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"
There should be one space before and after all operators.