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from typing import Dict, List
import numpy as np
from evaluation.evaluate_utils.utils import _align_bags
def calculate_f1_score(precision, recall):
if precision + recall == 0:
return 0 # Handle the case to avoid division by zero
return 2 * (precision * recall) / (precision + recall)
def calc_recall(pred: Dict, gold: Dict, use_gold_for_eval: bool):
from evaluation.evaluate_utils.evaluate_factory import get_evaluator_from_gold_answer
recall = []
for gold_key, gold_value in gold.items():
pred_value = pred.get(gold_key)
gold_value = fix_number(gold_value)
pred_value = fix_number(pred_value)
if gold_key not in pred:
recall.append(0)
else:
evaluator = (
get_evaluator_from_gold_answer(type(gold_value))
if use_gold_for_eval
else get_evaluator_from_gold_answer(type(pred_value))
)
if type(pred_value) != type(gold_value):
recall.append(0)
continue
recall.append(evaluator(pred_value, gold_value))
avg_recall = np.average(recall)
return avg_recall
def fix_number(number):
if type(number) == str:
copy_ans = number
copy_ans = ' '.join(' '.join(' '.join(copy_ans.split('$')).split('%')).split('sqft')).strip()
copy_ans = copy_ans.strip()
copy_ans = copy_ans.replace(',', '.')
try:
return float(copy_ans)
except:
return number
elif type(number) == int:
return float(number)
else:
return number
def evaluate_pair_of_dicts(pred: Dict, gold: Dict):
recall = calc_recall(pred, gold, True)
precision = calc_recall(gold, pred, False)
f1 = calculate_f1_score(precision, recall)
return f1
def evaluate_dicts(pred: List[Dict], gold: List[Dict]):
if not (
type(pred) == dict
or len(pred) == 0
or (type(pred) == list and type(pred[0]) == dict)
):
return 0
max_alignment_scores = _align_bags(pred, gold, evaluate_pair_of_dicts)
return np.average(max_alignment_scores) |