Change AverageStability in fct of distance and radius

[DavidPetiteau]

class AverageStability(ExplainerMetric):
"""
Used to compute the average sensitivity metric (or stability). This metric ensure that close
inputs with similar predictions yields similar explanations. For each inputs we randomly
sample noise to add to the inputs and compute the explanation for the noisy inputs. We then
get the average distance between the original explanations and the noisy explanations.
Ref. Bhatt & al., Evaluating and Aggregating Feature-based Model Explanations (2020).
https://arxiv.org/abs/2005.00631 (def. 2)
Parameters
----------
model
Model used for computing metric.
inputs
Input samples under study.
targets
One-hot encoded labels or regression target (e.g {+1, -1}), one for each sample.
batch_size
Number of samples to explain at once, if None compute all at once.
radius
Radius defining the neighborhood of the inputs with respect to l1 distance.
distance
Distance metric between the explanations.
nb_samples
Number of different neighbors points to try on each input to measure the stability.
"""

def __init__(self,
             model: Callable,
             inputs: Union[tf.data.Dataset, tf.Tensor, np.ndarray],
             targets: Optional[Union[tf.Tensor, np.ndarray]] = None,
             batch_size: Optional[int] = 64,
             radius: float = 0.1,
             distance: Union[str, Callable] = 'l2',
             nb_samples: int = 20):
    # pylint: disable=R0913
    super().__init__(model, inputs, targets, batch_size)
    self.nb_samples = nb_samples

    if distance == 'l1':
        self.distance = lambda x, y: tf.reduce_sum(tf.abs(x - y))
    elif distance == 'l2':
        self.distance = lambda x, y: tf.reduce_sum((x-y)**2.0)
    elif hasattr(distance, '__call__'):
        self.distance = distance
    else:
        raise ValueError(f"{distance} is not a valid distance.")

    # prepare the noisy masks that will be used to generate the neighbors
    nb_variables = np.prod(inputs.shape[1:])
    if distance == 'l1':
        self.noisy_masks = tf.random.uniform((nb_samples, *inputs.shape[1:]), 0, radius/nb_variables)
    elif distance == 'l2':
        self.noisy_masks = tf.random.uniform((nb_samples, *inputs.shape[1:]), 0, np.sqrt(radius/nb_variables))
    elif hasattr(distance, '__call__'):
        # find the right radius by evaluating the distance when selecting inputs and by repeating the process
        epsilon = 1e-6
        radius_tp = radius
        radius_min = 0.
        radius_max = 10 * radius
        while np.array(self.distance(radius_max, np.zeros(nb_variables))) <= radius:
            radius_max *= 10
        while (np.abs(self.distance(radius_tp, np.zeros(nb_variables)) - radius) > epsilon) and (radius_max - radius_min > epsilon):
            if np.array(self.distance(radius_tp, np.zeros(nb_variables))) > radius:
                radius_max = radius_tp
                radius_tp = (radius_tp - radius_min)/2
            else:
                radius_min = radius_tp
                radius_tp = (radius_max - radius_tp)/2 + radius_min
        radius = radius_tp
        self.noisy_masks = tf.random.uniform((nb_samples, *inputs.shape[1:]), 0, radius)

def evaluate(self,

[fel-thomas]

Good catch, nevertheless, you have to re-open the PR with a real message explaining the changes, and include only the necessary commits, no old commits.
I'll re-open a new PR, rebase, include your change and put you on it. Also, I think adding a test would be good to avoid this mistake in the future.
Thanks David !

[fel-thomas]

See #83 :)