class_docstr: Module level tests for all sktime classifiers.
docstr: Test classifier on unit test data.
docstr_tok: ["Test","classifier","on","unit","test","data","."]
code: def test_classifier_on_unit_test_data(self, estimator_class):
      """Test classifier on unit test data."""
      # we only use the first estimator instance for testing
      classname = estimator_class.__name__
  
      # retrieve expected predict_proba output, and skip test if not available
      if classname in unit_test_proba.keys():
          expected_probas = unit_test_proba[classname]
      else:
          # skip test if no expected probas are registered
          return None
  
      # if numba is not installed, some estimators may still try to construct
      # numba dependenct estimators in results_comparison
      # if that is the case, we skip the test
      try:
          # we only use the first estimator instance for testing
          estimator_instance = estimator_class.create_test_instance(
              parameter_set="results_comparison"
          )
      except ModuleNotFoundError as e:
          if not _check_soft_dependencies("numba", severity="none"):
              return None
          else:
              raise e
  
      # set random seed if possible
      if "random_state" in estimator_instance.get_params().keys():
          estimator_instance.set_params(random_state=0)
  
      # load unit test data
      X_train, y_train = load_unit_test(split="train")
      X_test, _ = load_unit_test(split="test")
      indices = np.random.RandomState(0).choice(
          len(y_train), 10, replace=False
      )
  
      # train classifier and predict probas
      estimator_instance.fit(X_train, y_train)
      y_proba = estimator_instance.predict_proba(X_test.iloc[indices])
  
      # assert probabilities are the same
      _assert_array_almost_equal(y_proba, expected_probas, decimal=2)
  
imports: import numpy
  import pandas
  import pytest
  from sktime.classification.tests._expected_outputs import basic_motions_proba, unit_test_proba
  from sktime.datasets import load_basic_motions, load_unit_test
  from sktime.datatypes import check_is_scitype
  from sktime.tests.test_all_estimators import BaseFixtureGenerator, QuickTester
  from sktime.utils._testing.estimator_checks import _assert_array_almost_equal
  from sktime.utils._testing.panel import make_classification_problem
  from sktime.utils._testing.scenarios_classification import ClassifierFitPredictMultivariate
  from sktime.utils.validation._dependencies import _check_soft_dependencies
label_desc: Use image node_id 3 for calling the TestAllClassifiers obj's underlying member method code with example usage: obj.test_classifier_on_unit_test_data(estimator_class) and returns: None, None