| |
| import numpy as np |
| import pandas as pd |
| import matplotlib.pyplot as plt |
| import seaborn as sns |
| from scipy.stats import pearsonr, spearmanr |
| from pathlib import Path |
|
|
|
|
| def evaluate(preds: np.ndarray, labels: np.ndarray) -> dict: |
| return { |
| 'R': round(pearsonr(preds, labels)[0], 4), |
| 'Sp': round(spearmanr(preds, labels)[0], 4), |
| 'RMSE': round(float(np.sqrt(np.mean((preds - labels)**2))), 4), |
| 'MAE': round(float(np.mean(np.abs(preds - labels))), 4), |
| 'SD': round(float(np.std(preds - labels)), 4), |
| } |
|
|
|
|
| def print_row(name: str, m: dict, note: str = ''): |
| print(f" {name:<32} R={m['R']:.4f} Sp={m['Sp']:.4f} " |
| f"RMSE={m['RMSE']:.4f} MAE={m['MAE']:.4f} {note}") |
|
|
|
|
| COMPETITORS = [ |
| |
| ("DeepDTA", 0.709, 1.584, 1.211, "1D seq"), |
| ("GraphDTA", 0.687, 1.638, 1.287, "1D seq"), |
| ("S2DTA", 0.728, 1.553, 1.236, "1D seq"), |
| ("MREDTA", 0.749, 1.449, 1.108, "1D seq"), |
| ("IGN", 0.758, 1.447, 1.108, "3D pocket"), |
| ("DeepDTAF", 0.744, 1.468, 1.123, "3D pocket"), |
| ("MDF-DTA", 0.772, 1.386, 1.048, "3D pocket"), |
| ("MMPD-DTA", 0.795, 1.342, 1.058, "3D pocket"), |
| ("CAPLA", 0.786, 1.362, 1.054, "3D pocket"), |
| ("PocketDTA", 0.806, 1.105, 0.861, "3D pocket"), |
| ("HPDAF", 0.849, 0.991, 0.766, "3D pocket"), |
| ] |
|
|
|
|
| def print_comparison_table(prism_m: dict, n_test: int): |
| print("\n" + "=" * 72) |
| print(f"CASF-2016 COMPARISON (N={n_test})") |
| print("=" * 72) |
| print(f" {'Model':<22} {'Input':<12} {'R':>7} {'RMSE':>7} {'MAE':>7}") |
| print(" " + "-" * 60) |
| for name, r, rmse, mae, inp in COMPETITORS: |
| print(f" {name:<22} {inp:<12} {r:>7.3f} {rmse:>7.3f} {mae:>7.3f}") |
| print(" " + "-" * 60) |
| print(f" {'PRISM (ours)':<22} {'1D seq':<12} " |
| f"{prism_m['R']:>7.4f} {prism_m['RMSE']:>7.4f} {prism_m['MAE']:>7.4f}") |
| print("=" * 72) |
|
|
|
|
| def ablation_table(rows: list): |
| """ |
| rows = list of (name, R, RMSE) tuples. |
| Prints a clean ablation table. |
| """ |
| print("\n── Ablation ──────────────────────────────────────────────") |
| print(f" {'Configuration':<40} {'R':>7} {'RMSE':>7}") |
| print(" " + "-" * 55) |
| for name, r, rmse in rows: |
| r_s = f"{r:.4f}" if r is not None else " — " |
| rmse_s = f"{rmse:.4f}" if rmse is not None else " — " |
| print(f" {name:<40} {r_s:>7} {rmse_s:>7}") |
| print(" " + "-" * 55) |
|
|
|
|
| def scatter_plot(y_true: np.ndarray, y_pred: np.ndarray, |
| m: dict, title: str, out_path: Path): |
| fig, ax = plt.subplots(figsize=(6, 6)) |
| lo = min(y_true.min(), y_pred.min()) - 0.3 |
| hi = max(y_true.max(), y_pred.max()) + 0.3 |
| ax.plot([lo, hi], [lo, hi], 'k--', alpha=0.4, lw=1.5) |
| ax.scatter(y_true, y_pred, alpha=0.65, s=28, |
| color='royalblue', edgecolors='white', lw=0.3) |
| sns.regplot(x=y_true, y=y_pred, scatter=False, ax=ax, |
| color='crimson', line_kws={'lw': 2}) |
| ax.set_xlabel("Experimental pKd", fontsize=12) |
| ax.set_ylabel("Predicted pKd", fontsize=12) |
| ax.set_title(f"{title}\n" |
| f"R={m['R']} Sp={m['Sp']} RMSE={m['RMSE']} MAE={m['MAE']}", |
| fontsize=11, weight='bold') |
| ax.grid(True, alpha=0.2) |
| plt.tight_layout() |
| plt.savefig(out_path, dpi=300, bbox_inches='tight') |
| plt.close() |
| print(f" Plot saved: {out_path.name}") |
|
|
