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| import unittest | |
| import os | |
| import shutil | |
| import pickle | |
| import pandas as pd | |
| from sklearn.linear_model import LinearRegression | |
| from model_testing import test_model | |
| class TestModelTesting(unittest.TestCase): | |
| def setUp(self): | |
| # Create a temporary directory | |
| self.temp_dir = 'temp_test' | |
| os.mkdir(self.temp_dir) | |
| # Create temporary test data directory | |
| self.test_data_dir = os.path.join(self.temp_dir, 'data') | |
| os.mkdir(self.test_data_dir) | |
| # Create temporary test data with the required pattern | |
| test_features = pd.DataFrame({ | |
| 'open': [0.6095, 0.5759, 0.6418], | |
| 'high': [0.6112, -0.7703, 0.13523], | |
| 'low': [1.7676, 0.23695, 0.6469], | |
| 'close': [1.0887, 0.4828, 2.657], | |
| 'volume': [-0.10299, 1.173, 1.345] | |
| }) | |
| test_target = pd.Series([1.624, 2.223, 0.064], name='target') | |
| test_features.to_csv(os.path.join(self.test_data_dir, | |
| 'test_features.csv'), index=True) | |
| test_target.to_csv(os.path.join(self.test_data_dir, 'test_target.csv'), | |
| index=True) | |
| # Create a simple linear regression model and save it to a pickle file | |
| model = LinearRegression() | |
| model.fit(test_features, test_target) | |
| with open(os.path.join(self.temp_dir, | |
| 'test_model.pkl'), 'wb') as model_file: | |
| pickle.dump(model, model_file) | |
| def tearDown(self): | |
| # Clean up temporary directory and files | |
| shutil.rmtree(self.temp_dir) | |
| def test_test_model(self): | |
| # Call the function with the temporary directory and model path | |
| result = test_model(model_path=os.path.join(self.temp_dir, | |
| 'test_model.pkl'), | |
| data_dir=self.test_data_dir) | |
| # Assert that the result is as expected | |
| self.assertTrue("Mean Squared Error:" in result) | |
| self.assertTrue("R-squared:" in result) | |
| if __name__ == '__main__': | |
| unittest.main() | |