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import unittest
from result_data_processor import ResultDataProcessor
import pandas as pd
class TestResultDataProcessor(unittest.TestCase):
def setUp(self):
self.processor = ResultDataProcessor()
# check that the result is a pandas dataframe
def test_process_data(self):
data = self.processor.data
self.assertIsInstance(data, pd.DataFrame)
# check that pandas dataframe has the right columns
def test_columns(self):
data = self.processor.data
self.assertIn('Parameters', data.columns)
self.assertIn('MMLU_average', data.columns)
# check number of columns
self.assertEqual(len(data.columns), 63)
# check that the number of rows is correct
def test_rows(self):
data = self.processor.data
self.assertEqual(len(data), 992)
# check that mc1 column exists
def test_mc1(self):
data = self.processor.data
self.assertIn('harness|truthfulqa:mc1', data.columns)
# test that a column that contains truthfulqa:mc does not exist
def test_truthfulqa_mc(self):
data = self.processor.data
self.assertNotIn('truthfulqa:mc', data.columns)
# check for extreme outliers in mc1 column
def test_mc1_outliers(self):
data = self.processor.data
mc1 = data['harness|truthfulqa:mc1']
self.assertLess(mc1.max(), 1.0)
self.assertGreater(mc1.min(), 0.0)
if __name__ == '__main__':
unittest.main() |