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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/api_attributions/index.html b/v1.1.0/api/attributions/api_attributions/index.html new file mode 100755 index 00000000..7c0a1ecf --- /dev/null +++ b/v1.1.0/api/attributions/api_attributions/index.html @@ -0,0 +1,1553 @@ + + + + + + + + + + + + + + + + + + + + + + + API Description - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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API: Attributions Methods

+ +

Context

+

In 2013, Simonyan et al. proposed a first attribution method, opening the way to a wide range of approaches which could be defined as follow:

+
+

Definition

+

The main objective in attributions techniques is to highlight the discriminating variables for decision-making. For instance, with Computer Vision (CV) tasks, the main goal is to underline the pixels contributing the most in the input image(s) leading to the model’s output(s).

+
+

Common API

+

All attribution methods inherit from the Base class BlackBoxExplainer. This base class can be instanciated with three parameters:

+
    +
  • model: the model from which we want to obtain attributions (e.g: InceptionV3, ResNet, ...), see the model expectations for more details
    • +
  • batch_size: an integer which allows to either process inputs per batch (gradient-based methods) or process perturbed samples of an input per batch (inputs are therefore process one by one)
    • +
  • operator: function g to explain, see the Operator documentation for more details
    • +
+

In addition, all class inheriting from BlackBoxExplainer should implement an explain method:

+
@abstractmethod
+def explain(self,
+            inputs: Union[tf.data.Dataset, tf.Tensor, np.array],
+            targets: Optional[Union[tf.Tensor, np.array]] = None) -> tf.Tensor:
+    raise NotImplementedError()
+
+def __call__(self,
+             inputs: tf.Tensor,
+             labels: tf.Tensor) -> tf.Tensor:
+    """Explain alias"""
+    return self.explain(inputs, labels)
+
+

inputs: Must be one of the following: a tf.data.Dataset (in which case you should not provide targets), a tf.Tensor or a np.ndarray.

+
    +
  • +

    If inputs are images, the expected shape of inputs is \((N, H, W, C)\) following the TF's conventions where:

    +
      +
    • \(N\) is the number of inputs
      • +
    • \(H\) is the height of the images
      • +
    • \(W\) is the width of the images
      • +
    • \(C\) is the number of channels (works for \(C=3\) or \(C=1\), other values might not work or need further customization)
      • +
    +
    • +
  • +

    If inputs are tabular data, the expected shape of inputs is \((N, W)\) where:

    +
      +
    • \(N\) is the number of inputs
      • +
    • \(W\) is the feature dimension of a single input
      • +
    +
    +

    Tip

    +

    Please refer to the table to see which methods might work with Tabular Data

    +
    +
    • +
  • +

    (Experimental) If inputs are Time Series, the expected shape of inputs is \((N, T, W)\)

    +
      +
    • \(N\) is the number of inputs
      • +
    • \(T\) is the temporal dimension of a single input
      • +
    • +

      \(W\) is the feature dimension of a single input

      +
      +

      Warning

      +

      By default Lime & KernelShap will treat such inputs as grey images. You will need to define a custom map_to_interpret_space function when instantiating those methods in order to create a meaningful mapping of Time-Series data into an interpretable space when building such explainers. Such an example is provided at the end of the Lime's documentation.

      +
      +
      • +
    +
    +

    Note

    +

    If your model is not following the same conventions, please refer to the Model documentation.

    +
    +
    • +
+

targets: Must be one of the following: a tf.Tensor or a np.ndarray. Its format depends on the task at end and what you want an explanation of. Thus, if you want to know more about targets you should read the documentation of Model or Operator.

+

Even though we made an harmonized API for all attributions methods it might be relevant for the user to distinguish Gradient based and Perturbation based methods, also often referenced respectively as white-box and black-box methods, as their hyperparameters settings might be quite different.

+

Perturbation-based approaches

+

Perturbation based methods focus on perturbing an input with a variety of techniques and, with the analysis of the resulting outputs, define an attribution representation. Thus, there is no need to explicitly know the model architecture as long as forward pass is available, which explain why they are also referenced as black-box methods.

+

Therefore, to use perturbation-based approaches you do not need a TF model. To know more, please see the Callable documentation.

+

Xplique includes the following black-box attributions:

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Method NameTutorialAvailable with TFAvailable with PyTorch*
KernelShapOpen In Colab✔✔
LimeOpen In Colab✔✔
OcclusionOpen In Colab✔✔
RiseOpen In Colab✔✔
Sobol AttributionOpen In Colab✔✔
Hsic AttributionOpen In Colab✔✔
+

*: Before using a PyTorch's model it is highly recommended to read the dedicated documentation

+

Gradient-based approaches

+

Those approaches are also called white-box methods as they require a full access to the model architecture, notably it should allow computing gradients. Indeed, the core idea with the gradient-based approach is to use back-propagation, along other techniques, not to update the model’s weights (which is already trained) but to reveal the most contributing inputs, potentially in a specific layer. All methods are available when the model work with TensorFlow but most methods also works with PyTorch (see Xplique for PyTorch documentation)

+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Method NameTutorialAvailable with TFAvailable with PyTorch*
DeconvNetOpen In Colab✔❌
GradCAMOpen In Colab✔❌
GradCAM++Open In Colab✔❌
GradientInputOpen In Colab✔✔
GuidedBackpropagationOpen In Colab✔❌
IntegratedGradientsOpen In Colab✔✔
SaliencyOpen In Colab✔✔
SmoothGradOpen In Colab✔✔
SquareGradOpen In Colab✔✔
VarGradOpen In Colab✔✔
+

*: Before using a PyTorch's model it is highly recommended to read the dedicated documentation

+

In addition, those methods inherits from WhiteBoxExplainer (itself inheriting from BlackBoxExplainer). Thus, an additional __init__ argument is added: output_layer. It is the layer to target for the output (e.g logits or after softmax). If an int is provided, it will be interpreted as a layer index, if a string is provided it will look for the layer name. Default to the last layer.

+
+

Tip

+

It is recommended to use the layer before Softmax.

+
+
+

Warning

+

The output_layer parameter will work well with TensorFlow's model. However, it will not work with PyTorch's model. For PyTorch, one should directly manipulate the model to focus on the layers of interest.

+
+
+

Info

+

The "white-box" explainers that work with Pytorch are those that only require the gradient of the model without having to "modify" some part of the model (e.g. Deconvnet will commute all original ReLU by a custom ReLU policy)

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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/deconvnet/index.html b/v1.1.0/api/attributions/deconvnet/index.html new file mode 100755 index 00000000..48dbc4eb --- /dev/null +++ b/v1.1.0/api/attributions/deconvnet/index.html @@ -0,0 +1,1424 @@ + + + + + + + + + + + + + + + + + + + + + + + DeconvNet - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Deconvnet

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

Deconvnet is one of the first attribution method and was proposed in 2013. +Its operation is similar to Saliency: it consists in backpropagating the output score with respect to the input, +however, at each non-linearity (the ReLUs), only the positive gradient (even of negative activations) are backpropagated.

+

More precisely, with \(f\) our classifier and \(f_l(x)\) the activation at layer \(l\), we usually have:

+
\[ \frac{\partial f(x)}{\partial f_{l}(x)} = \frac{\partial f(x)}{\partial \text{ReLU}(f_{l}(x))} \frac{\partial \text{ReLU}(f_l(x))}{\partial f_{l}(x)} += \frac{\partial f(x)}{\partial \text{ReLU}(f_{l}(x))} \odot \mathbb{1}(f_{l}(x)) +\]
+

with \(\mathbb{1}(.)\) the indicator function. With Deconvnet, the backpropagation is modified such that :

+
\[ +\frac{\partial f(x)}{\partial f_{l}(x)} = +\frac{\partial f(x)}{\partial \text{ReLU}(f_{l}(x))} \odot \mathbb{1}(\frac{\partial f(x)}{\partial \text{ReLU}(f_{l}(x))}) +\]
+

Example

+
from xplique.attributions import DeconvNet
+
+# load images, labels and model
+# ...
+
+method = DeconvNet(model)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

DeconvNet

+

Used to compute the DeconvNet method, which modifies the classic Saliency procedure on +ReLU's non linearities, allowing only the positive gradients (even from negative inputs) to +pass through. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         output_layer: Union[str, int, None] = None,
         batch_size: Optional[int] = 32,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    output_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for the outputs (e.g logits or after softmax).

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      Default to the last layer.

      It is recommended to use the layer before Softmax.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 32

    +
      +

    • Number of inputs to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute DeconvNet for a batch of samples. +Accept Tensor, numpy array or tf.data.Dataset (in that case targets is None) +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
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Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Deconv maps.

      • +
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FORGrad

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+ + View colab tutorial | + + + View source | +📰 Paper

+

ForGrad is an enhancement for any attribution method by effectively filtering +out high-frequency noise in gradient-based attribution maps, resulting in improved explainability scores and +promoting the adoption of more computationally efficient techniques for model interpretability.

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Quote

+

The application of an optimal low-pass filter to attribution maps improves gradient-based attribution methods significantly, resulting in higher explainability scores across multiple models and elevating gradient-based methods to a top ranking among state-of-the-art techniques, sparking renewed interest in simpler and more computationally efficient explainability approaches.

+

-- Gradient strikes back: How filtering out high frequencies improves explanations (2023)1

+
+

In a more precise manner, to obtain an attribution map \(\varphi_\sigma(x)\), we apply a filter \(w_\sigma\) with a cutoff value \(\sigma\) to remove high frequencies, as shown in the equation:

+
\[ \varphi_\sigma(x) = \mathcal{F}^{-1}((\mathcal{F} \cdot \varphi)(x) \odot w_\sigma) \]
+

The parameter \(\sigma\) controls the amount of frequencies retained and ranges between \((0, W]\), where \(W\) represents the dimension of the squared image. A value of \(0\) eliminates all frequencies, while \(W\) retains all frequencies. The paper presents a method to estimate the optimal cutoff, and for ImageNet images, the recommended default value for the optimal sigma is typically around 15.

+

Example

+
from xplique.attributions import Saliency
+from xplique.common import forgrad
+
+# load images, labels and model
+# ...
+
+method = Saliency(model)
+explanations = method.explain(images, labels)
+explanations_filtered = forgrad(explanations, sigma=15)
+
+

Notebooks

+ +

forgrad(explanations: tf.Tensor,
        sigma: int = 15) -> tf.Tensor

+

ForGRAD is a method that enhances any attributions explanations (particularly useful on +gradients based attribution method) by eliminating high frequencies in the explanations. +

+

Parameters

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • List of explanations to filter. Explanation should be at least 3D (batch, height, width) and should have the same height and width.

      • +
    +
    • +
  • +

    sigma : int = 15

    +
      +

    • Bandwith of the low pass filter. The higher the sigma, the more frequencies are kept.

      Sigma should be positive and less than image size.

      Default to paper recommendation, 15 for image size 224.

      • +
    +
    • +
+

Return

+
    +
  • +

    filtered_explanations : tf.Tensor

    +
      +

    • Explanations low-pass filtered.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Gradient strikes back: How filtering out high frequencies improves explanations (2023) ↩

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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/grad_cam/index.html b/v1.1.0/api/attributions/grad_cam/index.html new file mode 100755 index 00000000..671fbf0a --- /dev/null +++ b/v1.1.0/api/attributions/grad_cam/index.html @@ -0,0 +1,1447 @@ + + + + + + + + + + + + + + + + + + + + + + + Grad-CAM - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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GradCAM

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

Grad-CAM is a technique for producing visual explanations that can be used on Convolutional Neural +Network (CNN) which uses both gradients and the feature maps of the last convolutional layer.

+
+

Quote

+

Grad-CAM uses the gradients of any target concept (say logits for “dog” or even a caption), flowing +into the final convolutional layer to produce a coarse localization map highlighting the important +regions in the image for predicting the concept.

+

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

+
+

More precisely, to obtain the localization map for a prediction \(f(x)\), we need to compute the weights +\(w_k\) associated to each of the feature map channel \(A^k \in \mathbb{R}^{W \times H}\). As we use the last +convolutionnal layer, \(k\) will be the number of filters, \(Z\) is the number of pixels in each feature +map (\(Z = W \times H\), e.g. 7x7 for ResNet50).

+
\[ +w_k = \frac{1}{Z} \sum_i \sum_j \frac{\partial f(x)}{\partial A^k_{i,j}} +\]
+

We now use this weight to ponderate and aggregate the feature maps to obtain our grad-cam attribution \(\phi\):

+
\[ +\phi = \text{max}(0, \sum_k w_k A^k) +\]
+

Notice that \(\phi \in \mathbb{R}^{W \times H}\) and thus the size of the explanation depends on the +size of the feature map (\(W, H\)) of the last feature map. In order to compare it to the original input \(x\), +we upsample \(\phi\) using bicubic interpolation.

+

Example

+
from xplique.attributions import GradCAM
+
+# load images, labels and model
+# ...
+
+method = GradCAM(model)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

GradCAM

+

Used to compute the Grad-CAM visualization method. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         output_layer: Union[str, int, None] = None,
         batch_size: Optional[int] = 32,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
         conv_layer: Union[str, int, None] = None)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    output_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for the outputs (e.g logits or after softmax).

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      Default to the last layer.

      It is recommended to use the layer before Softmax.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 32

    +
      +

    • Number of inputs to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    conv_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for Grad-CAM algorithm.

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute and resize explanations to match inputs shape. +Accept Tensor, numpy array or tf.data.Dataset (in that case targets is None) +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    grad_cam : tf.Tensor

    +
      +

    • Grad-CAM explanations, same shape as the inputs except for the channels.

      • +
    +
    • +
+


+
+
+
    +
  1. +

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

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
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+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/grad_cam_pp/index.html b/v1.1.0/api/attributions/grad_cam_pp/index.html new file mode 100755 index 00000000..3d837982 --- /dev/null +++ b/v1.1.0/api/attributions/grad_cam_pp/index.html @@ -0,0 +1,1437 @@ + + + + + + + + + + + + + + + + + + + + + + + Grad-CAM++ - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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Grad-CAM ++

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

Grad-CAM++ is a technique for producing visual explanations that can be used on Convolutional Neural +Network (CNN) which uses both gradients and the feature maps of the last convolutional layer.

+

More precisely, to obtain the localization map for a prediction \(f(x)\), we need to compute the weights +\(w_k\) associated to each of the feature map channel \(A^k \in \mathbb{R}^{W \times H}\). As we use the last +convolutionnal layer, \(k\) will be the number of filters, \(Z\) is the number of pixels in each feature +map (\(Z = W \times H\), e.g. 7x7 for ResNet50). once this weights are obtained, we use them to ponderate +and aggregate the feature maps to obtain our grad-cam++ attribution \(\phi\):

+
\[ +\phi = \text{max}(0, \sum_k w_k A^k) +\]
+

Notice that \(\phi \in \mathbb{R}^{W \times H}\) and thus the size of the explanation depends on the +size of the feature map (\(W, H\)) of the last feature map. In order to compare it to the original input \(x\), +we upsample \(\phi\) using bicubic interpolation.

+

Example

+
from xplique.attributions import GradCAMPP
+
+# load images, labels and model
+# ...
+
+method = GradCAMPP(model)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

GradCAMPP

+

Used to compute the Grad-CAM++ visualization method. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         output_layer: Union[str, int, None] = None,
         batch_size: Optional[int] = 32,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
         conv_layer: Union[str, int, None] = None)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    output_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for the outputs (e.g logits or after softmax).

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      Default to the last layer.

      It is recommended to use the layer before Softmax.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 32

    +
      +

    • Number of inputs to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    conv_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for Grad-CAM++ algorithm.

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute and resize explanations to match inputs shape. +Accept Tensor, numpy array or tf.data.Dataset (in that case targets is None) +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    grad_cam : tf.Tensor

    +
      +

    • Grad-CAM explanations, same shape as the inputs except for the channels.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks (2017). ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/gradient_input/index.html b/v1.1.0/api/attributions/gradient_input/index.html new file mode 100755 index 00000000..310023d4 --- /dev/null +++ b/v1.1.0/api/attributions/gradient_input/index.html @@ -0,0 +1,1440 @@ + + + + + + + + + + + + + + + + + + + + + + + Gradient Input - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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Gradient \(\odot\) Input

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

Gradient \(\odot\) Input is a visualization techniques based on the gradient of a class score relative to +the input, element-wise with the input. This method was introduced by Shrikumar et al., 20161, in +an old version of their DeepLIFT paper2.

+
+

Quote

+

Gradient inputs was at first proposed as a technique to improve the sharpness of the attribution maps. +The attribution is computed taking the (signed) partial derivatives of the output with respect to +the input and multiplying them with the input itself.

+

-- Towards better understanding of the gradient-based attribution methods for Deep Neural Networks (2017)3

+
+

A theoretical analysis conducted by Ancona et al, 20183 showed that Gradient \(\odot\) Input is +equivalent to \(\epsilon\)-LRP and DeepLift methods under certain conditions: using a baseline of zero, and with +all biases to zero.

+

More precisely, the explanation \(\phi\) for an input \(x\) and a classifier \(f\) is defined as

+
\[ \phi = x \odot \nabla_x f(x) \]
+

with \(\odot\) the Hadamard product.

+

Example

+
from xplique.attributions import GradientInput
+
+# load images, labels and model
+# ...
+
+method = GradientInput(model)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

GradientInput

+

Used to compute elementwise product between the saliency maps of Simonyan et al. and the +input (Gradient x Input). +

+

__init__(self,
         model: keras.src.engine.training.Model,
         output_layer: Union[str, int, None] = None,
         batch_size: Optional[int] = 64,
         operator: Optional[Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float]] = None)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    output_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for the outputs (e.g logits or after softmax).

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      Default to the last layer.

      It is recommended to use the layer before Softmax.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 64

    +
      +

    • Number of inputs to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    operator : Optional[Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float]] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute gradients x inputs for a batch of samples. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Gradients x Inputs, with the same shape as the inputs.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Not Just a Black Box: Learning Important Features Through Propagating Activation Differences ↩

    +
    2. +
  2. +

    Learning Important Features Through Propagating Activation Differences ↩

    +
    3. +
  3. +

    Towards better understanding of gradient-based attribution methods for Deep Neural Networks ↩↩

    +
    4. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/guided_backpropagation/index.html b/v1.1.0/api/attributions/guided_backpropagation/index.html new file mode 100755 index 00000000..49d866fd --- /dev/null +++ b/v1.1.0/api/attributions/guided_backpropagation/index.html @@ -0,0 +1,1424 @@ + + + + + + + + + + + + + + + + + + + + + + + Guided Backprop - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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Guided Backpropagation

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

Guided-backprop is one of the first attribution method and was proposed in 2014. +Its operation is similar to Saliency: it consists in backpropagating the output score with respect to the input, +however, at each non-linearity (the ReLUs), only the positive gradient of positive activations are backpropagated. +We can see this as a filter on the backprop.

+

More precisely, with \(f\) our classifier and \(f_l(x)\) the activation at layer \(l\), we usually have:

+
\[ \frac{\partial f(x)}{\partial f_{l}(x)} = \frac{\partial f(x)}{\partial \text{ReLU}(f_{l}(x))} \frac{\partial \text{ReLU}(f_l(x))}{\partial f_{l}(x)} += \frac{\partial f(x)}{\partial \text{ReLU}(f_{l}(x))} \odot \mathbb{1}(f_{l}(x)) +\]
+

with \(\mathbb{1}(.)\) the indicator function. With Guided-backprop, the backpropagation is modified such that :

+
\[ +\frac{\partial f(x)}{\partial f_{l}(x)} = +\frac{\partial f(x)}{\partial \text{ReLU}(f_{l}(x))} \odot \mathbb{1}(f_{l}(x)) \odot \mathbb{1}(\frac{\partial f(x)}{\partial \text{ReLU}(f_{l}(x))}) +\]
+

Example

+
from xplique.attributions import GuidedBackprop
+
+# load images, labels and model
+# ...
+
+method = GuidedBackprop(model)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

GuidedBackprop

+

Used to compute the Guided Backpropagation, which modifies the classic Saliency procedure on +ReLU's non linearities, allowing only the positive gradients from positive activations to pass +through. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         output_layer: Union[str, int, None] = None,
         batch_size: Optional[int] = 32,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    output_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for the outputs (e.g logits or after softmax).

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      Default to the last layer.

      It is recommended to use the layer before Softmax.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 32

    +
      +

    • Number of inputs to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute Guided Backpropagation for a batch of samples. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Guided Backpropagation maps.

      • +
    +
    • +
+


+ + + + + + +
+
+ + +
+ +
+ + + +
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Hsic Attribution Method

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

The Hsic attribution method from Novello, Fel, Vigouroux1 explains a neural network's prediction for a given input image by assessing the dependence between the output and patches of the input. Thanks to the sample efficiency of HSIC Estimator, this black box method requires fewer forward passes to produce relevant explanations.

+

Let's consider two random variables which are the perturbation associated with each patch of the input image, \(X_i, i \in \{1,...d\}\) with \(d= \text{grid_size}^2\) image patches and the output \(Y\). Let \(X^1_i,...,X^p_i\) and \(Y^1,...,Y^p\) be \(p\) samples of \(X_i\) and \(Y\). HSIC attribution method requires selecting a kernel for the input and the output to construct an RKHS on which is computed the Maximum Mean Discrepancy, a dissimilarity metric between distributions. Let \(k:\mathbb{R}^2 \rightarrow \mathbb{R}\) and \(l:\mathbb{R}^2 \rightarrow \mathbb{R}\) the kernels selected for \(X_i\) and \(Y\), HSIC is estimated with an error \(\mathcal{O}(1/\sqrt{p})\) using the estimator +$$ +\mathcal{H}^p_{X_i, Y} = \frac{1}{(p-1)^2} \operatorname{tr} (KHLH), +$$ +where \(H, L, K \in \mathbb{R}^{p \times p}\) and \(K_{ij} = k(x_i, x_j), L_{i,j} = l(y_i, y_j)\) and \(H_{ij} = \delta(i=j) - p^{-1}\) where \(\delta(i=j) = 1\) if \(i=j\) and \(0\) otherwise.

+

In the paper Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure, the sampler LatinHypercube is used to sample the perturbations. Note however that the present implementation uses TFSobolSequence as default sampler because LatinHypercube requires scipy \(\geq\) 1.7.0. you can nevertheless use this sampler -- which is included in the library -- by specifying it during the init of your explainer.

+

For the kernel \(k\) applied on \(X_i\), a modified Dirac kernel is used to enable an ANOVA-like decomposition property that allows assessing pairwise patch interactions (see the paper for more details). For the kernel \(l\) of output \(Y\), a Radial Basis Function (RBF) is used.

+
+

Tip

+

We recommend using a grid size of \(7 \times 7\) to define the image patches. The paper uses a number of forwards of \(1500\) to obtain the most faithful explanations and \(750\) for a more budget - but still faithful - version.

+
+

Example

+

Low budget version

+
from xplique.attributions import HsicAttributionMethod
+
+# load images, labels and model
+# ...
+
+explainer = HsicAttributionMethod(model, grid_size=7, nb_design=750)
+explanations = explainer(images, labels)
+
+

High budget version

+
from xplique.attributions import HsicAttributionMethod
+
+# load images, labels and model
+# ...
+
+explainer = HsicAttributionMethod(model, grid_size=7, nb_design=1500)
+explanations = explainer(images, labels)
+
+

Recommended version, (you need scipy \(\geq\) 1.7.0)

+
from xplique.attributions import HsicAttributionMethod
+from xplique.attributions.global_sensitivity_analysis import LatinHypercube
+
+# load images, labels and model
+# ...
+
+explainer = HsicAttributionMethod(model, 
+                                  grid_size=7, nb_design=1500,
+                                  sampler = LatinHypercube(binary=True))
+explanations = explainer(images, labels)
+
+

Notebooks

+ +

HsicAttributionMethod

+

HSIC Attribution Method. +Compute the dependance of each input dimension wrt the output using Hilbert-Schmidt Independance +Criterion, a perturbation function on a grid and an adapted sampling as described in +the original paper. +

+

__init__(self,
         model,
         grid_size: int = 8,
         nb_design: int = 500,
         sampler: Optional[xplique.attributions.global_sensitivity_analysis.samplers.Sampler] = None,
         estimator: Optional[xplique.attributions.global_sensitivity_analysis.hsic_estimators.HsicEstimator] = None,
         perturbation_function: Union[Callable, str, None] = 'inpainting',
         batch_size=256,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None)

+

Parameters

+
    +
  • +

    model : model

    +
      +

    • Model used for computing explanations.

      • +
    +
    • +
  • +

    grid_size : int = 8

    +
      +

    • Cut the image in a grid of (grid_size, grid_size) to estimate an indice per cell.

      • +
    +
    • +
  • +

    nb_design : int = 500

    +
      +

    • Number of design for the sampler.

      • +
    +
    • +
  • +

    sampler : Optional[xplique.attributions.global_sensitivity_analysis.samplers.Sampler] = None

    +
      +

    • Sampler used to generate the (quasi-)monte carlo samples, LHS or QMC.

      For more option, see the sampler module. Note that the original paper uses LHS but here the default sampler is TFSobolSequence as LHS requires scipy 1.7.0.

      • +
    +
    • +
  • +

    estimator : Optional[xplique.attributions.global_sensitivity_analysis.hsic_estimators.HsicEstimator] = None

    +
      +

    • Estimator used to compute the HSIC score.

      • +
    +
    • +
  • +

    perturbation_function : Union[Callable, str, None] = 'inpainting'

    +
      +

    • Function to call to apply the perturbation on the input. Can also be string in 'inpainting', 'blur'.

      • +
    +
    • +
  • +

    batch_size : batch_size=256

    +
      +

    • Batch size to use for the forwards.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute the total Sobol' indices according to the explainer parameter (perturbation +function, grid size...). Accept Tensor, numpy array or tf.data.Dataset (in that case +targets is None). +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Images to be explained, either tf.dataset, Tensor or numpy array.

      If Dataset, targets should not be provided (included in Dataset).

      Expected shape (N, W, H, C) or (N, W, H).

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • One-hot encoding for classification or direction {-1, +1} for regression.

      Tensor or numpy array.

      Expected shape (N, C) or (N).

      • +
    +
    • +
+

Return

+
    +
  • +

    attributions_maps : tf.Tensor

    +
      +

    • GSA Attribution Method explanations, same shape as the inputs except for the channels.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure (2022) ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/integrated_gradients/index.html b/v1.1.0/api/attributions/integrated_gradients/index.html new file mode 100755 index 00000000..a55b644a --- /dev/null +++ b/v1.1.0/api/attributions/integrated_gradients/index.html @@ -0,0 +1,1455 @@ + + + + + + + + + + + + + + + + + + + + + + + Integrated Gradient - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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Integrated Gradients

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

Integrated Gradients is a visualization technique resulting of a theoretical search for an +explanatory method that satisfies two axioms, Sensitivity and Implementation Invariance +(Sundararajan et al1).

+
+

Quote

+

We consider the straightline path (in \(R^n\)) from the baseline \(\bar{x}\) to the input \(x\), and compute the +gradients at all points along the path. Integrated gradients are obtained by cumulating these +gradients.

+

-- Axiomatic Attribution for Deep Networks (2017)1

+
+

Rather than calculating only the gradient relative to the image, the method consists of averaging +the gradient values along the path from a baseline state to the current value. The baseline state +is often set to zero, representing the complete absence of features.

+

More precisely, with \(x_0\) the baseline state, \(x\) the image and \(f\) our classifier, +the Integrated Gradient attribution is defined as

+
\[\phi = (x - x_0) \cdot \int_0^1{ \nabla_x f(x_0 + \alpha (x - x_0) ) d\alpha }\]
+

In order to approximate from a finite number of steps, the implementation here use the +Trapezoidal rule3 and not a left-Riemann summation, which allows for more accurate results +and improved performance. (see the paper below for a comparison of the methods2).

+

Example

+
from xplique.attributions import IntegratedGradients
+
+# load images, labels and model
+# ...
+
+method = IntegratedGradients(model, steps=50, baseline_value=0.0)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

IntegratedGradients

+

Used to compute the Integrated Gradients, by cumulating the gradients along a path from a +baseline to the desired point. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         output_layer: Union[str, int, None] = None,
         batch_size: Optional[int] = 32,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
         steps: int = 50,
         baseline_value: float = 0.0)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    output_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for the outputs (e.g logits or after softmax).

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      Default to the last layer.

      It is recommended to use the layer before Softmax.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 32

    +
      +

    • Number of inputs to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    steps : int = 50

    +
      +

    • Number of points to interpolate between the baseline and the desired point.

      • +
    +
    • +
  • +

    baseline_value : float = 0.0

    +
      +

    • Scalar used to create the the baseline point.

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute Integrated Gradients for a batch of samples. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Integrated gradients, same shape as the inputs.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Axiomatic Attribution for Deep Networks ↩↩

    +
    2. +
  2. +

    Computing Linear Restrictions of Neural Networks ↩

    +
    3. +
  3. +

    Trapezoidal rule ↩

    +
    4. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/kernel_shap/index.html b/v1.1.0/api/attributions/kernel_shap/index.html new file mode 100755 index 00000000..460740c5 --- /dev/null +++ b/v1.1.0/api/attributions/kernel_shap/index.html @@ -0,0 +1,1506 @@ + + + + + + + + + + + + + + + + + + + + + + + KernelSHAP - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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Kernel Shap

+

View colab tutorial | View source

+

By setting appropriately the perturbation function, the similarity kernel and the interpretable +model in the LIME framework we can theoretically obtain the Shapley Values more efficiently. +Therefore, KernelShap is a method based on LIME with specific attributes.

+
+

Quote

+

The exact computation of SHAP values is challenging. However, by combining insights from current +additive feature attribution methods, we can approximate them. We describe two model-agnostic +approximation methods, [...] and another that is novel (Kernel SHAP)

+

-- A Unified Approach to Interpreting Model Predictions1

+
+

Example

+
from xplique.attributions import KernelShap
+
+# load images, labels and model
+# define a custom map_to_interpret_space function
+# ...
+
+method = KernelShap(model, map_to_interpret_space=custom_map)
+explanations = method.explain(images, labels)
+
+

The choice of the map function will have a great deal toward the quality of explanation. +By default, the map function use the quickshift segmentation of scikit-images

+

Notebooks

+ +

KernelShap

+

By setting appropriately the perturbation function, the similarity kernel and the interpretable +model in the LIME framework we can theoretically obtain the Shapley Values more efficiently. +Therefore, KernelShap is a method based on LIME with specific attributes. +

+

__init__(self,
         model: Callable,
         batch_size: int = 64,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
         map_to_interpret_space: Optional[Callable] = None,
         nb_samples: int = 800,
         ref_value: Optional[numpy.ndarray] = None)

+

Parameters

+
    +
  • +

    model : Callable

    +
      +

    • The model from which we want to obtain explanations.

      • +
    +
    • +
  • +

    batch_size : int = 64

    +
      +

    • Number of perturbed samples to process at once, mandatory when nb_samples is huge.

      Notice, it is different compare to WhiteBox explainers which batch the inputs.

      Here inputs are process one by one.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    map_to_interpret_space : Optional[Callable] = None

    +
      +

    • Function which group features of an input corresponding to the same interpretable feature (e.g super-pixel).

      It allows to transpose from (resp. to) the original input space to (resp. from) the interpretable space.

      • +
    +
    • +
  • +

    nb_samples : int = 800

    +
      +

    • The number of perturbed samples you want to generate for each input sample.

      Default to 800.

      • +
    +
    • +
  • +

    ref_value : Optional[numpy.ndarray] = None

    +
      +

    • It defines reference value which replaces each feature when the corresponding interpretable feature is set to 0.

      It should be provided as: a ndarray of shape (1) if there is no channels in your input and (C,) otherwise.

      The default ref value is set to (0.5,0.5,0.5) for inputs with 3 channels (corresponding to a grey pixel when inputs are normalized by 255) and to 0 otherwise.

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

This method attributes the output of the model with given targets +to the inputs of the model using the approach described above, +training an interpretable model and returning a representation of the +interpretable model. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Interpretable coefficients, same shape as the inputs, except for the channels.

      Coefficients of the interpretable model. Those coefficients having the size of the interpretable space will be given the same value to coefficient which were grouped together (e.g belonging to the same super-pixel).

      • +
    +
    • +
+


+
+

Warning

+

The computation time might be very long depending on the hyperparameters settings. +A huge number of perturbed samples and a fine-grained mapping may lead to better +results but it is long to compute.

+
+

Parameters in-depth

+

map_to_interpret_space:

+

Function which group features of an input corresponding to the same interpretable +feature (e.g super-pixel).

+

It allows to transpose from (resp. to) the original input space to (resp. from) +the interpretable space.

+

The default mappings are:

+
    +
  • - the quickshift segmentation algorithm for inputs with \((N, W, H, C)\) shape, +we assume here such shape is used to represent \((W, H, C)\) images.
    • +
  • - the felzenszwalb segmentation algorithm for inputs with \((N, W, H)\) shape, +we assume here such shape is used to represent \((W, H)\) images.
    • +
  • - an identity mapping if inputs has shape \((N, W)\), we assume here your inputs +are tabular data.
    • +
+

To use your own custom map function you should use the following scheme:

+
def custom_map_to_interpret_space(inputs: tf.tensor) ->
+tf.tensor:
+    **some grouping techniques**
+    return mappings
+
+

mappings should have the same dimension as input except for channels.

+

For instance you can use the scikit-image (as we did for the quickshift algorithm) +library to defines super pixels on your images.

+
+

Info

+

The quality of your explanation relies strongly on this mapping.

+
+
+

Warning

+

Depending on the mapping you might have a huge number of interpretable_features +(e.g you map pixels 2 by 2 on a 299x299 image). Thus, the compuation time might +be very long!

+
+
+

Danger

+

As you may have noticed, by default Time Series are not handled. Consequently, a custom mapping should be implented. Either to assign each feature to a different group or to group consecutive features together, by group of 4 timesteps for example. In the second example, we try to cover patterns. An example is provided below.

+
+
def map_time_series(single_input: tf.tensor) -> tf.Tensor:
+    time_dim = single_input.shape[0]
+    feat_dim = single_input.shape[1]
+    mapping = tf.range(time_dim*feat_dim)
+    mapping = tf.reshape(mapping, (time_dim, feat_dim))
+    return mapping
+
+
+
+
    +
  1. +

    A Unified Approach to Interpreting Model Predictions ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/lime/index.html b/v1.1.0/api/attributions/lime/index.html new file mode 100755 index 00000000..9684b1db --- /dev/null +++ b/v1.1.0/api/attributions/lime/index.html @@ -0,0 +1,1618 @@ + + + + + + + + + + + + + + + + + + + + + + + Lime - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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LIME

+

View colab tutorial | View source

+

The Lime method use an interpretable model to provide an explanation. +More specifically, you map inputs (\(x \in R^d\)) to an interpretable space (e.g super-pixels) of size num_interpetable_features. +From there you generate perturbed interpretable samples (\(z' \in \{0,1\}^{num\_interpretable\_samples}\) +where \(1\) means we keep this specific interpretable feature).

+

Once you have your interpretable samples you can map them back to their original space +(the perturbed samples \(z \in R^d\)) and obtain the label prediction of your model for each perturbed +samples.

+

In the Lime method you define a similarity kernel which compute the similarity between an input and +its perturbed representations (either in the original input space or in the interpretable space): +\(\pi_x(z',z)\).

+

Finally, you train an interpretable model per input, using interpretable samples along the +corresponding perturbed labels and it will draw interpretable samples weighted by the similarity kernel. +Thus, you will have an interpretable explanation (i.e in the interpretable space) which can be +broadcasted afterwards to the original space considering the mapping you used.

+
+

Quote

+

The overall goal of LIME is to identify an interpretable model over the interpretable representation that is locally faithful to the classifier.

+

-- "Why Should I Trust You?": Explaining the Predictions of Any Classifier.1

+
+

Example

+
from xplique.attributions import Lime
+
+# load images, labels and model
+# define a custom map_to_interpret_space function
+# ...
+
+method = Lime(model, map_to_interpret_space=custom_map)
+explanations = method.explain(images, labels)
+
+

The choice of the interpretable model and the map function will have a great deal toward the quality of explanation. +By default, the map function use the quickshift segmentation of scikit-images

+

Notebooks

+ +

Lime

+

Used to compute the LIME method. +

+

__init__(self,
         model: Callable,
         batch_size: Optional[int] = None,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
         interpretable_model: Any = Ridge(alpha=2),
         similarity_kernel: Optional[Callable[[tf.Tensor, tf.Tensor, tf.Tensor], tf.Tensor]] = None,
         pertub_func: Optional[Callable[[Union[int, tf.Tensor], int], tf.Tensor]] = None,
         map_to_interpret_space: Optional[Callable[[tf.Tensor], tf.Tensor]] = None,
         ref_value: Optional[numpy.ndarray] = None,
         nb_samples: int = 150,
         distance_mode: str = 'euclidean',
         kernel_width: float = 45.0,
         prob: float = 0.5)

+

Parameters

+
    +
  • +

    model : Callable

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    batch_size : Optional[int] = None

    +
      +

    • Number of perturbed samples to process at once, mandatory when nb_samples is huge.

      Notice, it is different compare to WhiteBox explainers which batch the inputs.

      Here inputs are process one by one.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    interpretable_model : Any = Ridge(alpha=2)

    +
      +

    • Model object to train interpretable model.

      See the documentation for more information.

      • +
    +
    • +
  • +

    similarity_kernel : Optional[Callable[[tf.Tensor, tf.Tensor, tf.Tensor], tf.Tensor]] = None

    +
      +

    • Function which considering an input, perturbed instances of these input and the interpretable version of those perturbed samples compute the similarities between the input and the perturbed samples.

      See the documentation for more information.

      • +
    +
    • +
  • +

    pertub_func : Optional[Callable[[Union[int, tf.Tensor], int], tf.Tensor]] = None

    +
      +

    • Function which generate perturbed interpretable samples in the interpretation space from the number of interpretable features (e.g nb of super pixel) and the number of perturbed samples you want per original input.

      See the documentation for more information.

      • +
    +
    • +
  • +

    ref_value : Optional[numpy.ndarray] = None

    +
      +

    • It defines reference value which replaces each feature when the corresponding interpretable feature is set to 0.

      It should be provided as: a ndarray of shape (1) if there is no channels in your input and (C,) otherwise The default ref value is set to (0.5,0.5,0.5) for inputs with 3 channels (corresponding to a grey pixel when inputs are normalized by 255) and to 0 otherwise.

      • +
    +
    • +
  • +

    map_to_interpret_space : Optional[Callable[[tf.Tensor], tf.Tensor]] = None

    +
      +

    • Function which group features of an input corresponding to the same interpretable feature (e.g super-pixel).

      It allows to transpose from (resp. to) the original input space to (resp. from) the interpretable space.

      See the documentation for more information.

      • +
    +
    • +
  • +

    nb_samples : int = 150

    +
      +

    • The number of perturbed samples you want to generate for each input sample.

      Default to 150.

      • +
    +
    • +
  • +

    prob : float = 0.5

    +
      +

    • The probability argument for the default pertub function.

      • +
    +
    • +
  • +

    distance_mode : str = 'euclidean'

    +
      +

    • The distance mode used in the default similarity kernel, you can choose either "euclidean" or "cosine" (will compute cosine similarity).

      Default value set to "euclidean".

      • +
    +
    • +
  • +

    kernel_width : float = 45.0

    +
      +

    • Width of your kernel. It is important to make it evolving depending on your inputs size otherwise you will get all similarity close to 0 leading to poor performance or NaN values.

      Default to 45 (i.e adapted for RGB images).

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

This method attributes the output of the model with given targets +to the inputs of the model using the approach described above, +training an interpretable model and returning a representation of the +interpretable model. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Interpretable coefficients, same shape as the inputs, except for the channels.

      Coefficients of the interpretable model. Those coefficients having the size of the interpretable space will be given the same value to coefficient which were grouped together (e.g belonging to the same super-pixel).

      • +
    +
    • +
+


+
+

Warning

+

The computation time might be very long depending on the hyperparameters settings. +A huge number of perturbed samples and a fine-grained mapping may lead to better +results but it is long to compute.

+
+

Parameters in-depth

+

interpretable_model:

+

A Model object providing a fit method that train the model with the following inputs:

+
    +
  • - interpretable_inputs: 2D ndarray of shape (\(nb\_samples\) x \(num\_interp\_features\)),
    • +
  • - expected_outputs: 1D ndarray of shape (\(nb\_samples\)),
    • +
  • - weights: 1D ndarray of shape (\(nb\_samples\))
    • +
+

The model object should also provide a predict and fit method.

+

It should also have a coef_ attributes (the interpretable explanations) at least +once fit is called.

+

As interpretable model you can use linear models from scikit-learn.

+
+

Warning

+

Note that here nb_samples doesn't indicates the length of inputs but the number of +perturbed samples we want to generate for each input.

+
+

similarity_kernel:

+

Function which considering an input, perturbed instances of these input and +the interpretable version of those perturbed samples compute the similarities between +the input and the perturbed samples.

+
+

Info

+

The similarities can be computed in the original input space or in the interpretable +space.

+
+

You can provide a custom function. Note that to use a custom function, you have to +follow the following scheme:

+
def custom_similarity(
+    original_input, interpret_samples , perturbed_samples
+) -> tf.tensor (shape=(nb_samples,), dtype = tf.float32):
+    ** some tf actions **
+    return similarities
+
+

where:

+
    +
  • - original_input has shape among \((W)\), \((W, H)\), \((W, H, C)\)
    • +
  • - interpret_samples is a tf.tensor of shape \((nb\_samples, num\_interp\_features)\)
    • +
  • - perturbed_samples is a tf.tensor of shape \((nb\_samples, *original\_input.shape)\)
    • +
+

If it is possible you can add the @tf.function decorator.

+
+

Warning

+

Note that here nb_samples doesn't indicates the length of inputs but the number of +perturbed samples we want to generate for each input.

+
+
+

Info

+

The default similarity kernel use the euclidean distance between the original input and +the perturbed samples in the input space.

+
+

pertub_func:

+

Function which generate perturbed interpretable samples in the interpretation space from +the number of interpretable features (e.g nb of super pixel) and the number of perturbed +samples you want per original input.

+

The generated interp_samples belong to \(\{0,1\}^{num\_features}\). Where \(1\) indicates that we +keep the corresponding feature (e.g super pixel) in the mapping.

+

To use your own custom pertub function you should use the following scheme:

+
@tf.function
+def custom_pertub_function(num_features, nb_samples) ->
+tf.tensor (shape=(nb_samples, num_interp_features), dtype=tf.int32):
+    ** some tf actions**
+    return perturbed_sample
+
+
+

Info

+

The default pertub function provided keep a feature (e.g super pixel) with a +probability 0.5. +If you want to change it, define the prob value when initiating the explainer or define your own function.

+
+

map_to_interpret_space:

+

Function which group features of an input corresponding to the same interpretable +feature (e.g super-pixel).

+

It allows to transpose from (resp. to) the original input space to (resp. from) +the interpretable space.

+

The default mappings are:

+
    +
  • - the quickshift segmentation algorithm for inputs with \((N, W, H, C)\) shape, +we assume here such shape is used to represent \((W, H, C)\) images.
    • +
  • - the felzenszwalb segmentation algorithm for inputs with \((N, W, H)\) shape, +we assume here such shape is used to represent \((W, H)\) images.
    • +
  • - an identity mapping if inputs has shape \((N, W)\), we assume here your inputs +are tabular data.
    • +
+

To use your own custom map function you should use the following scheme:

+
def custom_map_to_interpret_space(single_inp: tf.tensor) ->
+tf.tensor:
+    **some grouping techniques**
+    return mapping
+
+

mapping should have the same dimension as single input except for channels.

+

For instance you can use the scikit-image (as we did for the quickshift algorithm) +library to defines super pixels on your images.

+
+

Info

+

The quality of your explanation relies strongly on this mapping.

+
+
+

Warning

+

Depending on the mapping you might have a huge number of interpretable_features +(e.g you map pixels 2 by 2 on a 299x299 image). Thus, the compuation time might +be very long!

+
+
+

Danger

+

As you may have noticed, by default Time Series are not handled. Consequently, a custom mapping should be implented. Either to assign each feature to a different group or to group consecutive features together, by group of 4 timesteps for example. In the second example, we try to cover patterns. An example is provided below.

+
+
def map_time_series(single_input: tf.tensor) -> tf.Tensor:
+    time_dim = single_input.shape[0]
+    feat_dim = single_input.shape[1]
+    mapping = tf.range(time_dim*feat_dim)
+    mapping = tf.reshape(mapping, (time_dim, feat_dim))
+    return mapping
+
+
+
+
    +
  1. +

    "Why Should I Trust You?": Explaining the Predictions of Any Classifier. ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/model/index.html b/v1.1.0/api/attributions/model/index.html new file mode 100755 index 00000000..06d99c55 --- /dev/null +++ b/v1.1.0/api/attributions/model/index.html @@ -0,0 +1,1507 @@ + + + + + + + + + + + + + + + + + + + + + + + Model Specifications - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Model expectations: What should be the model provided ?

+

Even though we tried to cover a wide-range of models for the XAI methods to work we based our frameworks on some assumptions which we propose to see here.

+

The inputs have expected shape

+

As a reminder any attribution methods are instanciated with at least three parameters:

+
    +
  • model: the model from which we want to obtain attributions (e.g: InceptionV3, ResNet, ...)
    • +
  • batch_size: an integer which allows to either process inputs per batch (gradient-based methods) or process perturbed samples of an input per batch (inputs are therefore process one by one)
    • +
  • operator: function g to explain, see the Operator documentation for more details
    • +
+

And an explainer is called with the explain method that takes as parameters:

+
    +
  • +

    inputs: One of the following: a tf.data.Dataset (in which case you should not provide targets), a tf.Tensor or a np.ndarray

    +
    • +
  • +

    targets: One of the following: a tf.Tensor or a np.ndarray

    +
    • +
+
+

Info

+

targets' format is defined depending on the task you are doing. See the Tasks section.

+
+

General

+

In practice, we expect the model to be callable for the inputs parameters -- i.e. we can do model(inputs). We expect this call to produce the outputs variables that is the predictions of the model on those inputs. As for most attribution methods we need to manipulate and/or link the outputs to the inputs we assume that the latter have conventional shape described in the sections below.

+

Inputs Format

+

Images data

+

If inputs are images, the expected shape of inputs is \((N, H, W, C)\) following the TF's conventions where:

+
    +
  • \(N\) is the number of inputs
    • +
  • \(H\) is the height of the images
    • +
  • \(W\) is the width of the images
    • +
  • \(C\) is the number of channels (works for \(C=3\) or \(C=1\), other values might not work or need further customization)
    • +
+

In the case where inputs is a tf.data.Dataset with images, then we expect each sample of the dataset to be a tuple (image, target) with image having \((H, W, C)\) shape and target being defined as explained in the Tasks section

+
+

Warning

+

If your model is not following the same conventions it might lead to poor results or yield errors.

+
+

Tabular data

+

If inputs are tabular data, the expected shape of inputs is \((N, W)\) where:

+
    +
  • \(N\) is the number of inputs
    • +
  • \(W\) is the feature dimension of a single input
    • +
+

In the case where inputs is a tf.data.Dataset with tabular data, then we expect each sample of the dataset to be a tuple (features, target) with features having \(W\) shape and target being a one-hot encoding of the output you want an explanation of.

+
+

Info

+

All attribution methods does not work well with tabular data.

+
+
+

Tip

+

Please refer to the table to see which methods might work with Tabular Data

+
+

Time-Series data

+

If inputs are Time Series, the expected shape of inputs is \((N, T, W)\)

+
    +
  • \(N\) is the number of inputs
    • +
  • \(T\) is the temporal dimension of a single input
    • +
  • \(W\) is the feature dimension of a single input
    • +
+
+

Warning

+

By default Lime & KernelShap will treat such inputs as grey images. You will need to define a custom map_to_interpret_space function when instantiating those methods in order to create a meaningful mapping of Time-Series data into an interpretable space when building such explainers. Such an example is provided at the end of the Lime's documentation.

+
+

Tasks

+

Classification Tasks

+
+

Tip

+

In general, if you are doing classification tasks it is better to not include the final softmax layer in your model but to work with logits instead!

+
+

For classification tasks, it is expected for the user to use the predictions_operator (see the Operator documentation) when initializing an explainer:

+
@tf.function
+def predictions_operator(model: Callable,
+                         inputs: tf.Tensor,
+                         targets: tf.Tensor) -> tf.Tensor:
+    """
+    Compute predictions scores, only for the label class, for a batch of samples.
+
+    Parameters
+    ----------
+    model
+        Model used for computing predictions.
+    inputs
+        Input samples to be explained.
+    targets
+        One-hot encoded labels or regression target (e.g {+1, -1}), one for each sample.
+
+    Returns
+    -------
+    scores
+        Predictions scores computed, only for the label class.
+    """
+    scores = tf.reduce_sum(model(inputs) * targets, axis=-1)
+    return scores
+
+

model(inputs)

+

Consequently, we expect model(inputs) to yield a \((N, C)\) tensor or array where \(N\) is the number of input samples and \(C\) is the number of classes.

+

targets

+

If you use the default operator for classification task we expect targets to be a \((N, C)\) tensor or array which is a one-hot encoding of the class you want to explain where \(N\) is the number of input samples and \(C\) is the number of classes.

+

Regression Tasks

+

If the task at end is regression, then the user should instantiate the explainer with the regression_operator (see the Operator documentation):

+
@tf.function
+def regression_operator(model: Callable,
+                        inputs: tf.Tensor,
+                        targets: tf.Tensor) -> tf.Tensor:
+    """
+    Compute the the mean absolute error between model prediction and the target.
+    Target should the model prediction on non-perturbed input.
+    This operator can be used to compute attributions for all outputs of a regression model.
+
+    Parameters
+    ----------
+    model
+        Model used for computing predictions.
+    inputs
+        Input samples to be explained.
+    targets
+        Model prediction on non-perturbed inputs.
+
+    Returns
+    -------
+    scores
+        MAE between model prediction and targets.
+    """
+    scores = tf.reduce_mean(tf.abs(model(inputs) - targets), axis=-1)
+    return scores
+
+

model(inputs)

+

Consequently, we expect model(inputs) to yield a \((N, D)\) tensor or array where \(N\) is the number of input samples and \(D\) is the number of variables the model should predict (possibly one).

+

targets

+

If you are using the regression_operator, it is expected that targets to be a \((N, D)\) tensor or array of the expected multi-variate output where \(N\) is the number of input samples and \(D\) is the number of variables (possibly one).

+

Object-Detection Tasks

+

Work In Progress

+

Segmentation Tasks

+

Work In Progress

+

What if my inputs and/or targets and/or my model does not follow the previous assumptions ?

+
+

Warning

+

In any case, when you are out of the scope of the original API, you should take a deep look at the source code to be sure that your Use Case will make sense.

+
+

My inputs follow a different shape convention

+

In the case where you want to handle images or time series data that does not follow the previous conventions, it is recommended to reshape the data to the expected shape for the explainers (attribution methods) to handle them correctly. Then, you can simply define a wrapper of your model so that data is reshape to your model convenience when it is called.

+

For example, if you have a model that classifies images but want the images to be channel-first (i.e. with \((N, C, H, W)\) shape) then you should:

+
    +
  • Move the axis so inputs are \((N, H, W, C)\) for the explainers
    • +
  • Write the following wrapper for your model:
    • +
+
class ModelWrapper(tf.keras.models.Model):
+    def __init__(self, nchw_model):
+        super(ModelWrapper, self).__init__()
+        self.model = nchw_model
+
+    def __call__(self, nhwc_inputs):
+        # transform the NHWC inputs (wanted for the explainers) back to NCHW inputs
+        nchw_inputs = self._transform_inputs(nhwc_inputs)
+        # make predictions
+        outputs = self.nchw_model(nchw_inputs)
+
+        return outputs
+
+    def _transform_inputs(self, nhwc_inputs):
+        # include in this function all transformation
+        # needed for your model to work with NHWC inputs
+        # , here for example we moveaxis from channels last
+        # to channels first
+        nchw_inputs = np.moveaxis(nhwc_inputs, [3, 1, 2], [1, 2, 3])
+
+        return nchw_inputs
+
+wrapped_model = ModelWrapper(model)
+explainer = Saliency(wrapped_model)
+# images should be (N, H, W, C) for the explain call
+explanations = explainer.explain(images, labels)
+
+

My inputs are a dictionnary (ex: Attention Model)

+

Work In Progress

+

My model is neither for classification nor regression tasks

+

Then you could define your own operator! (see the Operator documentation)

+

I have a PyTorch model

+

Then you should definetely have a look on the dedicated documentation!

+

I have a model that is neither a tf.keras.Model nor a torch.nn.Module

+

Then you should take a look on the Callable documentation or you could take inspiration on the PyTorch Wrapper to write a wrapper that will integrate your model into our API!

+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/object_detector/index.html b/v1.1.0/api/attributions/object_detector/index.html new file mode 100755 index 00000000..47979148 --- /dev/null +++ b/v1.1.0/api/attributions/object_detector/index.html @@ -0,0 +1,1225 @@ + + + + + + + + + + + + + + + + + + + Object detector - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ +
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+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Object detector

+ +

Work In Progress

+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/occlusion/index.html b/v1.1.0/api/attributions/occlusion/index.html new file mode 100755 index 00000000..71dde481 --- /dev/null +++ b/v1.1.0/api/attributions/occlusion/index.html @@ -0,0 +1,1444 @@ + + + + + + + + + + + + + + + + + + + + + + + Occlusion sensitivity - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Occlusion sensitivity

+

View colab tutorial | View source

+

The Occlusion sensitivity method sweep a patch that occludes pixels over the +images, and use the variations of the model prediction to deduce critical areas.1

+
+

Quote

+

[...] this method, referred to as Occlusion, replacing one feature \(x_i\) at the time with a + baseline and measuring the effect of this perturbation on the target output.

+

-- Towards better understanding of the gradient-based attribution methods for Deep Neural Networks (2017)2

+
+

with \(S_c\) the unormalized class score (layer before softmax) and \(\bar{x}\) a baseline, the Occlusion +sensitivity map \(\phi\) is defined as :

+
\[ \phi_i = S_c(x) - S_c(x_{[x_i = \bar{x}]}) \]
+

Example

+
from xplique.attributions import Occlusion
+
+# load images, labels and model
+# ...
+
+method = Occlusion(model, patch_size=(10, 10),
+                   patch_stride=(2, 2), occlusion_value=0.5)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

Occlusion

+

Used to compute the Occlusion sensitivity method, sweep a patch that occludes pixels over the +images and use the variations of the model prediction to deduce critical areas. +

+

__init__(self,
         model: Callable,
         batch_size: Optional[int] = 32,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
         patch_size: Union[int, Tuple[int, int]] = 3,
         patch_stride: Union[int, Tuple[int, int]] = 3,
         occlusion_value: float = 0.0)

+

Parameters

+
    +
  • +

    model : Callable

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 32

    +
      +

    • Number of pertubed samples to explain at once.

      Default to 32.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    patch_size : Union[int, Tuple[int, int]] = 3

    +
      +

    • Size of the patches to apply, if integer then assume an hypercube.

      • +
    +
    • +
  • +

    patch_stride : Union[int, Tuple[int, int]] = 3

    +
      +

    • Stride between two patches, if integer then assume an hypercube.

      • +
    +
    • +
  • +

    occlusion_value : float = 0.0

    +
      +

    • Value used as occlusion.

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute Occlusion sensitivity for a batch of samples. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Occlusion sensitivity, same shape as the inputs, except for the channels.

      • +
    +
    • +
+


+
+

Info

+

patch_size and patch_stride will define patch to apply to the original input. Thus, a combination of patches will generate pertubed samples of the original input (masked by patches with occlusion_value value). +Consequently, the number of pertubed instances of an input depend on those parameters. Too little value of those two arguments on large image might lead to an incredible amount of pertubed samples and increase compuation time. On another hand too huge values might not be accurate enough.

+
+
+
+
    +
  1. +

    Visualizing and Understanding Convolutional Networks (2014). ↩

    +
    2. +
  2. +

    Towards better understanding of gradient-based attribution methods for Deep Neural Networks ↩

    +
    3. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/operator/index.html b/v1.1.0/api/attributions/operator/index.html new file mode 100755 index 00000000..b61cf55a --- /dev/null +++ b/v1.1.0/api/attributions/operator/index.html @@ -0,0 +1,1587 @@ + + + + + + + + + + + + + + + + + + + + + + + Operator - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ +
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+ + + + + + + +

Operator

+

operator is one of the main parameters for both attribution methods and metrics. It defines the function \(g\) that we want to explain. E.g.: In the case we have a classifier model the function that we might want to explain is the one that given a target gives us the score of the model for that specific target -- i.e \(model(input)[target]\).

+
+

Note

+

The operator parameter is a feature avaible for version > \(1.\). The operator default values are the ones used before the introduction of this new feature!

+
+

Leitmotiv

+

The operator parameter was introduced to offer users a flexible way to adapt current attribution methods or metrics. It should help them to empirically tackle new use-cases/new tasks. Broadly speaking, it should amplify the user's ability to experiment. However, this also imply that it is the user responsability to make sure that its derivations are in-scope of the original method and make sense.

+

Operators' Signature

+

An operator is a function \(g\) that we want to explain. This function take as input \(3\) parameters:

+
    +
  • model, the model under investigation
    • +
  • inputs: One of the following: a tf.data.Dataset (in which case you should not provide targets), a tf.Tensor or a np.ndarray
    • +
  • targets: One of the following: a tf.Tensor or a np.ndarray
    • +
+
+

Info

+

More specification concerning model or inputs can be found in the model's documentation. More information on targets can be found here or also in the model's documentation

+
+

This function \(g\) should return a vector of scalar value of size \((N,)\) where \(N\) is the number of input in inputs -- i.e a scalar score per input.

+

How is the operator used in Xplique ?

+

Black-box attribution methods

+

For attribution approaches that do not require gradient computation we mostly need to query the model. Thus, those methods need an inference function. If you provide an operator, it will be the inference function.

+

More concretely, for this kind of approach you want to compare some valued function for an original input and perturbed version of it:

+
original_scores = operator(model, original_inputs, original_targets)
+
+# depending on the attribution method this `perturbation_function` is different
+perturbed_inputs, perturbed_targets = perturbation_function(original_inputs, original_targets)
+perturbed_scores = operator(model, perturbed_inputs, perturbed_targets)
+
+# exemple of comparison of interest
+diff_scores = math.sqrt((original_scores - perturbed_scores)**2)
+
+

White-box attribution methods

+

Those methods usually require some gradients computation. The gradients that will be used are the one of the operator function (see the get_gradient_of_operator method in the Providing custom operator section).

+

Default Behavior

+

A lot of attribution methods are initially intended for classification tasks. Thus, the default operator predictions_operator assume such a setting

+
@tf.function
+def predictions_operator(model: Callable,
+                         inputs: tf.Tensor,
+                         targets: tf.Tensor) -> tf.Tensor:
+    """
+    Compute predictions scores, only for the label class, for a batch of samples.
+
+    Parameters
+    ----------
+    model
+        Model used for computing predictions.
+    inputs
+        Input samples to be explained.
+    targets
+        One-hot encoded labels, one for each sample.
+
+    Returns
+    -------
+    scores
+        Predictions scores computed, only for the label class.
+    """
+    scores = tf.reduce_sum(model(inputs) * targets, axis=-1)
+    return scores
+
+

That is a setting where the variable model(inputs) is a vector of size \((N, C)\) where: \(N\) is the number of input and \(C\) is the number of class.

+
+

Info

+

Explaining the logits is to explain the class, while explaining the softmax is to explain why this class is more likely. Thus, it is recommended to explain the logit and exclude the softmax layer if any.

+
+

Existing operators and how to use them

+

At present, there are at present 2 operators available (and 2 others should be released soon) in the library that tackle different tasks.

+

Tasks

+

Classification Tasks

+
+

Tip

+

In general, if you are doing classification tasks it is better to not include the final softmax layer in your model but to work with logits instead!

+
+

For classification tasks, it is expected for the user to use the predictions_operator when initializing an explainer:

+
@tf.function
+def predictions_operator(model: Callable,
+                         inputs: tf.Tensor,
+                         targets: tf.Tensor) -> tf.Tensor:
+    """
+    Compute predictions scores, only for the label class, for a batch of samples.
+
+    Parameters
+    ----------
+    model
+        Model used for computing predictions.
+    inputs
+        Input samples to be explained.
+    targets
+        One-hot encoded labels or regression target (e.g {+1, -1}), one for each sample.
+
+    Returns
+    -------
+    scores
+        Predictions scores computed, only for the label class.
+    """
+    scores = tf.reduce_sum(model(inputs) * targets, axis=-1)
+    return scores
+
+
    +
  • +

    model(inputs) +Consequently, we expect model(inputs) to yield a \((N, C)\) tensor or array where \(N\) is the number of input samples and \(C\) is the number of classes.

    +
    • +
  • +

    targets +If you use the default operator for classification task we expect targets to be a \((N, C)\) tensor or array which is a one-hot encoding of the class you want to explain where \(N\) is the number of input samples and \(C\) is the number of classes.

    +
    • +
+

Regression Tasks

+

If the task at end is regression, then the user should instantiate the explainer with the regression_operator:

+
@tf.function
+def regression_operator(model: Callable,
+                        inputs: tf.Tensor,
+                        targets: tf.Tensor) -> tf.Tensor:
+    """
+    Compute the the mean absolute error between model prediction and the target.
+    Target should the model prediction on non-perturbed input.
+    This operator can be used to compute attributions for all outputs of a regression model.
+
+    Parameters
+    ----------
+    model
+        Model used for computing predictions.
+    inputs
+        Input samples to be explained.
+    targets
+        Model prediction on non-perturbed inputs.
+
+    Returns
+    -------
+    scores
+        MAE between model prediction and targets.
+    """
+    scores = tf.reduce_mean(tf.abs(model(inputs) - targets), axis=-1)
+    return scores
+
+
    +
  • +

    model(inputs): +Consequently, we expect model(inputs) to yield a \((N, D)\) tensor or array where \(N\) is the number of input samples and \(D\) is the number of variables the model should predict (possibly one).

    +
    • +
  • +

    targets: +If you are using the regression_operator, it is expected that targets to be a \((N, D)\) tensor or array of the expected multi-variate output where \(N\) is the number of input samples and \(D\) is the number of variables (possibly one).

    +
    • +
+

Object-Detection Tasks

+

Work In Progress

+

Segmentation Tasks

+

Work In Progress

+

How to use them with an explainer ?

+

You can build attribution methods with those operator in three ways:

+
    +
  • Explicitly importing them
    • +
+
from xplique.attributions import Saliency
+from xplique.commons.operators import regression_operator
+
+explainer = Saliency(model, operator=regression_operator)
+explanations = explainer(inputs, targets)
+
+

At present, the available operators are: predictions_operator and regression_operator

+
    +
  • Use their name
    • +
+
from xplique.attributions import Saliency
+
+explainer = Saliency(model, operator='regression')
+explanations = explainer(inputs, targets)
+
+

At present you can select a name in ["classification", "regression"]

+
    +
  • Use the Tasks enumeration
    • +
+
from xplique.commons import Tasks
+from xplique.attributions import Saliency
+
+explainer = Saliency(model, operator=Tasks.REGRESSION)
+explanations = explainer(inputs, targets)
+
+

At present the Tasks enum has two members: CLASSIFICATION and REGRESSION

+

Providing custom operator

+

If you provide a custom operator you should be aware that:

+
    +
  • An assertion will be made to ensure it respects the signature describe in the previous section
    • +
  • If you use any white-box explainer your operator will go through the get_gradient_of_operator function
    • +
+
def get_gradient_of_operator(operator):
+    """
+    Get the gradient of an operator.
+
+    Parameters
+    ----------
+    operator
+        Operator to compute the gradient of.
+
+    Returns
+    -------
+    gradient
+        Gradient of the operator.
+    """
+    @tf.function
+    def gradient(model, inputs, targets):
+        with tf.GradientTape() as tape:
+            tape.watch(inputs)
+            scores = operator(model, inputs, targets)
+
+        return tape.gradient(scores, inputs)
+
+    return gradient
+
+
+

Tip

+

Writing your operator with only tensorflow functions should increase your chance that this method does not yield any errors. In addition, providing a @tf.function decorator is also welcomed!

+
+
+

Warning

+

The targets parameter is the key to specifying what to explain and differs greatly depending on the operator.

+
+ + + + + + +
+
+ + +
+ +
+ + + +
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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/rise/index.html b/v1.1.0/api/attributions/rise/index.html new file mode 100755 index 00000000..c3e35fcc --- /dev/null +++ b/v1.1.0/api/attributions/rise/index.html @@ -0,0 +1,1448 @@ + + + + + + + + + + + + + + + + + + + + + + + Rise - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ + + +
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+ + + + + + + +

RISE

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

The RISE method consist of probing the model with randomly masked versions of the input image and +obtaining the corresponding outputs to deduce critical areas.

+
+

Quote

+

[...] we estimate the importance of pixels by dimming them in random combinations, +reducing their intensities down to zero. We model this by multiplying an image with a [0,1] +valued mask.

+

-- RISE: Randomized Input Sampling for Explanation of Black-box Models (2018)1

+
+

with \(f(x)\) the prediction of a classifier, for an input \(x\) and \(m \sim \mathcal{M}\) a mask with value in \([0,1]\) created from a low dimension (\(m\) is in \({0, 1}^{w \times h}\) with \(w \ll W\) and \(h \ll H\) then upsampled, see the paper for more details).

+

The RISE importance estimator is defined as:

+
\[ +\phi_i = \mathbb{E}( f(x \odot m) | m_i = 1) +\approx \frac{1}{\mathbb{E}(\mathcal{M}) N} \sum_{i=1}^N f(x \odot m_i) m_i +\]
+

The most important parameters here are (1) the grid_size that control \(w\) and \(h\) and (2) +nb_samples that control \(N\). +The pourcentage of visible pixels \(\mathbb{E}(\mathcal{M})\) is controlled using the preservation_probability parameter.

+

Example

+
from xplique.attributions import Rise
+
+# load images, labels and model
+# ...
+
+method = Rise(model, nb_samples=4000, grid_size=7, preservation_probability=0.5)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

Rise

+

Used to compute the RISE method, by probing the model with randomly masked versions of +the input image and obtaining the corresponding outputs to deduce critical areas. +

+

__init__(self,
         model: Callable,
         batch_size: Optional[int] = 32,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
         nb_samples: int = 4000,
         grid_size: Union[int, Tuple[int]] = 7,
         preservation_probability: float = 0.5)

+

Parameters

+
    +
  • +

    model : Callable

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 32

    +
      +

    • Number of pertubed samples to explain at once.

      Default to 32.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    nb_samples : int = 4000

    +
      +

    • Number of masks generated for Monte Carlo sampling.

      • +
    +
    • +
  • +

    grid_size : Union[int, Tuple[int]] = 7

    +
      +

    • Size of the grid used to generate the scaled-down masks. Masks are then rescale to and cropped to input_size. Can be a tuple for different cutting depending on the dimension.

      • +
    +
    • +
  • +

    preservation_probability : float = 0.5

    +
      +

    • Probability of preservation for each pixel (or the percentage of non-masked pixels in each masks), also the expectation value of the mask.

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute RISE for a batch of samples. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • RISE maps, same shape as the inputs, except for the channels.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    RISE: Randomized Input Sampling for Explanation of Black-box Models (2018) ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/saliency/index.html b/v1.1.0/api/attributions/saliency/index.html new file mode 100755 index 00000000..3e358bc1 --- /dev/null +++ b/v1.1.0/api/attributions/saliency/index.html @@ -0,0 +1,1431 @@ + + + + + + + + + + + + + + + + + + + + + + + Saliency - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + + + +
+ + +
+ +
+ + + + + + +
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+ + + +
+
+
+ + + + + +
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+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

Saliency Maps

+

+ + View colab tutorial | + + + View source | +📰 Paper

+

Saliency is one of the most easy explanation method based on the gradient of a class score relative to the +input.

+
+

Quote

+

An interpretation of computing the image-specific class saliency using the class score derivative +is that the magnitude of the derivative indicates which pixels need to be changed the least +to affect the class score the most. One can expect that such pixels correspond to the object location +in the image.

+

-- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps (2013)1

+
+

More precisely, for an image \(x\) the importance map \(\phi\) according to a classifier \(f\) is defined as:

+
\[ \phi = | \nabla_{x} f(x) | \]
+

more precisely, in the image case, Xplique is faithful to the original method and returns the max on the axis of channels, +with \(\phi_i \in \mathbb{R}^3\) for RGB, the importance for the pixel \(i\) is given by \(||\phi_i||_{\infty}\)

+

Example

+
from xplique.attributions import Saliency
+
+# load images, labels and model
+# ...
+
+method = Saliency(model)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

Saliency

+

Used to compute the absolute gradient of the output relative to the input. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         output_layer: Union[str, int, None] = None,
         batch_size: Optional[int] = 64,
         operator: Optional[Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float]] = None)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    output_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for the outputs (e.g logits or after softmax).

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      Default to the last layer.

      It is recommended to use the layer before Softmax.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 64

    +
      +

    • Number of inputs to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    operator : Optional[Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float]] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute saliency maps for a batch of samples. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Saliency maps.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/smoothgrad/index.html b/v1.1.0/api/attributions/smoothgrad/index.html new file mode 100755 index 00000000..53d33e75 --- /dev/null +++ b/v1.1.0/api/attributions/smoothgrad/index.html @@ -0,0 +1,1453 @@ + + + + + + + + + + + + + + + + + + + + + + + SmoothGrad - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
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+ + + +
+
+
+ + + +
+
+ + + + + + + +

SmoothGrad

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

SmoothGrad is a gradient-based explanation method, which, as the name suggests, averages the +gradient at several points corresponding to small perturbations around the point of interest. +The smoothing effect induced by the average help reducing the visual noise, and hence improve the +explanations.

+
+

Quote

+

[...] The gradient at any given point will be less meaningful than a local average of gradient +values. This suggests a new way to create improved sensitivity maps: instead of basing a +visualization directly on the gradient, we could base it on a smoothing of the gradients with a +Gaussian kernel.

+

-- SmoothGrad: removing noise by adding noise (2017)1

+
+

More precisely, the explanation \(\phi\) for an input \(x\) and a classifier \(f\) is defined as

+
\[ +\phi = \mathbb{E}_{\delta ~\sim~ \mathcal{N}(0, \sigma^2) }( \nabla_x f(x + \delta) ) +\approx \frac{1}{N} \sum_{i=0}^N \nabla_x f(x + \delta_i) +\]
+

The \(\sigma\) in the formula is controlled using the noise +parameter, and the expectation is estimated using \(N\) samples controlled by the nb_samples parameter.

+
+

Tip

+

It is recommended to have a noise level \(\sigma\) at about 20% of the range of your inputs, i.e. \(\sigma=0.2\) if your inputs are between \([0, 1]\) or \(\sigma=0.4\) if your inputs are between \([-1, 1]\).

+
+

Example

+
from xplique.attributions import SmoothGrad
+
+# load images, labels and model
+# ...
+
+method = SmoothGrad(model, nb_samples=50, noise=0.15)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

SmoothGrad

+

Used to compute the SmoothGrad, by averaging Saliency maps of noisy samples centered on the +original sample. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         output_layer: Union[str, int, None] = None,
         batch_size: Optional[int] = 32,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
         nb_samples: int = 50,
         noise: float = 0.2)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    output_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for the outputs (e.g logits or after softmax).

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      Default to the last layer.

      It is recommended to use the layer before Softmax.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 32

    +
      +

    • Number of inputs to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    nb_samples : int = 50

    +
      +

    • Number of noisy samples generated for the smoothing procedure.

      • +
    +
    • +
  • +

    noise : float = 0.2

    +
      +

    • Scalar, noise used as standard deviation of a normal law centered on zero.

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute SmoothGrad for a batch of samples. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Smoothed gradients, same shape as the inputs.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    SmoothGrad: removing noise by adding noise (2017) ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/sobol/index.html b/v1.1.0/api/attributions/sobol/index.html new file mode 100755 index 00000000..1ccd1357 --- /dev/null +++ b/v1.1.0/api/attributions/sobol/index.html @@ -0,0 +1,1494 @@ + + + + + + + + + + + + + + + + + + + + + + + Sobol Attribution Method - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + + + +
+ + +
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+ + + + + + +
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+ + + + + +
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+ + + +
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+ + + +
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+ + + +
+
+ + + + + + + +

Sobol Attribution Method

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

The Sobol attribution method from Fel, Cadène & al.1 is an attribution method grounded in Sensitivity Analysis. +Beyond modeling the individual contributions of image regions, Sobol indices provide +efficient way to capture higher-order interactions between image regions and their +contributions to a neural network’s prediction through the lens of variance.

+
+

Quote

+

The total Sobol index \(ST_i\) which measures the contribution +of the variable \(X_i\) as well as its interactions of any order with any other input variables to the model +output variance.

+

-- Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis (2021)1

+
+

More precisely, the attribution score \(\phi_i\) for an input variable \(x_i\), is defined as

+
\[ \phi_i = \frac{\mathbb{E}_{X \sim i}(Var_{X_i}(f(x) | X_{\sim i}))} {Var +(f(X +))} \]
+

Where \(\mathbb{E}_{X \sim i}(Var_{X_i}(f(x) | X_{\sim i}))\) is the expected variance +that would be left if all variables but \(X_{\sim i}\) were to be fixed.

+

In order to generate stochasticity(\(X_i\)), a perturbation function is used and uses perturbation masks +to modulate the generated perturbation. The perturbation functions available are inpainting +that modulates pixel regions to a baseline state, amplitude and blurring.

+

The calculation of the indices also requires an estimator -- in practice this parameter does not +change the results much -- JansenEstimator being recommended.

+

Finally the exploration of the manifold exploration is made using a sampling method, several samplers are proposed: Quasi-Monte +Carlo (ScipySobolSequence, recommended) using Scipy's sobol sequence, Latin hypercubes + -- LHSAmpler -- or Halton's sequences HaltonSequence.

+
+

Tip

+

For a quick a faithful explanations, we recommend to use grid_size in \([7, 12)\), +nb_design in \(\{16, 32, 64\}\) (more is useless), and a QMC sampler.

+
+

Example

+
from xplique.attributions import SobolAttributionMethod
+from xplique.attributions.global_sensitivity_analysis import (
+    JansenEstimator, GlenEstimator,
+    LHSampler, ScipySobolSequence,
+    HaltonSequence)
+
+# load images, labels and model
+# ...
+
+# default explainer (recommended)
+explainer = SobolAttributionMethod(model, grid_size=8, nb_design=32)
+explanations = method(images, labels) # one-hot encoded labels
+
+

If you want to change the estimator or the sampling:

+
from xplique.attributions import SobolAttributionMethod
+from xplique.attributions.global_sensitivity_analysis import (
+    JansenEstimator, GlenEstimator,
+    LHSampler, ScipySobolSequence,
+    HaltonSequence)
+
+# load images, labels and model
+# ...
+
+explainer_lhs = SobolAttributionMethod(model, grid_size=8, nb_design=32, 
+                                       sampler=LHSampler(), 
+                                       estimator=GlenEstimator())
+explanations_lhs = explainer_lhs(images, labels)
+
+

Notebooks

+ +

SobolAttributionMethod

+

Sobol' Attribution Method. +Compute the total order Sobol' indices using a perturbation function on a grid and an +adapted sampling as described in the original paper. +

+

__init__(self,
         model,
         grid_size: int = 8,
         nb_design: int = 32,
         sampler: Optional[xplique.attributions.global_sensitivity_analysis.replicated_designs.ReplicatedSampler] = None,
         estimator: Optional[xplique.attributions.global_sensitivity_analysis.sobol_estimators.SobolEstimator] = None,
         perturbation_function: Union[Callable, str, None] = 'inpainting',
         batch_size=256,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None)

+

Parameters

+
    +
  • +

    model : model

    +
      +

    • Model used for computing explanations.

      • +
    +
    • +
  • +

    grid_size : int = 8

    +
      +

    • Cut the image in a grid of (grid_size, grid_size) to estimate an indice per cell.

      • +
    +
    • +
  • +

    nb_design : int = 32

    +
      +

    • Must be a power of two. Number of design, the number of forward will be: nb_design * (grid_size**2 + 2). Generally not above 32.

      • +
    +
    • +
  • +

    sampler : Optional[xplique.attributions.global_sensitivity_analysis.replicated_designs.ReplicatedSampler] = None

    +
      +

    • Sampler used to generate the (quasi-)monte carlo samples, QMC (sobol sequence recommended). For more option, see the sampler module.

      • +
    +
    • +
  • +

    estimator : Optional[xplique.attributions.global_sensitivity_analysis.sobol_estimators.SobolEstimator] = None

    +
      +

    • Estimator used to compute the total order sobol' indices, Jansen recommended. For more option, see the estimator module.

      • +
    +
    • +
  • +

    perturbation_function : Union[Callable, str, None] = 'inpainting'

    +
      +

    • Function to call to apply the perturbation on the input. Can also be string in 'inpainting', 'blur'.

      • +
    +
    • +
  • +

    batch_size : batch_size=256

    +
      +

    • Batch size to use for the forwards.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute the total Sobol' indices according to the explainer parameter (perturbation +function, grid size...). Accept Tensor, numpy array or tf.data.Dataset (in that case +targets is None). +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Images to be explained, either tf.dataset, Tensor or numpy array.

      If Dataset, targets should not be provided (included in Dataset).

      Expected shape (N, W, H, C) or (N, W, H).

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • One-hot encoding for classification or direction {-1, +1} for regression.

      Tensor or numpy array.

      Expected shape (N, C) or (N).

      • +
    +
    • +
+

Return

+
    +
  • +

    attributions_maps : tf.Tensor

    +
      +

    • GSA Attribution Method explanations, same shape as the inputs except for the channels.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis (2021) ↩↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/square_grad/index.html b/v1.1.0/api/attributions/square_grad/index.html new file mode 100755 index 00000000..0c52f5f3 --- /dev/null +++ b/v1.1.0/api/attributions/square_grad/index.html @@ -0,0 +1,1437 @@ + + + + + + + + + + + + + + + + + + + + + + + SquareGrad - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
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+ + + +
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+ + + +
+
+
+ + + +
+
+ + + + + + + +

Square Grad

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

Similar to SmoothGrad, Square-Grad is a gradient-based explanation method, which, as the name suggests, averages the +square of the gradient at several points corresponding to small perturbations around the point of interest. +The smoothing effect induced by the average help reducing the visual noise, and hence improve the +explanations.

+

More precisely, the explanation \(\phi\) for an input \(x\) and a classifier \(f\) is defined as

+
\[ +\phi = \mathbb{E}_{\delta ~\sim~ \mathcal{N}(0, \sigma^2) }( (\nabla_x f(x + \delta))^2 ) +\approx \frac{1}{N} \sum_{i=0}^N (\nabla_x f(x + \delta_i))^2 +\]
+

The \(\sigma\) in the formula is controlled using the noise +parameter, and the expectation is estimated using \(N\) samples controlled by the nb_samples parameter.

+
+

Tip

+

It is recommended to have a noise level \(\sigma\) at about 20% of the range of your inputs, i.e. \(\sigma=0.2\) if your inputs are between \([0, 1]\) or \(\sigma=0.4\) if your inputs are between \([-1, 1]\).

+
+

Example

+
from xplique.attributions import SquareGrad
+
+# load images, labels and model
+# ...
+
+method = SquareGrad(model, nb_samples=50, noise=0.15)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

SquareGrad

+

SquareGrad (or SmoothGrad^2) is an unpublished variant of classic SmoothGrad which squares +each gradients of the noisy inputs before averaging. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         output_layer: Union[str, int, None] = None,
         batch_size: Optional[int] = 32,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
         nb_samples: int = 50,
         noise: float = 0.2)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    output_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for the outputs (e.g logits or after softmax).

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      Default to the last layer.

      It is recommended to use the layer before Softmax.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 32

    +
      +

    • Number of inputs to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    nb_samples : int = 50

    +
      +

    • Number of noisy samples generated for the smoothing procedure.

      • +
    +
    • +
  • +

    noise : float = 0.2

    +
      +

    • Scalar, noise used as standard deviation of a normal law centered on zero.

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute SmoothGrad for a batch of samples. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Smoothed gradients, same shape as the inputs.

      • +
    +
    • +
+


+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/attributions/vargrad/index.html b/v1.1.0/api/attributions/vargrad/index.html new file mode 100755 index 00000000..5ef82337 --- /dev/null +++ b/v1.1.0/api/attributions/vargrad/index.html @@ -0,0 +1,1437 @@ + + + + + + + + + + + + + + + + + + + + + + + VarGrad - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
+
+ +
+ + + + + + + + +
+ + +
+ +
+ + + + + + +
+
+ + + +
+
+
+ + + + + +
+
+
+ + + +
+
+
+ + + +
+
+
+ + + +
+
+ + + + + + + +

VarGrad

+

+ +View colab tutorial | + + +View source | +📰 Paper

+

Similar to SmoothGrad, VarGrad is a gradient-based explanation method, which, as the name suggests, return the +variance of the gradient at several points corresponding to small perturbations around the point of interest. +The smoothing effect induced by the average help reducing the visual noise, and hence improve the +explanations.

+

More precisely, the explanation \(\phi\) for an input \(x\) and a classifier \(f\) is defined as

+
\[ +\phi = \mathbb{V}_{\delta ~\sim~ \mathcal{N}(0, \sigma^2) }( \nabla_x f(x + \delta) ) +\approx \frac{1}{N-1} \sum_{i=0}^N (\nabla_x f(x + \delta_i) - \hat{\mu})^2 +\]
+

Where \(\hat{\mu} = \frac{1}{N} \sum_{i=0}^N \nabla_x f(x + \delta_i)\) is the empirical mean. +The \(\sigma\) in the formula is controlled using the noise +parameter, and the expectation is estimated using \(N\) samples controlled by the nb_samples parameter.

+
+

Tip

+

It is recommended to have a noise level \(\sigma\) at about 20% of the range of your inputs, i.e. \(\sigma=0.2\) if your inputs are between \([0, 1]\) or \(\sigma=0.4\) if your inputs are between \([-1, 1]\).

+
+

Example

+
from xplique.attributions import VarGrad
+
+# load images, labels and model
+# ...
+
+method = VarGrad(model, nb_samples=50, noise=0.15)
+explanations = method.explain(images, labels)
+
+

Notebooks

+ +

VarGrad

+

VarGrad is a variance analog to SmoothGrad. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         output_layer: Union[str, int, None] = None,
         batch_size: Optional[int] = 32,
         operator: Union[xplique.commons.operators.Tasks, str,
         Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None,
         nb_samples: int = 50,
         noise: float = 0.2)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • The model from which we want to obtain explanations

      • +
    +
    • +
  • +

    output_layer : Union[str, int, None] = None

    +
      +

    • Layer to target for the outputs (e.g logits or after softmax).

      If an int is provided it will be interpreted as a layer index.

      If a string is provided it will look for the layer name.

      Default to the last layer.

      It is recommended to use the layer before Softmax.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 32

    +
      +

    • Number of inputs to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    operator : Union[xplique.commons.operators.Tasks, str, Callable[[keras.src.engine.training.Model, tf.Tensor, tf.Tensor], float], None] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    nb_samples : int = 50

    +
      +

    • Number of noisy samples generated for the smoothing procedure.

      • +
    +
    • +
  • +

    noise : float = 0.2

    +
      +

    • Scalar, noise used as standard deviation of a normal law centered on zero.

      • +
    +
    • +
+

explain(self,
        inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
        targets: Union[tf.Tensor, numpy.ndarray, None] = None) -> tf.Tensor

+

Compute SmoothGrad for a batch of samples. +

+

Parameters

+
    +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Dataset, Tensor or Array. Input samples to be explained.

      If Dataset, targets should not be provided (included in Dataset).

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

      More information in the documentation.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Tensor or Array. One-hot encoding of the model's output from which an explanation is desired. One encoding per input and only one output at a time. Therefore, the expected shape is (N, output_size).

      More information in the documentation.

      • +
    +
    • +
+

Return

+
    +
  • +

    explanations : tf.Tensor

    +
      +

    • Smoothed gradients, same shape as the inputs.

      • +
    +
    • +
+


+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/concepts/cav/index.html b/v1.1.0/api/concepts/cav/index.html new file mode 100755 index 00000000..552a25d4 --- /dev/null +++ b/v1.1.0/api/concepts/cav/index.html @@ -0,0 +1,1412 @@ + + + + + + + + + + + + + + + + + + + + + + + Cav - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ + + +
+
+ + + + + + + +

CAV

+

CAV or Concept Activation Vector represent a high-level concept as a vector that +indicate the direction to take (for activations of a layer) to maximise this concept.

+
+

Quote

+

[...] CAV for a concept is simply a vector in the direction of the values +(e.g., activations) of that concept’s set of examples… we derive CAVs by +training a linear classifier between a concept’s examples and random counter +examples and then taking the vector orthogonal to the decision boundary.

+

-- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) (2018).1

+
+

For a layer \(f_l\) of a model, we seek the linear classifier \(v_l \in \mathbb{R}^d\) +that separate the activations of the positive examples \(\{ f_l(x) : x \in \mathcal{P} \}\), +and the activations of the random/negative examples \(\{ f_l(x) : x \in \mathcal{R} \}\).

+

Example

+
from xplique.concepts import Cav
+
+cav_renderer = Cav(model, 'mixed4d', classifier='SGD', test_fraction=0.1)
+cav = cav_renderer(positive_examples, random_examples)
+
+

Cav

+

Used to compute the Concept Activation Vector, which is a vector in the direction of the +activations of that concept’s set of examples. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         target_layer: Union[str, int],
         classifier: Union[str, Callable] = 'SGD',
         test_fraction: float = 0.2,
         batch_size: int = 64,
         verbose: bool = False)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • Model to extract concept from.

      • +
    +
    • +
  • +

    target_layer : Union[str, int]

    +
      +

    • Index of the target layer or name of the layer.

      • +
    +
    • +
  • +

    classifier : 'SGD' or 'SVC' or Sklearn model, optional

    +
      +

    • Default implementation use SGD with hinge classifier (linear SVM), SVC use libsvm but the computation time is longer.

      • +
    +
    • +
  • +

    test_fraction : float = 0.2

    +
      +

    • Fraction of the dataset used for test

      • +
    +
    • +
  • +

    batch_size : int = 64

    +
      +

    • Batch size during the activations extraction

      • +
    +
    • +
  • +

    verbose : bool = False

    +
      +

    • If true, display information while training the classifier

      • +
    +
    • +
+

fit(self,
    positive_dataset: tf.Tensor,
    negative_dataset: tf.Tensor) -> tf.Tensor

+

Compute and return the Concept Activation Vector (CAV) associated to the dataset and the +layer targeted. +

+

Parameters

+
    +
  • +

    positive_dataset : tf.Tensor

    +
      +

    • Dataset of positive samples : samples containing the concept.

      • +
    +
    • +
  • +

    negative_dataset : tf.Tensor

    +
      +

    • Dataset of negative samples : samples without the concept

      • +
    +
    • +
+

Return

+
    +
  • +

    cav : tf.Tensor

    +
      +

    • Vector of the same shape as the layer output

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) (2018). ↩

    +
    2. +
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TCAV

+

TCAV or Testing with Concept Activation Vector consist consists in using a +concept activation vector (CAV) to quantify the relationship between this +concept and a class.

+

This is done by using the directional derivative of the concept vector on +several samples of a given class and measuring the percentage of positive +(a positive directional derivative indicating that an infinitesimal addition +of the concept increases the probability of the class).

+

For a Concept Activation Vector \(v_l\) of a layer \(f_l\) of a model, and \(f_{c}\) +the logit of the class \(c\), we measure the directional derivative +\(S_c(x) = v_l \cdot \frac{ \partial{f_c(x)} } { \partial{f_l}(x) }\).

+

The TCAV score is the percentage of elements of the class \(c\) for which the \(S_c\) +is positive.

+
\[ TCAV_c = \frac{|x \in \mathcal{X}^c : S_c(x) > 0 |}{ | \mathcal{X}^c | } \]
+

Example

+
from xplique.concepts import Tcav
+
+tcav_renderer = Tcav(model, 'mixed4d') # you can also pass the layer index (e.g -1)
+tcav_score = tcav_renderer(samples, class_index, cav)
+
+

Tcav

+

Used to Test a Concept Activation Vector, using the sign of the directional derivative of a +concept vector relative to a class. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         target_layer: Union[str, int],
         batch_size: Optional[int] = 64)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • Model to extract concept from.

      • +
    +
    • +
  • +

    target_layer : Union[str, int]

    +
      +

    • Index of the target layer or name of the layer.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 64

    +
      +

    • Batch size during the predictions.

      • +
    +
    • +
+

directional_derivative(multi_head_model: keras.src.engine.training.Model,
                       inputs: tf.Tensor,
                       label: int,
                       cav: tf.Tensor) -> tf.Tensor

+

Compute the gradient of the label relative to the activations of the CAV layer. +

+

Parameters

+
    +
  • +

    multi_head_model : keras.src.engine.training.Model

    +
      +

    • Model reconfigured, first output is the activations of the CAV layer, and the second output is the prediction layer.

      • +
    +
    • +
  • +

    inputs : tf.Tensor

    +
      +

    • Input sample on which to test the influence of the concept.

      • +
    +
    • +
  • +

    label : int

    +
      +

    • Index of the class to test.

      • +
    +
    • +
  • +

    cav : tf.Tensor

    +
      +

    • Concept Activation Vector, same shape as the activations output.

      • +
    +
    • +
+

Return

+
    +
  • +

    directional_derivative : tf.Tensor

    +
      +

    • Directional derivative values of each samples.

      • +
    +
    • +
+


+

score(self,
      inputs: tf.Tensor,
      label: int,
      cav: tf.Tensor) -> float

+

Compute and return the TCAV score of the CAV associated to class tested. +

+

Parameters

+
    +
  • +

    inputs : tf.Tensor

    +
      +

    • Input sample on which to test the influence of the concept.

      • +
    +
    • +
  • +

    label : int

    +
      +

    • Index of the class to test.

      • +
    +
    • +
  • +

    cav : tf.Tensor

    +
      +

    • Concept Activation Vector, see CAV module.

      • +
    +
    • +
+

Return

+
    +
  • +

    tcav : float

    +
      +

    • Percentage of sample for which increasing the concept has a positive impact on the class logit.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) (2018). ↩

    +
    2. +
+
+ + + + + + +
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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/feature_viz/feature_viz/index.html b/v1.1.0/api/feature_viz/feature_viz/index.html new file mode 100755 index 00000000..45cdd305 --- /dev/null +++ b/v1.1.0/api/feature_viz/feature_viz/index.html @@ -0,0 +1,1626 @@ + + + + + + + + + + + + + + + + + + + + + + + Feature visualization - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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Feature Visualization

+

One of the specificities of neural networks is their differentiability. This characteristic allows us to compute gradients, either the gradient of a loss with respect to the parameters, or in the case we are interested in here, of a part of the network with respect to the input. +This gradient then allows us to iteratively modify the input in order to maximize an objective such as a neuron, a channel or a combination of objectives.

+
+

Quote

+

If we want to understand individual features, we can search for examples where they have high values +either for a neuron at an individual position, or for an entire channel. +-- Feature Visualization -- How neural networks build up their understanding of images (2017)1

+
+

More precisely, the explanation of a neuron \(n\) denoted as \(\phi^{(n)}\) is an input \(x* \in \mathcal{X}\) such that

+
\[ \phi^{(n)} = \underset{x}{arg\ max}\ f(x)^{(n)} - \mathcal{R}(x) \]
+

with \(f(x)^{(n)}\) the neuron score for an input \(x\) and \(\mathcal{R}(x)\) a regularization term. +In practice it turns out that preconditioning the input in a decorrelated space such as the frequency domain allows to obtain more consistent results and to better formulate the regularization (e.g. by controlling the rate of high frequency and low frequency desired).

+

Examples

+

Optimize the ten logits of a neural network (we recommend to remove the softmax activation of your network).

+
from xplique.features_visualizations import Objective
+from xplique.features_visualizations import optimize
+
+# load a model...
+
+# targeting the 10 logits of the layer 'logits'
+# we can also target a layer by its index, like -1 for the last layer
+logits_obj = Objective.neuron(model, "logits", list(range(10)))
+images, obj_names = optimize(logits_obj) # 10 images, one for each logits
+
+

Create a combination of multiple objectives and aggregate them

+
from xplique.features_visualizations import Objective
+from xplique.features_visualizations import optimize
+
+# load a model...
+
+# target the first logits neuron
+logits_obj = Objective.neuron(model, "logits", 0)
+# target the third layer
+layer_obj = Objective.layer(model, "conv2d_1")
+# target the second channel of another layer
+channel_obj = Objective.channel(model, "mixed4_2", 2)
+
+# combine the objective
+obj = logits_obj * 1.0 + layer_obj * 3.0 + channel_obj * (-5.0)
+images, obj_names = optimize(logits_obj) # 1 resulting image
+
+

Objective

+

Use to combine several sub-objectives into one. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         layers: List[keras.src.engine.base_layer.Layer],
         masks: List[tf.Tensor],
         funcs: List[Callable],
         multipliers: List[float],
         names: List[str])

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • Model used for optimization.

      • +
    +
    • +
  • +

    layers : List[keras.src.engine.base_layer.Layer]

    +
      +

    • A list of the layers output for each sub-objectives.

      • +
    +
    • +
  • +

    masks : List[tf.Tensor]

    +
      +

    • A list of masks that will be applied on the targeted layer for each sub-objectives.

      • +
    +
    • +
  • +

    funcs : List[Callable]

    +
      +

    • A list of loss functions for each sub-objectives.

      • +
    +
    • +
  • +

    multipliers : List[float]

    +
      +

    • A list of multiplication factor for each sub-objectives

      • +
    +
    • +
  • +

    names : List[str]

    +
      +

    • A list of name for each sub-objectives

      • +
    +
    • +
+

channel(model: keras.src.engine.training.Model,
        layer: Union[str, int],
        channel_ids: Union[int, List[int]],
        multiplier: float = 1.0,
        names: Union[str, List[str], None] = None)

+

Util to build an objective to maximise a channel. +

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • Model used for optimization.

      • +
    +
    • +
  • +

    layer : Union[str, int]

    +
      +

    • Index or name of the targeted layer.

      • +
    +
    • +
  • +

    channel_ids : Union[int, List[int]]

    +
      +

    • Indexes of the channels to maximise.

      • +
    +
    • +
  • +

    multiplier : float = 1.0

    +
      +

    • Multiplication factor of the objectives.

      • +
    +
    • +
  • +

    names : Union[str, List[str], None] = None

    +
      +

    • Names for each objectives.

      • +
    +
    • +
+

Return

+
    +
  • +

    objective

    +
      +

    • An objective containing a sub-objective for each channels.

      • +
    +
    • +
+


+

compile(self) -> Tuple[keras.src.engine.training.Model,
        Callable,
        List[str], Tuple]

+

Compile all the sub-objectives into one and return the objects +for the optimisation process. +

+

Return

+
    +
  • +

    model_reconfigured : Tuple[keras.src.engine.training.Model, Callable, List[str], Tuple]

    +
      +

    • Model with the outputs needed for the optimization.

      • +
    +
    • +
  • +

    objective_function : Tuple[keras.src.engine.training.Model, Callable, List[str], Tuple]

    +
      +

    • Function to call that compute the loss for the objectives.

      • +
    +
    • +
  • +

    names : Tuple[keras.src.engine.training.Model, Callable, List[str], Tuple]

    +
      +

    • Names of each objectives.

      • +
    +
    • +
  • +

    input_shape : Tuple[keras.src.engine.training.Model, Callable, List[str], Tuple]

    +
      +

    • Shape of the input, one sample for each optimization.

      • +
    +
    • +
+


+

direction(model: keras.src.engine.training.Model,
          layer: Union[str, int],
          vectors: Union[tf.Tensor, List[tf.Tensor]],
          multiplier: float = 1.0,
          cossim_pow: float = 2.0,
          names: Union[str, List[str], None] = None)

+

Util to build an objective to maximise a direction of a layer. +

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • Model used for optimization.

      • +
    +
    • +
  • +

    layer : Union[str, int]

    +
      +

    • Index or name of the targeted layer.

      • +
    +
    • +
  • +

    vectors : Union[tf.Tensor, List[tf.Tensor]]

    +
      +

    • Direction(s) to optimize.

      • +
    +
    • +
  • +

    multiplier : float = 1.0

    +
      +

    • Multiplication factor of the objective.

      • +
    +
    • +
  • +

    cossim_pow : float = 2.0

    +
      +

    • Power of the cosine similarity, higher value encourage the objective to care more about the angle of the activations.

      • +
    +
    • +
  • +

    names : Union[str, List[str], None] = None

    +
      +

    • A name for each objectives.

      • +
    +
    • +
+

Return

+
    +
  • +

    objective

    +
      +

    • An objective ready to be compiled

      • +
    +
    • +
+


+

layer(model: keras.src.engine.training.Model,
      layer: Union[str, int],
      reducer: str = 'magnitude',
      multiplier: float = 1.0,
      name: Optional[str] = None)

+

Util to build an objective to maximise a layer. +

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • Model used for optimization.

      • +
    +
    • +
  • +

    layer : Union[str, int]

    +
      +

    • Index or name of the targeted layer.

      • +
    +
    • +
  • +

    reducer : str = 'magnitude'

    +
      +

    • Type of reduction to apply, 'mean' will optimize the mean value of the layer, 'magnitude' will optimize the mean of the absolute values.

      • +
    +
    • +
  • +

    multiplier : float = 1.0

    +
      +

    • Multiplication factor of the objective.

      • +
    +
    • +
  • +

    name : Optional[str] = None

    +
      +

    • A name for the objective.

      • +
    +
    • +
+

Return

+
    +
  • +

    objective

    +
      +

    • An objective ready to be compiled

      • +
    +
    • +
+


+

neuron(model: keras.src.engine.training.Model,
       layer: Union[str, int],
       neurons_ids: Union[int, List[int]],
       multiplier: float = 1.0,
       names: Union[str, List[str], None] = None)

+

Util to build an objective to maximise a neuron. +

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • Model used for optimization.

      • +
    +
    • +
  • +

    layer : Union[str, int]

    +
      +

    • Index or name of the targeted layer.

      • +
    +
    • +
  • +

    neurons_ids : Union[int, List[int]]

    +
      +

    • Indexes of the neurons to maximise.

      • +
    +
    • +
  • +

    multiplier : float = 1.0

    +
      +

    • Multiplication factor of the objectives.

      • +
    +
    • +
  • +

    names : Union[str, List[str], None] = None

    +
      +

    • Names for each objectives.

      • +
    +
    • +
+

Return

+
    +
  • +

    objective

    +
      +

    • An objective containing a sub-objective for each neurons.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Feature Visualization -- How neural networks build up their understanding of images (2017) ↩

    +
    2. +
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+ + + + + + +
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+ + +
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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/feature_viz/maco/index.html b/v1.1.0/api/feature_viz/maco/index.html new file mode 100755 index 00000000..5be75340 --- /dev/null +++ b/v1.1.0/api/feature_viz/maco/index.html @@ -0,0 +1,1437 @@ + + + + + + + + + + + + + + + + + + + + + + + Modern Feature Visualization (MaCo) - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Modern Feature Visualization (MaCo)

+

Feature visualization has become increasingly popular, especially after the groundbreaking work by Olah et al. 1, which established it as a vital tool for enhancing explainability. Despite its significance, the widespread adoption of feature visualization has been hindered by the reliance on various tricks to create interpretable images, making it challenging to scale the method effectively for deeper neural networks.

+

Addressing these limitations, a recent method called MaCo 2 offers a straightforward solution. The core concept involves generating images by optimizing the phase spectrum while keeping the magnitude of the Fourier spectrum constant. This ensures that the generated images reside in the space of natural images in the Fourier domain, providing a more stable and interpretable approach.

+
+

Quote

+

It is known that human recognition of objects in images is driven not by magnitude but by phase. +Motivated by this, we propose to optimize the phase of the Fourier spectrum while fixing +its magnitude to a typical value of a natural image (with few high frequencies). In particular, the +magnitude is kept constant at the average magnitude computed over a set of natural images (such as ImageNet)

+

MaCo -- Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization (2023)2

+
+

To put it more precisely, let \(\phi^{(n)}\) be an explanation of a neuron \(n\), and let \(x* \in \mathcal{X}\) be the corresponding input defined as:

+
\[ \varphi* = \underset{\varphi}{arg\ max}\ f(\mathcal{F}^{-1}(r e^{i \varphi}))^{(n)} \]
+

where \(x* = \mathcal{F}^{-1}(r e^{i \varphi*})\), \(f(x)^{(n)}\) represents the neuron score for a given input, and \(\mathcal{F}^{-1}\) denotes the 2-D inverse Fourier transform.

+

In the optimization process, MaCo also generates an alpha mask, which is used to identify the most important area of the generated image. For the purpose of correctly visualizing the image blended with the alpha mask, we provide utilities in the xplique.plot module.

+

Notebooks

+
    +
  • MaCo: Getting started In this notebook, you'll be introduced to the fundamentals of MaCo while also experimenting with various hyperparameters.
    • +
+

Examples

+

To optimize the logit 1 of your neural network (we recommend to remove the softmax activation of your network).

+
from xplique.features_visualizations import Objective
+from xplique.features_visualizations import maco
+from xplique.plot import plot_maco
+# load a model...
+
+# targeting the logit 1 of the layer 'logits'
+# we can also target a layer by its index, like -1 for the last layer
+logits_obj = Objective.neuron(model, "logits", 1)
+image, alpha = maco(logits_obj)
+plot_maco(image, alpha)
+
+

Or if you want to visualize a specific CAV (or any direction, like multiple neurons) in your models:

+
from xplique.features_visualizations import Objective
+from xplique.features_visualizations import maco
+from xplique.plot import plot_maco
+# load a model...
+
+# cav is a vector of the shape of an activation in the -2 layer
+# e.g 2048 for Resnet50
+logits_obj = Objective.direction(model, -2, cav)
+image, alpha = maco(logits_obj)
+plot_maco(image, alpha)
+
+

maco(objective: xplique.features_visualizations.objectives.Objective,
     optimizer: Optional[keras.src.optimizers.optimizer.Optimizer] = None,
     nb_steps: int = 256,
     noise_intensity: Union[float, Callable, None] = 0.08,
     box_size: Union[float, Callable, None] = None,
     nb_crops: Optional[int] = 32,
     values_range: Tuple[float, float] = (-1, 1),
     custom_shape: Optional[Tuple] = (512, 512)) -> Tuple[tf.Tensor, tf.Tensor]

+

Optimise a single objective using MaCo method. Note that, unlike classic fourier optimization, +we can only optimize for one objective at a time. +

+

Parameters

+
    +
  • +

    objective : xplique.features_visualizations.objectives.Objective

    +
      +

    • Objective object.

      • +
    +
    • +
  • +

    optimizer : Optional[keras.src.optimizers.optimizer.Optimizer] = None

    +
      +

    • Optimizer used for gradient ascent, default Nadam(lr=1.0).

      • +
    +
    • +
  • +

    nb_steps : int = 256

    +
      +

    • Number of iterations.

      • +
    +
    • +
  • +

    noise_intensity : Union[float, Callable, None] = 0.08

    +
      +

    • Control the noise injected at each step. Either a float : each step we add noise with same std, or a function that associate for each step a noise intensity.

      • +
    +
    • +
  • +

    box_size : Union[float, Callable, None] = None

    +
      +

    • Control the average size of the crop at each step. Either a fixed float (e.g 0.5 means the crops will be 50% of the image size) or a function that take as parameter the step and return the average box size. Default to linear decay from 50% to 5%.

      • +
    +
    • +
  • +

    nb_crops : Optional[int] = 32

    +
      +

    • Number of crops used at each steps, higher make the optimisation slower but make the results more stable. Default to 32.

      • +
    +
    • +
  • +

    values_range : Tuple[float, float] = (-1, 1)

    +
      +

    • Range of values of the inputs that will be provided to the model, e.g (0, 1) or (-1, 1).

      • +
    +
    • +
  • +

    custom_shape : Optional[Tuple] = (512, 512)

    +
      +

    • If specified, optimizes images of the given size. Used with a low box size to optimize bigger images crop by crop.

      • +
    +
    • +
+

Return

+
    +
  • +

    image_optimized : Tuple[tf.Tensor, tf.Tensor]

    +
      +

    • Optimized image for the given objective.

      • +
    +
    • +
  • +

    transparency : Tuple[tf.Tensor, tf.Tensor]

    +
      +

    • Transparency of the image, i.e the sum of the absolute value of the gradients of the image with respect to the objective.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Feature Visualization -- How neural networks build up their understanding of images (2017) ↩

    +
    2. +
  2. +

    MaCo -- Unlocking Feature Visualization for Deeper Networks with MAgnitude Constrained Optimization (2023) ↩↩

    +
    3. +
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+ + + + + + +
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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/metrics/api_metrics/index.html b/v1.1.0/api/metrics/api_metrics/index.html new file mode 100755 index 00000000..6e893b71 --- /dev/null +++ b/v1.1.0/api/metrics/api_metrics/index.html @@ -0,0 +1,1440 @@ + + + + + + + + + + + + + + + + + + + + + + + API Description - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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API: Metrics

+ +

Context

+

As the XAI field continues on being trendy, the quantity of materials at disposal to explain DL models keeps on growing. Especially, there is an increasing need to benchmark and evaluate those different approaches. Mainly, there is an urge to evaluate the quality of explanations provided by attribution methods.

+
+

Info

+

Note that, even though some work exists for other tasks, this challenge has been mainly tackled in the context of Computer Vision tasks.

+
+

As pointed out by Petsiuk et al. most explanations approaches are used to be evaluated in a human-centred way. For instance, an attribution method was considered as good if it pointed out the same relevant pixels as the ones highlighted by human users. While this kind of evaluation allows giving some user trust it can easily be biased. Therefore, the authors introduced two automatic evaluation metrics that rely solely on the drop or rise in the probability of a class as important pixels (defined by the saliency map) are removed or added. Those are not the only available metrics and we propose here to present the API we used as common ground and then to dive into more specifity.

+

Common API

+

All metrics inherits from the base class BaseAttributionMetric which has the following __init__ arguments:

+
    +
  • model: The model from which we want to obtain explanations
    • +
  • +

    inputs: Input samples to be explained

    +
    +

    Info

    +

    Inputs should be the same as defined in the model's documentation

    +
    +
    • +
  • +

    targets: Specify the kind of explanations we want depending on the task at end (e.g. a one-hot encoding of a class of interest, a difference to a ground-truth value..)

    +
    +

    Info

    +

    Targets should be the same as defined in the model's documentation

    +
    +
    • +
  • +

    batch_size

    +
    • +
  • +

    activation: A string that belongs to [None, 'sigmoid', 'softmax']. See the dedicated section for details

    +
    • +
+

Then we can distinguish two category of metrics:

+
    +
  • Those which only need the attribution ouputs of an explainer: ExplanationMetric, namely those which evaluate Fidelity (MuFidelity, Deletion, Insertion)
    • +
  • Those which need the explainer: ExplainerMetric (AverageStability)
    • +
+

ExplanationMetric

+

Those metrics are agnostic of the explainer used and rely only on the attributions mappings it gives.

+
+

Tip

+

Therefore, you can use them with other explainer than those provided in Xplique!

+
+

All metrics inheriting from this class have another argument in their __init__ method:

+
    +
  • operator: Optionnal function wrapping the model. It can be seen as a metric which allow to evaluate model evolution. For more details, see the attribution's API Description and the operator documentation.
    • +
+
+

Info

+

The operator used here should match the one used to compute the explanations!

+
+

All metrics inheriting from this class have to define a method evaluate which will take as input the attributions given by an explainer. Those attributions should correspond to the model, inputs and targets used to build the metric object.

+

ExplainerMetric

+

Those metrics will not assess the quality of the explanations provided but (also) the explainer itself.

+

All metrics inheriting from this class have to define a method evaluate which will take as input the explainer evaluated.

+
+

Info

+

It is even more important that inputs and targets are the same as defined in the attribution's API Description

+
+

Currently, there is only one Stability metric inheriting from this class:

+

Activation

+

This parameter specify if an additional activation layer should be added once a model has been called on the inputs when you have to compute the metric.

+

Indeed, most of the times it is recommended when you instantiate an explainer (i.e. an attribution methods) to provide a model which gives logits as explaining the logits is to explain the class, while explaining the softmax is to explain why this class rather than another.

+

However, when you compute metrics some were thought to measure a "drop of probability" when you occlude the "most relevant" part of an input. Thus, once you get your explanations (computed from the logits), you might need to have access to a probability score of occluded inputs of a specific class, thus to have access to the logits after a softmax or sigmoid layer.

+

Consequently, we add this activation parameter so one can provide a model that predicts logits but add an activation layer for the purpose of having probability when using a metric method.

+

The default behavior is to compute the metric without adding any activation layer to the model.

+
+

Note

+

There does not appear to be a consensus on the activation function to be used for metrics. Some papers use logits values (e.g., with mu-fidelity), while others use sigmoid or softmax (with deletion and insertion). We can only observe that changing the activation function has an effect on the ranking of the best methods.

+
+

Other Metrics

+

A Representatibity metric: MeGe is also available. Documentation about it should be added soon.

+

Notebooks

+ + + + + + + +
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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/metrics/avg_stability/index.html b/v1.1.0/api/metrics/avg_stability/index.html new file mode 100755 index 00000000..34be49c4 --- /dev/null +++ b/v1.1.0/api/metrics/avg_stability/index.html @@ -0,0 +1,1430 @@ + + + + + + + + + + + + + + + + + + + + + + + AverageStability - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ + + + + + + +

Average Stability

+

Average Stability is a Stability metric measuring how similar are explanations of similar inputs.

+
+

Quote

+

[...] We want to ensure that, if inputs are near each other and their model outputs are similar, then their explanations should be close to each other.

+

-- Evaluating and Aggregating Feature-based Model Explanations (2020)1

+
+

Formally, given a predictor \(f\), an explanation function \(g\), a point \(x\), a radius \(r\) and a two distance metric: \(\rho\) over the inputs and \(D\) over the explanations, the AverageStability is defined as:

+
\[ S = \underset{z : \rho(x, z) \leq r}{\int} D(g(f, x), g(f, z))\ dz \]
+
+

Info

+

The better the method, the smaller the score.

+
+

Example

+
from xplique.metrics import AverageStability
+from xplique.attributions import Saliency
+
+# load images, labels and model
+# ...
+explainer = Saliency(model)
+
+metric = AverageStability(model, inputs, labels)
+score = metric.evaluate(explainer)
+
+

AverageStability

+

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. +

+

__init__(self,
         model: Callable,
         inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
         targets: Union[tf.Tensor, numpy.ndarray, None] = None,
         batch_size: Optional[int] = 64,
         radius: float = 0.1,
         distance: Union[str, Callable] = 'l2',
         nb_samples: int = 20)

+

Parameters

+
    +
  • +

    model : Callable

    +
      +

    • Model used for computing metric.

      • +
    +
    • +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Input samples under study.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • One-hot encoded labels or regression target (e.g {+1, -1}), one for each sample.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 64

    +
      +

    • Number of samples to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    radius : float = 0.1

    +
      +

    • Maximum value of the uniform noise added to the inputs before recalculating their explanations.

      • +
    +
    • +
  • +

    distance : Union[str, Callable] = 'l2'

    +
      +

    • Distance metric between the explanations.

      • +
    +
    • +
  • +

    nb_samples : int = 20

    +
      +

    • Number of different neighbors points to try on each input to measure the stability.

      • +
    +
    • +
+

evaluate(self,
         explainer: Callable,
         base_explanations: Union[tf.Tensor, numpy.ndarray, None] = None) -> float

+

Evaluate the fidelity score. +

+

Parameters

+
    +
  • +

    explainer : Callable

    +
      +

    • Explainer or Explanations associated to each inputs.

      • +
    +
    • +
  • +

    base_explanations : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • Explanation for the inputs under study. Calculates them automatically if they are not provided.

      • +
    +
    • +
+

Return

+
    +
  • +

    stability_score : float

    +
      +

    • Average distance between the explanations

      • +
    +
    • +
+


+
+

Warning

+

AverageStability will compute several time explanations for all the inputs (pertubed more or less severly). +Thus, it might be very long to compute (especially if the explainer is already time consumming).

+
+
+
+
    +
  1. +

    Evaluating and Aggregating Feature-based Model Explanations (2020) ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/metrics/deletion/index.html b/v1.1.0/api/metrics/deletion/index.html new file mode 100755 index 00000000..dd00f3ba --- /dev/null +++ b/v1.1.0/api/metrics/deletion/index.html @@ -0,0 +1,1502 @@ + + + + + + + + + + + + + + + + + + + + + + + Deletion - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ +
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+ + +
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+ + + + + +
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+ + + +
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+ + + + + + + +

Deletion

+

The Deletion Fidelity metric measures how well a saliency-map–based explanation localizes the important features.

+
+

Quote

+

The deletion metric measures the drop in the probability of a class as important pixels (given +by the saliency map) are gradually removed from the image. A sharp drop, and thus a small +area under the probability curve, are indicative of a good explanation.

+

-- RISE: Randomized Input Sampling for Explanation of Black-box Models (2018)1

+
+

Score interpretation

+

The interpretation of the score depends on your operator, which represents the metrics you use to evaluate your model. +For metrics where the score increases with the performance of the model (such as accuracy). + If explanations are accurate, the score will quickly fall to the score of a random model. + Thus, in this case, a lower score represent a more accurate explanation.

+

For metrics where the score decreases with the performance of the model (such as losses). + If explanations are accurate, the score will quickly rise to the score of a random model. + Thus, in this case, a higher score represent a more accurate explanation.

+

Remarks

+

This metric only evaluate the order of importance between features.

+

The parameters metric, steps and max_percentage_perturbed may drastically change the score :

+
    +
  • +

    For inputs with many features, increasing the number of steps will allow you to capture more efficiently the difference between attributions methods.

    +
    • +
  • +

    The order of importance of features with low importance may not matter, hence, decreasing the max_percentage_perturbed, +may make the score more relevant.

    +
    • +
+

Sometimes, attributions methods also returns negative attributions, +for those methods, do not take the absolute value before computing insertion and deletion metrics. +Otherwise, negative attributions may have higher absolute values, and the order of importance between features will change. +Therefore, take those previous remarks into account to get a relevant score.

+

Example

+
from xplique.metrics import Deletion
+from xplique.attributions import Saliency
+
+# load images, targets and model
+# ...
+explainer = Saliency(model)
+explanations = explainer(inputs, targets)
+
+metric = Deletion(model, inputs, targets)
+score = metric.evaluate(explanations)
+
+

Deletion

+

The deletion metric measures the drop in the probability of a class as important pixels (given +by the saliency map) are gradually removed from the image. A sharp drop, and thus a small +area under the probability curve, are indicative of a good explanation. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
         targets: Union[tf.Tensor, numpy.ndarray, None] = None,
         batch_size: Optional[int] = 64,
         baseline_mode: Union[float, Callable] = 0.0,
         steps: int = 10,
         max_percentage_perturbed: float = 1.0,
         operator: Optional[Callable] = None,
         activation: Optional[str] = None)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • Model used for computing metric.

      • +
    +
    • +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Input samples under study.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • One-hot encoded labels or regression target (e.g {+1, -1}), one for each sample.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 64

    +
      +

    • Number of samples to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    baseline_mode : Union[float, Callable] = 0.0

    +
      +

    • Value of the baseline state, will be called with the inputs if it is a function.

      • +
    +
    • +
  • +

    steps : int = 10

    +
      +

    • Number of steps between the start and the end state.

      Can be set to -1 for all possible steps to be computed.

      • +
    +
    • +
  • +

    max_percentage_perturbed : float = 1.0

    +
      +

    • Maximum percentage of the input perturbed.

      • +
    +
    • +
  • +

    operator : Optional[Callable] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    activation : Optional[str] = None

    +
      +

    • A string that belongs to [None, 'sigmoid', 'softmax']. Specify if we should add an activation layer once the model has been called. It is useful, for instance if you want to measure a 'drop of probability' by adding a sigmoid or softmax after getting your logits. If None does not add a layer to your model.

      • +
    +
    • +
+

detailed_evaluate(self,
                  explanations: Union[tf.Tensor, numpy.ndarray]) -> Dict[int, float]

+

Evaluate model performance for successive perturbations of an input. +Used to compute causal score. +

+

Parameters

+
    +
  • +

    explanations : Union[tf.Tensor, numpy.ndarray]

    +
      +

    • Explanation for the inputs, labels to evaluate.

      • +
    +
    • +
+

Return

+
    +
  • +

    causal_score_dict : Dict[int, float]

    +
      +

    • Dictionary of scores obtain for different perturbations Keys are the steps, i.e the number of features perturbed Values are the scores, the score of the model on the inputs with the corresponding number of features perturbed

      • +
    +
    • +
+


+

evaluate(self,
         explanations: Union[tf.Tensor, numpy.ndarray]) -> float

+

Evaluate the causal score. +

+

Parameters

+
    +
  • +

    explanations : Union[tf.Tensor, numpy.ndarray]

    +
      +

    • Explanation for the inputs, labels to evaluate.

      • +
    +
    • +
+

Return

+
    +
  • +

    causal_score : float

    +
      +

    • Metric score, area over the deletion (lower is better) or insertion (higher is better) curve.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    RISE: Randomized Input Sampling for Explanation of Black-box Models (2018) ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
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+
+
+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/metrics/insertion/index.html b/v1.1.0/api/metrics/insertion/index.html new file mode 100755 index 00000000..3353a457 --- /dev/null +++ b/v1.1.0/api/metrics/insertion/index.html @@ -0,0 +1,1503 @@ + + + + + + + + + + + + + + + + + + + + + + + Insertion - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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Insertion

+

The Insertion Fidelity metric measures how well a saliency-map–based explanation can find elements that are minimal for the predictions.

+
+

Quote

+

The insertion metric, on the other hand, captures the importance of the +pixels in terms of their ability to synthesize an image and is measured by the rise in the +probability of the class of interest as pixels are added according to the generated importance +map.

+

-- RISE: Randomized Input Sampling for Explanation of Black-box Models (2018)1

+
+

Score interpretation

+

The interpretation of the score depends on your operator, which represents the metrics you use to evaluate your model. +For metrics where the score increases with the performance of the model (such as accuracy). + If explanations are accurate, the score will quickly rise to the score on non-perturbed input. + Thus, in this case, a higher score represent a more accurate explanation.

+

For metrics where the score decreases with the performance of the model (such as losses). + If explanations are accurate, the score will quickly fall to the score on non-perturbed input. + Thus, in this case, a lower score represent a more accurate explanation.

+

Remarks

+

This metric only evaluate the order of importance between features.

+

The parameters metric, steps and max_percentage_perturbed may drastically change the score :

+
    +
  • +

    For inputs with many features, increasing the number of steps will allow you to capture more efficiently the difference between attributions methods.

    +
    • +
  • +

    The order of importance of features with low importance may not matter, hence, decreasing the max_percentage_perturbed, +may make the score more relevant.

    +
    • +
+

Sometimes, attributions methods also returns negative attributions, +for those methods, do not take the absolute value before computing insertion and deletion metrics. +Otherwise, negative attributions may have higher absolute values, and the order of importance between features will change. +Therefore, take those previous remarks into account to get a relevant score.

+

Example

+
from xplique.metrics import Insertion
+from xplique.attributions import Saliency
+
+# load images, labels and model
+# ...
+explainer = Saliency(model)
+explanations = explainer(inputs, labels)
+
+metric = Insertion(model, inputs, labels)
+score = metric.evaluate(explanations)
+
+

Insertion

+

The insertion metric, on the other hand, captures the importance of the pixels in terms of +their ability to synthesize an image and is measured by the rise in the probability of the +class of interest as pixels are added according to the generated importance map. +

+

__init__(self,
         model: keras.src.engine.training.Model,
         inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
         targets: Union[tf.Tensor, numpy.ndarray, None] = None,
         batch_size: Optional[int] = 64,
         baseline_mode: Union[float, Callable] = 0.0,
         steps: int = 10,
         max_percentage_perturbed: float = 1.0,
         operator: Optional[Callable] = None,
         activation: Optional[str] = None)

+

Parameters

+
    +
  • +

    model : keras.src.engine.training.Model

    +
      +

    • Model used for computing metric.

      • +
    +
    • +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Input samples under study.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • One-hot encoded labels or regression target (e.g {+1, -1}), one for each sample.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 64

    +
      +

    • Number of samples to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    baseline_mode : Union[float, Callable] = 0.0

    +
      +

    • Value of the baseline state, will be called with the inputs if it is a function.

      • +
    +
    • +
  • +

    steps : int = 10

    +
      +

    • Number of steps between the start and the end state.

      Can be set to -1 for all possible steps to be computed.

      • +
    +
    • +
  • +

    max_percentage_perturbed : float = 1.0

    +
      +

    • Maximum percentage of the input perturbed.

      • +
    +
    • +
  • +

    operator : Optional[Callable] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    activation : Optional[str] = None

    +
      +

    • A string that belongs to [None, 'sigmoid', 'softmax']. Specify if we should add an activation layer once the model has been called. It is useful, for instance if you want to measure a 'drop of probability' by adding a sigmoid or softmax after getting your logits. If None does not add a layer to your model.

      • +
    +
    • +
+

detailed_evaluate(self,
                  explanations: Union[tf.Tensor, numpy.ndarray]) -> Dict[int, float]

+

Evaluate model performance for successive perturbations of an input. +Used to compute causal score. +

+

Parameters

+
    +
  • +

    explanations : Union[tf.Tensor, numpy.ndarray]

    +
      +

    • Explanation for the inputs, labels to evaluate.

      • +
    +
    • +
+

Return

+
    +
  • +

    causal_score_dict : Dict[int, float]

    +
      +

    • Dictionary of scores obtain for different perturbations Keys are the steps, i.e the number of features perturbed Values are the scores, the score of the model on the inputs with the corresponding number of features perturbed

      • +
    +
    • +
+


+

evaluate(self,
         explanations: Union[tf.Tensor, numpy.ndarray]) -> float

+

Evaluate the causal score. +

+

Parameters

+
    +
  • +

    explanations : Union[tf.Tensor, numpy.ndarray]

    +
      +

    • Explanation for the inputs, labels to evaluate.

      • +
    +
    • +
+

Return

+
    +
  • +

    causal_score : float

    +
      +

    • Metric score, area over the deletion (lower is better) or insertion (higher is better) curve.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    RISE: Randomized Input Sampling for Explanation of Black-box Models (2018) ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
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+
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+ + + + + + + + + + + + + + + \ No newline at end of file diff --git a/v1.1.0/api/metrics/mu_fidelity/index.html b/v1.1.0/api/metrics/mu_fidelity/index.html new file mode 100755 index 00000000..f99ff4b7 --- /dev/null +++ b/v1.1.0/api/metrics/mu_fidelity/index.html @@ -0,0 +1,1440 @@ + + + + + + + + + + + + + + + + + + + + + + + MuFidelity - Xplique + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + Skip to content + + +
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+ + + + + + + +

MuFidelity

+

MuFidelity is a fidelity metric measuring the correlation between important variables defined by the explanation method and the decline in the model score when these variables are reset to a baseline state.

+
+

Quote

+

[...] when we set particular features \(x_s\) to a baseline value \(x_0\) the change in predictor’s +output should be proportional to the sum of attribution scores.

+

-- Evaluating and Aggregating Feature-based Model Explanations (2020)1

+
+

Formally, given a predictor \(f\), an explanation function \(g\), a point \(x \in \mathbb{R}^n\) and a subset size \(k\) the MuFidelity metric is defined as:

+
\[ \mu F = \underset{S \subseteq \{1, ..., d\} \\ |S| = k}{Corr}( \sum_{i \in S} g(f, x)_i, f(x) - f(x_{[x_i = x_0 | i \in S]})) \]
+
+

Info

+

The better the method, the higher the score.

+
+

Example

+
from xplique.metrics import MuFidelity
+from xplique.attributions import Saliency
+
+# load images, labels and model
+# ...
+explainer = Saliency(model)
+explanations = explainer(inputs, lablels)
+
+metric = MuFidelity(model, inputs, labels)
+score = metric.evaluate(explainations)
+
+

MuFidelity

+

Used to compute the fidelity correlation metric. This metric ensure there is a correlation +between a random subset of pixels and their attribution score. For each random subset +created, we set the pixels of the subset at a baseline state and obtain the prediction score. +This metric measures the correlation between the drop in the score and the importance of the +explanation. +

+

__init__(self,
         model: Callable,
         inputs: Union[tf.Dataset, tf.Tensor, numpy.ndarray],
         targets: Union[tf.Tensor, numpy.ndarray, None] = None,
         batch_size: Optional[int] = 64,
         grid_size: Optional[int] = 9,
         subset_percent: float = 0.2,
         baseline_mode: Union[Callable, float] = 0.0,
         nb_samples: int = 200,
         operator: Optional[Callable] = None,
         activation: Optional[str] = None)

+

Parameters

+
    +
  • +

    model : Callable

    +
      +

    • Model used for computing metric.

      • +
    +
    • +
  • +

    inputs : Union[tf.Dataset, tf.Tensor, numpy.ndarray]

    +
      +

    • Input samples under study.

      • +
    +
    • +
  • +

    targets : Union[tf.Tensor, numpy.ndarray, None] = None

    +
      +

    • One-hot encoded labels or regression target (e.g {+1, -1}), one for each sample.

      • +
    +
    • +
  • +

    batch_size : Optional[int] = 64

    +
      +

    • Number of samples to explain at once, if None compute all at once.

      • +
    +
    • +
  • +

    grid_size : Optional[int] = 9

    +
      +

    • If none, compute the original metric, else cut the image in (grid_size, grid_size) and each element of the subset will be a super pixel representing one element of the grid.

      You should use this when dealing with medium / large size images.

      • +
    +
    • +
  • +

    subset_percent : float = 0.2

    +
      +

    • Percent of the image that will be set to baseline.

      • +
    +
    • +
  • +

    baseline_mode : Union[Callable, float] = 0.0

    +
      +

    • Value of the baseline state, will be called with the a single input if it is a function.

      • +
    +
    • +
  • +

    nb_samples : int = 200

    +
      +

    • Number of different subsets to try on each input to measure the correlation.

      • +
    +
    • +
  • +

    operator : Optional[Callable] = None

    +
      +

    • Function g to explain, g take 3 parameters (f, x, y) and should return a scalar, with f the model, x the inputs and y the targets. If None, use the standard operator g(f, x, y) = f(x)[y].

      • +
    +
    • +
  • +

    activation : Optional[str] = None

    +
      +

    • A string that belongs to [None, 'sigmoid', 'softmax']. Specify if we should add an activation layer once the model has been called. It is useful, for instance if you want to measure a 'drop of probability' by adding a sigmoid or softmax after getting your logits. If None does not add a layer to your model.

      • +
    +
    • +
+

evaluate(self,
         explanations: Union[tf.Tensor, numpy.ndarray]) -> float

+

Evaluate the fidelity score. +

+

Parameters

+
    +
  • +

    explanations : Union[tf.Tensor, numpy.ndarray]

    +
      +

    • Explanation for the inputs, labels to evaluate.

      • +
    +
    • +
+

Return

+
    +
  • +

    fidelity_score : float

    +
      +

    • Metric score, average correlation between the drop in score when variables are set to a baseline state and the importance of these variables according to the explanations.

      • +
    +
    • +
+


+
+
+
    +
  1. +

    Evaluating and Aggregating Feature-based Model Explanations (2020) ↩

    +
    2. +
+
+ + + + + + +
+
+ + +
+ +
+ + + +
+
+
+
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c=o.pipe(w(()=>a.innerHTML=""),v(({items:m})=>M(H(...m.slice(0,10)),H(...m.slice(10)).pipe(Ce(4),Ir(n),v(([f])=>f)))),l(mn),le());return c.subscribe(m=>a.appendChild(m)),c.pipe(se(m=>{let f=ce("details",m);return typeof f=="undefined"?T:h(f,"toggle").pipe(K(o),l(()=>f))})).subscribe(m=>{m.open===!1&&m.offsetTop<=i.scrollTop&&i.scrollTo({top:m.offsetTop})}),t.pipe(L(mr),l(({data:m})=>m)).pipe(w(m=>o.next(m)),k(()=>o.complete()),l(m=>I({ref:e},m)))}function Ha(e,{query$:t}){return t.pipe(l(({value:r})=>{let o=fe();return o.hash="",r=r.replace(/\s+/g,"+").replace(/&/g,"%26").replace(/=/g,"%3D"),o.search=`q=${r}`,{url:o}}))}function zn(e,t){let r=new x,o=r.pipe(J(),ee(!0));return r.subscribe(({url:n})=>{e.setAttribute("data-clipboard-text",e.href),e.href=`${n}`}),h(e,"click").pipe(K(o)).subscribe(n=>n.preventDefault()),Ha(e,t).pipe(w(n=>r.next(n)),k(()=>r.complete()),l(n=>I({ref:e},n)))}function qn(e,{worker$:t,keyboard$:r}){let o=new x,n=ye("search-query"),i=M(h(n,"keydown"),h(n,"focus")).pipe(_e(ae),l(()=>n.value),G());return o.pipe(Ge(i),l(([{suggest:a},c])=>{let p=c.split(/([\s-]+)/);if(a!=null&&a.length&&p[p.length-1]){let m=a[a.length-1];m.startsWith(p[p.length-1])&&(p[p.length-1]=m)}else p.length=0;return p})).subscribe(a=>e.innerHTML=a.join("").replace(/\s/g," ")),r.pipe(L(({mode:a})=>a==="search")).subscribe(a=>{switch(a.type){case"ArrowRight":e.innerText.length&&n.selectionStart===n.value.length&&(n.value=e.innerText);break}}),t.pipe(L(mr),l(({data:a})=>a)).pipe(w(a=>o.next(a)),k(()=>o.complete()),l(()=>({ref:e})))}function Kn(e,{index$:t,keyboard$:r}){let o=ue();try{let n=Un(o.search,t),i=ye("search-query",e),s=ye("search-result",e);h(e,"click").pipe(L(({target:c})=>c instanceof Element&&!!c.closest("a"))).subscribe(()=>Ke("search",!1)),r.pipe(L(({mode:c})=>c==="search")).subscribe(c=>{let p=Re();switch(c.type){case"Enter":if(p===i){let m=new Map;for(let f of z(":first-child [href]",s)){let 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ei(e,{viewport$:t,header$:r,main$:o,target$:n}){let i=new x,s=i.pipe(J(),ee(!0));return i.subscribe({next({hidden:a}){e.hidden=a,a?(e.setAttribute("tabindex","-1"),e.blur()):e.removeAttribute("tabindex")},complete(){e.style.top="",e.hidden=!0,e.removeAttribute("tabindex")}}),r.pipe(K(s),X("height")).subscribe(({height:a})=>{e.style.top=`${a+16}px`}),h(e,"click").subscribe(a=>{a.preventDefault(),window.scrollTo({top:0})}),ja(e,{viewport$:t,main$:o,target$:n}).pipe(w(a=>i.next(a)),k(()=>i.complete()),l(a=>I({ref:e},a)))}function ti({document$:e,tablet$:t}){e.pipe(v(()=>z(".md-toggle--indeterminate")),w(r=>{r.indeterminate=!0,r.checked=!1}),se(r=>h(r,"change").pipe($r(()=>r.classList.contains("md-toggle--indeterminate")),l(()=>r))),oe(t)).subscribe(([r,o])=>{r.classList.remove("md-toggle--indeterminate"),o&&(r.checked=!1)})}function Wa(){return/(iPad|iPhone|iPod)/.test(navigator.userAgent)}function 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ni;((ni=Qr.version)==null?void 0:ni.provider)==="mike"&&Nn({document$:nt});M(At,gt).pipe(ze(125)).subscribe(()=>{Ke("drawer",!1),Ke("search",!1)});Yr.pipe(L(({mode:e})=>e==="global")).subscribe(e=>{switch(e.type){case"p":case",":let t=ce("link[rel=prev]");typeof t!="undefined"&&ot(t);break;case"n":case".":let r=ce("link[rel=next]");typeof r!="undefined"&&ot(r);break;case"Enter":let o=Re();o instanceof HTMLLabelElement&&o.click()}});ti({document$:nt,tablet$:lr});ri({document$:nt});oi({viewport$:Se,tablet$:lr});var Xe=An(ye("header"),{viewport$:Se}),_t=nt.pipe(l(()=>ye("main")),v(e=>Hn(e,{viewport$:Se,header$:Xe})),B(1)),Na=M(...ne("consent").map(e=>an(e,{target$:gt})),...ne("dialog").map(e=>_n(e,{alert$:Br})),...ne("header").map(e=>Cn(e,{viewport$:Se,header$:Xe,main$:_t})),...ne("palette").map(e=>$n(e)),...ne("search").map(e=>Kn(e,{index$:si,keyboard$:Yr})),...ne("source").map(e=>Jn(e))),Da=$(()=>M(...ne("announce").map(e=>nn(e)),...ne("content").map(e=>Ln(e,{viewport$:Se,target$:gt,print$:ai})),...ne("content").map(e=>te("search.highlight")?Qn(e,{index$:si,location$:At}):T),...ne("header-title").map(e=>kn(e,{viewport$:Se,header$:Xe})),...ne("sidebar").map(e=>e.getAttribute("data-md-type")==="navigation"?jr(ii,()=>Kr(e,{viewport$:Se,header$:Xe,main$:_t})):jr(lr,()=>Kr(e,{viewport$:Se,header$:Xe,main$:_t}))),...ne("tabs").map(e=>Xn(e,{viewport$:Se,header$:Xe})),...ne("toc").map(e=>Zn(e,{viewport$:Se,header$:Xe,main$:_t,target$:gt})),...ne("top").map(e=>ei(e,{viewport$:Se,header$:Xe,main$:_t,target$:gt})))),ci=nt.pipe(v(()=>Da),qe(Na),B(1));ci.subscribe();window.document$=nt;window.location$=At;window.target$=gt;window.keyboard$=Yr;window.viewport$=Se;window.tablet$=lr;window.screen$=ii;window.print$=ai;window.alert$=Br;window.component$=ci;})(); 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"src/assets/javascripts/components/search/highlight/index.ts", "src/assets/javascripts/components/sidebar/index.ts", "src/assets/javascripts/components/source/facts/github/index.ts", "src/assets/javascripts/components/source/facts/gitlab/index.ts", "src/assets/javascripts/components/source/facts/_/index.ts", "src/assets/javascripts/components/source/_/index.ts", "src/assets/javascripts/components/tabs/index.ts", "src/assets/javascripts/components/toc/index.ts", "src/assets/javascripts/components/top/index.ts", "src/assets/javascripts/patches/indeterminate/index.ts", "src/assets/javascripts/patches/scrollfix/index.ts", "src/assets/javascripts/patches/scrolllock/index.ts", "src/assets/javascripts/polyfills/index.ts"], + "sourceRoot": "../../..", + "sourcesContent": ["(function (global, factory) {\n typeof exports === 'object' && typeof module !== 'undefined' ? factory() :\n typeof define === 'function' && define.amd ? define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "/*!\n * clipboard.js v2.0.11\n * https://clipboardjs.com/\n *\n * Licensed MIT \u00A9 Zeno Rocha\n */\n(function webpackUniversalModuleDefinition(root, factory) {\n\tif(typeof exports === 'object' && typeof module === 'object')\n\t\tmodule.exports = factory();\n\telse if(typeof define === 'function' && define.amd)\n\t\tdefine([], factory);\n\telse if(typeof exports === 'object')\n\t\texports[\"ClipboardJS\"] = factory();\n\telse\n\t\troot[\"ClipboardJS\"] = factory();\n})(this, function() {\nreturn /******/ (function() { // webpackBootstrap\n/******/ \tvar __webpack_modules__ = ({\n\n/***/ 686:\n/***/ (function(__unused_webpack_module, __webpack_exports__, __webpack_require__) {\n\n\"use strict\";\n\n// EXPORTS\n__webpack_require__.d(__webpack_exports__, {\n \"default\": function() { return /* binding */ clipboard; }\n});\n\n// EXTERNAL MODULE: ./node_modules/tiny-emitter/index.js\nvar tiny_emitter = __webpack_require__(279);\nvar tiny_emitter_default = /*#__PURE__*/__webpack_require__.n(tiny_emitter);\n// EXTERNAL MODULE: ./node_modules/good-listener/src/listen.js\nvar listen = __webpack_require__(370);\nvar listen_default = /*#__PURE__*/__webpack_require__.n(listen);\n// EXTERNAL MODULE: ./node_modules/select/src/select.js\nvar src_select = __webpack_require__(817);\nvar select_default = /*#__PURE__*/__webpack_require__.n(src_select);\n;// CONCATENATED MODULE: ./src/common/command.js\n/**\n * Executes a given operation type.\n * @param {String} type\n * @return {Boolean}\n */\nfunction command(type) {\n try {\n return document.execCommand(type);\n } catch (err) {\n return false;\n }\n}\n;// CONCATENATED MODULE: ./src/actions/cut.js\n\n\n/**\n * Cut action wrapper.\n * @param {String|HTMLElement} target\n * @return {String}\n */\n\nvar ClipboardActionCut = function ClipboardActionCut(target) {\n var selectedText = select_default()(target);\n command('cut');\n return selectedText;\n};\n\n/* harmony default export */ var actions_cut = (ClipboardActionCut);\n;// CONCATENATED MODULE: ./src/common/create-fake-element.js\n/**\n * Creates a fake textarea element with a value.\n * @param {String} value\n * @return {HTMLElement}\n */\nfunction createFakeElement(value) {\n var isRTL = document.documentElement.getAttribute('dir') === 'rtl';\n var fakeElement = document.createElement('textarea'); // Prevent zooming on iOS\n\n fakeElement.style.fontSize = '12pt'; // Reset box model\n\n fakeElement.style.border = '0';\n fakeElement.style.padding = '0';\n fakeElement.style.margin = '0'; // Move element out of screen horizontally\n\n fakeElement.style.position = 'absolute';\n fakeElement.style[isRTL ? 'right' : 'left'] = '-9999px'; // Move element to the same position vertically\n\n var yPosition = window.pageYOffset || document.documentElement.scrollTop;\n fakeElement.style.top = \"\".concat(yPosition, \"px\");\n fakeElement.setAttribute('readonly', '');\n fakeElement.value = value;\n return fakeElement;\n}\n;// CONCATENATED MODULE: ./src/actions/copy.js\n\n\n\n/**\n * Create fake copy action wrapper using a fake element.\n * @param {String} target\n * @param {Object} options\n * @return {String}\n */\n\nvar fakeCopyAction = function fakeCopyAction(value, options) {\n var fakeElement = createFakeElement(value);\n options.container.appendChild(fakeElement);\n var selectedText = select_default()(fakeElement);\n command('copy');\n fakeElement.remove();\n return selectedText;\n};\n/**\n * Copy action wrapper.\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @return {String}\n */\n\n\nvar ClipboardActionCopy = function ClipboardActionCopy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n var selectedText = '';\n\n if (typeof target === 'string') {\n selectedText = fakeCopyAction(target, options);\n } else if (target instanceof HTMLInputElement && !['text', 'search', 'url', 'tel', 'password'].includes(target === null || target === void 0 ? void 0 : target.type)) {\n // If input type doesn't support `setSelectionRange`. Simulate it. https://developer.mozilla.org/en-US/docs/Web/API/HTMLInputElement/setSelectionRange\n selectedText = fakeCopyAction(target.value, options);\n } else {\n selectedText = select_default()(target);\n command('copy');\n }\n\n return selectedText;\n};\n\n/* harmony default export */ var actions_copy = (ClipboardActionCopy);\n;// CONCATENATED MODULE: ./src/actions/default.js\nfunction _typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { _typeof = function _typeof(obj) { return typeof obj; }; } else { _typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return _typeof(obj); }\n\n\n\n/**\n * Inner function which performs selection from either `text` or `target`\n * properties and then executes copy or cut operations.\n * @param {Object} options\n */\n\nvar ClipboardActionDefault = function ClipboardActionDefault() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n // Defines base properties passed from constructor.\n var _options$action = options.action,\n action = _options$action === void 0 ? 'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*\n * Copyright (c) 2016-2023 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"focus-visible\"\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getActiveElement,\n getOptionalElement,\n requestJSON,\n setLocation,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchScript,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountAnnounce,\n mountBackToTop,\n mountConsent,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantLoading,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Functions - @todo refactor\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch search index\n *\n * @returns Search index observable\n */\nfunction fetchSearchIndex(): Observable {\n if (location.protocol === \"file:\") {\n return watchScript(\n `${new URL(\"search/search_index.js\", config.base)}`\n )\n .pipe(\n // @ts-ignore - @todo fix typings\n map(() => __index),\n shareReplay(1)\n )\n } else {\n return requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget(location$)\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? fetchSearchIndex()\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantLoading({ location$, viewport$ })\n .subscribe(document$)\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"link[rel=prev]\")\n if (typeof prev !== \"undefined\")\n setLocation(prev)\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"link[rel=next]\")\n if (typeof next !== \"undefined\")\n setLocation(next)\n break\n\n /* Expand navigation, see https://bit.ly/3ZjG5io */\n case \"Enter\":\n const active = getActiveElement()\n if (active instanceof HTMLLabelElement)\n active.click()\n }\n })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Consent */\n ...getComponentElements(\"consent\")\n .map(el => mountConsent(el, { target$ })),\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Announcement bar */\n ...getComponentElements(\"announce\")\n .map(el => mountAnnounce(el)),\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { viewport$, target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, {\n viewport$, header$, main$, target$\n })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.component$ = component$ /* Component observable */\n", "/*! *****************************************************************************\r\nCopyright (c) Microsoft Corporation.\r\n\r\nPermission to use, copy, modify, and/or distribute this software for any\r\npurpose with or without fee is hereby granted.\r\n\r\nTHE SOFTWARE IS PROVIDED \"AS IS\" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH\r\nREGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY\r\nAND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT,\r\nINDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM\r\nLOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR\r\nOTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR\r\nPERFORMANCE OF THIS SOFTWARE.\r\n***************************************************************************** */\r\n/* global Reflect, Promise */\r\n\r\nvar extendStatics = function(d, b) {\r\n extendStatics = Object.setPrototypeOf ||\r\n ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||\r\n function (d, b) { for (var p in b) if (Object.prototype.hasOwnProperty.call(b, p)) d[p] = b[p]; };\r\n return extendStatics(d, b);\r\n};\r\n\r\nexport function __extends(d, b) {\r\n if (typeof b !== \"function\" && b !== null)\r\n throw new TypeError(\"Class extends value \" + String(b) + \" is not a constructor or null\");\r\n extendStatics(d, b);\r\n function __() { this.constructor = d; }\r\n d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());\r\n}\r\n\r\nexport var __assign = function() {\r\n __assign = Object.assign || function __assign(t) {\r\n for (var s, i = 1, n = arguments.length; i < n; i++) {\r\n s = arguments[i];\r\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];\r\n }\r\n return t;\r\n }\r\n return __assign.apply(this, arguments);\r\n}\r\n\r\nexport function __rest(s, e) {\r\n var t = {};\r\n for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)\r\n t[p] = s[p];\r\n if (s != null && typeof Object.getOwnPropertySymbols === \"function\")\r\n for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) {\r\n if (e.indexOf(p[i]) < 0 && Object.prototype.propertyIsEnumerable.call(s, p[i]))\r\n t[p[i]] = s[p[i]];\r\n }\r\n return t;\r\n}\r\n\r\nexport function __decorate(decorators, target, key, desc) {\r\n var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;\r\n if (typeof Reflect === \"object\" && typeof Reflect.decorate === \"function\") r = Reflect.decorate(decorators, target, key, desc);\r\n else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;\r\n return c > 3 && r && Object.defineProperty(target, key, r), r;\r\n}\r\n\r\nexport function __param(paramIndex, decorator) {\r\n return function (target, key) { decorator(target, key, paramIndex); }\r\n}\r\n\r\nexport function __metadata(metadataKey, metadataValue) {\r\n if (typeof Reflect === \"object\" && typeof Reflect.metadata === \"function\") return Reflect.metadata(metadataKey, metadataValue);\r\n}\r\n\r\nexport function __awaiter(thisArg, _arguments, P, generator) {\r\n function adopt(value) { return value instanceof P ? value : new P(function (resolve) { resolve(value); }); }\r\n return new (P || (P = Promise))(function (resolve, reject) {\r\n function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }\r\n function rejected(value) { try { step(generator[\"throw\"](value)); } catch (e) { reject(e); } }\r\n function step(result) { result.done ? resolve(result.value) : adopt(result.value).then(fulfilled, rejected); }\r\n step((generator = generator.apply(thisArg, _arguments || [])).next());\r\n });\r\n}\r\n\r\nexport function __generator(thisArg, body) {\r\n var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;\r\n return g = { next: verb(0), \"throw\": verb(1), \"return\": verb(2) }, typeof Symbol === \"function\" && (g[Symbol.iterator] = function() { return this; }), g;\r\n function verb(n) { return function (v) { return step([n, v]); }; }\r\n function step(op) {\r\n if (f) throw new TypeError(\"Generator is already executing.\");\r\n while (_) try {\r\n if (f = 1, y && (t = op[0] & 2 ? y[\"return\"] : op[0] ? y[\"throw\"] || ((t = y[\"return\"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;\r\n if (y = 0, t) op = [op[0] & 2, t.value];\r\n switch (op[0]) {\r\n case 0: case 1: t = op; break;\r\n case 4: _.label++; return { value: op[1], done: false };\r\n case 5: _.label++; y = op[1]; op = [0]; continue;\r\n case 7: op = _.ops.pop(); _.trys.pop(); continue;\r\n default:\r\n if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }\r\n if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }\r\n if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }\r\n if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }\r\n if (t[2]) _.ops.pop();\r\n _.trys.pop(); continue;\r\n }\r\n op = body.call(thisArg, _);\r\n } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }\r\n if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };\r\n }\r\n}\r\n\r\nexport var __createBinding = Object.create ? (function(o, m, k, k2) {\r\n if (k2 === undefined) k2 = k;\r\n Object.defineProperty(o, k2, { enumerable: true, get: function() { return m[k]; } });\r\n}) : (function(o, m, k, k2) {\r\n if (k2 === undefined) k2 = k;\r\n o[k2] = m[k];\r\n});\r\n\r\nexport function __exportStar(m, o) {\r\n for (var p in m) if (p !== \"default\" && !Object.prototype.hasOwnProperty.call(o, p)) __createBinding(o, m, p);\r\n}\r\n\r\nexport function __values(o) {\r\n var s = typeof Symbol === \"function\" && Symbol.iterator, m = s && o[s], i = 0;\r\n if (m) return m.call(o);\r\n if (o && typeof o.length === \"number\") return {\r\n next: function () {\r\n if (o && i >= o.length) o = void 0;\r\n return { value: o && o[i++], done: !o };\r\n }\r\n };\r\n throw new TypeError(s ? \"Object is not iterable.\" : \"Symbol.iterator is not defined.\");\r\n}\r\n\r\nexport function __read(o, n) {\r\n var m = typeof Symbol === \"function\" && o[Symbol.iterator];\r\n if (!m) return o;\r\n var i = m.call(o), r, ar = [], e;\r\n try {\r\n while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);\r\n }\r\n catch (error) { e = { error: error }; }\r\n finally {\r\n try {\r\n if (r && !r.done && (m = i[\"return\"])) m.call(i);\r\n }\r\n finally { if (e) throw e.error; }\r\n }\r\n return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spread() {\r\n for (var ar = [], i = 0; i < arguments.length; i++)\r\n ar = ar.concat(__read(arguments[i]));\r\n return ar;\r\n}\r\n\r\n/** @deprecated */\r\nexport function __spreadArrays() {\r\n for (var s = 0, i = 0, il = arguments.length; i < il; i++) s += arguments[i].length;\r\n for (var r = Array(s), k = 0, i = 0; i < il; i++)\r\n for (var a = arguments[i], j = 0, jl = a.length; j < jl; j++, k++)\r\n r[k] = a[j];\r\n return r;\r\n}\r\n\r\nexport function __spreadArray(to, from, pack) {\r\n if (pack || arguments.length === 2) for (var i = 0, l = from.length, ar; i < l; i++) {\r\n if (ar || !(i in from)) {\r\n if (!ar) ar = Array.prototype.slice.call(from, 0, i);\r\n ar[i] = from[i];\r\n }\r\n }\r\n return to.concat(ar || Array.prototype.slice.call(from));\r\n}\r\n\r\nexport function __await(v) {\r\n return this instanceof __await ? (this.v = v, this) : new __await(v);\r\n}\r\n\r\nexport function __asyncGenerator(thisArg, _arguments, generator) {\r\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n var g = generator.apply(thisArg, _arguments || []), i, q = [];\r\n return i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i;\r\n function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; }\r\n function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }\r\n function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }\r\n function fulfill(value) { resume(\"next\", value); }\r\n function reject(value) { resume(\"throw\", value); }\r\n function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }\r\n}\r\n\r\nexport function __asyncDelegator(o) {\r\n var i, p;\r\n return i = {}, verb(\"next\"), verb(\"throw\", function (e) { throw e; }), verb(\"return\"), i[Symbol.iterator] = function () { return this; }, i;\r\n function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === \"return\" } : f ? f(v) : v; } : f; }\r\n}\r\n\r\nexport function __asyncValues(o) {\r\n if (!Symbol.asyncIterator) throw new TypeError(\"Symbol.asyncIterator is not defined.\");\r\n var m = o[Symbol.asyncIterator], i;\r\n return m ? m.call(o) : (o = typeof __values === \"function\" ? __values(o) : o[Symbol.iterator](), i = {}, verb(\"next\"), verb(\"throw\"), verb(\"return\"), i[Symbol.asyncIterator] = function () { return this; }, i);\r\n function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }\r\n function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }\r\n}\r\n\r\nexport function __makeTemplateObject(cooked, raw) {\r\n if (Object.defineProperty) { Object.defineProperty(cooked, \"raw\", { value: raw }); } else { cooked.raw = raw; }\r\n return cooked;\r\n};\r\n\r\nvar __setModuleDefault = Object.create ? (function(o, v) {\r\n Object.defineProperty(o, \"default\", { enumerable: true, value: v });\r\n}) : function(o, v) {\r\n o[\"default\"] = v;\r\n};\r\n\r\nexport function __importStar(mod) {\r\n if (mod && mod.__esModule) return mod;\r\n var result = {};\r\n if (mod != null) for (var k in mod) if (k !== \"default\" && Object.prototype.hasOwnProperty.call(mod, k)) __createBinding(result, mod, k);\r\n __setModuleDefault(result, mod);\r\n return result;\r\n}\r\n\r\nexport function __importDefault(mod) {\r\n return (mod && mod.__esModule) ? mod : { default: mod };\r\n}\r\n\r\nexport function __classPrivateFieldGet(receiver, state, kind, f) {\r\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a getter\");\r\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot read private member from an object whose class did not declare it\");\r\n return kind === \"m\" ? f : kind === \"a\" ? f.call(receiver) : f ? f.value : state.get(receiver);\r\n}\r\n\r\nexport function __classPrivateFieldSet(receiver, state, value, kind, f) {\r\n if (kind === \"m\") throw new TypeError(\"Private method is not writable\");\r\n if (kind === \"a\" && !f) throw new TypeError(\"Private accessor was defined without a setter\");\r\n if (typeof state === \"function\" ? receiver !== state || !f : !state.has(receiver)) throw new TypeError(\"Cannot write private member to an object whose class did not declare it\");\r\n return (kind === \"a\" ? f.call(receiver, value) : f ? f.value = value : state.set(receiver, value)), value;\r\n}\r\n", "/**\n * Returns true if the object is a function.\n * @param value The value to check\n */\nexport function isFunction(value: any): value is (...args: any[]) => any {\n return typeof value === 'function';\n}\n", "/**\n * Used to create Error subclasses until the community moves away from ES5.\n *\n * This is because compiling from TypeScript down to ES5 has issues with subclassing Errors\n * as well as other built-in types: https://github.com/Microsoft/TypeScript/issues/12123\n *\n * @param createImpl A factory function to create the actual constructor implementation. The returned\n * function should be a named function that calls `_super` internally.\n */\nexport function createErrorClass(createImpl: (_super: any) => any): T {\n const _super = (instance: any) => {\n Error.call(instance);\n instance.stack = new Error().stack;\n };\n\n const ctorFunc = createImpl(_super);\n ctorFunc.prototype = Object.create(Error.prototype);\n ctorFunc.prototype.constructor = ctorFunc;\n return ctorFunc;\n}\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface UnsubscriptionError extends Error {\n readonly errors: any[];\n}\n\nexport interface UnsubscriptionErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (errors: any[]): UnsubscriptionError;\n}\n\n/**\n * An error thrown when one or more errors have occurred during the\n * `unsubscribe` of a {@link Subscription}.\n */\nexport const UnsubscriptionError: UnsubscriptionErrorCtor = createErrorClass(\n (_super) =>\n function UnsubscriptionErrorImpl(this: any, errors: (Error | string)[]) {\n _super(this);\n this.message = errors\n ? `${errors.length} errors occurred during unsubscription:\n${errors.map((err, i) => `${i + 1}) ${err.toString()}`).join('\\n ')}`\n : '';\n this.name = 'UnsubscriptionError';\n this.errors = errors;\n }\n);\n", "/**\n * Removes an item from an array, mutating it.\n * @param arr The array to remove the item from\n * @param item The item to remove\n */\nexport function arrRemove(arr: T[] | undefined | null, item: T) {\n if (arr) {\n const index = arr.indexOf(item);\n 0 <= index && arr.splice(index, 1);\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { UnsubscriptionError } from './util/UnsubscriptionError';\nimport { SubscriptionLike, TeardownLogic, Unsubscribable } from './types';\nimport { arrRemove } from './util/arrRemove';\n\n/**\n * Represents a disposable resource, such as the execution of an Observable. A\n * Subscription has one important method, `unsubscribe`, that takes no argument\n * and just disposes the resource held by the subscription.\n *\n * Additionally, subscriptions may be grouped together through the `add()`\n * method, which will attach a child Subscription to the current Subscription.\n * When a Subscription is unsubscribed, all its children (and its grandchildren)\n * will be unsubscribed as well.\n *\n * @class Subscription\n */\nexport class Subscription implements SubscriptionLike {\n /** @nocollapse */\n public static EMPTY = (() => {\n const empty = new Subscription();\n empty.closed = true;\n return empty;\n })();\n\n /**\n * A flag to indicate whether this Subscription has already been unsubscribed.\n */\n public closed = false;\n\n private _parentage: Subscription[] | Subscription | null = null;\n\n /**\n * The list of registered finalizers to execute upon unsubscription. Adding and removing from this\n * list occurs in the {@link #add} and {@link #remove} methods.\n */\n private _finalizers: Exclude[] | null = null;\n\n /**\n * @param initialTeardown A function executed first as part of the finalization\n * process that is kicked off when {@link #unsubscribe} is called.\n */\n constructor(private initialTeardown?: () => void) {}\n\n /**\n * Disposes the resources held by the subscription. May, for instance, cancel\n * an ongoing Observable execution or cancel any other type of work that\n * started when the Subscription was created.\n * @return {void}\n */\n unsubscribe(): void {\n let errors: any[] | undefined;\n\n if (!this.closed) {\n this.closed = true;\n\n // Remove this from it's parents.\n const { _parentage } = this;\n if (_parentage) {\n this._parentage = null;\n if (Array.isArray(_parentage)) {\n for (const parent of _parentage) {\n parent.remove(this);\n }\n } else {\n _parentage.remove(this);\n }\n }\n\n const { initialTeardown: initialFinalizer } = this;\n if (isFunction(initialFinalizer)) {\n try {\n initialFinalizer();\n } catch (e) {\n errors = e instanceof UnsubscriptionError ? e.errors : [e];\n }\n }\n\n const { _finalizers } = this;\n if (_finalizers) {\n this._finalizers = null;\n for (const finalizer of _finalizers) {\n try {\n execFinalizer(finalizer);\n } catch (err) {\n errors = errors ?? [];\n if (err instanceof UnsubscriptionError) {\n errors = [...errors, ...err.errors];\n } else {\n errors.push(err);\n }\n }\n }\n }\n\n if (errors) {\n throw new UnsubscriptionError(errors);\n }\n }\n }\n\n /**\n * Adds a finalizer to this subscription, so that finalization will be unsubscribed/called\n * when this subscription is unsubscribed. If this subscription is already {@link #closed},\n * because it has already been unsubscribed, then whatever finalizer is passed to it\n * will automatically be executed (unless the finalizer itself is also a closed subscription).\n *\n * Closed Subscriptions cannot be added as finalizers to any subscription. Adding a closed\n * subscription to a any subscription will result in no operation. (A noop).\n *\n * Adding a subscription to itself, or adding `null` or `undefined` will not perform any\n * operation at all. (A noop).\n *\n * `Subscription` instances that are added to this instance will automatically remove themselves\n * if they are unsubscribed. Functions and {@link Unsubscribable} objects that you wish to remove\n * will need to be removed manually with {@link #remove}\n *\n * @param teardown The finalization logic to add to this subscription.\n */\n add(teardown: TeardownLogic): void {\n // Only add the finalizer if it's not undefined\n // and don't add a subscription to itself.\n if (teardown && teardown !== this) {\n if (this.closed) {\n // If this subscription is already closed,\n // execute whatever finalizer is handed to it automatically.\n execFinalizer(teardown);\n } else {\n if (teardown instanceof Subscription) {\n // We don't add closed subscriptions, and we don't add the same subscription\n // twice. Subscription unsubscribe is idempotent.\n if (teardown.closed || teardown._hasParent(this)) {\n return;\n }\n teardown._addParent(this);\n }\n (this._finalizers = this._finalizers ?? []).push(teardown);\n }\n }\n }\n\n /**\n * Checks to see if a this subscription already has a particular parent.\n * This will signal that this subscription has already been added to the parent in question.\n * @param parent the parent to check for\n */\n private _hasParent(parent: Subscription) {\n const { _parentage } = this;\n return _parentage === parent || (Array.isArray(_parentage) && _parentage.includes(parent));\n }\n\n /**\n * Adds a parent to this subscription so it can be removed from the parent if it\n * unsubscribes on it's own.\n *\n * NOTE: THIS ASSUMES THAT {@link _hasParent} HAS ALREADY BEEN CHECKED.\n * @param parent The parent subscription to add\n */\n private _addParent(parent: Subscription) {\n const { _parentage } = this;\n this._parentage = Array.isArray(_parentage) ? (_parentage.push(parent), _parentage) : _parentage ? [_parentage, parent] : parent;\n }\n\n /**\n * Called on a child when it is removed via {@link #remove}.\n * @param parent The parent to remove\n */\n private _removeParent(parent: Subscription) {\n const { _parentage } = this;\n if (_parentage === parent) {\n this._parentage = null;\n } else if (Array.isArray(_parentage)) {\n arrRemove(_parentage, parent);\n }\n }\n\n /**\n * Removes a finalizer from this subscription that was previously added with the {@link #add} method.\n *\n * Note that `Subscription` instances, when unsubscribed, will automatically remove themselves\n * from every other `Subscription` they have been added to. This means that using the `remove` method\n * is not a common thing and should be used thoughtfully.\n *\n * If you add the same finalizer instance of a function or an unsubscribable object to a `Subscription` instance\n * more than once, you will need to call `remove` the same number of times to remove all instances.\n *\n * All finalizer instances are removed to free up memory upon unsubscription.\n *\n * @param teardown The finalizer to remove from this subscription\n */\n remove(teardown: Exclude): void {\n const { _finalizers } = this;\n _finalizers && arrRemove(_finalizers, teardown);\n\n if (teardown instanceof Subscription) {\n teardown._removeParent(this);\n }\n }\n}\n\nexport const EMPTY_SUBSCRIPTION = Subscription.EMPTY;\n\nexport function isSubscription(value: any): value is Subscription {\n return (\n value instanceof Subscription ||\n (value && 'closed' in value && isFunction(value.remove) && isFunction(value.add) && isFunction(value.unsubscribe))\n );\n}\n\nfunction execFinalizer(finalizer: Unsubscribable | (() => void)) {\n if (isFunction(finalizer)) {\n finalizer();\n } else {\n finalizer.unsubscribe();\n }\n}\n", "import { Subscriber } from './Subscriber';\nimport { ObservableNotification } from './types';\n\n/**\n * The {@link GlobalConfig} object for RxJS. It is used to configure things\n * like how to react on unhandled errors.\n */\nexport const config: GlobalConfig = {\n onUnhandledError: null,\n onStoppedNotification: null,\n Promise: undefined,\n useDeprecatedSynchronousErrorHandling: false,\n useDeprecatedNextContext: false,\n};\n\n/**\n * The global configuration object for RxJS, used to configure things\n * like how to react on unhandled errors. Accessible via {@link config}\n * object.\n */\nexport interface GlobalConfig {\n /**\n * A registration point for unhandled errors from RxJS. These are errors that\n * cannot were not handled by consuming code in the usual subscription path. For\n * example, if you have this configured, and you subscribe to an observable without\n * providing an error handler, errors from that subscription will end up here. This\n * will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onUnhandledError: ((err: any) => void) | null;\n\n /**\n * A registration point for notifications that cannot be sent to subscribers because they\n * have completed, errored or have been explicitly unsubscribed. By default, next, complete\n * and error notifications sent to stopped subscribers are noops. However, sometimes callers\n * might want a different behavior. For example, with sources that attempt to report errors\n * to stopped subscribers, a caller can configure RxJS to throw an unhandled error instead.\n * This will _always_ be called asynchronously on another job in the runtime. This is because\n * we do not want errors thrown in this user-configured handler to interfere with the\n * behavior of the library.\n */\n onStoppedNotification: ((notification: ObservableNotification, subscriber: Subscriber) => void) | null;\n\n /**\n * The promise constructor used by default for {@link Observable#toPromise toPromise} and {@link Observable#forEach forEach}\n * methods.\n *\n * @deprecated As of version 8, RxJS will no longer support this sort of injection of a\n * Promise constructor. If you need a Promise implementation other than native promises,\n * please polyfill/patch Promise as you see appropriate. Will be removed in v8.\n */\n Promise?: PromiseConstructorLike;\n\n /**\n * If true, turns on synchronous error rethrowing, which is a deprecated behavior\n * in v6 and higher. This behavior enables bad patterns like wrapping a subscribe\n * call in a try/catch block. It also enables producer interference, a nasty bug\n * where a multicast can be broken for all observers by a downstream consumer with\n * an unhandled error. DO NOT USE THIS FLAG UNLESS IT'S NEEDED TO BUY TIME\n * FOR MIGRATION REASONS.\n *\n * @deprecated As of version 8, RxJS will no longer support synchronous throwing\n * of unhandled errors. All errors will be thrown on a separate call stack to prevent bad\n * behaviors described above. Will be removed in v8.\n */\n useDeprecatedSynchronousErrorHandling: boolean;\n\n /**\n * If true, enables an as-of-yet undocumented feature from v5: The ability to access\n * `unsubscribe()` via `this` context in `next` functions created in observers passed\n * to `subscribe`.\n *\n * This is being removed because the performance was severely problematic, and it could also cause\n * issues when types other than POJOs are passed to subscribe as subscribers, as they will likely have\n * their `this` context overwritten.\n *\n * @deprecated As of version 8, RxJS will no longer support altering the\n * context of next functions provided as part of an observer to Subscribe. Instead,\n * you will have access to a subscription or a signal or token that will allow you to do things like\n * unsubscribe and test closed status. Will be removed in v8.\n */\n useDeprecatedNextContext: boolean;\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetTimeoutFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearTimeoutFunction = (handle: TimerHandle) => void;\n\ninterface TimeoutProvider {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n delegate:\n | {\n setTimeout: SetTimeoutFunction;\n clearTimeout: ClearTimeoutFunction;\n }\n | undefined;\n}\n\nexport const timeoutProvider: TimeoutProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setTimeout(handler: () => void, timeout?: number, ...args) {\n const { delegate } = timeoutProvider;\n if (delegate?.setTimeout) {\n return delegate.setTimeout(handler, timeout, ...args);\n }\n return setTimeout(handler, timeout, ...args);\n },\n clearTimeout(handle) {\n const { delegate } = timeoutProvider;\n return (delegate?.clearTimeout || clearTimeout)(handle as any);\n },\n delegate: undefined,\n};\n", "import { config } from '../config';\nimport { timeoutProvider } from '../scheduler/timeoutProvider';\n\n/**\n * Handles an error on another job either with the user-configured {@link onUnhandledError},\n * or by throwing it on that new job so it can be picked up by `window.onerror`, `process.on('error')`, etc.\n *\n * This should be called whenever there is an error that is out-of-band with the subscription\n * or when an error hits a terminal boundary of the subscription and no error handler was provided.\n *\n * @param err the error to report\n */\nexport function reportUnhandledError(err: any) {\n timeoutProvider.setTimeout(() => {\n const { onUnhandledError } = config;\n if (onUnhandledError) {\n // Execute the user-configured error handler.\n onUnhandledError(err);\n } else {\n // Throw so it is picked up by the runtime's uncaught error mechanism.\n throw err;\n }\n });\n}\n", "/* tslint:disable:no-empty */\nexport function noop() { }\n", "import { CompleteNotification, NextNotification, ErrorNotification } from './types';\n\n/**\n * A completion object optimized for memory use and created to be the\n * same \"shape\" as other notifications in v8.\n * @internal\n */\nexport const COMPLETE_NOTIFICATION = (() => createNotification('C', undefined, undefined) as CompleteNotification)();\n\n/**\n * Internal use only. Creates an optimized error notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function errorNotification(error: any): ErrorNotification {\n return createNotification('E', undefined, error) as any;\n}\n\n/**\n * Internal use only. Creates an optimized next notification that is the same \"shape\"\n * as other notifications.\n * @internal\n */\nexport function nextNotification(value: T) {\n return createNotification('N', value, undefined) as NextNotification;\n}\n\n/**\n * Ensures that all notifications created internally have the same \"shape\" in v8.\n *\n * TODO: This is only exported to support a crazy legacy test in `groupBy`.\n * @internal\n */\nexport function createNotification(kind: 'N' | 'E' | 'C', value: any, error: any) {\n return {\n kind,\n value,\n error,\n };\n}\n", "import { config } from '../config';\n\nlet context: { errorThrown: boolean; error: any } | null = null;\n\n/**\n * Handles dealing with errors for super-gross mode. Creates a context, in which\n * any synchronously thrown errors will be passed to {@link captureError}. Which\n * will record the error such that it will be rethrown after the call back is complete.\n * TODO: Remove in v8\n * @param cb An immediately executed function.\n */\nexport function errorContext(cb: () => void) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n const isRoot = !context;\n if (isRoot) {\n context = { errorThrown: false, error: null };\n }\n cb();\n if (isRoot) {\n const { errorThrown, error } = context!;\n context = null;\n if (errorThrown) {\n throw error;\n }\n }\n } else {\n // This is the general non-deprecated path for everyone that\n // isn't crazy enough to use super-gross mode (useDeprecatedSynchronousErrorHandling)\n cb();\n }\n}\n\n/**\n * Captures errors only in super-gross mode.\n * @param err the error to capture\n */\nexport function captureError(err: any) {\n if (config.useDeprecatedSynchronousErrorHandling && context) {\n context.errorThrown = true;\n context.error = err;\n }\n}\n", "import { isFunction } from './util/isFunction';\nimport { Observer, ObservableNotification } from './types';\nimport { isSubscription, Subscription } from './Subscription';\nimport { config } from './config';\nimport { reportUnhandledError } from './util/reportUnhandledError';\nimport { noop } from './util/noop';\nimport { nextNotification, errorNotification, COMPLETE_NOTIFICATION } from './NotificationFactories';\nimport { timeoutProvider } from './scheduler/timeoutProvider';\nimport { captureError } from './util/errorContext';\n\n/**\n * Implements the {@link Observer} interface and extends the\n * {@link Subscription} class. While the {@link Observer} is the public API for\n * consuming the values of an {@link Observable}, all Observers get converted to\n * a Subscriber, in order to provide Subscription-like capabilities such as\n * `unsubscribe`. Subscriber is a common type in RxJS, and crucial for\n * implementing operators, but it is rarely used as a public API.\n *\n * @class Subscriber\n */\nexport class Subscriber extends Subscription implements Observer {\n /**\n * A static factory for a Subscriber, given a (potentially partial) definition\n * of an Observer.\n * @param next The `next` callback of an Observer.\n * @param error The `error` callback of an\n * Observer.\n * @param complete The `complete` callback of an\n * Observer.\n * @return A Subscriber wrapping the (partially defined)\n * Observer represented by the given arguments.\n * @nocollapse\n * @deprecated Do not use. Will be removed in v8. There is no replacement for this\n * method, and there is no reason to be creating instances of `Subscriber` directly.\n * If you have a specific use case, please file an issue.\n */\n static create(next?: (x?: T) => void, error?: (e?: any) => void, complete?: () => void): Subscriber {\n return new SafeSubscriber(next, error, complete);\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected isStopped: boolean = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n protected destination: Subscriber | Observer; // this `any` is the escape hatch to erase extra type param (e.g. R)\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * There is no reason to directly create an instance of Subscriber. This type is exported for typings reasons.\n */\n constructor(destination?: Subscriber | Observer) {\n super();\n if (destination) {\n this.destination = destination;\n // Automatically chain subscriptions together here.\n // if destination is a Subscription, then it is a Subscriber.\n if (isSubscription(destination)) {\n destination.add(this);\n }\n } else {\n this.destination = EMPTY_OBSERVER;\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `next` from\n * the Observable, with a value. The Observable may call this method 0 or more\n * times.\n * @param {T} [value] The `next` value.\n * @return {void}\n */\n next(value?: T): void {\n if (this.isStopped) {\n handleStoppedNotification(nextNotification(value), this);\n } else {\n this._next(value!);\n }\n }\n\n /**\n * The {@link Observer} callback to receive notifications of type `error` from\n * the Observable, with an attached `Error`. Notifies the Observer that\n * the Observable has experienced an error condition.\n * @param {any} [err] The `error` exception.\n * @return {void}\n */\n error(err?: any): void {\n if (this.isStopped) {\n handleStoppedNotification(errorNotification(err), this);\n } else {\n this.isStopped = true;\n this._error(err);\n }\n }\n\n /**\n * The {@link Observer} callback to receive a valueless notification of type\n * `complete` from the Observable. Notifies the Observer that the Observable\n * has finished sending push-based notifications.\n * @return {void}\n */\n complete(): void {\n if (this.isStopped) {\n handleStoppedNotification(COMPLETE_NOTIFICATION, this);\n } else {\n this.isStopped = true;\n this._complete();\n }\n }\n\n unsubscribe(): void {\n if (!this.closed) {\n this.isStopped = true;\n super.unsubscribe();\n this.destination = null!;\n }\n }\n\n protected _next(value: T): void {\n this.destination.next(value);\n }\n\n protected _error(err: any): void {\n try {\n this.destination.error(err);\n } finally {\n this.unsubscribe();\n }\n }\n\n protected _complete(): void {\n try {\n this.destination.complete();\n } finally {\n this.unsubscribe();\n }\n }\n}\n\n/**\n * This bind is captured here because we want to be able to have\n * compatibility with monoid libraries that tend to use a method named\n * `bind`. In particular, a library called Monio requires this.\n */\nconst _bind = Function.prototype.bind;\n\nfunction bind any>(fn: Fn, thisArg: any): Fn {\n return _bind.call(fn, thisArg);\n}\n\n/**\n * Internal optimization only, DO NOT EXPOSE.\n * @internal\n */\nclass ConsumerObserver implements Observer {\n constructor(private partialObserver: Partial>) {}\n\n next(value: T): void {\n const { partialObserver } = this;\n if (partialObserver.next) {\n try {\n partialObserver.next(value);\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n\n error(err: any): void {\n const { partialObserver } = this;\n if (partialObserver.error) {\n try {\n partialObserver.error(err);\n } catch (error) {\n handleUnhandledError(error);\n }\n } else {\n handleUnhandledError(err);\n }\n }\n\n complete(): void {\n const { partialObserver } = this;\n if (partialObserver.complete) {\n try {\n partialObserver.complete();\n } catch (error) {\n handleUnhandledError(error);\n }\n }\n }\n}\n\nexport class SafeSubscriber extends Subscriber {\n constructor(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((e?: any) => void) | null,\n complete?: (() => void) | null\n ) {\n super();\n\n let partialObserver: Partial>;\n if (isFunction(observerOrNext) || !observerOrNext) {\n // The first argument is a function, not an observer. The next\n // two arguments *could* be observers, or they could be empty.\n partialObserver = {\n next: (observerOrNext ?? undefined) as (((value: T) => void) | undefined),\n error: error ?? undefined,\n complete: complete ?? undefined,\n };\n } else {\n // The first argument is a partial observer.\n let context: any;\n if (this && config.useDeprecatedNextContext) {\n // This is a deprecated path that made `this.unsubscribe()` available in\n // next handler functions passed to subscribe. This only exists behind a flag\n // now, as it is *very* slow.\n context = Object.create(observerOrNext);\n context.unsubscribe = () => this.unsubscribe();\n partialObserver = {\n next: observerOrNext.next && bind(observerOrNext.next, context),\n error: observerOrNext.error && bind(observerOrNext.error, context),\n complete: observerOrNext.complete && bind(observerOrNext.complete, context),\n };\n } else {\n // The \"normal\" path. Just use the partial observer directly.\n partialObserver = observerOrNext;\n }\n }\n\n // Wrap the partial observer to ensure it's a full observer, and\n // make sure proper error handling is accounted for.\n this.destination = new ConsumerObserver(partialObserver);\n }\n}\n\nfunction handleUnhandledError(error: any) {\n if (config.useDeprecatedSynchronousErrorHandling) {\n captureError(error);\n } else {\n // Ideal path, we report this as an unhandled error,\n // which is thrown on a new call stack.\n reportUnhandledError(error);\n }\n}\n\n/**\n * An error handler used when no error handler was supplied\n * to the SafeSubscriber -- meaning no error handler was supplied\n * do the `subscribe` call on our observable.\n * @param err The error to handle\n */\nfunction defaultErrorHandler(err: any) {\n throw err;\n}\n\n/**\n * A handler for notifications that cannot be sent to a stopped subscriber.\n * @param notification The notification being sent\n * @param subscriber The stopped subscriber\n */\nfunction handleStoppedNotification(notification: ObservableNotification, subscriber: Subscriber) {\n const { onStoppedNotification } = config;\n onStoppedNotification && timeoutProvider.setTimeout(() => onStoppedNotification(notification, subscriber));\n}\n\n/**\n * The observer used as a stub for subscriptions where the user did not\n * pass any arguments to `subscribe`. Comes with the default error handling\n * behavior.\n */\nexport const EMPTY_OBSERVER: Readonly> & { closed: true } = {\n closed: true,\n next: noop,\n error: defaultErrorHandler,\n complete: noop,\n};\n", "/**\n * Symbol.observable or a string \"@@observable\". Used for interop\n *\n * @deprecated We will no longer be exporting this symbol in upcoming versions of RxJS.\n * Instead polyfill and use Symbol.observable directly *or* use https://www.npmjs.com/package/symbol-observable\n */\nexport const observable: string | symbol = (() => (typeof Symbol === 'function' && Symbol.observable) || '@@observable')();\n", "/**\n * This function takes one parameter and just returns it. Simply put,\n * this is like `(x: T): T => x`.\n *\n * ## Examples\n *\n * This is useful in some cases when using things like `mergeMap`\n *\n * ```ts\n * import { interval, take, map, range, mergeMap, identity } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(5));\n *\n * const result$ = source$.pipe(\n * map(i => range(i)),\n * mergeMap(identity) // same as mergeMap(x => x)\n * );\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * Or when you want to selectively apply an operator\n *\n * ```ts\n * import { interval, take, identity } from 'rxjs';\n *\n * const shouldLimit = () => Math.random() < 0.5;\n *\n * const source$ = interval(1000);\n *\n * const result$ = source$.pipe(shouldLimit() ? take(5) : identity);\n *\n * result$.subscribe({\n * next: console.log\n * });\n * ```\n *\n * @param x Any value that is returned by this function\n * @returns The value passed as the first parameter to this function\n */\nexport function identity(x: T): T {\n return x;\n}\n", "import { identity } from './identity';\nimport { UnaryFunction } from '../types';\n\nexport function pipe(): typeof identity;\nexport function pipe(fn1: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction): UnaryFunction;\nexport function pipe(fn1: UnaryFunction, fn2: UnaryFunction, fn3: UnaryFunction): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction\n): UnaryFunction;\nexport function pipe(\n fn1: UnaryFunction,\n fn2: UnaryFunction,\n fn3: UnaryFunction,\n fn4: UnaryFunction,\n fn5: UnaryFunction,\n fn6: UnaryFunction,\n fn7: UnaryFunction,\n fn8: UnaryFunction,\n fn9: UnaryFunction,\n ...fns: UnaryFunction[]\n): UnaryFunction;\n\n/**\n * pipe() can be called on one or more functions, each of which can take one argument (\"UnaryFunction\")\n * and uses it to return a value.\n * It returns a function that takes one argument, passes it to the first UnaryFunction, and then\n * passes the result to the next one, passes that result to the next one, and so on. \n */\nexport function pipe(...fns: Array>): UnaryFunction {\n return pipeFromArray(fns);\n}\n\n/** @internal */\nexport function pipeFromArray(fns: Array>): UnaryFunction {\n if (fns.length === 0) {\n return identity as UnaryFunction;\n }\n\n if (fns.length === 1) {\n return fns[0];\n }\n\n return function piped(input: T): R {\n return fns.reduce((prev: any, fn: UnaryFunction) => fn(prev), input as any);\n };\n}\n", "import { Operator } from './Operator';\nimport { SafeSubscriber, Subscriber } from './Subscriber';\nimport { isSubscription, Subscription } from './Subscription';\nimport { TeardownLogic, OperatorFunction, Subscribable, Observer } from './types';\nimport { observable as Symbol_observable } from './symbol/observable';\nimport { pipeFromArray } from './util/pipe';\nimport { config } from './config';\nimport { isFunction } from './util/isFunction';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A representation of any set of values over any amount of time. This is the most basic building block\n * of RxJS.\n *\n * @class Observable\n */\nexport class Observable implements Subscribable {\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n source: Observable | undefined;\n\n /**\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n */\n operator: Operator | undefined;\n\n /**\n * @constructor\n * @param {Function} subscribe the function that is called when the Observable is\n * initially subscribed to. This function is given a Subscriber, to which new values\n * can be `next`ed, or an `error` method can be called to raise an error, or\n * `complete` can be called to notify of a successful completion.\n */\n constructor(subscribe?: (this: Observable, subscriber: Subscriber) => TeardownLogic) {\n if (subscribe) {\n this._subscribe = subscribe;\n }\n }\n\n // HACK: Since TypeScript inherits static properties too, we have to\n // fight against TypeScript here so Subject can have a different static create signature\n /**\n * Creates a new Observable by calling the Observable constructor\n * @owner Observable\n * @method create\n * @param {Function} subscribe? the subscriber function to be passed to the Observable constructor\n * @return {Observable} a new observable\n * @nocollapse\n * @deprecated Use `new Observable()` instead. Will be removed in v8.\n */\n static create: (...args: any[]) => any = (subscribe?: (subscriber: Subscriber) => TeardownLogic) => {\n return new Observable(subscribe);\n };\n\n /**\n * Creates a new Observable, with this Observable instance as the source, and the passed\n * operator defined as the new observable's operator.\n * @method lift\n * @param operator the operator defining the operation to take on the observable\n * @return a new observable with the Operator applied\n * @deprecated Internal implementation detail, do not use directly. Will be made internal in v8.\n * If you have implemented an operator using `lift`, it is recommended that you create an\n * operator by simply returning `new Observable()` directly. See \"Creating new operators from\n * scratch\" section here: https://rxjs.dev/guide/operators\n */\n lift(operator?: Operator): Observable {\n const observable = new Observable();\n observable.source = this;\n observable.operator = operator;\n return observable;\n }\n\n subscribe(observerOrNext?: Partial> | ((value: T) => void)): Subscription;\n /** @deprecated Instead of passing separate callback arguments, use an observer argument. Signatures taking separate callback arguments will be removed in v8. Details: https://rxjs.dev/deprecations/subscribe-arguments */\n subscribe(next?: ((value: T) => void) | null, error?: ((error: any) => void) | null, complete?: (() => void) | null): Subscription;\n /**\n * Invokes an execution of an Observable and registers Observer handlers for notifications it will emit.\n *\n * Use it when you have all these Observables, but still nothing is happening.\n *\n * `subscribe` is not a regular operator, but a method that calls Observable's internal `subscribe` function. It\n * might be for example a function that you passed to Observable's constructor, but most of the time it is\n * a library implementation, which defines what will be emitted by an Observable, and when it be will emitted. This means\n * that calling `subscribe` is actually the moment when Observable starts its work, not when it is created, as it is often\n * the thought.\n *\n * Apart from starting the execution of an Observable, this method allows you to listen for values\n * that an Observable emits, as well as for when it completes or errors. You can achieve this in two\n * of the following ways.\n *\n * The first way is creating an object that implements {@link Observer} interface. It should have methods\n * defined by that interface, but note that it should be just a regular JavaScript object, which you can create\n * yourself in any way you want (ES6 class, classic function constructor, object literal etc.). In particular, do\n * not attempt to use any RxJS implementation details to create Observers - you don't need them. Remember also\n * that your object does not have to implement all methods. If you find yourself creating a method that doesn't\n * do anything, you can simply omit it. Note however, if the `error` method is not provided and an error happens,\n * it will be thrown asynchronously. Errors thrown asynchronously cannot be caught using `try`/`catch`. Instead,\n * use the {@link onUnhandledError} configuration option or use a runtime handler (like `window.onerror` or\n * `process.on('error)`) to be notified of unhandled errors. Because of this, it's recommended that you provide\n * an `error` method to avoid missing thrown errors.\n *\n * The second way is to give up on Observer object altogether and simply provide callback functions in place of its methods.\n * This means you can provide three functions as arguments to `subscribe`, where the first function is equivalent\n * of a `next` method, the second of an `error` method and the third of a `complete` method. Just as in case of an Observer,\n * if you do not need to listen for something, you can omit a function by passing `undefined` or `null`,\n * since `subscribe` recognizes these functions by where they were placed in function call. When it comes\n * to the `error` function, as with an Observer, if not provided, errors emitted by an Observable will be thrown asynchronously.\n *\n * You can, however, subscribe with no parameters at all. This may be the case where you're not interested in terminal events\n * and you also handled emissions internally by using operators (e.g. using `tap`).\n *\n * Whichever style of calling `subscribe` you use, in both cases it returns a Subscription object.\n * This object allows you to call `unsubscribe` on it, which in turn will stop the work that an Observable does and will clean\n * up all resources that an Observable used. Note that cancelling a subscription will not call `complete` callback\n * provided to `subscribe` function, which is reserved for a regular completion signal that comes from an Observable.\n *\n * Remember that callbacks provided to `subscribe` are not guaranteed to be called asynchronously.\n * It is an Observable itself that decides when these functions will be called. For example {@link of}\n * by default emits all its values synchronously. Always check documentation for how given Observable\n * will behave when subscribed and if its default behavior can be modified with a `scheduler`.\n *\n * #### Examples\n *\n * Subscribe with an {@link guide/observer Observer}\n *\n * ```ts\n * import { of } from 'rxjs';\n *\n * const sumObserver = {\n * sum: 0,\n * next(value) {\n * console.log('Adding: ' + value);\n * this.sum = this.sum + value;\n * },\n * error() {\n * // We actually could just remove this method,\n * // since we do not really care about errors right now.\n * },\n * complete() {\n * console.log('Sum equals: ' + this.sum);\n * }\n * };\n *\n * of(1, 2, 3) // Synchronously emits 1, 2, 3 and then completes.\n * .subscribe(sumObserver);\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Subscribe with functions ({@link deprecations/subscribe-arguments deprecated})\n *\n * ```ts\n * import { of } from 'rxjs'\n *\n * let sum = 0;\n *\n * of(1, 2, 3).subscribe(\n * value => {\n * console.log('Adding: ' + value);\n * sum = sum + value;\n * },\n * undefined,\n * () => console.log('Sum equals: ' + sum)\n * );\n *\n * // Logs:\n * // 'Adding: 1'\n * // 'Adding: 2'\n * // 'Adding: 3'\n * // 'Sum equals: 6'\n * ```\n *\n * Cancel a subscription\n *\n * ```ts\n * import { interval } from 'rxjs';\n *\n * const subscription = interval(1000).subscribe({\n * next(num) {\n * console.log(num)\n * },\n * complete() {\n * // Will not be called, even when cancelling subscription.\n * console.log('completed!');\n * }\n * });\n *\n * setTimeout(() => {\n * subscription.unsubscribe();\n * console.log('unsubscribed!');\n * }, 2500);\n *\n * // Logs:\n * // 0 after 1s\n * // 1 after 2s\n * // 'unsubscribed!' after 2.5s\n * ```\n *\n * @param {Observer|Function} observerOrNext (optional) Either an observer with methods to be called,\n * or the first of three possible handlers, which is the handler for each value emitted from the subscribed\n * Observable.\n * @param {Function} error (optional) A handler for a terminal event resulting from an error. If no error handler is provided,\n * the error will be thrown asynchronously as unhandled.\n * @param {Function} complete (optional) A handler for a terminal event resulting from successful completion.\n * @return {Subscription} a subscription reference to the registered handlers\n * @method subscribe\n */\n subscribe(\n observerOrNext?: Partial> | ((value: T) => void) | null,\n error?: ((error: any) => void) | null,\n complete?: (() => void) | null\n ): Subscription {\n const subscriber = isSubscriber(observerOrNext) ? observerOrNext : new SafeSubscriber(observerOrNext, error, complete);\n\n errorContext(() => {\n const { operator, source } = this;\n subscriber.add(\n operator\n ? // We're dealing with a subscription in the\n // operator chain to one of our lifted operators.\n operator.call(subscriber, source)\n : source\n ? // If `source` has a value, but `operator` does not, something that\n // had intimate knowledge of our API, like our `Subject`, must have\n // set it. We're going to just call `_subscribe` directly.\n this._subscribe(subscriber)\n : // In all other cases, we're likely wrapping a user-provided initializer\n // function, so we need to catch errors and handle them appropriately.\n this._trySubscribe(subscriber)\n );\n });\n\n return subscriber;\n }\n\n /** @internal */\n protected _trySubscribe(sink: Subscriber): TeardownLogic {\n try {\n return this._subscribe(sink);\n } catch (err) {\n // We don't need to return anything in this case,\n // because it's just going to try to `add()` to a subscription\n // above.\n sink.error(err);\n }\n }\n\n /**\n * Used as a NON-CANCELLABLE means of subscribing to an observable, for use with\n * APIs that expect promises, like `async/await`. You cannot unsubscribe from this.\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * #### Example\n *\n * ```ts\n * import { interval, take } from 'rxjs';\n *\n * const source$ = interval(1000).pipe(take(4));\n *\n * async function getTotal() {\n * let total = 0;\n *\n * await source$.forEach(value => {\n * total += value;\n * console.log('observable -> ' + value);\n * });\n *\n * return total;\n * }\n *\n * getTotal().then(\n * total => console.log('Total: ' + total)\n * );\n *\n * // Expected:\n * // 'observable -> 0'\n * // 'observable -> 1'\n * // 'observable -> 2'\n * // 'observable -> 3'\n * // 'Total: 6'\n * ```\n *\n * @param next a handler for each value emitted by the observable\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n */\n forEach(next: (value: T) => void): Promise;\n\n /**\n * @param next a handler for each value emitted by the observable\n * @param promiseCtor a constructor function used to instantiate the Promise\n * @return a promise that either resolves on observable completion or\n * rejects with the handled error\n * @deprecated Passing a Promise constructor will no longer be available\n * in upcoming versions of RxJS. This is because it adds weight to the library, for very\n * little benefit. If you need this functionality, it is recommended that you either\n * polyfill Promise, or you create an adapter to convert the returned native promise\n * to whatever promise implementation you wanted. Will be removed in v8.\n */\n forEach(next: (value: T) => void, promiseCtor: PromiseConstructorLike): Promise;\n\n forEach(next: (value: T) => void, promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n const subscriber = new SafeSubscriber({\n next: (value) => {\n try {\n next(value);\n } catch (err) {\n reject(err);\n subscriber.unsubscribe();\n }\n },\n error: reject,\n complete: resolve,\n });\n this.subscribe(subscriber);\n }) as Promise;\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): TeardownLogic {\n return this.source?.subscribe(subscriber);\n }\n\n /**\n * An interop point defined by the es7-observable spec https://github.com/zenparsing/es-observable\n * @method Symbol.observable\n * @return {Observable} this instance of the observable\n */\n [Symbol_observable]() {\n return this;\n }\n\n /* tslint:disable:max-line-length */\n pipe(): Observable;\n pipe(op1: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction): Observable;\n pipe(op1: OperatorFunction, op2: OperatorFunction, op3: OperatorFunction): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction\n ): Observable;\n pipe(\n op1: OperatorFunction,\n op2: OperatorFunction,\n op3: OperatorFunction,\n op4: OperatorFunction,\n op5: OperatorFunction,\n op6: OperatorFunction,\n op7: OperatorFunction,\n op8: OperatorFunction,\n op9: OperatorFunction,\n ...operations: OperatorFunction[]\n ): Observable;\n /* tslint:enable:max-line-length */\n\n /**\n * Used to stitch together functional operators into a chain.\n * @method pipe\n * @return {Observable} the Observable result of all of the operators having\n * been called in the order they were passed in.\n *\n * ## Example\n *\n * ```ts\n * import { interval, filter, map, scan } from 'rxjs';\n *\n * interval(1000)\n * .pipe(\n * filter(x => x % 2 === 0),\n * map(x => x + x),\n * scan((acc, x) => acc + x)\n * )\n * .subscribe(x => console.log(x));\n * ```\n */\n pipe(...operations: OperatorFunction[]): Observable {\n return pipeFromArray(operations)(this);\n }\n\n /* tslint:disable:max-line-length */\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: typeof Promise): Promise;\n /** @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise */\n toPromise(PromiseCtor: PromiseConstructorLike): Promise;\n /* tslint:enable:max-line-length */\n\n /**\n * Subscribe to this Observable and get a Promise resolving on\n * `complete` with the last emission (if any).\n *\n * **WARNING**: Only use this with observables you *know* will complete. If the source\n * observable does not complete, you will end up with a promise that is hung up, and\n * potentially all of the state of an async function hanging out in memory. To avoid\n * this situation, look into adding something like {@link timeout}, {@link take},\n * {@link takeWhile}, or {@link takeUntil} amongst others.\n *\n * @method toPromise\n * @param [promiseCtor] a constructor function used to instantiate\n * the Promise\n * @return A Promise that resolves with the last value emit, or\n * rejects on an error. If there were no emissions, Promise\n * resolves with undefined.\n * @deprecated Replaced with {@link firstValueFrom} and {@link lastValueFrom}. Will be removed in v8. Details: https://rxjs.dev/deprecations/to-promise\n */\n toPromise(promiseCtor?: PromiseConstructorLike): Promise {\n promiseCtor = getPromiseCtor(promiseCtor);\n\n return new promiseCtor((resolve, reject) => {\n let value: T | undefined;\n this.subscribe(\n (x: T) => (value = x),\n (err: any) => reject(err),\n () => resolve(value)\n );\n }) as Promise;\n }\n}\n\n/**\n * Decides between a passed promise constructor from consuming code,\n * A default configured promise constructor, and the native promise\n * constructor and returns it. If nothing can be found, it will throw\n * an error.\n * @param promiseCtor The optional promise constructor to passed by consuming code\n */\nfunction getPromiseCtor(promiseCtor: PromiseConstructorLike | undefined) {\n return promiseCtor ?? config.Promise ?? Promise;\n}\n\nfunction isObserver(value: any): value is Observer {\n return value && isFunction(value.next) && isFunction(value.error) && isFunction(value.complete);\n}\n\nfunction isSubscriber(value: any): value is Subscriber {\n return (value && value instanceof Subscriber) || (isObserver(value) && isSubscription(value));\n}\n", "import { Observable } from '../Observable';\nimport { Subscriber } from '../Subscriber';\nimport { OperatorFunction } from '../types';\nimport { isFunction } from './isFunction';\n\n/**\n * Used to determine if an object is an Observable with a lift function.\n */\nexport function hasLift(source: any): source is { lift: InstanceType['lift'] } {\n return isFunction(source?.lift);\n}\n\n/**\n * Creates an `OperatorFunction`. Used to define operators throughout the library in a concise way.\n * @param init The logic to connect the liftedSource to the subscriber at the moment of subscription.\n */\nexport function operate(\n init: (liftedSource: Observable, subscriber: Subscriber) => (() => void) | void\n): OperatorFunction {\n return (source: Observable) => {\n if (hasLift(source)) {\n return source.lift(function (this: Subscriber, liftedSource: Observable) {\n try {\n return init(liftedSource, this);\n } catch (err) {\n this.error(err);\n }\n });\n }\n throw new TypeError('Unable to lift unknown Observable type');\n };\n}\n", "import { Subscriber } from '../Subscriber';\n\n/**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional teardown logic here. This will only be called on teardown if the\n * subscriber itself is not already closed. This is called after all other teardown logic is executed.\n */\nexport function createOperatorSubscriber(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n onFinalize?: () => void\n): Subscriber {\n return new OperatorSubscriber(destination, onNext, onComplete, onError, onFinalize);\n}\n\n/**\n * A generic helper for allowing operators to be created with a Subscriber and\n * use closures to capture necessary state from the operator function itself.\n */\nexport class OperatorSubscriber extends Subscriber {\n /**\n * Creates an instance of an `OperatorSubscriber`.\n * @param destination The downstream subscriber.\n * @param onNext Handles next values, only called if this subscriber is not stopped or closed. Any\n * error that occurs in this function is caught and sent to the `error` method of this subscriber.\n * @param onError Handles errors from the subscription, any errors that occur in this handler are caught\n * and send to the `destination` error handler.\n * @param onComplete Handles completion notification from the subscription. Any errors that occur in\n * this handler are sent to the `destination` error handler.\n * @param onFinalize Additional finalization logic here. This will only be called on finalization if the\n * subscriber itself is not already closed. This is called after all other finalization logic is executed.\n * @param shouldUnsubscribe An optional check to see if an unsubscribe call should truly unsubscribe.\n * NOTE: This currently **ONLY** exists to support the strange behavior of {@link groupBy}, where unsubscription\n * to the resulting observable does not actually disconnect from the source if there are active subscriptions\n * to any grouped observable. (DO NOT EXPOSE OR USE EXTERNALLY!!!)\n */\n constructor(\n destination: Subscriber,\n onNext?: (value: T) => void,\n onComplete?: () => void,\n onError?: (err: any) => void,\n private onFinalize?: () => void,\n private shouldUnsubscribe?: () => boolean\n ) {\n // It's important - for performance reasons - that all of this class's\n // members are initialized and that they are always initialized in the same\n // order. This will ensure that all OperatorSubscriber instances have the\n // same hidden class in V8. This, in turn, will help keep the number of\n // hidden classes involved in property accesses within the base class as\n // low as possible. If the number of hidden classes involved exceeds four,\n // the property accesses will become megamorphic and performance penalties\n // will be incurred - i.e. inline caches won't be used.\n //\n // The reasons for ensuring all instances have the same hidden class are\n // further discussed in this blog post from Benedikt Meurer:\n // https://benediktmeurer.de/2018/03/23/impact-of-polymorphism-on-component-based-frameworks-like-react/\n super(destination);\n this._next = onNext\n ? function (this: OperatorSubscriber, value: T) {\n try {\n onNext(value);\n } catch (err) {\n destination.error(err);\n }\n }\n : super._next;\n this._error = onError\n ? function (this: OperatorSubscriber, err: any) {\n try {\n onError(err);\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._error;\n this._complete = onComplete\n ? function (this: OperatorSubscriber) {\n try {\n onComplete();\n } catch (err) {\n // Send any errors that occur down stream.\n destination.error(err);\n } finally {\n // Ensure finalization.\n this.unsubscribe();\n }\n }\n : super._complete;\n }\n\n unsubscribe() {\n if (!this.shouldUnsubscribe || this.shouldUnsubscribe()) {\n const { closed } = this;\n super.unsubscribe();\n // Execute additional teardown if we have any and we didn't already do so.\n !closed && this.onFinalize?.();\n }\n }\n}\n", "import { Subscription } from '../Subscription';\n\ninterface AnimationFrameProvider {\n schedule(callback: FrameRequestCallback): Subscription;\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n delegate:\n | {\n requestAnimationFrame: typeof requestAnimationFrame;\n cancelAnimationFrame: typeof cancelAnimationFrame;\n }\n | undefined;\n}\n\nexport const animationFrameProvider: AnimationFrameProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n schedule(callback) {\n let request = requestAnimationFrame;\n let cancel: typeof cancelAnimationFrame | undefined = cancelAnimationFrame;\n const { delegate } = animationFrameProvider;\n if (delegate) {\n request = delegate.requestAnimationFrame;\n cancel = delegate.cancelAnimationFrame;\n }\n const handle = request((timestamp) => {\n // Clear the cancel function. The request has been fulfilled, so\n // attempting to cancel the request upon unsubscription would be\n // pointless.\n cancel = undefined;\n callback(timestamp);\n });\n return new Subscription(() => cancel?.(handle));\n },\n requestAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.requestAnimationFrame || requestAnimationFrame)(...args);\n },\n cancelAnimationFrame(...args) {\n const { delegate } = animationFrameProvider;\n return (delegate?.cancelAnimationFrame || cancelAnimationFrame)(...args);\n },\n delegate: undefined,\n};\n", "import { createErrorClass } from './createErrorClass';\n\nexport interface ObjectUnsubscribedError extends Error {}\n\nexport interface ObjectUnsubscribedErrorCtor {\n /**\n * @deprecated Internal implementation detail. Do not construct error instances.\n * Cannot be tagged as internal: https://github.com/ReactiveX/rxjs/issues/6269\n */\n new (): ObjectUnsubscribedError;\n}\n\n/**\n * An error thrown when an action is invalid because the object has been\n * unsubscribed.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n *\n * @class ObjectUnsubscribedError\n */\nexport const ObjectUnsubscribedError: ObjectUnsubscribedErrorCtor = createErrorClass(\n (_super) =>\n function ObjectUnsubscribedErrorImpl(this: any) {\n _super(this);\n this.name = 'ObjectUnsubscribedError';\n this.message = 'object unsubscribed';\n }\n);\n", "import { Operator } from './Operator';\nimport { Observable } from './Observable';\nimport { Subscriber } from './Subscriber';\nimport { Subscription, EMPTY_SUBSCRIPTION } from './Subscription';\nimport { Observer, SubscriptionLike, TeardownLogic } from './types';\nimport { ObjectUnsubscribedError } from './util/ObjectUnsubscribedError';\nimport { arrRemove } from './util/arrRemove';\nimport { errorContext } from './util/errorContext';\n\n/**\n * A Subject is a special type of Observable that allows values to be\n * multicasted to many Observers. Subjects are like EventEmitters.\n *\n * Every Subject is an Observable and an Observer. You can subscribe to a\n * Subject, and you can call next to feed values as well as error and complete.\n */\nexport class Subject extends Observable implements SubscriptionLike {\n closed = false;\n\n private currentObservers: Observer[] | null = null;\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n observers: Observer[] = [];\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n isStopped = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n hasError = false;\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n thrownError: any = null;\n\n /**\n * Creates a \"subject\" by basically gluing an observer to an observable.\n *\n * @nocollapse\n * @deprecated Recommended you do not use. Will be removed at some point in the future. Plans for replacement still under discussion.\n */\n static create: (...args: any[]) => any = (destination: Observer, source: Observable): AnonymousSubject => {\n return new AnonymousSubject(destination, source);\n };\n\n constructor() {\n // NOTE: This must be here to obscure Observable's constructor.\n super();\n }\n\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n lift(operator: Operator): Observable {\n const subject = new AnonymousSubject(this, this);\n subject.operator = operator as any;\n return subject as any;\n }\n\n /** @internal */\n protected _throwIfClosed() {\n if (this.closed) {\n throw new ObjectUnsubscribedError();\n }\n }\n\n next(value: T) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n if (!this.currentObservers) {\n this.currentObservers = Array.from(this.observers);\n }\n for (const observer of this.currentObservers) {\n observer.next(value);\n }\n }\n });\n }\n\n error(err: any) {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.hasError = this.isStopped = true;\n this.thrownError = err;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.error(err);\n }\n }\n });\n }\n\n complete() {\n errorContext(() => {\n this._throwIfClosed();\n if (!this.isStopped) {\n this.isStopped = true;\n const { observers } = this;\n while (observers.length) {\n observers.shift()!.complete();\n }\n }\n });\n }\n\n unsubscribe() {\n this.isStopped = this.closed = true;\n this.observers = this.currentObservers = null!;\n }\n\n get observed() {\n return this.observers?.length > 0;\n }\n\n /** @internal */\n protected _trySubscribe(subscriber: Subscriber): TeardownLogic {\n this._throwIfClosed();\n return super._trySubscribe(subscriber);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._checkFinalizedStatuses(subscriber);\n return this._innerSubscribe(subscriber);\n }\n\n /** @internal */\n protected _innerSubscribe(subscriber: Subscriber) {\n const { hasError, isStopped, observers } = this;\n if (hasError || isStopped) {\n return EMPTY_SUBSCRIPTION;\n }\n this.currentObservers = null;\n observers.push(subscriber);\n return new Subscription(() => {\n this.currentObservers = null;\n arrRemove(observers, subscriber);\n });\n }\n\n /** @internal */\n protected _checkFinalizedStatuses(subscriber: Subscriber) {\n const { hasError, thrownError, isStopped } = this;\n if (hasError) {\n subscriber.error(thrownError);\n } else if (isStopped) {\n subscriber.complete();\n }\n }\n\n /**\n * Creates a new Observable with this Subject as the source. You can do this\n * to create custom Observer-side logic of the Subject and conceal it from\n * code that uses the Observable.\n * @return {Observable} Observable that the Subject casts to\n */\n asObservable(): Observable {\n const observable: any = new Observable();\n observable.source = this;\n return observable;\n }\n}\n\n/**\n * @class AnonymousSubject\n */\nexport class AnonymousSubject extends Subject {\n constructor(\n /** @deprecated Internal implementation detail, do not use directly. Will be made internal in v8. */\n public destination?: Observer,\n source?: Observable\n ) {\n super();\n this.source = source;\n }\n\n next(value: T) {\n this.destination?.next?.(value);\n }\n\n error(err: any) {\n this.destination?.error?.(err);\n }\n\n complete() {\n this.destination?.complete?.();\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n return this.source?.subscribe(subscriber) ?? EMPTY_SUBSCRIPTION;\n }\n}\n", "import { TimestampProvider } from '../types';\n\ninterface DateTimestampProvider extends TimestampProvider {\n delegate: TimestampProvider | undefined;\n}\n\nexport const dateTimestampProvider: DateTimestampProvider = {\n now() {\n // Use the variable rather than `this` so that the function can be called\n // without being bound to the provider.\n return (dateTimestampProvider.delegate || Date).now();\n },\n delegate: undefined,\n};\n", "import { Subject } from './Subject';\nimport { TimestampProvider } from './types';\nimport { Subscriber } from './Subscriber';\nimport { Subscription } from './Subscription';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * A variant of {@link Subject} that \"replays\" old values to new subscribers by emitting them when they first subscribe.\n *\n * `ReplaySubject` has an internal buffer that will store a specified number of values that it has observed. Like `Subject`,\n * `ReplaySubject` \"observes\" values by having them passed to its `next` method. When it observes a value, it will store that\n * value for a time determined by the configuration of the `ReplaySubject`, as passed to its constructor.\n *\n * When a new subscriber subscribes to the `ReplaySubject` instance, it will synchronously emit all values in its buffer in\n * a First-In-First-Out (FIFO) manner. The `ReplaySubject` will also complete, if it has observed completion; and it will\n * error if it has observed an error.\n *\n * There are two main configuration items to be concerned with:\n *\n * 1. `bufferSize` - This will determine how many items are stored in the buffer, defaults to infinite.\n * 2. `windowTime` - The amount of time to hold a value in the buffer before removing it from the buffer.\n *\n * Both configurations may exist simultaneously. So if you would like to buffer a maximum of 3 values, as long as the values\n * are less than 2 seconds old, you could do so with a `new ReplaySubject(3, 2000)`.\n *\n * ### Differences with BehaviorSubject\n *\n * `BehaviorSubject` is similar to `new ReplaySubject(1)`, with a couple of exceptions:\n *\n * 1. `BehaviorSubject` comes \"primed\" with a single value upon construction.\n * 2. `ReplaySubject` will replay values, even after observing an error, where `BehaviorSubject` will not.\n *\n * @see {@link Subject}\n * @see {@link BehaviorSubject}\n * @see {@link shareReplay}\n */\nexport class ReplaySubject extends Subject {\n private _buffer: (T | number)[] = [];\n private _infiniteTimeWindow = true;\n\n /**\n * @param bufferSize The size of the buffer to replay on subscription\n * @param windowTime The amount of time the buffered items will stay buffered\n * @param timestampProvider An object with a `now()` method that provides the current timestamp. This is used to\n * calculate the amount of time something has been buffered.\n */\n constructor(\n private _bufferSize = Infinity,\n private _windowTime = Infinity,\n private _timestampProvider: TimestampProvider = dateTimestampProvider\n ) {\n super();\n this._infiniteTimeWindow = _windowTime === Infinity;\n this._bufferSize = Math.max(1, _bufferSize);\n this._windowTime = Math.max(1, _windowTime);\n }\n\n next(value: T): void {\n const { isStopped, _buffer, _infiniteTimeWindow, _timestampProvider, _windowTime } = this;\n if (!isStopped) {\n _buffer.push(value);\n !_infiniteTimeWindow && _buffer.push(_timestampProvider.now() + _windowTime);\n }\n this._trimBuffer();\n super.next(value);\n }\n\n /** @internal */\n protected _subscribe(subscriber: Subscriber): Subscription {\n this._throwIfClosed();\n this._trimBuffer();\n\n const subscription = this._innerSubscribe(subscriber);\n\n const { _infiniteTimeWindow, _buffer } = this;\n // We use a copy here, so reentrant code does not mutate our array while we're\n // emitting it to a new subscriber.\n const copy = _buffer.slice();\n for (let i = 0; i < copy.length && !subscriber.closed; i += _infiniteTimeWindow ? 1 : 2) {\n subscriber.next(copy[i] as T);\n }\n\n this._checkFinalizedStatuses(subscriber);\n\n return subscription;\n }\n\n private _trimBuffer() {\n const { _bufferSize, _timestampProvider, _buffer, _infiniteTimeWindow } = this;\n // If we don't have an infinite buffer size, and we're over the length,\n // use splice to truncate the old buffer values off. Note that we have to\n // double the size for instances where we're not using an infinite time window\n // because we're storing the values and the timestamps in the same array.\n const adjustedBufferSize = (_infiniteTimeWindow ? 1 : 2) * _bufferSize;\n _bufferSize < Infinity && adjustedBufferSize < _buffer.length && _buffer.splice(0, _buffer.length - adjustedBufferSize);\n\n // Now, if we're not in an infinite time window, remove all values where the time is\n // older than what is allowed.\n if (!_infiniteTimeWindow) {\n const now = _timestampProvider.now();\n let last = 0;\n // Search the array for the first timestamp that isn't expired and\n // truncate the buffer up to that point.\n for (let i = 1; i < _buffer.length && (_buffer[i] as number) <= now; i += 2) {\n last = i;\n }\n last && _buffer.splice(0, last + 1);\n }\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Subscription } from '../Subscription';\nimport { SchedulerAction } from '../types';\n\n/**\n * A unit of work to be executed in a `scheduler`. An action is typically\n * created from within a {@link SchedulerLike} and an RxJS user does not need to concern\n * themselves about creating and manipulating an Action.\n *\n * ```ts\n * class Action extends Subscription {\n * new (scheduler: Scheduler, work: (state?: T) => void);\n * schedule(state?: T, delay: number = 0): Subscription;\n * }\n * ```\n *\n * @class Action\n */\nexport class Action extends Subscription {\n constructor(scheduler: Scheduler, work: (this: SchedulerAction, state?: T) => void) {\n super();\n }\n /**\n * Schedules this action on its parent {@link SchedulerLike} for execution. May be passed\n * some context object, `state`. May happen at some point in the future,\n * according to the `delay` parameter, if specified.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler.\n * @return {void}\n */\n public schedule(state?: T, delay: number = 0): Subscription {\n return this;\n }\n}\n", "import type { TimerHandle } from './timerHandle';\ntype SetIntervalFunction = (handler: () => void, timeout?: number, ...args: any[]) => TimerHandle;\ntype ClearIntervalFunction = (handle: TimerHandle) => void;\n\ninterface IntervalProvider {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n delegate:\n | {\n setInterval: SetIntervalFunction;\n clearInterval: ClearIntervalFunction;\n }\n | undefined;\n}\n\nexport const intervalProvider: IntervalProvider = {\n // When accessing the delegate, use the variable rather than `this` so that\n // the functions can be called without being bound to the provider.\n setInterval(handler: () => void, timeout?: number, ...args) {\n const { delegate } = intervalProvider;\n if (delegate?.setInterval) {\n return delegate.setInterval(handler, timeout, ...args);\n }\n return setInterval(handler, timeout, ...args);\n },\n clearInterval(handle) {\n const { delegate } = intervalProvider;\n return (delegate?.clearInterval || clearInterval)(handle as any);\n },\n delegate: undefined,\n};\n", "import { Action } from './Action';\nimport { SchedulerAction } from '../types';\nimport { Subscription } from '../Subscription';\nimport { AsyncScheduler } from './AsyncScheduler';\nimport { intervalProvider } from './intervalProvider';\nimport { arrRemove } from '../util/arrRemove';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncAction extends Action {\n public id: TimerHandle | undefined;\n public state?: T;\n // @ts-ignore: Property has no initializer and is not definitely assigned\n public delay: number;\n protected pending: boolean = false;\n\n constructor(protected scheduler: AsyncScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n public schedule(state?: T, delay: number = 0): Subscription {\n if (this.closed) {\n return this;\n }\n\n // Always replace the current state with the new state.\n this.state = state;\n\n const id = this.id;\n const scheduler = this.scheduler;\n\n //\n // Important implementation note:\n //\n // Actions only execute once by default, unless rescheduled from within the\n // scheduled callback. This allows us to implement single and repeat\n // actions via the same code path, without adding API surface area, as well\n // as mimic traditional recursion but across asynchronous boundaries.\n //\n // However, JS runtimes and timers distinguish between intervals achieved by\n // serial `setTimeout` calls vs. a single `setInterval` call. An interval of\n // serial `setTimeout` calls can be individually delayed, which delays\n // scheduling the next `setTimeout`, and so on. `setInterval` attempts to\n // guarantee the interval callback will be invoked more precisely to the\n // interval period, regardless of load.\n //\n // Therefore, we use `setInterval` to schedule single and repeat actions.\n // If the action reschedules itself with the same delay, the interval is not\n // canceled. If the action doesn't reschedule, or reschedules with a\n // different delay, the interval will be canceled after scheduled callback\n // execution.\n //\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, delay);\n }\n\n // Set the pending flag indicating that this action has been scheduled, or\n // has recursively rescheduled itself.\n this.pending = true;\n\n this.delay = delay;\n // If this action has already an async Id, don't request a new one.\n this.id = this.id ?? this.requestAsyncId(scheduler, this.id, delay);\n\n return this;\n }\n\n protected requestAsyncId(scheduler: AsyncScheduler, _id?: TimerHandle, delay: number = 0): TimerHandle {\n return intervalProvider.setInterval(scheduler.flush.bind(scheduler, this), delay);\n }\n\n protected recycleAsyncId(_scheduler: AsyncScheduler, id?: TimerHandle, delay: number | null = 0): TimerHandle | undefined {\n // If this action is rescheduled with the same delay time, don't clear the interval id.\n if (delay != null && this.delay === delay && this.pending === false) {\n return id;\n }\n // Otherwise, if the action's delay time is different from the current delay,\n // or the action has been rescheduled before it's executed, clear the interval id\n if (id != null) {\n intervalProvider.clearInterval(id);\n }\n\n return undefined;\n }\n\n /**\n * Immediately executes this action and the `work` it contains.\n * @return {any}\n */\n public execute(state: T, delay: number): any {\n if (this.closed) {\n return new Error('executing a cancelled action');\n }\n\n this.pending = false;\n const error = this._execute(state, delay);\n if (error) {\n return error;\n } else if (this.pending === false && this.id != null) {\n // Dequeue if the action didn't reschedule itself. Don't call\n // unsubscribe(), because the action could reschedule later.\n // For example:\n // ```\n // scheduler.schedule(function doWork(counter) {\n // /* ... I'm a busy worker bee ... */\n // var originalAction = this;\n // /* wait 100ms before rescheduling the action */\n // setTimeout(function () {\n // originalAction.schedule(counter + 1);\n // }, 100);\n // }, 1000);\n // ```\n this.id = this.recycleAsyncId(this.scheduler, this.id, null);\n }\n }\n\n protected _execute(state: T, _delay: number): any {\n let errored: boolean = false;\n let errorValue: any;\n try {\n this.work(state);\n } catch (e) {\n errored = true;\n // HACK: Since code elsewhere is relying on the \"truthiness\" of the\n // return here, we can't have it return \"\" or 0 or false.\n // TODO: Clean this up when we refactor schedulers mid-version-8 or so.\n errorValue = e ? e : new Error('Scheduled action threw falsy error');\n }\n if (errored) {\n this.unsubscribe();\n return errorValue;\n }\n }\n\n unsubscribe() {\n if (!this.closed) {\n const { id, scheduler } = this;\n const { actions } = scheduler;\n\n this.work = this.state = this.scheduler = null!;\n this.pending = false;\n\n arrRemove(actions, this);\n if (id != null) {\n this.id = this.recycleAsyncId(scheduler, id, null);\n }\n\n this.delay = null!;\n super.unsubscribe();\n }\n }\n}\n", "import { Action } from './scheduler/Action';\nimport { Subscription } from './Subscription';\nimport { SchedulerLike, SchedulerAction } from './types';\nimport { dateTimestampProvider } from './scheduler/dateTimestampProvider';\n\n/**\n * An execution context and a data structure to order tasks and schedule their\n * execution. Provides a notion of (potentially virtual) time, through the\n * `now()` getter method.\n *\n * Each unit of work in a Scheduler is called an `Action`.\n *\n * ```ts\n * class Scheduler {\n * now(): number;\n * schedule(work, delay?, state?): Subscription;\n * }\n * ```\n *\n * @class Scheduler\n * @deprecated Scheduler is an internal implementation detail of RxJS, and\n * should not be used directly. Rather, create your own class and implement\n * {@link SchedulerLike}. Will be made internal in v8.\n */\nexport class Scheduler implements SchedulerLike {\n public static now: () => number = dateTimestampProvider.now;\n\n constructor(private schedulerActionCtor: typeof Action, now: () => number = Scheduler.now) {\n this.now = now;\n }\n\n /**\n * A getter method that returns a number representing the current time\n * (at the time this function was called) according to the scheduler's own\n * internal clock.\n * @return {number} A number that represents the current time. May or may not\n * have a relation to wall-clock time. May or may not refer to a time unit\n * (e.g. milliseconds).\n */\n public now: () => number;\n\n /**\n * Schedules a function, `work`, for execution. May happen at some point in\n * the future, according to the `delay` parameter, if specified. May be passed\n * some context object, `state`, which will be passed to the `work` function.\n *\n * The given arguments will be processed an stored as an Action object in a\n * queue of actions.\n *\n * @param {function(state: ?T): ?Subscription} work A function representing a\n * task, or some unit of work to be executed by the Scheduler.\n * @param {number} [delay] Time to wait before executing the work, where the\n * time unit is implicit and defined by the Scheduler itself.\n * @param {T} [state] Some contextual data that the `work` function uses when\n * called by the Scheduler.\n * @return {Subscription} A subscription in order to be able to unsubscribe\n * the scheduled work.\n */\n public schedule(work: (this: SchedulerAction, state?: T) => void, delay: number = 0, state?: T): Subscription {\n return new this.schedulerActionCtor(this, work).schedule(state, delay);\n }\n}\n", "import { Scheduler } from '../Scheduler';\nimport { Action } from './Action';\nimport { AsyncAction } from './AsyncAction';\nimport { TimerHandle } from './timerHandle';\n\nexport class AsyncScheduler extends Scheduler {\n public actions: Array> = [];\n /**\n * A flag to indicate whether the Scheduler is currently executing a batch of\n * queued actions.\n * @type {boolean}\n * @internal\n */\n public _active: boolean = false;\n /**\n * An internal ID used to track the latest asynchronous task such as those\n * coming from `setTimeout`, `setInterval`, `requestAnimationFrame`, and\n * others.\n * @type {any}\n * @internal\n */\n public _scheduled: TimerHandle | undefined;\n\n constructor(SchedulerAction: typeof Action, now: () => number = Scheduler.now) {\n super(SchedulerAction, now);\n }\n\n public flush(action: AsyncAction): void {\n const { actions } = this;\n\n if (this._active) {\n actions.push(action);\n return;\n }\n\n let error: any;\n this._active = true;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions.shift()!)); // exhaust the scheduler queue\n\n this._active = false;\n\n if (error) {\n while ((action = actions.shift()!)) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\n/**\n *\n * Async Scheduler\n *\n * Schedule task as if you used setTimeout(task, duration)\n *\n * `async` scheduler schedules tasks asynchronously, by putting them on the JavaScript\n * event loop queue. It is best used to delay tasks in time or to schedule tasks repeating\n * in intervals.\n *\n * If you just want to \"defer\" task, that is to perform it right after currently\n * executing synchronous code ends (commonly achieved by `setTimeout(deferredTask, 0)`),\n * better choice will be the {@link asapScheduler} scheduler.\n *\n * ## Examples\n * Use async scheduler to delay task\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * const task = () => console.log('it works!');\n *\n * asyncScheduler.schedule(task, 2000);\n *\n * // After 2 seconds logs:\n * // \"it works!\"\n * ```\n *\n * Use async scheduler to repeat task in intervals\n * ```ts\n * import { asyncScheduler } from 'rxjs';\n *\n * function task(state) {\n * console.log(state);\n * this.schedule(state + 1, 1000); // `this` references currently executing Action,\n * // which we reschedule with new state and delay\n * }\n *\n * asyncScheduler.schedule(task, 3000, 0);\n *\n * // Logs:\n * // 0 after 3s\n * // 1 after 4s\n * // 2 after 5s\n * // 3 after 6s\n * ```\n */\n\nexport const asyncScheduler = new AsyncScheduler(AsyncAction);\n\n/**\n * @deprecated Renamed to {@link asyncScheduler}. Will be removed in v8.\n */\nexport const async = asyncScheduler;\n", "import { AsyncAction } from './AsyncAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\nimport { SchedulerAction } from '../types';\nimport { animationFrameProvider } from './animationFrameProvider';\nimport { TimerHandle } from './timerHandle';\n\nexport class AnimationFrameAction extends AsyncAction {\n constructor(protected scheduler: AnimationFrameScheduler, protected work: (this: SchedulerAction, state?: T) => void) {\n super(scheduler, work);\n }\n\n protected requestAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle {\n // If delay is greater than 0, request as an async action.\n if (delay !== null && delay > 0) {\n return super.requestAsyncId(scheduler, id, delay);\n }\n // Push the action to the end of the scheduler queue.\n scheduler.actions.push(this);\n // If an animation frame has already been requested, don't request another\n // one. If an animation frame hasn't been requested yet, request one. Return\n // the current animation frame request id.\n return scheduler._scheduled || (scheduler._scheduled = animationFrameProvider.requestAnimationFrame(() => scheduler.flush(undefined)));\n }\n\n protected recycleAsyncId(scheduler: AnimationFrameScheduler, id?: TimerHandle, delay: number = 0): TimerHandle | undefined {\n // If delay exists and is greater than 0, or if the delay is null (the\n // action wasn't rescheduled) but was originally scheduled as an async\n // action, then recycle as an async action.\n if (delay != null ? delay > 0 : this.delay > 0) {\n return super.recycleAsyncId(scheduler, id, delay);\n }\n // If the scheduler queue has no remaining actions with the same async id,\n // cancel the requested animation frame and set the scheduled flag to\n // undefined so the next AnimationFrameAction will request its own.\n const { actions } = scheduler;\n if (id != null && actions[actions.length - 1]?.id !== id) {\n animationFrameProvider.cancelAnimationFrame(id as number);\n scheduler._scheduled = undefined;\n }\n // Return undefined so the action knows to request a new async id if it's rescheduled.\n return undefined;\n }\n}\n", "import { AsyncAction } from './AsyncAction';\nimport { AsyncScheduler } from './AsyncScheduler';\n\nexport class AnimationFrameScheduler extends AsyncScheduler {\n public flush(action?: AsyncAction): void {\n this._active = true;\n // The async id that effects a call to flush is stored in _scheduled.\n // Before executing an action, it's necessary to check the action's async\n // id to determine whether it's supposed to be executed in the current\n // flush.\n // Previous implementations of this method used a count to determine this,\n // but that was unsound, as actions that are unsubscribed - i.e. cancelled -\n // are removed from the actions array and that can shift actions that are\n // scheduled to be executed in a subsequent flush into positions at which\n // they are executed within the current flush.\n const flushId = this._scheduled;\n this._scheduled = undefined;\n\n const { actions } = this;\n let error: any;\n action = action || actions.shift()!;\n\n do {\n if ((error = action.execute(action.state, action.delay))) {\n break;\n }\n } while ((action = actions[0]) && action.id === flushId && actions.shift());\n\n this._active = false;\n\n if (error) {\n while ((action = actions[0]) && action.id === flushId && actions.shift()) {\n action.unsubscribe();\n }\n throw error;\n }\n }\n}\n", "import { AnimationFrameAction } from './AnimationFrameAction';\nimport { AnimationFrameScheduler } from './AnimationFrameScheduler';\n\n/**\n *\n * Animation Frame Scheduler\n *\n * Perform task when `window.requestAnimationFrame` would fire\n *\n * When `animationFrame` scheduler is used with delay, it will fall back to {@link asyncScheduler} scheduler\n * behaviour.\n *\n * Without delay, `animationFrame` scheduler can be used to create smooth browser animations.\n * It makes sure scheduled task will happen just before next browser content repaint,\n * thus performing animations as efficiently as possible.\n *\n * ## Example\n * Schedule div height animation\n * ```ts\n * // html:
      \n * import { animationFrameScheduler } from 'rxjs';\n *\n * const div = document.querySelector('div');\n *\n * animationFrameScheduler.schedule(function(height) {\n * div.style.height = height + \"px\";\n *\n * this.schedule(height + 1); // `this` references currently executing Action,\n * // which we reschedule with new state\n * }, 0, 0);\n *\n * // You will see a div element growing in height\n * ```\n */\n\nexport const animationFrameScheduler = new AnimationFrameScheduler(AnimationFrameAction);\n\n/**\n * @deprecated Renamed to {@link animationFrameScheduler}. Will be removed in v8.\n */\nexport const animationFrame = animationFrameScheduler;\n", "import { Observable } from '../Observable';\nimport { SchedulerLike } from '../types';\n\n/**\n * A simple Observable that emits no items to the Observer and immediately\n * emits a complete notification.\n *\n * Just emits 'complete', and nothing else.\n *\n * ![](empty.png)\n *\n * A simple Observable that only emits the complete notification. It can be used\n * for composing with other Observables, such as in a {@link mergeMap}.\n *\n * ## Examples\n *\n * Log complete notification\n *\n * ```ts\n * import { EMPTY } from 'rxjs';\n *\n * EMPTY.subscribe({\n * next: () => console.log('Next'),\n * complete: () => console.log('Complete!')\n * });\n *\n * // Outputs\n * // Complete!\n * ```\n *\n * Emit the number 7, then complete\n *\n * ```ts\n * import { EMPTY, startWith } from 'rxjs';\n *\n * const result = EMPTY.pipe(startWith(7));\n * result.subscribe(x => console.log(x));\n *\n * // Outputs\n * // 7\n * ```\n *\n * Map and flatten only odd numbers to the sequence `'a'`, `'b'`, `'c'`\n *\n * ```ts\n * import { interval, mergeMap, of, EMPTY } from 'rxjs';\n *\n * const interval$ = interval(1000);\n * const result = interval$.pipe(\n * mergeMap(x => x % 2 === 1 ? of('a', 'b', 'c') : EMPTY),\n * );\n * result.subscribe(x => console.log(x));\n *\n * // Results in the following to the console:\n * // x is equal to the count on the interval, e.g. (0, 1, 2, 3, ...)\n * // x will occur every 1000ms\n * // if x % 2 is equal to 1, print a, b, c (each on its own)\n * // if x % 2 is not equal to 1, nothing will be output\n * ```\n *\n * @see {@link Observable}\n * @see {@link NEVER}\n * @see {@link of}\n * @see {@link throwError}\n */\nexport const EMPTY = new Observable((subscriber) => subscriber.complete());\n\n/**\n * @param scheduler A {@link SchedulerLike} to use for scheduling\n * the emission of the complete notification.\n * @deprecated Replaced with the {@link EMPTY} constant or {@link scheduled} (e.g. `scheduled([], scheduler)`). Will be removed in v8.\n */\nexport function empty(scheduler?: SchedulerLike) {\n return scheduler ? emptyScheduled(scheduler) : EMPTY;\n}\n\nfunction emptyScheduled(scheduler: SchedulerLike) {\n return new Observable((subscriber) => scheduler.schedule(() => subscriber.complete()));\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport function isScheduler(value: any): value is SchedulerLike {\n return value && isFunction(value.schedule);\n}\n", "import { SchedulerLike } from '../types';\nimport { isFunction } from './isFunction';\nimport { isScheduler } from './isScheduler';\n\nfunction last(arr: T[]): T | undefined {\n return arr[arr.length - 1];\n}\n\nexport function popResultSelector(args: any[]): ((...args: unknown[]) => unknown) | undefined {\n return isFunction(last(args)) ? args.pop() : undefined;\n}\n\nexport function popScheduler(args: any[]): SchedulerLike | undefined {\n return isScheduler(last(args)) ? args.pop() : undefined;\n}\n\nexport function popNumber(args: any[], defaultValue: number): number {\n return typeof last(args) === 'number' ? args.pop()! : defaultValue;\n}\n", "export const isArrayLike = ((x: any): x is ArrayLike => x && typeof x.length === 'number' && typeof x !== 'function');", "import { isFunction } from \"./isFunction\";\n\n/**\n * Tests to see if the object is \"thennable\".\n * @param value the object to test\n */\nexport function isPromise(value: any): value is PromiseLike {\n return isFunction(value?.then);\n}\n", "import { InteropObservable } from '../types';\nimport { observable as Symbol_observable } from '../symbol/observable';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being Observable (but not necessary an Rx Observable) */\nexport function isInteropObservable(input: any): input is InteropObservable {\n return isFunction(input[Symbol_observable]);\n}\n", "import { isFunction } from './isFunction';\n\nexport function isAsyncIterable(obj: any): obj is AsyncIterable {\n return Symbol.asyncIterator && isFunction(obj?.[Symbol.asyncIterator]);\n}\n", "/**\n * Creates the TypeError to throw if an invalid object is passed to `from` or `scheduled`.\n * @param input The object that was passed.\n */\nexport function createInvalidObservableTypeError(input: any) {\n // TODO: We should create error codes that can be looked up, so this can be less verbose.\n return new TypeError(\n `You provided ${\n input !== null && typeof input === 'object' ? 'an invalid object' : `'${input}'`\n } where a stream was expected. You can provide an Observable, Promise, ReadableStream, Array, AsyncIterable, or Iterable.`\n );\n}\n", "export function getSymbolIterator(): symbol {\n if (typeof Symbol !== 'function' || !Symbol.iterator) {\n return '@@iterator' as any;\n }\n\n return Symbol.iterator;\n}\n\nexport const iterator = getSymbolIterator();\n", "import { iterator as Symbol_iterator } from '../symbol/iterator';\nimport { isFunction } from './isFunction';\n\n/** Identifies an input as being an Iterable */\nexport function isIterable(input: any): input is Iterable {\n return isFunction(input?.[Symbol_iterator]);\n}\n", "import { ReadableStreamLike } from '../types';\nimport { isFunction } from './isFunction';\n\nexport async function* readableStreamLikeToAsyncGenerator(readableStream: ReadableStreamLike): AsyncGenerator {\n const reader = readableStream.getReader();\n try {\n while (true) {\n const { value, done } = await reader.read();\n if (done) {\n return;\n }\n yield value!;\n }\n } finally {\n reader.releaseLock();\n }\n}\n\nexport function isReadableStreamLike(obj: any): obj is ReadableStreamLike {\n // We don't want to use instanceof checks because they would return\n // false for instances from another Realm, like an