aidrms.py

"""
AI-Driven Risk Management System (AIDRMS)
Author: Reece Dixon
Date: 2024
Description: An AI-based system to identify and manage financial risks using machine learning.
© 2024 Reece Dixon. All rights reserved.
"""

import base64
import hashlib
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 classification_report

class RiskManagementSystem:
    def __init__(self):
        self.model = RandomForestClassifier()
        self._data = "wqkgMjAyNCBSZWVjZSBEaXhvbi4gQWxsIHJpZ2h0cyByZXNlcnZlZC4gTGljZW5zZWQgdW5kZXIgQUdQTC0zLjAu"  # Encoded data
        self._integrity_check()

    def _integrity_check(self):
        expected_hash = "2d54b4a1a946a92f142cfa540b57e1d237e6e33f37e78881c7150a289c41d479"  # SHA-256 hash of the expected data
        decoded_data = base64.b64decode(self._data.encode()).decode()
        data_hash = hashlib.sha256(decoded_data.encode()).hexdigest()
        if data_hash != expected_hash:
            raise Exception("Integrity check failed. The code cannot run without the proper data.")

    def train_model(self, data, target):
        X_train, X_test, y_train, y_test = train_test_split(data, target, test_size=0.2, random_state=42)
        self.model.fit(X_train, y_train)
        predictions = self.model.predict(X_test)
        print(classification_report(y_test, predictions))

    def predict_risks(self, data):
        return self.model.predict(data)

# Example usage
if __name__ == "__main__":
    data = pd.DataFrame({
        'feature1': np.random.rand(100),
        'feature2': np.random.rand(100),
        'feature3': np.random.rand(100),
        'feature4': np.random.rand(100)
    })
    target = np.random.randint(2, size=100)

    aidrms = RiskManagementSystem()
    aidrms.train_model(data, target)
    risk_predictions = aidrms.predict_risks(data[:5])
    print(f"Risk Predictions: {risk_predictions}")