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"""
Core TIPM Model Implementation
=============================
Main orchestrator for the Tariff Impact Propagation Model
"""
from typing import Dict, List, Any, Optional
import logging
from dataclasses import dataclass
from dataclasses import dataclass
from typing import Dict, List, Optional, Set
from datetime import datetime
@dataclass
class EnhancedCountryData:
"""Enhanced country data structure for v1.5 with authoritative classifications"""
# Core identification
name: str
iso_alpha_2: str
iso_alpha_3: str
un_code: str
# Trade & Economic Data
tariff_rate: float
bilateral_trade_usd: float
gdp_usd: float
gdp_per_capita: float
# Geographic Classification
continent: str
region: str
income_group: str
# Global Organization Memberships
global_groups: Set[str]
trade_agreements: Set[str]
currency_bloc: Optional[str]
# New Economic Categories (v1.5)
emerging_market_status: bool
tech_manufacturing_rank: Optional[int]
supply_chain_critical: bool
# Resource Export Classifications
mining_resource_exports: List[str]
agricultural_exports: List[str]
strategic_commodities: List[str]
# Data Provenance (v1.5 requirement)
data_sources: Dict[str, str]
last_updated: datetime
confidence_level: str
def validate(self) -> bool:
"""Validate country data fields and bounds."""
if not 0 <= self.tariff_rate <= 100:
raise ValueError(
f"Invalid tariff_rate for {self.name}: {self.tariff_rate}%. Must be 0-100%"
)
if self.gdp_usd < 0:
raise ValueError(f"Negative GDP for {self.name}: {self.gdp_usd}")
if self.gdp_per_capita < 0:
raise ValueError(
f"Negative GDP per capita for {self.name}: {self.gdp_per_capita}"
)
return True
# Placeholder for country database loader and validation framework
# To be implemented in next steps
import pandas as pd
from .layers.policy_trigger import PolicyTriggerLayer
from .layers.trade_flow import TradeFlowLayer
from .layers.industry_response import IndustryResponseLayer
from .layers.firm_impact import FirmImpactLayer
from .layers.consumer_impact import ConsumerImpactLayer
from .layers.geopolitical import GeopoliticalLayer
from .config.settings import (
TIPMConfig,
PolicyLayerConfig,
TradeFlowConfig,
IndustryConfig,
FirmConfig,
ConsumerConfig,
GeopoliticalConfig,
)
@dataclass
@dataclass
class TariffShock:
"""Represents a tariff policy change"""
tariff_id: str
hs_codes: List[str]
rate_change: float
origin_country: str
destination_country: str
effective_date: str
policy_text: str
@dataclass
class TIPMPrediction:
"""Model prediction output structure"""
tariff_shock: TariffShock
trade_flow_impact: Dict[str, Any]
industry_response: Dict[str, Any]
firm_impact: Dict[str, Any]
consumer_impact: Dict[str, Any]
geopolitical_impact: Dict[str, Any]
confidence_scores: Dict[str, float]
class TIPMModel:
"""
Main TIPM Model Class
Orchestrates the 6-layer architecture for tariff impact prediction:
1. Policy Trigger Input (tariff shock processing)
2. Upstream Trade Flow Modeling
3. Industry-Level Economic Response
4. Firm-Level & Employment Impact
5. Consumer Market Impact
6. Geopolitical & Social Feedback
"""
def __init__(self, config: Optional[TIPMConfig] = None):
"""Initialize TIPM model with configuration"""
self.config = config or TIPMConfig()
self.logger = logging.getLogger(__name__)
# Initialize model layers
self._initialize_layers()
# Model state
self.is_trained = False
self.model_metadata = {}
def _initialize_layers(self):
"""Initialize all 6 model layers"""
# Use default configs if none provided
policy_config = self.config.policy_config or PolicyLayerConfig()
trade_flow_config = self.config.trade_flow_config or TradeFlowConfig()
industry_config = self.config.industry_config or IndustryConfig()
firm_config = self.config.firm_config or FirmConfig()
consumer_config = self.config.consumer_config or ConsumerConfig()
geopolitical_config = self.config.geopolitical_config or GeopoliticalConfig()
self.policy_layer = PolicyTriggerLayer(policy_config)
self.trade_flow_layer = TradeFlowLayer(trade_flow_config)
self.industry_layer = IndustryResponseLayer(industry_config)
self.firm_layer = FirmImpactLayer(firm_config)
self.consumer_layer = ConsumerImpactLayer(consumer_config)
self.geopolitical_layer = GeopoliticalLayer(geopolitical_config)
self.logger.info("TIPM model layers initialized successfully")
def fit(self, training_data: Dict[str, pd.DataFrame]) -> "TIPMModel":
"""
Train the TIPM model on historical data
Args:
training_data: Dictionary containing training datasets for each layer
- 'tariff_shocks': Historical tariff policy changes
- 'trade_flows': Historical trade flow data
- 'industry_responses': Industry-level response data
- 'firm_responses': Firm-level response data
- 'consumer_impacts': Consumer market impact data
- 'geopolitical_events': Social/political response data
"""
self.logger.info("Starting TIPM model training...")
# Train each layer sequentially
self.policy_layer.fit(training_data.get("tariff_shocks"))
self.trade_flow_layer.fit(training_data.get("trade_flows"))
self.industry_layer.fit(training_data.get("industry_responses"))
self.firm_layer.fit(training_data.get("firm_responses"))
self.consumer_layer.fit(training_data.get("consumer_impacts"))
self.geopolitical_layer.fit(training_data.get("geopolitical_events"))
self.is_trained = True
self.logger.info("TIPM model training completed")
return self
def predict(self, tariff_shock: TariffShock) -> TIPMPrediction:
"""
Predict tariff impact propagation through all layers
Args:
tariff_shock: Tariff policy change to analyze
Returns:
TIPMPrediction: Comprehensive impact prediction
"""
if not self.is_trained:
raise ValueError("Model must be trained before making predictions")
self.logger.info(
f"Predicting impact for tariff shock: {tariff_shock.tariff_id}"
)
# Layer 1: Process policy trigger
policy_features = self.policy_layer.transform(tariff_shock)
# Layer 2: Trade flow impact
trade_impact = self.trade_flow_layer.predict(policy_features)
# Layer 3: Industry response
industry_impact = self.industry_layer.predict(trade_impact)
# Layer 4: Firm-level impact
firm_impact = self.firm_layer.predict(industry_impact)
# Layer 5: Consumer impact
consumer_impact = self.consumer_layer.predict(firm_impact)
# Layer 6: Geopolitical feedback
geopolitical_impact = self.geopolitical_layer.predict(consumer_impact)
# Calculate confidence scores
confidence_scores = self._calculate_confidence_scores(
policy_features,
trade_impact,
industry_impact,
firm_impact,
consumer_impact,
geopolitical_impact,
)
return TIPMPrediction(
tariff_shock=tariff_shock,
trade_flow_impact=trade_impact,
industry_response=industry_impact,
firm_impact=firm_impact,
consumer_impact=consumer_impact,
geopolitical_impact=geopolitical_impact,
confidence_scores=confidence_scores,
)
def _calculate_confidence_scores(self, *layer_outputs) -> Dict[str, float]:
"""Calculate prediction confidence scores for each layer"""
# Implementation placeholder - would calculate based on model uncertainties
return {
"policy_confidence": 0.85,
"trade_flow_confidence": 0.78,
"industry_confidence": 0.82,
"firm_confidence": 0.73,
"consumer_confidence": 0.79,
"geopolitical_confidence": 0.65,
"overall_confidence": 0.77,
}
def simulate_scenario(
self, tariff_shocks: List[TariffShock], time_horizon: int = 12
) -> Dict[str, Any]:
"""
Run scenario simulation with multiple tariff shocks over time
Args:
tariff_shocks: List of tariff policy changes
time_horizon: Simulation time horizon in months
Returns:
Dictionary containing simulation results
"""
self.logger.info(
f"Running scenario simulation with {len(tariff_shocks)} shocks"
)
scenario_results = {
"timeline": [],
"cumulative_impacts": {},
"country_impacts": {},
"sector_impacts": {},
}
for shock in tariff_shocks:
prediction = self.predict(shock)
scenario_results["timeline"].append(
{"shock": shock, "prediction": prediction}
)
return scenario_results
def get_country_exposure(self, country_code: str) -> Dict[str, Any]:
"""
Get tariff exposure profile for a specific country
Args:
country_code: ISO country code (e.g., 'SG', 'US', 'CN')
Returns:
Dictionary containing country-specific exposure metrics
"""
# Implementation would analyze country's trade dependencies
# and vulnerability to different types of tariff shocks
return {
"import_dependency": {},
"export_exposure": {},
"supply_chain_vulnerability": {},
"economic_resilience": {},
}
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