docstr: Return the time that a single feature importance computation round can take up to. Currently the time it
  takes to fully evaluated minimum of 50 features or the number of features in the dataset.
docstr_tok: ["Return","the","time","that","a","single","feature","importance","computation","round","can","take","up","to",".","Currently","the","time","it","takes","to","fully","evaluated","minimum","of","50","features","or","the","number","of","features","in","the","dataset","."]
code: def compute_time_budget_fi(
      self, X_fi: pd.DataFrame, n_subsample: int, **kwargs
  ):
      """
      Return the time that a single feature importance computation round can take up to. Currently the time it
      takes to fully evaluated minimum of 50 features or the number of features in the dataset.
      """
      min_fi_samples = kwargs.get("min_fi_samples", 10000)
      max_fi_samples = kwargs.get("max_fi_samples", 50000)
      n_total_samples = max(
          min_fi_samples, min(max_fi_samples, len(X_fi))
      )
      baseline_time = self.model_predict_time
      fi_time_single = self.compute_expected_fi_time_single(
          X_fi=X_fi,
          model_predict_time=self.model_predict_time,
          n_subsample=n_subsample,
          n_total_sample=n_total_samples,
      )
      return fi_time_single * min(len(X_fi.columns), 50) + baseline_time
  
imports: import logging
  import time
  import traceback
  import uuid
  from copy import deepcopy
  from typing import List, Set, Tuple, Union
  import numpy
  import pandas
  from autogluon.common.features.types import R_FLOAT
  from ..models.abstract.abstract_model import AbstractModel
  from ..models.ensemble.bagged_ensemble_model import BaggedEnsembleModel
  from ..utils.exceptions import TimeLimitExceeded
  from ..utils.utils import generate_train_test_split, unevaluated_fi_df_template
label_desc: Use image node_id 7 for calling the FeatureSelector obj's underlying member method code with example usage: obj.compute_time_budget_fi(X_fi, n_subsample) and returns: unknown