DeepFlarePred

Predicting the likelihood that a flare occur

By using the Liu dataset, that contains SHARP parameters (not images), we train a DNN to predict the likelihood of a flare erupting from a sunspot.

Installation

Conda install

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh

bash Miniconda3-latest-Linux-x86_64.sh

Git clone

git clone https://github.com/Dewald928/DeepFlarePred.git

Create virtual environment

Install conda and install environment:

conda env create -f environment.yml

Activate environment:

conda activate DeepFlarePred

If you have errors with the predefined validation set do get dev branch 0.9:

pip install git+https://github.com/skorch-dev/skorch.git

Download and prepare data

The Liu data should be downloaded from Liu dataset. For the main_LSTM_Liu.py and main_TCN_Liu.py the Liu dataset needs to be downloaded and extracted to the "/Data/Liu" folder such that:

./Data/
├── Liu
│   ├── C
│   ├── M
│   └── M5

To fix the incorrect column names used, run the following (Not needed if repo is cloned, dataset already the fixed version): python liu_data_fix

To create the normalized dataset run:

• for z_train: python normalize_z_train.py
• for z_minmax_train: python normalize_z_minmax_train.py
• fot z_minmax_all: cp Data/Liu/M5/ Data/Liu/z_minmax_all -rf

To create the power transformed dataset run: python normality_check.py

Run script

To run the script, you can either do a Weight & Biases sweep, or just simply run:

python main_TCN_Liu.py

The default configurations can be changed in config-defaults.yaml.

Docker cloud gpu setup (not working atm)

Build container

Follow instructions here

• Install docker
• Install GPU drivers
• Install nvidia docker Quickstart
• Then build dockerfile and push image to hub (useful Link1 Link2)

Dockerhub container

dvd928/deep_flare_pred:latest

Example of runstring on paperspace

paperspace jobs create --container dvd928/deep_flare_pred:latest --machineType P4000 --command wandb agent 5ks0xbql --ports 5000:5000 --project Liu_pytorch

Test/Analysis Scripts

Script	Description
cme_svm_updated_for_pyastro.ipynb	Example notebook of Bobra's CME SVM
data_aquisition_pipeline.ipynb	Notebook for generating Liu et al. data (WIP)
feature_selection.py	For Univariate Feature selection and RFE
inspectdata.py	Basic data analysis & Pair plot generation
nested_crossval.py	Example script for nested crossval
plot_classifier_comparison.py	Sklearn script
plot_cv_indices.py	Sklearn script
regression.py	Synthetic LSTM regression testing
roc_test.py	ROC vs. PR for imbalanced dataset
skorchCV.py	Used for generating Toy Unbalanced Classification
test_tcn.py	For analysis of TCN and 1D convolution using sequences
Titanic_Basic_Interpret.py	Captum Example
moving_std_protocol.py	Protocol for downloading wandb runs and model selection, based on smooth training
WNBtestscript.py	wandb setup script
workers_test.py	Pytorch optimal workers test

Plans for the Project

Preliminary tests

• Copy Liu's code to pytorch somewhat.
• Copy Liu architecture completely
• Cross-validation: Skorch library
• Regularization: L2 + Dropout
• Shuffled vs. Unshuffled. Shuffling is not very advers.
• GPU integration
• GPU optimization, just use larger batch sizes
• Implement Weight and Biases
• W&B sweeps check if it work
• W&B multiple gpu sweep. How to by using tmux
• Pytorch bottleneck test: Inconclusive, revisit
• Attention models Tested, but not sweeped
• Understand LSTM + TCN better
• GRU, LSTM and RNN switchable between
• hdf5 test script: Chen data uses hdf5, but unable to read the data
• MLP skorch test: RNN and custom logs not well supported
• TCN networks : better so far, slightly
• Early stopping and checkpointing on best validation TSS (LSTM only, so far)
• Test data? best wat to test network?
• LR scheduler

Main Objectives

• Create MLP that is equivalent to Nishzuka et al paper
• Establish a baseline MLP
• Understand TCN operation
• Synthetic dataset
• Change sequence length with TCN
• LSTM vs. TCN?
• TCN baseline (Liu dataset (20/40 features?))
• ROC + Precision Recall curves, with AUC (train, val & test set)
• Find best features out of the 40. (captum)
• Occlusions method compare.
• What do these best features mean? (fits with other literature?)
• SHARP only TCN
• Case studies
• What does TSS mean in this context?
• How to interpret W&B parameters?

Future plans

• SHARP query and infer model pipeline (un-normalize data)
• Incorporate SHARP magnetogram images like Chen article
• Use GAN for detecting anomalies
• MLP/LSTM attention models
• See a regression problem? LSTM regression is possible.

Questions

Data sets

• Chen et al. 2019 data (some of it)
• Liu dataset