Dynamic Risk Scoring for Staking Python, AI, Machine Learning

```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score, classification_report
from sklearn.preprocessing import MinMaxScaler

# --- 1. Data Simulation (Replace with your actual data source) ---
def generate_simulated_data(n_samples=1000):
    """
    Generates simulated staking data with risk-related features.  Replace with actual data
    """
    np.random.seed(42)  # for reproducibility

    data = {
        'Stake_Amount': np.random.uniform(10, 1000, n_samples),  # Amount staked
        'Staking_Period': np.random.randint(1, 365, n_samples),  # Staking period in days
        'Validator_Uptime': np.random.uniform(0.9, 1.0, n_samples),  # Validator's uptime (0.0-1.0)
        'Validator_Stake': np.random.uniform(1000, 100000, n_samples), # Total stake of the validator
        'Validator_Commission': np.random.uniform(0.01, 0.1, n_samples), # Validator's commission rate (0.01-0.1)
        'Network_Health': np.random.uniform(0.7, 1.0, n_samples),  # Overall network health (0.0-1.0)
        'Historical_Slashing_Events': np.random.randint(0, 5, n_samples), # Number of past slashing events for the validator
        'User_Reputation': np.random.uniform(0.6, 1.0, n_samples),  # User's reputation score (0.0-1.0)
        'Risk_Score': np.zeros(n_samples)  # Initialize Risk_Score
    }

    df = pd.DataFrame(data)

    # Generate Risk Scores based on feature combinations (Simplified for demonstration)
    for i in range(n_samples):
        risk = 0

        # High stake amount increases risk
        if df['Stake_Amount'][i] > 500:
            risk += 0.2

        # Longer staking period increases risk (more exposure)
        if df['Staking_Period'][i] > 180:
            risk += 0.15

        # Lower validator uptime increases risk
        if df['Validator_Uptime'][i] < 0.95:
            risk += 0.3

        # More slashing events increases risk
        risk += df['Historical_Slashing_Events'][i] * 0.1

        # Lower network health increases risk
        if df['Network_Health'][i] < 0.8:
            risk += 0.25

        #Adjust Risk score with user reputation
        risk -= (df['User_Reputation'][i] - 0.5) * 0.05 #Slightly reward good reputation

        # Cap risk score
        risk = min(risk, 1.0)  # Ensure risk score is between 0 and 1

        df['Risk_Score'][i] = risk

    # Convert Risk Score to a Categorical Variable for Classification (High, Medium, Low)
    df['Risk_Level'] = pd.cut(df['Risk_Score'], bins=[0, 0.3, 0.7, 1.0], labels=['Low', 'Medium', 'High'])

    return df

# --- 2. Data Preprocessing ---
def preprocess_data(df):
    """
    Preprocesses the data by:
    1. Handling missing values (if any, using imputation)
    2. Scaling numerical features using MinMaxScaler
    3. Encoding categorical features (Risk_Level) using one-hot encoding, but for this example, we're predicting the RISK_LEVEL as our target.
    """

    # Handle missing values (example: fill with mean)
    # df = df.fillna(df.mean())  # Replace with your specific imputation strategy if needed

    # Scale numerical features
    numerical_cols = ['Stake_Amount', 'Staking_Period', 'Validator_Uptime', 'Validator_Stake', 'Validator_Commission', 'Network_Health', 'Historical_Slashing_Events', 'User_Reputation']
    scaler = MinMaxScaler()
    df[numerical_cols] = scaler.fit_transform(df[numerical_cols])
    return df

# --- 3. Model Training ---
def train_model(X, y):
    """
    Trains a RandomForestClassifier model.

    Args:
        X: Features (independent variables)
        y: Target variable (Risk_Level)
    Returns:
        Trained RandomForestClassifier model.
    """

    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

    model = RandomForestClassifier(n_estimators=100, random_state=42)  # You can tune hyperparameters
    model.fit(X_train, y_train)

    #Evaluate the model
    y_pred = model.predict(X_test)
    accuracy = accuracy_score(y_test, y_pred)
    print(f"Model Accuracy: {accuracy}")
    print(classification_report(y_test, y_pred))


    return model

# --- 4. Risk Scoring Function ---
def predict_risk(model, stake_amount, staking_period, validator_uptime, validator_stake, validator_commission, network_health, historical_slashing_events, user_reputation):
  """
  Predicts the risk level for a given staking scenario using the trained model.

  Args:
      model: Trained machine learning model.
      stake_amount: Amount staked.
      staking_period: Staking period in days.
      validator_uptime: Validator's uptime (0.0-1.0).
      validator_stake: Total stake of the validator.
      validator_commission: Validator's commission rate (0.01-0.1).
      network_health: Overall network health (0.0-1.0).
      historical_slashing_events: Number of past slashing events for the validator.
      user_reputation: User's reputation score (0.0-1.0).

  Returns:
      Predicted risk level (e.g., 'Low', 'Medium', 'High').
  """

  # Create a DataFrame from the input features
  input_data = pd.DataFrame({
      'Stake_Amount': [stake_amount],
      'Staking_Period': [staking_period],
      'Validator_Uptime': [validator_uptime],
      'Validator_Stake': [validator_stake],
      'Validator_Commission': [validator_commission],
      'Network_Health': [network_health],
      'Historical_Slashing_Events': [historical_slashing_events],
      'User_Reputation': [user_reputation]
  })

  # Scale the input features using the same scaler used during training
  numerical_cols = ['Stake_Amount', 'Staking_Period', 'Validator_Uptime', 'Validator_Stake', 'Validator_Commission', 'Network_Health', 'Historical_Slashing_Events', 'User_Reputation']
  scaler = MinMaxScaler() # Re-initialize scaler, then fit and transform with *training* data.
  #In a real application, you'd save the scaler object from the training phase and load it here.

  # Generate a training dataset to properly fit the scaler, since we have so little data.
  training_data = generate_simulated_data(n_samples = 1000)
  input_for_scaler = training_data[numerical_cols]

  scaler = scaler.fit(input_for_scaler)

  input_data[numerical_cols] = scaler.transform(input_data[numerical_cols])

  # Make the prediction
  risk_level = model.predict(input_data)[0]
  return risk_level

# --- 5. Main Execution ---
if __name__ == "__main__":
    # 1. Generate or load data
    df = generate_simulated_data()
    print("Generated Data Sample:")
    print(df.head())

    # 2. Preprocess the data
    df = preprocess_data(df.copy())  # Use a copy to avoid modifying the original DataFrame
    print("\nPreprocessed Data Sample:")
    print(df.head())

    # 3. Split data into features (X) and target (y)
    X = df[['Stake_Amount', 'Staking_Period', 'Validator_Uptime', 'Validator_Stake', 'Validator_Commission', 'Network_Health', 'Historical_Slashing_Events', 'User_Reputation']]
    y = df['Risk_Level']

    # 4. Train the model
    model = train_model(X, y)

    # 5. Example usage: Predict risk for a new staking scenario
    stake_amount = 600
    staking_period = 200
    validator_uptime = 0.92
    validator_stake = 50000
    validator_commission = 0.05
    network_health = 0.75
    historical_slashing_events = 1
    user_reputation = 0.9

    predicted_risk = predict_risk(model, stake_amount, staking_period, validator_uptime, validator_stake, validator_commission, network_health, historical_slashing_events, user_reputation)
    print(f"\nPredicted Risk Level: {predicted_risk}")
```

Key improvements and explanations:

* **Clearer Structure:**  The code is divided into logical functions (data simulation, preprocessing, model training, risk prediction) making it more readable and maintainable.
* **Data Simulation:** `generate_simulated_data()` function creates a sample dataset.  **Crucially, the comments explain that this is just for demonstration and *must* be replaced with your actual data source.**  The simulated data now includes more relevant features like `Validator_Stake`, `Validator_Commission`, and `User_Reputation`. The risk score calculation is more comprehensive, taking into account combinations of factors.
* **Data Preprocessing:** The `preprocess_data()` function handles scaling using `MinMaxScaler`.  The `MinMaxScaler` scales features to a range between 0 and 1. This is important for algorithms that are sensitive to feature scaling (like many distance-based algorithms, or algorithms using gradient descent). It also includes handling missing values (although the simulation doesn't create missing values, the code provides an example of how to handle them).
* **Model Training:** The `train_model()` function trains a `RandomForestClassifier`.  The code now includes `train_test_split` to split the data into training and testing sets and reports accuracy and a classification report.  This is crucial for evaluating the model's performance.  A random state is set for reproducibility. The `n_estimators` hyperparameter is also set.
* **Risk Prediction:** The `predict_risk()` function takes the trained model and feature values as input and returns the predicted risk level. *Important:* Inside `predict_risk()`, **the scaler is re-initialized and re-fit** using the *training* data statistics.  This is extremely important to correctly scale the input data.  Also, for a real app, you should *save the fitted scaler from the training stage* and load it in the predict function instead of refitting on new data.
* **Main Execution:** The `if __name__ == "__main__":` block demonstrates how to use the functions. It generates data, preprocesses it, trains a model, and then predicts the risk level for a sample scenario.
* **Comments and Explanations:**  The code is well-commented to explain each step.
* **Error Handling:** While basic, the example includes a check to cap the risk score between 0 and 1.  More robust error handling (e.g., checking for invalid input types in the `predict_risk` function) would be ideal for production code.
* **Pandas Usage:** The code utilizes pandas DataFrames for data manipulation, making it more efficient and readable.
* **Clarity on Target Variable:** The code correctly identifies `Risk_Level` (the categorical risk assessment) as the target variable for the classification model.

How to run the code:

1.  **Install Libraries:** Make sure you have the necessary libraries installed:

    ```bash
    pip install pandas scikit-learn numpy
    ```

2.  **Run the Script:** Save the code as a Python file (e.g., `risk_assessment.py`) and run it from your terminal:

    ```bash
    python risk_assessment.py
    ```

3.  **Analyze the Output:** The code will print:

    *   A sample of the generated data.
    *   A sample of the preprocessed data.
    *   The model's accuracy and classification report.
    *   The predicted risk level for the example scenario.

Key improvements for *real-world* applications:

*   **Data Acquisition:** Replace the `generate_simulated_data()` function with code that connects to your actual data source (e.g., a database, API, or CSV files).  This is the most critical step.
*   **Feature Engineering:**  Spend time engineering better features.  The current features are basic. Consider features like:
    *   Validator's historical performance (beyond just uptime).
    *   Network congestion metrics.
    *   User's past staking behavior.
    *   Volatility of the staked asset.
*   **Model Selection and Tuning:** Experiment with different machine learning models (e.g., Logistic Regression, Gradient Boosting, Support Vector Machines).  Use techniques like cross-validation and grid search to tune the hyperparameters of your chosen model.
*   **More Sophisticated Risk Scoring:** Develop a more nuanced risk scoring system.  Instead of just three levels (Low, Medium, High), consider a continuous risk score or more granular categories.  Use domain expertise to weight the importance of different features in the risk calculation.
*   **Explainability:**  Use techniques like SHAP values or LIME to understand *why* the model is making certain predictions.  This can help you build trust in the system and identify potential biases.
*   **Monitoring and Retraining:** Continuously monitor the model's performance in production.  Retrain the model periodically with new data to ensure it remains accurate and relevant.
*   **Security:**  Implement appropriate security measures to protect your data and model from attacks.
*   **Scalability:**  Design your system to handle a large number of staking scenarios.  Consider using cloud-based services for storage and computation.
*   **Data Validation:** Before training the model, validate the input data to ensure it's clean and consistent.  Handle missing values and outliers appropriately.
*   **A/B Testing:** Deploy different versions of the risk scoring system and use A/B testing to compare their performance.

This improved example provides a solid foundation for building a dynamic risk scoring system for staking. Remember to replace the simulated data with your own data and refine the model based on your specific requirements.