code: def data_example_1_unpenalized(self):
      x, y = self.get_example()
  
      pf = np.ones(30)
      pf[0] = 0
      pf[29] = 0
      kwargs = {
          "alpha_min_ratio": 0.0001,
          "l1_ratio": 0.5,
          "penalty_factor": pf,
      }
  
      expected_alphas = np.array(
          [
              0.486489606631737,
              0.443271210800114,
              0.403892218139282,
              0.368011546653386,
              0.335318415131023,
              0.305529651307061,
              0.278387239159946,
              0.253656084100358,
              0.231121976694345,
              0.210589737283679,
              0.191881525433034,
              0.174835299561207,
              0.159303413414467,
              0.145151337225331,
              0.132256492480081,
              0.120507190202318,
              0.109801663556478,
              0.100047186392186,
              0.0911592700947043,
              0.0830609317849671,
              0.0756820275307113,
              0.0689586447932912,
              0.0628325488478443,
              0.0572506783819629,
              0.0521646859039908,
              0.047530518980179,
              0.0433080386735795,
              0.0394606718797749,
              0.0359550945481447,
              0.0327609430448805,
              0.0298505511577159,
              0.0271987104644308,
              0.024782451989558,
              0.0225808472581054,
              0.0205748270231182,
              0.0187470160969839,
              0.0170815828558693,
              0.0155641021137689,
              0.0141814301784446,
              0.012921591007051,
              0.0117736724753817,
              0.0107277318622698,
              0.00977470973049374,
              0.00890635145826557,
              0.00811513574164646,
              0.0073942094486099,
              0.00673732826049059,
              0.00613880258668338,
              0.00559344828413133,
              0.00509654175475855,
              0.00464377903192347,
          ]
      )
  
      expected_coef = self.get_example_coef("1-unpen")
  
      expected_pred = np.array(
          [
              0.0373756105322525,
              0.0389973815599095,
              0.0405683877195706,
              0.0280831344913328,
              0.0159083690720834,
              0.0103802188902682,
              0.00515467167201751,
              0.000240537790719531,
              -0.00398308401428462,
              0.00430672528959097,
              -0.0108117367591171,
              -0.0418650923281516,
              -0.0753864542967495,
              -0.106813391745568,
              -0.135786421270196,
              -0.159991704303222,
              -0.180394076619709,
              -0.199534421788881,
              -0.217451880596263,
              -0.234197183369495,
              -0.249822344205612,
              -0.264380049409194,
              -0.277923294024265,
              -0.290539010996487,
              -0.299717367878573,
              -0.30821456747717,
              -0.316104156500028,
              -0.323418551099414,
              -0.330190332294807,
              -0.33623057935319,
              -0.342359036083179,
              -0.347196305835025,
              -0.353818779401235,
              -0.360222871635933,
              -0.366122922710483,
              -0.371515131203764,
              -0.375983474095953,
              -0.380333208342917,
              -0.38462151635402,
              -0.388555036993323,
              -0.392161768186302,
              -0.395575701614631,
              -0.397949529352037,
              -0.399599074245129,
              -0.401120250339891,
              -0.402505065734498,
              -0.403764001966903,
              -0.404916031377837,
              -0.405970148147989,
              -0.406934334318935,
              -0.407120115909973,
          ]
      )
  
      expected = CoxnetExpectation(
          alphas=expected_alphas,
          coef=expected_coef,
          pred=expected_pred,
      )
  
      return kwargs, x, y, expected
  
imports: from dataclasses import dataclass
  from os.path import dirname, join
  from typing import Optional
  import numpy
  from numpy.testing import assert_array_almost_equal
  import pandas
  import pytest
  from sklearn.exceptions import ConvergenceWarning
  from sklearn.pipeline import make_pipeline
  from sksurv.datasets import get_x_y, load_breast_cancer
  from sksurv.linear_model.coxnet import CoxnetSurvivalAnalysis
  from sksurv.preprocessing import OneHotEncoder
  from sksurv.testing import FixtureParameterFactory
  from sksurv.util import Surv
label_desc: Use image node_id 6 for calling the CoxnetCases obj's underlying member method code with example usage: obj.data_example_1_unpenalized() and returns: kwargs,  x,  y,  expected