3 -m evaluation.baselines --data DATASET_NAME --explainer EXPLAINER_MODEL --train

In addition to FIT, you can also run experiments on different baseline explainers such as retain, deep lift, feature occlusion, etc.

Owner
Sana
Sana
UAV-Networks-Routing is a Python simulator for experimenting routing algorithms and mac protocols on unmanned aerial vehicle networks.

UAV-Networks Simulator - Autonomous Networking - A.A. 20/21 UAV-Networks-Routing is a Python simulator for experimenting routing algorithms and mac pr

0 Nov 13, 2021
Official code for "Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021".

Simpler is Better: Few-shot Semantic Segmentation with Classifier Weight Transformer. ICCV2021. Introduction We proposed a novel model training paradi

Lucas 103 Dec 14, 2022
Unofficial Pytorch Lightning implementation of Contrastive Syn-to-Real Generalization (ICLR, 2021)

Unofficial Pytorch Lightning implementation of Contrastive Syn-to-Real Generalization (ICLR, 2021)

Gyeongjae Choi 17 Sep 23, 2021
This is the official code for the paper "Ad2Attack: Adaptive Adversarial Attack for Real-Time UAV Tracking".

Ad^2Attack:Adaptive Adversarial Attack on Real-Time UAV Tracking Demo video 📹 Our video on bilibili demonstrates the test results of Ad^2Attack on se

Intelligent Vision for Robotics in Complex Environment 10 Nov 07, 2022
Official PyTorch Implementation of Learning Architectures for Binary Networks

Learning Architectures for Binary Networks An Pytorch Implementation of the paper Learning Architectures for Binary Networks (BNAS) (ECCV 2020) If you

Computer Vision Lab. @ GIST 25 Jun 09, 2022
Dialect classification

Dialect-Classification This repository presents the data that was used in a talk at ICKL-5 (5th International Conference on Kurdish Linguistics) at th

Kurdish-BLARK 0 Nov 12, 2021
Train DeepLab for Semantic Image Segmentation

Train DeepLab for Semantic Image Segmentation Martin Kersner, [email protected]

Martin Kersner 172 Dec 14, 2022
LSTM-VAE Implementation and Relevant Evaluations

LSTM-VAE Implementation and Relevant Evaluations Before using any file in this repository, please create two directories under the root directory name

Lan Zhang 5 Oct 08, 2022
This code is part of the reproducibility package for the SANER 2022 paper "Generating Clarifying Questions for Query Refinement in Source Code Search".

Clarifying Questions for Query Refinement in Source Code Search This code is part of the reproducibility package for the SANER 2022 paper "Generating

Zachary Eberhart 0 Dec 04, 2021
Equivariant layers for RC-complement symmetry in DNA sequence data

Equi-RC Equivariant layers for RC-complement symmetry in DNA sequence data This is a repository that implements the layers as described in "Reverse-Co

7 May 19, 2022
Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Deepak Nandwani 1 Dec 31, 2021
TensorFlow implementation of ENet, trained on the Cityscapes dataset.

segmentation TensorFlow implementation of ENet (https://arxiv.org/pdf/1606.02147.pdf) based on the official Torch implementation (https://github.com/e

Fredrik Gustafsson 248 Dec 16, 2022
The code for our paper Semi-Supervised Learning with Multi-Head Co-Training

Semi-Supervised Learning with Multi-Head Co-Training (PyTorch) Abstract Co-training, extended from self-training, is one of the frameworks for semi-su

cmc 6 Dec 04, 2022
Keep CALM and Improve Visual Feature Attribution

Keep CALM and Improve Visual Feature Attribution Jae Myung Kim1*, Junsuk Choe1*, Zeynep Akata2, Seong Joon Oh1† * Equal contribution † Corresponding a

NAVER AI 90 Dec 07, 2022
Unofficial implementation of the paper: PonderNet: Learning to Ponder in TensorFlow

PonderNet-TensorFlow This is an Unofficial Implementation of the paper: PonderNet: Learning to Ponder in TensorFlow. Official PyTorch Implementation:

1 Oct 23, 2022
A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

Segnet is deep fully convolutional neural network architecture for semantic pixel-wise segmentation. This is implementation of http://arxiv.org/pdf/15

Pradyumna Reddy Chinthala 190 Dec 15, 2022
Production First and Production Ready End-to-End Speech Recognition Toolkit

WeNet 中文版 Discussions | Docs | Papers | Runtime (x86) | Runtime (android) | Pretrained Models We share neural Net together. The main motivation of WeN

2.7k Jan 04, 2023
Implementation for Paper "Inverting Generative Adversarial Renderer for Face Reconstruction"

StyleGAR TODO: add arxiv link Implementation of Inverting Generative Adversarial Renderer for Face Reconstruction TODO: for test Currently, some model

155 Oct 27, 2022
Collapse by Conditioning: Training Class-conditional GANs with Limited Data

Collapse by Conditioning: Training Class-conditional GANs with Limited Data Moha

Mohamad Shahbazi 33 Dec 06, 2022
A clear, concise, simple yet powerful and efficient API for deep learning.

The Gluon API Specification The Gluon API specification is an effort to improve speed, flexibility, and accessibility of deep learning technology for

Gluon API 2.3k Dec 17, 2022