semvec/pypi-packages huggingface dataset:
django-location-api 0.1.1
Django package for location services
Requires Python: <4,>=3.10

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Django Location API
===================

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Django package for location services.


* Free software: BSD-3-Clause
* Documentation: https://django-location-api.readthedocs.io.


django-location-api is a Django app for location services.

Detailed documentation is in the "docs" directory.

Quick start
-----------

1. Add "location_api" and the other required packages to your INSTALLED_APPS setting like this::

    INSTALLED_APPS = [
        ...,
        "rest_framework",
        "rest_framework_gis",
        "location_api"
    ]

2. Include the location_api URL conf in your project urls.py like this::

    path("api/", include("location_api.urls")),
    path("", include("location_api.urls.search")),

3. Run ``python manage.py migrate`` to create the location models.

4. Start the development server and visit the admin site to create a location.

5. Access the ``/locations/`` URL.

Development
-----------

This project uses `uv <https://docs.astral.sh/uv/>`_ for dependency management.

Install dependencies::

    uv sync

Run tests::

    docker compose run --rm django python ./runtests.py

Format code::

    uv run invoke format

Run linting::

    uv run invoke lint

Create migrations:

    docker compose run --rm django python example/manage.py makemigrations


Credits
-------

This package was created with Cookiecutter_ and the `briggySmalls/cookiecutter-pypackage`_ project template.

.. _Cookiecutter: https://github.com/audreyr/cookiecutter
.. _`briggySmalls/cookiecutter-pypackage`: https://github.com/briggySmalls/cookiecutter-pypackage
DrugAutoML 0.1.4
Automated Machine Learning pipeline for bioactivity prediction using molecular fingerprints
Requires Python: >=3.8

# DrugAutoML

**DrugAutoML** is an **end-to-end Automated Machine Learning (AutoML) pipeline** for **bioactivity prediction** in drug discovery.  
It automates every stage — from reading raw datasets to generating predictions for new molecules — and produces both **high-performance models** and **explainable outputs**.

---

## 🚀 Features

- **Data Preprocessing**  
  Reads raw datasets, cleans and standardizes SMILES, removes invalid molecules, and labels compounds as *active* or *inactive* based on pChEMBL cutoffs or existing binary labels.

- **Molecular Featurization**  
  Generates **ECFP (Extended-Connectivity Fingerprints)** using RDKit with customizable radius, bit size, and count-based features.

- **Data Splitting**  
  Splits data into training and testing sets using:
  - **Scaffold Split** (structure-aware)
  - **Stratified Random Split** (class-proportion preserving)

- **Model Selection**  
  Hyperparameter optimization with **Optuna** for:
  - Random Forest, Extra Trees, Logistic Regression, Linear SVC, XGBoost, LightGBM  
  Uses repeated stratified k-fold CV and produces a ranked **leaderboard**.

- **Model Finalization**  
  Trains the best model, applies **probability calibration**, selects optimal classification threshold, evaluates on the test set, and saves the model.

- **Explainability**  
  - **SHAP** global importance plots (beeswarm, bar, signed bar)  
  - **Bit Gallery** visualizations: highlights ECFP bits in test molecules that strongly influence predictions.

- **Prediction on New Data**  
  Scores unlabeled or labeled molecules, outputs probabilities and predictions, and computes metrics if labels are available.