Try a method¶

All explanation methods in XAIB have the same input and output interface which allows to use them easily and compare. If you want to run an Explainer and see the results you can do this:

from xaib.explainers.feature_importance.lime_explainer import LimeExplainer
from xaib.evaluation import DatasetFactory, ModelFactory

# Get the dataset and train the model
train_ds, test_ds = DatasetFactory().get('synthetic')
model = ModelFactory(train_ds, test_ds).get('svm')

# You can also get the default one using ExplainerFactory
explainer = LimeExplainer(train_ds, labels=[0, 1])

# Obtain batch from dataset
sample = [test_ds[i]['item'] for i in range(10)]

# Obtain explanations
explanations = explainer.predict(sample, model)

print(explanations)