Update src/vis_utils.py

src/vis_utils.py

@@ -186,6 +186,14 @@ def plot_family_results(method_names, dataset, family_path="/tmp/family_results.
     # Reshape the DataFrame to long format
     df_long = pd.melt(df, id_vars=["Method"], value_vars=value_vars, var_name="Dataset_Metric_Fold", value_name="Value")
 
+    print(df_long)
+
+    # Convert the "Value" column to numeric
+    df_long["Value"] = pd.to_numeric(df_long["Value"], errors="coerce")
+
+    # Drop rows with NaN values in "Value"
+    df_long = df_long.dropna(subset=["Value"])
+    
     # Split the "Dataset_Metric_Fold" column into "Metric" and "Fold"
     df_long[["Metric", "Fold"]] = df_long["Dataset_Metric_Fold"].str[len(dataset) + 1:].str.split("_", expand=True)
     df_long["Fold"] = df_long["Fold"].astype(int)
@@ -241,8 +249,6 @@ def plot_affinity_results(method_names, metric, affinity_path="/tmp/affinity_res
     df = df.fillna(0)
 
     df = df.T
-
-    print(df)
     # Set up the plot
     sns.set(rc={'figure.figsize': (11.7, 8.27)})
     sns.set_theme(style="whitegrid", color_codes=True)
@@ -304,7 +310,7 @@ def update_metric_choices(benchmark_type):
         return (
             gr.update(visible=False), gr.update(visible=False), gr.update(visible=False),
             gr.update(choices=datasets, value=datasets[0], visible=True),
-            gr.update(choices=metrics, value=metrics[0], visible=True)
+            gr.update(visible=False)
         )
     elif benchmark_type == 'affinity':
         # Show single metric selector for affinity