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e11d84b996
* Move from tomllib to tomli * added example code for magentic-one + code comments * adding benchmarks temporarily * add license for datasets * revert changes to magentic-one * change license location --------- Co-authored-by: Ryan Sweet <rysweet@microsoft.com>
25 lines
916 B
Python
25 lines
916 B
Python
from typing import List, Set, Tuple, Union, Callable
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import numpy as np
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from scipy.optimize import linear_sum_assignment
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def _align_bags(
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predicted: List[Set[str]],
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gold: List[Set[str]],
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method: Callable[[object, object], float],
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) -> List[float]:
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"""
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Takes gold and predicted answer sets and first finds the optimal 1-1 alignment
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between them and gets maximum metric values over all the answers.
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"""
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scores = np.zeros([len(gold), len(predicted)])
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for gold_index, gold_item in enumerate(gold):
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for pred_index, pred_item in enumerate(predicted):
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scores[gold_index, pred_index] = method(pred_item, gold_item)
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row_ind, col_ind = linear_sum_assignment(-scores)
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max_scores = np.zeros([max(len(gold), len(predicted))])
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for row, column in zip(row_ind, col_ind):
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max_scores[row] = max(max_scores[row], scores[row, column])
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return max_scores
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