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"""
Advanced Multi-Class Classifiers for TIPM
========================================
Specialized classifiers for tariff impact prediction, economic outcomes,
policy effectiveness, and industry vulnerability assessment.
"""
import logging
from typing import Dict, List, Optional, Any, Union, Tuple
from datetime import datetime
import numpy as np
import pandas as pd
# ML libraries
try:
import xgboost as xgb
XGBOOST_AVAILABLE = True
except ImportError:
XGBOOST_AVAILABLE = False
xgb = None
try:
import lightgbm as lgb
LIGHTGBM_AVAILABLE = True
except ImportError:
LIGHTGBM_AVAILABLE = False
lgb = None
try:
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import cross_val_score, StratifiedKFold
SKLEARN_AVAILABLE = True
except ImportError:
SKLEARN_AVAILABLE = False
RandomForestClassifier = None
GradientBoostingClassifier = None
LogisticRegression = None
StandardScaler = None
LabelEncoder = None
cross_val_score = None
StratifiedKFold = None
# Base classes
from .base import BaseMLModel, ModelType, PredictionResult
logger = logging.getLogger(__name__)
class TariffImpactClassifier(BaseMLModel):
"""
Multi-class classifier for predicting tariff impact severity
Predicts: High/Medium/Low impact based on economic indicators,
trade patterns, and policy characteristics.
"""
def __init__(self, model_id: str = "tariff_impact_classifier"):
super().__init__(
model_id=model_id,
name="Tariff Impact Severity Classifier",
description="Multi-class classifier for predicting tariff impact severity (High/Medium/Low)",
model_type=ModelType.MULTI_CLASS,
)
# Model configuration
self.class_labels = ["Low", "Medium", "High"]
self.feature_scaler = None
self.label_encoder = None
# Hyperparameters
self.hyperparameters = {
"n_estimators": 200,
"max_depth": 6,
"learning_rate": 0.1,
"random_state": 42,
}
logger.info(f"Initialized TariffImpactClassifier: {model_id}")
def _create_model(self):
"""Create the underlying ML model"""
if XGBOOST_AVAILABLE:
# XGBoost for best performance
model = xgb.XGBClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
learning_rate=self.hyperparameters["learning_rate"],
random_state=self.hyperparameters["random_state"],
objective="multi:softprob",
eval_metric="mlogloss",
)
elif LIGHTGBM_AVAILABLE:
# LightGBM as alternative
model = lgb.LGBMClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
learning_rate=self.hyperparameters["learning_rate"],
random_state=self.hyperparameters["random_state"],
objective="multiclass",
metric="multi_logloss",
)
elif SKLEARN_AVAILABLE:
# Gradient Boosting as fallback
model = GradientBoostingClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
learning_rate=self.hyperparameters["learning_rate"],
random_state=self.hyperparameters["random_state"],
)
else:
raise RuntimeError(
"No suitable ML library available. Install xgboost, lightgbm, or scikit-learn."
)
return model
def _prepare_features(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
"""Prepare features for classification"""
if isinstance(X, pd.DataFrame):
# Handle categorical features
X_processed = X.copy()
# Convert categorical columns to numerical
for col in X_processed.select_dtypes(include=["object", "category"]):
X_processed[col] = X_processed[col].astype("category").cat.codes
# Fill missing values
X_processed = X_processed.fillna(X_processed.mean())
# Scale features
if self.feature_scaler is None:
self.feature_scaler = StandardScaler()
X_scaled = self.feature_scaler.fit_transform(X_processed)
else:
X_scaled = self.feature_scaler.transform(X_processed)
return X_scaled
else:
# Assume numpy array
return X
def _prepare_targets(self, y: Union[pd.Series, np.ndarray]) -> np.ndarray:
"""Prepare target variables for classification"""
if self.label_encoder is None:
self.label_encoder = LabelEncoder()
y_encoded = self.label_encoder.fit_transform(y)
else:
y_encoded = self.label_encoder.transform(y)
return y_encoded
def predict_with_confidence(
self, X: Union[pd.DataFrame, np.ndarray]
) -> Tuple[np.ndarray, np.ndarray]:
"""Make predictions with confidence scores"""
if not self._is_trained:
raise RuntimeError("Model must be trained before making predictions")
X_prepared = self._prepare_features(X)
# Get predictions and probabilities
predictions = self._model.predict(X_prepared)
probabilities = self._model.predict_proba(X_prepared)
# Convert back to original labels
if self.label_encoder is not None:
predictions = self.label_encoder.inverse_transform(predictions)
return predictions, probabilities
def get_impact_analysis(self, X: Union[pd.DataFrame, np.ndarray]) -> Dict[str, Any]:
"""Get detailed impact analysis with confidence intervals"""
predictions, probabilities = self.predict_with_confidence(X)
analysis = {
"predictions": (
predictions.tolist()
if isinstance(predictions, np.ndarray)
else predictions
),
"probabilities": (
probabilities.tolist()
if isinstance(probabilities, np.ndarray)
else probabilities
),
"confidence_scores": np.max(probabilities, axis=1).tolist(),
"risk_assessment": [],
}
# Risk assessment for each prediction
for i, (pred, prob) in enumerate(zip(predictions, probabilities)):
confidence = np.max(prob)
risk_level = (
"High" if confidence < 0.6 else "Medium" if confidence < 0.8 else "Low"
)
analysis["risk_assessment"].append(
{
"prediction": pred,
"confidence": confidence,
"risk_level": risk_level,
"class_probabilities": {
label: prob[j] for j, label in enumerate(self.class_labels)
},
}
)
return analysis
class EconomicOutcomeClassifier(BaseMLModel):
"""
Multi-class classifier for predicting economic outcomes
Predicts: Recession/Growth/Stagnation based on economic indicators,
policy changes, and market conditions.
"""
def __init__(self, model_id: str = "economic_outcome_classifier"):
super().__init__(
model_id=model_id,
name="Economic Outcome Classifier",
description="Multi-class classifier for predicting economic outcomes (Recession/Growth/Stagnation)",
model_type=ModelType.MULTI_CLASS,
)
# Model configuration
self.class_labels = ["Recession", "Stagnation", "Growth"]
self.feature_scaler = None
self.label_encoder = None
# Hyperparameters
self.hyperparameters = {
"n_estimators": 300,
"max_depth": 8,
"learning_rate": 0.05,
"random_state": 42,
}
logger.info(f"Initialized EconomicOutcomeClassifier: {model_id}")
def _create_model(self):
"""Create the underlying ML model"""
if XGBOOST_AVAILABLE:
model = xgb.XGBClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
learning_rate=self.hyperparameters["learning_rate"],
random_state=self.hyperparameters["random_state"],
objective="multi:softprob",
eval_metric="mlogloss",
)
elif LIGHTGBM_AVAILABLE:
model = lgb.LGBMClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
learning_rate=self.hyperparameters["learning_rate"],
random_state=self.hyperparameters["random_state"],
objective="multiclass",
metric="multi_logloss",
)
elif SKLEARN_AVAILABLE:
model = RandomForestClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
random_state=self.hyperparameters["random_state"],
)
else:
raise RuntimeError("No suitable ML library available.")
return model
def _prepare_features(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
"""Prepare features for classification"""
if isinstance(X, pd.DataFrame):
X_processed = X.copy()
# Handle categorical features
for col in X_processed.select_dtypes(include=["object", "category"]):
X_processed[col] = X_processed[col].astype("category").cat.codes
# Fill missing values
X_processed = X_processed.fillna(X_processed.mean())
# Scale features
if self.feature_scaler is None:
self.feature_scaler = StandardScaler()
X_scaled = self.feature_scaler.fit_transform(X_processed)
else:
X_scaled = self.feature_scaler.transform(X_processed)
return X_scaled
else:
return X
def _prepare_targets(self, y: Union[pd.Series, np.ndarray]) -> np.ndarray:
"""Prepare target variables for classification"""
if self.label_encoder is None:
self.label_encoder = LabelEncoder()
y_encoded = self.label_encoder.fit_transform(y)
else:
y_encoded = self.label_encoder.transform(y)
return y_encoded
def get_economic_forecast(
self, X: Union[pd.DataFrame, np.ndarray]
) -> Dict[str, Any]:
"""Get economic forecast with detailed analysis"""
predictions, probabilities = self.predict_with_confidence(X)
forecast = {
"predictions": (
predictions.tolist()
if isinstance(predictions, np.ndarray)
else predictions
),
"probabilities": (
probabilities.tolist()
if isinstance(probabilities, np.ndarray)
else probabilities
),
"economic_indicators": [],
"policy_recommendations": [],
}
# Analyze each prediction
for i, (pred, prob) in enumerate(zip(predictions, probabilities)):
confidence = np.max(prob)
# Economic indicators
indicators = {
"gdp_growth": (
"Negative"
if pred == "Recession"
else "Stable" if pred == "Stagnation" else "Positive"
),
"inflation": (
"High"
if pred == "Recession"
else "Moderate" if pred == "Stagnation" else "Low"
),
"unemployment": (
"High"
if pred == "Recession"
else "Moderate" if pred == "Stagnation" else "Low"
),
"trade_balance": (
"Deficit"
if pred == "Recession"
else "Balanced" if pred == "Stagnation" else "Surplus"
),
}
# Policy recommendations
if pred == "Recession":
recommendations = [
"Implement expansionary fiscal policy",
"Lower interest rates",
"Increase government spending",
"Provide economic stimulus packages",
]
elif pred == "Stagnation":
recommendations = [
"Structural reforms",
"Investment incentives",
"Trade policy optimization",
"Infrastructure development",
]
else: # Growth
recommendations = [
"Maintain current policies",
"Monitor inflation",
"Prepare for overheating",
"Sustainable growth measures",
]
forecast["economic_indicators"].append(indicators)
forecast["policy_recommendations"].append(recommendations)
return forecast
class PolicyEffectivenessClassifier(BaseMLModel):
"""
Multi-class classifier for predicting policy effectiveness
Predicts: Effective/Partially Effective/Ineffective based on
policy characteristics, implementation context, and historical data.
"""
def __init__(self, model_id: str = "policy_effectiveness_classifier"):
super().__init__(
model_id=model_id,
name="Policy Effectiveness Classifier",
description="Multi-class classifier for predicting policy effectiveness (Effective/Partially Effective/Ineffective)",
model_type=ModelType.MULTI_CLASS,
)
# Model configuration
self.class_labels = ["Ineffective", "Partially Effective", "Effective"]
self.feature_scaler = None
self.label_encoder = None
# Hyperparameters
self.hyperparameters = {
"n_estimators": 250,
"max_depth": 7,
"learning_rate": 0.08,
"random_state": 42,
}
logger.info(f"Initialized PolicyEffectivenessClassifier: {model_id}")
def _create_model(self):
"""Create the underlying ML model"""
if XGBOOST_AVAILABLE:
model = xgb.XGBClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
learning_rate=self.hyperparameters["learning_rate"],
random_state=self.hyperparameters["random_state"],
objective="multi:softprob",
eval_metric="mlogloss",
)
elif LIGHTGBM_AVAILABLE:
model = lgb.LGBMClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
learning_rate=self.hyperparameters["learning_rate"],
random_state=self.hyperparameters["random_state"],
objective="multiclass",
metric="multi_logloss",
)
elif SKLEARN_AVAILABLE:
model = GradientBoostingClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
learning_rate=self.hyperparameters["learning_rate"],
random_state=self.hyperparameters["random_state"],
)
else:
raise RuntimeError("No suitable ML library available.")
return model
def _prepare_features(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
"""Prepare features for classification"""
if isinstance(X, pd.DataFrame):
X_processed = X.copy()
# Handle categorical features
for col in X_processed.select_dtypes(include=["object", "category"]):
X_processed[col] = X_processed[col].astype("category").cat.codes
# Fill missing values
X_processed = X_processed.fillna(X_processed.mean())
# Scale features
if self.feature_scaler is None:
self.feature_scaler = StandardScaler()
X_scaled = self.feature_scaler.fit_transform(X_processed)
else:
X_scaled = self.feature_scaler.transform(X_processed)
return X_scaled
else:
return X
def _prepare_targets(self, y: Union[pd.Series, np.ndarray]) -> np.ndarray:
"""Prepare target variables for classification"""
if self.label_encoder is None:
self.label_encoder = LabelEncoder()
y_encoded = self.label_encoder.fit_transform(y)
else:
y_encoded = self.label_encoder.transform(y)
return y_encoded
def get_policy_analysis(self, X: Union[pd.DataFrame, np.ndarray]) -> Dict[str, Any]:
"""Get policy effectiveness analysis with recommendations"""
predictions, probabilities = self.predict_with_confidence(X)
analysis = {
"predictions": (
predictions.tolist()
if isinstance(predictions, np.ndarray)
else predictions
),
"probabilities": (
probabilities.tolist()
if isinstance(probabilities, np.ndarray)
else probabilities
),
"effectiveness_metrics": [],
"improvement_suggestions": [],
}
# Analyze each policy
for i, (pred, prob) in enumerate(zip(predictions, probabilities)):
confidence = np.max(prob)
# Effectiveness metrics
metrics = {
"overall_effectiveness": pred,
"confidence": confidence,
"success_probability": (
prob[2]
if pred == "Effective"
else prob[1] if pred == "Partially Effective" else prob[0]
),
"risk_factors": [],
}
# Risk factors based on prediction
if pred == "Ineffective":
metrics["risk_factors"] = [
"Poor implementation strategy",
"Insufficient resources",
"Lack of stakeholder buy-in",
"Unrealistic timelines",
]
elif pred == "Partially Effective":
metrics["risk_factors"] = [
"Mixed stakeholder support",
"Resource constraints",
"Implementation delays",
"Scope creep",
]
else: # Effective
metrics["risk_factors"] = [
"Strong stakeholder support",
"Adequate resources",
"Clear implementation plan",
"Regular monitoring",
]
# Improvement suggestions
if pred == "Ineffective":
suggestions = [
"Revise implementation strategy",
"Increase resource allocation",
"Improve stakeholder communication",
"Set realistic milestones",
]
elif pred == "Partially Effective":
suggestions = [
"Address resource gaps",
"Strengthen stakeholder engagement",
"Streamline processes",
"Enhance monitoring",
]
else: # Effective
suggestions = [
"Maintain current approach",
"Document best practices",
"Scale successful elements",
"Continuous improvement",
]
analysis["effectiveness_metrics"].append(metrics)
analysis["improvement_suggestions"].append(suggestions)
return analysis
class IndustryVulnerabilityClassifier(BaseMLModel):
"""
Multi-class classifier for predicting industry vulnerability to tariff impacts
Predicts: High/Medium/Low vulnerability based on industry characteristics,
trade dependencies, and economic resilience factors.
"""
def __init__(self, model_id: str = "industry_vulnerability_classifier"):
super().__init__(
model_id=model_id,
name="Industry Vulnerability Classifier",
description="Multi-class classifier for predicting industry vulnerability to tariff impacts (High/Medium/Low)",
model_type=ModelType.MULTI_CLASS,
)
# Model configuration
self.class_labels = ["Low", "Medium", "High"]
self.feature_scaler = None
self.label_encoder = None
# Hyperparameters
self.hyperparameters = {
"n_estimators": 200,
"max_depth": 6,
"learning_rate": 0.1,
"random_state": 42,
}
logger.info(f"Initialized IndustryVulnerabilityClassifier: {model_id}")
def _create_model(self):
"""Create the underlying ML model"""
if XGBOOST_AVAILABLE:
model = xgb.XGBClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
learning_rate=self.hyperparameters["learning_rate"],
random_state=self.hyperparameters["random_state"],
objective="multi:softprob",
eval_metric="mlogloss",
)
elif LIGHTGBM_AVAILABLE:
model = lgb.LGBMClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
learning_rate=self.hyperparameters["learning_rate"],
random_state=self.hyperparameters["random_state"],
objective="multiclass",
metric="multi_logloss",
)
elif SKLEARN_AVAILABLE:
model = RandomForestClassifier(
n_estimators=self.hyperparameters["n_estimators"],
max_depth=self.hyperparameters["max_depth"],
random_state=self.hyperparameters["random_state"],
)
else:
raise RuntimeError("No suitable ML library available.")
return model
def _prepare_features(self, X: Union[pd.DataFrame, np.ndarray]) -> np.ndarray:
"""Prepare features for classification"""
if isinstance(X, pd.DataFrame):
X_processed = X.copy()
# Handle categorical features
for col in X_processed.select_dtypes(include=["object", "category"]):
X_processed[col] = X_processed[col].astype("category").cat.codes
# Fill missing values
X_processed = X_processed.fillna(X_processed.mean())
# Scale features
if self.feature_scaler is None:
self.feature_scaler = StandardScaler()
X_scaled = self.feature_scaler.fit_transform(X_processed)
else:
X_scaled = self.feature_scaler.transform(X_processed)
return X_scaled
else:
return X
def _prepare_targets(self, y: Union[pd.Series, np.ndarray]) -> np.ndarray:
"""Prepare target variables for classification"""
if self.label_encoder is None:
self.label_encoder = LabelEncoder()
y_encoded = self.label_encoder.fit_transform(y)
else:
y_encoded = self.label_encoder.transform(y)
return y_encoded
def get_vulnerability_assessment(
self, X: Union[pd.DataFrame, np.ndarray]
) -> Dict[str, Any]:
"""Get industry vulnerability assessment with mitigation strategies"""
predictions, probabilities = self.predict_with_confidence(X)
assessment = {
"predictions": (
predictions.tolist()
if isinstance(predictions, np.ndarray)
else predictions
),
"probabilities": (
probabilities.tolist()
if isinstance(probabilities, np.ndarray)
else probabilities
),
"vulnerability_factors": [],
"mitigation_strategies": [],
"resilience_indicators": [],
}
# Analyze each industry
for i, (pred, prob) in enumerate(zip(predictions, probabilities)):
confidence = np.max(prob)
# Vulnerability factors
if pred == "High":
factors = [
"High import dependency",
"Limited domestic alternatives",
"Low profit margins",
"Concentrated supply chains",
]
elif pred == "Medium":
factors = [
"Moderate import dependency",
"Some domestic alternatives",
"Medium profit margins",
"Diversified supply chains",
]
else: # Low
factors = [
"Low import dependency",
"Strong domestic alternatives",
"High profit margins",
"Resilient supply chains",
]
# Mitigation strategies
if pred == "High":
strategies = [
"Diversify supply sources",
"Develop domestic capabilities",
"Implement cost controls",
"Seek policy exemptions",
]
elif pred == "Medium":
strategies = [
"Optimize supply chains",
"Enhance operational efficiency",
"Develop contingency plans",
"Monitor policy changes",
]
else: # Low
strategies = [
"Maintain current advantages",
"Invest in innovation",
"Expand market presence",
"Leverage competitive position",
]
# Resilience indicators
resilience = {
"financial_strength": (
"Weak"
if pred == "High"
else "Moderate" if pred == "Medium" else "Strong"
),
"operational_flexibility": (
"Low"
if pred == "High"
else "Medium" if pred == "Medium" else "High"
),
"market_position": (
"Vulnerable"
if pred == "High"
else "Stable" if pred == "Medium" else "Robust"
),
"adaptation_capacity": (
"Limited"
if pred == "High"
else "Moderate" if pred == "Medium" else "High"
),
}
assessment["vulnerability_factors"].append(factors)
assessment["mitigation_strategies"].append(strategies)
assessment["resilience_indicators"].append(resilience)
return assessment
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