Model Risk Profiler

What It Does

Installation

pip install numpy

Usage

python model_risk_profiler.py --model_path model.pkl --report_path risk_profile.json --test_config test_config.json

Source Code

import argparse
import json
import numpy as np

class ModelRiskProfiler:
    def __init__(self, model, test_config=None):
        self.model = model
        self.test_config = test_config or {}

    def evaluate_edge_cases(self):
        """Simulates edge case inputs to evaluate model behavior."""
        edge_cases = self.test_config.get("edge_cases", [
            np.array([0, 0, 0]),  # Example edge case: all zeros
            np.array([1e10, -1e10, 1e10]),  # Large values
        ])
        results = []
        for case in edge_cases:
            case = np.array(case)  # Ensure input is a numpy array
            try:
                prediction = self.model.predict(case.reshape(1, -1))
                results.append({"input": case.tolist(), "prediction": prediction})
            except Exception as e:
                results.append({"input": case.tolist(), "error": str(e)})
        return results

    def evaluate_adversarial_inputs(self):
        """Simulates adversarial inputs to evaluate model robustness."""
        adversarial_inputs = self.test_config.get("adversarial_inputs", [
            np.random.normal(size=(1, 3)) * 1e6  # Random large noise
        ])
        results = []
        for adv_input in adversarial_inputs:
            adv_input = np.array(adv_input)  # Ensure input is a numpy array
            try:
                prediction = self.model.predict(adv_input.reshape(1, -1))
                results.append({"input": adv_input.tolist(), "prediction": prediction})
            except Exception as e:
                results.append({"input": adv_input.tolist(), "error": str(e)})
        return results

    def generate_risk_profile(self):
        """Generates a comprehensive risk profile for the model."""
        profile = {
            "edge_case_evaluation": self.evaluate_edge_cases(),
            "adversarial_input_evaluation": self.evaluate_adversarial_inputs(),
        }
        return profile

def main():
    parser = argparse.ArgumentParser(description="Model Risk Profiler")
    parser.add_argument("--model_path", required=True, help="Path to the serialized model file (pickle format).")
    parser.add_argument("--report_path", required=True, help="Path to save the risk profile report (JSON format).")
    parser.add_argument("--test_config", help="Optional JSON file for test configurations.")
    args = parser.parse_args()

    try:
        import pickle
        with open(args.model_path, "rb") as f:
            model = pickle.load(f)
    except Exception as e:
        print(f"Error loading model: {e}")
        return

    test_config = {}
    if args.test_config:
        try:
            with open(args.test_config, "r") as f:
                test_config = json.load(f)
        except Exception as e:
            print(f"Error loading test configuration: {e}")
            return

    profiler = ModelRiskProfiler(model, test_config)
    risk_profile = profiler.generate_risk_profile()

    try:
        with open(args.report_path, "w") as f:
            json.dump(risk_profile, f, indent=4)
        print(f"Risk profile saved to {args.report_path}")
    except Exception as e:
        print(f"Error saving risk profile: {e}")

if __name__ == "__main__":
    main()