Source code for autogluon.core.models.ensemble.bagged_ensemble_model

import copy
import logging
import os
import time
from collections import Counter
from statistics import mean
from functools import reduce

import numpy as np
import pandas as pd

from ...utils.exceptions import TimeLimitExceeded
from ...utils.loaders import load_pkl
from ...utils.savers import save_pkl
from ...utils.utils import generate_kfold, _compute_fi_with_stddev, extract_column

from ..abstract.abstract_model import AbstractModel

logger = logging.getLogger(__name__)

# TODO: Add metadata object with info like score on each model, train time on each model, etc.
[docs]class BaggedEnsembleModel(AbstractModel): """ Bagged ensemble meta-model which fits a given model multiple times across different splits of the training data. """ _oof_filename = 'oof.pkl' def __init__(self, model_base: AbstractModel, random_state=0, **kwargs): self.model_base = model_base self._child_type = type(self.model_base) self.models = [] self._oof_pred_proba = None self._oof_pred_model_repeats = None self._n_repeats = 0 # Number of n_repeats with at least 1 model fit, if kfold=5 and 8 models have been fit, _n_repeats is 2 self._n_repeats_finished = 0 # Number of n_repeats finished, if kfold=5 and 8 models have been fit, _n_repeats_finished is 1 self._k_fold_end = 0 # Number of models fit in current n_repeat (0 if completed), if kfold=5 and 8 models have been fit, _k_fold_end is 3 self._k = None # k models per n_repeat, equivalent to kfold value self._k_per_n_repeat = [] # k-fold used for each n_repeat. == [5, 10, 3] if first kfold was 5, second was 10, and third was 3 self._random_state = random_state self.low_memory = True self.bagged_mode = None try: feature_metadata = self.model_base.feature_metadata except: feature_metadata = None eval_metric = kwargs.pop('eval_metric', self.model_base.eval_metric) stopping_metric = kwargs.pop('stopping_metric', self.model_base.stopping_metric) super().__init__(problem_type=self.model_base.problem_type, eval_metric=eval_metric, stopping_metric=stopping_metric, feature_metadata=feature_metadata, **kwargs) def _set_default_params(self): default_params = {'save_bag_folds': True} for param, val in default_params.items(): self._set_default_param_value(param, val) super()._set_default_params() def is_valid(self): return self.is_fit() and (self._n_repeats == self._n_repeats_finished) def can_infer(self): return self.is_fit() and self.params.get('save_bag_folds', True) def is_stratified(self): if self.problem_type == REGRESSION or self.problem_type == SOFTCLASS: return False else: return True def is_fit(self): return len(self.models) != 0 # TODO: This assumes bagged ensemble has a complete k_fold and no partial k_fold models, this is likely fine but will act incorrectly if called when only a partial k_fold has been completed # Solving this is memory intensive, requires all oof_pred_probas from all n_repeats, so its probably not worth it. @property def oof_pred_proba(self): # TODO: Require is_valid == True (add option param to ignore is_valid) return self._oof_pred_proba_func(self._oof_pred_proba, self._oof_pred_model_repeats) @staticmethod def _oof_pred_proba_func(oof_pred_proba, oof_pred_model_repeats): oof_pred_model_repeats_without_0 = np.where(oof_pred_model_repeats == 0, 1, oof_pred_model_repeats) if oof_pred_proba.ndim == 2: oof_pred_model_repeats_without_0 = oof_pred_model_repeats_without_0[:, None] return oof_pred_proba / oof_pred_model_repeats_without_0 def preprocess(self, X, preprocess_nonadaptive=True, model=None, **kwargs): if preprocess_nonadaptive: if model is None: if not self.models: return X model = self.models[0] model = self.load_child(model) return model.preprocess(X, preprocess_stateful=False) else: return X def _fit(self, X, y, k_fold=5, k_fold_start=0, k_fold_end=None, n_repeats=1, n_repeat_start=0, time_limit=None, sample_weight=None, **kwargs): if k_fold < 1: k_fold = 1 if k_fold_end is None: k_fold_end = k_fold if self._oof_pred_proba is None and (k_fold_start != 0 or n_repeat_start != 0): self._load_oof() if n_repeat_start != self._n_repeats_finished: raise ValueError(f'n_repeat_start must equal self._n_repeats_finished, values: ({n_repeat_start}, {self._n_repeats_finished})') if n_repeats <= n_repeat_start: raise ValueError(f'n_repeats must be greater than n_repeat_start, values: ({n_repeats}, {n_repeat_start})') if k_fold_start != self._k_fold_end: raise ValueError(f'k_fold_start must equal previous k_fold_end, values: ({k_fold_start}, {self._k_fold_end})') if k_fold_start >= k_fold_end: # TODO: Remove this limitation if n_repeats > 1 raise ValueError(f'k_fold_end must be greater than k_fold_start, values: ({k_fold_end}, {k_fold_start})') if (n_repeats - n_repeat_start) > 1 and k_fold_end != k_fold: # TODO: Remove this limitation raise ValueError(f'k_fold_end must equal k_fold when (n_repeats - n_repeat_start) > 1, values: ({k_fold_end}, {k_fold})') if self._k is not None and self._k != k_fold: raise ValueError(f'k_fold must equal previously fit k_fold value for the current n_repeat, values: (({k_fold}, {self._k})') fold_start = n_repeat_start * k_fold + k_fold_start fold_end = (n_repeats - 1) * k_fold + k_fold_end time_start = time.time() model_base = self._get_model_base() model_base.rename(name='') if self.features is not None: model_base.features = self.features model_base.feature_metadata = self.feature_metadata # TODO: Don't pass this here if self.model_base is not None: self.save_model_base(self.model_base) self.model_base = None if k_fold == 1: if self._n_repeats != 0: raise ValueError(f'n_repeats must equal 0 when fitting a single model with k_fold < 2, values: ({self._n_repeats}, {k_fold})') = f'{}S1F1' model_base.set_contexts(path_context=self.path + + os.path.sep) time_start_fit = time.time(), y=y, time_limit=time_limit, **kwargs) model_base.fit_time = time.time() - time_start_fit model_base.predict_time = None self._oof_pred_proba = model_base.predict_proba(X=X) # TODO: Cheater value, will be overfit to valid set self._oof_pred_model_repeats = np.ones(shape=len(X), dtype=np.uint8) self._n_repeats = 1 self._n_repeats_finished = 1 self._k_per_n_repeat = [1] self.bagged_mode = False model_base.reduce_memory_size(remove_fit=True, remove_info=False, requires_save=True) if not self.params.get('save_bag_folds', True): model_base.model = None if self.low_memory: self.save_child(model_base, verbose=False) self.models = [] else: self.models = [model_base] self._add_child_times_to_bag(model=model_base) return # TODO: Preprocess data here instead of repeatedly kfolds = generate_kfold(X=X, y=y, n_splits=k_fold, stratified=self.is_stratified(), random_state=self._random_state, n_repeats=n_repeats) oof_pred_proba, oof_pred_model_repeats = self._construct_empty_oof(X=X, y=y) models = [] folds_to_fit = fold_end - fold_start for j in range(n_repeat_start, n_repeats): # For each n_repeat cur_repeat_count = j - n_repeat_start fold_start_n_repeat = fold_start + cur_repeat_count * k_fold fold_end_n_repeat = min(fold_start_n_repeat + k_fold, fold_end) # TODO: Consider moving model fit inner for loop to a function to simply this code for i in range(fold_start_n_repeat, fold_end_n_repeat): # For each fold fold_num_in_repeat = i - (j * k_fold) # The fold in the current repeat set (first fold in set = 0) folds_finished = i - fold_start folds_left = fold_end - i fold = kfolds[i] time_elapsed = time.time() - time_start if time_limit is not None: time_left = time_limit - time_elapsed required_time_per_fold = time_left / folds_left time_limit_fold = required_time_per_fold * 0.8 if folds_finished > 0: expected_time_required = time_elapsed * folds_to_fit / folds_finished expected_remaining_time_required = expected_time_required * folds_left / folds_to_fit if expected_remaining_time_required > time_left: raise TimeLimitExceeded if time_left <= 0: raise TimeLimitExceeded else: time_limit_fold = None time_start_fold = time.time() train_index, val_index = fold X_fold, X_val_fold = X.iloc[train_index, :], X.iloc[val_index, :] y_fold, y_val_fold = y.iloc[train_index], y.iloc[val_index] fold_model = copy.deepcopy(model_base) = f'{}S{j+1}F{fold_num_in_repeat+1}' # S5F3 = 3rd fold of the 5th repeat set fold_model.set_contexts(self.path + + os.path.sep) kwargs_fold = kwargs.copy() if sample_weight is not None: kwargs_fold['sample_weight'] = sample_weight[train_index] kwargs_fold['sample_weight_val'] = sample_weight[val_index], y=y_fold, X_val=X_val_fold, y_val=y_val_fold, time_limit=time_limit_fold, **kwargs_fold) time_train_end_fold = time.time() if time_limit is not None: # Check to avoid unnecessarily predicting and saving a model when an Exception is going to be raised later if i != (fold_end - 1): time_elapsed = time.time() - time_start time_left = time_limit - time_elapsed expected_time_required = time_elapsed * folds_to_fit / (folds_finished + 1) expected_remaining_time_required = expected_time_required * (folds_left - 1) / folds_to_fit if expected_remaining_time_required > time_left: raise TimeLimitExceeded pred_proba = fold_model.predict_proba(X_val_fold) time_predict_end_fold = time.time() fold_model.fit_time = time_train_end_fold - time_start_fold fold_model.predict_time = time_predict_end_fold - time_train_end_fold fold_model.val_score = fold_model.score_with_y_pred_proba(y=y_val_fold, y_pred_proba=pred_proba) fold_model.reduce_memory_size(remove_fit=True, remove_info=False, requires_save=True) if not self.params.get('save_bag_folds', True): fold_model.model = None if self.low_memory: self.save_child(fold_model, verbose=False) models.append( else: models.append(fold_model) oof_pred_proba[val_index] += pred_proba oof_pred_model_repeats[val_index] += 1 self._add_child_times_to_bag(model=fold_model) if (fold_end_n_repeat != fold_end) or (k_fold == k_fold_end): self._k_per_n_repeat.append(k_fold) self.models += models self.bagged_mode = True if self._oof_pred_proba is None: self._oof_pred_proba = oof_pred_proba self._oof_pred_model_repeats = oof_pred_model_repeats else: self._oof_pred_proba += oof_pred_proba self._oof_pred_model_repeats += oof_pred_model_repeats self._n_repeats = n_repeats if k_fold == k_fold_end: self._k = None self._k_fold_end = 0 self._n_repeats_finished = self._n_repeats else: self._k = k_fold self._k_fold_end = k_fold_end self._n_repeats_finished = self._n_repeats - 1 def predict_proba(self, X, normalize=None, **kwargs): model = self.load_child(self.models[0]) X = self.preprocess(X, model=model, **kwargs) pred_proba = model.predict_proba(X=X, preprocess_nonadaptive=False, normalize=normalize) for model in self.models[1:]: model = self.load_child(model) pred_proba += model.predict_proba(X=X, preprocess_nonadaptive=False, normalize=normalize) pred_proba = pred_proba / len(self.models) return pred_proba def _predict_proba(self, X, normalize=False, **kwargs): return self.predict_proba(X=X, normalize=normalize, **kwargs) def score_with_oof(self, y, sample_weight=None): self._load_oof() valid_indices = self._oof_pred_model_repeats > 0 y = y[valid_indices] y_pred_proba = self.oof_pred_proba[valid_indices] if sample_weight is not None: sample_weight = sample_weight[valid_indices] return self.score_with_y_pred_proba(y=y, y_pred_proba=y_pred_proba, sample_weight=sample_weight) # TODO: Augment to generate OOF after shuffling each column in X (Batching), this is the fastest way. # TODO: v0.1 Reduce logging clutter during OOF importance calculation (Currently logs separately for each child) # Generates OOF predictions from pre-trained bagged models, assuming X and y are in the same row order as used in .fit(X, y) def compute_feature_importance(self, X, y, features=None, is_oof=True, time_limit=None, silent=False, **kwargs) -> pd.DataFrame: if features is None: features = self.load_child(model=self.models[0]).features if not is_oof: return super().compute_feature_importance(X, y, features=features, time_limit=time_limit, silent=silent, **kwargs) fi_fold_list = [] model_index = 0 num_children = len(self.models) if time_limit is not None: time_limit_per_child = time_limit / num_children else: time_limit_per_child = None if not silent: logging_message = f'Computing feature importance via permutation shuffling for {len(features)} features using out-of-fold (OOF) data aggregated across {num_children} child models...' if time_limit is not None: logging_message = f'{logging_message} Time limit: {time_limit}s...' logger.log(20, logging_message) time_start = time.time() early_stop = False children_completed = 0 log_final_suffix = '' for n_repeat, k in enumerate(self._k_per_n_repeat): if is_oof: if not self.bagged_mode: raise AssertionError('Model trained with no validation data cannot get feature importances on training data, please specify new test data to compute feature importances (model=%s)' % kfolds = generate_kfold(X=X, y=y, n_splits=k, stratified=self.is_stratified(), random_state=self._random_state, n_repeats=n_repeat + 1) cur_kfolds = kfolds[n_repeat * k:(n_repeat+1) * k] else: cur_kfolds = [(None, list(range(len(X))))]*k for i, fold in enumerate(cur_kfolds): _, test_index = fold model = self.load_child(self.models[model_index + i]) fi_fold = model.compute_feature_importance(X=X.iloc[test_index, :], y=y.iloc[test_index], features=features, time_limit=time_limit_per_child, silent=silent, log_prefix='\t', importance_as_list=True, **kwargs) fi_fold_list.append(fi_fold) children_completed += 1 if time_limit is not None and children_completed != num_children: time_now = time.time() time_left = time_limit - (time_now - time_start) time_child_average = (time_now - time_start) / children_completed if time_left < (time_child_average * 1.1): log_final_suffix = f' (Early stopping due to lack of time...)' early_stop = True break if early_stop: break model_index += k # TODO: DON'T THROW AWAY SAMPLES! USE LARGER N fi_list_dict = dict() for val in fi_fold_list: val = val['importance'].to_dict() # TODO: Don't throw away stddev information of children for key in val: if key not in fi_list_dict: fi_list_dict[key] = [] fi_list_dict[key] += val[key] fi_df = _compute_fi_with_stddev(fi_list_dict) if not silent: logger.log(20, f'\t{round(time.time() - time_start, 2)}s\t= Actual runtime (Completed {children_completed} of {num_children} children){log_final_suffix}') return fi_df def load_child(self, model, verbose=False) -> AbstractModel: if isinstance(model, str): child_path = self.create_contexts(self.path + model + os.path.sep) return self._child_type.load(path=child_path, verbose=verbose) else: return model def save_child(self, model, verbose=False): child = self.load_child(model) child.set_contexts(self.path + + os.path.sep) # TODO: Multiply epochs/n_iterations by some value (such as 1.1) to account for having more training data than bagged models def convert_to_refit_full_template(self): init_args = self._get_init_args() init_args['hyperparameters']['save_bag_folds'] = True # refit full models must save folds init_args['model_base'] = self.convert_to_refitfull_template_child() init_args['name'] = init_args['name'] + REFIT_FULL_SUFFIX model_full_template = self.__class__(**init_args) return model_full_template def convert_to_refitfull_template_child(self): compressed_params = self._get_compressed_params() child_compressed = copy.deepcopy(self._get_model_base()) child_compressed.feature_metadata = self.feature_metadata # TODO: Don't pass this here child_compressed.params = compressed_params return child_compressed def _get_init_args(self): init_args = dict( model_base=self._get_model_base(), random_state=self._random_state, ) init_args.update(super()._get_init_args()) init_args.pop('problem_type') init_args.pop('feature_metadata') return init_args def _get_compressed_params(self, model_params_list=None): if model_params_list is None: model_params_list = [ self.load_child(child).get_trained_params() for child in self.models ] model_params_compressed = dict() for param in model_params_list[0].keys(): model_param_vals = [model_params[param] for model_params in model_params_list] if all(isinstance(val, bool) for val in model_param_vals): counter = Counter(model_param_vals) compressed_val = counter.most_common(1)[0][0] elif all(isinstance(val, int) for val in model_param_vals): compressed_val = round(mean(model_param_vals)) elif all(isinstance(val, float) for val in model_param_vals): compressed_val = mean(model_param_vals) else: try: counter = Counter(model_param_vals) compressed_val = counter.most_common(1)[0][0] except TypeError: compressed_val = model_param_vals[0] model_params_compressed[param] = compressed_val return model_params_compressed def _get_compressed_params_trained(self): model_params_list = [ self.load_child(child).params_trained for child in self.models ] return self._get_compressed_params(model_params_list=model_params_list) def _get_model_base(self): if self.model_base is None: return self.load_model_base() else: return self.model_base def _add_child_times_to_bag(self, model): if self.fit_time is None: self.fit_time = model.fit_time else: self.fit_time += model.fit_time if self.predict_time is None: self.predict_time = model.predict_time else: self.predict_time += model.predict_time @classmethod def load(cls, path: str, reset_paths=True, low_memory=True, load_oof=False, verbose=True): model = super().load(path=path, reset_paths=reset_paths, verbose=verbose) if not low_memory: model.persist_child_models(reset_paths=reset_paths) if load_oof: model._load_oof() return model @classmethod def load_oof(cls, path, verbose=True): try: oof = load_pkl.load(path=path + 'utils' + os.path.sep + cls._oof_filename, verbose=verbose) oof_pred_proba = oof['_oof_pred_proba'] oof_pred_model_repeats = oof['_oof_pred_model_repeats'] except FileNotFoundError: model = cls.load(path=path, reset_paths=True, verbose=verbose) model._load_oof() oof_pred_proba = model._oof_pred_proba oof_pred_model_repeats = model._oof_pred_model_repeats return cls._oof_pred_proba_func(oof_pred_proba=oof_pred_proba, oof_pred_model_repeats=oof_pred_model_repeats) def _load_oof(self): if self._oof_pred_proba is not None: pass else: oof = load_pkl.load(path=self.path + 'utils' + os.path.sep + self._oof_filename) self._oof_pred_proba = oof['_oof_pred_proba'] self._oof_pred_model_repeats = oof['_oof_pred_model_repeats'] def persist_child_models(self, reset_paths=True): for i, model_name in enumerate(self.models): if isinstance(model_name, str): child_path = self.create_contexts(self.path + model_name + os.path.sep) child_model = self._child_type.load(path=child_path, reset_paths=reset_paths, verbose=True) self.models[i] = child_model def load_model_base(self): return load_pkl.load(path=self.path + 'utils' + os.path.sep + 'model_template.pkl') def save_model_base(self, model_base): + 'utils' + os.path.sep + 'model_template.pkl', object=model_base) def save(self, path=None, verbose=True, save_oof=True, save_children=False) -> str: if path is None: path = self.path if save_children: model_names = [] for child in self.models: child = self.load_child(child) child.set_contexts(path + + os.path.sep) model_names.append( self.models = model_names if save_oof and self._oof_pred_proba is not None: + 'utils' + os.path.sep + self._oof_filename, object={ '_oof_pred_proba': self._oof_pred_proba, '_oof_pred_model_repeats': self._oof_pred_model_repeats, }) self._oof_pred_proba = None self._oof_pred_model_repeats = None return super().save(path=path, verbose=verbose) # If `remove_fit_stack=True`, variables will be removed that are required to fit more folds and to fit new stacker models which use this model as a base model. # This includes OOF variables. def reduce_memory_size(self, remove_fit_stack=False, remove_fit=True, remove_info=False, requires_save=True, reduce_children=False, **kwargs): super().reduce_memory_size(remove_fit=remove_fit, remove_info=remove_info, requires_save=requires_save, **kwargs) if remove_fit_stack: try: os.remove(self.path + 'utils' + os.path.sep + self._oof_filename) except FileNotFoundError: pass if requires_save: self._oof_pred_proba = None self._oof_pred_model_repeats = None try: os.remove(self.path + 'utils' + os.path.sep + 'model_template.pkl') except FileNotFoundError: pass if requires_save: self.model_base = None try: os.rmdir(self.path + 'utils') except OSError: pass if reduce_children: for model in self.models: model = self.load_child(model) model.reduce_memory_size(remove_fit=remove_fit, remove_info=remove_info, requires_save=requires_save, **kwargs) if requires_save and self.low_memory: self.save_child(model=model) def _get_model_names(self): model_names = [] for model in self.models: if isinstance(model, str): model_names.append(model) else: model_names.append( return model_names def get_info(self): info = super().get_info() children_info = self._get_child_info() child_memory_sizes = [child['memory_size'] for child in children_info.values()] sum_memory_size_child = sum(child_memory_sizes) if child_memory_sizes: max_memory_size_child = max(child_memory_sizes) else: max_memory_size_child = 0 if self.low_memory: max_memory_size = info['memory_size'] + sum_memory_size_child min_memory_size = info['memory_size'] + max_memory_size_child else: max_memory_size = info['memory_size'] min_memory_size = info['memory_size'] - sum_memory_size_child + max_memory_size_child bagged_info = dict( child_model_type=self._child_type.__name__, num_child_models=len(self.models), child_model_names=self._get_model_names(), _n_repeats=self._n_repeats, # _n_repeats_finished=self._n_repeats_finished, # commented out because these are too technical # _k_fold_end=self._k_fold_end, # _k=self._k, _k_per_n_repeat=self._k_per_n_repeat, _random_state=self._random_state, low_memory=self.low_memory, # If True, then model will attempt to use at most min_memory_size memory by having at most one child in memory. If False, model will use max_memory_size memory. bagged_mode=self.bagged_mode, max_memory_size=max_memory_size, # Memory used when all children are loaded into memory at once. min_memory_size=min_memory_size, # Memory used when only the largest child is loaded into memory. child_hyperparameters=self._get_model_base().params, child_hyperparameters_fit = self._get_compressed_params_trained(), child_ag_args_fit = self._get_model_base().params_aux, ) info['bagged_info'] = bagged_info info['children_info'] = children_info child_features_full = list(set().union(*[child['features'] for child in children_info.values()])) info['features'] = child_features_full info['num_features'] = len(child_features_full) return info def get_memory_size(self): models = self.models self.models = None memory_size = super().get_memory_size() self.models = models return memory_size def _get_child_info(self): child_info_dict = dict() for model in self.models: if isinstance(model, str): child_path = self.create_contexts(self.path + model + os.path.sep) child_info_dict[model] = self._child_type.load_info(child_path) else: child_info_dict[] = model.get_info() return child_info_dict def _construct_empty_oof(self, X, y): if self.problem_type == MULTICLASS: oof_pred_proba = np.zeros(shape=(len(X), len(y.unique())), dtype=np.float32) elif self.problem_type == SOFTCLASS: oof_pred_proba = np.zeros(shape=y.shape, dtype=np.float32) else: oof_pred_proba = np.zeros(shape=len(X), dtype=np.float32) oof_pred_model_repeats = np.zeros(shape=len(X), dtype=np.uint8) return oof_pred_proba, oof_pred_model_repeats def _preprocess_fit_resources(self, silent=False, **kwargs): """Pass along to child models to avoid altering up-front""" return kwargs # TODO: Currently double disk usage, saving model in HPO and also saving model in bag def _hyperparameter_tune(self, X, y, k_fold, scheduler_options, preprocess_kwargs=None, **kwargs): if len(self.models) != 0: raise ValueError('self.models must be empty to call hyperparameter_tune, value: %s' % self.models) self.model_base.feature_metadata = self.feature_metadata # TODO: Move this self.model_base.set_contexts(self.path + 'hpo' + os.path.sep) # TODO: Preprocess data here instead of repeatedly if preprocess_kwargs is None: preprocess_kwargs = dict() X = self.preprocess(X=X, preprocess=False, fit=True, **preprocess_kwargs) kfolds = generate_kfold(X=X, y=y, n_splits=k_fold, stratified=self.is_stratified(), random_state=self._random_state, n_repeats=1) train_index, test_index = kfolds[0] X_fold, X_val_fold = X.iloc[train_index, :], X.iloc[test_index, :] y_fold, y_val_fold = y.iloc[train_index], y.iloc[test_index] orig_time = scheduler_options[1]['time_out'] scheduler_options[1]['time_out'] = orig_time * 0.8 # TODO: Scheduler doesn't early stop on final model, this is a safety net. Scheduler should be updated to early stop hpo_models, hpo_model_performances, hpo_results = self.model_base.hyperparameter_tune(X=X_fold, y=y_fold, X_val=X_val_fold, y_val=y_val_fold, scheduler_options=scheduler_options, **kwargs) scheduler_options[1]['time_out'] = orig_time bags = {} bags_performance = {} for i, (model_name, model_path) in enumerate(hpo_models.items()): child: AbstractModel = self._child_type.load(path=model_path) y_pred_proba = child.predict_proba(X_val_fold) # TODO: Create new Ensemble Here bag = copy.deepcopy(self) bag.rename(f"{}{os.path.sep}T{i}") bag.set_contexts(self.path_root + + os.path.sep) oof_pred_proba, oof_pred_model_repeats = self._construct_empty_oof(X=X, y=y) oof_pred_proba[test_index] += y_pred_proba oof_pred_model_repeats[test_index] += 1 bag.model_base = None child.rename('') child.set_contexts(bag.path + + os.path.sep) bag.save_model_base(child.convert_to_template()) bag._k = k_fold bag._k_fold_end = 1 bag._n_repeats = 1 bag._oof_pred_proba = oof_pred_proba bag._oof_pred_model_repeats = oof_pred_model_repeats child.rename('S1F1') child.set_contexts(bag.path + + os.path.sep) if not self.params.get('save_bag_folds', True): child.model = None if bag.low_memory: bag.save_child(child, verbose=False) bag.models.append( else: bag.models.append(child) bag.val_score = child.val_score bag._add_child_times_to_bag(model=child) bags[] = bag.path bags_performance[] = bag.val_score # TODO: hpo_results likely not correct because no renames return bags, bags_performance, hpo_results