class_docstr: Analyze results of machine learning experiments.
docstr: Wilcoxon signed-rank test.
  
  http://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test
  `Wilcoxon signed-rank test
  <https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test>`_.
  Tests whether two  related paired samples come from the same
  distribution. In particular, it tests whether the distribution of the
  differences x-y is symmetric about zero
docstr_tok: ["Wilcoxon","signed-rank","test",".","http",":","\/\/en.wikipedia.org\/wiki\/Wilcoxon_signed-rank_test","`","Wilcoxon","signed-rank","test","<","https",":","\/\/en.wikipedia.org\/wiki\/Wilcoxon_signed-rank_test",">","`","_",".","Tests","whether","two","related","paired","samples","come","from","the","same","distribution",".","In","particular",",","it","tests","whether","the","distribution","of","the","differences","x-y","is","symmetric","about","zero"]
code: def wilcoxon_test(self, metric_name=None):
      """Wilcoxon signed-rank test.
  
      http://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test
      `Wilcoxon signed-rank test
      <https://en.wikipedia.org/wiki/Wilcoxon_signed-rank_test>`_.
      Tests whether two  related paired samples come from the same
      distribution. In particular, it tests whether the distribution of the
      differences x-y is symmetric about zero
      """
      self._check_is_evaluated()
      metric_name = self._validate_metric_name(metric_name)
      metrics_per_estimator_dataset = (
          self._get_metrics_per_estimator_dataset(metric_name)
      )
  
      wilcoxon_df = pd.DataFrame()
      prod = itertools.combinations(
          metrics_per_estimator_dataset.keys(), 2
      )
      for p in prod:
          estim_1 = p[0]
          estim_2 = p[1]
          w, p_val = stats.wilcoxon(
              metrics_per_estimator_dataset[p[0]],
              metrics_per_estimator_dataset[p[1]],
          )
  
          w_test = {
              "estimator_1": estim_1,
              "estimator_2": estim_2,
              "statistic": w,
              "p_val": p_val,
          }
  
          wilcoxon_df = pd.concat(
              [wilcoxon_df, pd.DataFrame(w_test, index=[0])],
              ignore_index=True,
          )
  
      return wilcoxon_df
  
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 13 for calling the Evaluator obj's underlying member method code with example usage: obj.wilcoxon_test(metric_name) and returns: wilcoxon_df