code: def _evaluation_test_helper_no_probability(self, labels, allow_slow):
      # Generate some random data.
      # This unit test should not rely on scikit learn for test data.
      x, y = [], []
      random.seed(42)
      for _ in range(50):
          x.append(
              [
                  random.gauss(200, 30),
                  random.gauss(-100, 22),
                  random.gauss(100, 42),
              ]
          )
          y.append(random.choice(labels))
      # make sure first label is seen first, second is seen second, and so on.
      for i, val in enumerate(labels):
          y[i] = val
      column_names = ["x1", "x2", "x3"]
      prob = svmutil.svm_problem(y, x)
  
      df = pd.DataFrame(x, columns=column_names)
  
      for param1 in self.non_kernel_parameters:
          for param2 in self.kernel_parameters:
              param_str = " ".join([self.base_param, param1, param2])
              param = svm_parameter(param_str)
  
              model = svm_train(prob, param)
  
              # Get predictions with probabilities as dictionaries
              (df["target"], _, _) = svm_predict(y, x, model, " -q")
  
              spec = libsvm.convert(model, column_names, "target")
  
              if _is_macos() and _macos_version() >= (10, 13):
                  metrics = evaluate_classifier(spec, df, verbose=False)
                  self.assertEqual(metrics["num_errors"], 0)
  
              if not allow_slow:
                  break
  
          if not allow_slow:
              break
  
imports: import copy
  import random
  import tempfile
  import unittest
  import numpy
  import pandas
  import pytest
  from coremltools._deps import _HAS_LIBSVM, _HAS_SKLEARN
  from coremltools.models.utils import _is_macos, _macos_version, evaluate_classifier, evaluate_classifier_with_probabilities
label_desc: Use image node_id 12 for calling the CSVCLibSVMTest obj's underlying member method code with example usage: obj._evaluation_test_helper_no_probability(labels, allow_slow) without return types