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life_expectancy.py

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+"""
+Life Expectancy Energy-Based Model
+==================================
+
+THRML-based probabilistic model for life expectancy prediction with 
+uncertainty quantification and demographic factor interactions.
+"""
+
+import jax
+import jax.numpy as jnp
+from typing import List, Dict, Tuple, Optional
+import numpy as np
+from dataclasses import dataclass
+
+from thrml.pgm import CategoricalNode
+from thrml.block_management import Block
+from thrml.block_sampling import BlockGibbsSpec, sample_states
+from thrml.factor import FactorSamplingProgram
+from thrml.conditional_samplers import AbstractConditionalSampler
+
+from thermal.graph.mortality_graph import MortalityGraphBuilder, MortalityRecord
+
+
+@dataclass
+class LifeExpectancyPrediction:
+    """Result of life expectancy prediction with uncertainty."""
+    mean_life_expectancy: float
+    confidence_interval: Tuple[float, float]
+    uncertainty: float
+    risk_factors: Dict[str, float]
+    samples: Optional[jnp.ndarray] = None
+
+
+class LifeExpectancySampler:
+    """Custom sampler for life expectancy nodes in the EBM."""
+    
+    def __init__(self, mortality_data: List[MortalityRecord]):
+        """
+        Initialize with mortality data for informed sampling.
+        
+        Args:
+            mortality_data: List of MortalityRecord objects
+        """
+        self.mortality_data = mortality_data
+        self._build_empirical_distributions()
+    
+    def _build_empirical_distributions(self):
+        """Build empirical distributions from mortality data."""
+        # Group data by demographics for empirical priors
+        self.life_exp_by_demographics = {}
+        
+        for record in self.mortality_data:
+            key = (record.country, record.age, record.sex)
+            if key not in self.life_exp_by_demographics:
+                self.life_exp_by_demographics[key] = []
+            self.life_exp_by_demographics[key].append(record.lifeExpectancy)
+        
+        # Convert to arrays and compute statistics
+        for key in self.life_exp_by_demographics:
+            values = self.life_exp_by_demographics[key]
+            self.life_exp_by_demographics[key] = {
+                'mean': np.mean(values),
+                'std': np.std(values),
+                'values': np.array(values)
+            }
+    
+    def sample(self, key, interactions, active_flags, states, sampler_state, output_sd):
+        """
+        Sample life expectancy values based on interactions and empirical data.
+        
+        Args:
+            key: JAX random key
+            interactions: Factor interactions affecting this node
+            active_flags: Which interactions are active
+            states: Current states of other nodes
+            sampler_state: Current sampler state
+            output_sd: Output shape description
+            
+        Returns:
+            Tuple of (new_samples, updated_sampler_state)
+        """
+        # Start with empirical prior
+        batch_size = output_sd.shape[0] if len(output_sd.shape) > 0 else 1
+        
+        # Default to global average if no specific data
+        global_mean = 75.0  # Reasonable global life expectancy
+        global_std = 10.0
+        
+        # Compute bias from interactions
+        bias = jnp.zeros(batch_size)
+        variance = jnp.full(batch_size, global_std**2)
+        
+        # Process interactions to adjust bias and variance
+        for interaction in interactions:
+            if active_flags[id(interaction)]:
+                # Extract demographic information from interaction
+                interaction_bias, interaction_var = self._process_interaction(
+                    interaction, states
+                )
+                bias += interaction_bias
+                variance += interaction_var
+        
+        # Ensure positive variance
+        variance = jnp.maximum(variance, 0.1)
+        std = jnp.sqrt(variance)
+        
+        # Sample from adjusted normal distribution
+        samples = (global_mean + bias + 
+                  std * jax.random.normal(key, (batch_size,)))
+        
+        # Clip to reasonable life expectancy range
+        samples = jnp.clip(samples, 0.0, 120.0)
+        
+        return samples, sampler_state
+    
+    def _process_interaction(self, interaction, states) -> Tuple[jnp.ndarray, jnp.ndarray]:
+        """Process interaction to compute bias and variance adjustments."""
+        # This is a simplified interaction processing
+        # In practice, would extract demographic info and look up empirical data
+        
+        # Default small adjustments
+        bias_adjustment = jax.random.normal(jax.random.PRNGKey(0), ()) * 2.0
+        var_adjustment = jax.random.exponential(jax.random.PRNGKey(1), ()) * 1.0
+        
+        return jnp.array([bias_adjustment]), jnp.array([var_adjustment])
+
+
+class LifeExpectancyEBM:
+    """
+    Energy-Based Model for life expectancy prediction using THRML.
+    
+    This model captures complex interactions between demographic factors
+    (age, country, sex, year) and provides probabilistic predictions with
+    uncertainty quantification.
+    """
+    
+    def __init__(self, mortality_data: List[MortalityRecord]):
+        """
+        Initialize the Life Expectancy EBM.
+        
+        Args:
+            mortality_data: List of MortalityRecord objects for training
+        """
+        self.mortality_data = mortality_data
+        self.graph_builder = MortalityGraphBuilder(mortality_data)
+        
+        # Build the probabilistic graph
+        self.graph = self.graph_builder.build_mortality_graph()
+        self.blocks = self.graph_builder.create_sampling_blocks("demographic")
+        self.factors = self.graph_builder.create_interaction_factors()
+        
+        # Create custom sampler
+        self.life_exp_sampler = LifeExpectancySampler(mortality_data)
+        
+        # Initialize sampling program
+        self._initialize_sampling_program()
+    
+    def _initialize_sampling_program(self):
+        """Initialize the THRML sampling program."""
+        # Create Gibbs specification with empty clamped blocks
+        self.gibbs_spec = BlockGibbsSpec(self.blocks, [])
+        
+        # For now, skip the complex sampling program setup
+        # In a full implementation, would create proper THRML factor objects
+        self.sampling_program = None
+        
+        # Default sampling schedule
+        self.default_schedule = {
+            'n_steps': 1000,
+            'burn_in': 200,
+            'thin': 2
+        }
+    
+    def predict_life_expectancy(self, 
+                              age: int,
+                              country: str, 
+                              sex: int,
+                              year: Optional[int] = None,
+                              n_samples: int = 1000,
+                              confidence_level: float = 0.95) -> LifeExpectancyPrediction:
+        """
+        Predict life expectancy with uncertainty quantification.
+        
+        Args:
+            age: Age of individual
+            country: Country name
+            sex: Sex (1=male, 2=female, 3=both)
+            year: Year for prediction (optional)
+            n_samples: Number of MCMC samples
+            confidence_level: Confidence level for intervals (default 0.95)
+            
+        Returns:
+            LifeExpectancyPrediction with mean, confidence interval, and uncertainty
+        """
+        # Get relevant nodes for this prediction
+        prediction_nodes = self.graph_builder.get_mortality_prediction_nodes(
+            age, country, sex
+        )
+        
+        if prediction_nodes['age_node'] is None:
+            raise ValueError(f"Age {age} not found in training data")
+        if prediction_nodes['country_node'] is None:
+            raise ValueError(f"Country {country} not found in training data")
+        if prediction_nodes['sex_node'] is None:
+            raise ValueError(f"Sex {sex} not found in training data")
+        
+        # Set evidence (observed demographic factors)
+        evidence = {
+            'age': age,
+            'country': country, 
+            'sex': sex
+        }
+        if year is not None:
+            evidence['year'] = year
+        
+        # Initialize states for sampling
+        initial_states = self._initialize_states_for_prediction(evidence)
+        
+        # Run MCMC sampling
+        samples = self._run_sampling(
+            initial_states, 
+            n_samples=n_samples,
+            evidence=evidence
+        )
+        
+        # Extract life expectancy samples
+        life_exp_samples = self._extract_life_expectancy_samples(samples)
+        
+        # Compute statistics
+        mean_life_exp = float(jnp.mean(life_exp_samples))
+        
+        # Confidence interval
+        alpha = 1 - confidence_level
+        lower_percentile = (alpha / 2) * 100
+        upper_percentile = (1 - alpha / 2) * 100
+        
+        ci_lower = float(jnp.percentile(life_exp_samples, lower_percentile))
+        ci_upper = float(jnp.percentile(life_exp_samples, upper_percentile))
+        
+        # Uncertainty (standard deviation)
+        uncertainty = float(jnp.std(life_exp_samples))
+        
+        # Risk factor analysis
+        risk_factors = self._analyze_risk_factors(evidence, samples)
+        
+        return LifeExpectancyPrediction(
+            mean_life_expectancy=mean_life_exp,
+            confidence_interval=(ci_lower, ci_upper),
+            uncertainty=uncertainty,
+            risk_factors=risk_factors,
+            samples=life_exp_samples
+        )
+    
+    def _initialize_states_for_prediction(self, evidence: Dict) -> Dict:
+        """Initialize states for MCMC sampling given evidence."""
+        # This is a simplified initialization
+        # In practice, would set observed nodes to evidence values
+        # and initialize unobserved nodes from priors
+        
+        initial_states = {}
+        
+        # Set demographic factors from evidence
+        if 'age' in evidence:
+            initial_states['age'] = evidence['age']
+        if 'country' in evidence:
+            initial_states['country'] = evidence['country']
+        if 'sex' in evidence:
+            initial_states['sex'] = evidence['sex']
+        if 'year' in evidence:
+            initial_states['year'] = evidence['year']
+        
+        # Initialize life expectancy bins with uniform distribution
+        n_life_exp_bins = len(self.graph_builder.life_expectancy_nodes)
+        initial_states['life_expectancy_bin'] = jax.random.choice(
+            jax.random.PRNGKey(42), n_life_exp_bins
+        )
+        
+        return initial_states
+    
+    def _run_sampling(self, 
+                     initial_states: Dict, 
+                     n_samples: int,
+                     evidence: Dict) -> jnp.ndarray:
+        """Run MCMC sampling to generate posterior samples."""
+        # Create JAX random keys
+        key = jax.random.PRNGKey(42)
+        keys = jax.random.split(key, n_samples)
+        
+        # Initialize memory for sampling program
+        init_memory = {}  # Simplified - would contain program state
+        
+        # Mock sampling - in practice would call THRML's sample_states
+        # This is a placeholder implementation
+        
+        # Generate samples using simplified normal distribution
+        # based on empirical data for the given demographics
+        samples = []
+        
+        # Look up empirical data for these demographics
+        demographic_key = (
+            evidence.get('country', 'USA'),
+            evidence.get('age', 50), 
+            evidence.get('sex', 3)
+        )
+        
+        # Use empirical distribution if available
+        if hasattr(self.life_exp_sampler, 'life_exp_by_demographics'):
+            if demographic_key in self.life_exp_sampler.life_exp_by_demographics:
+                data = self.life_exp_sampler.life_exp_by_demographics[demographic_key]
+                mean_le = data['mean']
+                std_le = data['std']
+            else:
+                # Use nearby demographics or global average
+                mean_le = 75.0
+                std_le = 10.0
+        else:
+            mean_le = 75.0
+            std_le = 10.0
+        
+        # Generate samples with some noise for uncertainty
+        for i in range(n_samples):
+            sample = jax.random.normal(keys[i]) * std_le + mean_le
+            # Add interaction effects
+            if evidence.get('sex') == 1:  # Male
+                sample -= 2.0  # Males typically have lower life expectancy
+            elif evidence.get('sex') == 2:  # Female
+                sample += 2.0  # Females typically have higher life expectancy
+                
+            # Age effects
+            age = evidence.get('age', 50)
+            if age > 80:
+                sample -= (age - 80) * 0.5  # Older starting age
+            
+            samples.append(sample)
+        
+        return jnp.array(samples)
+    
+    def _extract_life_expectancy_samples(self, samples: jnp.ndarray) -> jnp.ndarray:
+        """Extract life expectancy values from raw samples."""
+        # In this simplified implementation, samples are already life expectancy values
+        return jnp.clip(samples, 0.0, 120.0)
+    
+    def _analyze_risk_factors(self, 
+                            evidence: Dict, 
+                            samples: jnp.ndarray) -> Dict[str, float]:
+        """Analyze contribution of different risk factors."""
+        risk_factors = {}
+        
+        # Age risk
+        age = evidence.get('age', 50)
+        if age < 30:
+            risk_factors['age_risk'] = 0.1  # Low risk
+        elif age < 60:
+            risk_factors['age_risk'] = 0.3  # Medium risk
+        else:
+            risk_factors['age_risk'] = 0.6  # Higher risk
+        
+        # Sex risk
+        sex = evidence.get('sex', 3)
+        if sex == 1:  # Male
+            risk_factors['sex_risk'] = 0.4
+        elif sex == 2:  # Female
+            risk_factors['sex_risk'] = 0.2
+        else:
+            risk_factors['sex_risk'] = 0.3
+        
+        # Country risk (simplified)
+        country = evidence.get('country', 'USA')
+        country_risk_map = {
+            'USA': 0.3, 'JPN': 0.1, 'DEU': 0.2, 'GBR': 0.3,
+            'FRA': 0.2, 'ITA': 0.2, 'ESP': 0.2, 'CAN': 0.2,
+            'AUS': 0.2, 'CHN': 0.4
+        }
+        risk_factors['country_risk'] = country_risk_map.get(country, 0.3)
+        
+        # Uncertainty risk (based on sample variance)
+        risk_factors['uncertainty_risk'] = min(float(jnp.std(samples)) / 20.0, 1.0)
+        
+        return risk_factors
+    
+    def batch_predict(self, 
+                     demographics: List[Dict],
+                     n_samples: int = 1000) -> List[LifeExpectancyPrediction]:
+        """
+        Batch prediction for multiple demographic profiles.
+        
+        Args:
+            demographics: List of dicts with age, country, sex keys
+            n_samples: Number of samples per prediction
+            
+        Returns:
+            List of LifeExpectancyPrediction objects
+        """
+        predictions = []
+        
+        for demo in demographics:
+            try:
+                prediction = self.predict_life_expectancy(
+                    age=demo['age'],
+                    country=demo['country'],
+                    sex=demo['sex'],
+                    year=demo.get('year'),
+                    n_samples=n_samples
+                )
+                predictions.append(prediction)
+            except Exception as e:
+                # Return default prediction for invalid demographics
+                predictions.append(LifeExpectancyPrediction(
+                    mean_life_expectancy=75.0,
+                    confidence_interval=(65.0, 85.0),
+                    uncertainty=10.0,
+                    risk_factors={'error': 1.0}
+                ))
+        
+        return predictions
+    
+    def get_model_info(self) -> Dict:
+        """Get information about the trained model."""
+        return {
+            'n_mortality_records': len(self.mortality_data),
+            'countries': self.graph_builder.countries,
+            'age_range': (min(self.graph_builder.ages), max(self.graph_builder.ages)),
+            'year_range': (min(self.graph_builder.years), max(self.graph_builder.years)),
+            'n_nodes': len(self.graph.nodes),
+            'n_edges': len(self.graph.edges),
+            'n_factors': len(self.factors),
+            'version': '0.1.1'
+        }