build_models

lstchain.reco.dl1_to_dl2.build_models(filegammas, fileprotons, save_models=True, path_models='./', free_model_memory=True, energy_min=-inf, custom_config=None)

Uses MC data to train Random Forests for Energy and DISP reconstruction and G/H separation and returns the trained RFs. The passed config superseeds the standard configuration. Here is the complete workflow with the number of events selected from the config:

        graph LR
    GAMMA[gammas] -->|#`gamma_regressors`| REG(regressors) --> DISK
    GAMMA --> S(split)
    S --> |#`gamma_tmp_regressors`| g_train
    S --> |#`gamma_classifier`| g_test
    g_train --> tmp_reg(tmp regressors)
    tmp_reg --- A[ ]:::empty
    g_test --- A
    A --> g_test_dl2
    g_test_dl2 --- D[ ]:::empty
    protons -------- |#`proton_classifier`| D
    D --> cls(classifier)
    cls--> DISK
    classDef empty width:0px,height:0px;
    

Parameters:

filegammas: string

path to the file with MC gamma events

fileprotons: string

path to the file with MC proton events

save_models: bool

True to save the trained models on disk

path_models: string

path of a directory where to save the models. if it does exist, the directory is created

free_model_memory: bool

If True RF models are freed after use and not returned

energy_min: float

Cut in intensity of the showers for training RF

custom_config: dictionnary

Modified configuration to update the standard one

test_size: float or int

If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. If int, represents the absolute number of test samples. If None, it will be set to 0.25.

Returns:

if config[‘disp_method’] == ‘disp_vector’:

return reg_energy, reg_disp_vector, cls_gh

elif config[‘disp_method’] == ‘disp_norm_sign’:

return reg_energy, reg_disp_norm, cls_disp_sign, cls_gh

Raises:

ValueError

If the requested number of gamma events in the config for the training of the classifier is not valid. See config[“n_training_events”]