Add explainer¶

Explainers are the heart of this benchmark, they are being thorougly tested and the more of them added, the more we know

Create explainer wrapper¶

Explainers wrappers are less demanding than model’s which makes them easier to be implemented

import numpy as np
from xaib import Explainer


class NewExplainer(Explainer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        # name is essential to put explainer into
        # results table correctly
        self.name = 'new_explainer'

    def predict(self, x, model, *args, **kwargs):
        return np.random.rand(len(x), len(x[0]))

Test new explainer¶

Before adding your implementation directly into source code, it would be useful to test how it will work with standard XAIB setup

from xaib.evaluation import DatasetFactory, ModelFactory
from xaib.evaluation.feature_importance import ExperimentFactory
from xaib.evaluation.utils import visualize_results


train_ds, test_ds = DatasetFactory().get('synthetic')
model = ModelFactory(train_ds, test_ds).get('svm')

explainers = {'new_explainer': NewExplainer()}

experiment_factory = ExperimentFactory(
    repo_path='results',
    explainers=explainers,
    test_ds=test_ds,
    model=model,
    batch_size=10
)

experiments = experiment_factory.get('all')
for name in experiments:
    experiments[name]()

visualize_results('results', 'results/results.png')

Integrate new explainer¶

Finally you can integrate your explainer into the source code. To do that you need to add it into xaib.explainers module and then make a constructor for the Factory.

# xaib/evaluation/feature_importance/explainer_factory.py
# ...
from ...explainers.feature_importance.new_explainer import NewExplainer
# ...

# Create a constructor - function that will build your explainer
def new_explainer():
    return NewExplainer()


class ExplainerFactory(Factory):
    def __init__(self) -> None:

        # ...
        # add it to the factory
        self._constructors['new_explainer'] = lambda: new_explainer()