tabensemb.model.RFE

class tabensemb.model.RFE(trainer: Trainer, modelbase: AbstractModel, model_subset=None, program=None, metric: str = 'Validation RMSE', impor_method: str = 'shap', cross_validation=5, min_features=1, **kwargs)

Bases: TorchModel

Methods

__init__(trainer: Trainer, modelbase: AbstractModel, model_subset=None, program=None, metric: str = 'Validation RMSE', impor_method: str = 'shap', cross_validation=5, min_features=1, **kwargs)

Parameters:

trainer:

A Trainer instance that contains all information and datasets and will be linked to the model base. The trainer has loaded configs and data.

program:

The name of the model base. If None, the name from _get_program_name() is used.

model_subset:

The names of models selected to be trained in the model base.

exclude_models:

The names of models that should not be trained. Only one of model_subset and exclude_models can be specified.

store_in_harddisk:

Whether to save models in the hard disk. If the global setting tabensemb.setting["low_memory"] is True, True is used.

optimizers

A dictionary of optimizer names (choose from those in torch.optim) and their hyperparameters for each model. Remember to change _initial_values() and _space() to optimize its hyperparameters.

lr_schedulers

A dictionary of lr scheduler names (choose from those in torch.optim.lr_scheduler) and their hyperparameters for each model. Remember to change _initial_values() and _space() to optimize its hyperparameters.

**kwargs:

Ignored.

run(model_name[, verbose])
_get_model_names()	Get names of all available models implemented in the model base.
_get_program_name()	Get the default name of the model base.
_new_model(model_name, verbose, **kwargs)	Generate a new selected model based on kwargs.
_predict(df, model_name[, derived_data])	Make prediction based on a tabular dataset using the selected model.
_predict_all(**kwargs)	Make inferences on training/validation/testing datasets to evaluate the performance of all models.
_train([verbose, model_subset, warm_start])	The basic framework of training models, including processing the dataset, training each model (with/without bayesian hyperparameter optimization), and evaluating them on the dataset.