Generalize to sample any number of dev sets

1 files changed, 74 insertions, 0 deletions

diff --git a/corpus/sample-dev-sets.py b/corpus/sample-dev-sets.py
new file mode 100755
index 00000000..3c969bbe
--- /dev/null
+++ b/corpus/sample-dev-sets.py
@@ -0,0 +1,74 @@
+#!/usr/bin/env python
+
+import gzip
+import os
+import sys
+
+HELP = '''Process an input corpus by dividing it into pseudo-documents and uniformly
+sampling train and dev sets (simulate uniform sampling at the document level
+when document boundaries are unknown)
+
+usage: {} in_file out_prefix doc_size docs_per_dev_set dev_sets [-lc]
+recommended: doc_size=20, docs_per_dev_set=100, dev_sets=2 (dev and test)
+'''
+
+def gzopen(f):
+    return gzip.open(f, 'rb') if f.endswith('.gz') else open(f, 'r')
+
+def wc(f):
+    return sum(1 for _ in gzopen(f))
+
+def main(argv):
+    
+    if len(argv[1:]) < 5:
+        sys.stderr.write(HELP.format(os.path.basename(argv[0])))
+        sys.exit(2)
+    
+    # Args
+    in_file = os.path.abspath(argv[1])
+    out_prefix = os.path.abspath(argv[2])
+    doc_size = int(argv[3])
+    docs_per_dev_set = int(argv[4])
+    dev_sets = int(argv[5])
+    lc = (len(argv[1:]) == 6 and argv[6] == '-lc')
+
+    # Compute sizes
+    corpus_size = wc(in_file)
+    total_docs = corpus_size / doc_size
+    leftover = corpus_size % doc_size
+    train_docs = total_docs - (dev_sets * docs_per_dev_set)
+    train_batch_size = (train_docs / docs_per_dev_set)
+
+    # Report
+    sys.stderr.write('Splitting {} lines ({} documents)\n'.format(corpus_size, total_docs + (1 if leftover else 0)))
+    sys.stderr.write('Train: {} ({})\n'.format((train_docs * doc_size) + leftover, train_docs + (1 if leftover else 0)))
+    sys.stderr.write('Dev: {} x {} ({})\n'.format(dev_sets, docs_per_dev_set * doc_size, docs_per_dev_set))
+
+    inp = gzopen(in_file)
+    train_out = open('{}.train'.format(out_prefix), 'w')
+    dev_out = [open('{}.dev.{}'.format(out_prefix, i + 1), 'w') for i in range(dev_sets)]
+    i = 0
+
+    # For each set of documents
+    for _ in range(docs_per_dev_set):
+        # Write several documents to train
+        for _ in range(train_batch_size):
+            for _ in range(doc_size):
+                i += 1
+                train_out.write('{} ||| {}'.format(i, inp.readline()) if lc else inp.readline())
+        # Write a document to each dev
+        for out in dev_out:
+            for _ in range(doc_size):
+                i += 1
+                out.write('{} ||| {}'.format(i, inp.readline()) if lc else inp.readline())
+    # Write leftover lines to train
+    for line in inp:
+        i += 1
+        train_out.write('{} ||| {}'.format(i, line) if lc else line)
+
+    train_out.close()
+    for out in dev_out:
+        out.close()
+
+if __name__ == '__main__':
+    main(sys.argv)