🧰 TRAPI Predict Kit

A package to help create and deploy Translator Reasoner APIs (TRAPI) from any prediction model exposed as a regular python function.

The TRAPI Predict Kit helps data scientists to build, and publish prediction models in a FAIR and reproducible manner. It provides helpers for various steps of the process:

• A template to help user quickly bootstrap a new prediction project with the recommended structure (MaastrichtU-IDS/cookiecutter-trapi-predict-kit)
• Helper function to easily save a generated model, its metadata, and the data used to generate it. It uses tools such as dvc to store large model outside of the git repository.
• Deploy API endpoints for retrieving predictions, which comply with the NCATS Biomedical Data Translator standards (Translator Reasoner API and BioLink model), using a decorator @trapi_predict to simply annotate the function that produces predicted associations for a given input entity

Predictions are usually generated from machine learning models (e.g. predict disease treated by drug), but it can adapt to generic python function, as long as the input params and return object follow the expected structure.

Checkout the documentation website at maastrichtu-ids.github.io/trapi-predict-kit for more details.

📦️ Installation

This package requires Python >=3.7, simply install it with:

pip install trapi-predict-kit

To also include uvicorn/gunicorn for deployment:

pip install trapi-predict-kit[web]

🪄 Usage

🍪 Start a new prediction project

A template to help user quickly bootstrap a new prediction project with the recommended structure (MaastrichtU-IDS/cookiecutter-openpredict-api)

You can use our cookiecutter template to quickly bootstrap a repository with everything ready to start developing your prediction models, to then easily publish, and integrate them in the Translator ecosystem

pip install cookiecutter
cookiecutter https://github.com/MaastrichtU-IDS/cookiecutter-openpredict-api

🔮 Define the prediction endpoint(s)

The trapi_predict_kit package provides a decorator @trapi_predict to annotate your functions that generate predictions. Predictions generated from functions decorated with @trapi_predict can easily be imported in the Translator OpenPredict API, exposed as an API endpoint to get predictions from the web, and queried through the Translator Reasoner API (TRAPI).

The annotated predict functions are expected to take 2 input arguments: the input ID (string) and options for the prediction (dictionary). And it should return a dictionary with a list of predicted associated entities hits. Here is an example:

from trapi_predict_kit import trapi_predict, PredictInput, PredictOutput

@trapi_predict(
   path='/predict',
   name="Get predicted targets for a given entity",
   description="Return the predicted targets for a given entity: drug (DrugBank ID) or disease (OMIM ID), with confidence scores.",
   edges=[
       {
           'subject': 'biolink:Drug',
           'predicate': 'biolink:treats',
           'inverse': 'biolink:treated_by',
           'object': 'biolink:Disease',
       },
   ],
   nodes={
       "biolink:Disease": {
           "id_prefixes": [
               "OMIM"
           ]
       },
       "biolink:Drug": {
           "id_prefixes": [
               "DRUGBANK"
           ]
       }
   }
)
def get_predictions(request: PredictInput) -> PredictOutput:
   predictions = []
   # Add the code the load the model and get predictions here
   # Available props: request.subjects, request.objects, request.options
   for subj in request.subjects:
       predictions.append({
           "subject": subj,
           "object": "OMIM:246300",
           "score": 0.12345,
           "object_label": "Leipirudin",
           "object_type": "biolink:Drug",
       })
   for obj in request.objects:
       predictions.append({
           "subject": "DRUGBANK:DB00001",
           "object": obj,
           "score": 0.12345,
           "object_label": "Leipirudin",
           "object_type": "biolink:Drug",
       })
   return {"hits": predictions, "count": len(predictions)}

Define the TRAPI object

You will need to instantiate a TRAPI class to deploy a Translator Reasoner API serving a list of prediction functions that have been decorated with @trapi_predict. For example:

import logging

from trapi_predict_kit.config import settings
from trapi_predict_kit import TRAPI
# TODO: change to your module name
from my_model.predict import get_predictions

log_level = logging.INFO
logging.basicConfig(level=log_level)

openapi_info = {
    "contact": {
        "name": "Firstname Lastname",
        "email": "email@example.com",
        # "x-id": "https://orcid.org/0000-0000-0000-0000",
        "x-role": "responsible developer",
    },
    "license": {
        "name": "MIT license",
        "url": "https://opensource.org/licenses/MIT",
    },
    "termsOfService": 'https://github.com/your-org-or-username/my-model/blob/main/LICENSE.txt',

    "x-translator": {
        "component": 'KP',
        # TODO: update the Translator team to yours
        "team": [ "Clinical Data Provider" ],
        "biolink-version": settings.BIOLINK_VERSION,
        "infores": 'infores:openpredict',
        "externalDocs": {
            "description": "The values for component and team are restricted according to this external JSON schema. See schema and examples at url",
            "url": "https://github.com/NCATSTranslator/translator_extensions/blob/production/x-translator/",
        },
    },
    "x-trapi": {
        "version": settings.TRAPI_VERSION,
        "asyncquery": False,
        "operations": [
            "lookup",
        ],
        "externalDocs": {
            "description": "The values for version are restricted according to the regex in this external JSON schema. See schema and examples at url",
            "url": "https://github.com/NCATSTranslator/translator_extensions/blob/production/x-trapi/",
        },
    }
}

app = TRAPI(
    predict_endpoints=[ get_predictions ],
    info=openapi_info,
    title='OpenPredict TRAPI',
    version='1.0.0',
    openapi_version='3.0.1',
    description="""Machine learning models to produce predictions that can be integrated to Translator Reasoner APIs.
\n\nService supported by the [NCATS Translator project](https://ncats.nih.gov/translator/about)""",
    itrb_url_prefix="openpredict",
    dev_server_url="https://openpredict.semanticscience.org",
)

Deploy the API

Run the webserver with the path to the api file:

uvicorn src.my_model.api:app --port 8808 --reload

💾 Save a generated model

Helper function to easily save a generated model, its metadata, and the data used to generate it. It uses tools such as dvc to store large model outside of the git repository.

from trapi_predict_kit import save

hyper_params = {
    'penalty': 'l2',
    'dual': False,
    'tol': 0.0001,
    'C': 1.0,
    'random_state': 100
}

saved_model = save(
    model=clf,
    path="models/my_model",
    sample_data=sample_data,
    hyper_params=hyper_params,
    scores=scores,
)

🧑‍💻 Development setup

The final section of the README is for if you want to run the package in development, and get involved by making a code contribution.

📥️ Clone

Clone the repository:

git clone https://github.com/MaastrichtU-IDS/trapi-predict-kit
cd trapi-predict-kit

🐣 Install dependencies

Install Hatch, this will automatically handle virtual environments and make sure all dependencies are installed when you run a script in the project:

pip install --upgrade hatch

Install the dependencies in a local virtual environment:

hatch -v env create

To test it locally with python 3.7 use mamba or conda:

mamba create -n py37 python=3.7

🧑‍💻 Develop

Run the API for development defined in tests/dev.py:

hatch run api

☑️ Run tests

Make sure the existing tests still work by running pytest. Note that any pull requests to the fairworkflows repository on github will automatically trigger running of the test suite;

hatch run test

To display all logs when debugging:

hatch run test -s

🧹 Code formatting

The code will be automatically formatted when you commit your changes using pre-commit. But you can also run the script to format the code yourself:

hatch run fmt

📖 Update docs

Serve docs locally with mkdocs:

hatch run docs

The documentation website is automatically updated by a GitHub action workflow.

♻️ Reset the environment

In case you are facing issues with dependencies not updating properly you can easily reset the virtual environment with:

hatch env prune

🏷️ New release process

The deployment of new releases is done automatically by a GitHub Action workflow when a new release is created on GitHub. To release a new version:

1. Make sure the PYPI_TOKEN secret has been defined in the GitHub repository (in Settings > Secrets > Actions). You can get an API token from PyPI at pypi.org/manage/account.

2. Increment the version number in the pyproject.toml file in the root folder of the repository.

   hatch version fix

3. Create a new release on GitHub, which will automatically trigger the publish workflow, and publish the new release to PyPI.

You can also manually trigger the workflow from the Actions tab in your GitHub repository webpage.

Or use hatch:

hatch build
hatch publish -u "__token__"

And create the release with gh:

gh release create