TabularPredictor.fit_extra#

TabularPredictor.fit_extra(hyperparameters, time_limit=None, base_model_names=None, fit_weighted_ensemble=True, num_cpus='auto', num_gpus='auto', **kwargs)[source]#

Fits additional models after the original TabularPredictor.fit() call. The original train_data and tuning_data will be used to train the models.

Parameters

  • hyperparameters (str or dict) – Refer to argument documentation in TabularPredictor.fit(). If base_model_names is specified and hyperparameters is using the level-based key notation, the key of the level which directly uses the base models should be 1. The level in the hyperparameters dictionary is relative, not absolute.

  • time_limit (int, default = None) – Refer to argument documentation in TabularPredictor.fit().

  • base_model_names (list, default = None) – The names of the models to use as base models for this fit call. Base models will provide their out-of-fold predictions as additional features to the models in hyperparameters. If specified, all models trained will be stack ensembles. If None, models will be trained as if they were specified in TabularPredictor.fit(), without depending on existing models. Only valid if bagging is enabled.

  • fit_weighted_ensemble (bool, default = True) – If True, a WeightedEnsembleModel will be fit in each stack layer. A weighted ensemble will often be stronger than an individual model while being very fast to train. It is recommended to keep this value set to True to maximize predictive quality.

  • num_cpus (int, default = "auto") – The total amount of cpus you want AutoGluon predictor to use. Auto means AutoGluon will make the decision based on the total number of cpus available and the model requirement for best performance. Users generally don’t need to set this value

  • num_gpus (int, default = "auto") – The total amount of gpus you want AutoGluon predictor to use. Auto means AutoGluon will make the decision based on the total number of gpus available and the model requirement for best performance. Users generally don’t need to set this value

  • **kwargs –

    Refer to kwargs documentation in TabularPredictor.fit(). Note that the following kwargs are not available in fit_extra as they cannot be changed from their values set in fit():

    [holdout_frac, num_bag_folds, auto_stack, feature_generator, unlabeled_data]

    pseudo_datapd.DataFrame, default = None

    Data that has been self labeled by Autogluon model and will be incorporated into training during ‘fit_extra’