class_docstr: A suite of commands that are useful for analyzing the results
  of a completed scan, or during a scan.
  
  filename :: the name of the experiment log from Scan()
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docstr: Get the best parameters of the experiment based on a metric.
  Returns a numpy array with the values in a format that can be used
  with the talos backend in Scan(). Adds an index as the last column.
  
  metric | str or list | Column labels for the metric to correlate with
  exclude | list | Column label/s to be excluded from the correlation
  n | int | Number of hyperparameter permutations to be returned
  ascending | bool | Set to True when `metric` is to be minimized eg. loss
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code: def best_params(self, metric, exclude, n=10, ascending=False):
      """Get the best parameters of the experiment based on a metric.
      Returns a numpy array with the values in a format that can be used
      with the talos backend in Scan(). Adds an index as the last column.
  
      metric | str or list | Column labels for the metric to correlate with
      exclude | list | Column label/s to be excluded from the correlation
      n | int | Number of hyperparameter permutations to be returned
      ascending | bool | Set to True when `metric` is to be minimized eg. loss
  
      """
  
      cols = self._cols(metric, exclude)
      out = self.data[cols].sort_values(metric, ascending=ascending)
      out = out.drop(metric, axis=1).head(n)
      out.insert(out.shape[1], "index_num", range(len(out)))
  
      return out.values
  
file_path: talos/talos/commands/analyze.py
label_desc: Use image node_id 15 for calling the Analyze obj's underlying member method code with example usage: obj.best_params(metric, exclude, n, ascending) and returns: out