docstr: ...Test CoxRegression score
          
docstr_tok: ["...","Test","CoxRegression","score"]
code: def test_CoxRegression_score(self):
      """...Test CoxRegression score"""
      features, times, censoring = Test.get_train_data()
      learner = CoxRegression()
      learner.fit(features, times, censoring)
      self.assertAlmostEqual(learner.score(), 3.856303803547875)
  
      features, times, censoring = Test.get_train_data(seed=123)
      self.assertAlmostEqual(
          learner.score(features, times, censoring), 5.556509086276002
      )
  
      msg = "^You must fit the model first$"
      learner = CoxRegression()
      with self.assertRaisesRegex(RuntimeError, msg):
          learner.score()
  
      msg = "^Passed ``features`` is None$"
      learner = CoxRegression().fit(features, times, censoring)
      with self.assertRaisesRegex(ValueError, msg):
          learner.score(None, times, censoring)
  
      msg = "^Passed ``times`` is None$"
      learner = CoxRegression().fit(features, times, censoring)
      with self.assertRaisesRegex(ValueError, msg):
          learner.score(times, None, censoring)
  
      msg = "^Passed ``censoring`` is None$"
      learner = CoxRegression().fit(features, times, censoring)
      with self.assertRaisesRegex(ValueError, msg):
          learner.score(features, times, None)
  
file_path: tick/tick/survival/tests/cox_regression_test.py
imports: import unittest
  import numpy
  from tick.base.inference import InferenceTest
  from tick.survival import SimuCoxReg, CoxRegression
  from tick.simulation import weights_sparse_gauss
  from tick.preprocessing.features_binarizer import FeaturesBinarizer
  from tick.prox import ProxZero, ProxL1, ProxL2Sq, ProxElasticNet, ProxTV, ProxBinarsity
  from tick.solver import GD, AGD
label_desc: Use image node_id 9 for calling the Test obj's underlying member method code with example usage: obj.test_CoxRegression_score() without return types