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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.metrics import roc_curve, auc
import logging
import os
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
def plot_roc_curve(results_path, output_image_path):
"""
Reads predictions CSV, calculates AUC, and plots ROC curve.
"""
if not os.path.exists(results_path):
logger.error(f"Results file not found: {results_path}")
return
try:
df = pd.read_csv(results_path)
logger.info(f"Loaded {len(df)} predictions from {results_path}")
# Filter out errors
df = df.dropna(subset=['pneumothorax_score'])
if len(df) == 0:
logger.error("No valid predictions found.")
return
# Prepare True Labels (Binary)
# Kaggle Labels: 'Pneumothorax' vs 'No Pneumothorax'
y_true = (df['true_label'] == 'Pneumothorax').astype(int)
y_scores = df['pneumothorax_score']
# Calculate ROC and AUC
fpr, tpr, thresholds = roc_curve(y_true, y_scores)
roc_auc = auc(fpr, tpr)
logger.info(f"Calculated AUC: {roc_auc:.4f}")
# Plot
plt.figure(figsize=(8, 6))
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve - Zero-Shot Pneumothorax Classification (Kaggle)')
plt.legend(loc="lower right")
plt.grid(True, alpha=0.3)
plt.savefig(output_image_path)
logger.info(f"ROC curve saved to {output_image_path}")
plt.close()
except Exception as e:
logger.error(f"Failed to plot ROC curve: {e}")
if __name__ == "__main__":
results_file = "results/kaggle_predictions.csv"
output_image = "results/kaggle_roc_curve.png"
plot_roc_curve(results_file, output_image)
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