Add model¶

New models and model classes provide information on how good explainers are in some particular cases.

Create model wrapper¶

First model wrapper should be implemented. It has many required methods that should be implemented. For example fit and evaluate methods are needed to be able to train the model on different datasets see specification in xaib/base and examples in xaib/evaluation/model_factory.py

import numpy as np
from xaib import Model


class NewModel(Model):
    """
    Here the documentation on model should be filled
    """
    def __init__(self, const, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self.const = const

        # It is important to set the name
        # the name will be used to identify a model
        self.name = 'new_model'

    def fit(self, x, y):
        pass

    def evaluate(self, x, y):
        pass

    def predict(self, x):
        return np.array([self.const for _ in range(len(x))])

    def save(self, filepath, *args, **kwargs):
        with open(filepath, 'w') as f:
            f.write(str(self.const))

    def load(self, filepath, *args, **kwargs):
        with open(filepath, 'r') as f:
            self.const = float(f.read())
        # load does not return anything - just fills
        # internal state

Test new model¶

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
from xaib.evaluation.feature_importance import ExplainerFactory, ExperimentFactory
from xaib.evaluation.utils import visualize_results


# Create your model
model = NewModel(const=1)

train_ds, test_ds = DatasetFactory().get('synthetic')
explainers = {'shap': ExplainerFactory(train_ds, model, labels=[0, 1]).get('shap')}

experiment_factory = ExperimentFactory(
    repo_path='results',
    explainers=explainers,
    test_ds=test_ds,
    model=model, # and put it here
    batch_size=10
)

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


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

Integrate new model¶

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

# xaib/evaluation/model_factory.py
# ...
from ..models.new_model import NewModel
# ...

# Create a constructor - function that will build your model
def new_model(const):
    return NewModel(const=const)


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

        # ...
        # add it to the factory
        self._constructors['new_model'] = lambda: new_model(const=1)