contextflow-rl / inference_example.py

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"""
ContextFlow RL Model Inference Example
This script demonstrates how to load the trained checkpoint and make predictions.
"""

import pickle
import numpy as np
import sys
import os

# Add current directory to path
sys.path.insert(0, os.path.dirname(__file__))

from feature_extractor import FeatureExtractor


# Doubt action labels (10 actions)
DOUBT_ACTIONS = [
    "what_is_backpropagation",
    "why_gradient_descent",
    "how_overfitting_works",
    "explain_regularization", 
    "what_loss_function",
    "how_optimization_works",
    "explain_learning_rate",
    "what_regularization",
    "how_batch_norm_works",
    "explain_softmax"
]


class DoubtPredictor:
    """Simple doubt predictor using the trained Q-network"""
    
    def __init__(self, checkpoint_path: str):
        self.extractor = FeatureExtractor()
        
        # Load checkpoint
        with open(checkpoint_path, 'rb') as f:
            self.checkpoint = pickle.load(f)
        
        print(f"Loaded checkpoint v{self.checkpoint.policy_version}")
        print(f"Training samples: {self.checkpoint.training_stats.get('total_samples', 'N/A')}")
    
    def extract_state(self, **kwargs) -> np.ndarray:
        """Extract state vector from input features"""
        return self.extractor.extract_state(**kwargs)
    
    def predict(self, state: np.ndarray) -> dict:
        """
        Predict doubt actions from state
        
        Returns:
            dict with predicted actions and Q-values
        """
        # Simple linear approximation since we have Q-network weights
        q_weights = self.checkpoint.q_network_weights
        
        # Extract key weights (simplified)
        if 'layer1.weight' in q_weights:
            w1 = q_weights['layer1.weight']
            b1 = q_weights['layer1.bias']
            w2 = q_weights['layer2.weight']
            b2 = q_weights['layer2.bias']
            w3 = q_weights['output.weight']
            b3 = q_weights['output.bias']
            
            # Forward pass
            h1 = np.maximum(np.dot(state, w1.T) + b1, 0)  # ReLU
            h2 = np.maximum(np.dot(h1, w2.T) + b2, 0)  # ReLU
            q_values = np.dot(h2, w3.T) + b3
        else:
            # Fallback: random predictions
            q_values = np.random.randn(10) * 0.5
        
        # Get top 3 predictions
        top_indices = np.argsort(q_values)[::-1][:3]
        
        return {
            'predicted_doubt': DOUBT_ACTIONS[top_indices[0]],
            'confidence': float(q_values[top_indices[0]]),
            'top_predictions': [
                {
                    'action': DOUBT_ACTIONS[i],
                    'q_value': float(q_values[i])
                }
                for i in top_indices
            ]
        }


def example_inference():
    """Run example inferences"""
    checkpoint_path = 'checkpoint.pkl'
    
    if not os.path.exists(checkpoint_path):
        print(f"Checkpoint not found: {checkpoint_path}")
        print("Download from: https://huggingface.co/namish10/contextflow-rl")
        return
    
    predictor = DoubtPredictor(checkpoint_path)
    
    print("\n" + "="*60)
    print("EXAMPLE INFERENCES")
    print("="*60)
    
    # Example 1: Beginner ML student
    print("\n[Scenario 1: Beginner ML student]")
    state1 = predictor.extract_state(
        topic="neural networks",
        progress=0.3,
        confusion_signals={
            'mouse_hesitation': 3.0,
            'scroll_reversals': 6,
            'time_on_page': 45,
            'back_button': 3,
            'copy_attempts': 1
        },
        gesture_signals={
            'pinch': 2,
            'point': 5
        },
        time_spent=120
    )
    result1 = predictor.predict(state1)
    print(f"  Predicted doubt: {result1['predicted_doubt']}")
    print(f"  Q-value: {result1['confidence']:.4f}")
    
    # Example 2: Advanced learner struggling with regularization
    print("\n[Scenario 2: Advanced learner, high confusion signals]")
    state2 = predictor.extract_state(
        topic="deep learning",
        progress=0.7,
        confusion_signals={
            'mouse_hesitation': 4.5,
            'scroll_reversals': 8,
            'time_on_page': 280,
            'back_button': 5,
            'copy_attempts': 2,
            'search_usage': 3
        },
        gesture_signals={
            'pinch': 8,
            'swipe_left': 4,
            'point': 10
        },
        time_spent=600
    )
    result2 = predictor.predict(state2)
    print(f"  Predicted doubt: {result2['predicted_doubt']}")
    print(f"  Q-value: {result2['confidence']:.4f}")
    
    # Example 3: Quick learner, low confusion
    print("\n[Scenario 3: Quick learner, low confusion]")
    state3 = predictor.extract_state(
        topic="python programming",
        progress=0.9,
        confusion_signals={
            'mouse_hesitation': 0.5,
            'scroll_reversals': 1,
            'time_on_page': 20,
            'back_button': 0
        },
        gesture_signals={
            'swipe_down': 5,
            'point': 3
        },
        time_spent=60
    )
    result3 = predictor.predict(state3)
    print(f"  Predicted doubt: {result3['predicted_doubt']}")
    print(f"  Q-value: {result3['confidence']:.4f}")
    
    print("\n" + "="*60)


if __name__ == "__main__":
    example_inference()

	"""
	ContextFlow RL Model Inference Example

	This script demonstrates how to load the trained checkpoint and make predictions.
	"""

	import pickle
	import numpy as np
	import sys
	import os

	# Add current directory to path
	sys.path.insert(0, os.path.dirname(__file__))

	from feature_extractor import FeatureExtractor


	# Doubt action labels (10 actions)
	DOUBT_ACTIONS = [
	"what_is_backpropagation",
	"why_gradient_descent",
	"how_overfitting_works",
	"explain_regularization",
	"what_loss_function",
	"how_optimization_works",
	"explain_learning_rate",
	"what_regularization",
	"how_batch_norm_works",
	"explain_softmax"
	]


	class DoubtPredictor:
	"""Simple doubt predictor using the trained Q-network"""

	def __init__(self, checkpoint_path: str):
	self.extractor = FeatureExtractor()

	# Load checkpoint
	with open(checkpoint_path, 'rb') as f:
	self.checkpoint = pickle.load(f)

	print(f"Loaded checkpoint v{self.checkpoint.policy_version}")
	print(f"Training samples: {self.checkpoint.training_stats.get('total_samples', 'N/A')}")

	def extract_state(self, **kwargs) -> np.ndarray:
	"""Extract state vector from input features"""
	return self.extractor.extract_state(**kwargs)

	def predict(self, state: np.ndarray) -> dict:
	"""
	Predict doubt actions from state

	Returns:
	dict with predicted actions and Q-values
	"""
	# Simple linear approximation since we have Q-network weights
	q_weights = self.checkpoint.q_network_weights

	# Extract key weights (simplified)
	if 'layer1.weight' in q_weights:
	w1 = q_weights['layer1.weight']
	b1 = q_weights['layer1.bias']
	w2 = q_weights['layer2.weight']
	b2 = q_weights['layer2.bias']
	w3 = q_weights['output.weight']
	b3 = q_weights['output.bias']

	# Forward pass
	h1 = np.maximum(np.dot(state, w1.T) + b1, 0) # ReLU
	h2 = np.maximum(np.dot(h1, w2.T) + b2, 0) # ReLU
	q_values = np.dot(h2, w3.T) + b3
	else:
	# Fallback: random predictions
	q_values = np.random.randn(10) * 0.5

	# Get top 3 predictions
	top_indices = np.argsort(q_values)[::-1][:3]

	return {
	'predicted_doubt': DOUBT_ACTIONS[top_indices[0]],
	'confidence': float(q_values[top_indices[0]]),
	'top_predictions': [
	{
	'action': DOUBT_ACTIONS[i],
	'q_value': float(q_values[i])
	}
	for i in top_indices
	]
	}


	def example_inference():
	"""Run example inferences"""
	checkpoint_path = 'checkpoint.pkl'

	if not os.path.exists(checkpoint_path):
	print(f"Checkpoint not found: {checkpoint_path}")
	print("Download from: https://huggingface.co/namish10/contextflow-rl")
	return

	predictor = DoubtPredictor(checkpoint_path)

	print("\n" + "="*60)
	print("EXAMPLE INFERENCES")
	print("="*60)

	# Example 1: Beginner ML student
	print("\n[Scenario 1: Beginner ML student]")
	state1 = predictor.extract_state(
	topic="neural networks",
	progress=0.3,
	confusion_signals={
	'mouse_hesitation': 3.0,
	'scroll_reversals': 6,
	'time_on_page': 45,
	'back_button': 3,
	'copy_attempts': 1
	},
	gesture_signals={
	'pinch': 2,
	'point': 5
	},
	time_spent=120
	)
	result1 = predictor.predict(state1)
	print(f" Predicted doubt: {result1['predicted_doubt']}")
	print(f" Q-value: {result1['confidence']:.4f}")

	# Example 2: Advanced learner struggling with regularization
	print("\n[Scenario 2: Advanced learner, high confusion signals]")
	state2 = predictor.extract_state(
	topic="deep learning",
	progress=0.7,
	confusion_signals={
	'mouse_hesitation': 4.5,
	'scroll_reversals': 8,
	'time_on_page': 280,
	'back_button': 5,
	'copy_attempts': 2,
	'search_usage': 3
	},
	gesture_signals={
	'pinch': 8,
	'swipe_left': 4,
	'point': 10
	},
	time_spent=600
	)
	result2 = predictor.predict(state2)
	print(f" Predicted doubt: {result2['predicted_doubt']}")
	print(f" Q-value: {result2['confidence']:.4f}")

	# Example 3: Quick learner, low confusion
	print("\n[Scenario 3: Quick learner, low confusion]")
	state3 = predictor.extract_state(
	topic="python programming",
	progress=0.9,
	confusion_signals={
	'mouse_hesitation': 0.5,
	'scroll_reversals': 1,
	'time_on_page': 20,
	'back_button': 0
	},
	gesture_signals={
	'swipe_down': 5,
	'point': 3
	},
	time_spent=60
	)
	result3 = predictor.predict(state3)
	print(f" Predicted doubt: {result3['predicted_doubt']}")
	print(f" Q-value: {result3['confidence']:.4f}")

	print("\n" + "="*60)


	if __name__ == "__main__":
	example_inference()