Client interface for twinLab machine-learning in the cloud.
Project description
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 for some reason
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 cloud
where test.py
can be replaced with any of the scripts in the script
directory.
You need to have a local server for the (private) twinlab-cloud
repository running for local testing. But local testing can then be run with
poetry run python scripts/test.py local
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 twinLab cloud
df = pd.DataFrame({'X': [1, 2, 3, 4], 'y': [1, 4, 9, 16]})
tl.upload_dataset('test.csv', df)
# 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
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