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"""
Adaptive Learning Environment for Codette
Provides a dynamic learning environment that adapts to user interactions
"""
import logging
from typing import Dict, List, Any, Optional
from datetime import datetime
from .dynamic_learning import DynamicLearner
logger = logging.getLogger(__name__)
try:
import numpy as np
except Exception:
np = None
class AdaptiveLearningEnvironment:
"""Manages an adaptive learning environment for Codette"""
def __init__(self,
initial_learning_rate: float = 0.01,
adaptation_threshold: float = 0.75,
max_memory_size: int = 1000):
"""Initialize the adaptive learning environment"""
self.learning_rate = initial_learning_rate
self.adaptation_threshold = adaptation_threshold
self.max_memory_size = max_memory_size
# Initialize components
self.dynamic_learner = DynamicLearner(
learning_rate=initial_learning_rate,
memory_size=max_memory_size,
adaptation_threshold=adaptation_threshold
)
# Initialize state
self.state = {
"learning_phase": "initialization",
"adaptivity_level": 1.0,
"performance_metrics": {},
"current_focus": None
}
# Learning history
self.history = []
logger.info("Adaptive Learning Environment initialized")
def process_interaction(self,
input_data: Dict[str, Any],
context: Optional[Dict[str, Any]] = None) -> Dict[str, Any]:
"""Process a new interaction and adapt the learning environment"""
try:
# Prepare interaction data
interaction_data = {
"input": input_data,
"context": context or {},
"timestamp": datetime.now().isoformat()
}
# Update dynamic learner
adaptation_score = self.dynamic_learner.update(interaction_data)
# Analyze and adapt
analysis_result = self._analyze_interaction(interaction_data)
self._adapt_environment(analysis_result, adaptation_score)
# Update state
self.state["performance_metrics"] = {
"adaptation_score": adaptation_score,
"learning_efficiency": self._calculate_learning_efficiency(),
"environment_stability": self._assess_stability()
}
# Record in history
self.history.append({
"interaction": interaction_data,
"analysis": analysis_result,
"state": self.state.copy(),
"timestamp": datetime.now().isoformat()
})
return self._prepare_response(analysis_result)
except Exception as e:
logger.error(f"Error processing interaction: {e}")
return {"error": str(e)}
def _analyze_interaction(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Analyze an interaction for learning opportunities"""
try:
# Extract features for analysis
features = self._extract_features(data)
# Calculate metrics
complexity = self._calculate_complexity(features)
relevance = self._assess_relevance(features)
novelty = self._evaluate_novelty(features)
return {
"features": features,
"metrics": {
"complexity": complexity,
"relevance": relevance,
"novelty": novelty
},
"learning_focus": self._determine_learning_focus(
complexity, relevance, novelty
)
}
except Exception as e:
logger.error(f"Error analyzing interaction: {e}")
return {}
def _adapt_environment(self,
analysis: Dict[str, Any],
adaptation_score: float):
"""Adapt the learning environment based on analysis"""
try:
# Update learning phase
current_metrics = analysis.get("metrics", {})
if adaptation_score > self.adaptation_threshold:
if self.state["learning_phase"] == "initialization":
self.state["learning_phase"] = "active_learning"
elif current_metrics.get("novelty", 0) > 0.8:
self.state["learning_phase"] = "exploration"
elif adaptation_score < self.adaptation_threshold * 0.5:
self.state["learning_phase"] = "reinforcement"
# Adjust adaptivity level
self.state["adaptivity_level"] = self._calculate_adaptivity_level(
current_metrics
)
# Update current focus
self.state["current_focus"] = analysis.get("learning_focus")
except Exception as e:
logger.error(f"Error adapting environment: {e}")
def _extract_features(self, data: Dict[str, Any]) -> Dict[str, Any]:
"""Extract relevant features from interaction data"""
features = {}
try:
input_data = data.get("input", {})
context = data.get("context", {})
# Extract basic features
features.update({
"input_type": type(input_data).__name__,
"context_size": len(context),
"timestamp": data.get("timestamp")
})
# Add derived features
if isinstance(input_data, dict):
features["input_complexity"] = len(str(input_data))
features["input_depth"] = self._calculate_depth(input_data)
except Exception as e:
logger.error(f"Error extracting features: {e}")
return features
def _calculate_complexity(self, features: Dict[str, Any]) -> float:
"""Calculate complexity score for the interaction"""
try:
complexity_factors = [
features.get("input_complexity", 0) / 1000, # Normalize
features.get("input_depth", 0) / 10, # Normalize
features.get("context_size", 0) / 100 # Normalize
]
if np is not None:
return min(1.0, float(np.mean([f for f in complexity_factors if f is not None])))
# Fallback to pure Python mean
vals = [f for f in complexity_factors if f is not None]
return min(1.0, float(sum(vals) / len(vals))) if vals else 0.0
except Exception as e:
logger.error(f"Error calculating complexity: {e}")
return 0.0
def _assess_relevance(self, features: Dict[str, Any]) -> float:
"""Assess relevance of the interaction to current learning focus"""
try:
if not self.state["current_focus"]:
return 1.0
# Compare with current focus
focus_similarity = self._calculate_similarity(
features,
self.state["current_focus"]
)
return focus_similarity
except Exception as e:
logger.error(f"Error assessing relevance: {e}")
return 0.0
def _evaluate_novelty(self, features: Dict[str, Any]) -> float:
"""Evaluate how novel the interaction is"""
try:
if not self.history:
return 1.0
# Compare with recent history
recent_features = [
h["analysis"].get("features", {})
for h in self.history[-10:] # Last 10 interactions
]
similarities = [
self._calculate_similarity(features, hist_features)
for hist_features in recent_features
if hist_features
]
if not similarities:
return 1.0
if np is not None:
return float(1.0 - np.mean(similarities))
return float(1.0 - (sum(similarities) / len(similarities))) if similarities else 1.0
except Exception as e:
logger.error(f"Error evaluating novelty: {e}")
return 0.0
def _determine_learning_focus(self,
complexity: float,
relevance: float,
novelty: float) -> Dict[str, float]:
"""Determine what the learning should focus on"""
return {
"complexity_focus": complexity > 0.7,
"exploration_focus": novelty > 0.7,
"reinforcement_focus": relevance < 0.3,
"metrics": {
"complexity": complexity,
"relevance": relevance,
"novelty": novelty
}
}
def _calculate_depth(self, d: Dict) -> int:
"""Calculate the depth of a nested dictionary"""
if not isinstance(d, dict) or not d:
return 0
return 1 + max(self._calculate_depth(v) if isinstance(v, dict) else 0
for v in d.values())
def _calculate_similarity(self,
features1: Dict[str, Any],
features2: Dict[str, Any]) -> float:
"""Calculate similarity between feature sets"""
try:
# Extract comparable features
f1 = {k: v for k, v in features1.items() if not isinstance(v, (dict, list))}
f2 = {k: v for k, v in features2.items() if not isinstance(v, (dict, list))}
# Find common keys
common_keys = set(f1.keys()) & set(f2.keys())
if not common_keys:
return 0.0
# Calculate similarity for each common feature
similarities = []
for key in common_keys:
if isinstance(f1[key], (int, float)) and isinstance(f2[key], (int, float)):
# Numeric comparison
max_val = max(abs(f1[key]), abs(f2[key]))
if max_val == 0:
similarities.append(1.0)
else:
similarities.append(1.0 - abs(f1[key] - f2[key]) / max_val)
else:
# String comparison
similarities.append(1.0 if f1[key] == f2[key] else 0.0)
if np is not None:
return float(np.mean(similarities))
return float(sum(similarities) / len(similarities)) if similarities else 0.0
except Exception as e:
logger.error(f"Error calculating similarity: {e}")
return 0.0
def _calculate_learning_efficiency(self) -> float:
"""Calculate current learning efficiency"""
try:
if not self.history:
return 1.0
# Get recent adaptation scores
recent_scores = [
h["state"]["performance_metrics"].get("adaptation_score", 0)
for h in self.history[-5:] # Last 5 interactions
]
if np is not None:
return float(np.mean(recent_scores)) if recent_scores else 1.0
return float(sum(recent_scores) / len(recent_scores)) if recent_scores else 1.0
except Exception as e:
logger.error(f"Error calculating learning efficiency: {e}")
return 0.0
def _assess_stability(self) -> float:
"""Assess the stability of the learning environment"""
try:
if len(self.history) < 2:
return 1.0
# Calculate state changes
recent_states = [
h["state"]["adaptivity_level"]
for h in self.history[-5:] # Last 5 states
]
if len(recent_states) < 2:
return 1.0
if np is not None:
stability = 1.0 - float(np.std(recent_states))
else:
# Simple python std fallback
mean = sum(recent_states) / len(recent_states)
variance = sum((x - mean) ** 2 for x in recent_states) / len(recent_states)
stability = 1.0 - variance ** 0.5
return max(0.0, min(1.0, stability))
except Exception as e:
logger.error(f"Error assessing stability: {e}")
return 1.0
def _calculate_adaptivity_level(self, metrics: Dict[str, float]) -> float:
"""Calculate the current adaptivity level"""
try:
factors = [
metrics.get("complexity", 0),
metrics.get("relevance", 0),
metrics.get("novelty", 0),
self.state.get("adaptivity_level", 1.0)
]
# Weight current adaptivity level more heavily
weights = [0.2, 0.2, 0.2, 0.4]
if np is not None:
return float(np.average(factors, weights=weights))
# Weighted average fallback
total_weight = sum(weights)
return float(sum(f * w for f, w in zip(factors, weights)) / total_weight)
except Exception as e:
logger.error(f"Error calculating adaptivity level: {e}")
return 1.0
def _prepare_response(self, analysis: Dict[str, Any]) -> Dict[str, Any]:
"""Prepare the response with current state and analysis"""
return {
"state": self.state.copy(),
"analysis": analysis,
"metrics": self.state["performance_metrics"],
"recommendations": self._generate_recommendations(analysis)
}
def _generate_recommendations(self,
analysis: Dict[str, Any]) -> List[str]:
"""Generate learning recommendations based on analysis"""
recommendations = []
try:
metrics = analysis.get("metrics", {})
focus = analysis.get("learning_focus", {})
# Add recommendations based on metrics
if metrics.get("complexity", 0) > 0.8:
recommendations.append(
"Consider breaking down complex interactions into simpler components"
)
if metrics.get("novelty", 0) > 0.7:
recommendations.append(
"Explore this new pattern further to enhance learning"
)
if metrics.get("relevance", 0) < 0.3:
recommendations.append(
"Review alignment with current learning objectives"
)
# Add focus-based recommendations
if focus.get("complexity_focus"):
recommendations.append(
"Focus on understanding complex patterns"
)
if focus.get("exploration_focus"):
recommendations.append(
"Prioritize exploration of new concepts"
)
if focus.get("reinforcement_focus"):
recommendations.append(
"Reinforce existing knowledge patterns"
)
except Exception as e:
logger.error(f"Error generating recommendations: {e}")
recommendations.append(
"Unable to generate specific recommendations at this time"
)
return recommendations
def get_state(self) -> Dict[str, Any]:
"""Get current state of the learning environment"""
return self.state.copy()
def get_history(self) -> List[Dict[str, Any]]:
"""Get learning history"""
return self.history.copy() |