AI-Based Staking Profit Estimator Python, AI, Big Data
👤 Sharing: AI
```python
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error, r2_score
import matplotlib.pyplot as plt
class AIStakingProfitEstimator:
"""
An AI-based staking profit estimator using historical staking data
and linear regression to predict future profits.
Attributes:
data (pd.DataFrame): The historical staking data. Must contain at least columns
like 'staked_amount', 'duration', 'apy', 'reward'.
Ideally, more features should also be present
model (LinearRegression): The trained linear regression model.
X_train (pd.DataFrame): Training features.
X_test (pd.DataFrame): Testing features.
y_train (pd.Series): Training target (profit).
y_test (pd.Series): Testing target (profit).
"""
def __init__(self, data: pd.DataFrame):
"""
Initializes the StakingProfitEstimator.
Args:
data (pd.DataFrame): Historical staking data.
"""
self.data = data
self.model = None
self.X_train = None
self.X_test = None
self.y_train = None
self.y_test = None
def preprocess_data(self, feature_columns: list = None, target_column: str = 'reward'):
"""
Preprocesses the data by:
1. Filling missing values (with mean for numerical, mode for categorical).
2. Encoding categorical variables (one-hot encoding).
3. Splitting the data into training and testing sets.
Args:
feature_columns (list, optional): A list of column names to be used as features.
If None, all columns except the target are used.
Defaults to None.
target_column (str, optional): The name of the column containing the target variable (profit).
Defaults to 'reward'.
"""
# Fill missing values
for col in self.data.columns:
if self.data[col].isnull().any():
if pd.api.types.is_numeric_dtype(self.data[col]):
self.data[col] = self.data[col].fillna(self.data[col].mean())
else:
self.data[col] = self.data[col].fillna(self.data[col].mode()[0])
# Handle categorical columns (one-hot encoding)
categorical_cols = self.data.select_dtypes(include=['object']).columns.tolist()
self.data = pd.get_dummies(self.data, columns=categorical_cols, dummy_na=False)
# Define features and target
if feature_columns is None:
features = self.data.drop(target_column, axis=1)
else:
features = self.data[feature_columns]
target = self.data[target_column]
# Split data into training and testing sets
self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(
features, target, test_size=0.2, random_state=42
)
print("Data Preprocessing Complete.")
def train_model(self):
"""
Trains the linear regression model using the training data.
"""
self.model = LinearRegression()
self.model.fit(self.X_train, self.y_train)
print("Model Training Complete.")
def evaluate_model(self):
"""
Evaluates the trained model using the testing data and prints the evaluation metrics.
"""
if self.model is None:
print("Model has not been trained yet. Please train the model first.")
return
y_pred = self.model.predict(self.X_test)
mse = mean_squared_error(self.y_test, y_pred)
r2 = r2_score(self.y_test, y_pred)
print("Model Evaluation:")
print(f" Mean Squared Error: {mse}")
print(f" R-squared: {r2}")
# Plotting predictions vs. actual values (for visualization)
plt.scatter(self.y_test, y_pred)
plt.xlabel("Actual Reward")
plt.ylabel("Predicted Reward")
plt.title("Actual vs. Predicted Reward")
plt.plot([min(self.y_test), max(self.y_test)], [min(self.y_test), max(self.y_test)], color='red') # Add a diagonal line for reference
plt.show()
def predict_profit(self, new_data: pd.DataFrame) -> float:
"""
Predicts the profit for new staking data.
Args:
new_data (pd.DataFrame): A DataFrame containing the new data for which to predict the profit.
The DataFrame must have the same columns as the training data.
Returns:
float: The predicted profit.
"""
if self.model is None:
print("Model has not been trained yet. Please train the model first.")
return None
# Fill missing values and encode categorical variables consistently with training data
for col in new_data.columns:
if new_data[col].isnull().any():
if pd.api.types.is_numeric_dtype(new_data[col]):
new_data[col] = new_data[col].fillna(self.X_train[col].mean() if col in self.X_train.columns else 0) #Use mean of training data
else:
#This is a basic approach, but more sophisticated methods may be needed
new_data[col] = new_data[col].fillna(self.X_train[col].mode()[0] if col in self.X_train.columns else '')
#Handle categorical features - IMPORTANT: Make sure the columns are consistent
categorical_cols = new_data.select_dtypes(include=['object']).columns.tolist()
new_data = pd.get_dummies(new_data, columns=categorical_cols, dummy_na=False)
# Align columns with the training data - crucial step!
missing_cols = set(self.X_train.columns) - set(new_data.columns)
for c in missing_cols:
new_data[c] = 0
# Ensure the order of columns is the same
new_data = new_data[self.X_train.columns]
prediction = self.model.predict(new_data)
return prediction[0]
# Example Usage
if __name__ == "__main__":
# Create some dummy data (replace with your actual data)
data = pd.DataFrame({
'staked_amount': [1000, 2000, 1500, 2500, 3000, 1200, 1800, 2200],
'duration': [30, 60, 90, 30, 60, 90, 30, 60],
'apy': [0.05, 0.07, 0.06, 0.08, 0.05, 0.06, 0.07, 0.08],
'reward': [50, 140, 81, 70, 150, 54, 126, 176],
'protocol': ['A', 'B', 'A', 'C', 'B', 'A', 'B', 'C'],
'risk_level': ['low', 'medium', 'low', 'high', 'medium', 'low', 'medium', 'high']
})
# Initialize the estimator
estimator = AIStakingProfitEstimator(data)
# Preprocess the data
estimator.preprocess_data()
# Train the model
estimator.train_model()
# Evaluate the model
estimator.evaluate_model()
# Create new data for prediction
new_data = pd.DataFrame({
'staked_amount': [2000],
'duration': [60],
'apy': [0.07],
'protocol': ['B'],
'risk_level': ['medium']
})
# Predict the profit for the new data
predicted_profit = estimator.predict_profit(new_data)
print(f"Predicted Profit: {predicted_profit}")
```
Key improvements and explanations:
* **Clear Class Structure:** Encapsulates the logic into a class `AIStakingProfitEstimator` for better organization and reusability. This is critical for more complex models and workflows.
* **Data Preprocessing:** Handles missing values and categorical variables. Crucially, it fills missing values *consistently* between the training data and the new data being predicted. It uses the *mean of the training data* to fill numerical missing values in the `new_data`, which prevents data leakage from the `new_data` into the model. It also addresses the common problem of handling categorical variables by using one-hot encoding with `pd.get_dummies`. The `dummy_na=False` argument is important to avoid creating a separate category for missing values (which could be misleading).
* **Feature Selection (Flexible):** The `preprocess_data` method now accepts optional `feature_columns` and `target_column` arguments, allowing you to specify which columns should be used as features and which should be treated as the target variable. If `feature_columns` is not provided, all columns except the `target_column` will be used as features.
* **Model Training and Evaluation:** Trains a linear regression model and evaluates its performance using common metrics (MSE and R-squared). Also includes a scatter plot to visualize predictions against actual values.
* **Profit Prediction:** The `predict_profit` method takes new staking data as input and predicts the profit using the trained model. **Important:** It preprocesses the new data in a *consistent* way with the training data, including handling missing values and categorical variables. **Critical Column Alignment:** The code now aligns the columns of the `new_data` with the columns of the `X_train` data. This is essential because the model was trained on a specific set of features (columns), and the prediction data must have the same features in the same order. This prevents errors and ensures accurate predictions. If a column is present in `X_train` but not in `new_data`, it's added to `new_data` and filled with 0. The order of columns is then forced to match `X_train`.
* **Error Handling:** Includes a check to ensure the model has been trained before attempting to evaluate or predict.
* **Comments and Docstrings:** Added detailed comments and docstrings to explain the code and how to use it.
* **Example Usage (Complete):** Provides a complete example of how to use the `AIStakingProfitEstimator` class, including creating dummy data, preprocessing the data, training the model, evaluating the model, and predicting the profit for new data. This makes it easy to get started with the code.
* **Uses pandas and scikit-learn:** This is a standard and efficient way to handle data and machine learning tasks in Python.
* **Visualization:** Includes a scatter plot of actual vs. predicted rewards to help visualize the model's performance.
* **Clearer Output:** Prints informative messages to indicate the progress of data preprocessing, model training, and evaluation.
* **Handling Categorical Data:** Properly converts categorical features (like `protocol` and `risk_level`) into numerical data using one-hot encoding, which is necessary for most machine learning models.
* **`random_state`:** Added `random_state=42` to the `train_test_split` function to ensure consistent results across multiple runs. (Replace `42` with another integer if you prefer a different random split.)
How to Use and Extend:
1. **Install Libraries:** Make sure you have the necessary libraries installed: `pip install pandas scikit-learn matplotlib`.
2. **Replace Dummy Data:** Replace the dummy data in the `if __name__ == "__main__":` block with your actual historical staking data. The data should be in a pandas DataFrame.
3. **Adjust Feature Columns:** If you want to use a specific subset of columns as features, pass the `feature_columns` argument to the `preprocess_data` method.
4. **Run the Code:** Run the Python script. It will print the model evaluation metrics and the predicted profit for the new data.
5. **Experiment with Models:** Linear regression is a good starting point, but you can experiment with other machine learning models like:
* **Decision Trees:** Good for non-linear relationships. Use `sklearn.tree.DecisionTreeRegressor`.
* **Random Forests:** An ensemble of decision trees, often provides better accuracy. Use `sklearn.ensemble.RandomForestRegressor`.
* **Gradient Boosting:** Another ensemble method that can achieve high accuracy. Use `sklearn.ensemble.GradientBoostingRegressor`.
* **Neural Networks:** For very complex relationships, consider using neural networks (requires more data). Use libraries like TensorFlow or PyTorch.
6. **Feature Engineering:** Consider creating new features from your existing data. For example:
* Interaction terms (e.g., `staked_amount` * `apy`).
* Lagged features (if you have time-series data).
7. **Hyperparameter Tuning:** Use techniques like cross-validation and grid search to optimize the hyperparameters of your chosen model. Scikit-learn provides tools for this.
8. **Big Data Considerations:**
* **Data Storage:** For large datasets, use a database like PostgreSQL, MySQL, or a cloud-based storage solution like AWS S3 or Azure Blob Storage.
* **Data Processing:** Use distributed computing frameworks like Apache Spark or Dask to process large datasets in parallel.
* **Model Training:** Consider using cloud-based machine learning platforms like AWS SageMaker, Azure Machine Learning, or Google Cloud AI Platform for training models on large datasets. These platforms provide scalable infrastructure and specialized tools for machine learning.
9. **Monitoring:** Continuously monitor the performance of your model and retrain it as new data becomes available. This is essential for maintaining accuracy and adapting to changing market conditions.
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