Source code for captum._utils.models.model

#!/usr/bin/env python3

from abc import ABC, abstractmethod
from typing import Dict, Optional, Union

from captum._utils.typing import TensorOrTupleOfTensorsGeneric
from torch import Tensor
from import DataLoader

[docs] class Model(ABC): r""" Abstract Class to describe the interface of a trainable model to be used within the algorithms of captum. Please note that this is an experimental feature. """
[docs] @abstractmethod def fit( self, train_data: DataLoader, **kwargs ) -> Optional[Dict[str, Union[int, float, Tensor]]]: r""" Override this method to actually train your model. The specification of the dataloader will be supplied by the algorithm you are using within captum. This will likely be a supervised learning task, thus you should expect batched (x, y) pairs or (x, y, w) triples. Args: train_data (DataLoader): The data to train on Returns: Optional statistics about training, e.g. iterations it took to train, training loss, etc. """ pass
[docs] @abstractmethod def representation(self) -> Tensor: r""" Returns the underlying representation of the interpretable model. For a linear model this is simply a tensor (the concatenation of weights and bias). For something slightly more complicated, such as a decision tree, this could be the nodes of a decision tree. Returns: A Tensor describing the representation of the model. """ pass
@abstractmethod def __call__( self, x: TensorOrTupleOfTensorsGeneric ) -> TensorOrTupleOfTensorsGeneric: r""" Predicts with the interpretable model. Args: x (TensorOrTupleOfTensorsGeneric) A batched input of tensor(s) to the model to predict Returns: The prediction of the input as a TensorOrTupleOfTensorsGeneric. """ pass