twinlab 1.1.3

Client interface for twinLab machine-learning in the cloud.

twinLab Client

Headless interface to the twinLab library.

Installation

Most users should use pip

pip install twinlab

If you want to modify the client-side code, or have a local installation, you will need to have git, poetry, and a python version of 3.9 or higher installed. Then you can do:

git clone https://github.com/digiLab-ai/twinLab-client.git
cd twinlab-client
poetry install

Environment setup

You will need a .env file in your project directory that looks like the .env.example file in this repository

cp .env.example .env

and fill in your twinLab user details.

Commands

Testing:

poetry run python scripts/test.py

where test.py can be replaced with any of the scripts in the script directory.

Example

Here we create some mock data (which has a quadratic relationship between X and y) and use twinLab to create a surrogate model with quantified uncertainty.

# Import libraries
import twinlab as tl
import pandas as pd

# Create a dataset and upload to the twinLab cloud
df = pd.DataFrame({'X': [1, 2, 3, 4], 'y': [1, 4, 9, 16]})
tl.upload_dataset(df, 'test.csv')

# Train a machine-learning model for the data
params = {
    'filename': 'test.csv',
    'inputs': ['X'],
    'outputs': ['y'],
}
tl.train_campaign(params, campaign='test')

# Evaluate the model on some unseen data
df = pd.DataFrame({'X': [1.5, 2.5, 3.5]})
df_mean, df_std = tl.predict_campaign(df, campaign='test')

Notebooks

Check out the notebooks directory for some additional examples to get started!

Documentation

See the live documentation at https://digilab-ai.github.io/twinLab-client/. Or build a copy locally:

cd docs
yarn start