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60b21d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | #!/usr/bin/env python3
# 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
"""
Parse sleep baseline evaluation results from a structured JSON file and compute
accuracy and F1 statistics. Designed for JSON files with the following shape:
{
"model_name": "...",
"dataset_name": "SleepEDFCoTQADataset",
"total_samples": 930,
"successful_inferences": 930,
"success_rate": 1.0,
"metrics": {"accuracy": 10.75},
"detailed_results": [
{
"sample_idx": 0,
"input_text": "...",
"target_answer": "... Answer: Wake",
"generated_answer": "... Answer: Wake",
"metrics": {
"accuracy": 1,
"gt_label": "wake",
"pred_label": "wake"
}
},
...
]
}
The script prioritizes labels under detailed_results[i]["metrics"]["gt_label"|"pred_label"],
falling back to extracting the trailing "Answer: <label>" from the target and
generated texts if labels are not provided.
"""
import argparse
import json
import re
from pathlib import Path
from typing import Dict, List
from evaluation.opentslm.parse_predictions import (
calculate_f1_score,
calculate_f1_stats,
calculate_accuracy_stats,
extract_answer,
)
def normalize_label(s: str) -> str:
"""Utility to normalize a label string for comparison/printing.
Collapses answers like "(a) ...", "a) ...", "A.", or even text that
contains these patterns anywhere (e.g., "The answer is (a) ...") into the
canonical form "(a)" (case-insensitive, letters a-e), so artifacts in
generated answers are treated as just the option choice.
"""
if s is None:
return ""
s = s.strip()
# 1) Look for explicit parenthesized option anywhere: (a), (B), etc.
m = re.search(r"\(([a-eA-E])\)", s)
if m:
return f"({m.group(1).lower()})"
# 2) Look for patterns like "a)" or "B." anywhere in the string
m = re.search(r"\b([a-eA-E])[)\.]\b", s)
if m:
return f"({m.group(1).lower()})"
# 3) As a weaker fallback, detect standalone option letter bounded by word boundaries
# and followed by a colon or hyphen (e.g., "Answer a: ...")
m = re.search(r"\b([a-eA-E])\s*[:\-]", s)
if m:
return f"({m.group(1).lower()})"
return s
def extract_structured_data(obj: Dict) -> List[Dict]:
"""Extract structured per-sample data points from the Sleep JSON results object.
Returns a list of dicts with keys:
- generated
- model_prediction
- ground_truth
- accuracy (bool)
- f1_score, precision, recall
- prediction_normalized, ground_truth_normalized
"""
items = obj.get("detailed_results", [])
data_points: List[Dict] = []
for it in items:
metrics = it.get("metrics", {}) or {}
gt_label = metrics.get("gt_label")
pred_label = metrics.get("pred_label")
# Fallback to parsing the textual answers if labels are missing
if not gt_label:
gt_label = extract_answer(it.get("target_answer", ""))
if not pred_label:
pred_label = extract_answer(it.get("generated_answer", ""))
ground_truth = normalize_label(gt_label)
model_prediction = normalize_label(pred_label)
generated = it.get("generated_answer", "")
# Binary exact-match accuracy on normalized labels handled in calculate_f1_score, but keep explicit flag
f1_result = calculate_f1_score(model_prediction, ground_truth)
accuracy = f1_result["f1_score"] == 1.0
# Use canonicalized labels for both values and the normalized fields used by class grouping
data_point = {
"generated": generated,
"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": model_prediction,
"ground_truth_normalized": ground_truth,
}
data_points.append(data_point)
return data_points
def main():
ap = argparse.ArgumentParser(
description="Compute accuracy and F1 from a Sleep baseline results JSON (with detailed_results)."
)
ap.add_argument(
"--detailed-json",
type=Path,
required=True,
help="Path to a single results JSON file containing 'detailed_results'",
)
ap.add_argument(
"--clean-out",
type=Path,
help="Optional path to write clean JSONL of parsed per-sample points",
)
args = ap.parse_args()
with args.detailed_json.open("r", encoding="utf-8") as f:
obj = json.load(f)
# Extract per-sample points
data_points = extract_structured_data(obj)
# Print high-level info if available
model_name = obj.get("model_name")
dataset_name = obj.get("dataset_name")
total_samples = obj.get("total_samples")
top_metrics = obj.get("metrics", {}) or {}
if model_name or dataset_name or total_samples is not None:
print("\nRun Metadata:")
if model_name:
print(f"Model: {model_name}")
if dataset_name:
print(f"Dataset: {dataset_name}")
if total_samples is not None:
print(f"Total samples (reported): {total_samples}")
if "accuracy" in top_metrics:
print(f"Reported accuracy: {top_metrics['accuracy']}")
# Accuracy stats (computed from per-sample)
accuracy_stats = calculate_accuracy_stats(data_points)
print(f"\nAccuracy Statistics:")
print(f"Total samples: {accuracy_stats.get('total_samples', 0)}")
print(f"Correct predictions: {accuracy_stats.get('correct_predictions', 0)}")
print(f"Incorrect predictions: {accuracy_stats.get('incorrect_predictions', 0)}")
print(f"Accuracy: {accuracy_stats.get('accuracy_percentage', 0.0):.2f}%")
# F1 stats
f1_stats = calculate_f1_stats(data_points)
print(f"\nF1 Score Statistics:")
print(f"Average F1 Score: {f1_stats.get('average_f1', 0.0):.4f}")
print(f"Macro-F1 Score: {f1_stats.get('macro_f1', 0.0):.4f}")
print(f"Total Classes: {f1_stats.get('total_classes', 0)}")
if f1_stats.get("class_f1_scores"):
print(f"\nPer-Class F1 Scores:")
for class_name, scores in f1_stats["class_f1_scores"].items():
print(
f" {class_name}: F1={scores['f1']:.4f}, "
f"P={scores['precision']:.4f}, R={scores['recall']:.4f}"
)
# Optional clean JSONL output
if args.clean_out:
with args.clean_out.open("w", encoding="utf-8") as f:
for item in data_points:
f.write(json.dumps(item, indent=2) + "\n")
print(f"\nData saved to {args.clean_out}")
if __name__ == "__main__":
main()
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