new option --total-symbols in learn-bpe

redefines "--symbols" to be the number of merge operations,
minus the character vocabulary size, so that "--symbols" becomes
an estimate of the final symbol vocabulary size.

thx @phikoehn

subword_nmt/learn_bpe.py

@@ -55,6 +55,9 @@ def create_parser(subparsers=None):
         help='Stop if no symbol pair has frequency >= FREQ (default: %(default)s))')
     parser.add_argument('--dict-input', action="store_true",
         help="If set, input file is interpreted as a dictionary where each line contains a word-count pair")
+    parser.add_argument(
+        '--total-symbols', '-t', action="store_true",
+        help="subtract number of characters from the symbols to be generated (so that '--symbols' becomes an estimate for the total number of symbols needed to encode text).")
     parser.add_argument(
         '--verbose', '-v', action="store_true",
         help="verbose mode.")
@@ -197,7 +200,7 @@ def prune_stats(stats, big_stats, threshold):
                 big_stats[item] = freq
 
 
-def learn_bpe(infile, outfile, num_symbols, min_frequency=2, verbose=False, is_dict=False):
+def learn_bpe(infile, outfile, num_symbols, min_frequency=2, verbose=False, is_dict=False, total_symbols=False):
     """Learn num_symbols BPE operations from vocabulary, and write to outfile.
     """
 
@@ -211,6 +214,19 @@ def learn_bpe(infile, outfile, num_symbols, min_frequency=2, verbose=False, is_d
 
     stats, indices = get_pair_statistics(sorted_vocab)
     big_stats = copy.deepcopy(stats)
+
+    if total_symbols:
+        uniq_char_internal = set()
+        uniq_char_final = set()
+        for word in vocab:
+            for char in word[:-1]:
+                uniq_char_internal.add(char)
+            uniq_char_final.add(char[-1])
+        sys.stderr.write('Number of word-internal characters: {0}\n'.format(len(uniq_char_internal)))
+        sys.stderr.write('Number of word-final characters: {0}\n'.format(len(uniq_char_final)))
+        sys.stderr.write('Reducing number of merge operations by {0}\n'.format(len(uniq_char_internal) + len(uniq_char_final)))
+        num_symbols -= len(uniq_char_internal) + len(uniq_char_final)
+
     # threshold is inspired by Zipfian assumption, but should only affect speed
     threshold = max(stats.values()) / 10
     for i in range(num_symbols):
@@ -270,4 +286,4 @@ if __name__ == '__main__':
     if args.output.name != '<stdout>':
         args.output = codecs.open(args.output.name, 'w', encoding='utf-8')
 
-    learn_bpe(args.input, args.output, args.symbols, args.min_frequency, args.verbose, is_dict=args.dict_input)
+    learn_bpe(args.input, args.output, args.symbols, args.min_frequency, args.verbose, is_dict=args.dict_input, total_symbols=args.total_symbols)