How to Write a MedPredictor

MedPredictor is Classifier/Regressor that inputs the features matrix and output prediction/s. It is being executed after FeatureProcessors

MedPredictor in MedModel follow a defined sequence of method calls. Here’s the typical lifecycle:

1. Constructor - Initializes the MedPredictor object.

2. init_defaults() - Sets default values for the predictor. Be sure to update classifier_type to reflect the predictor type.

3. Initialization - During learning:
   Implement init(map<string, string>& mapper) to parse parameters from a key-value map (using SerializableObject::init_from_string).
   Medial pipeline will initialize features_count and model_features that when we apply the modle we can make sure all and only needed features are inputed to the model. You might want to set transpose_for_learn, transpose_for_predict - if transpose of features matrix is needed for this type of predictor You might want to set (legacy and almost not used. Please use Feature Processors) to normalize the inputs: normalize_for_learn, normalize_y_for_learn, normalize_for_predict - During application:
   Arguments are loaded from disk. Parameters stored via ADD_SERIALIZATION_FUNCS are restored automatically.

4. Learning Phase - learn()
   Implements any learning logic needed during training.

5. Applying Phase - apply() or predict_single() for single patient (more efficient setup for single patient, some preparation are done prior) Applies the predictor to calculate the score

Steps to Implement a MedPredictor

1. Create Class Files - Create a new .h header and .cpp source file for your predictor class. Include MedAlgo.h in your header.

2. Set Default Values - Implement init_defaults() or set defaults in the constructor.

3. Parameter Initialization - Override init(map<string, string>& mapper) to parse external parameters.

4. Serialization - Add MEDSERIALIZE_SUPPORT($CLASS_NAME) at the end of your header file (replace $CLASS_NAME). - Add ADD_CLASS_NAME($CLASS_NAME) in the public section of your class. - Use ADD_SERIALIZATION_FUNCS to specify which parameters should be saved after learning. Do not include temporary or repository-specific variables.

5. Learning - Implement learn() for any required training logic.

6. Apply - Implement apply() to calculate the score. If there is more efficient calculate for single call, than also implement predict_single()

7. Register Your Predictor in the Header (MedAlgo.h) - Add a new type to MedPredictorTypes before MODEL_LAST. In the documentation comment, specify the name in MedPredictorTypes for Doxygen reference.

8. Register Your Feature Processor in the Source (MedAlgo.cpp) - Add your type conversion to predictor_type_to_name dictionary - Add your class to MedPredictor::new_polymorphic - Add your class to MedPredictor::make_predictor(MedPredictorTypes model_type)