curia 0.0.33

A library for training and using risk & impactability models on Curia

Curia Python SDK

Curia Python SDK is a library for training and using risk & impactability models on Curia.

For detailed documentation, including the API reference, see our docs at https://foundryai.github.io/curia-python-sdk/.

Installing the Curia Python SDK

The Curia Python SDK is built to PyPi and can be installed with pip as follows:

pip install curia

You can install from source by cloning this repository and running a pip install command in the root directory of the repository:

git clone https://github.com/FoundryAI/curia-python-sdk.git
cd curia-python-sdk
pip install .

Supported Operating Systems

Curia Python SDK supports Unix/Linux and Mac.

Supported Python Versions

Curia Python SDK is tested on:

• Python 3.6
• Python 3.7
• Python 3.8

Curia Permissions

Curia Python SDK will utilize the Curia Platform when training models and generating predictions. You will need access to the platform with appropriate permissions to fully utilize the SDK.

Running tests

Curia Python SDK has unit tests. To run the tests:

python setup.py pytest

Building Sphinx docs

Curia Python SDK has Sphinx docs. To build the docs run:

cd doc
make html

To preview the site with a Python web server:

cd docs/_build/html
python -m http.server 8000

View the docs by visiting http://localhost:8080

Curia API Token

To use the Curia Python SDK you will need a Curia API Token. To access your API Token visit https://app.curia.ai/settings.

Use gnu-sed

Visit https://medium.com/@bramblexu/install-gnu-sed-on-mac-os-and-set-it-as-default-7c17ef1b8f64 to see how to install gnu-sed for consistency in fixing swagger imports

Using the Curia Python SDK

from curia.session import Session
from curia.risk import RiskModel
from curia.synthetic_data import generate_data

# Create synthetic data (demo/testing purposes only)
(X_train, X_test, _, _, y_train, y_test, _, _, _, _) = generate_data(binary_outcome=True)

# Create a session
curia_session = Session(api_token="YOUR_API_TOKEN")

# Instantiate a model
model = RiskModel(
    session=curia_session, 
    name="your-model-name",
    project_id="YOUR_PROJECT_ID",
    environment_id="YOUR ENVIRONMENT_ID"
)

# Train a model on the Curia Platform
model.train(features=X_train, label=y_train)

# Get predictions from your model on the Curia Platform
predictions = model.predict(features=X_test)

TODO

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