from abc import abstractmethod
from typing import Any, Union, Tuple, List, Dict
import torch
import numpy as np
from flambe import Component
[docs]class Field(Component):
"""Base Field interface.
A field processes raw examples and produces Tensors.
"""
[docs] def setup(self, *data: np.ndarray) -> None:
"""Setup the field.
This method will be called with all the data in the dataset and
it can be used to compute aggregated information (for example,
vocabulary in Fields that process text).
ATTENTION: this method could be called multiple times in case
the same field is used in different datasets. Take this into
account and build a stateful implementation.
Parameters
----------
*data: np.ndarray
Multiple 2d arrays (ex: train_data, dev_data, test_data).
First dimension is for the examples, second dimension for
the columns specified for this specific field.
"""
pass
@abstractmethod
[docs] def process(self, *example: Any) \
-> Union[torch.Tensor,
Tuple[torch.Tensor, ...],
List[torch.Tensor],
Dict[str, torch.Tensor]]:
"""Process an example into a Tensor or tuple of Tensor.
This method allows N to M mappings from example columns (N)
to tensors (M).
Parameters
----------
*example: Any
Column values of the example
Returns
-------
Union[torch.Tensor, Tuple[torch.Tensor, ...]]
The processed example, as a tensor or tuple of tensors
"""
pass