code: def test_performances():
      X, y = make_blobs(n_samples=1000, random_state=0, centers=2)
  
      # make labels imbalanced by remove all but 100 instances from class 1
      indexes = np.ones(X.shape[0]).astype(bool)
      ind = np.array([False] * 100 + list(((y == 1)[100:])))
      indexes[ind] = 0
      X = X[indexes]
      y = y[indexes]
      n_samples, n_features = X.shape
  
      clf = SkopeRules()
      # fit
      clf.fit(X, y)
      # with lists
      clf.fit(X.tolist(), y.tolist())
      y_pred = clf.predict(X)
      assert_equal(y_pred.shape, (n_samples,))
      # training set performance
      assert_greater(accuracy_score(y, y_pred), 0.83)
  
      # decision_function agrees with predict
      decision = -clf.decision_function(X)
      assert_equal(decision.shape, (n_samples,))
      dec_pred = (decision.ravel() < 0).astype(np.int)
      assert_array_equal(dec_pred, y_pred)
  
imports: import numpy
  from sklearn.model_selection import ParameterGrid
  from sklearn.datasets import load_iris, load_boston, make_blobs
  from sklearn.metrics import accuracy_score
  from sklearn.utils import check_random_state
  from sklearn.utils.testing import assert_array_equal
  from sklearn.utils.testing import assert_raises
  from sklearn.utils.testing import assert_warns_message
  from sklearn.utils.testing import assert_equal
  from sklearn.utils.testing import assert_in
  from sklearn.utils.testing import assert_not_in
  from sklearn.utils.testing import assert_not_equal
  from sklearn.utils.testing import assert_no_warnings
  from sklearn.utils.testing import assert_greater
  from sklearn.utils.testing import ignore_warnings
  from skrules import SkopeRules
label_desc: Use image node_id 6 for calling a global function with example usage: test_performances() without return types