import unittest from unittest.mock import patch import pandas as pd import src.backend.evaluate_model as evaluate_model import src.envs as envs class TestEvaluator(unittest.TestCase): def setUp(self): self.model_name = 'test_model' self.revision = 'test_revision' self.precision = 'test_precision' self.batch_size = 10 self.device = 'test_device' self.no_cache = False self.limit = 10 @patch('src.backend.evaluate_model.SummaryGenerator') @patch('src.backend.evaluate_model.EvaluationModel') def test_evaluator_initialization(self, mock_eval_model, mock_summary_generator): evaluator = evaluate_model.Evaluator(self.model_name, self.revision, self.precision, self.batch_size, self.device, self.no_cache, self.limit) mock_summary_generator.assert_called_once_with(self.model_name, self.revision) mock_eval_model.assert_called_once_with(envs.HEM_PATH) self.assertEqual(evaluator.model, self.model_name) @patch('src.backend.evaluate_model.EvaluationModel') @patch('src.backend.evaluate_model.SummaryGenerator') def test_evaluator_initialization_error(self, mock_summary_generator, mock_eval_model): mock_eval_model.side_effect = Exception('test_exception') with self.assertRaises(Exception): evaluate_model.Evaluator(self.model_name, self.revision, self.precision, self.batch_size, self.device, self.no_cache, self.limit) @patch('src.backend.evaluate_model.SummaryGenerator') @patch('src.backend.evaluate_model.EvaluationModel') @patch('src.backend.evaluate_model.pd.read_csv') @patch('src.backend.util.format_results') def test_evaluate_method(self, mock_format_results, mock_read_csv, mock_eval_model, mock_summary_generator): evaluator = evaluate_model.Evaluator(self.model_name, self.revision, self.precision, self.batch_size, self.device, self.no_cache, self.limit) # Mock setup mock_format_results.return_value = {'test': 'result'} mock_read_csv.return_value = pd.DataFrame({'column1': ['data1', 'data2']}) mock_summary_generator.return_value.generate_summaries.return_value = pd.DataFrame({'column1': ['summary1', 'summary2']}) mock_summary_generator.return_value.avg_length = 100 mock_summary_generator.return_value.answer_rate = 1.0 mock_summary_generator.return_value.error_rate = 0.0 mock_eval_model.return_value.compute_accuracy.return_value = 1.0 mock_eval_model.return_value.hallucination_rate = 0.0 mock_eval_model.return_value.evaluate_hallucination.return_value = [0.5] # Method call and assertions results = evaluator.evaluate() mock_format_results.assert_called_once_with(model_name=self.model_name, revision=self.revision, precision=self.precision, accuracy=1.0, hallucination_rate=0.0, answer_rate=1.0, avg_summary_len=100, error_rate=0.0) mock_read_csv.assert_called_once_with(envs.SOURCE_PATH) @patch('src.backend.evaluate_model.SummaryGenerator') @patch('src.backend.evaluate_model.EvaluationModel') @patch('src.backend.evaluate_model.pd.read_csv') def test_evaluate_with_file_not_found(self, mock_read_csv, mock_eval_model, mock_summary_generator): mock_read_csv.side_effect = FileNotFoundError('test_exception') evaluator = evaluate_model.Evaluator(self.model_name, self.revision, self.precision, self.batch_size, self.device, self.no_cache, self.limit) with self.assertRaises(FileNotFoundError): evaluator.evaluate() if __name__ == '__main__': unittest.main()