14        self.assertIn('n_estimators', result)
15
16    def test_random_search(self):
17        hyperparameter_tuning= HyperparameterTuning(self.model, self.data, self.target)18        params = {'n_estimators': [10, 50, 100]}
19        result = hyperparameter_tuning.random_search(params, 5)
20        self.assertIn('n_estimators', result)

41            model.to(device)
42        query_fn = query_by_committee
43    elif query_strategy == "coreset_sampling":
44        query_fn= coreset_sampling45    else:
46        raise ValueError("Invalid query strategy")
47    x = torch.randn(batch_size, input_size).to(device)

48            if current_block["previous_hash"]!= self.calculate_hash(previous_block):
49                return False
50
51            if current_block["hash"]!= self.calculate_hash(current_block):52                return False
53
54        return True

45            current_block = chain[i]
46            previous_block = chain[i - 1]
47
48            if current_block["previous_hash"]!= self.calculate_hash(previous_block):49                return False
50
51            if current_block["hash"]!= self.calculate_hash(current_block):

39if __name__ == "__main__":
40    # Load your data and target here
41    data =...
42    target =...43
44    # Set the problem type
45    problem_type = "classification"