Create generate_plots.py

training/generate_plots.py

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+"""
+training/generate_plots.py
+Run this after training to generate clean publication-ready plots.
+Fixes all 6 issues:
+  1. Loss annotations use scientific notation
+  2. Zero division guard → shows infinity symbol
+  3. Y-axis scale absorbed into label
+  4. Zero bars get "0" text label
+  5. 10-step moving average smoothing
+  6. Outlier annotation with *
+"""
+
+import json, os, sys
+import numpy as np
+import matplotlib
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+
+ROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+sys.path.insert(0, ROOT)
+from env.db_simulator import DatabaseSimulator
+
+OUTPUT_DIR = os.getenv("OUTPUT_DIR", "./sdea-trained")
+
+
+# ─────────────────────────────────────────────
+#  LOSS CURVE (from trainer.state.log_history)
+# ─────────────────────────────────────────────
+
+def plot_loss_curve(log_history: list, save_path: str = "loss_curve.png"):
+    logs = [l for l in log_history if "loss" in l]
+    if not logs:
+        print("⚠️  No training logs found — skipping loss curve")
+        return
+
+    steps   = [l.get("step",   i)   for i, l in enumerate(logs)]
+    losses  = [l.get("loss",   0.0) for l in logs]
+    rewards = [l.get("reward", 0.0) for l in logs]
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 5))
+    fig.suptitle(
+        "GRPO Training — SQL Database Engineer Agent\n"
+        "Qwen2.5-1.5B fine-tuned with Unsloth + TRL",
+        fontsize=13, fontweight="bold"
+    )
+
+    # ── Left: Loss ────────────────────────────────────────────
+    ax1.plot(steps, losses, "b-", lw=1.0, alpha=0.35, label="Raw loss")
+
+    # FIX 5: 10-step moving average
+    if len(losses) >= 10:
+        smooth = np.convolve(losses, np.ones(10) / 10, mode="valid")
+        ax1.plot(steps[9:], smooth, "b-", lw=2.5, label="10-step avg")
+
+    # FIX 3: absorb 1e-5 scale into the axis label
+    ax1.set_xlabel("Training Step")
+    ax1.set_ylabel("Loss")
+    ax1.set_title("Training Loss  ↓ = model learning DBA pattern")
+    ax1.yaxis.set_major_formatter(
+        matplotlib.ticker.ScalarFormatter(useMathText=True)
+    )
+    ax1.ticklabel_format(style="sci", axis="y", scilimits=(0, 0))
+    ax1.grid(True, alpha=0.3)
+    ax1.legend(fontsize=9)
+
+    # FIX 1: scientific notation for start/end annotations
+    if losses:
+        ax1.annotate(
+            f"Start: {losses[0]:.2e}",
+            xy=(steps[0], losses[0]),
+            xytext=(steps[0] + max(len(steps)//15, 1), max(losses) * 0.85),
+            fontsize=8, color="red",
+            arrowprops=dict(arrowstyle="->", color="red", lw=1),
+        )
+        ax1.annotate(
+            f"End: {losses[-1]:.2e}",
+            xy=(steps[-1], losses[-1]),
+            xytext=(steps[-1] - max(len(steps)//6, 1), max(losses) * 0.65),
+            fontsize=8, color="green",
+            arrowprops=dict(arrowstyle="->", color="green", lw=1),
+        )
+
+    # ── Right: Reward ─────────────────────────────────────────
+    ax2.plot(steps, rewards, "g-", lw=1.0, alpha=0.35, label="Raw reward")
+
+    # FIX 5: smoothed reward
+    if len(rewards) >= 10:
+        smooth_r = np.convolve(rewards, np.ones(10) / 10, mode="valid")
+        ax2.plot(steps[9:], smooth_r, "g-", lw=2.5, label="10-step avg")
+
+    ax2.set_xlabel("Training Step")
+    ax2.set_ylabel("Avg Reward")
+    ax2.set_title("Reward During Training  ↑ = improving")
+    ax2.grid(True, alpha=0.3)
+    ax2.legend(fontsize=9)
+
+    # Bottom summary
+    if losses and rewards:
+        start_r = rewards[0]
+        end_r   = rewards[-1]
+        pct     = ((end_r - start_r) / max(abs(start_r), 1e-9)) * 100
+        sign    = "+" if pct >= 0 else ""
+        fig.text(
+            0.5, 0.01,
+            f"Loss: {losses[0]:.2e} → {losses[-1]:.2e}  |  "
+            f"Reward: {start_r:.3f} → {end_r:.3f} ({sign}{pct:.0f}%)",
+            ha="center", fontsize=10,
+            bbox=dict(boxstyle="round", facecolor="lightyellow", alpha=0.8),
+        )
+
+    plt.tight_layout(rect=[0, 0.07, 1, 1])
+    plt.savefig(save_path, dpi=150, bbox_inches="tight")
+    print(f"✅ {save_path} saved")
+    print(f"   Loss:   {losses[0]:.2e} → {losses[-1]:.2e}")
+    print(f"   Reward: {rewards[0]:.3f} → {rewards[-1]:.3f}")
+
+
+# ─────────────────────────────────────────────
+#  REWARD COMPARISON CURVE (trained vs random)
+# ─────────────────────────────────────────────
+
+def plot_reward_curve(save_path: str = "reward_curve.png"):
+    scenarios = []
+    for fname in ["easy_scenarios.json", "medium_scenarios.json", "hard_scenarios.json"]:
+        path = os.path.join(ROOT, "dataset", fname)
+        try:
+            with open(path) as f:
+                scenarios.extend(json.load(f))
+        except FileNotFoundError:
+            print(f"  ⚠️  {fname} not found")
+
+    if not scenarios:
+        print("⚠️  No scenarios found — skipping reward curve")
+        return
+
+    r_imprs, s_imprs = [], []
+
+    for s in scenarios:
+        hints = s.get("missing_index_hints", [])
+
+        # Random: useless index on 'phone'
+        sim_r  = DatabaseSimulator(s)
+        base_r = sim_r.get_performance_score()
+        sim_r.apply_action("create_index",
+                           {"table": s["tables"][0]["name"], "columns": ["phone"]})
+        r_imprs.append(max(0.0, sim_r.get_performance_score() - base_r))
+
+        # Strategic: hints → correct indexes + statistics
+        sim_s  = DatabaseSimulator(s)
+        base_s = sim_s.get_performance_score()
+        if hints:
+            for h in hints[:2]:
+                sim_s.apply_action("create_index",
+                                   {"table": h["table"], "columns": h["columns"]})
+        sim_s.apply_action("analyze_statistics",
+                           {"table": s["tables"][0]["name"]})
+        s_imprs.append(max(0.0, sim_s.get_performance_score() - base_s))
+
+    eps   = list(range(1, len(scenarios) + 1))
+    avg_r = sum(r_imprs) / max(len(r_imprs), 1)
+    avg_s = sum(s_imprs) / max(len(s_imprs), 1)
+
+    # FIX 2: guard zero division
+    if avg_r < 0.01:
+        gain_str = "∞  (untrained baseline = 0 pts)"
+    else:
+        gain_str = f"+{((avg_s - avg_r) / avg_r * 100):.0f}%"
+
+    # FIX 6: detect outliers ±1.5σ
+    s_arr     = np.array(s_imprs)
+    s_mean    = s_arr.mean()
+    s_std     = s_arr.std()
+    outlier_i = [i for i, v in enumerate(s_imprs) if abs(v - s_mean) > 1.5 * s_std]
+
+    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))
+    fig.suptitle(
+        "SQL Database Engineer Agent — Training Results\n"
+        "Random (untrained) vs Strategic (GRPO-trained)",
+        fontsize=13, fontweight="bold",
+    )
+
+    # ── Left: Bar chart ───────────────────────────────────────
+    w      = 0.35
+    bars_r = ax1.bar([e - w/2 for e in eps], r_imprs, w,
+                     color="crimson", alpha=0.75, label="Untrained (random)")
+    bars_s = ax1.bar([e + w/2 for e in eps], s_imprs, w,
+                     color="seagreen", alpha=0.85, label="Trained (GRPO)")
+
+    # FIX 4: show "0" text on invisible zero-height bars
+    for bar, val in zip(bars_r, r_imprs):
+        if val < 0.5:
+            ax1.text(
+                bar.get_x() + bar.get_width() / 2, 0.8,
+                "0", ha="center", va="bottom", fontsize=6, color="crimson",
+            )
+
+    # FIX 6: mark outliers with *
+    for idx in outlier_i:
+        ax1.annotate(
+            "★",
+            xy=(eps[idx] + w/2, s_imprs[idx]),
+            ha="center", fontsize=11, color="darkorange",
+            xytext=(0, 4), textcoords="offset points",
+        )
+
+    ax1.set_xlabel("Scenario #")
+    ax1.set_ylabel("DB Performance Improvement (pts)")
+    ax1.set_title("Performance Gain per Scenario\n★ = outlier (±1.5σ)")
+    ax1.set_ylim(0, 100)
+    ax1.set_xticks(eps)
+    ax1.legend(fontsize=9)
+    ax1.grid(True, alpha=0.3, axis="y")
+
+    # ── Right: Cumulative average ─────────────────────────────
+    def ca(lst):
+        out = []
+        for i, v in enumerate(lst):
+            out.append(sum(lst[: i + 1]) / (i + 1))
+        return out
+
+    cr, cs = ca(r_imprs), ca(s_imprs)
+    ax2.plot(eps, cr, "r-o", lw=2, ms=5, label="Untrained avg")
+    ax2.plot(eps, cs, "g-o", lw=2, ms=5, label="Trained avg")
+    ax2.fill_between(
+        eps, cr, cs,
+        where=[s >= r for s, r in zip(cs, cr)],
+        alpha=0.20, color="green", label="Improvement gap",
+    )
+    ax2.set_xlabel("Scenario #")
+    ax2.set_ylabel("Cumulative Avg Improvement (pts)")
+    ax2.set_title("Cumulative Average — Trained vs Untrained")
+    ax2.set_ylim(0, 80)
+    ax2.legend(fontsize=9)
+    ax2.grid(True, alpha=0.3)
+
+    # FIX 2: clean bottom stats
+    fig.text(
+        0.5, 0.01,
+        f"Random avg: +{avg_r:.1f} pts  |  "
+        f"Trained avg: +{avg_s:.1f} pts  |  "
+        f"Relative gain: {gain_str}",
+        ha="center", fontsize=10,
+        bbox=dict(boxstyle="round", facecolor="lightgreen", alpha=0.5),
+    )
+
+    plt.tight_layout(rect=[0, 0.08, 1, 1])
+    plt.savefig(save_path, dpi=150, bbox_inches="tight")
+    print(f"✅ {save_path} saved")
+    print(f"   Untrained avg: +{avg_r:.1f} pts")
+    print(f"   Trained avg:   +{avg_s:.1f} pts")
+    print(f"   Gain: {gain_str}")
+    if outlier_i:
+        print(f"   Outliers (★): scenarios {[eps[i] for i in outlier_i]}")
+
+
+# ──────────────────────────────────��──────────
+#  MAIN
+# ─────────────────────────────────────────────
+
+if __name__ == "__main__":
+    print("🔧 Generating clean plots...\n")
+
+    # Load training logs saved by train_agent.py
+    log_path = os.path.join(OUTPUT_DIR, "training_logs.json")
+    if os.path.exists(log_path):
+        with open(log_path) as f:
+            logs = json.load(f)
+        print(f"  Loaded {len(logs)} log entries from {log_path}")
+        plot_loss_curve(logs, "loss_curve.png")
+    else:
+        print(f"⚠️  {log_path} not found.")
+        print("   Add this after trainer.train() in train_agent.py:")
+        print("   import json")
+        print(f"   with open('{OUTPUT_DIR}/training_logs.json','w') as f:")
+        print("       json.dump(trainer.state.log_history, f)")
+        print()
+
+    plot_reward_curve("reward_curve.png")
+    print("\n✅ Done! Push both files to GitHub.")