docstr: ...Test PoissonRegression basic settings
          
docstr_tok: ["...","Test","PoissonRegression","basic","settings"]
code: def test_PoissonRegression_settings(self):
      """...Test PoissonRegression basic settings"""
      # solver
      from tick.solver import AGD, GD, BFGS, SGD, SVRG, SDCA
  
      solvers = {
          "AGD": AGD,
          "BFGS": BFGS,
          "GD": GD,
          "SGD": SGD,
          "SVRG": SVRG,
          "SDCA": SDCA,
      }
      solver_class_map = PoissonRegression._solvers
      for solver in PoissonRegression._solvers.keys():
          learner = PoissonRegression(solver=solver)
          solver_class = solvers[solver_class_map[solver]]
          self.assertTrue(isinstance(learner._solver_obj, solver_class))
  
      msg = (
          "^``solver`` must be one of agd, bfgs, gd, sgd, svrg, "
          "got wrong_name$"
      )
      with self.assertRaisesRegex(ValueError, msg):
          PoissonRegression(solver="wrong_name")
  
      prox_class_map = PoissonRegression._penalties
      for penalty in PoissonRegression._penalties.keys():
          if penalty == "binarsity":
              learner = PoissonRegression(
                  penalty=penalty, blocks_start=[0], blocks_length=[1]
              )
          else:
              learner = PoissonRegression(penalty=penalty)
          prox_class = prox_class_map[penalty]
          self.assertTrue(isinstance(learner._prox_obj, prox_class))
  
      msg = (
          "^``penalty`` must be one of binarsity, elasticnet, l1, l2, "
          "none, tv, got wrong_name$"
      )
      with self.assertRaisesRegex(ValueError, msg):
          PoissonRegression(penalty="wrong_name")
  
imports: import unittest
  from itertools import product
  import numpy
  from tick.base.inference import InferenceTest
  from tick.linear_model import SimuPoisReg, PoissonRegression
  from tick.simulation import weights_sparse_gauss
label_desc: Use image node_id 5 for calling the Test obj's underlying member method code with example usage: obj.test_PoissonRegression_settings() without return types