Merge pull request #69 from Ashwani132003/new_enhancement

dropped ensemble requirement to single model

Author: teubert

src/progpy/ensemble_model.py

@@ -35,8 +35,8 @@ class EnsembleModel(PrognosticsModel):
     def __init__(self, models: list, **kwargs):
         if not isinstance(models, Sequence):
             raise TypeError(f'EnsembleModel must be initialized with a list of models, got {type(models)}')
-        if len(models) < 2:
-            raise ValueError('EnsembleModel requires at least two models')
+        if len(models) < 1:
+            raise ValueError('EnsembleModel requires at least one model')
         for i, m in enumerate(models):
             if not isinstance(m, PrognosticsModel):
                 raise TypeError(f'EnsembleModel requires all models to be PrognosticsModel instances. models[{i}] was {type(m)}')

tests/test_ensemble.py

@@ -27,8 +27,8 @@ def test_single_model(self):
 
         m = OneInputOneOutputOneEventLM()
 
-        with self.assertRaises(ValueError):
-            EnsembleModel([m])
+
+        EnsembleModel([m])
 
     def test_wrong_type(self):
         # An ensemble model with a non-model should raise an exception
@@ -43,6 +43,48 @@ def test_wrong_type(self):
         with self.assertRaises(TypeError):
             EnsembleModel([m, m, m, 77])
 
+    def test_single_model(self):
+        """
+        This tests that the ensemble model works with a single model, ensuring that inputs, states, outputs, and events are correctly handled.
+        """
+        m = OneInputOneOutputOneEventLM()
+        em = EnsembleModel([m])
+
+        # inputs, states, outputs, and events should be the same as the single model
+        self.assertSetEqual(set(em.inputs), {'u1'})
+        self.assertSetEqual(set(em.states), {'x1'})
+        self.assertSetEqual(set(em.outputs), {'z1'})
+        self.assertSetEqual(set(em.events), {'x1 == 10'})
+
+        # Initialize
+        x_t0 = em.initialize()
+        self.assertEqual(x_t0['x1'], 0)
+
+        # State transition
+        u = em.InputContainer({'u1': 1})
+        x_t1 = em.next_state(x_t0, u, 1)
+        self.assertEqual(x_t1['x1'], 1)
+
+        # Output
+        z = em.output(x_t1)
+        self.assertEqual(z['z1'], 1)
+
+        # Event state
+        es = em.event_state(x_t1)
+        self.assertEqual(es['x1 == 10'], 0.9)
+
+        # Threshold met
+        self.assertFalse(em.threshold_met(x_t1)['x1 == 10'])
+
+        # Transition again
+        x_t2 = em.next_state(x_t1, u, 2)
+
+        # Threshold met
+        # x1 == 3
+        self.assertFalse(em.threshold_met(x_t2)['x1 == 10'])
+
+        
+
     def test_two_models_identical(self):
         """
         This tests that the ensemble model works with two identical model-types with identical states, inputs, etc., one with slightly altered parameters.