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"""
Advanced Learning System for FitScore Feedback Agent
"""
import uuid
from datetime import datetime
from typing import Dict, Any, Optional
from sqlalchemy.orm import Session
from .database import get_db, LearningEvent, ContrastivePair, FewShotExample, CurriculumStage, BayesianUpdate, ActiveLearningRequest
class AdvancedLearningSystem:
"""Advanced learning system with multiple AI approaches"""
def __init__(self):
self.learning_methods = [
"rlhf", "contrastive", "few_shot", "curriculum",
"online", "bayesian", "active"
]
def process_learning_event(self, event_data: Dict[str, Any]) -> Dict[str, Any]:
"""Process a learning event using multiple AI approaches"""
try:
db = next(get_db())
event_id = str(uuid.uuid4())
# Create learning event
learning_event = LearningEvent(
event_id=event_id,
event_type=event_data["event_type"],
candidate_id=event_data["candidate_id"],
job_id=event_data["job_id"],
submission_id=event_data.get("submission_id"),
input_data=event_data["input_data"],
outcome_data=event_data["outcome_data"],
confidence_score=event_data.get("confidence_score", 0.0),
model_version=event_data.get("model_version", "v1.0"),
learning_signal=0.0, # Will be calculated based on event type
processed=False
)
# Process based on event type
if event_data["event_type"] == "rlhf":
learning_event.learning_signal = self._process_rlhf_event(event_data)
elif event_data["event_type"] == "contrastive":
learning_event.learning_signal = self._process_contrastive_event(event_data)
elif event_data["event_type"] == "few_shot":
learning_event.learning_signal = self._process_few_shot_event(event_data)
elif event_data["event_type"] == "curriculum":
learning_event.learning_signal = self._process_curriculum_event(event_data)
elif event_data["event_type"] == "online":
learning_event.learning_signal = self._process_online_event(event_data)
elif event_data["event_type"] == "bayesian":
learning_event.learning_signal = self._process_bayesian_event(event_data)
elif event_data["event_type"] == "active":
learning_event.learning_signal = self._process_active_event(event_data)
learning_event.processed = True
db.add(learning_event)
db.commit()
return {
"success": True,
"event_id": event_id,
"learning_signal": learning_event.learning_signal,
"message": f"Learning event processed using {event_data['event_type']} method"
}
except Exception as e:
print(f"❌ Error processing learning event: {e}")
raise
def get_learning_analytics(self) -> Dict[str, Any]:
"""Get comprehensive learning analytics"""
try:
db = next(get_db())
total_events = db.query(LearningEvent).count()
processed_events = db.query(LearningEvent).filter(LearningEvent.processed == True).count()
# Events by type
events_by_type = {}
for event_type in self.learning_methods:
count = db.query(LearningEvent).filter(LearningEvent.event_type == event_type).count()
events_by_type[event_type] = count
# Average learning signals
avg_learning_signal = db.query(LearningEvent.learning_signal).filter(
LearningEvent.processed == True
).all()
avg_signal = sum(signal[0] for signal in avg_learning_signal) / len(avg_learning_signal) if avg_learning_signal else 0
return {
"total_events": total_events,
"processed_events": processed_events,
"processing_rate": (processed_events / total_events * 100) if total_events > 0 else 0,
"events_by_type": events_by_type,
"average_learning_signal": avg_signal,
"learning_methods": self.learning_methods
}
except Exception as e:
print(f"❌ Error getting learning analytics: {e}")
return {
"total_events": 0,
"processed_events": 0,
"processing_rate": 0,
"events_by_type": {},
"average_learning_signal": 0,
"learning_methods": self.learning_methods
}
def _process_rlhf_event(self, event_data: Dict[str, Any]) -> float:
"""Process RLHF (Reinforcement Learning from Human Feedback) event"""
# Simplified RLHF processing
outcome_data = event_data.get("outcome_data", {})
# Calculate reward based on outcome
if outcome_data.get("outcome") == "hired":
return 10.0
elif outcome_data.get("outcome") == "accepted":
return 5.0
elif outcome_data.get("outcome") == "interviewed":
return 1.0
elif outcome_data.get("outcome") == "rejected":
return -1.0
return 0.0
def _process_contrastive_event(self, event_data: Dict[str, Any]) -> float:
"""Process contrastive learning event"""
# Simplified contrastive learning
input_data = event_data.get("input_data", {})
# Calculate separation score between positive and negative examples
positive_score = input_data.get("positive_score", 0.0)
negative_score = input_data.get("negative_score", 0.0)
separation_score = positive_score - negative_score
return max(0.0, separation_score)
def _process_few_shot_event(self, event_data: Dict[str, Any]) -> float:
"""Process few-shot learning event"""
# Simplified few-shot learning
confidence_score = event_data.get("confidence_score", 0.0)
# Higher confidence in few-shot examples indicates better learning
return confidence_score * 2.0
def _process_curriculum_event(self, event_data: Dict[str, Any]) -> float:
"""Process curriculum learning event"""
# Simplified curriculum learning
input_data = event_data.get("input_data", {})
# Calculate progress through curriculum stages
current_stage = input_data.get("current_stage", 1)
total_stages = input_data.get("total_stages", 5)
progress = current_stage / total_stages
return progress * 5.0
def _process_online_event(self, event_data: Dict[str, Any]) -> float:
"""Process online learning event"""
# Simplified online learning
confidence_score = event_data.get("confidence_score", 0.0)
# Online learning benefits from continuous updates
return confidence_score * 1.5
def _process_bayesian_event(self, event_data: Dict[str, Any]) -> float:
"""Process Bayesian learning event"""
# Simplified Bayesian learning
input_data = event_data.get("input_data", {})
# Calculate uncertainty reduction
prior_uncertainty = input_data.get("prior_uncertainty", 1.0)
posterior_uncertainty = input_data.get("posterior_uncertainty", 0.5)
uncertainty_reduction = prior_uncertainty - posterior_uncertainty
return max(0.0, uncertainty_reduction * 3.0)
def _process_active_event(self, event_data: Dict[str, Any]) -> float:
"""Process active learning event"""
# Simplified active learning
input_data = event_data.get("input_data", {})
# Calculate information gain
uncertainty_score = input_data.get("uncertainty_score", 0.0)
# Higher uncertainty means more potential for learning
return uncertainty_score * 2.0 |