vcalderon2009 / SDSS_Groups_ML

Estimation of Group Masses using a set of Machine-Learning techniques

SDSS_Groups_ML

Estimation of Group Masses using a set of Machine-Learning techniques

Author: Victor Calderon ([e-mail])

Installing Environment & Dependencies

To use the scripts in this repository, you must have Anaconda installed on the systems that will be running the scripts. This will simplify the process of installing all the dependencies.

For reference, see: https://conda.io/docs/user-guide/tasks/manage-environments.html

The package counts with a Makefile with useful functions. You must use this Makefile to ensure that you have all the necessary dependencies, as well as the correct conda environment.

• Show all available functions in the Makefile

$:  make show-help
    
    Available rules:
    
    clean               Delete all compiled Python files
    environment         Set up python interpreter environment - Using environment.yml
    lint                Lint using flake8
    remove_environment  Delete python interpreter environment
    test_environment    Test python environment is setup correctly
    update_environment  Update python interpreter environment

• Create the environment from the environment.yml file:

    make environment

• Activate the new environment sdss_groups_ml.

    source activate sdss_groups_ml

• To update the environment.yml file (when the required packages have changed):

  make update_environment

• Deactivate the new environment:

    source deactivate

Auto-activate environment

To make it easier to activate the necessary environment, one can check out conda-auto-env, which activates the necessary environment automatically.

Usage

Project Organization

├── LICENSE
├── Makefile           <- Makefile with commands like `make data` or `make train`
├── README.md          <- The top-level README for developers using this project.
├── data
│   ├── external       <- Data from third party sources.
│   ├── interim        <- Intermediate data that has been transformed.
│   ├── processed      <- The final, canonical data sets for modeling.
│   └── raw            <- The original, immutable data dump.
│
├── docs               <- A default Sphinx project; see sphinx-doc.org for details
│
├── models             <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks          <- Jupyter notebooks. Naming convention is a number (for ordering),
│                         the creator's initials, and a short `-` delimited description, e.g.
│                         `1.0-jqp-initial-data-exploration`.
│
├── references         <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures        <- Generated graphics and figures to be used in reporting
│
├── requirements.txt   <- The requirements file for reproducing the analysis environment, e.g.
│                         generated with `pip freeze > requirements.txt`
│
├── src                <- Source code for use in this project.
│   ├── __init__.py    <- Makes src a Python module
│   │
│   ├── data           <- Scripts to download or generate data
│   │   │
│   │   ├── utilities_python    <- General Python scripts to make the flow of the project a little easier.
│   │   │
│   │   └── make_dataset.py
│   │
│   ├── features       <- Scripts to turn raw data into features for modeling
│   │   └── build_features.py
│   │
│   ├── models         <- Scripts to train models and then use trained models to make
│   │   │                 predictions
│   │   ├── predict_model.py
│   │   └── train_model.py
│   │
│   └── visualization  <- Scripts to create exploratory and results oriented visualizations
│       └── visualize.py
│
└── tox.ini            <- tox file with settings for running tox; see tox.testrun.org

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Project based on the cookiecutter data science project template. #cookiecutterdatascience