Update run.py #11967
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Update run.py #11967
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Hyperparameter Optimization: The CONFIG dictionary allows easy tuning of model parameters. You can expand it to include other models or settings as needed. Additional Models: Introduced Random Forest and XGBoost regressors for improved prediction performance, allowing for a more robust ensemble approach. Feature Engineering: Added a new feature_engineering function that generates new features from the date, like the day of the week and week of the year, which can significantly enhance model performance. Handling Missing Values: You can further extend the load_data function to handle missing values based on your dataset's characteristics. Comprehensive Evaluation Metrics: Added a new evaluate_predictions function to provide mean squared error (MSE), mean absolute error (MAE), and R² metrics, giving a better understanding of model performance. Command-Line Arguments: Enabled loading of the CSV file via command-line arguments for greater flexibility. Model Persistence: Added functionality to save trained models using joblib, allowing for easy reuse without retraining. Visualization Enhancements: The plotting function can be further enhanced by adding residual plots or feature importance plots if using tree-based models.
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Key Improvements:
Hyperparameter Configuration:
Introduced a CONFIG dictionary to easily manage and tune hyperparameters for various models (SVR, Random Forest, XGBoost, and SARIMAX).
Model Variety:
Added Random Forest and XGBoost regressors, enhancing the predictive capability through ensemble learning methods.
Feature Engineering:
Included a feature_engineering function that creates new features such as day of the week and week of the year from the date, potentially improving model performance.
Advanced Prediction Methods:
Implemented SARIMAX for time series forecasting, providing a more comprehensive approach to predict total users based on historical data and external factors.
Enhanced Data Normalization:
Incorporated normalization of input data using Normalizer from sklearn to standardize the data before training the models.
Model Evaluation Metrics:
Added an evaluate_predictions function that calculates and logs multiple evaluation metrics: Mean Squared Error (MSE), Mean Absolute Error (MAE), and R² score for better insights into model performance.
Improved Data Safety Check:
Refined the data_safety_checker function to assess the safety of predictions compared to actual results, using a more comprehensive logic to determine safety status.
Command-Line Interface:
Implemented argument parsing for loading CSV data through command-line arguments, enhancing usability and flexibility.
Model Persistence:
Added functionality to save trained models using joblib, allowing for easy reuse and deployment without the need for retraining.
Visualization Improvements:
Enhanced the plot_results function to visualize predictions against actual results clearly, making it easier to interpret model performance visually.