Create test_npae.py

Author: KOSASIH

tests/test_neuromorphic_analytics/test_npae.py

@@ -0,0 +1,34 @@
+# tests/test_neuromorphic_analytics/test_npae.py
+
+import unittest
+from src.neuromorphic_analytics.npae import NeuromorphicPredictiveAnalyticsEngine
+from src.neuromorphic_analytics.model import SpikingNeuralNetworkModel
+from unittest.mock import patch
+
+class TestNPAE(unittest.TestCase):
+    def setUp(self):
+        """Set up the Neuromorphic Predictive Analytics Engine for testing."""
+        model_params = {'num_neurons': 10, 'threshold': 1.0, 'decay': 0.9}
+        data_sources = ['source1', 'source2']
+        self.npae = NeuromorphicPredictiveAnalyticsEngine(model_params, data_sources)
+
+    @patch('src.neuromorphic_analytics.data_pipeline.DataPipeline.collect_data')
+    @patch('src.neuromorphic_analytics.data_pipeline.DataPipeline.preprocess_data')
+    def test_process_data(self, mock_preprocess_data, mock_collect_data):
+        """Test the data processing functionality."""
+        mock_collect_data.return_value = [0.5, 0.7, 0.2]
+        mock_preprocess_data.return_value = [0.5, 0.7, 0.2]
+
+        predictions = self.npae.process_data()
+        self.assertEqual(len(predictions), 3)  # Expecting 3 predictions
+        self.assertTrue(all(isinstance(pred, int) for pred in predictions))  # Predictions should be integers (0 or 1)
+
+    @patch('src.neuromorphic_analytics.model.SpikingNeuralNetworkModel.evaluate')
+    def test_evaluate_model(self, mock_evaluate):
+        """Test the model evaluation functionality."""
+        mock_evaluate.return_value = 0.85  # Simulate an accuracy of 85%
+        accuracy = self.npae.evaluate_model([[0.5]], [1])
+        self.assertEqual(accuracy, 0.85)  # Check if the returned accuracy matches the mock
+
+if __name__ == '__main__':
+    unittest.main()