ProtoDash

View colab tutorial | View source | 📰 Paper

ProtoDash associated non-negative weights to prototypes which are indicative of their importance. This approach allows for identifying both prototypes and criticisms (the least weighted examples among prototypes) by maximmizing the same weighted objective function.

Quote

Our work notably generalizes the recent work by Kim et al. (2016) where in addition to selecting prototypes, we also associate non-negative weights which are indicative of their importance. This extension provides a single coherent framework under which both prototypes and criticisms (i.e. outliers) can be found. Furthermore, our framework works for any symmetric positive definite kernel thus addressing one of the key open questions laid out in Kim et al. (2016).

-- Efficient Data Representation by Selecting Prototypes with Importance Weights (2019).

More precisely, the weighted objective \(F(\mathcal{P},w)\) is defined as: \begin{equation}
F(\mathcal{P},w)=\frac{2}{n}\sum_{i,j=1}^{|\mathcal{P}|,n}w_i\kappa(p_i,x_j)-\sum_{i,j=1}^{|\mathcal{P}|}w_iw_j\kappa(p_i,p_j), \end{equation} where \(w\) are non-negative weights for each prototype. The problem then consist on finding a subset \(\mathcal{P}\) with a corresponding \(w\) that maximizes \(J(\mathcal{P}) \equiv \max_{w:supp(w)\in \mathcal{P},w\ge 0} J(\mathcal{P},w)\) s.t. \(|\mathcal{P}| \leq m=m_p+m_c\).

Info

For ProtoDash, any kernel can be used, as these methods rely on weak submodularity instead of full submodularity.

Example

from xplique.example_based import ProtoDash
from xplique.example_based.projections import LatentSpaceProjection

# load the training dataset and the model
cases_dataset = ... # load the training dataset
model = ...

# load the test samples
test_samples = ... # load the test samples to search for

# parameters
case_returns = "all"  # elements returned by the explain function
latent_layer = "last_conv"  # where to split your model for the projection
nb_global_prototypes = 5
nb_local_prototypes = 1
kernel_fn = None  # the default rbf kernel will be used, the distance will be based on this

# construct a projection with your model
projection = LatentSpaceProjection(model, latent_layer=latent_layer)

protodash = ProtoDash(
    cases_dataset=cases_dataset,
    nb_global_prototypes=nb_global_prototypes,
    nb_local_prototypes=nb_local_prototypes,
    projection=projection,
    case_returns=case_returns,
)

# compute global explanation
global_prototypes = protodash.get_global_prototypes()

# compute local explanation
local_prototypes = protodash.explain(test_samples)

Notebooks

• Example-based: Prototypes

ProtoDash

__init__(self,
         cases_dataset: ~DatasetOrTensor,
         labels_dataset: Optional[~DatasetOrTensor] = None,
         targets_dataset: Optional[~DatasetOrTensor] = None,
         nb_global_prototypes: int = 1,
         nb_local_prototypes: int = 1,
         projection: Union[xplique.example_based.projections.base.Projection, Callable] = None,
         case_returns: Union[List[str], str] = 'examples',
         batch_size: Optional[int] = None,
         distance: Union[int, str, Callable, None] = None,
         kernel_fn: = None,
         gamma: float = None)

explain(self,
        inputs: Union[tensorflow.python.framework.tensor.Tensor, numpy.ndarray],
        targets: Union[tensorflow.python.framework.tensor.Tensor, numpy.ndarray, None] = None)

Return the relevant examples to explain the (inputs, targets). It projects inputs with self.projection in the search space and find examples with the self.search_method.

Parameters

• inputs : Union[tensorflow.python.framework.tensor.Tensor, numpy.ndarray]

  • Tensor or Array. Input samples to be explained.

    Expected shape among (N, W), (N, T, W), (N, W, H, C).

    More information in the documentation.

• targets : Union[tensorflow.python.framework.tensor.Tensor, numpy.ndarray, None] = None

  • Targets associated to the inputs for projection.

    Shape: (n, nb_classes) where n is the number of samples and nb_classes is the number of classes.

    It is used in the projection. But projection can compute it internally.

Return

• return_dict

  • Dictionary with listed elements in self.returns.

    The elements that can be returned are defined with the _returns_possibilities static attribute of the class.

format_search_output(self,
                     search_output: Dict[str, tensorflow.python.framework.tensor.Tensor],
                     inputs: Union[tensorflow.python.framework.tensor.Tensor, numpy.ndarray])

Format the output of the search_method to match the expected returns in self.returns.

Parameters

• search_output : Dict[str, tensorflow.python.framework.tensor.Tensor]

  • Dictionary with the required outputs from the search_method.

• inputs : Union[tensorflow.python.framework.tensor.Tensor, numpy.ndarray]

  • Tensor or Array. Input samples to be explained.

    Expected shape among (N, W), (N, T, W), (N, W, H, C).

Return

• return_dict

  • Dictionary with listed elements in self.returns.

    The elements that can be returned are defined with the _returns_possibilities static attribute of the class.

get_global_prototypes(self) -> Dict[str, tensorflow.python.framework.tensor.Tensor]

Provide the global prototypes computed at the initialization. Prototypes and their labels are extracted from the indices. The weights of the prototypes and their indices are also returned.

Return

• prototypes_dict : Dict[str, tf.Tensor]

  • A dictionary with the following - 'prototypes': The prototypes found by the method.

    - 'prototype_labels': The labels of the prototypes.

    - 'prototype_weights': The weights of the prototypes.

    - 'prototype_indices': The indices of the prototypes.

---

1. Visual Explanations from Deep Networks via Gradient-based Localization (2016). ↩