clean

lium_baseline/interactive.py

@@ -84,8 +84,8 @@ def create_bottomline_clustering(model, model_cfg, show, current_diar, file_path
                                            model_cfg,
                                            "reference")
 
-        print(f"cluster_{cluster} with {len(diar_per_cluster)} segs changed to "
-              f"{len(diar_per_cluster.unique('cluster'))} clusters")
+        #print(f"cluster_{cluster} with {len(diar_per_cluster)} segs changed to "
+        #      f"{len(diar_per_cluster.unique('cluster'))} clusters")
         # Creat a diarization with bottom line clustering
         for seg in diar_per_cluster:
             bottomline_diar.append(show=seg["show"],
@@ -1051,7 +1051,7 @@ def run_active_learning_tree(link,
     stop_clustering_list = []  # a list of nodes that have gotten confirmation for clustering question
 
     der, new_diar = check_der(init_diar, bottomline_cluster_list, temporary_link_list, ref, uem)
-    print("Initial DER based on bottomline_diar and linkage : ", der)
+    #print("Initial DER based on bottomline_diar and linkage : ", der)
 
     der_track_show = {"der_log": [der], "correction": ["initial"]}
 
@@ -1117,7 +1117,7 @@ def run_active_learning_tree(link,
                         link_tmp = copy.deepcopy(temporary_link_list)
                         diar_tmp = copy.deepcopy(init_diar)
                         der, new_diar = check_der(diar_tmp, bottomline_cluster_list, link_tmp, ref, uem)
-                        print(f"not_separation:", der)
+                        #print(f"not_separation:", der)
                         der_track_show["der_log"].append(der)
                         der_track_show["correction"].append("not_separation")
                         separation_confirm += 1
@@ -1210,7 +1210,7 @@ def run_active_learning_tree(link,
                         link_tmp = copy.deepcopy(temporary_link_list)
                         diar_tmp = copy.deepcopy(init_diar)
                         der, new_diar = check_der(diar_tmp, bottomline_cluster_list, link_tmp, ref, uem)
-                        print("not_clustering :", der)
+                        #print("not_clustering :", der)
                         der_track_show["der_log"].append(der)
                         der_track_show["correction"].append("not_clustering")
                         clustering_confirm += 1
@@ -1297,17 +1297,21 @@ def allies_within_show_hal(model_cfg,
     #       check does bottomline_diar plus sub cluster linkage make current_diar          #
     hyp = s4d_to_allies(init_diar)
     der_init, fa_rate, miss_rate, conf_rate, error, time, newspkmap = compute_der([ref], [hyp], [uem], collar = 0.250)
+    #print("DER of init_diar: ", der_init)
 
-    print("Current DER (original) : ", der_init)
+    #print("Current DER (original) : ", der_init)
 
 
     hyp = s4d_to_allies(bottomline_diar)
     der, fa_rate, miss_rate, conf_rate, error, time, newspkmap = compute_der([ref], [hyp], [uem], collar = 0.250)
-    print("DER of bottomline_diar: ", der)
+    #print("DER of bottomline_diar: ", der)
+    
+#     import pdb
+#     pdb.set_trace()
 
     vec_per_seg.modelset = vec_per_seg.modelset.astype(object)
-    #for idx in range(len(bottomline_diar)):
-    #    vec_per_seg.modelset[idx] = bottomline_diar[idx]['cluster']
+    for idx in range(len(bottomline_diar)):
+        vec_per_seg.modelset[idx] = bottomline_diar[idx]['cluster']
 
     if c2s != 'ideal':
 

lium_baseline/utils.py

@@ -35,7 +35,8 @@ import sklearn.mixture
 import s4d
 
 from ..user_simulation import Reference
-from ..der_single import compute_der
+# from ..der_single import compute_der
+from ..der import der_cross as compute_der
 
 def allies_write_diar(current_diar, filename):
     """
@@ -233,9 +234,12 @@ def check_der(bottomline_diar, bottomline_cluster_list, full_link, ref, uem):
     full_link_tmp = copy.deepcopy(full_link)
 
     current_diar = apply_link_on_diar(diar_tmp, cluster_list, full_link_tmp)
-
+    
     hyp = s4d_to_allies(current_diar)
-    der, fa_rate, miss_rate, conf_rate, time, newspkmap = compute_der(ref, hyp, uem, {}, 0.250)
+    der, fa_rate, miss_rate, conf_rate, error, time, newspkmap = compute_der([ref], [hyp], [uem], collar = 0.250)
+    
+#     hyp = s4d_to_allies(current_diar)
+#     der, fa_rate, miss_rate, conf_rate, time, newspkmap = compute_der(ref, hyp, uem, {}, 0.250)
 
     return der, current_diar