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"""
TIPM ML Models Package
======================
Advanced machine learning models for Tariff Impact Propagation Model (TIPM).
This package provides a comprehensive suite of ML models including:
- Multi-class classifiers for tariff impact analysis
- LSTM-based time series forecasters
- Advanced ensemble methods
- SHAP-based explainability
- Policy insight generation
The package is designed for enterprise-grade performance and interpretability.
"""
__version__ = "1.0.0"
__author__ = "TIPM Development Team"
# Import base classes
from .base import (
BaseMLModel,
ModelType,
ModelStatus,
PredictionResult,
TrainingResult,
ModelMetadata,
)
# Import classifiers
from .classifiers import (
TariffImpactClassifier,
EconomicOutcomeClassifier,
PolicyEffectivenessClassifier,
IndustryVulnerabilityClassifier,
)
# Import forecasters
from .forecasters import (
BaseTimeSeriesForecaster,
GDPImpactForecaster,
TradeFlowForecaster,
EmploymentForecaster,
PriceImpactForecaster,
LSTMNetwork,
)
# Import ensemble methods
from .ensemble import TIPMEnsemble, ModelVoting, StackingEnsemble, DynamicEnsemble
# Import explainability components
from .explainability import SHAPExplainer, PolicyInsightGenerator
# Import model manager
from .manager import MLModelManager
# Public API
__all__ = [
# Base classes
"BaseMLModel",
"ModelType",
"ModelStatus",
"PredictionResult",
"TrainingResult",
"ModelMetadata",
# Classifiers
"TariffImpactClassifier",
"EconomicOutcomeClassifier",
"PolicyEffectivenessClassifier",
"IndustryVulnerabilityClassifier",
# Forecasters
"BaseTimeSeriesForecaster",
"GDPImpactForecaster",
"TradeFlowForecaster",
"EmploymentForecaster",
"PriceImpactForecaster",
"LSTMNetwork",
# Ensemble methods
"TIPMEnsemble",
"ModelVoting",
"StackingEnsemble",
"DynamicEnsemble",
# Explainability
"SHAPExplainer",
"PolicyInsightGenerator",
# Model management
"MLModelManager",
]
# Package metadata
PACKAGE_INFO = {
"name": "TIPM ML Models",
"version": __version__,
"description": "Advanced machine learning models for tariff impact analysis",
"author": __author__,
"components": {
"classifiers": 4,
"forecasters": 4,
"ensemble_methods": 4,
"explainability_tools": 2,
"total_models": 14,
},
"features": [
"Multi-class classification for tariff impact severity",
"LSTM-based time series forecasting",
"Advanced ensemble methods (voting, stacking, dynamic)",
"SHAP-based model explainability",
"Policy insight generation",
"Model lifecycle management",
"Performance tracking and optimization",
],
"supported_model_types": [
"Classification",
"Multi-class Classification",
"Binary Classification",
"Time Series Forecasting",
"Ensemble Methods",
"Voting Ensembles",
"Stacking Ensembles",
],
}
def get_package_info():
"""Get comprehensive package information"""
return PACKAGE_INFO.copy()
def get_available_models():
"""Get list of all available model classes"""
return {
"classifiers": [
TariffImpactClassifier,
EconomicOutcomeClassifier,
PolicyEffectivenessClassifier,
IndustryVulnerabilityClassifier,
],
"forecasters": [
GDPImpactForecaster,
TradeFlowForecaster,
EmploymentForecaster,
PriceImpactForecaster,
],
"ensembles": [TIPMEnsemble, ModelVoting, StackingEnsemble, DynamicEnsemble],
}
def create_default_model_manager():
"""Create and configure a default ML model manager"""
manager = MLModelManager()
manager.create_default_models()
return manager
def get_model_summary():
"""Get a summary of all available models"""
models = get_available_models()
summary = {
"total_models": sum(len(model_list) for model_list in models.values()),
"categories": {},
}
for category, model_list in models.items():
summary["categories"][category] = {
"count": len(model_list),
"models": [model.__name__ for model in model_list],
}
return summary
# Version compatibility check
def check_compatibility():
"""Check package compatibility and dependencies"""
import sys
compatibility_info = {
"python_version": sys.version,
"python_version_compatible": sys.version_info >= (3, 8),
"required_packages": ["numpy", "pandas", "scikit-learn", "torch", "shap"],
"optional_packages": ["xgboost", "lightgbm", "optuna"],
}
return compatibility_info
# Quick start function
def quick_start():
"""Quick start guide for using TIPM ML models"""
print("🚀 TIPM ML Models Quick Start")
print("=" * 40)
# Create model manager
manager = create_default_model_manager()
print(f"✅ Created {len(manager.models)} default models")
print(f"📊 Classifiers: {len(manager.classifiers)}")
print(f"📈 Forecasters: {len(manager.forecasters)}")
print(f"🎯 Ensembles: {len(manager.ensembles)}")
print("\n🔧 Next steps:")
print("1. Prepare your training data (X, y)")
print("2. Train models: manager.train_all_models(X, y)")
print("3. Make predictions: manager.predict(model_id, X)")
print("4. Get explanations: manager.explain_prediction(model_id, X)")
print(
"5. Generate insights: manager.generate_policy_insights(model_id, explanation)"
)
return manager
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