Skip to content

Overview#

The Predictor class serves as a base class for implementing predictive models within the Quick-Tune-Tool. It provides core functionality for model setup, data handling, training, and persistence (saving/loading), allowing specific predictive models to extend and customize these methods.

Core Methods#

  • fit and _fit:

    • fit: Public method for training the model. It takes feature data X, target labels y, verbosity level, and any additional arguments.
    • _fit: Abstract method where specific model training logic is implemented. Models inheriting from Predictor should override _fit to implement their own fitting procedures.

  • preprocess and _preprocess:

    • preprocess: Wrapper method that calls _preprocess to prepare data for fitting or prediction.
    • _preprocess: Abstract method where data transformation logic should be added. Designed to clean and structure input data before model training or inference.

  • load and save:

    • load: Class method to load a saved model from disk, optionally resetting its path and logging the location.
    • save: Saves the current model to disk in a specified path, providing persistence for trained models.

  • predict:
    Abstract method for generating predictions on new data. Specific predictive models should implement this method based on their inference logic.

This Predictor class offers a foundation for different predictive models, providing essential methods for data handling, training, and saving/loading, with extensibility for custom implementations.

Available Predictors#

  • PerfPredictor Predicts the performance of a configuration on a new dataset.
  • CostPredictor Predicts the cost of training a configuration on a new dataset.