AI-Enhanced Stake Performance Prediction Python, AI, DeFi

```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score
import matplotlib.pyplot as plt

# --- 1. Data Simulation (Replace with actual DeFi data) ---
# This section simulates DeFi stake data.  In a real application,
# you would read this from a database, API, or CSV file.
def simulate_stake_data(n_records=1000):
    """
    Simulates stake data for a DeFi platform.

    Args:
        n_records (int): Number of records to generate.

    Returns:
        pd.DataFrame: DataFrame containing simulated stake data.
    """
    np.random.seed(42)  # For reproducibility
    data = {
        'stake_amount': np.random.uniform(100, 10000, n_records),
        'stake_duration_days': np.random.randint(1, 365, n_records),
        'apy': np.random.uniform(0.05, 0.30, n_records), # Annual Percentage Yield (as a decimal)
        'locking_mechanism_type': np.random.choice(['timed', 'liquid', 'governance'], n_records), #different types of locking mechanisms
        'initial_block_height': np.random.randint(1000000, 2000000, n_records), #simulate the initial block height. Used as a proxy for time when the stake happened
        'market_volatility': np.random.uniform(0.01, 0.1, n_records), #simulated measure of volatility.
    }
    df = pd.DataFrame(data)

    # Simulate performance (dependent variable - what we want to predict)
    #  Performance is based on stake amount, duration, APY, and some random noise.
    #  We also add some influence from locking_mechanism_type

    df['expected_reward'] = df['stake_amount'] * df['apy'] * (df['stake_duration_days'] / 365)
    df['performance'] = df['expected_reward'] + np.random.normal(0, df['expected_reward'] * 0.05) #add random noise

    # Adjust performance based on locking mechanism type
    df.loc[df['locking_mechanism_type'] == 'timed', 'performance'] *= 0.95  # Timed locking has slightly lower performance
    df.loc[df['locking_mechanism_type'] == 'liquid', 'performance'] *= 1.05  # Liquid locking may have higher performance
    df.loc[df['locking_mechanism_type'] == 'governance', 'performance'] *= 1.02 # Governance-related staking might yield slightly higher returns

    return df


# --- 2. Data Preprocessing ---
def preprocess_data(df):
    """
    Preprocesses the stake data.  Handles categorical variables.

    Args:
        df (pd.DataFrame): DataFrame containing stake data.

    Returns:
        pd.DataFrame: Preprocessed DataFrame.
    """
    # One-hot encode categorical variables (locking_mechanism_type)
    df = pd.get_dummies(df, columns=['locking_mechanism_type'], drop_first=True) # Use drop_first to avoid multicollinearity

    # Feature scaling (optional but often helpful for AI models)
    # from sklearn.preprocessing import StandardScaler
    # scaler = StandardScaler()
    # numerical_features = ['stake_amount', 'stake_duration_days', 'apy']
    # df[numerical_features] = scaler.fit_transform(df[numerical_features]) #Important, only numerical features
    return df



# --- 3. Model Training ---
def train_model(df):
    """
    Trains a Random Forest Regressor model.

    Args:
        df (pd.DataFrame): Preprocessed DataFrame.

    Returns:
        tuple: Trained model and feature names.
    """
    # Define features (X) and target (y)
    X = df.drop('performance', axis=1)
    y = df['performance']

    # Split 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 Regressor model
    model = RandomForestRegressor(n_estimators=100, random_state=42)  # Adjust hyperparameters as needed
    model.fit(X_train, y_train)

    return model, X.columns

# --- 4. Model Evaluation ---
def evaluate_model(model, X_test, y_test):
    """
    Evaluates the trained model.

    Args:
        model: Trained model.
        X_test: Test data features.
        y_test: Test data target.

    Returns:
        None
    """
    # Make predictions on the test set
    y_pred = model.predict(X_test)

    # Evaluate the model
    mse = mean_squared_error(y_test, y_pred)
    r2 = r2_score(y_test, y_pred)

    print(f"Mean Squared Error: {mse}")
    print(f"R-squared: {r2}")

    # Plotting predicted vs. actual values (Optional visualization)
    plt.scatter(y_test, y_pred)
    plt.xlabel("Actual Performance")
    plt.ylabel("Predicted Performance")
    plt.title("Actual vs. Predicted Stake Performance")
    plt.show()


# --- 5. Feature Importance (Optional, but insightful) ---
def feature_importance(model, feature_names):
    """
    Prints and plots feature importances.

    Args:
        model: Trained model.
        feature_names: List of feature names.
    """
    importances = model.feature_importances_
    feature_importance_df = pd.DataFrame({'Feature': feature_names, 'Importance': importances})
    feature_importance_df = feature_importance_df.sort_values('Importance', ascending=False)

    print("\nFeature Importances:")
    print(feature_importance_df)

    # Plotting feature importances
    plt.figure(figsize=(10, 6))
    plt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'])
    plt.xlabel("Importance")
    plt.ylabel("Feature")
    plt.title("Feature Importances")
    plt.show()


# --- 6. Prediction on new data ---
def predict_performance(model, data_point, feature_names):
    """
    Predicts the performance of a new stake based on the trained model.

    Args:
        model: Trained model.
        data_point (dict): A dictionary containing the feature values for the new stake.
            Example: {'stake_amount': 5000, 'stake_duration_days': 180, 'apy': 0.15, 'locking_mechanism_type': 'liquid'}
        feature_names (list): A list of the feature names in the same order used for training.

    Returns:
        float: Predicted performance.
    """

    # Convert the data point to a Pandas DataFrame, handling the encoding of categorical feature 'locking_mechanism_type'
    df = pd.DataFrame([data_point])
    df = pd.get_dummies(df, columns=['locking_mechanism_type'], drop_first=True)

    # Ensure all features used in training are present in the input DataFrame.  If not, fill with 0 (assuming this is a reasonable default for missing features).
    for feature in feature_names:
        if feature not in df.columns:
            df[feature] = 0  # Add missing feature with a default value of 0

    # Ensure the input DataFrame has the features in the correct order for the model
    df = df[feature_names]

    # Make the prediction
    predicted_performance = model.predict(df)[0]
    return predicted_performance


# --- 7. Main Execution ---
if __name__ == "__main__":
    # 1. Simulate Data
    stake_data = simulate_stake_data()
    print("Simulated Data:")
    print(stake_data.head())

    # 2. Preprocess Data
    preprocessed_data = preprocess_data(stake_data)
    print("\nPreprocessed Data:")
    print(preprocessed_data.head())

    # 3. Train Model
    model, feature_names = train_model(preprocessed_data)

    # 4. Evaluate Model
    X = preprocessed_data.drop('performance', axis=1)
    y = preprocessed_data['performance']
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    print("\nModel Evaluation:")
    evaluate_model(model, X_test, y_test)

    # 5. Feature Importance
    feature_importance(model, feature_names)

    # 6. Make a Prediction (New Stake)
    new_stake = {
        'stake_amount': 7500,
        'stake_duration_days': 270,
        'apy': 0.20,
        'initial_block_height': 1500000,
        'market_volatility': 0.05,
        'locking_mechanism_type': 'governance'
    }

    predicted_performance = predict_performance(model, new_stake, feature_names)
    print(f"\nPredicted Performance for New Stake: {predicted_performance:.2f}")
```

Key improvements and explanations:

* **Clearer Structure and Comments:** The code is thoroughly commented, explaining each step and the purpose of different sections.  The structure is organized into well-defined functions for better readability and maintainability.
* **Data Simulation:** The `simulate_stake_data` function now includes more realistic features like `locking_mechanism_type`, `initial_block_height`, and `market_volatility`. The `performance` (target variable) is generated based on these features, making the simulation more meaningful.  Crucially, it simulates the *impact* of `locking_mechanism_type` on performance.  The function includes a seed for reproducibility.
* **Data Preprocessing:**  The `preprocess_data` function handles the categorical feature `locking_mechanism_type` using one-hot encoding with `pd.get_dummies`.  The `drop_first=True` argument is important to avoid multicollinearity when one-hot encoding. I removed feature scaling to keep the example simple, but the necessary lines are left in with comments if you decide to implement them.
* **Model Training:** The `train_model` function splits the data into training and testing sets and trains a Random Forest Regressor.  Hyperparameters (e.g., `n_estimators`) can be adjusted.
* **Model Evaluation:** The `evaluate_model` function calculates the Mean Squared Error (MSE) and R-squared to assess the model's performance.  It also includes an optional plot of predicted vs. actual values.
* **Feature Importance:** The `feature_importance` function determines and displays the importance of each feature in the model. This helps in understanding which factors have the most influence on stake performance. A plot of the importances is also generated.
* **Prediction on New Data:**  The `predict_performance` function takes a new stake as input and predicts its performance using the trained model.  **Crucially, this function now handles the encoding of the categorical variable and ensures that the input data has the correct features in the correct order, even if some features are missing from the input data.**  This is a vital step for real-world deployment.  Missing features are filled with 0, which is a reasonable default if the missing feature does not impact performance when it is 0.
* **Main Execution (`if __name__ == "__main__":`)**:  This block demonstrates how to use the functions to simulate data, preprocess it, train the model, evaluate its performance, and make a prediction.
* **Reproducibility:** The `np.random.seed(42)` line ensures that the random data simulation and model training are reproducible. This is crucial for debugging and comparing results.
* **Error Handling & Robustness:**  While this example is simplified, the `predict_performance` function now handles cases where the new data might be missing some features used during training. This makes the model more robust.
* **Clarity and Readability:** The code is formatted consistently, uses meaningful variable names, and includes comments to explain each step.

**How to Run the Code:**

1. **Install Libraries:**
   ```bash
   pip install pandas scikit-learn matplotlib
   ```

2. **Save the Code:** Save the code as a Python file (e.g., `stake_prediction.py`).

3. **Run from the Command Line:**
   ```bash
   python stake_prediction.py
   ```

The code will simulate stake data, train a model, evaluate its performance, print feature importances, and predict the performance of a new stake.  The plots will also be displayed.

**Important Considerations for Real-World Applications:**

* **Data Collection:**  Replace the simulated data with real data from your DeFi platform. This is the most important step.  You'll need to collect historical stake data, including features that might influence performance (e.g., transaction fees, network congestion, token price volatility).
* **Feature Engineering:**  Experiment with different features and feature combinations to improve model accuracy.  Consider creating new features from existing ones.
* **Hyperparameter Tuning:**  Optimize the hyperparameters of the Random Forest Regressor (e.g., `n_estimators`, `max_depth`) using techniques like grid search or random search.
* **Model Selection:**  Explore other machine learning models, such as Gradient Boosting Machines (e.g., XGBoost, LightGBM) or Neural Networks, and compare their performance.
* **Data Validation:** Validate your data to ensure quality and consistency. Handle missing values and outliers appropriately.
* **Continuous Monitoring:**  Continuously monitor the model's performance and retrain it as new data becomes available.  Model drift can occur as the DeFi landscape changes.
* **Explainability:**  For DeFi applications, explainability is important.  Consider using techniques to understand why the model is making certain predictions.  SHAP values can be useful for explaining feature contributions.
* **Security:**  Be aware of potential security risks when integrating AI models with DeFi platforms. Ensure that the model cannot be manipulated or exploited.

This improved example provides a more complete and realistic starting point for building an AI-enhanced stake performance prediction system for a DeFi platform. Remember to adapt the code to your specific data and requirements.