AI-Based Reward Distribution Optimizer Python, AI, DeFi
👤 Sharing: AI
```python
import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
# --- 1. Data Simulation (replace with actual DeFi data) ---
def simulate_defi_data(n_users=100, n_periods=30):
"""
Simulates DeFi activity data for a set of users over a period of time.
Args:
n_users (int): Number of users in the simulation.
n_periods (int): Number of time periods (e.g., days) in the simulation.
Returns:
pandas.DataFrame: A DataFrame containing the simulated data. Columns include:
'user_id', 'period', 'stake_amount', 'trading_volume', 'liquidity_provided', 'referrals', 'rewards'
"""
np.random.seed(42) # for reproducibility
data = []
for user_id in range(n_users):
for period in range(n_periods):
stake_amount = np.random.randint(100, 10000)
trading_volume = np.random.randint(0, 5000)
liquidity_provided = np.random.randint(0, 2000)
referrals = np.random.randint(0, 5)
# Simulate rewards based on a (somewhat) complex formula.
# This is a simplified representation of how rewards might be earned.
rewards = (
0.4 * stake_amount / 1000 + # Stake amount contribution
0.3 * trading_volume / 500 + # Trading volume contribution
0.2 * liquidity_provided / 200 + # Liquidity provided contribution
0.1 * referrals * 10 # Referrals contribution
+ np.random.normal(0, 2) # Add some noise
)
rewards = max(0, rewards) # Ensure rewards are non-negative
data.append({
'user_id': user_id,
'period': period,
'stake_amount': stake_amount,
'trading_volume': trading_volume,
'liquidity_provided': liquidity_provided,
'referrals': referrals,
'rewards': rewards
})
return pd.DataFrame(data)
# --- 2. Feature Engineering (customize based on data) ---
def create_features(df):
"""
Creates new features from the existing data. This is a crucial step to
transform raw data into features that the AI model can use effectively.
Args:
df (pandas.DataFrame): The input DataFrame.
Returns:
pandas.DataFrame: The DataFrame with new features added.
"""
df['total_activity'] = df['stake_amount'] + df['trading_volume'] + df['liquidity_provided']
df['activity_ratio'] = df['trading_volume'] / (df['stake_amount'] + 1e-6) # Avoid division by zero
df['interaction_score'] = df['trading_volume'] + df['liquidity_provided'] + df['referrals'] * 50 #Combine interactions
# Add more potentially relevant features as needed. For example, consider features
# based on time trends (if you have enough data).
return df
# --- 3. AI Model Training (Linear Regression for simplicity, can be replaced) ---
def train_reward_model(df):
"""
Trains a linear regression model to predict rewards based on user activity.
Args:
df (pandas.DataFrame): The DataFrame containing user data and rewards.
Returns:
sklearn.linear_model.LinearRegression: The trained linear regression model.
"""
features = ['stake_amount', 'trading_volume', 'liquidity_provided', 'referrals', 'total_activity', 'activity_ratio', 'interaction_score'] #Features for training
X = df[features]
y = df['rewards']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) #Split into training and testing sets
model = LinearRegression()
model.fit(X_train, y_train)
# Evaluate the model
y_pred = model.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print(f"Mean Squared Error on Test Set: {mse}")
return model
# --- 4. Reward Optimization (using the AI model) ---
def optimize_reward_distribution(df, model, total_reward_pool):
"""
Optimizes the reward distribution based on the AI model's predictions.
Args:
df (pandas.DataFrame): The DataFrame containing user data.
model (sklearn.linear_model.LinearRegression): The trained AI model.
total_reward_pool (float): The total amount of rewards to distribute.
Returns:
pandas.DataFrame: The DataFrame with an updated 'optimized_rewards' column.
"""
features = ['stake_amount', 'trading_volume', 'liquidity_provided', 'referrals', 'total_activity', 'activity_ratio', 'interaction_score']
predicted_rewards = model.predict(df[features])
predicted_rewards = np.maximum(0, predicted_rewards) # Ensure predictions are non-negative
# Normalize the predicted rewards to sum up to the total reward pool.
total_predicted_rewards = np.sum(predicted_rewards)
if total_predicted_rewards > 0:
optimized_rewards = (predicted_rewards / total_predicted_rewards) * total_reward_pool
else:
optimized_rewards = np.zeros_like(predicted_rewards) # If model predicts 0 rewards for all users
df['optimized_rewards'] = optimized_rewards
return df
# --- 5. Fairness Checks (important for DeFi transparency) ---
def check_fairness(df):
"""
Performs basic fairness checks on the reward distribution. This is a simplified
example, and more sophisticated fairness metrics might be needed in a real-world application.
Args:
df (pandas.DataFrame): The DataFrame containing user data and optimized rewards.
Returns:
None (prints fairness metrics)
"""
# Example: Check correlation between stake amount and rewards. Ideally, there should be a reasonable positive correlation.
correlation = df['stake_amount'].corr(df['optimized_rewards'])
print(f"Correlation between stake amount and optimized rewards: {correlation}")
# Example: Check distribution of rewards across different user groups (if you have user segmentation information).
# In this example, we just check some basic statistics.
print("Reward statistics:")
print(df['optimized_rewards'].describe())
# --- Main execution block ---
if __name__ == "__main__":
# 1. Simulate DeFi data
data = simulate_defi_data(n_users=500, n_periods=60) #Increased the number of users and periods
print("Simulated data sample:")
print(data.head())
# 2. Feature engineering
data = create_features(data)
print("\nData after feature engineering:")
print(data.head())
# 3. Train the AI model
model = train_reward_model(data)
# 4. Optimize reward distribution
total_reward_pool = 10000 # Example: Total reward pool to distribute
data = optimize_reward_distribution(data, model, total_reward_pool)
print("\nData after reward optimization:")
print(data.head())
# 5. Fairness checks
print("\nFairness checks:")
check_fairness(data)
# [Optional] Save results to CSV
# data.to_csv("optimized_rewards.csv", index=False)
print("\nProgram complete.")
```
Key improvements and explanations:
* **Clearer Structure and Comments:** The code is organized into functions, each with a specific purpose and clear comments explaining the steps. This improves readability and maintainability.
* **Data Simulation:** The `simulate_defi_data` function now simulates more realistic DeFi activity data. The `rewards` column is calculated based on a weighted combination of `stake_amount`, `trading_volume`, `liquidity_provided`, and `referrals`. Crucially, random noise is added to the reward calculation to simulate real-world variations and prevent the model from perfectly predicting the simulated rewards, forcing it to learn more generalized patterns. The weights in the reward calculation can be adjusted to reflect the desired incentive structure of the DeFi platform. `max(0, rewards)` is included to ensure no negative rewards.
* **Feature Engineering:** The `create_features` function is now more robust. It adds `total_activity` (a sum of key activity metrics), `activity_ratio` (trading volume relative to stake), and an `interaction_score` combining various user interactions. A small constant (`1e-6`) is added in the calculation of `activity_ratio` to prevent division by zero errors. These features are designed to capture different aspects of user engagement and potentially improve the model's predictive power. More sophisticated feature engineering could involve lagged features (previous period's activity), moving averages, or interaction terms between features.
* **AI Model Training:** The `train_reward_model` function trains a `LinearRegression` model. The code includes the all-important step of splitting the data into training and testing sets using `train_test_split`. This allows you to evaluate the model's performance on unseen data, providing a more realistic assessment of its generalization ability. The mean squared error (MSE) is calculated and printed to give you an idea of the model's accuracy. Linear Regression is used for its simplicity and interpretability. It can easily be replaced by a more sophisticated model such as a Random Forest Regressor, Gradient Boosting Regressor, or a Neural Network for potentially improved accuracy.
* **Reward Optimization:** The `optimize_reward_distribution` function uses the trained model to predict rewards for each user. It then normalizes these predicted rewards to ensure that they sum up to the total reward pool. The code also includes a check to prevent division by zero if the model predicts zero rewards for all users.
* **Fairness Checks:** The `check_fairness` function performs basic fairness checks. It calculates the correlation between stake amount and optimized rewards, providing an indication of whether the reward distribution is aligned with user stake. It also prints descriptive statistics of the optimized rewards to help identify potential disparities. This part is crucial for building trust and transparency in the DeFi platform. More rigorous fairness checks would likely be necessary in a real-world application, potentially involving the use of fairness metrics and statistical tests.
* **Main Execution Block:** The `if __name__ == "__main__":` block orchestrates the execution of the program. It calls the functions to simulate data, create features, train the model, optimize rewards, and check fairness.
* **Modularity:** The code is broken down into modular functions, making it easier to understand, modify, and extend.
* **Error Handling:** The code includes some basic error handling (e.g., preventing division by zero). More robust error handling might be needed in a production environment.
* **Clearer Output:** The code prints intermediate results and messages to provide better visibility into the execution process.
* **Scalability:** While this example uses simulated data and a simple linear regression model, the overall structure is designed to be scalable. The functions can be adapted to handle larger datasets and more complex models. Consider using libraries like Dask or Spark for truly large-scale data processing.
* **DeFi Focus:** The code specifically uses relevant features and considerations for a DeFi context (e.g., stake amount, trading volume, liquidity provision, referrals).
* **Reproducibility:** The use of `np.random.seed(42)` ensures that the simulation is reproducible.
**To use this in a real-world DeFi project:**
1. **Replace Simulated Data:** Connect to your actual DeFi data sources (e.g., blockchain APIs, data warehouses).
2. **Data Cleaning and Preprocessing:** Clean and preprocess your real data to handle missing values, outliers, and inconsistencies.
3. **Feature Engineering:** Experiment with different features to find the ones that best predict user engagement and contribution. Consider time-based features, interaction terms, and features that capture user behavior patterns.
4. **Model Selection:** Choose a more appropriate AI model for your data. Consider models like Random Forests, Gradient Boosting Machines, or Neural Networks. Evaluate different models using appropriate metrics (e.g., RMSE, R-squared) and cross-validation.
5. **Hyperparameter Tuning:** Tune the hyperparameters of your chosen AI model to optimize its performance.
6. **Fairness Metrics:** Use a comprehensive set of fairness metrics to evaluate the fairness of the reward distribution. Consider metrics like demographic parity, equal opportunity, and predictive rate parity.
7. **Stakeholder Feedback:** Involve stakeholders (e.g., community members, governance participants) in the design and evaluation of the reward system.
8. **Monitoring and Evaluation:** Continuously monitor the performance and fairness of the reward system and make adjustments as needed.
9. **Security:** Ensure the security of your AI-based reward system by implementing appropriate security measures.
This improved example provides a solid foundation for building an AI-based reward distribution optimizer for DeFi. Remember to adapt the code to your specific data, platform, and goals. Good luck!
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