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evaluate.py

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+"""
+evaluate.py — Baseline vs trained agent comparison
+Run before and after training to measure improvement.
+
+Usage:
+    python evaluate.py --url http://localhost:8000
+    python evaluate.py --url https://your-hf-username-datacentric-env.hf.space
+"""
+
+import requests
+import json
+import random
+import argparse
+import matplotlib.pyplot as plt
+
+parser = argparse.ArgumentParser(description="Evaluate DataCentric-Env agent")
+parser.add_argument("--url", default="http://localhost:8000", help="Environment server URL")
+parser.add_argument("--episodes", type=int, default=20, help="Number of evaluation episodes")
+args = parser.parse_args()
+
+ENV_URL = args.url.rstrip("/")
+N_EPISODES = args.episodes
+
+
+def random_agent_episode():
+    """Baseline: random tool selection."""
+    obs = requests.post(f"{ENV_URL}/reset").json()
+    tools = ["cleaner", "augmenter", "balancer", "validator"]
+    total_reward = 0.0
+    success = False
+    for _ in range(10):
+        action = {"agent": random.choice(tools), "target": "all"}
+        result = requests.post(f"{ENV_URL}/step", json=action).json()
+        total_reward += result.get("reward", 0)
+        if result.get("done"):
+            success = result.get("info", {}).get("success", False)
+            break
+    return total_reward, success
+
+
+# ─── Run baseline ─────────────────────────────────────────────────────────────
+print(f"Running {N_EPISODES} baseline (random) episodes against {ENV_URL}...")
+baseline_rewards = []
+baseline_successes = []
+for i in range(N_EPISODES):
+    reward, success = random_agent_episode()
+    baseline_rewards.append(reward)
+    baseline_successes.append(success)
+    print(f"  Episode {i+1:02d}: reward={reward:.3f} success={success}")
+
+mean_baseline = sum(baseline_rewards) / len(baseline_rewards)
+success_rate_baseline = sum(baseline_successes) / len(baseline_successes)
+print(f"\nBaseline mean reward:  {mean_baseline:.3f}")
+print(f"Baseline success rate: {success_rate_baseline:.1%}")
+
+# ─── Plot reward curve ────────────────────────────────────────────────────────
+plt.figure(figsize=(10, 4))
+
+plt.subplot(1, 2, 1)
+plt.plot(range(1, N_EPISODES + 1), baseline_rewards, marker="o", color="#5B8FF9", label="Random baseline")
+plt.xlabel("Episode")
+plt.ylabel("Total Reward")
+plt.title("Baseline Reward per Episode")
+plt.legend()
+plt.grid(alpha=0.3)
+
+plt.subplot(1, 2, 2)
+mean_trained = mean_baseline * 1.0  # placeholder — replace with trained agent result
+plt.bar(["Random baseline", "Trained agent"],
+        [mean_baseline, mean_trained],
+        color=["#5B8FF9", "#5AD8A6"])
+plt.ylabel("Mean Episode Reward")
+plt.title("Baseline vs Trained Agent")
+plt.grid(alpha=0.3, axis="y")
+
+plt.tight_layout()
+plt.savefig("results.png", dpi=150)
+print("\nSaved results.png")