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# SPDX-FileCopyrightText: 2025 This source file is part of the OpenTSLM open-source project.
#
# SPDX-License-Identifier: MIT
import json
import os
from collections import Counter
def calculate_f1_score(prediction, ground_truth):
"""Calculate F1 score for classification labels"""
# Normalize labels for comparison (lowercase, strip whitespace and trailing punctuation)
pred_normalized = prediction.lower().strip().rstrip(".,!?;:")
truth_normalized = ground_truth.lower().strip().rstrip(".,!?;:")
# For single prediction vs single ground truth, F1 is binary
f1 = 1.0 if pred_normalized == truth_normalized else 0.0
return {
"f1_score": f1,
"precision": f1, # For single-label classification, precision = recall = f1
"recall": f1,
"prediction_normalized": pred_normalized,
"ground_truth_normalized": truth_normalized,
}
def calculate_f1_stats(data_points, allowed_labels=None):
"""Calculate both macro-F1 and average F1 (micro-F1) statistics.
If allowed_labels is provided, predictions not in this set will:
- contribute False Negatives to the ground-truth class, and
- NOT count as False Positives for any (new) predicted class.
This prevents introducing new classes into per-class/macro metrics.
"""
if not data_points:
return {}
# Calculate average F1 (micro-F1) - simple average across all predictions
f1_scores = [point.get("f1_score", 0) for point in data_points]
average_f1 = sum(f1_scores) / len(f1_scores) if f1_scores else 0
# Group by ground truth class for macro-F1
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}
# True positive: prediction matches ground truth
if pred_class == gt_class:
class_predictions[gt_class]["tp"] += 1
else:
# False negative: ground truth class was not predicted
class_predictions[gt_class]["fn"] += 1
# False positive: predicted class that wasn't ground truth
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}
# Calculate F1 per class
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
# Calculate macro-F1 (average across all classes)
macro_f1 = total_f1 / valid_classes if valid_classes > 0 else 0
return {
"average_f1": average_f1,
"macro_f1": macro_f1,
"class_f1_scores": class_f1_scores,
"total_classes": valid_classes,
}
# Path to your JSONL file
file_path = "evaluation_results_openai-gpt-4o_tsqadataset.json"
# Check if file exists
if not os.path.exists(file_path):
print(f"File not found: {file_path}")
exit(1)
if os.path.getsize(file_path) == 0:
print(f"File is empty: {file_path}")
exit(1)
# Counters
total = 0
correct = 0
data_points = []
labels = ["(a)", "(b)", "(c)"]
label_to_idx = {l: i for i, l in enumerate(labels)}
confusion = [[0, 0, 0] for _ in range(3)] # rows: gold, cols: pred
support = {l: 0 for l in labels}
# Read and process the file
with open(file_path, "r", encoding="utf-8") as f:
for line_num, line in enumerate(f, 1):
line = line.strip()
if not line:
print(f"Skipping empty line at {line_num}")
continue
try:
entry = json.loads(line)
except json.JSONDecodeError as e:
print(f"JSON decode error on line {line_num}: {e}")
continue
generated_raw = entry.get("generated", "").strip()
gold_raw = entry.get("gold", "").strip()
# Use only the first three characters for comparison (e.g., "(a)")
generated = generated_raw[:3]
gold = gold_raw[:3]
total += 1
is_correct = generated == gold
if is_correct:
correct += 1
else:
# Only print incorrect predictions
print(
f"Line {line_num} - Generated: {generated_raw} -> {generated}, Gold: {gold_raw} -> {gold}"
)
# Calculate F1 score for this prediction
f1_result = calculate_f1_score(generated, gold)
data_point = {
"generated": generated,
"gold": gold,
"accuracy": is_correct,
"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)
# Update confusion matrix and supports when labels are recognized
if gold in label_to_idx and generated in label_to_idx:
gi = label_to_idx[gold]
pi = label_to_idx[generated]
confusion[gi][pi] += 1
support[gold] += 1
# Compute and print accuracy
if total == 0:
print("No valid entries found.")
else:
accuracy = correct / total
print(f"\nAccuracy: {accuracy:.2%} ({correct}/{total})")
# Calculate and display F1 statistics
allowed_labels = {point.get("ground_truth_normalized", "") for point in data_points}
f1_stats = calculate_f1_stats(data_points, allowed_labels=allowed_labels)
print(f"\nF1 Score Statistics:")
print(f"Average F1 Score: {f1_stats['average_f1']:.4f}")
print(f"Macro-F1 Score: {f1_stats['macro_f1']:.4f}")
print(f"Total Classes: {f1_stats['total_classes']}")
# Display per-class F1 scores
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}: F1={scores['f1']:.4f}, P={scores['precision']:.4f}, R={scores['recall']:.4f}"
)
# Print class supports
print("\nClass support (gold counts):")
for l in labels:
print(f" {l}: {support.get(l, 0)}")
# Print confusion matrix
print("\nConfusion matrix (rows=gold, cols=pred):")
header = " " + " ".join(labels)
print(header)
for i, l in enumerate(labels):
row = " ".join(str(x) for x in confusion[i])
print(f" {l} {row}")
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