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TSR

TSR

Wrapper Class for Saliency Calculation. Automatically calls the corresponding PYT or TF implementation.

__new__(model, NumTimeSteps, NumFeatures, method='GRAD', mode='time', device='cpu', normalize=True, tsr=True)

Initialization Arguments: model [torch.nn.Module, tf.keras.Model]: model to be explained NumTimeSteps int : Number of Time Step NumFeatures int : Number Features method str: Saliency Methode to be used mode str: Second dimension 'time'->(1,time,feat) or 'feat'->(1,feat,time)

Saliency_PTY(model, NumTimeSteps, NumFeatures, method='GRAD', mode='time', tsr=True, normalize=True, device='cpu')

Bases: Saliency

PyTorch Implementation for Saliency Calculation based on [1]. The Saliency Methods are based on the library captum [2]. For PyTorch the following saliency methods are available: + Gradients (GRAD) + Integrated Gradients (IG) + Gradient Shap (GS) + DeepLift (DL) + DeepLiftShap (DLS) + SmoothGrad (SG) + Shapley Value Sampling(SVS) + Feature Ablatiom (FA) + Occlusion (FO) References

[1] Ismail, Aya Abdelsalam, et al. "Benchmarking deep learning interpretability in time series predictions." Advances in neural information processing systems 33 (2020): 6441-6452. [2] Kokhlikyan, Narine, et al. "Captum: A unified and generic model interpretability library for pytorch." arXiv preprint arXiv:2009.07896 (2020).

Initialization Arguments: model [torch.nn.Module]: model to be explained NumTimeSteps int : Number of Time Step NumFeatures int : Number Features method str: Saliency Methode to be used mode str: Second dimension 'time'->(1,time,feat) or 'feat'->(1,feat,time)

explain(item, labels, TSR=None, **kwargs)

Method to explain the model based on the item. Arguments: item np.array: item to get feature attribution for, if mode = time->(1,time,feat) or mode = feat->(1,feat,time) labels int: label TSR bool: if True time series rescaling according to [1] is used, else plain (scaled) weights are returened Returns: np.array: feature attribution weights mode = time->(time,feat) or mode = feat->(feat,time)

Saliency_TF(model, NumTimeSteps, NumFeatures, method='GRAD', mode='time', tsr=True, device='cpu')

Bases: Saliency

Tensorflow Implementation for Saliency Calculation based on [1]. The Saliency Methods are based on the library tf-explain [2] and shap [3]. For Tensorflow the following saliency methods are available: + Gradients (GRAD) + Integrated Gradients (IG) + Gradient Shap (GS)) + DeepLiftShap (DLS) + SmoothGrad (SG) + Occlusion (FO)

Attention: GS and DLS only work for Python < 3.10.

References

[1] Ismail, Aya Abdelsalam, et al. "Benchmarking deep learning interpretability in time series predictions." Advances in neural information processing systems 33 (2020): 6441-6452.

[2] Meudec, Raphael: , tf-explain. https://github.com/sicara/tf-explain

[3] Lundberg, Scott M., and Su-In Lee. "A unified approach to interpreting model predictions." Advances in neural information processing systems 30 (2017). https://shap.readthedocs.io/

PARAMETER DESCRIPTION
model

model to be explained

TYPE: [models]

NumTimeSteps

Number of Time Step

TYPE: int

NumFeatures

Number Features

TYPE: int

method

Saliency Methode to be used

TYPE: str DEFAULT: 'GRAD'

mode

Second dimension 'time'->(1,time,feat) or 'feat'->(1,feat,time)

TYPE: str DEFAULT: 'time'

explain(item, labels, TSR=None)

Method to explain the model based on the item. Arguments: item np.array: item to get feature attribution for, if mode = time->(1,time,feat) or mode = feat->(1,feat,time) labels int: label TSR bool: if True time series rescaling according to [1] is used, else plain (scaled) weights are returened Returns: np.array: feature attribution weights mode = time->(time,feat) or mode = feat->(feat,time)