fix: experimental classification create custom plot defaults to group by experiemnt group

Signed-off-by: Ilana Nguyen <inguyen@nvidia.com>

Author: ilana-n

src/aiperf/plot/core/plot_generator.py

@@ -587,7 +587,8 @@ def create_pareto_plot(
                 group_data = df_sorted[df_sorted[group_by] == group]
                 group_color = group_colors[group]
                 # Use display name if available, otherwise use group ID
-                group_name = display_names.get(group, group)
+                # Convert to string to ensure compatibility with Plotly (handles numpy types)
+                group_name = str(display_names.get(group, group))
 
             # Calculate Pareto frontier for this group based on metric directions
             x_dir = self._get_metric_direction(x_metric)
@@ -763,7 +764,8 @@ def create_scatter_line_plot(
                 group_data = df_sorted[df_sorted[group_by] == group]
                 group_color = group_colors[group]
                 # Use display name if available, otherwise use group ID
-                group_name = display_names.get(group, group)
+                # Convert to string to ensure compatibility with Plotly (handles numpy types)
+                group_name = str(display_names.get(group, group))
 
             # Determine shadow and main modes based on mode parameter
             shadow_mode = mode
@@ -864,7 +866,8 @@ def create_multi_run_bar_chart(
             else:
                 group_data = df[df[group_by] == group]
                 group_color = group_colors[group]
-                group_name = display_names.get(group, group)
+                # Convert to string to ensure compatibility with Plotly (handles numpy types)
+                group_name = str(display_names.get(group, group))
 
             r, g, b = mcolors.to_rgb(group_color)
             fillcolor = f"rgba({int(r * 255)}, {int(g * 255)}, {int(b * 255)}, 0.7)"

src/aiperf/plot/dashboard/builder.py

@@ -1291,6 +1291,14 @@ def _build_custom_plot_modal(self) -> dbc.Modal:
             {"label": "Model", "value": "model"},
             {"label": "Concurrency", "value": "concurrency"},
         ]
+
+        # Add experiment group if experimental classification is enabled
+        exp_class_config = self.plot_config.get_experiment_classification_config()
+        if exp_class_config is not None:
+            group_by_options.append(
+                {"label": "Experiment Group", "value": "experiment_group"}
+            )
+
         group_by_options.extend(metadata_options)
 
         # Add swept parameters (exclude already listed options)
@@ -1299,6 +1307,7 @@ def _build_custom_plot_modal(self) -> dbc.Modal:
                 "endpoint_type",
                 "request_count",
                 "duration_seconds",
+                "experiment_group",
             ]:
                 display_name = param.replace("_", " ").replace(".", " ").title()
                 group_by_options.append({"label": display_name, "value": param})
@@ -1339,6 +1348,13 @@ def _build_custom_plot_modal(self) -> dbc.Modal:
                 ),
                 dbc.ModalBody(
                     [
+                        create_label("Plot Type", self.theme),
+                        dcc.Dropdown(
+                            id="custom-plot-type",
+                            options=MULTI_RUN_PLOT_TYPES,
+                            placeholder="Select plot type",
+                            style={"font-size": "12px", "margin-bottom": "12px"},
+                        ),
                         create_label("X-Axis Metric", self.theme),
                         dcc.Dropdown(
                             id="custom-x-metric",
@@ -1385,13 +1401,6 @@ def _build_custom_plot_modal(self) -> dbc.Modal:
                                 "margin-bottom": "16px",
                             },
                         ),
-                        create_label("Plot Type", self.theme),
-                        dcc.Dropdown(
-                            id="custom-plot-type",
-                            options=MULTI_RUN_PLOT_TYPES,
-                            placeholder="Select plot type",
-                            style={"font-size": "12px", "margin-bottom": "12px"},
-                        ),
                         create_label("Label Points By", self.theme),
                         dcc.Dropdown(
                             id="custom-label-by",