code: def make_grid(scores, dataset, model, normalize=True, transform=True):
      color_vals = {}
      metric_sort_order = {}
      for (_, _, method, metric), (fcounts, score) in filter(
          lambda x: x[0][0] == dataset and x[0][1] == model, scores
      ):
          metric_sort_order[metric] = get_metric_attr(
              metric, "sort_order"
          )
          if metric not in color_vals:
              color_vals[metric] = {}
  
          if transform:
              transform_type = get_metric_attr(metric, "transform")
              if transform_type == "negate":
                  score = -score
              elif transform_type == "one_minus":
                  score = 1 - score
              elif transform_type == "negate_log":
                  score = -np.log10(score)
  
          if fcounts is None:
              color_vals[metric][method] = score
          elif fcounts == "human":
              color_vals[metric][method] = _human_score_map(*score)
          else:
              auc = sklearn.metrics.auc(fcounts, score) / fcounts[-1]
              color_vals[metric][method] = auc
      # print(metric_sort_order)
      # col_keys = sorted(list(color_vals.keys()), key=lambda v: metric_sort_order[v])
      # print(col_keys)
      col_keys = list(color_vals.keys())
      row_keys = list(
          {v for k in col_keys for v in color_vals[k].keys()}
      )
  
      data = -28567 * np.ones((len(row_keys), len(col_keys)))
  
      for i in range(len(row_keys)):
          for j in range(len(col_keys)):
              data[i, j] = color_vals[col_keys[j]][row_keys[i]]
  
      assert (
          np.sum(data == -28567) == 0
      ), "There are missing data values!"
  
      if normalize:
          data = (data - data.min(0)) / (
              data.max(0) - data.min(0) + 1e-8
          )
  
      # sort by performans
      inds = np.argsort(-data.mean(1))
      row_keys = [row_keys[i] for i in inds]
      data = data[inds, :]
  
      return row_keys, col_keys, data
  
file_path: shap/shap/benchmark/plots.py
imports: import base64
  import io
  import os
  import numpy
  import sklearn
  from matplotlib.colors import LinearSegmentedColormap
  from ..None import __version__
  from ..plots import colors
  from .None import methods, metrics, models
  from .experiments import run_experiments
label_desc: Use image node_id 7 for calling a global function with example usage: make_grid(scores, dataset, model, normalize, transform) and returns: row_keys,  col_keys,  data