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from typing import Dict, List
import evaluate
from datasets import Features, Sequence, Value
from sklearn.metrics import accuracy_score
from itertools import chain
from random import choice
from typing import Any, Dict, List, Optional, Tuple
_CITATION = """
"""
_DESCRIPTION = """
Evaluation metrics for Aspect-Based Sentiment Analysis (ABSA) including precision, recall, and F1 score for aspect terms and polarities.
"""
_KWARGS_DESCRIPTION = """
Computes precision, recall, and F1 score for aspect terms and polarities in Aspect-Based Sentiment Analysis (ABSA).
Args:
predictions: List of ABSA predictions with the following structure:
- 'aspects': Sequence of aspect annotations, each with the following keys:
- 'term': Aspect term
- 'polarity': Polarity of the aspect term
references: List of ABSA references with the same structure as predictions.
Returns:
aspect_precision: Precision score for aspect terms
aspect_recall: Recall score for aspect terms
aspect_f1: F1 score for aspect terms
polarity_precision: Precision score for aspect polarities
polarity_recall: Recall score for aspect polarities
polarity_f1: F1 score for aspect polarities
"""
class AbsaEvaluatorTest(evaluate.Metric):
def _info(self):
return evaluate.MetricInfo(
description=_DESCRIPTION,
citation=_CITATION,
inputs_description=_KWARGS_DESCRIPTION,
features=Features(
{
"predictions": Features(
{
"aspects": Features(
{
"term": Sequence(Value("string")),
"polarity": Sequence(Value("string")),
}
),
"category": Features(
{
"category": Sequence(Value("string")),
"polarity": Sequence(Value("string")),
}
),
}
),
"references": Features(
{
"aspects": Features(
{
"term": Sequence(Value("string")),
"polarity": Sequence(Value("string")),
}
),
"category": Features(
{
"category": Sequence(Value("string")),
"polarity": Sequence(Value("string")),
}
),
}
),
}
),
)
def _compute(self, predictions, references):
# preprocess aspect term
(
truth_aspect_terms,
pred_aspect_terms,
truth_term_polarities,
pred_term_polarities,
) = absa_term_preprocess(
references=references,
predictions=predictions,
subtask_key="aspects",
subtask_value="term",
)
# evaluate
term_results = self.semeval_metric(
truth_aspect_terms, pred_aspect_terms
)
term_polarity_acc = accuracy_score(
truth_term_polarities, pred_term_polarities
)
# preprocess category detection
(
truth_categories,
pred_categories,
truth_cat_polarities,
pred_cat_polarities,
) = absa_term_preprocess(
references=references,
predictions=predictions,
subtask_key="category",
subtask_value="category",
)
# evaluate
category_results = self.semeval_metric(
truth_categories, pred_categories
)
cat_polarity_acc = accuracy_score(
truth_cat_polarities, pred_cat_polarities
)
return {
"term_extraction_results": term_results,
"term_polarity_results_accuracy": term_polarity_acc,
"category_detection_results": category_results,
"category_polarity_results_accuracy": cat_polarity_acc,
}
def semeval_metric(
self, truths: List[List[str]], preds: List[List[str]]
) -> Dict[str, float]:
"""
Implements evaluation for extraction tasks using precision, recall, and F1 score.
Parameters:
- truths: List of lists, where each list contains the ground truth labels for a sample.
- preds: List of lists, where each list contains the predicted labels for a sample.
Returns:
- A dictionary containing the precision, recall, F1 score, and counts of common, retrieved, and relevant.
link for code: link for this code: https://github.com/davidsbatista/Aspect-Based-Sentiment-Analysis/blob/1d9c8ec1131993d924e96676fa212db6b53cb870/libraries/baselines.py#L387
"""
b = 1
common, relevant, retrieved = 0.0, 0.0, 0.0
for truth, pred in zip(truths, preds):
common += len([a for a in pred if a in truth])
retrieved += len(pred)
relevant += len(truth)
precision = common / retrieved if retrieved > 0 else 0.0
recall = common / relevant if relevant > 0 else 0.0
f1 = (
(1 + (b**2))
* precision
* recall
/ ((precision * b**2) + recall)
if precision > 0 and recall > 0
else 0.0
)
return {
"precision": precision,
"recall": recall,
"f1_score": f1,
"common": common,
"retrieved": retrieved,
"relevant": relevant,
}
def adjust_predictions(refs, preds, choices):
"""Adjust predictions to match the length of references with either a special token or random choice."""
adjusted_preds = []
for ref, pred in zip(refs, preds):
if len(pred) < len(ref):
missing_count = len(ref) - len(pred)
pred.extend([choice(choices) for _ in range(missing_count)])
adjusted_preds.append(pred)
return adjusted_preds
def extract_aspects(data, specific_key, specific_val):
"""Extracts and returns a list of specified aspect details from the nested 'aspects' data."""
return [item[specific_key][specific_val] for item in data]
def absa_term_preprocess(references, predictions, subtask_key, subtask_value):
"""
Preprocess the terms and polarities for aspect-based sentiment analysis.
Args:
references (List[Dict]): A list of dictionaries containing the actual terms and polarities under 'aspects'.
predictions (List[Dict]): A list of dictionaries containing predicted aspect categories to terms and their sentiments.
Returns:
Tuple[List[str], List[str], List[str], List[str]]: A tuple containing lists of true aspect terms,
adjusted predicted aspect terms, true polarities, and adjusted predicted polarities.
"""
# Extract aspect terms and polarities
truth_aspect_terms = extract_aspects(references, subtask_key, subtask_value)
pred_aspect_terms = extract_aspects(predictions, subtask_key, subtask_value)
truth_polarities = extract_aspects(references, subtask_key, "polarity")
pred_polarities = extract_aspects(predictions, subtask_key, "polarity")
# Define adjustment parameters
special_token = "NONE" # For missing aspect terms
sentiment_choices = [
"positive",
"negative",
"neutral",
"conflict",
] # For missing polarities
# Adjust the predictions to match the length of references
adjusted_pred_terms = adjust_predictions(
truth_aspect_terms, pred_aspect_terms, [special_token]
)
adjusted_pred_polarities = adjust_predictions(
truth_polarities, pred_polarities, sentiment_choices
)
return (
flatten_list(truth_aspect_terms),
flatten_list(adjusted_pred_terms),
flatten_list(truth_polarities),
flatten_list(adjusted_pred_polarities),
)
def flatten_list(nested_list):
"""Flatten a nested list into a single-level list."""
return list(chain.from_iterable(nested_list))
def extract_pred_terms(
all_predictions: List[Dict[str, Dict[str, str]]]
) -> List[List]:
"""Extract and organize predicted terms from the sentiment analysis results."""
pred_aspect_terms = []
for pred in all_predictions:
terms = [term for cat in pred.values() for term in cat.keys()]
pred_aspect_terms.append(terms)
return pred_aspect_terms
def merge_aspects_and_categories(aspects, categories):
result = []
# Assuming both lists are of the same length and corresponding indices match
for aspect, category in zip(aspects, categories):
combined_entry = {
"aspects": {"term": [], "polarity": []},
"category": {"category": [], "polarity": []},
}
# Process aspect entries
for cat_key, terms_dict in aspect.items():
for term, polarity in terms_dict.items():
combined_entry["aspects"]["term"].append(term)
combined_entry["aspects"]["polarity"].append(polarity)
# Add category details based on the aspect's key if available in categories
if cat_key in category:
combined_entry["category"]["category"].append(cat_key)
combined_entry["category"]["polarity"].append(
category[cat_key]
)
# Ensure all keys in category are accounted for
for cat_key, polarity in category.items():
if cat_key not in combined_entry["category"]["category"]:
combined_entry["category"]["category"].append(cat_key)
combined_entry["category"]["polarity"].append(polarity)
result.append(combined_entry)
return result
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