docstr: Test if the model can learn a linearly separable dataset
code: def test_linear_separability(self):
      """Test if the model can learn a linearly separable dataset"""
      x = np.array([0.8, 0.8, 0.3, 0.3, 0.3, 0.3]).reshape(-1, 1)
      y = np.array([0, 0, 1, 1, 1, 1])
      self.m.fit(x, y, verbose=True)
      yhat = self.m.predict(x)
      acc = np.mean(y == yhat) * 100
      assert len(self.m.rules_) == 2
      assert acc == 100
  
file_path: imodels/tests/grl_test.py
imports: import unittest
  import numpy
  from imodels.rule_list.greedy_rule_list import GreedyRuleListClassifier
  import sklearn
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 3 for calling the TestGRL obj's underlying member method code with example usage: obj.test_linear_separability() without return types