Merge pull request #90 from MSusik/py3

examples: Python 3 compability

Author: MSusik

examples/applications/author-disambiguation/clustering.py

@@ -296,4 +296,5 @@ def clustering(input_signatures, input_records, distance_model,
  args.input_clusters, args.output_clusters,
  args.verbose, args.n_jobs, args.clustering_method,
  args.train_signatures, args.clustering_threshold,
- args.results_file)
+ args.results_file, args.blocking_function,
+ args.blocking_threshold, args.blocking_phonetic_alg)

examples/applications/author-disambiguation/sampling.py

@@ -41,6 +41,7 @@
 import math
 import numpy as np
 import random
+import six
 
 from beard.clustering import block_phonetic
 from beard.clustering import block_last_name_first_initial
@@ -50,7 +51,7 @@ def _noblocking_sampling(sample_size, train_signatures, clusters_reversed):
  pairs = []
  # Pairs dict will prevent duplicates
  pairs_dict = {}
- category_size = sample_size / 2
+ category_size = sample_size // 2
  negative = 0
  while negative < category_size:
  s1 = random.choice(train_signatures)['signature_id']
@@ -78,7 +79,7 @@ def _noblocking_sampling(sample_size, train_signatures, clusters_reversed):
  for i in range(100):
  print("sampling positive examples: %s out of 100 folds" % (i+1))
  some_signatures = random.sample(train_signatures,
- len(train_signatures)/20)
+ len(train_signatures)//20)
  for i, s1 in enumerate(some_signatures):
  for s2 in some_signatures[i+1:]:
  s1_id = s1['signature_id']
@@ -88,7 +89,7 @@ def _noblocking_sampling(sample_size, train_signatures, clusters_reversed):
  if s1_cluster == s2_cluster:
  positive_pairs.append((s1_id, s2_id, 0))
 
- sampled = random.sample(positive_pairs, category_size/100)
+ sampled = random.sample(positive_pairs, category_size//100)
  pairs += sampled
  for s1, s2, _ in sampled:
  if s1 > s2:
@@ -169,7 +170,7 @@ def pair_sampling(blocking_function,
  """
  # Load ground-truth
  true_clusters = json.load(open(clusters_filename, "r"))
- clusters_reversed = {v: k for k, va in true_clusters.iteritems()
+ clusters_reversed = {v: k for k, va in six.iteritems(true_clusters)
  for v in va}
 
  train_signatures = json.load(open(train_filename, "r"))