docstr: ...Test simulation of a Cox Regression with cut-points
          
docstr_tok: ["...","Test","simulation","of","a","Cox","Regression","with","cut-points"]
code: def test_SimuCoxRegWithCutPoints(self):
      """...Test simulation of a Cox Regression with cut-points"""
      # Simulate a Cox model with cut-points with specific seed
      n_samples = 10
      n_features = 3
      n_cut_points = 2
      cov_corr = 0.5
      sparsity = 0.2
  
      seed = 123
      simu = SimuCoxRegWithCutPoints(
          n_samples=n_samples,
          n_features=n_features,
          seed=seed,
          verbose=False,
          n_cut_points=n_cut_points,
          shape=2,
          scale=0.1,
          cov_corr=cov_corr,
          sparsity=sparsity,
      )
      (
          features_,
          times_,
          censoring_,
          cut_points_,
          coeffs_binarized_,
          S_,
      ) = simu.simulate()
  
      times = np.array(
          [
              6.12215425,
              6.74403919,
              5.2148425,
              5.42903238,
              2.42953933,
              9.50705158,
              18.49545933,
              19.7929349,
              0.39267278,
              1.24799812,
          ]
      )
  
      censoring = np.array(
          [1, 0, 0, 1, 0, 1, 1, 1, 0, 1], dtype=np.ushort
      )
  
      features = np.array(
          [
              [1.4912667, 0.80881799, 0.26977298],
              [1.23227551, 0.50697013, 1.9409132],
              [1.8891494, 1.49834791, 2.41445794],
              [0.19431319, 0.80245126, 1.02577552],
              [-1.61687582, -1.08411865, -0.83438387],
              [2.30419894, -0.68987056, -0.39750262],
              [-0.28826405, -1.23635074, -0.76124386],
              [-1.32869473, -1.8752391, -0.182537],
              [0.79464218, 0.65055633, 1.57572506],
              [0.71524202, 1.66759831, 0.88679047],
          ]
      )
  
      cut_points = {
          "0": np.array([-np.inf, -0.28826405, 0.79464218, np.inf]),
          "1": np.array([-np.inf, -1.23635074, 0.50697013, np.inf]),
          "2": np.array([-np.inf, -0.182537, 0.88679047, np.inf]),
      }
  
      coeffs_binarized = np.array(
          [
              -1.26789642,
              1.31105319,
              -0.04315676,
              0.0,
              0.0,
              0.0,
              0.01839684,
              0.4075832,
              -0.42598004,
          ]
      )
  
      S = np.array([1])
  
      np.testing.assert_almost_equal(features, features_)
      np.testing.assert_almost_equal(times, times_)
      np.testing.assert_almost_equal(censoring, censoring_)
      np.testing.assert_almost_equal(cut_points_["0"], cut_points["0"])
      np.testing.assert_almost_equal(cut_points_["1"], cut_points["1"])
      np.testing.assert_almost_equal(cut_points_["2"], cut_points["2"])
      np.testing.assert_almost_equal(
          coeffs_binarized, coeffs_binarized_
      )
      np.testing.assert_almost_equal(S, S_)
  
imports: import unittest
  import numpy
  from tick.survival import SimuCoxReg, SimuCoxRegWithCutPoints
label_desc: Use image node_id 2 for calling the Test obj's underlying member method code with example usage: obj.test_SimuCoxRegWithCutPoints() without return types