solubility_model

Exploring ML models to predict solubility of small organic compounds, trained on data provided by the AI4SD summer school 2022.

Installation guide

Prerequisites

• Conda
• Optional Mamba

Create environment

conda env create -f environment.yml
activate solubility_model

or

mamba env create -f environment.yml
activate solubility_model

The packages necessary to run the project are now installed inside the conda environment.

Note: The following sections assume you are located in your conda environment.

Set up project's module

To move beyond notebook prototyping, all reusable code should go into the solubility_model/ folder package. To use that package inside your project, install the project's module in editable mode, so you can edit files in the solubility_model folder and use the modules inside your notebooks :

pip install --editable .

To use the module inside your notebooks, add %autoreload at the top of your notebook :

%load_ext autoreload
%autoreload 2

Example of module usage :

from solubility_model.utils.paths import data_dir
data_dir()

Project Organization

├── 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.
│
├── 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.
│
├── environment.yml    <- The requirements file for reproducing the analysis environment.
│
├── .here              <- File that will stop the search if none of the other criteria
│                         apply when searching head of project.
│
├── setup.py           <- Makes project pip installable (pip install -e .)
│                         so solubility_model can be imported.
│
└── solubility_model               <- Source code for use in this project.
    ├── __init__.py    <- Makes solubility_model a Python module.
    │
    ├── data           <- Scripts to download or generate data.
    │   └── 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
    │
    ├── utils          <- Scripts to help with common tasks.
    |   └── paths.py   <- Helper functions to relative file referencing across project.
    │
    └── visualization  <- Scripts to create exploratory and results oriented visualizations.
        └── visualize.py

---

Project based on the cookiecutter data science project template.

Made by Jon Swain, contact at [email protected].