code style fixes to the tfidf module (#1313)

Author: piskvorky

gensim/models/tfidfmodel.py

@@ -28,8 +28,7 @@ def precompute_idfs(wglobal, dfs, total_docs):
     """Precompute the inverse document frequency mapping for all terms."""
     # not strictly necessary and could be computed on the fly in TfidfModel__getitem__.
     # this method is here just to speed things up a little.
-    return dict((termid, wglobal(df, total_docs))
-                for termid, df in iteritems(dfs))
+    return dict((termid, wglobal(df, total_docs)) for termid, df in iteritems(dfs))
 
 
 class TfidfModel(interfaces.TransformationABC):
@@ -49,8 +48,9 @@ class TfidfModel(interfaces.TransformationABC):
 
     Model persistency is achieved via its load/save methods.
     """
-    def __init__(self, corpus=None, id2word=None, dictionary=None,
-                 wlocal=utils.identity, wglobal=df2idf, normalize=True):
+    def __init__(
+            self, corpus=None, id2word=None, dictionary=None,
+            wlocal=utils.identity, wglobal=df2idf, normalize=True):
         """
         Compute tf-idf by multiplying a local component (term frequency) with a
         global component (inverse document frequency), and normalizing
@@ -87,11 +87,13 @@ def __init__(self, corpus=None, id2word=None, dictionary=None,
             # statistics we need to construct the IDF mapping. we can skip the
             # step that goes through the corpus (= an optimization).
             if corpus is not None:
-                logger.warning("constructor received both corpus and explicit "
-                               "inverse document frequencies; ignoring the corpus")
+                logger.warning(
+                    "constructor received both corpus and explicit inverse document frequencies; ignoring the corpus")
             self.num_docs, self.num_nnz = dictionary.num_docs, dictionary.num_nnz
             self.dfs = dictionary.dfs.copy()
             self.idfs = precompute_idfs(self.wglobal, self.dfs, self.num_docs)
+            if id2word is None:
+                self.id2word = dictionary
         elif corpus is not None:
             self.initialize(corpus)
         else:
@@ -114,7 +116,7 @@ def initialize(self, corpus):
         numnnz, docno = 0, -1
         for docno, bow in enumerate(corpus):
             if docno % 10000 == 0:
-                logger.info("PROGRESS: processing document #%i" % docno)
+                logger.info("PROGRESS: processing document #%i", docno)
             numnnz += len(bow)
             for termid, _ in bow:
                 dfs[termid] = dfs.get(termid, 0) + 1
@@ -126,8 +128,9 @@ def initialize(self, corpus):
 
         # and finally compute the idf weights
         n_features = max(dfs) if dfs else 0
-        logger.info("calculating IDF weights for %i documents and %i features (%i matrix non-zeros)" %
-                     (self.num_docs, n_features, self.num_nnz))
+        logger.info(
+            "calculating IDF weights for %i documents and %i features (%i matrix non-zeros)",
+            self.num_docs, n_features, self.num_nnz)
         self.idfs = precompute_idfs(self.wglobal, self.dfs, self.num_docs)
 
 
@@ -142,8 +145,10 @@ def __getitem__(self, bow, eps=1e-12):
 
         # unknown (new) terms will be given zero weight (NOT infinity/huge weight,
         # as strict application of the IDF formula would dictate)
-        vector = [(termid, self.wlocal(tf) * self.idfs.get(termid))
-                  for termid, tf in bow if self.idfs.get(termid, 0.0) != 0.0]
+        vector = [
+            (termid, self.wlocal(tf) * self.idfs.get(termid))
+            for termid, tf in bow if self.idfs.get(termid, 0.0) != 0.0
+        ]
 
         # and finally, normalize the vector either to unit length, or use a
         # user-defined normalization function