class_docstr: Constructs a weighted ensemble using the greedy Ensemble Selection algorithm.
class_docstr_tok: ["Constructs","a","weighted","ensemble","using","the","greedy","Ensemble","Selection","algorithm","."]
code: def remap_base_models(self, model_refit_map: Dict[str, str]) -> None:
      updated_weights = {}
      for model, weight in self.model_to_weight.items():
          model_full_name = model_refit_map.get(model, model)
          updated_weights[model_full_name] = weight
      self.model_to_weight = updated_weights
  
imports: import copy
  import logging
  import pprint
  from typing import Dict, List, Optional
  import numpy
  import autogluon.core
  from autogluon.common.utils.log_utils import set_logger_verbosity
  from autogluon.core.models.greedy_ensemble.ensemble_selection import EnsembleSelection
  from autogluon.timeseries import TimeSeriesDataFrame
  from autogluon.timeseries.metrics import TimeSeriesScorer
  from autogluon.timeseries.models.ensemble import AbstractTimeSeriesEnsembleModel
  from autogluon.timeseries.utils.datetime import get_seasonality
label_desc: Use image node_id 7 for calling the TimeSeriesGreedyEnsemble obj's underlying member method code with example usage: obj.remap_base_models(model_refit_map) without return types