Predictor Event Strategy

src/progpy/predictors/monte_carlo.py

@@ -28,7 +28,8 @@ class MonteCarlo(Predictor):
     __DEFAULT_N_SAMPLES = 100 # Default number of samples to use, if none specified and not UncertainData
 
     default_parameters = { 
-        'n_samples': None
+        'n_samples': None,
+        'event_strategy': 'all'
     }
 
     def predict(self, state: UncertainData, future_loading_eqn: Callable=None, events=None, **kwargs) -> PredictionResults:
@@ -50,6 +51,10 @@ def predict(self, state: UncertainData, future_loading_eqn: Callable=None, event
             Simulation step size (s), e.g., 0.1
         events : list[str], optional
             Events to predict (subset of model.events) e.g., ['event1', 'event2']
+        event_strategy: str, optional
+            Strategy for stopping evaluation. Default is 'all'. One of:\n
+            'first': Will stop when first event in `events` list is reached.
+            'all': Will stop when all events in `events` list have been reached
         horizon : float, optional
             Prediction horizon (s)
         n_samples : int, optional
@@ -84,6 +89,8 @@ def predict(self, state: UncertainData, future_loading_eqn: Callable=None, event
         params.update(kwargs)  # update for specific run
         params['print'] = False
         params['progress'] = False
+        # Remove event_strategy from params to not confuse simulate_to method call
+        event_strategy = params.pop('event_strategy')
 
         if not isinstance(state, UnweightedSamples) and params['n_samples'] is None:
             # if not unweighted samples, some sample number is required, so set to default.
@@ -184,7 +191,12 @@ def predict(self, state: UncertainData, future_loading_eqn: Callable=None, event
 
                     # An event has occured
                     time_of_event[event] = times[-1]
-                    events_remaining.remove(event)  # No longer an event to predict to
+                    if event_strategy == 'all':
+                        events_remaining.remove(event)  # No longer an event to predict to
+                    elif event_strategy in ('first', 'any'):
+                        events_remaining = []
+                    else:
+                        raise ValueError(f"Invalid value for `event_strategy`: {event_strategy}. Should be either 'all' or 'first'")
 
                     # Remove last state (event)
                     params['t0'] = times.pop()

src/progpy/predictors/unscented_transform.py

@@ -89,6 +89,7 @@ class UnscentedTransformPredictor(Predictor):
         'kappa': -1,
         't0': 0,
         'dt': 0.5,
+        'event_strategy': 'all',
         'horizon': 1e99,
         'save_pts': [],
         'save_freq': 1e99
@@ -175,6 +176,9 @@ def predict(self, state, future_loading_eqn: Callable = None, events=None, **kwa
         params = deepcopy(self.parameters) # copy parameters
         params.update(kwargs) # update for specific run
 
+        if params['event_strategy'] != 'all':
+            raise ValueError(f"`event_strategy` {params['event_strategy']} not supported. Currently, only 'all' event strategy is supported")
+
         if events is None:
             # Predict to all events
             # change to list because of limits of jsonify
@@ -252,7 +256,7 @@ def update_all():
                         all_failed = False  # This event for this sigma point hasn't been met yet
             if all_failed:
                 # If all events have been reched for every sigma point
-                break 
+                break
 
         # Prepare Results
         pts = array([[e for e in ToE[key]] for key in ToE.keys()])

tests/test_predictors.py

@@ -62,6 +62,15 @@ def test_pred_template(self):
         m = MockProgModel()
         pred = TemplatePredictor(m)
 
+    def test_UTP_Broken(self):
+        m = ThrownObject()
+        pred = UnscentedTransformPredictor(m)
+        samples = MultivariateNormalDist(['x', 'v'], [1.83, 40], [[0.1, 0.01], [0.01, 0.1]])
+
+        with self.assertRaises(ValueError):
+            # Invalid event strategy - first (not supported)
+            pred.predict(samples, dt=0.2, num_samples=3, save_freq=1, event_strategy='first')
+
     def test_UTP_ThrownObject(self):
         m = ThrownObject()
         pred = UnscentedTransformPredictor(m)
@@ -122,7 +131,15 @@ def future_loading(t, x=None):
         s = results.time_of_event.sample(100).key('EOD')
         samples.eol_metrics(s)  # Kept for backwards compatibility
 
-    def test_MC(self):
+    def test_MC_Broken(self):
+        m = ThrownObject()
+        mc = MonteCarlo(m)
+
+        with self.assertRaises(ValueError):
+            # Invalid event strategy
+            mc.predict(m.initialize(), dt=0.2, num_samples=3, save_freq=1, event_strategy='fdksl')
+
+    def test_MC_ThrownObject(self):
         m = ThrownObject()
         mc = MonteCarlo(m)
 
@@ -131,6 +148,22 @@ def test_MC(self):
         self.assertAlmostEqual(results.time_of_event.mean['impact'], 8.0, 5)
         self.assertAlmostEqual(results.time_of_event.mean['falling'], 3.8, 5)
 
+        # event_strategy='all' should act the same
+        results = mc.predict(m.initialize(), dt=0.2, num_samples=3, save_freq=1, event_strategy='all')
+        self.assertAlmostEqual(results.time_of_event.mean['impact'], 8.0, 5)
+        self.assertAlmostEqual(results.time_of_event.mean['falling'], 3.8, 5)
+
+    def test_MC_ThrownObject_First(self):
+        # Test thrown object, similar to test_UKP_ThrownObject, but with only the first event (i.e., 'falling')
+
+        m = ThrownObject()
+        mc = MonteCarlo(m)
+        mc_results = mc.predict(m.initialize(), dt=0.2, event_strategy='first', num_samples=3, save_freq=1)
+
+        self.assertAlmostEqual(mc_results.time_of_event.mean['falling'], 3.8, 10)
+        self.assertTrue('impact' not in mc_results.time_of_event.mean)
+        self.assertAlmostEqual(mc_results.times[-1], 3, 1)  # Saving every second, last time should be around the nearest 1s before falling event
+
     def test_prediction_mvnormaldist(self):
         times = list(range(10))
         covar = [[0.1, 0.01], [0.01, 0.1]]