Create risk_assessment.py

Author: KOSASIH

quantum-ai-arbitration/src/main/arbitration/risk_assessment.py

@@ -0,0 +1,94 @@
+# src/main/arbitration/risk_assessment.py
+
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report
+import joblib
+import logging
+
+class RiskAssessment:
+    def __init__(self, model_path='./models/risk_assessment_model.pkl'):
+        self.model_path = model_path
+        self.model = None
+        logging.info("RiskAssessment initialized with model path: %s", self.model_path)
+
+    def load_data(self, data):
+        """
+        Load and preprocess the input data for risk assessment.
+        
+        Args:
+            data (dict): A dictionary containing the features for risk assessment.
+
+        Returns:
+            pd.DataFrame: Processed features for the model.
+        """
+        logging.info("Loading data for risk assessment...")
+        df = pd.DataFrame(data)
+        logging.info("Data loaded with %d samples.", len(df))
+        return df
+
+    def train(self, data):
+        """
+        Train the risk assessment model using the provided data.
+        
+        Args:
+            data (dict): A dictionary containing the features and target variable.
+        """
+        df = self.load_data(data)
+
+        # Assuming the last column is the target variable (1 for high risk, 0 for low risk)
+        X = df.drop(columns=['risk_level'])  # Features
+        y = df['risk_level']  # Target variable
+
+        # Split the data into training and testing sets
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+        # Initialize and train the Random Forest model
+        self.model = RandomForestClassifier(n_estimators=100, random_state=42)
+        self.model.fit(X_train, y_train)
+
+        # Evaluate the model
+        y_pred = self.model.predict(X_test)
+        accuracy = accuracy_score(y_test, y_pred)
+        logging.info("Model trained successfully with accuracy: %.2f%%", accuracy * 100)
+        logging.info("Classification report:\n%s", classification_report(y_test, y_pred))
+
+        # Save the trained model
+        self.save_model()
+
+    def assess_risk(self, data):
+        """
+        Assess the risk based on the input data.
+        
+        Args:
+            data (dict): A dictionary containing the features for risk assessment.
+
+        Returns:
+            dict: The risk assessment result indicating the risk level.
+        """
+        if self.model is None:
+            logging.error("Model is not trained. Please train the model before assessment.")
+            return None
+
+        # Convert input data to DataFrame
+        input_df = pd.DataFrame(data, index=[0])
+        prediction = self.model.predict(input_df)
+        risk_level = prediction[0]  # 1 for high risk, 0 for low risk
+
+        logging.info("Risk assessment result: %s", "High risk" if risk_level == 1 else "Low risk")
+        return {"risk_level": risk_level}
+
+    def save_model(self):
+        """Save the trained model to a file."""
+        joblib.dump(self.model, self.model_path)
+        logging.info("Model saved to %s", self.model_path)
+
+    def load_model(self):
+        """Load the trained model from a file."""
+        try:
+            self.model = joblib.load(self.model_path)
+            logging.info("Model loaded from %s", self.model_path)
+        except FileNotFoundError:
+            logging.error("Model file not found. Please train the model first.")