code: import pandas as pd
  import numpy as np
  from scipy.stats import spearmanr
  from sklearn.metrics import matthews_corrcoef, ConfusionMatrixDisplay
  from alaska2.submissions import blend_predictions_ranked, blend_predictions_mean
  import matplotlib.pyplot as plt
  from sklearn.metrics import plot_confusion_matrix
  
  v25_xl_NR_moreTTA = pd.read_csv("submission_v25_xl_NR_moreTTA.csv").sort_values(by="Id").reset_index()
  v25_xl_NR_moreTTA_b4mish = pd.read_csv("submission_v25_xl_NR_moreTTA_b4mish.csv").sort_values(by="Id").reset_index()
  
  mean_09406 = pd.read_csv("mean_0.9406_cls_Cf2cauc_Df1cauc_Df2cauc_Ff0cauc_Gf0cauc_Gf1cauc_Gf2cauc_Gf3cauc.csv")
  xgb_cls_gs_09445 = pd.read_csv(
      "xgb_cls_gs_0.9445_Cf2cauc_Df1cauc_Df2cauc_Ff0cauc_Gf0cauc_Gf1cauc_Gf2cauc_Gf3cauc_.csv"
  )
  
  # Force 1.01 value of OOR values in my submission
  oor_mask = v25_xl_NR_moreTTA.Label > 1.0
  
  mean_09406.loc[oor_mask, "Label"] = 1.01
  xgb_cls_gs_09445.loc[oor_mask, "Label"] = 1.01
  
  submissions = [v25_xl_NR_moreTTA, v25_xl_NR_moreTTA_b4mish, mean_09406, xgb_cls_gs_09445]
  
  cm = np.zeros((len(submissions), len(submissions)))
  for i in range(len(submissions)):
      for j in range(len(submissions)):
          cm[i, j] = spearmanr(submissions[i].Label, submissions[j].Label).correlation
  
  print(cm)
  
  disp = ConfusionMatrixDisplay(
      confusion_matrix=cm,
      display_labels=["v25_xl_NR_moreTTA", "v25_xl_NR_moreTTA_b4mish", "mean_09406", "xgb_cls_gs_09445"],
  )
  plt.figure(figsize=(8, 8))
  disp.plot(include_values=True, cmap="Blues", ax=plt.gca(), xticks_rotation=45)
  plt.show()
  
  blend_6_ranked = blend_predictions_ranked([v25_xl_NR_moreTTA_b4mish, xgb_cls_gs_09445])
  blend_6_ranked.to_csv("blend_6_ranked_v25_xl_NR_moreTTA_b4mish_with_xgb_cls_gs_09445.csv", index=False)
  
apis: sklearn.metrics.ConfusionMatrixDisplay pandas.read_csv matplotlib.pyplot.figure matplotlib.pyplot.gca matplotlib.pyplot.show scipy.stats.spearmanr