docstr: Test Prof Weight Computation 
docstr_tok: ["Test","Prof","Weight","Computation"]
code: def test_prof_weight_compute(self):
      """Test Prof Weight Computation"""
  
      ## Create a fixed probability matrix (samples=3,num_classes=9) and rotate it 3 times along the columns to generate 3 layers.
      ## The Labels are appropriately generated such that the prof_weight computation would compute the normalized sum of the first 3 probabilities for the
      ## first sample, the next 3 probabilities for the second sample, the next 3 probabilities for the third sample etc..
  
      ## Generate a random probability vector and replicate it 3 times.
      a = np.random.random_sample((1, 9))
      a = a / np.sum(a)
      data_matrix = np.outer(np.ones((3, 1)), a)
      np.save("./test_case_data_1", data_matrix)
      np.save("./test_case_data_2", np.roll(data_matrix, -1, axis=1))
      np.save("./test_case_data_3", np.roll(data_matrix, -2, axis=1))
  
      y = np.zeros((1, 9))
      y[0, 0] = 1
      y1 = np.roll(y, 3, axis=1)
      y2 = np.roll(y1, 3, axis=1)
      Y = np.vstack((np.vstack((y, y1)), y2))
  
      np.save("./test_case_train_labels", Y)
  
      # Load the probes already stored numbered by layers.
      list_probe_filenames = [
          "./test_case_data_" + str(x) + ".npy" for x in range(1, 4)
      ]
      print(list_probe_filenames)
      # Load the label corresponding to this
      train_label_path = "./test_case_train_labels.npy"
      y_train = np.load(train_label_path)
  
      # Compute Prof-Weight by calling prof_weight_compute function
      start_layer = 0
      final_layer = 2
      w = prof_weight_compute(
          list_probe_filenames, start_layer, final_layer, y_train
      )
      w = w.reshape(
          w.shape[0],
      )
      self.assertTrue(
          (abs(3 * w[0] - np.sum(a[0, 0:3])) < 0.0001)
          & (abs(3 * w[1] - np.sum(a[0, 3:6])) < 0.0001)
          & (abs(3 * w[2] - np.sum(a[0, 6:9])) < 0.0001),
          "Weight Computation has an error",
      )
  
      print("prof_weight_compute Test passed..")
      # Delete the npy files created
      print("Removing Created test files....")
      if os.path.isfile(train_label_path):
          os.remove(train_label_path)
      for f in list_probe_filenames:
          if os.path.isfile(f):
              os.remove(f)
  
imports: import unittest
  import sys
  import numpy
  import os
  from aix360.algorithms.profwt import prof_weight_compute
label_desc: Use image node_id 1 for calling the Testprofwexplainer obj's underlying member method code with example usage: obj.test_prof_weight_compute() without return types