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import json
import pandas as pd
import numpy as np
import os
def load_ground_truth_from_csv(csv_path):
"""Load ground truth labels from CSV file"""
df = pd.read_csv(csv_path)
ground_truth = {}
for _, row in df.iterrows():
filename = row['filename']
# Extract ground truth labels (all columns except filename)
gt_cols = [col for col in df.columns if col.startswith('pixel_')]
gt_labels = row[gt_cols].values.astype(int)
ground_truth[filename] = gt_labels
print(f"Loaded {len(ground_truth)} samples from CSV")
return ground_truth
def calculate_score_from_csv(csv_path, ground_truth, bonus=50):
df = pd.read_csv(csv_path)
total_score = 0
total_theo = 0
for _, row in df.iterrows():
filename = row['filename']
if filename not in ground_truth:
raise ValueError(f"Missing ground truth for file: {filename}")
pred_cols = [col for col in df.columns if col.startswith('pixel_')]
if len(pred_cols) == 0:
raise ValueError("No prediction columns found (e.g., pixel_0, pixel_1, ...)")
predictions = row[pred_cols].values.astype(int)
gt_labels = ground_truth[filename]
if len(predictions) != len(gt_labels):
raise ValueError(f"Length mismatch for {filename}: prediction({len(predictions)}), ground truth({len(gt_labels)})")
equal_mask = predictions == gt_labels
neg_one_mask = gt_labels == -1
score_neg_one = np.sum(equal_mask & neg_one_mask) * 1
score_other = np.sum(equal_mask & ~neg_one_mask) * bonus
score_theo = np.sum(neg_one_mask) * 1 + np.sum(~neg_one_mask) * bonus
total_score += score_neg_one + score_other
total_theo += score_theo
if total_theo == 0:
print("Warning: Theoretical total score is zero. Returning score 0.0")
return 0.0
score = total_score / total_theo
if not np.isfinite(score) or score < 0 or score > 1:
print(f"Warning: Score {score} is invalid. Returning 0.0")
return 0.0
return score
def main():
"""Main function to calculate scores for both validation and testing sets"""
bonus = 50 # Score weights for non-background categories
if os.environ.get('METRIC_PATH'):
METRIC_PATH = os.environ.get("METRIC_PATH")+"/"
else:
METRIC_PATH =""
# Paths for predictions
val_csv_path = 'submission_val.csv'
test_csv_path = 'submission_test.csv'
# Paths for ground truth CSV files
val_gt_csv_path = METRIC_PATH + 'ground_truth_val.csv'
test_gt_csv_path = METRIC_PATH + 'ground_truth_test.csv'
# Load ground truth for validation set from CSV
val_ground_truth = load_ground_truth_from_csv(val_gt_csv_path)
# Load ground truth for testing set from CSV
test_ground_truth = load_ground_truth_from_csv(test_gt_csv_path)
# Calculate scores
print("\nCalculating scores...")
# Validation set score
val_score = calculate_score_from_csv(val_csv_path, val_ground_truth, bonus)
print(f"Validation set score: {val_score:.4f}")
if val_score > 1:
val_score = 0
# Testing set score
test_score = calculate_score_from_csv(test_csv_path, test_ground_truth, bonus)
print(f"Testing set score: {test_score:.4f}")
if test_score > 1:
test_score = 0
# Save results
score = {
"public_a": val_score,
"private_b": test_score,
}
ret_json = {
"status": True,
"score": score,
"msg": "Success!",
}
with open('score.json', 'w') as f:
json.dump(ret_json, f, indent=2)
if __name__ == '__main__':
main() |