code: def independentlinear60(display=False, n_points=1_000):
      """A simulated dataset with tight correlations among distinct groups of features."""
  
      # set a constant seed
      old_seed = np.random.seed()
      np.random.seed(0)
  
      # generate dataset with known correlation
      N, M = n_points, 60
  
      # set one coefficient from each group of 3 to 1
      beta = np.zeros(M)
      beta[0:30:3] = 1
  
      def f(X):
          return np.matmul(X, beta)
  
      # Make sure the sample correlation is a perfect match
      X_start = np.random.randn(N, M)
      X = X_start - X_start.mean(0)
      y = f(X) + np.random.randn(N) * 1e-2
  
      # restore the previous numpy random seed
      np.random.seed(old_seed)
  
      return pd.DataFrame(X), y
  
file_path: shap/shap/datasets.py
imports: import os
  from urllib.request import urlretrieve
  import numpy
  import pandas
  import sklearn.datasets
  import shap
label_desc: Use image node_id 11 for calling a global function with example usage: independentlinear60(display, n_points) and returns: pd,  y, np