labels no id or unnamed

pages/3_Oxford_Vases_Disentanglement.py

@@ -54,9 +54,11 @@ with input_col_1:
         type_col = st.selectbox('Concept category:', tuple(['Provenance', 'Shape Name', 'Fabric', 'Technique']))
         
         ann_df = pd.read_csv(f'./data/vase_annotated_files/sim_{type_col}_seeds0000-20000.csv')
-        labels = ann_df.columns
-
-        concept_ids = st.multiselect('Concepts:', tuple(labels), max_selections=2, default=[labels[0], labels[1]])
+        labels = list(ann_df.columns)
+        labels.remove('ID')
+        labels.remove('Unnamed: 0')
+        
+        concept_ids = st.multiselect('Concepts:', tuple(labels), max_selections=2, default=[labels[2], labels[3]])
 
         st.write('**Choose a latent space to disentangle**')
         space_id = st.selectbox('Space:', tuple(['W', 'Z']))
@@ -93,7 +95,7 @@ smoothgrad_col_1, smoothgrad_col_2, smoothgrad_col_3, smoothgrad_col_4, smoothgr
 
 # ---------------------------- DISPLAY COL 1 ROW 1 ------------------------------
 with output_col_1:
-    separation_vector, number_important_features, imp_nodes, performance = get_separation_space(concept_ids, annotations, ann_df, latent_space=st.session_state.space_id, samples=100)
+    separation_vector, number_important_features, imp_nodes, performance = get_separation_space(concept_ids, annotations, ann_df, latent_space=st.session_state.space_id, samples=150)
     # st.write(f'Class ID {input_id} - {input_label}: {pred_prob*100:.3f}% confidence')
     st.write('Concept vector', separation_vector)
     header_col_1.write(f'Concept {st.session_state.concept_ids} - Space {st.session_state.space_id} - Number of relevant nodes: {number_important_features} - Val classification performance: {performance}')# - Nodes {",".join(list(imp_nodes))}')
@@ -146,8 +148,8 @@ else:
     original_image_vec = annotations['w_vectors'][st.session_state.image_id]
 
 img = generate_original_image(original_image_vec, model, latent_space=st.session_state.space_id)
-cols = list(ann_df.columns)
-top_pred = ann_df.loc[st.session_state.image_id, cols].astype(float).idxmax()
+
+top_pred = ann_df.loc[st.session_state.image_id, labels].astype(float).idxmax()
 # input_image = original_image_dict['image']
 # input_label = original_image_dict['label']
 # input_id = original_image_dict['id']