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import re
import argparse
from typing import List, Union
# Keywords used for entity extraction
KEYWORDS = {
"bigger", "change", "cleared", "constant", "decrease", "decreased", "decreasing", "elevated", "elevation",
"enlarged", "enlargement", "enlarging", "expanded", "greater", "growing", "improved", "improvement",
"improving", "increase", "increased", "increasing", "larger", "new", "persistence", "persistent",
"persisting", "progression", "progressive", "reduced", "removal", "resolution", "resolved", "resolving",
"smaller", "stability", "stable", "stably", "unchanged", "unfolded", "worse", "worsen", "worsened",
"worsening", "unaltered"
}
def clean_text(text: str) -> str:
"""
Clean the input text by removing special characters and redundant spaces or newlines.
Args:
text (str): Input text.
Returns:
str: Cleaned text.
"""
# Remove special characters and redundant newlines
text = re.sub(r'\n+', ' ', text) # Replace multiple newlines with a single space
text = re.sub(r'[_-]+', ' ', text) # Replace underscores and dashes with spaces
text = re.sub(r'\(___, __, __\)', '', text) # Remove irrelevant underscore patterns
text = re.sub(r'---, ---, ---', '', text) # Remove dashed patterns
text = re.sub(r'\(__, __, ___\)', '', text) # Remove similar underscore patterns
text = re.sub(r'[_-]+', ' ', text) # Replace underscores and dashes again (if any remain)
text = re.sub(r'[^\w\s.,:;()-]', '', text) # Remove non-alphanumeric characters except common punctuation
# Remove extra spaces
text = re.sub(r'\s{2,}', ' ', text).strip()
return text
def extract_entities(text: str, keywords: set) -> set:
"""
Extract entities from the given text based on the provided keywords.
Args:
text (str): Input text.
keywords (set): Set of keywords to extract entities.
Returns:
set: Set of matched keywords found in the text.
"""
# Clean the text before extracting entities
text = clean_text(text)
# Create a regex pattern that matches any of the keywords as whole words
pattern = r'\b(' + '|'.join(re.escape(word) for word in keywords) + r')\b'
# Find all matches and return them as a set
return {match.group().lower() for match in re.finditer(pattern, text.lower())}
def calculate_tem_score(prediction_text: str, reference_text: Union[str, List[str]], epsilon: float = 1e-10) -> float:
"""
Calculate the Temporal Entity Matching (TEM) score (similar to F1-score).
Args:
reference_text (Union[str, List[str]]): Reference text or a list of reference texts.
prediction_text (str): Prediction text.
epsilon (float): Small value to avoid division by zero.
Returns:
float: TEM score.
"""
if isinstance(reference_text, list):
reference_entities = set()
for ref in reference_text:
reference_entities.update(extract_entities(ref, KEYWORDS))
else:
reference_entities = extract_entities(reference_text, KEYWORDS)
prediction_entities = extract_entities(prediction_text, KEYWORDS)
if len(reference_entities) == 0:
if len(prediction_entities) == 0:
return {
"f1": 1.0,
"prediction_entities": prediction_entities,
"reference_entities": reference_entities
} # Perfect match when both are empty
else:
return {
"f1": epsilon,
"prediction_entities": prediction_entities,
"reference_entities": reference_entities
} # Minimal score when reference is empty but prediction is not
# Calculate intersection of entities
true_positives = len(prediction_entities & reference_entities)
# Calculate precision and recall with epsilon to avoid division by zero
precision = (true_positives + epsilon) / (len(prediction_entities) + epsilon)
recall = (true_positives + epsilon) / (len(reference_entities) + epsilon)
# Calculate TEM score (F1 score)
tem_score = (2 * precision * recall) / (precision + recall + epsilon)
return {
"f1": tem_score,
"prediction_entities": prediction_entities,
"reference_entities": reference_entities
}
def temporal_f1_score(predictions: List[str], references: List[Union[str, List[str]]], epsilon: float = 1e-10) -> float:
"""
Calculate the average TEM score over a list of reference and prediction texts.
Args:
references (List[Union[str, List[str]]]): List of reference texts or lists of reference texts.
predictions (List[str]): List of prediction texts.
epsilon (float): Small value to avoid division by zero.
Returns:
float: Average TEM score.
"""
assert len(references) == len(predictions), "Reference and prediction lists must have the same length."
tem_scores = []
prediction_entities = []
reference_entities = []
for pred, ref in zip(predictions, references):
result = calculate_tem_score(pred, ref, epsilon)
tem_scores.append(result["f1"])
prediction_entities.append(result["prediction_entities"])
reference_entities.append(result["reference_entities"])
average_f1 = sum(tem_scores) / len(tem_scores)
return {
"f1": average_f1,
"prediction_entities": prediction_entities,
"reference_entities": reference_entities
}
# Command-line interface
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Calculate the average TEM score for reference and prediction texts.")
parser.add_argument("--predictions", nargs='+', required=True, help="List of prediction texts.")
parser.add_argument("--reference", nargs='+', required=True, help="List of reference texts or lists of reference texts.")
args = parser.parse_args()
# Convert references into a nested list if necessary
reference_list = [eval(ref) if ref.startswith('[') else ref for ref in args.reference]
# Calculate the average TEM score
temporal_f1_score(predictions=args.predictions, references=reference_list)
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