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Lime pro

LimePro ¤

Bases: XAImethod_Base

XAI Method for LIME Pro.

Normally, this class is used internally in the aucmedi.xai.decoder.xai_decoder in the AUCMEDI XAI module.

Reference - Implementation

Lime: Explaining the predictions of any machine learning classifier
GitHub: https://github.com/marcotcr/lime

Reference - Publication

Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin. 9 Aug 2016. "Why Should I Trust You?": Explaining the Predictions of Any Classifier
https://arxiv.org/abs/1602.04938

This class provides functionality for running the compute_heatmap function, which computes a Lime Pro Map for an image with a model.

Source code in aucmedi/xai/methods/lime_pro.py
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class LimePro(XAImethod_Base):
    """ XAI Method for LIME Pro.

    Normally, this class is used internally in the [aucmedi.xai.decoder.xai_decoder][] in the AUCMEDI XAI module.

    ??? abstract "Reference - Implementation"
        Lime: Explaining the predictions of any machine learning classifier <br>
        GitHub: [https://github.com/marcotcr/lime](https://github.com/marcotcr/lime) <br>

    ??? abstract "Reference - Publication"
        Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin. 9 Aug 2016.
        "Why Should I Trust You?": Explaining the Predictions of Any Classifier
        <br>
        [https://arxiv.org/abs/1602.04938](https://arxiv.org/abs/1602.04938)

    This class provides functionality for running the compute_heatmap function,
    which computes a Lime Pro Map for an image with a model.
    """
    def __init__(self, model, layerName=None, num_samples=1000):
        """ Initialization function for creating a Lime Pro Map as XAI Method object.

        Args:
            model (keras.model):            Keras model object.
            layerName (str):                Not required in LIME Pro/Con Maps, but defined by Abstract Base Class.
            num_samples (int):              Number of iterations for LIME instance explanation.
        """
        # Cache class parameters
        self.model = model
        self.num_samples = num_samples

    #---------------------------------------------#
    #             Heatmap Computation             #
    #---------------------------------------------#
    def compute_heatmap(self, image, class_index, eps=1e-8):
        """ Core function for computing the Lime Pro Map for a provided image and for specific classification outcome.

        ???+ attention
            Be aware that the image has to be provided in batch format.

        Args:
            image (numpy.ndarray):              Image matrix encoded as NumPy Array (provided as one-element batch).
            class_index (int):                  Classification index for which the heatmap should be computed.
            eps (float):                        Epsilon for rounding.

        The returned heatmap is encoded within a range of [0,1]

        ???+ attention
            The shape of the returned heatmap is 2D -> batch and channel axis will be removed.

        Returns:
            heatmap (numpy.ndarray):            Computed Lime Pro Map for provided image.
        """
        # Initialize LIME explainer
        explainer = lime_image.LimeImageExplainer()
        explanation = explainer.explain_instance(image[0].astype("double"),
                                self.model.predict, hide_color=0,
                                labels=(class_index,),
                                num_samples=self.num_samples)
        # Obtain PRO explanation mask
        temp, mask = explanation.get_image_and_mask(class_index, hide_rest=True,
                                positive_only=True, negative_only=False)
        heatmap = mask
        # Intensity normalization to [0,1]
        numer = heatmap - np.min(heatmap)
        denom = (heatmap.max() - heatmap.min()) + eps
        heatmap = numer / denom
        # Return the resulting heatmap
        return heatmap

__init__(model, layerName=None, num_samples=1000) ¤

Initialization function for creating a Lime Pro Map as XAI Method object.

Parameters:

Name Type Description Default
model keras.model

Keras model object.

required
layerName str

Not required in LIME Pro/Con Maps, but defined by Abstract Base Class.

None
num_samples int

Number of iterations for LIME instance explanation.

1000
Source code in aucmedi/xai/methods/lime_pro.py
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def __init__(self, model, layerName=None, num_samples=1000):
    """ Initialization function for creating a Lime Pro Map as XAI Method object.

    Args:
        model (keras.model):            Keras model object.
        layerName (str):                Not required in LIME Pro/Con Maps, but defined by Abstract Base Class.
        num_samples (int):              Number of iterations for LIME instance explanation.
    """
    # Cache class parameters
    self.model = model
    self.num_samples = num_samples

compute_heatmap(image, class_index, eps=1e-08) ¤

Core function for computing the Lime Pro Map for a provided image and for specific classification outcome.

Attention

Be aware that the image has to be provided in batch format.

Parameters:

Name Type Description Default
image numpy.ndarray

Image matrix encoded as NumPy Array (provided as one-element batch).

required
class_index int

Classification index for which the heatmap should be computed.

required
eps float

Epsilon for rounding.

1e-08

The returned heatmap is encoded within a range of [0,1]

Attention

The shape of the returned heatmap is 2D -> batch and channel axis will be removed.

Returns:

Name Type Description
heatmap numpy.ndarray

Computed Lime Pro Map for provided image.

Source code in aucmedi/xai/methods/lime_pro.py
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def compute_heatmap(self, image, class_index, eps=1e-8):
    """ Core function for computing the Lime Pro Map for a provided image and for specific classification outcome.

    ???+ attention
        Be aware that the image has to be provided in batch format.

    Args:
        image (numpy.ndarray):              Image matrix encoded as NumPy Array (provided as one-element batch).
        class_index (int):                  Classification index for which the heatmap should be computed.
        eps (float):                        Epsilon for rounding.

    The returned heatmap is encoded within a range of [0,1]

    ???+ attention
        The shape of the returned heatmap is 2D -> batch and channel axis will be removed.

    Returns:
        heatmap (numpy.ndarray):            Computed Lime Pro Map for provided image.
    """
    # Initialize LIME explainer
    explainer = lime_image.LimeImageExplainer()
    explanation = explainer.explain_instance(image[0].astype("double"),
                            self.model.predict, hide_color=0,
                            labels=(class_index,),
                            num_samples=self.num_samples)
    # Obtain PRO explanation mask
    temp, mask = explanation.get_image_and_mask(class_index, hide_rest=True,
                            positive_only=True, negative_only=False)
    heatmap = mask
    # Intensity normalization to [0,1]
    numer = heatmap - np.min(heatmap)
    denom = (heatmap.max() - heatmap.min()) + eps
    heatmap = numer / denom
    # Return the resulting heatmap
    return heatmap