To get a copy of this repository type git clone https://github.com/MichelleLochner/ml_esac.git in the command line or click "Clone or download" and click "download zip" if you don't have git installed.
Key files:
machine_learning_notes.pdf -> The notes from the lecture (without the answers)
pre_workshop_questions.pdf -> A set of questions for you to spend ~30 minutes investigating that I'll be asking throughout the lecture
supernova_tutorial.ipynb -> A Jupyter Notebook tutorial for supernova classification with machine learning.
I strongly recommend using anaconda to run the tutorial code:
-
Install anaconda if you don't already have it (https://www.continuum.io/downloads)
-
Create a new anaconda environment by typing (inside the
ml_esacfolder):
conda env create --name ml --file environment.yml
- Activate the environment by typing:
source activate ml
Note: If you have tsch instead of bash this will not work!
A simple workaround is to manually edit your PATH environment variable to point to the new anaconda environment:
setenv PATH <your path to anaconda>/envs/snmachine/bin/:$PATH
If you don't want to use anaconda or create a separate environment, the requirements to run this tutorial code are
dependencies:
- python>=3
- astropy>=1.1.2
- jupyter>=1.0.0
- matplotlib>=1.5.1
- numpy>=1.11.0
- scikit-learn>=0.18.1
- scipy>=0.17.0
- iminuit>=0.12
- sncosmo>=1.3.0
The notebook has not been tested with python 2 but should still work.
Type jupyter notebook supernova_tutorial.ipynb into the command line after activating the environment.
There is also a very basic deep learning tutorial based on tflearn. You'll need to install tflearn yourself (pip install tflearn). WARNING: this thing is very slow unless you have a GPU and install the GPU version tensorflow. It's also highly incomplete so use at your own risk...