[FEATURE] The 'extract_ngrams' method on Node seems to be working!

Still needs to be fully tested.

node/models.py

@@ -75,8 +75,8 @@ class Node(CTENode):
     def extract_ngrams(self, keys, cache):
         # TODO: instanciate the ngrams extractors
         # WHERE TO PUT THEIR CACHE?
-        extractor = extractor_cache[self.language.iso2]
-        ngrams = ngrams_cache[self.language.iso2]
+        extractor = cache.extractors[self.language.iso2]
+        ngrams = cache.ngrams[self.language]
         # find & count all the occurrences
         associations = defaultdict(float) # float or int?
         if isinstance(keys, dict):

parsing/Caches.py

+import collections
+from node.models import Ngram
+from parsing.NgramsExtractors import EnglishNgramsExtractor, FrenchNgramsExtractor
 
 
-class NgramsCache:
+
+class NgramsCache(collections.defaultdict):
     """This allows the fast retrieval of ngram ids
     from a cache instead of calling the database every time
     """
     
     def __init__(self, language):
-        self._cache = dict()
-        self._language = language
+        self.language = language
             
-    def __getitem__(self, terms):
-        terms = terms.strip().lower()
-        if terms not in self._cache:
-            try:
-                ngram = Ngram.get(terms=terms, language=self._language)
-            except:
-                ngram = Ngram(terms=terms, n=len(terms.split()), language=self._language)
-                ngram.save()
-            self._cache[terms] = ngram
-        return self._cache[terms]
-
+    def __missing__(self, terms):
+        try:
+            ngram = Ngram.get(terms=terms, language=self.language)
+        except:
+            ngram = Ngram(terms=terms, n=len(terms.split()), language=self.language)
+            ngram.save()
+        self[terms] = ngram
+        return self[terms]
         
 class NgramsCaches(collections.defaultdict):
 
     def __missing__(self, language):
         self[language] = NgramsCache(language)
         return self[language]
-        
+   
+class NgramsExtractorsCache(collections.defaultdict):
+    """This allows the fast retrieval of ngram ids
+    from a cache instead of calling the database every time
+    """
+    
+    def __missing__(self, key):
+        # format the language
+        if isinstance(key, str):
+            language = key.strip().lower()
+        else:
+            language = key.iso3
+        # find the proper extractor
+        if language in ["en", "eng", "english"]:
+            Extractor = EnglishNgramsExtractor
+        elif language in ["fr", "fra", "fre", "french"]:
+            Extractor = FrenchNgramsExtractor
+        else:
+            Extractor = NgramsExtractor
+        # try to see if already instanciated, otherwise do it
+        found = False
+        for extractor in self.values():
+            if type(extractor) == Extractor:
+                self[key] = extractor
+                found = True
+                break
+        if not found:
+            self[key] = Extractor()
+        # return the proper extractor
+        return self[key]
+
+   
 
 class Cache:
-    
+    """This is THE cache of the caches."""
     def __init__(self):
-        self.ngrams_caches = NgramsCaches()
-        self.
\ No newline at end of file
+        self.ngrams = NgramsCaches()
+        self.extractors = NgramsExtractorsCache()

parsing/NgramsExtractors/__init__.py

@@ -2,36 +2,3 @@ from parsing.NgramsExtractors.FrenchNgramsExtractor import FrenchNgramsExtractor
 from parsing.NgramsExtractors.EnglishNgramsExtractor import EnglishNgramsExtractor
 from parsing.NgramsExtractors.NgramsExtractor import NgramsExtractor
 
-import collections
-
-
-class NgramsExtractorsCache(collections.defaultdict):
-    """This allows the fast retrieval of ngram ids
-    from a cache instead of calling the database every time
-    """
-    
-    def __missing__(self, key):
-        # format the language
-        if isinstance(key, str):
-            language = key.strip().lower()
-        else:
-            language = key.iso3
-        # find the proper extractor
-        if language in ["en", "eng", "english"]:
-            Extractor = EnglishNgramsExtractor
-        elif language in ["fr", "fra", "fre", "french"]:
-            Extractor = FrenchNgramsExtractor
-        else:
-            Extractor = NgramsExtractor
-        # try to see if already instanciated, otherwise do it
-        found = False
-        for extractor in self.values():
-            if type(extractor) == Extractor:
-                self[key] = extractor
-                found = True
-                break
-        if not found:
-            self[key] = Extractor()
-        # return the proper extractor
-        return self[key]
-

test-parsing_from_node.py

 from node.models import Node, NodeType, User, Language
-from parsing.NgramsExtractors import NgramsExtractorsCache
 from parsing.Caches import Cache
 
 try:
@@ -39,8 +38,7 @@ except:
             parent      = corpus
         ).save()
 
-extractor_cache = NgramsExtractorsCache()
-ngrams_cache = NgramsCache()
+cache = Cache()
 for child in corpus.children.all():
     print(child.id)
-    child.extract_ngrams(['title'], extractor_cache)
\ No newline at end of file
+    child.extract_ngrams(['title'], cache)
\ No newline at end of file