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ef737d3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 | import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import random
from tasks.email_triage import EmailTriageTask
from tasks.devops_incident import DevOpsIncidentTask
from tasks.financial_request import FinancialRequestTask
from models import Action
def run_evaluation(num_episodes=100):
tasks = [EmailTriageTask, DevOpsIncidentTask, FinancialRequestTask]
results = []
print(f"Running {num_episodes} random agent episodes...")
for i in range(num_episodes):
TaskClass = random.choice(tasks)
task = TaskClass()
obs = task.reset()
episode_rewards = []
episode_actions = []
episode_ambiguity = task._scenario.get("ambiguity", 0.5)
investigated = False
done = False
while not done:
actions = obs.available_actions
if not actions:
break
action_type = random.choice(actions)
if action_type == "investigate":
investigated = True
obs, reward, done, info = task.step(Action(type=action_type))
episode_rewards.append(reward.value)
episode_actions.append(action_type)
results.append({
"task": TaskClass.__name__,
"total_reward": sum(episode_rewards),
"avg_reward": np.mean(episode_rewards) if episode_rewards else 0,
"actions": episode_actions,
"ambiguity": episode_ambiguity,
"investigated": investigated
})
if (i+1) % 20 == 0:
print(f"Progress: {i+1}/{num_episodes}")
return results
def plot_results(results):
rewards = [r["avg_reward"] for r in results]
all_actions = []
for r in results:
all_actions.extend(r["actions"])
# 1. Reward Distribution
plt.figure(figsize=(10, 6))
plt.hist(rewards, bins=20, color='#3498DB', edgecolor='white')
plt.title('Random Agent Reward Distribution', fontsize=14, fontweight='bold')
plt.xlabel('Average Step Reward')
plt.ylabel('Frequency')
plt.grid(alpha=0.3)
plt.savefig('reward_dist.png', dpi=150)
plt.close()
# 2. Action Distribution
from collections import Counter
counts = Counter(all_actions)
labels, values = zip(*counts.most_common(10))
plt.figure(figsize=(10, 6))
plt.bar(labels, values, color='#2ECC71')
plt.title('Top 10 Actions Distribution', fontsize=14, fontweight='bold')
plt.xticks(rotation=45, ha='right')
plt.ylabel('Count')
plt.tight_layout()
plt.savefig('action_dist.png', dpi=150)
plt.close()
# 3. Ambiguity vs Investigation
ambiguities = [r["ambiguity"] for r in results]
investigated = [1 if r["investigated"] else 0 for r in results]
# Bin ambiguity and calculate investigation rate
bins = np.linspace(0, 1, 6)
bin_indices = np.digitize(ambiguities, bins)
bin_rates = []
bin_centers = []
for i in range(1, len(bins)):
mask = bin_indices == i
if np.any(mask):
bin_rates.append(np.mean(np.array(investigated)[mask]))
bin_centers.append((bins[i-1] + bins[i]) / 2)
plt.figure(figsize=(10, 6))
plt.plot(bin_centers, bin_rates, 'o-', color='#E74C3C', linewidth=2, markersize=8)
plt.title('Ambiguity vs Investigation Rate (Random Policy)', fontsize=14, fontweight='bold')
plt.xlabel('Scenario Ambiguity')
plt.ylabel('Investigation Probability')
plt.ylim(-0.05, 1.05)
plt.grid(alpha=0.3)
plt.savefig('ambiguity_investigation.png', dpi=150)
plt.close()
print("✅ Evaluation plots generated: reward_dist.png, action_dist.png, ambiguity_investigation.png")
if __name__ == "__main__":
results = run_evaluation(100)
plot_results(results)
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