Update utils/conditional_classifier.py

utils/conditional_classifier.py

@@ -66,9 +66,12 @@ def conditional_classification(haystack_doc:pd.DataFrame,
     haystack_doc['Conditional Label'] = 'NA'
     haystack_doc['Conditional Score'] = 0.0
     haystack_doc['cond_check'] = False
+    haystack_doc['PA_check'] = haystack_doc['Policy-Action Label'].apply(lambda x: True if len(x) != 0 else False)
+                            
+    #df1 = haystack_doc[haystack_doc['PA_check'] == True]
+    #df = haystack_doc[haystack_doc['PA_check'] == False]                        
     haystack_doc['cond_check'] = haystack_doc.apply(lambda x: True if (
-                (x['Target Label'] == 'TARGET') | (x['Action Label'] == 'Action') |
-                (x['Policies_Plans Label'] == 'Policies and Plans')) else
+                (x['Target Label'] == 'TARGET') | (x['PA_check'] == True) else
                 False, axis=1)
     # we apply Netzero to only paragraphs which are classified as 'Target' related
     temp = haystack_doc[haystack_doc['cond_check'] == True]
@@ -85,7 +88,7 @@ def conditional_classification(haystack_doc:pd.DataFrame,
     # temp[' Label'] = temp['Netzero Label'].apply(lambda x: _lab_dict[x])
     # merging Target with Non Target dataframe
     df = pd.concat([df,temp])
-    df = df.drop(columns = ['cond_check'])
+    df = df.drop(columns = ['cond_check','PA_check'])
     df = df.reset_index(drop =True)
     df.index += 1