Ontolearn is an open-source software library for explainable structured machine learning in Python.
git clone https://github.com/dice-group/Ontolearn.git
cd Ontolearn
conda create --name temp python=3.8
conda activate temp
conda env update --name temp
python -c 'from setuptools import setup; setup()' develop
python -c "import ontolearn"
python -m pytest tests # Partial test with pytest
tox # full test with toxpip install ontolearn # currently it is only a place holder https://pypi.org/project/ontolearn/See the manual, tests and examples folder for details.
from ontolearn.concept_learner import CELOE
from ontolearn.model_adapter import ModelAdapter
from owlapy.model import OWLNamedIndividual, IRI
from owlapy.namespaces import Namespaces
from owlapy.render import DLSyntaxObjectRenderer
from examples.experiments_standard import ClosedWorld_ReasonerFactory
NS = Namespaces('ex', 'http://example.com/father#')
positive_examples = {OWLNamedIndividual(IRI.create(NS, 'stefan')),
OWLNamedIndividual(IRI.create(NS, 'markus')),
OWLNamedIndividual(IRI.create(NS, 'martin'))}
negative_examples = {OWLNamedIndividual(IRI.create(NS, 'heinz')),
OWLNamedIndividual(IRI.create(NS, 'anna')),
OWLNamedIndividual(IRI.create(NS, 'michelle'))}
# Only the class of the learning algorithm is specified
model = ModelAdapter(learner_type=CELOE,
reasoner_factory=ClosedWorld_ReasonerFactory,
path="KGs/father.owl")
model.fit(pos=positive_examples,
neg=negative_examples)
dlsr = DLSyntaxObjectRenderer()
for desc in model.best_hypotheses(1):
print('The result:', dlsr.render(desc.concept), 'has quality', desc.quality)Some resources like pre-calculated embeddings or pre_trained_agents
are not included in the Git repository directly. Use the following
command to download them from our data server.
./big_gitext/download_big.sh examples/pre_trained_agents.zip.link
./big_gitext/download_big.sh -A # to download them all into examples folderTo update or upload resource files, follow the instructions here and use the following command.
./big_gitext/upload_big.sh pre_trained_agents.ziptox -e buildtox -e docsFeel free to create a pull request
Run
tox -e lint --This will run flake8 on the source code.
For any further questions, please contact: onto-learn@lists.uni-paderborn.de