code: def test_classifier_comparison():
      """Test the classifier comparison example works"""
  
      X, y = make_classification(
          n_features=2,
          n_redundant=0,
          n_informative=2,
          random_state=1,
          n_clusters_per_class=1,
      )
      rng = np.random.RandomState(2)
      X += 2 * rng.uniform(size=X.shape)
      linearly_separable = (X, y)
      datasets = [
          make_moons(noise=0.3, random_state=0),
          make_circles(noise=0.2, factor=0.5, random_state=1),
          linearly_separable,
      ]
      scores = []
      for ds in datasets:
          X, y = ds
          X = StandardScaler().fit_transform(X)
          X_train, X_test, y_train, y_test = train_test_split(
              X, y, test_size=0.4, random_state=42
          )
          clf = SymbolicClassifier(random_state=0)
          clf.fit(X_train, y_train)
          score = clf.score(X_test, y_test)
          scores.append(("%.2f" % score).lstrip("0"))
  
      assert scores == [".95", ".93", ".95"]
  
file_path: gplearn/gplearn/tests/test_examples.py
imports: import numpy
  from sklearn.datasets import load_diabetes, load_breast_cancer
  from sklearn.datasets import make_moons, make_circles, make_classification
  from sklearn.linear_model import Ridge
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import train_test_split
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils._testing import assert_almost_equal
  from sklearn.utils.validation import check_random_state
  from gplearn.genetic import SymbolicClassifier, SymbolicRegressor
  from gplearn.genetic import SymbolicTransformer
  from gplearn.functions import make_function
label_desc: Use image node_id 4 for calling a global function with example usage: test_classifier_comparison() without return types