code: def data_example_1_penalty_factor_1(self):
      x, y = self.get_example()
  
      pf = np.ones(30)
      pf[4] = 0.125
      kwargs = {
          "alpha_min_ratio": 0.0001,
          "l1_ratio": 0.5,
          "penalty_factor": pf,
      }
  
      expected_alphas = np.array(
          [
              1.58712986523032,
              1.44613362231646,
              1.31766309702114,
              1.20060553911345,
              1.09394705202614,
              0.99676381096855,
              0.908214061198282,
              0.827530827144269,
              0.754015269231302,
              0.687030630865956,
              0.625996723155588,
              0.57038489958971,
              0.519713477156817,
              0.47354356423657,
              0.431475259130545,
              0.393144186305032,
              0.358218340344475,
              0.326395210279388,
              0.297399159381062,
              0.270979037728078,
              0.246906006865839,
              0.224971557717347,
              0.204985704577333,
              0.186775339546958,
              0.170182733156013,
              0.1550641681857,
              0.141288694858838,
              0.128736996615539,
              0.117300356650245,
              0.106879716258767,
              0.0973848168391796,
              0.088733418114989,
              0.0808505858092015,
              0.0736680425994639,
              0.0671235767325608,
              0.0611605031759696,
              0.0557271726392266,
              0.0507765242124814,
              0.0462656777474094,
              0.0421555624498806,
              0.0384105784674319,
              0.0349982885403755,
              0.0318891370457737,
              0.0290561940007717,
              0.0264749218079695,
              0.0241229627224915,
              0.0219799451998966,
              0.0200273074476052,
              0.0182481366515322,
              0.0166270224853822,
              0.015149923633775,
              0.0138040461730896,
              0.0125777327566178,
              0.011460361644204,
              0.0104422547018131,
              0.00951459357416398,
              0.00866934330435397,
              0.00789918273890653,
              0.00719744111544197,
              0.00655804028372505,
              0.00597544205963532,
              0.00544460025606573,
              0.00496091697526395,
              0.00452020278404162,
          ]
      )
  
      expected_coef = self.get_example_coef("1-pf")
  
      expected_pred = np.array(
          [
              0,
              -0.0219470160430587,
              -0.0423872317402525,
              -0.0613777857697107,
              -0.0789932877972767,
              -0.0953091589085253,
              -0.110400665798654,
              -0.124342123109189,
              -0.137206242394496,
              -0.149063613776774,
              -0.159982305706598,
              -0.170027568241602,
              -0.179261625730142,
              -0.187743545598723,
              -0.195047607087029,
              -0.200036792234896,
              -0.220238913103036,
              -0.239384552773407,
              -0.253379565373219,
              -0.264180012747967,
              -0.274257453314554,
              -0.281696215417802,
              -0.278053682674572,
              -0.276239842910816,
              -0.288084434992464,
              -0.30234982200802,
              -0.315735609513988,
              -0.32825728518968,
              -0.335338196765289,
              -0.341667043049741,
              -0.347724135480807,
              -0.353500260626603,
              -0.358992026119048,
              -0.364198632953538,
              -0.369121567561978,
              -0.371981666745392,
              -0.374171559462367,
              -0.376100523422468,
              -0.377881839851226,
              -0.379598485711978,
              -0.38134957880695,
              -0.383108070287709,
              -0.385083683420684,
              -0.386218437874565,
              -0.387068637979054,
              -0.388095519736864,
              -0.391347677297057,
              -0.394371697177822,
              -0.396812702028453,
              -0.399074022881495,
              -0.401165142344145,
              -0.403587140384981,
              -0.405974801639294,
              -0.408793015715854,
              -0.411377351836687,
              -0.413496980133616,
              -0.414144504730355,
              -0.41438652016618,
              -0.414598984650713,
              -0.414796366581722,
              -0.414979630970168,
              -0.415149243472181,
              -0.415305936592074,
              -0.414884495552163,
          ]
      )
  
      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 4 for calling the CoxnetCases obj's underlying member method code with example usage: obj.data_example_1_penalty_factor_1() and returns: kwargs,  x,  y,  expected