#!/usr/bin/env python3
"""
Exp04: DINOv2単体でPublic LB確認
既存のDINOv2提出ファイルを使用（再実行なし）
"""

import pandas as pd
import shutil

print("="*70)
print("📊 Exp04: DINOv2 Giant + Lasso - Public LB Submission")
print("="*70)

print("\nModel Info:")
print("  • Model: DINOv2 Giant + Lasso")
print("  • OOF Score: 0.6847 (Weighted R²)")
print("  • Training: 5-Fold CV")
print("  • Source: dinov2-giant-simple-baseline notebook")

# Load existing submission from baseline notebook
submission = pd.read_csv('/kaggle/input/dinov2-giant-simple-baseline/submission.csv')

print(f"\n✅ Submission file loaded")
print(f"  • Total rows: {len(submission)}")
print(f"  • Columns: {submission.columns.tolist()}")

# Validate submission format
assert 'sample_id' in submission.columns, "Missing sample_id column"
assert 'target' in submission.columns, "Missing target column"
assert len(submission) == 1785, f"Expected 1785 rows, got {len(submission)}"

print("\n📋 Submission Preview:")
print(submission.head(10))

print("\n📊 Target Statistics:")
print(f"  • Min: {submission['target'].min():.4f}")
print(f"  • Max: {submission['target'].max():.4f}")
print(f"  • Mean: {submission['target'].mean():.4f}")
print(f"  • Median: {submission['target'].median():.4f}")

# Save submission
submission.to_csv('submission.csv', index=False)

print("\n✅ Submission saved to submission.csv")
print("\n" + "="*70)
print("📝 EXPERIMENT NOTES")
print("="*70)
print("""
この提出の目的:
  • DINOv2単体のPublic LBスコアを確認
  • OOF 0.6847 vs Public LB の乖離を分析
  • アンサンブル (LB 0.634) との比較

期待される結果:
  • Public LB 0.65以上
  • 現行アンサンブルを上回る可能性

次のアクション:
  1. この提出のPublic LBを確認
  2. LB > 0.634 なら、DINOv2重視のアンサンブルを試す
  3. LB < 0.634 なら、OOF過学習の可能性を調査
""")

print("\n✨ Exp04 ready for submission!")