Bugfix for non-ASCII chars in warings in semantic similarity evaluati…

…on (#1079)

gensim/models/keyedvectors.py

@@ -481,16 +481,16 @@ def accuracy(self, questions, restrict_vocab=30000, most_similar=most_similar, c
 
     @staticmethod
     def log_evaluate_word_pairs(pearson, spearman, oov, pairs):
-        logger.info('Pearson correlation coefficient against {0:s}: {1:.4f}'.format(pairs, pearson[0]))
-        logger.info('Spearman rank-order correlation coefficient against {0:s}: {1:.4f}'.format(pairs, spearman[0]))
-        logger.info('Pairs with unknown words ratio: {0:.1f}%'.format(oov))
+        logger.info('Pearson correlation coefficient against %s: %.4f' % (pairs, pearson[0]))
+        logger.info('Spearman rank-order correlation coefficient against %s: %.4f' % (pairs, spearman[0]))
+        logger.info('Pairs with unknown words ratio: %.1f%%' % oov)
 
     def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, case_insensitive=True, dummy4unknown=False):
         """
         Compute correlation of the model with human similarity judgments. `pairs` is a filename of a dataset where
         lines are 3-tuples, each consisting of a word pair and a similarity value, separated by `delimiter'.
-        Example datasets can be found at http://technion.ac.il/~ira.leviant/wordsim353.zip or at
-        https://www.cl.cam.ac.uk/~fh295/SimLex-999.zip.
+        An example dataset is included in Gensim (test/test_data/wordsim353.tsv). More datasets can be found at 
+        http://technion.ac.il/~ira.leviant/MultilingualVSMdata.html or https://www.cl.cam.ac.uk/~fh295/simlex.html.
 
         The model is evaluated using Pearson correlation coefficient and Spearman rank-order correlation coefficient
         between the similarities from the dataset and the similarities produced by the model itself.        .
@@ -532,7 +532,7 @@ def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, case
                         a, b, sim = [word for word in line.split(delimiter)]
                     sim = float(sim)
                 except:
-                    logger.info('skipping invalid line #{0:d} in {1:s}'.format(line_no, pairs.encode('utf-8')))
+                    logger.info('skipping invalid line #%d in %s' % (line_no, pairs))
                     continue
                 if a not in ok_vocab or b not in ok_vocab:
                     oov += 1
@@ -541,7 +541,7 @@ def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, case
                         similarity_gold.append(sim)
                         continue
                     else:
-                        logger.debug('skipping line #{0:d} with OOV words: {1:s}'.format(line_no, line.strip()))
+                        logger.debug('skipping line #%d with OOV words: %s' % (line_no, line.strip()))
                         continue
                 similarity_gold.append(sim)  # Similarity from the dataset
                 similarity_model.append(self.similarity(a, b))  # Similarity from the model
@@ -550,11 +550,11 @@ def evaluate_word_pairs(self, pairs, delimiter='\t', restrict_vocab=300000, case
         pearson = stats.pearsonr(similarity_gold, similarity_model)
         oov_ratio = float(oov) / (len(similarity_gold) + oov) * 100
 
-        logger.debug('Pearson correlation coefficient against {0:s}: {1:f} with p-value {2:f}'
-                     .format(pairs, pearson[0], pearson[1]))
-        logger.debug('Spearman rank-order correlation coefficient against {0:s}: {1:f} with p-value {2:f}'
-                     .format(pairs, spearman[0], spearman[1]))
-        logger.debug('Pairs with unknown words: {0:d}'.format(oov))
+        logger.debug('Pearson correlation coefficient against %s: %f with p-value %f'
+                     % (pairs, pearson[0], pearson[1]))
+        logger.debug('Spearman rank-order correlation coefficient against %s: %f with p-value %f'
+                     % (pairs, spearman[0], spearman[1]))
+        logger.debug('Pairs with unknown words: %d' % oov)
         self.log_evaluate_word_pairs(pearson, spearman, oov_ratio, pairs)
         return pearson, spearman, oov_ratio