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from typing import List, Dict, Any, Optional
import numpy as np
from tinytroupe.agent import TinyPerson
from tinytroupe.agent.social_types import Content, Reaction
from tinytroupe.social_network import NetworkTopology
from tinytroupe.features import ContentFeatureExtractor, PersonaFeatureExtractor, InteractionFeatureExtractor
from tinytroupe.agent.agent_traits import TraitBasedBehaviorModel
class EngagementPredictor:
"""Predicts whether persona will engage with content"""
def __init__(self):
self.content_extractor = ContentFeatureExtractor()
self.persona_extractor = PersonaFeatureExtractor()
self.interaction_extractor = InteractionFeatureExtractor()
self.trait_model = TraitBasedBehaviorModel()
def predict(self, persona: TinyPerson, content: Content, network: NetworkTopology) -> float:
"""Predict engagement probability"""
content_features = self.content_extractor.extract(content)
persona_features = self.persona_extractor.extract(persona)
interaction_features = self.interaction_extractor.extract(persona, content, network)
# Get base probability from trait-based model
trait_prob = self.trait_model.compute_action_probability(persona, "engage", content)
# Combine with other signals
prob = (trait_prob * 0.5 +
interaction_features["topic_alignment"] * 0.3 +
persona_features["engagement_rate"] * 0.1 +
content_features["sentiment_score"] * 0.1)
return max(0.0, min(1.0, prob))
class ViralityPredictor:
def predict_cascade_size(self, content: Content, seed_personas: List[str], network: NetworkTopology) -> int:
return len(seed_personas) * 2 # Placeholder
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