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| import pytest | |
| import numpy as np | |
| import os | |
| import sys | |
| from unittest.mock import patch, MagicMock | |
| sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../backend'))) | |
| from ensemble import EnsembleModel | |
| def test_ensemble_initialization(mock_exists, mock_load): | |
| mock_exists.return_value = True | |
| mock_model = MagicMock() | |
| mock_load.return_value = mock_model | |
| ensemble = EnsembleModel() | |
| assert len(ensemble.models) == len(ensemble.model_names) | |
| assert 'rf' in ensemble.models | |
| assert 'logreg' in ensemble.models | |
| def test_ensemble_prediction(mock_exists, mock_load): | |
| mock_exists.return_value = True | |
| mock_model = MagicMock() | |
| mock_model.predict_proba.return_value = np.array([[0.2, 0.8]]) | |
| mock_label_encoder = MagicMock() | |
| mock_label_encoder.inverse_transform.side_effect = lambda x: [f"Class_{i}" for i in x] | |
| def side_effect(path): | |
| if 'label_encoder' in path: | |
| return mock_label_encoder | |
| return mock_model | |
| mock_load.side_effect = side_effect | |
| ensemble = EnsembleModel() | |
| X_dummy = np.array([[1.0, 2.0, 3.0, 4.0, 5.0, 1.0]]) | |
| final_pred, confidence, individual_preds, class_probs, weights, individual_conf = ensemble.predict(X_dummy) | |
| assert confidence == 0.8 | |
| assert final_pred == "Class_1" | |
| assert "Class_0" in class_probs | |
| assert class_probs["Class_1"] == 0.8 | |