replace xrange with range + import from six.moves

Now the code contains both range and xrange. It's not consistent. Since
Python2 support will end soon, it's better to make the code more like in
Python3.

docs/notebooks/Wordrank_comparisons.ipynb

@@ -1071,7 +1071,7 @@
     "    # sort analogies according to their mean frequences \n",
     "    copy_mean_freq = sorted(copy_mean_freq.items(), key=lambda x: x[1][1])\n",
     "    # prepare analogies buckets according to given size\n",
-    "    for centre_p in xrange(bucket_size//2, len(copy_mean_freq), bucket_size):\n",
+    "    for centre_p in range(bucket_size//2, len(copy_mean_freq), bucket_size):\n",
     "        bucket = copy_mean_freq[centre_p-bucket_size//2:centre_p+bucket_size//2]\n",
     "        b_acc = 0\n",
     "        # calculate current bucket accuracy with b_acc count\n",

docs/notebooks/translation_matrix.ipynb

@@ -417,7 +417,7 @@
     "duration = []\n",
     "sizeofword = []\n",
     "\n",
-    "for idx in xrange(0, test_case):\n",
+    "for idx in range(0, test_case):\n",
     "    sub_pair = word_pair[: (idx + 1) * step]\n",
     "\n",
     "    startTime = time.time()\n",
@@ -1450,15 +1450,15 @@
     "small_train_docs = train_docs[:15000]\n",
     "# train for small corpus\n",
     "model1.build_vocab(small_train_docs)\n",
-    "for epoch in xrange(50):\n",
+    "for epoch in range(50):\n",
     "    shuffle(small_train_docs)\n",
     "    model1.train(small_train_docs, total_examples=len(small_train_docs), epochs=1)\n",
     "model.save(\"small_doc_15000_iter50.bin\")\n",
     "\n",
     "large_train_docs = train_docs + test_docs\n",
     "# train for large corpus\n",
     "model2.build_vocab(large_train_docs)\n",
-    "for epoch in xrange(50):\n",
+    "for epoch in range(50):\n",
     "    shuffle(large_train_docs)\n",
     "    model2.train(large_train_docs, total_examples=len(train_docs), epochs=1)\n",
     "# save the model\n",

gensim/corpora/_mmreader.pyx

@@ -8,7 +8,7 @@ from __future__ import with_statement
 from gensim import utils
 
 from six import string_types
-from six.moves import xrange
+from six.moves import range
 import logging
 
 cimport cython
@@ -148,7 +148,7 @@ cdef class MmReader(object):
 
                     # return implicit (empty) documents between previous id and new id
                     # too, to keep consistent document numbering and corpus length
-                    for previd in xrange(previd + 1, docid):
+                    for previd in range(previd + 1, docid):
                         yield previd, []
 
                     # from now on start adding fields to a new document, with a new id
@@ -163,7 +163,7 @@ cdef class MmReader(object):
 
         # return empty documents between the last explicit document and the number
         # of documents as specified in the header
-        for previd in xrange(previd + 1, self.num_docs):
+        for previd in range(previd + 1, self.num_docs):
             yield previd, []
 
     def docbyoffset(self, offset):

gensim/corpora/bleicorpus.py

@@ -14,7 +14,7 @@
 
 from gensim import utils
 from gensim.corpora import IndexedCorpus
-from six.moves import xrange
+from six.moves import range
 
 
 logger = logging.getLogger(__name__)
@@ -159,7 +159,7 @@ def save_corpus(fname, corpus, id2word=None, metadata=False):
         fname_vocab = utils.smart_extension(fname, '.vocab')
         logger.info("saving vocabulary of %i words to %s", num_terms, fname_vocab)
         with utils.smart_open(fname_vocab, 'wb') as fout:
-            for featureid in xrange(num_terms):
+            for featureid in range(num_terms):
                 fout.write(utils.to_utf8("%s\n" % id2word.get(featureid, '---')))
 
         return offsets

gensim/corpora/dictionary.py

@@ -16,8 +16,7 @@
 from gensim import utils
 
 from six import PY3, iteritems, iterkeys, itervalues, string_types
-from six.moves import xrange
-from six.moves import zip as izip
+from six.moves import zip, range
 
 if sys.version_info[0] >= 3:
     unicode = str
@@ -465,7 +464,7 @@ def compactify(self):
         logger.debug("rebuilding dictionary, shrinking gaps")
 
         # build mapping from old id -> new id
-        idmap = dict(izip(sorted(itervalues(self.token2id)), xrange(len(self.token2id))))
+        idmap = dict(zip(sorted(itervalues(self.token2id)), range(len(self.token2id))))
 
         # reassign mappings to new ids
         self.token2id = {token: idmap[tokenid] for token, tokenid in iteritems(self.token2id)}
@@ -702,7 +701,7 @@ def from_corpus(corpus, id2word=None):
 
         if id2word is None:
             # make sure length(result) == get_max_id(corpus) + 1
-            result.token2id = {unicode(i): i for i in xrange(max_id + 1)}
+            result.token2id = {unicode(i): i for i in range(max_id + 1)}
         else:
             # id=>word mapping given: simply copy it
             result.token2id = {utils.to_unicode(token): idx for idx, token in iteritems(id2word)}

gensim/corpora/lowcorpus.py

@@ -14,7 +14,7 @@
 from gensim import utils
 from gensim.corpora import IndexedCorpus
 from six import iterkeys
-from six.moves import xrange, zip as izip
+from six.moves import zip, range
 
 
 logger = logging.getLogger(__name__)
@@ -109,7 +109,7 @@ def __init__(self, fname, id2word=None, line2words=split_on_space):
                 all_terms.update(word for word, wordCnt in doc)
             all_terms = sorted(all_terms)  # sort the list of all words; rank in that list = word's integer id
             # build a mapping of word id(int) -> word (string)
-            self.id2word = dict(izip(xrange(len(all_terms)), all_terms))
+            self.id2word = dict(zip(range(len(all_terms)), all_terms))
         else:
             logger.info("using provided word mapping (%i ids)", len(id2word))
             self.id2word = id2word

gensim/corpora/sharded_corpus.py

@@ -26,7 +26,7 @@
 import scipy.sparse as sparse
 import time
 
-from six.moves import xrange
+from six.moves import range
 
 import gensim
 from gensim.corpora import IndexedCorpus
@@ -432,7 +432,7 @@ def resize_shards(self, shardsize):
         new_shard_names = []
         new_offsets = [0]
 
-        for new_shard_idx in xrange(n_new_shards):
+        for new_shard_idx in range(n_new_shards):
             new_start = shardsize * new_shard_idx
             new_stop = new_start + shardsize
 
@@ -461,7 +461,7 @@ def resize_shards(self, shardsize):
 
         # Move old shard files out, new ones in. Complicated due to possibility
         # of exceptions.
-        old_shard_names = [self._shard_name(n) for n in xrange(self.n_shards)]
+        old_shard_names = [self._shard_name(n) for n in range(self.n_shards)]
         try:
             for old_shard_n, old_shard_name in enumerate(old_shard_names):
                 os.remove(old_shard_name)
@@ -644,7 +644,7 @@ def __getitem__(self, offset):
             s_result = self.__add_to_slice(s_result, result_start, result_stop, shard_start, shard_stop)
 
             # First and last get special treatment, these are in between
-            for shard_n in xrange(first_shard + 1, last_shard):
+            for shard_n in range(first_shard + 1, last_shard):
                 self.load_shard(shard_n)
 
                 result_start = result_stop
@@ -735,7 +735,7 @@ def row_sparse2gensim(row_idx, csr_matrix):
             g_row = [(col_idx, csr_matrix[row_idx, col_idx]) for col_idx in indices]
             return g_row
 
-        output = (row_sparse2gensim(i, result) for i in xrange(result.shape[0]))
+        output = (row_sparse2gensim(i, result) for i in range(result.shape[0]))
 
         return output
 
@@ -745,7 +745,7 @@ def _getitem_dense2gensim(self, result):
             output = gensim.matutils.full2sparse(result)
         else:
             output = (gensim.matutils.full2sparse(result[i])
-                      for i in xrange(result.shape[0]))
+                      for i in range(result.shape[0]))
         return output
 
     # Overriding the IndexedCorpus and other corpus superclass methods
@@ -754,7 +754,7 @@ def __iter__(self):
         Yield dataset items one by one (generator).
 
         """
-        for i in xrange(len(self)):
+        for i in range(len(self)):
             yield self[i]
 
     def save(self, *args, **kwargs):

gensim/corpora/ucicorpus.py

@@ -17,7 +17,7 @@
 from gensim.corpora import IndexedCorpus
 from gensim.matutils import MmReader
 from gensim.matutils import MmWriter
-from six.moves import xrange
+from six.moves import range
 
 
 logger = logging.getLogger(__name__)
@@ -285,7 +285,7 @@ def save_corpus(fname, corpus, id2word=None, progress_cnt=10000, metadata=False)
         fname_vocab = utils.smart_extension(fname, '.vocab')
         logger.info("saving vocabulary of %i words to %s", num_terms, fname_vocab)
         with utils.smart_open(fname_vocab, 'wb') as fout:
-            for featureid in xrange(num_terms):
+            for featureid in range(num_terms):
                 fout.write(utils.to_utf8("%s\n" % id2word.get(featureid, '---')))
 
         logger.info("storing corpus in UCI Bag-of-Words format: %s", fname)

gensim/interfaces.py

@@ -19,7 +19,7 @@
 import logging
 
 from gensim import utils, matutils
-from six.moves import xrange
+from six.moves import range
 
 
 logger = logging.getLogger(__name__)
@@ -385,7 +385,7 @@ def __iter__(self):
             # assumes `self.corpus` holds the index as a 2-d numpy array.
             # this is true for MatrixSimilarity and SparseMatrixSimilarity, but
             # may not be true for other (future) classes..?
-            for chunk_start in xrange(0, self.index.shape[0], self.chunksize):
+            for chunk_start in range(0, self.index.shape[0], self.chunksize):
                 # scipy.sparse doesn't allow slicing beyond real size of the matrix
                 # (unlike numpy). so, clip the end of the chunk explicitly to make
                 # scipy.sparse happy

gensim/matutils.py

@@ -24,7 +24,7 @@
 from scipy.special import psi  # gamma function utils
 
 from six import iteritems, itervalues, string_types
-from six.moves import xrange, zip as izip
+from six.moves import zip, range
 
 
 logger = logging.getLogger(__name__)
@@ -586,7 +586,7 @@ def __iter__(self):
             Document in BoW format.
 
         """
-        for indprev, indnow in izip(self.sparse.indptr, self.sparse.indptr[1:]):
+        for indprev, indnow in zip(self.sparse.indptr, self.sparse.indptr[1:]):
             yield list(zip(self.sparse.indices[indprev:indnow], self.sparse.data[indprev:indnow]))
 
     def __len__(self):
@@ -1516,7 +1516,7 @@ def __iter__(self):
 
                         # return implicit (empty) documents between previous id and new id
                         # too, to keep consistent document numbering and corpus length
-                        for previd in xrange(previd + 1, docid):
+                        for previd in range(previd + 1, docid):
                             yield previd, []
 
                         # from now on start adding fields to a new document, with a new id
@@ -1531,7 +1531,7 @@ def __iter__(self):
 
             # return empty documents between the last explicit document and the number
             # of documents as specified in the header
-            for previd in xrange(previd + 1, self.num_docs):
+            for previd in range(previd + 1, self.num_docs):
                 yield previd, []
 
         def docbyoffset(self, offset):

gensim/models/atmodel.py

@@ -71,7 +71,7 @@
 from gensim.corpora import MmCorpus
 from itertools import chain
 from scipy.special import gammaln  # gamma function utils
-from six.moves import xrange
+from six.moves import range
 import six
 
 logger = logging.getLogger(__name__)
@@ -482,7 +482,7 @@ def inference(self, chunk, author2doc, doc2author, rhot, collect_sstats=False, c
             phinorm = self.compute_phinorm(expElogthetad, expElogbetad)
 
             # Iterate between gamma and phi until convergence
-            for _ in xrange(self.iterations):
+            for _ in range(self.iterations):
                 lastgamma = tilde_gamma.copy()
 
                 # Update gamma.
@@ -699,7 +699,7 @@ def update(self, corpus=None, author2doc=None, doc2author=None, chunksize=None,
             # Just keep training on the already available data.
             # Assumes self.update() has been called before with input documents and corresponding authors.
             assert self.total_docs > 0, 'update() was called with no documents to train on.'
-            train_corpus_idx = [d for d in xrange(self.total_docs)]
+            train_corpus_idx = [d for d in range(self.total_docs)]
             num_input_authors = len(self.author2doc)
         else:
             if doc2author is None and author2doc is None:
@@ -816,7 +816,7 @@ def update(self, corpus=None, author2doc=None, doc2author=None, chunksize=None,
         def rho():
             return pow(offset + pass_ + (self.num_updates / chunksize), -decay)
 
-        for pass_ in xrange(passes):
+        for pass_ in range(passes):
             if self.dispatcher:
                 logger.info('initializing %s workers', self.numworkers)
                 self.dispatcher.reset(self.state)

gensim/models/base_any2vec.py

@@ -36,7 +36,7 @@
 import logging
 from timeit import default_timer
 import threading
-from six.moves import xrange
+from six.moves import range
 from six import itervalues, string_types
 from gensim import matutils
 from numpy import float32 as REAL, ones, random, dtype, zeros
@@ -284,7 +284,7 @@ def _job_producer(self, data_iterator, job_queue, cur_epoch=0, total_examples=No
             )
 
         # give the workers heads up that they can finish -- no more work!
-        for _ in xrange(self.workers):
+        for _ in range(self.workers):
             job_queue.put(None)
         logger.debug("job loop exiting, total %i jobs", job_no)
 
@@ -472,7 +472,7 @@ def _train_epoch(self, data_iterable, cur_epoch=0, total_examples=None, total_wo
             threading.Thread(
                 target=self._worker_loop,
                 args=(job_queue, progress_queue,))
-            for _ in xrange(self.workers)
+            for _ in range(self.workers)
         ]
 
         workers.append(threading.Thread(

gensim/models/deprecated/doc2vec.py

@@ -81,7 +81,7 @@
 from gensim.models.deprecated.old_saveload import SaveLoad
 
 from gensim import matutils  # utility fnc for pickling, common scipy operations etc
-from six.moves import xrange, zip
+from six.moves import zip, range
 from six import string_types, integer_types
 
 logger = logging.getLogger(__name__)
@@ -490,7 +490,7 @@ def reset_weights(self, model):
             self.doctag_syn0 = empty((length, model.vector_size), dtype=REAL)
             self.doctag_syn0_lockf = ones((length,), dtype=REAL)  # zeros suppress learning
 
-        for i in xrange(length):
+        for i in range(length):
             # construct deterministic seed from index AND model seed
             seed = "%d %s" % (model.seed, self.index_to_doctag(i))
             self.doctag_syn0[i] = model.seeded_vector(seed)
@@ -510,7 +510,7 @@ def init_sims(self, replace=False):
         if getattr(self, 'doctag_syn0norm', None) is None or replace:
             logger.info("precomputing L2-norms of doc weight vectors")
             if replace:
-                for i in xrange(self.doctag_syn0.shape[0]):
+                for i in range(self.doctag_syn0.shape[0]):
                     self.doctag_syn0[i, :] /= sqrt((self.doctag_syn0[i, :] ** 2).sum(-1))
                 self.doctag_syn0norm = self.doctag_syn0
             else:

gensim/models/deprecated/keyedvectors.py

@@ -98,7 +98,7 @@
 from gensim import utils, matutils  # utility fnc for pickling, common scipy operations etc
 from gensim.corpora.dictionary import Dictionary
 from six import string_types, iteritems
-from six.moves import xrange
+from six.moves import range
 from scipy import stats
 
 
@@ -239,7 +239,7 @@ def add_word(word, weights):
 
             if binary:
                 binary_len = dtype(REAL).itemsize * vector_size
-                for _ in xrange(vocab_size):
+                for _ in range(vocab_size):
                     # mixed text and binary: read text first, then binary
                     word = []
                     while True:
@@ -254,7 +254,7 @@ def add_word(word, weights):
                     weights = fromstring(fin.read(binary_len), dtype=REAL)
                     add_word(word, weights)
             else:
-                for line_no in xrange(vocab_size):
+                for line_no in range(vocab_size):
                     line = fin.readline()
                     if line == b'':
                         raise EOFError("unexpected end of input; is count incorrect or file otherwise damaged?")
@@ -1084,7 +1084,7 @@ def init_sims(self, replace=False):
         if getattr(self, 'syn0norm', None) is None or replace:
             logger.info("precomputing L2-norms of word weight vectors")
             if replace:
-                for i in xrange(self.syn0.shape[0]):
+                for i in range(self.syn0.shape[0]):
                     self.syn0[i, :] /= sqrt((self.syn0[i, :] ** 2).sum(-1))
                 self.syn0norm = self.syn0
             else: