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pythondatasetsactive-learningtext-annotationdatasetnatural-language-processingdata-labelingmachine-learningannotation-tool
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82 lines
2.9 KiB
82 lines
2.9 KiB
import abc
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import dataclasses
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import enum
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import random
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from typing import List
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import numpy as np
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@dataclasses.dataclass
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class Assignment:
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user: int
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example: int
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class StrategyName(enum.Enum):
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weighted_sequential = enum.auto()
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weighted_random = enum.auto()
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sampling_without_replacement = enum.auto()
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def create_assignment_strategy(strategy_name: StrategyName, dataset_size: int, weights: List[int]) -> "BaseStrategy":
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if strategy_name == StrategyName.weighted_sequential:
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return WeightedSequentialStrategy(dataset_size, weights)
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elif strategy_name == StrategyName.weighted_random:
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return WeightedRandomStrategy(dataset_size, weights)
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elif strategy_name == StrategyName.sampling_without_replacement:
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return SamplingWithoutReplacementStrategy(dataset_size, weights)
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else:
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raise ValueError(f"Unknown strategy name: {strategy_name}")
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class BaseStrategy(abc.ABC):
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@abc.abstractmethod
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def assign(self) -> List[Assignment]:
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...
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class WeightedSequentialStrategy(BaseStrategy):
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def __init__(self, dataset_size: int, weights: List[int]):
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if sum(weights) != 100:
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raise ValueError("Sum of weights must be 100")
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self.dataset_size = dataset_size
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self.weights = weights
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def assign(self) -> List[Assignment]:
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assignments = []
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cumsum = np.cumsum([0] + self.weights)
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ratio = np.round(cumsum / 100 * self.dataset_size).astype(int)
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for user, (start, end) in enumerate(zip(ratio, ratio[1:])): # Todo: use itertools.pairwise
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assignments.extend([Assignment(user=user, example=example) for example in range(start, end)])
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return assignments
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class WeightedRandomStrategy(BaseStrategy):
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def __init__(self, dataset_size: int, weights: List[int]):
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if sum(weights) != 100:
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raise ValueError("Sum of weights must be 100")
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self.dataset_size = dataset_size
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self.weights = weights
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def assign(self) -> List[Assignment]:
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proba = np.array(self.weights) / 100
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assignees = np.random.choice(range(len(self.weights)), size=self.dataset_size, p=proba)
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return [Assignment(user=user, example=example) for example, user in enumerate(assignees)]
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class SamplingWithoutReplacementStrategy(BaseStrategy):
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def __init__(self, dataset_size: int, weights: List[int]):
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if not (0 <= sum(weights) <= 100 * len(weights)):
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raise ValueError("Sum of weights must be between 0 and 100 x number of members")
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self.dataset_size = dataset_size
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self.weights = weights
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def assign(self) -> List[Assignment]:
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assignments = []
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proba = np.array(self.weights) / 100
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for user, p in enumerate(proba):
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count = int(self.dataset_size * p)
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examples = random.sample(range(self.dataset_size), count)
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assignments.extend([Assignment(user=user, example=example) for example in examples])
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return assignments
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