from abc import ABC, abstractmethod
from typing import Any, List
import torch
from torch import Tensor
from pyrelational.data_managers import DataManager
from pyrelational.model_managers import ModelManager
from pyrelational.strategies.abstract_strategy import Strategy
[docs]
class RegressionStrategy(Strategy, ABC):
"""A base active learning strategy class for regression in which the top n indices,
according to user-specified scoring function, are queried at each iteration"""
def __init__(self) -> None:
super(RegressionStrategy, self).__init__()
[docs]
def __call__(
self, num_annotate: int, data_manager: DataManager, model_manager: ModelManager[Any, Any]
) -> List[int]:
"""
Call function which identifies samples which need to be labelled based on
user defined scoring function.
:param num_annotate: number of samples to annotate
:param data_manager: A pyrelational data manager
which keeps track of what has been labelled and creates data loaders for
active learning
:param model_manager: A pyrelational model manager
which wraps a user defined ML model to handle instantiation, training, testing,
as well as uncertainty quantification
:return: list of indices to annotate
"""
output = self.train_and_infer(data_manager=data_manager, model_manager=model_manager)
scores = self.scoring_function(output).squeeze(-1)
ixs = torch.argsort(scores, descending=True).tolist()
return [data_manager.u_indices[i] for i in ixs[:num_annotate]]
[docs]
@abstractmethod
def scoring_function(self, predictions: Tensor) -> Tensor:
"""
Compute score of each sample.
:param predictions: model predictions for each sample
:return: scores for each sample
"""