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# SPDX-FileCopyrightText: 2025 Stanford University, ETH Zurich, and the project authors (see CONTRIBUTORS.md)
# SPDX-FileCopyrightText: 2025 This source file is part of the OpenTSLM open-source project.
#
# SPDX-License-Identifier: MIT
"""Parser for converting baseline JSON files to clean format."""
import json
import re
import sys
import os
from pathlib import Path
from collections import Counter
from tqdm import tqdm
# Import the dataset class to get labels
from opentslm.time_series_datasets.har_cot.HARCoTQADataset import HARCoTQADataset
# Get the supported labels from the dataset class
SUPPORTED_LABELS = HARCoTQADataset.get_labels()
def calculate_f1_score(prediction, ground_truth):
"""Calculate F1 score for classification labels using token overlap (like SQuAD)."""
# Normalize labels
pred_normalized = prediction.lower().strip().rstrip(".,!?;:")
truth_normalized = ground_truth.lower().strip().rstrip(".,!?;:")
pred_tokens = pred_normalized.split()
truth_tokens = truth_normalized.split()
if not pred_tokens and not truth_tokens:
return {
"f1_score": 1.0,
"precision": 1.0,
"recall": 1.0,
"prediction_normalized": pred_normalized,
"ground_truth_normalized": truth_normalized,
}
if not pred_tokens or not truth_tokens:
return {
"f1_score": 0.0,
"precision": 0.0,
"recall": 0.0,
"prediction_normalized": pred_normalized,
"ground_truth_normalized": truth_normalized,
}
common = Counter(pred_tokens) & Counter(truth_tokens)
num_same = sum(common.values())
precision = num_same / len(pred_tokens) if pred_tokens else 0.0
recall = num_same / len(truth_tokens) if truth_tokens else 0.0
f1 = (
2 * precision * recall / (precision + recall) if precision + recall > 0 else 0.0
)
return {
"f1_score": f1,
"precision": precision,
"recall": recall,
"prediction_normalized": pred_normalized,
"ground_truth_normalized": truth_normalized,
}
def calculate_f1_stats(data_points, allowed_labels=None):
"""Calculate macro-F1 across classes from confusion counts."""
if not data_points:
return {}
class_predictions = {}
if allowed_labels:
for label in allowed_labels:
class_predictions[label] = {"tp": 0, "fp": 0, "fn": 0}
for point in data_points:
gt_class = point.get("ground_truth_normalized", "")
pred_class = point.get("prediction_normalized", "")
if gt_class not in class_predictions:
class_predictions[gt_class] = {"tp": 0, "fp": 0, "fn": 0}
if pred_class == gt_class:
class_predictions[gt_class]["tp"] += 1
else:
class_predictions[gt_class]["fn"] += 1
if (allowed_labels is None) or (pred_class in (allowed_labels or set())):
if pred_class in class_predictions:
class_predictions[pred_class]["fp"] += 1
else:
class_predictions[pred_class] = {"tp": 0, "fp": 1, "fn": 0}
class_f1_scores = {}
total_f1 = 0
valid_classes = 0
for class_name, counts in class_predictions.items():
tp, fp, fn = counts["tp"], counts["fp"], counts["fn"]
precision = tp / (tp + fp) if (tp + fp) > 0 else 0
recall = tp / (tp + fn) if (tp + fn) > 0 else 0
f1 = (
2 * precision * recall / (precision + recall)
if (precision + recall) > 0
else 0
)
class_f1_scores[class_name] = {
"f1": f1,
"precision": precision,
"recall": recall,
"tp": tp,
"fp": fp,
"fn": fn,
}
total_f1 += f1
valid_classes += 1
macro_f1 = total_f1 / valid_classes if valid_classes > 0 else 0
return {
"macro_f1": macro_f1,
"class_f1_scores": class_f1_scores,
"total_classes": valid_classes,
}
def calculate_accuracy_stats(data_points):
"""Calculate accuracy statistics from data points"""
if not data_points:
return {}
total = len(data_points)
correct = sum(1 for point in data_points if point.get("accuracy", False))
accuracy_percentage = (correct / total) * 100 if total > 0 else 0
return {
"total_samples": total,
"correct_predictions": correct,
"incorrect_predictions": total - correct,
"accuracy_percentage": accuracy_percentage,
}
def parse_baseline_json(input_file, output_file=None):
"""Parse baseline JSON file and extract structured data."""
if output_file is None:
input_path = Path(input_file)
output_file = str(input_path.parent / f"{input_path.stem}.clean.jsonl")
print(f"Parsing {input_file}")
print(f"Output will be saved to {output_file}")
print(f"Using {len(SUPPORTED_LABELS)} labels from HARCoTQADataset:")
for label in sorted(SUPPORTED_LABELS):
print(f" - {label}")
extracted_data = extract_structured_data(input_file)
allowed_labels = set(SUPPORTED_LABELS)
answer_count = sum(
1 for point in extracted_data if "Answer: " in point.get("generated", "")
)
excluded_count = 0
for point in extracted_data:
prediction_label = point.get("prediction_normalized", "")
is_valid_prediction = prediction_label in allowed_labels
point["excluded"] = not is_valid_prediction
if not is_valid_prediction:
excluded_count += 1
if extracted_data:
print(f"Extracted {len(extracted_data)} data points")
print(f"Predictions containing 'Answer:': {answer_count}/{len(extracted_data)}")
if excluded_count > 0:
print(
f"Excluded {excluded_count} predictions not in the label set from metrics"
)
accuracy_stats = calculate_accuracy_stats(extracted_data)
print(f"\nAccuracy Statistics:")
print(f"Total samples: {accuracy_stats['total_samples']}")
print(f"Correct predictions: {accuracy_stats['correct_predictions']}")
print(f"Incorrect predictions: {accuracy_stats['incorrect_predictions']}")
print(f"Accuracy: {accuracy_stats['accuracy_percentage']:.2f}%")
f1_stats = calculate_f1_stats(extracted_data, allowed_labels=allowed_labels)
print(f"\nF1 Score Statistics:")
print(f"Macro-F1 Score: {f1_stats['macro_f1']:.4f}")
print(f"Total Classes: {f1_stats['total_classes']}")
if f1_stats["class_f1_scores"]:
print(f"\nPer-Class F1 Scores:")
for class_name, scores in f1_stats["class_f1_scores"].items():
print(
f" {class_name}: "
f"F1={scores['f1']:.4f}, "
f"P={scores['precision']:.4f}, "
f"R={scores['recall']:.4f}, "
f"TP={scores['tp']}, FP={scores['fp']}, FN={scores['fn']}"
)
with open(output_file, "w", encoding="utf-8") as f:
for item in extracted_data:
f.write(json.dumps(item, indent=2) + "\n")
print(f"\nData saved to {output_file}")
return extracted_data
else:
print("No data could be extracted from the file.")
return []
def extract_structured_data(input_file):
"""Extract structured data from baseline JSON file"""
data_points = []
with open(input_file, "r", encoding="utf-8") as f:
data = json.load(f)
detailed_results = data.get("detailed_results", [])
for result in tqdm(detailed_results, desc="Processing results"):
try:
sample_idx = result.get("sample_idx", 0)
generated_answer = result.get("generated_answer", "")
target_answer = result.get("target_answer", "")
model_prediction = extract_answer(generated_answer).replace("<eos>", "")
ground_truth = extract_answer(target_answer).replace("<eos>", "")
accuracy = (
model_prediction.strip().lower() == ground_truth.strip().lower()
)
f1_result = calculate_f1_score(model_prediction, ground_truth)
data_point = {
"sample_idx": sample_idx,
"generated": generated_answer,
"model_prediction": model_prediction,
"ground_truth": ground_truth,
"accuracy": accuracy,
"f1_score": f1_result["f1_score"],
"precision": f1_result["precision"],
"recall": f1_result["recall"],
"prediction_normalized": f1_result["prediction_normalized"],
"ground_truth_normalized": f1_result["ground_truth_normalized"],
}
data_points.append(data_point)
except Exception as e:
print(
f"Error processing sample {result.get('sample_idx', 'unknown')}: {e}"
)
continue
return data_points
def extract_answer(text):
"""Extract the final answer from text"""
if "Answer: " not in text:
return text.strip()
answer = text.split("Answer: ")[-1].strip()
answer = re.sub(r"<\|.*?\|>$", "", answer).strip()
return answer
if __name__ == "__main__":
# Example usage with the baseline JSON file
input_file = "evaluation_results_meta-llama-llama-3-2-3b_harcotqadataset.json"
output_file = "out.jsonl"
parse_baseline_json(input_file, output_file)
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