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"""Visualization utilities for Teacher Agent system."""
import matplotlib.pyplot as plt
import numpy as np
from typing import Dict, List
from teacher_agent import TeacherAgent
def plot_learning_curves(history: Dict, save_path: str = 'learning_curves.png'):
"""
Plot student accuracy and teacher reward over time.
Args:
history: Dictionary with 'iterations', 'student_accuracies', 'teacher_rewards'
save_path: Where to save the plot
"""
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(12, 8))
iterations = history['iterations']
# Plot student accuracy
ax1.plot(iterations, history['student_accuracies'], label='Student Accuracy', linewidth=2)
ax1.set_xlabel('Iteration')
ax1.set_ylabel('Accuracy')
ax1.set_title('Student Learning Curve')
ax1.grid(True, alpha=0.3)
ax1.legend()
ax1.set_ylim([0, 1])
# Plot teacher reward (smoothed)
rewards = np.array(history['teacher_rewards'])
window = 50
if len(rewards) > window:
smoothed = np.convolve(rewards, np.ones(window)/window, mode='valid')
smoothed_iterations = iterations[window-1:]
ax2.plot(smoothed_iterations, smoothed, label=f'Smoothed Reward (window={window})', linewidth=2)
ax2.plot(iterations, rewards, alpha=0.3, label='Raw Reward', linewidth=0.5)
else:
ax2.plot(iterations, rewards, label='Reward', linewidth=2)
ax2.set_xlabel('Iteration')
ax2.set_ylabel('Reward')
ax2.set_title('Teacher Reward Over Time')
ax2.grid(True, alpha=0.3)
ax2.legend()
plt.tight_layout()
plt.savefig(save_path, dpi=150)
print(f"Saved learning curves to {save_path}")
plt.close()
def plot_curriculum_heatmap(history: Dict, save_path: str = 'curriculum_heatmap.png'):
"""
Visualize teacher's curriculum choices over time.
Args:
history: Dictionary with 'iterations', 'topics', 'difficulties', 'is_reviews'
save_path: Where to save the plot
"""
topics = list(set(history['topics']))
topics.sort()
# Create grid: time (iterations) vs topics
num_iterations = len(history['iterations'])
num_topics = len(topics)
# Map difficulty to numeric value
difficulty_map = {'easy': 1, 'medium': 2, 'hard': 3}
# Create heatmap data
heatmap_data = np.zeros((num_topics, num_iterations))
for i, (topic, difficulty, is_review) in enumerate(zip(
history['topics'],
history['difficulties'],
history['is_reviews']
)):
topic_idx = topics.index(topic)
diff_value = difficulty_map[difficulty]
if is_review:
diff_value = 0.5 # Mark reviews differently
heatmap_data[topic_idx, i] = diff_value
fig, ax = plt.subplots(figsize=(14, 6))
im = ax.imshow(heatmap_data, aspect='auto', cmap='viridis', interpolation='nearest')
ax.set_yticks(range(num_topics))
ax.set_yticklabels(topics)
ax.set_xlabel('Iteration')
ax.set_ylabel('Topic')
ax.set_title('Curriculum Heatmap (Light=Easy/Review, Dark=Hard)')
# Add colorbar
cbar = plt.colorbar(im, ax=ax)
cbar.set_label('Difficulty (0.5=Review, 1=Easy, 2=Medium, 3=Hard)')
# Sample iterations for x-axis labels
if num_iterations > 20:
step = num_iterations // 10
ax.set_xticks(range(0, num_iterations, step))
ax.set_xticklabels(range(0, num_iterations, step))
plt.tight_layout()
plt.savefig(save_path, dpi=150)
print(f"Saved curriculum heatmap to {save_path}")
plt.close()
def plot_action_distributions(teacher: TeacherAgent, save_path: str = 'action_dist.png'):
"""
Show which actions teacher prefers.
Args:
teacher: Trained TeacherAgent
save_path: Where to save the plot
"""
stats = teacher.get_statistics()
fig, axes = plt.subplots(2, 2, figsize=(14, 10))
# 1. Topic distribution
topic_counts = {}
for idx, count in enumerate(stats['action_counts']):
if count > 0:
action = teacher._index_to_action(idx)
topic_counts[action.topic] = topic_counts.get(action.topic, 0) + count
ax = axes[0, 0]
topics = list(topic_counts.keys())
counts = list(topic_counts.values())
ax.bar(topics, counts)
ax.set_xlabel('Topic')
ax.set_ylabel('Count')
ax.set_title('Topic Selection Distribution')
ax.tick_params(axis='x', rotation=45)
# 2. Difficulty distribution
difficulty_counts = {'easy': 0, 'medium': 0, 'hard': 0}
for idx, count in enumerate(stats['action_counts']):
if count > 0:
action = teacher._index_to_action(idx)
difficulty_counts[action.difficulty] += count
ax = axes[0, 1]
difficulties = list(difficulty_counts.keys())
counts = list(difficulty_counts.values())
ax.bar(difficulties, counts)
ax.set_xlabel('Difficulty')
ax.set_ylabel('Count')
ax.set_title('Difficulty Selection Distribution')
# 3. Review vs New
review_counts = {'New': 0, 'Review': 0}
for idx, count in enumerate(stats['action_counts']):
if count > 0:
action = teacher._index_to_action(idx)
key = 'Review' if action.is_review else 'New'
review_counts[key] += count
ax = axes[1, 0]
labels = list(review_counts.keys())
sizes = list(review_counts.values())
ax.pie(sizes, labels=labels, autopct='%1.1f%%', startangle=90)
ax.set_title('New vs Review Distribution')
# 4. Average reward per topic
topic_rewards = {}
for idx in range(len(stats['action_counts'])):
if stats['action_counts'][idx] > 0:
action = teacher._index_to_action(idx)
avg_reward = stats['action_rewards'][idx] / stats['action_counts'][idx]
topic_rewards[action.topic] = topic_rewards.get(action.topic, []) + [avg_reward]
# Compute mean reward per topic
topic_avg_rewards = {topic: np.mean(rewards) for topic, rewards in topic_rewards.items()}
ax = axes[1, 1]
topics = list(topic_avg_rewards.keys())
rewards = list(topic_avg_rewards.values())
ax.bar(topics, rewards)
ax.set_xlabel('Topic')
ax.set_ylabel('Average Reward')
ax.set_title('Average Reward per Topic')
ax.tick_params(axis='x', rotation=45)
plt.tight_layout()
plt.savefig(save_path, dpi=150)
print(f"Saved action distributions to {save_path}")
plt.close()
def plot_comparison(histories: Dict[str, Dict], save_path: str = 'comparison.png'):
"""
Compare teacher vs baselines.
Args:
histories: Dictionary mapping strategy name to history dict
e.g., {'teacher': history1, 'random': history2, 'fixed': history3}
save_path: Where to save the plot
"""
fig, axes = plt.subplots(2, 1, figsize=(12, 8))
# Plot accuracy comparison
ax = axes[0]
for name, history in histories.items():
iterations = history['iterations']
accuracies = history['student_accuracies']
ax.plot(iterations, accuracies, label=name, linewidth=2)
ax.set_xlabel('Iteration')
ax.set_ylabel('Accuracy')
ax.set_title('Student Accuracy Comparison')
ax.legend()
ax.grid(True, alpha=0.3)
ax.set_ylim([0, 1])
# Plot reward comparison (smoothed)
ax = axes[1]
window = 50
for name, history in histories.items():
rewards = np.array(history['teacher_rewards'])
iterations = history['iterations']
if len(rewards) > window:
smoothed = np.convolve(rewards, np.ones(window)/window, mode='valid')
smoothed_iterations = iterations[window-1:]
ax.plot(smoothed_iterations, smoothed, label=f'{name} (smoothed)', linewidth=2)
else:
ax.plot(iterations, rewards, label=name, linewidth=2)
ax.set_xlabel('Iteration')
ax.set_ylabel('Reward')
ax.set_title('Teacher Reward Comparison')
ax.legend()
ax.grid(True, alpha=0.3)
plt.tight_layout()
plt.savefig(save_path, dpi=150)
print(f"Saved comparison plot to {save_path}")
plt.close()
if __name__ == "__main__":
# Example usage
print("This module provides visualization functions.")
print("Import and use them with training results:")
print()
print(" from train_teacher import train_teacher")
print(" from visualize import *")
print()
print(" history, teacher, student = train_teacher(num_iterations=500)")
print(" plot_learning_curves(history)")
print(" plot_curriculum_heatmap(history)")
print(" plot_action_distributions(teacher)")
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