code: def test_LearnerSCCS_preprocess(self):
      features = [
          np.array([[0, 1, 0], [0, 0, 0], [0, 1, 1]], dtype="float64"),
          np.array([[1, 0, 1], [0, 0, 1], [1, 0, 0]], dtype="float64"),
          np.array([[1, 1, 1], [0, 0, 1], [1, 1, 0]], dtype="float64"),
      ]
      sparse_features = [csr_matrix(f, shape=(3, 3)) for f in features]
      labels = [
          np.array([0, 0, 1], dtype="uint64"),
          np.array([0, 1, 0], dtype="uint64"),
          np.array([0, 0, 0], dtype="uint64"),
      ]
      censoring = np.array([2, 3, 3], dtype="uint64")
      n_lags = np.array([1, 1, 0], dtype="uint64")
  
      expected_features = [
          np.array([[0, 0, 1, 0, 0], [0, 0, 0, 1, 0], [0, 0, 0, 0, 0]]),
          np.array([[1, 0, 0, 0, 1], [0, 1, 0, 0, 1], [1, 0, 0, 0, 0]]),
      ]
      expected_labels = [
          np.array([0, 0, 1], dtype="uint64"),
          np.array([0, 1, 0], dtype="uint64"),
      ]
      expected_censoring = np.array([2, 3], dtype="uint64")
  
      lrn = ConvSCCS(n_lags=n_lags, penalized_features=[])
      X, y, c = lrn._prefit(sparse_features, labels, censoring)
      [
          np.testing.assert_array_equal(
              f.toarray(), expected_features[i]
          )
          for i, f in enumerate(X)
      ]
      [
          np.testing.assert_array_equal(l, expected_labels[i])
          for i, l in enumerate(y)
      ]
      np.testing.assert_array_equal(c, expected_censoring)
  
file_path: tick/tick/survival/tests/convolutional_sccs_test.py
imports: import unittest
  import numpy
  from tick.survival import SimuSCCS, ConvSCCS
  from scipy.sparse import csr_matrix
  from tick.survival.sccs import *
label_desc: Use image node_id 3 for calling the Test obj's underlying member method code with example usage: obj.test_LearnerSCCS_preprocess() without return types