fix normalize issue for pickling

gensim/models/tfidfmodel.py

@@ -54,7 +54,7 @@ def precompute_idfs(wglobal, dfs, total_docs):
     return {termid: wglobal(df, total_docs) for termid, df in iteritems(dfs)}
 
 
-def wlocal_g(tf, n_tf):  # TODO rename it (to avoid confusion)
+def updated_wlocal(tf, n_tf):
     if n_tf == "n":
         return tf
     elif n_tf == "l":
@@ -67,7 +67,7 @@ def wlocal_g(tf, n_tf):  # TODO rename it (to avoid confusion)
         return (1 + np.log(tf) / np.log(2)) / (1 + np.log(tf.mean(axis=0) / np.log(2)))
 
 
-def wglobal_g(docfreq, totaldocs, n_df):  # TODO rename it (to avoid confusion)
+def updated_wglobal(docfreq, totaldocs, n_df):  # TODO rename it (to avoid confusion)
     if n_df == "n":
         return utils.identity(docfreq)
     elif n_df == "t":
@@ -76,14 +76,11 @@ def wglobal_g(docfreq, totaldocs, n_df):  # TODO rename it (to avoid confusion)
         return np.log((1.0 * totaldocs - docfreq) / docfreq) / np.log(2)
 
 
-def normalize_g(x, n_n):  # TODO rename it (to avoid confusion)
+def updated_normalize(x, n_n):  # TODO rename it (to avoid confusion)
     if n_n == "n":
         return x
     elif n_n == "c":
         return matutils.unitvec(x)
-    # TODO write byte-size normalisation
-    # elif n_n == "b":
-    #    pass
 
 
 class TfidfModel(interfaces.TransformationABC):
@@ -152,7 +149,7 @@ def __init__(self, corpus=None, id2word=None, dictionary=None, wlocal=utils.iden
                     Document frequency weighting:
                       none - `n`, idf - `t`, prob idf - `p`.
                     Document normalization:
-                      none - `n`, cosine - `c`, byte size - `b`.
+                      none - `n`, cosine - `c`.
 
                     for more information visit https://en.wikipedia.org/wiki/SMART_Information_Retrieval_System
 
@@ -167,18 +164,13 @@ def __init__(self, corpus=None, id2word=None, dictionary=None, wlocal=utils.iden
         self.num_docs, self.num_nnz, self.idfs = None, None, None
         self.smartirs = smartirs
 
-        if self.normalize is True:
-            self.normalize = matutils.unitvec
-        elif self.normalize is False:
-            self.normalize = utils.identity
-
         # If smartirs is not None, override wlocal, wglobal and normalize
         if smartirs is not None:
             n_tf, n_df, n_n = resolve_weights(smartirs)
 
-            self.wlocal = partial(wlocal_g, n_tf=n_tf)
-            self.wglobal = partial(wglobal_g, n_df=n_df)
-            self.normalize = partial(normalize_g, n_n=n_n)
+            self.wlocal = partial(updated_wlocal, n_tf=n_tf)
+            self.wglobal = partial(updated_wglobal, n_df=n_df)
+            self.normalize = partial(updated_normalize, n_n=n_n)
 
         if dictionary is not None:
             # user supplied a Dictionary object, which already contains all the
@@ -255,9 +247,13 @@ def __getitem__(self, bow, eps=1e-12):
             for termid, tf in zip(termid_array, tf_array) if self.idfs.get(termid, 0.0) != 0.0
         ]
 
+        if self.normalize is True:
+            self.normalize = matutils.unitvec
+        elif self.normalize is False:
+            self.normalize = utils.identity
+
         # and finally, normalize the vector either to unit length, or use a
         # user-defined normalization function
-
         vector = self.normalize(vector)
 
         # make sure there are no explicit zeroes in the vector (must be sparse)