message: BenchmarkRunnerMP test for EvaluatorLTL
diff: @@ -20,7 +20,7 @@ from bark.runtime.scenario.scenario_generation.configurable_scenario_generation
  from bark.runtime.viewer.matplotlib_viewer import MPViewer
  from bark.core.world.evaluation import *
  -# from bark.core.world.evaluation.ltl import ConstantLabelFunction
  +from bark.core.world.evaluation.ltl import ConstantLabelFunction
  from bark.runtime.commons.parameters import ParameterServer
  from bark.core.models.behavior import BehaviorIDMClassic, BehaviorConstantVelocity
  @@ -29,7 +29,7 @@ os.chdir("../benchmark_database/")
  class DatabaseRunnerTests(unittest.TestCase):
  - # @unittest.skip
  + @unittest.skip
  def test_database_runner(self):
  dbs = DatabaseSerializer(test_scenarios=2, test_world_steps=2, num_serialize_scenarios=5)
  cwd = os.getcwd()
  @@ -102,7 +102,7 @@ class DatabaseRunnerTests(unittest.TestCase):
  print(df)
  self.assertEqual(len(df.index), 40) # 2 Behaviors * 10 Serialize Scenarios * 2 scenario sets
  - @unittest.skip
  + #@unittest.skip
  def test_parameterized_evaluators(self):
  dbs = DatabaseSerializer(test_scenarios=2, test_world_steps=2, num_serialize_scenarios=1)
  dbs.process("data/database1")
  @@ -111,15 +111,17 @@ class DatabaseRunnerTests(unittest.TestCase):
  db = BenchmarkDatabase(database_root=local_release_filename)
  evaluators = {"true": {"type": "EvaluatorLTL",
  "params": {"ltl_formula": "G !test", "label_functions": [ConstantLabelFunction("test")]}}}
  - terminal_when = {"true": lambda x: x > 0}
  + terminal_when = {"true": lambda x: x}
  params = ParameterServer() # only for evaluated agents not passed to scenario!
  behaviors_tested = {"Const": BehaviorConstantVelocity(params)}
  - benchmark_runner = BenchmarkRunner(benchmark_database=db,
  + benchmark_runner = BenchmarkRunnerMP(benchmark_database=db,
  evaluators=evaluators,
  terminal_when=terminal_when,
  behaviors=behaviors_tested,
  - log_eval_avg_every=1)
  + num_scenarios=10,
  + log_eval_avg_every=1,
  + num_cpus=2)
  result = benchmark_runner.run()
  df = result.get_data_frame()
  