merge upstream/master

3 files changed, 187 insertions, 0 deletions

diff --git a/python/examples/cdec-mt.py b/python/examples/cdec-mt.py
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+++ b/python/examples/cdec-mt.py
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+#!/usr/bin/env python
+import sys
+import argparse
+import logging
+import multiprocessing as mp
+import cdec
+
+decoder = None
+def make_decoder(config, weights):
+    global decoder
+    decoder = cdec.Decoder(config)
+    decoder.read_weights(weights)
+
+def translate(sentence):
+    global decoder
+    return decoder.translate(sentence).viterbi()
+
+def main():
+    logging.basicConfig(level=logging.INFO, format='%(message)s')
+
+    parser = argparse.ArgumentParser(description='Run multiple decoders concurrentely')
+    parser.add_argument('-c', '--config', required=True,
+                        help='decoder configuration')
+    parser.add_argument('-w', '--weights', required=True,
+                        help='feature weights')
+    parser.add_argument('-j', '--jobs', type=int, default=mp.cpu_count(),
+                        help='number of decoder instances')
+    parser.add_argument('-s', '--chunksize', type=int, default=10,
+                        help='number of sentences / chunk')
+    args = parser.parse_args()
+
+    with open(args.config) as config:
+        config = config.read()
+    logging.info('Starting %d workers; chunk size: %d', args.jobs, args.chunksize)
+    pool = mp.Pool(args.jobs, make_decoder, (config, args.weights))
+    for output in pool.imap(translate, sys.stdin, args.chunksize):
+        print(output.encode('utf8'))
+    logging.info('Shutting down workers...')
+
+if __name__ == '__main__':
+    main()
diff --git a/python/examples/rampion.py b/python/examples/rampion.py
new file mode 100644
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+++ b/python/examples/rampion.py
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+import argparse
+import logging
+from itertools import izip
+import cdec, cdec.score
+
+def evaluate(hyp, ref):
+    """ Compute BLEU score for a set of hypotheses+references """
+    return sum(cdec.score.BLEU(r).evaluate(h) for h, r in izip(hyp, ref)).score
+
+T1, T2, T3 = 5, 10, 20 # number of iterations (global, CCCP, SSD)
+K = 500 # k-best list size
+C = 1 # regularization coefficient
+eta = 1e-4 # step size
+cost = lambda c: 10 * (1 - c.score) # cost definition
+
+def rampion(decoder, sources, references):
+    # Empty k-best lists
+    candidate_sets = [cdec.score.BLEU(refs).candidate_set() for refs in references]
+    # Weight vector -> sparse
+    w = decoder.weights.tosparse()
+    w0 = w.copy()
+
+    N = len(sources)
+    for t in range(T1):
+        logging.info('Iteration {0}: translating...'.format(t+1))
+        # Get the hypergraphs and extend the k-best lists
+        hgs = []
+        for src, candidates in izip(sources, candidate_sets):
+            hg = decoder.translate(src)
+            hgs.append(hg)
+            candidates.add_kbest(hg, K)
+        # BLEU score for the previous iteration
+        score = evaluate((hg.viterbi() for hg in hgs), references)
+        logging.info('BLEU: {:.2f}'.format(100 * score))
+        logging.info('Optimizing...')
+        for _ in range(T2):
+            # y_i^+, h_i^+; i=1..N
+            plus = [max(candidates, key=lambda c: w.dot(c.fmap) - cost(c)).fmap
+                    for candidates in candidate_sets]
+            for _ in range(T3):
+                for fp, candidates in izip(plus, candidate_sets):
+                    # y^-, h^-
+                    fm = max(candidates, key=lambda c: w.dot(c.fmap) + cost(c)).fmap
+                    # update weights (line 11-12)
+                    w += eta * ((fp - fm) - C/N * (w - w0))
+        logging.info('Updated weight vector: {0}'.format(dict(w)))
+        # Update decoder weights
+        decoder.weights = w
+
+def main():
+    logging.basicConfig(level=logging.INFO, format='%(message)s')
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', '--config', help='cdec config', required=True)
+    parser.add_argument('-w', '--weights', help='initial weights', required=True)
+    parser.add_argument('-r', '--reference', help='reference file', required=True)
+    parser.add_argument('-s', '--source', help='source file', required=True)
+    args = parser.parse_args()
+
+    with open(args.config) as fp:
+        config = fp.read()
+
+    decoder = cdec.Decoder(config)
+    decoder.read_weights(args.weights)
+    with open(args.reference) as fp:
+        references = fp.readlines()
+    with open(args.source) as fp:
+        sources = fp.readlines()
+    assert len(references) == len(sources)
+    rampion(decoder, sources, references)
+
+    for fname, fval in sorted(dict(decoder.weights).iteritems()):
+        print('{0}\t{1}'.format(fname, fval))
+
+if __name__ == '__main__':
+    main()
diff --git a/python/examples/test.py b/python/examples/test.py
new file mode 100644
index 00000000..eb9e6a95
--- /dev/null
+++ b/python/examples/test.py
@@ -0,0 +1,70 @@
+#coding: utf8
+import cdec
+import gzip
+
+weights = '../tests/system_tests/australia/weights'
+grammar_file = '../tests/system_tests/australia/australia.scfg.gz'
+
+# Load decoder width configuration
+decoder = cdec.Decoder(formalism='scfg')
+# Read weights
+decoder.read_weights(weights)
+
+print dict(decoder.weights)
+
+# Read grammar
+with gzip.open(grammar_file) as f:
+    grammar = f.read()
+
+# Input sentence
+sentence = u'澳洲 是 与 北韩 有 邦交 的 少数 国家 之一 。'
+print '    Input:', sentence.encode('utf8')
+
+# Decode
+forest = decoder.translate(sentence, grammar=grammar)
+
+# Get viterbi translation
+print 'Output[0]:', forest.viterbi().encode('utf8')
+f_tree, e_tree = forest.viterbi_trees()
+print ' FTree[0]:', f_tree.encode('utf8')
+print ' ETree[0]:', e_tree.encode('utf8')
+print 'LgProb[0]:', forest.viterbi_features().dot(decoder.weights)
+
+# Get k-best translations
+kbest = zip(forest.kbest(5), forest.kbest_trees(5), forest.kbest_features(5))
+for i, (sentence, (f_tree, e_tree), features) in enumerate(kbest, 1):
+    print 'Output[%d]:' % i, sentence.encode('utf8')
+    print ' FTree[%d]:' % i, f_tree.encode('utf8')
+    print ' ETree[%d]:' % i, e_tree.encode('utf8')
+    print ' FVect[%d]:' % i, dict(features)
+
+# Sample translations from the forest
+for sentence in forest.sample(5):
+    print 'Sample:', sentence.encode('utf8')
+
+# Get feature vector for 1best
+fsrc = forest.viterbi_features()
+
+# Feature expectations
+print 'Feature expectations:', dict(forest.inside_outside())
+
+# Reference lattice
+lattice = ((('australia',0,1),),(('is',0,1),),(('one',0,1),),(('of',0,1),),(('the',0,4),('a',0,4),('a',0,1),('the',0,1),),(('small',0,1),('tiny',0,1),('miniscule',0,1),('handful',0,2),),(('number',0,1),('group',0,1),),(('of',0,2),),(('few',0,1),),(('countries',0,1),),(('that',0,1),),(('has',0,1),('have',0,1),),(('diplomatic',0,1),),(('relations',0,1),),(('with',0,1),),(('north',0,1),),(('korea',0,1),),(('.',0,1),),)
+
+lat = cdec.Lattice(lattice)
+assert (lattice == tuple(lat))
+
+# Intersect forest and lattice
+assert forest.intersect(lat)
+
+# Get best synchronous parse
+f_tree, e_tree = forest.viterbi_trees()
+print 'FTree:', f_tree.encode('utf8')
+print 'ETree:', e_tree.encode('utf8')
+
+# Compare 1best and reference feature vectors
+fref = forest.viterbi_features()
+print dict(fsrc - fref)
+
+# Prune hypergraph
+forest.prune(density=100)