An ergonomic machine learning library for non-technical users. Save time. Blaze through ML.
Install latest release version:
pip install -U libra
Install directory from github:
git clone https://github.com/Palashio/libra.git cd libra pip install .
Alternatively you can build and use the docker image locally with:
docker build . -f docker/libra-normal/Dockerfile -t libra docker run -v /path/to/my/data:/data -it --rm libra
Or if you have nvidia-docker installed.
docker build . -f docker/libra-gpu/Dockerfile -t libra-gpu docker run -v /path/to/my/data:/data --gpus all -it --rm libra-gpu
The core functionality of libra works through the
client object. A new client object should be created for every dataset that you want to produce results for. All information about the models that're built, the plots that are generated, and the metrics are created will be stored in the object.
You can then call different queries on that client object, and the dataset you passed to it will be used.
from libra import client newClient = client('path/to/dataset') newClient.neural_network_query('please model the median number of households')
will return a dictionary of all the information that was generated:
dict_keys(['id', 'model', 'num_classes', 'plots', 'target', 'preprocesser', 'interpreter', 'test_data', 'losses', 'accuracy'])
Other queries can also be called on the same object, and will be appended to the
newClient.svm_query('predict the proximity to the ocean') newClient.model().keys() dict_keys(['regression_ANN', svm'])
Welcome to the Libra community!
Shoot me an email at email@example.com if you'd like to get in touch!
Don't forget to tag @Palashio in your comment, otherwise they may not be notified.