code: def approximate_interactions(
      index, shap_values, X, feature_names=None
  ):
      """Order other features by how much interaction they seem to have with the feature at the given index.
  
      This just bins the SHAP values for a feature along that feature's value. For true Shapley interaction
      index values for SHAP see the interaction_contribs option implemented in XGBoost.
      """
  
      # convert from DataFrames if we got any
      if isinstance(X, pd.DataFrame):
          if feature_names is None:
              feature_names = X.columns
          X = X.values
  
      index = convert_name(index, shap_values, feature_names)
  
      if X.shape[0] > 10000:
          a = np.arange(X.shape[0])
          np.random.shuffle(a)
          inds = a[:10000]
      else:
          inds = np.arange(X.shape[0])
  
      x = X[inds, index]
      srt = np.argsort(x)
      shap_ref = shap_values[inds, index]
      shap_ref = shap_ref[srt]
      inc = max(min(int(len(x) / 10.0), 50), 1)
      interactions = []
      for i in range(X.shape[1]):
          encoded_val_other = encode_array_if_needed(
              X[inds, i][srt], dtype=float
          )
  
          val_other = encoded_val_other
          v = 0.0
          if not (i == index or np.sum(np.abs(val_other)) < 1e-8):
              for j in range(0, len(x), inc):
                  if (
                      np.std(val_other[j : j + inc]) > 0
                      and np.std(shap_ref[j : j + inc]) > 0
                  ):
                      v += abs(
                          np.corrcoef(
                              shap_ref[j : j + inc],
                              val_other[j : j + inc],
                          )[0, 1]
                      )
          val_v = v
  
          val_other = np.isnan(encoded_val_other)
          v = 0.0
          if not (i == index or np.sum(np.abs(val_other)) < 1e-8):
              for j in range(0, len(x), inc):
                  if (
                      np.std(val_other[j : j + inc]) > 0
                      and np.std(shap_ref[j : j + inc]) > 0
                  ):
                      v += abs(
                          np.corrcoef(
                              shap_ref[j : j + inc],
                              val_other[j : j + inc],
                          )[0, 1]
                      )
          nan_v = v
  
          interactions.append(max(val_v, nan_v))
  
      return np.argsort(-np.abs(interactions))
  
imports: import copy
  import os
  import re
  import sys
  from contextlib import contextmanager
  import numpy
  import pandas
  import scipy.special
  import sklearn
label_desc: Use image node_id 6 for calling a global function with example usage: approximate_interactions(index, shap_values, X, feature_names) and returns: np