class_docstr: Analyze results of machine learning experiments.
docstr: Get old format back, to be deprecated.
docstr_tok: ["Get","old","format","back",",","to","be","deprecated","."]
code: def _get_metrics_per_estimator(self, metric_name):
      """Get old format back, to be deprecated."""
      # TODO deprecate in favor of new pandas data frame based data
      #  representation
      columns = [
          "strategy",
          "dataset",
          f"{metric_name}_mean",
          f"{metric_name}_stderr",
      ]
      df = self.metrics_by_strategy_dataset.loc[:, columns]
      d = {}
      for dataset in df.dataset.unique():
          results = []
          for strategy in df.strategy.unique():
              row = df.loc[
                  (df.strategy == strategy) & (df.dataset == dataset), :
              ]
              m = row["accuracy_mean"].values[0]
              s = row["accuracy_stderr"].values[0]
              results.append([strategy, m, s])
          d[dataset] = results
      return d
  
imports: import itertools
  import numpy
  import pandas
  from scipy import stats
  from scipy.stats import ranksums, ttest_ind
  from sktime.benchmarking.base import BaseResults
  from sktime.exceptions import NotEvaluatedError
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 22 for calling the Evaluator obj's underlying member method code with example usage: obj._get_metrics_per_estimator(metric_name) and returns: d