Small changes to enable specified answer_set_list on which the learning is based on

bt5/erp5_survey_ml/ExtensionTemplateItem/portal_components/extension.erp5.SurveyMachineLearning.py

@@ -3,27 +3,33 @@ import nltk
 import numpy as np
 from sklearn.feature_extraction.text import TfidfVectorizer
 
+from Products.ERP5Type.Log import log
+
 stemmer = nltk.stem.porter.PorterStemmer()
 def stemTokens(tokens):
   return [stemmer.stem(item) for item in tokens]
 
 
-def Answer_getCorrectionSuggestion(self):
+def Answer_getCorrectionSuggestion(self, answer_set_list=None, try_cache=True):
 
   memcached_tool = self.getPortalObject().portal_memcached
   memcached_dict = memcached_tool.getMemcachedDict(key_prefix='correctionSuggestion', plugin_path='portal_memcached/default_memcached_plugin')
-
-  try:
-    existing_suggestion = memcached_dict[self.getPath()]
-    return existing_suggestion
-  except KeyError:
-    pass
-
-  def getCorrectionSuggestion():
+  if try_cache:
+    try:
+      existing_suggestion = memcached_dict[self.getPath()]
+      return existing_suggestion
+    except KeyError:
+      pass
+  else:
+    log("Answer_getCorrectionSuggestion without caching!")
+
+  def getCorrectionSuggestion(answer_set_list):
     question_id = str(self.getResourceId())
 
     portal = self.getPortalObject()
-    answer_set_list = portal.portal_catalog(portal_type="Answer Set", validation_state="archived", version="001")
+    if answer_set_list is None:
+      log("Answer_getCorrectionSuggestion without trainingsdata!")
+      answer_set_list = portal.portal_catalog(portal_type="Answer Set", validation_state="archived", version="001")
 
     ## Get ALL the data from ERP and process within this cell so other cells do not depend on the ERP interface
     #  this is due to ERP being slightly unstable and causing trouble
@@ -90,32 +96,37 @@ def Answer_getCorrectionSuggestion(self):
   # Caching. Does not permanetly cache so not used anymore
   #getCorrectionSuggestion = CachingMethod(getCorrectionSuggestion, "getCorrectionSuggestion@" + self.getPath())
   #return getCorrectionSuggestion()
-
-  correctionSuggestion = getCorrectionSuggestion()
+  correctionSuggestion = getCorrectionSuggestion(answer_set_list)
   memcached_dict[self.getPath()] = correctionSuggestion
   return correctionSuggestion
 
 
 # returns a dict with similar, suggested configurations to the Answer Set this method is called on
 # the format of the returned dict is given by the Javascript processing it
-def AnswerSet_getSuggestionExampleData(self):
+def AnswerSet_getSuggestionExampleData(self, answer_set_list=None, try_cache=True):
 
   memcached_tool = self.getPortalObject().portal_memcached
   memcached_dict = memcached_tool.getMemcachedDict(key_prefix='correctionSuggestion', plugin_path='portal_memcached/default_memcached_plugin')
 
-  try:
-    existing_suggestion = memcached_dict[self.getPath()]
-    return existing_suggestion
-  except KeyError:
-    pass
+  if try_cache:
+    try:
+      existing_suggestion = memcached_dict[self.getPath()]
+      return existing_suggestion
+    except KeyError:
+      pass
+  else:
+    log("Answer_getSuggestionExampleData without caching!")
+
 
-  def getSuggestionExampleData():
+  def getSuggestionExampleData(answer_set_list):
     portal = self.getPortalObject()
     answer_set_module = portal.answer_set_module
 
     new_answer_set = (self.getId(), getTextBlob(self))
 
-    answer_set_list = self.portal_catalog(portal_type="Answer Set", validation_state="released")
+    if answer_set_list is None:
+      answer_set_list = self.portal_catalog(portal_type="Answer Set", validation_state="released")
+
     answer_set_list_tb = [(a.getId(), getTextBlob(a)) for a in answer_set_list]
     scoring_list = rateSimilarity(new_answer_set, answer_set_list_tb)[1:]
     ## for testing:
@@ -177,7 +188,7 @@ def AnswerSet_getSuggestionExampleData(self):
   #getSuggestionExampleData = CachingMethod(getSuggestionExampleData, "getSuggestionExampleData@" + self.getPath())
   #return getSuggestionExampleData()
 
-  exampleSuggestion = getSuggestionExampleData()
+  exampleSuggestion = getSuggestionExampleData(answer_set_list)
   memcached_dict[self.getPath()] = exampleSuggestion
   return exampleSuggestion
 

bt5/erp5_survey_ml/ExtensionTemplateItem/portal_components/extension.erp5.SurveyMachineLearning.xml

@@ -46,7 +46,7 @@
             <key> <string>text_content_warning_message</string> </key>
             <value>
               <tuple>
-                <string>W: 35,  4: Unused variable \'example_answer\' (unused-variable)</string>
+                <string>W: 41,  4: Unused variable \'example_answer\' (unused-variable)</string>
               </tuple>
             </value>
         </item>

bt5/erp5_survey_ml/bt/test_dependency_list

+erp5_survey_ml_test
\ No newline at end of file