Merge pull request #507 from alan-turing-institute/hm_bug

small bug in HM

Author: radka-j

autoemulate/history_matching.py

@@ -169,23 +169,13 @@ def sample_nroy(
 
         # Need to handle discontinuous NROY spaces
         # i.e., region within min/max bounds is RO
-        valid_samples = np.empty((0, nroy_samples.shape[1]))
-        while len(valid_samples) < n_samples:
-            # Generate candidates
-            candidate_samples = np.random.uniform(
-                min_bounds, max_bounds, size=(n_samples, nroy_samples.shape[1])
-            )
-
-            # Filter valid samples based on implausibility and concatenate
-            implausibility = self.calculate_implausibility(candidate_samples)
-            valid_candidates = candidate_samples[implausibility["NROY"]]
-            valid_samples = np.concatenate((valid_samples, valid_candidates), axis=0)
 
-            # Only return required number of samples
-            if len(valid_samples) > n_samples:
-                valid_samples = valid_samples[:n_samples]
+        # Generate candidates
+        candidate_samples = np.random.uniform(
+            min_bounds, max_bounds, size=(n_samples, nroy_samples.shape[1])
+        )
 
-        return valid_samples
+        return candidate_samples
 
     def predict(
         self,
@@ -298,13 +288,22 @@ def run(
 
         with tqdm(total=n_waves, desc="History Matching", unit="wave") as pbar:
             for wave in range(n_waves):
+                #  CHECK IF WE HAVE SAMPLES TO PROCESS
+                if len(current_samples) == 0:
+                    print(f"Wave {wave}: No valid samples found, skipping...")
+                    pbar.update(1)
+                    continue
+
                 # Run wave using batch processing
                 pred_means, pred_vars, successful_samples = self.predict(
                     x=current_samples,
                     # Emulate predictions unless emulator_predict=False
                     emulator=emulator if emulator_predict else None,
                 )
-
+                if len(successful_samples) == 0:
+                    print(f"Wave {wave}: All simulations failed, skipping...")
+                    pbar.update(1)
+                    continue
                 # Calculate implausibility in batch
                 implausibility = self.calculate_implausibility(pred_means, pred_vars)
 
@@ -328,18 +327,39 @@ def run(
                             emulator, successful_samples, pred_means
                         )
 
-                # Generate new samples for next wave
-                if wave < n_waves - 1:
-                    if nroy_samples.size > 0:
-                        current_samples = self.sample_nroy(
-                            nroy_samples, n_samples_per_wave
-                        )
-                    else:
-                        # If no NROY points, sample from full space
-                        current_samples = self.simulator.sample_inputs(
-                            n_samples_per_wave
-                        )
-
+                    # Generate new samples for next wave
+                    if wave < n_waves - 1:
+                        if nroy_samples.size > 0:
+                            # Sample candidates
+                            candidate_samples = self.sample_nroy(
+                                nroy_samples, n_samples_per_wave
+                            )
+
+                            # Filter candidates using emulator before simulation
+                            if not emulator_predict and emulator is not None:
+                                pred_means, pred_vars = emulator.predict(
+                                    candidate_samples, return_std=True
+                                )
+                                pred_vars = pred_vars**2
+
+                                # Ensure correct shape for single output case
+                                if len(pred_means.shape) == 1:
+                                    pred_means = pred_means.reshape(-1, 1)
+                                    pred_vars = pred_vars.reshape(-1, 1)
+
+                                implausibility = self.calculate_implausibility(
+                                    pred_means, pred_vars
+                                )
+                                current_samples = candidate_samples[
+                                    implausibility["NROY"]
+                                ]
+                            else:
+                                current_samples = candidate_samples
+                        else:
+                            # If no NROY points, sample from full space
+                            current_samples = self.simulator.sample_inputs(
+                                n_samples_per_wave
+                            )
                 pbar.update(1)
 
         # Concatenate all samples and implausibility scores

autoemulate/history_matching_dashboard.py

@@ -1209,26 +1209,8 @@ def display(self):
 
         heading = widgets.HTML(value="<h2>History Matching Dashboard</h2>")
 
-        instructions = widgets.HTML(
-            value="""
-            <div style="background-color: #f8f9fa; padding: 15px; border-radius: 5px; margin-bottom: 15px;">
-                <h3>Instructions:</h3>
-                <ul>
-                    <li><strong>Plot Type</strong>: Select the type of visualization from the dropdown.</li>
-                    <li><strong>Threshold</strong>: Adjust the implausibility threshold for NROY classification.</li>
-                    <li><strong>Parameters</strong>: Select parameters to visualize (availability depends on plot type).</li>
-                    <li><strong>Show only NROY points</strong>: When available, filters to show only viable parameter combinations.</li>
-                    <li><strong>Update Plot</strong>: Click to regenerate the visualization with current settings.</li>
-                </ul>
-                <p><em>Note: Some controls are only available for certain plot types.</em></p>
-            </div>
-            """
-        )
-
         # Display the heading and instructions first
         display(heading)
-        display(instructions)
-
         display(self.main_layout)
         # Initialize the first plot
         self._update_plot(None)