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"""
Risk scoring using gradient boosting and ensemble methods.
"""
import numpy as np
import pandas as pd
from typing import Dict, List, Optional, Any, Tuple
from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier
from sklearn.model_selection import cross_val_score
class RiskScorer:
"""
Nonlinear risk scoring using ensemble methods.
Uses Gradient Boosting and Random Forests to discover nonlinear
patterns in geopolitical risk factors.
"""
def __init__(self, method: str = 'gradient_boosting'):
"""
Initialize risk scorer.
Parameters
----------
method : str
Method to use ('gradient_boosting', 'random_forest', 'ensemble')
"""
self.method = method
self.model = None
self.feature_names = None
self.is_trained = False
def train(
self,
X: pd.DataFrame,
y: np.ndarray,
n_estimators: int = 100,
max_depth: int = 5
) -> Dict[str, Any]:
"""
Train risk scoring model.
Parameters
----------
X : pd.DataFrame
Feature matrix
y : np.ndarray
Risk labels (0 = low risk, 1 = high risk)
n_estimators : int
Number of estimators
max_depth : int
Maximum tree depth
Returns
-------
dict
Training results
"""
self.feature_names = X.columns.tolist()
if self.method == 'gradient_boosting':
self.model = GradientBoostingClassifier(
n_estimators=n_estimators,
max_depth=max_depth,
random_state=42
)
elif self.method == 'random_forest':
self.model = RandomForestClassifier(
n_estimators=n_estimators,
max_depth=max_depth,
random_state=42
)
else:
raise ValueError(f"Unknown method: {self.method}")
# Train
self.model.fit(X, y)
self.is_trained = True
# Cross-validation score
cv_scores = cross_val_score(self.model, X, y, cv=5)
return {
'cv_mean': cv_scores.mean(),
'cv_std': cv_scores.std(),
'feature_importance': self.get_feature_importance()
}
def predict_risk(self, X: pd.DataFrame) -> np.ndarray:
"""
Predict risk scores.
Parameters
----------
X : pd.DataFrame
Features
Returns
-------
np.ndarray
Risk probabilities
"""
if not self.is_trained:
raise ValueError("Model not trained yet")
return self.model.predict_proba(X)[:, 1]
def get_feature_importance(self) -> Dict[str, float]:
"""
Get feature importance scores.
Returns
-------
dict
Feature importance
"""
if not self.is_trained:
raise ValueError("Model not trained yet")
importance = self.model.feature_importances_
return dict(zip(self.feature_names, importance))
def explain_prediction(self, X: pd.DataFrame, index: int) -> Dict[str, Any]:
"""
Explain a specific prediction.
Parameters
----------
X : pd.DataFrame
Features
index : int
Index of sample to explain
Returns
-------
dict
Explanation
"""
if not self.is_trained:
raise ValueError("Model not trained yet")
sample = X.iloc[index:index+1]
risk_score = self.predict_risk(sample)[0]
# Feature contributions (simplified)
feature_values = sample.iloc[0].to_dict()
feature_importance = self.get_feature_importance()
contributions = {
feat: feature_values[feat] * feature_importance[feat]
for feat in feature_values.keys()
}
return {
'risk_score': risk_score,
'top_risk_factors': sorted(contributions.items(), key=lambda x: x[1], reverse=True)[:5],
'feature_values': feature_values
}
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