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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()
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