Bertalign

Bertalign

bin/bert_align.py

@@ -0,0 +1,400 @@
+import argparse
+import os
+import sys
+import numpy as np
+import numba as nb
+import faiss
+import time
+
+def _main():
+  # user-defined parameters
+  parser = argparse.ArgumentParser('Multilingual sentence alignment using BERT embeddings',
+                                   formatter_class = argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('--job', type=str, required=True, help='Job file for alignment task.')
+  parser.add_argument('--src_embed', type=str, required=True, nargs=2, help='Source overlap and embedding files.')
+  parser.add_argument('--tgt_embed', type=str, required=True, nargs=2, help='Target overlap and embedding files.')
+  parser.add_argument('--max_align', type=int, default=5, help='Maximum alignment types, n + m <= this value.')
+  parser.add_argument('--win', type=int, default=5, help='Window size for the second-pass alignment.')
+  parser.add_argument('--top_k', type=int, default=3, help='Top-k target neighbors of each source sentence.')
+  parser.add_argument('--skip', type=float, default=-0.1, help='Similarity score for 0-1 and 1-0 alignment.')
+  parser.add_argument('--margin', action='store_true', help='Margin-based cosine similarity')
+  args = parser.parse_args()
+  
+  # fixed parameters to determine the
+  # window size for the first-pass alignment  
+  min_win_size = 10
+  max_win_size = 600
+  win_per_100 = 8
+
+  # read in embeddings
+  src_sent2line, src_line_embeddings = read_in_embeddings(args.src_embed[0], args.src_embed[1])
+  tgt_sent2line, tgt_line_embeddings = read_in_embeddings(args.tgt_embed[0], args.tgt_embed[1])
+  embedding_size = src_line_embeddings.shape[1]
+  
+  # read in alignment jobs
+  job = read_job(args.job)
+
+  # start alignment
+  for rec in job:
+    src_file, tgt_file, align_file = rec.split("\t")
+    print("Aligning {} to {}".format(src_file, tgt_file))
+   
+    # read in source and target sentences
+    src_lines = open(src_file, 'rt', encoding="utf-8").readlines()
+    tgt_lines = open(tgt_file, 'rt', encoding="utf-8").readlines()
+    
+    # convert source and target texts into embeddings
+    # and calculate sentence length
+    t_0 = time.time()
+    src_vecs, src_lens = doc2feats(src_sent2line, src_line_embeddings, src_lines, args.max_align - 1)
+    tgt_vecs, tgt_lens = doc2feats(tgt_sent2line, tgt_line_embeddings, tgt_lines, args.max_align - 1)
+    char_ratio = np.sum(src_lens[0,]) / np.sum(tgt_lens[0,])
+    print("Reading embeddings takes {}".format(time.time() - t_0))
+    
+    # using faiss, find in the target text
+    # the k nearest neighbors of each source sentence
+    #index = faiss.IndexFlatIP(embedding_size) # use inter product to build index
+    t_1 = time.time()
+    #index.add(tgt_vecs[0,:])
+    #xq = src_vecs[0,:]
+    #D,I = index.search(xq, args.top_k)
+
+    res = faiss.StandardGpuResources() 
+    index = faiss.IndexFlatIP(embedding_size)
+    gpu_index = faiss.index_cpu_to_gpu(res, 0, index)
+    gpu_index.add(tgt_vecs[0,:]) 
+    xq = src_vecs[0,:]
+    D,I = gpu_index.search(xq,args.top_k) 
+    print("Finding top-k neighbors takes {}".format(time.time() - t_1))
+
+    # find 1-to-1 alignment
+    t_2 = time.time()
+    src_len = len(src_lines)
+    tgt_len = len(tgt_lines)
+    first_alignment_types = make_alignment_types(2) # 0-0， 1-0 and 1-1
+    first_w, first_search_path = find_first_search_path(src_len, tgt_len, min_win_size, max_win_size, win_per_100)
+    first_pointers = first_pass_align(src_len, tgt_len, first_w, first_search_path, first_alignment_types, D, I, args.top_k)
+    first_alignment = first_back_track(src_len, tgt_len, first_pointers, first_search_path, first_alignment_types)
+    print("First pass alignment takes {}".format(time.time() - t_2))
+
+    # find m-to-n alignment
+    t_3 = time.time()
+    second_w, second_search_path = find_second_search_path(first_alignment, args.win, src_len, tgt_len)
+    second_alignment_types = make_alignment_types(args.max_align)
+    second_pointers = second_pass_align(src_vecs, tgt_vecs, src_lens, tgt_lens, second_w, second_search_path, second_alignment_types, char_ratio, args.skip, margin=args.margin)
+    second_alignment = second_back_track(src_len, tgt_len, second_pointers, second_search_path, second_alignment_types)
+    print("Second pass alignment takes {}".format(time.time() - t_3))
+
+    # save alignment
+    out_f = open(align_file, 'w', encoding="utf-8")
+    print_alignments(second_alignment, file=out_f)
+    
+def print_alignments(alignments, file=sys.stdout):
+  for x, y in alignments:
+    print('%s:%s' % (x, y), file=file)
+
+def second_back_track(i, j, b, search_path, a_types):
+    alignment = []
+    while ( i !=0 and j != 0 ):
+        j_offset = j - search_path[i][0]
+        a = b[i][j_offset]
+        s = a_types[a][0]
+        t = a_types[a][1]
+        src_range = [i - offset - 1 for offset in range(s)][::-1]
+        tgt_range = [j - offset - 1 for offset in range(t)][::-1]
+        alignment.append((src_range, tgt_range))
+
+        i = i-s
+        j = j-t
+
+    return alignment[::-1]
+
+@nb.jit(nopython=True, fastmath=True, cache=True)
+def second_pass_align(src_vecs, tgt_vecs, src_lens, tgt_lens, w, search_path, align_types, char_ratio, skip, margin=False):
+  src_len = src_vecs.shape[1]
+  tgt_len = tgt_vecs.shape[1]
+
+  # intialize sum matrix
+  cost = np.zeros((src_len + 1, w))
+  #back = np.zeros((tgt_len + 1, w), dtype=nb.int64)
+  back = np.zeros((src_len + 1, w), dtype=nb.int64)
+  cost[0][0] = 0
+  back[0][0] = -1
+  
+  for i in range(1, src_len + 1):
+    i_start = search_path[i][0]
+    i_end = search_path[i][1]
+    for j in range(i_start, i_end + 1):
+      if i + j == 0:
+        continue
+      best_score = -np.inf
+      best_a = -1
+      for a in range(align_types.shape[0]):
+        a_1 = align_types[a][0]
+        a_2 = align_types[a][1]
+        prev_i = i - a_1
+        prev_j = j - a_2
+
+        if prev_i < 0 or prev_j < 0 :  # no previous cell in DP table 
+          continue
+        prev_i_start = search_path[prev_i][0]
+        prev_i_end =  search_path[prev_i][1]
+        if prev_j < prev_i_start or prev_j > prev_i_end: # out of bound of cost matrix
+            continue
+        prev_j_offset = prev_j - prev_i_start
+        score = cost[prev_i][prev_j_offset]
+        if score == -np.inf:
+          continue
+
+        if a_1 == 0 or a_2 == 0:  # deletion or insertion
+          cur_score = skip
+        else:
+          src_v = src_vecs[a_1-1,i-1,:]
+          tgt_v = tgt_vecs[a_2-1,j-1,:]
+          src_l = src_lens[a_1-1, i-1]
+          tgt_l = tgt_lens[a_2-1, j-1]
+          cur_score = get_score(src_v, tgt_v, a_1, a_2, i, j, src_vecs, tgt_vecs, src_len, tgt_len, margin=margin)
+          tgt_l = tgt_l * char_ratio
+          min_len = min(src_l, tgt_l)
+          max_len = max(src_l, tgt_l)
+          len_p = np.log2(1 + min_len / max_len)
+          cur_score *= len_p
+        
+        score += cur_score
+        if score > best_score:
+          best_score = score
+          best_a = a
+
+      j_offset = j - i_start
+      cost[i][j_offset] = best_score
+      back[i][j_offset] = best_a
+      
+  return back
+
+@nb.jit(nopython=True, fastmath=True, cache=True)
+def get_score(src_v, tgt_v, a_1, a_2, i, j, src_vecs, tgt_vecs, src_len, tgt_len, margin=False):
+  similarity = nb_dot(src_v, tgt_v)
+  if margin:
+    tgt_neighbor_ave_sim = get_neighbor_sim(src_v, a_2, j, tgt_len, tgt_vecs)
+    src_neighbor_ave_sim = get_neighbor_sim(tgt_v, a_1, i, src_len, src_vecs)
+    neighbor_ave_sim = (tgt_neighbor_ave_sim + src_neighbor_ave_sim)/2
+    similarity -= neighbor_ave_sim
+  return similarity
+
+@nb.jit(nopython=True, fastmath=True, cache=True)
+def get_neighbor_sim(vec, a, j, len, db):
+  left_idx = j - a
+  right_idx = j + 1
+    
+  if right_idx > len:
+    neighbor_right_sim = 0
+  else:
+    right_embed = db[0,right_idx-1,:]
+    neighbor_right_sim = nb_dot(vec, right_embed)
+    
+  if left_idx == 0:
+    neighbor_left_sim = 0
+  else:
+    left_embed = db[0,left_idx-1,:]
+    neighbor_left_sim = nb_dot(vec, left_embed)
+
+  #if right_idx > LEN or left_idx < 0:
+  if right_idx > len or left_idx == 0:
+    neighbor_ave_sim = neighbor_left_sim + neighbor_right_sim
+  else:
+    neighbor_ave_sim = (neighbor_left_sim + neighbor_right_sim) / 2
+  
+  return neighbor_ave_sim
+
+@nb.jit(nopython=True, fastmath=True, cache=True)
+def nb_dot(x, y):
+    return np.dot(x,y)
+
+def find_second_search_path(align, w, src_len, tgt_len):
+  '''
+  Convert 1-1 alignment from first-pass to the path for second-pass alignment.
+  The index along X-axis and Y-axis must be consecutive. 
+  '''
+  last_bead_src = align[-1][0]
+  last_bead_tgt = align[-1][1]
+  
+  if last_bead_src != src_len:
+    if last_bead_tgt == tgt_len:
+      align.pop()
+    align.append((src_len, tgt_len))
+  else:
+    if last_bead_tgt != tgt_len:
+      align.pop()
+      align.append((src_len, tgt_len))
+      
+  prev_src, prev_tgt = 0,0
+  path = []
+  max_w = -np.inf
+  for src, tgt in align:
+    lower_bound = max(0, prev_tgt - w)
+    upper_bound = min(tgt_len, tgt + w)
+    path.extend([(lower_bound, upper_bound) for id in range(prev_src+1, src+1)])
+    prev_src, prev_tgt = src, tgt
+    width = upper_bound - lower_bound
+    if width > max_w:
+      max_w = width
+  path = [path[0]] + path
+  return max_w + 1, np.array(path)
+
+def first_back_track(i, j, b, search_path, a_types):
+    alignment = []
+    while ( i !=0  and j != 0 ):
+        j_offset = j - search_path[i][0]
+        a = b[i][j_offset]
+        s = a_types[a][0]
+        t = a_types[a][1]
+        if a == 2:
+          alignment.append((i, j))
+
+        i = i-s
+        j = j-t
+        
+    return alignment[::-1]
+
+@nb.jit(nopython=True, fastmath=True, cache=True)
+def first_pass_align(src_len, tgt_len, w, search_path, align_types, dist, index, top_k):
+
+  #initialize cost and backpointer matrix
+  cost = np.zeros((src_len + 1, 2 * w + 1))
+  pointers = np.zeros((src_len + 1, 2 * w + 1), dtype=nb.int64)
+  cost[0][0] = 0
+  pointers[0][0] = -1
+
+  for i in range(1, src_len +  1):
+    i_start = search_path[i][0]
+    i_end = search_path[i][1]
+    for j in range(i_start, i_end + 1):
+      if i + j == 0:
+        continue
+      best_score = -np.inf
+      best_a = -1
+      for a in range(align_types.shape[0]):
+        a_1 = align_types[a][0]
+        a_2 = align_types[a][1]
+        prev_i = i - a_1
+        prev_j = j - a_2
+        if prev_i < 0 or prev_j < 0 :  # no previous cell 
+          continue
+        prev_i_start = search_path[prev_i][0]
+        prev_i_end =  search_path[prev_i][1]
+        if prev_j < prev_i_start or prev_j > prev_i_end: # out of bound of cost matrix
+            continue
+        prev_j_offset = prev_j - prev_i_start
+        score = cost[prev_i][prev_j_offset]
+        if score == -np.inf:
+          continue
+
+        if a_1 > 0 and a_2 > 0:
+          for k in range(top_k):
+            if index[i-1][k] == j - 1:
+              score += dist[i-1][k]
+        if score > best_score:
+          best_score = score
+          best_a = a
+
+      j_offset = j - i_start
+      cost[i][j_offset] = best_score
+      pointers[i][j_offset] = best_a
+
+  return pointers
+
+@nb.jit(nopython=True, fastmath=True, cache=True)
+def find_first_search_path(src_len, tgt_len, min_win_size, max_win_size, win_per_100):
+  yx_ratio = tgt_len / src_len
+  win_size_1 = int(yx_ratio * tgt_len * win_per_100 / 100)
+  win_size_2 = int(abs(tgt_len - src_len) * 3/4)
+  w_1 = min(max(min_win_size, max(win_size_1, win_size_2)), max_win_size)
+  #w_2 = int(max(src_len, tgt_len) * 0.05)
+  w_2 = int(max(src_len, tgt_len) * 0.06)
+  w = max(w_1, w_2)
+  search_path = np.zeros((src_len + 1, 2), dtype=nb.int64)
+  for i in range(0, src_len + 1):
+    center = int(yx_ratio * i)
+    w_start = max(0, center - w)
+    w_end = min(center + w, tgt_len)
+    search_path[i] = [w_start, w_end]
+  return w, search_path
+
+def doc2feats(sent2line, line_embeddings, lines, num_overlaps):
+    lines = [preprocess_line(line) for line in lines]
+    vecsize = line_embeddings.shape[1]
+    vecs0 = np.empty((num_overlaps, len(lines), vecsize), dtype=np.float32)
+    vecs1 = np.empty((num_overlaps, len(lines)), dtype=np.int)
+
+    for ii, overlap in enumerate(range(1, num_overlaps + 1)):
+        for jj, out_line in enumerate(layer(lines, overlap)):
+            try:
+                line_id = sent2line[out_line]
+            except KeyError:
+                logger.warning('Failed to find overlap=%d line "%s". Will use random vector.', overlap, out_line)
+                line_id = None
+
+            if line_id is not None:
+                vec = line_embeddings[line_id]
+            else:
+                vec = np.random.random(vecsize) - 0.5
+                vec = vec / np.linalg.norm(vec)
+                
+            vecs0[ii, jj, :] = vec
+            vecs1[ii, jj] = len(out_line.encode("utf-8"))
+
+    return vecs0, vecs1
+
+def preprocess_line(line):
+    line = line.strip()
+    if len(line) == 0:
+        line = 'BLANK_LINE'
+    return line
+
+def layer(lines, num_overlaps, comb=' '):
+    """
+    make front-padded overlapping sentences
+    """
+    if num_overlaps < 1:
+        raise Exception('num_overlaps must be >= 1')
+    out = ['PAD', ] * min(num_overlaps - 1, len(lines))
+    for ii in range(len(lines) - num_overlaps + 1):
+        out.append(comb.join(lines[ii:ii + num_overlaps]))
+    return out
+
+def read_in_embeddings(text_file, embed_file):
+    sent2line = dict()
+    with open(text_file, 'rt', encoding="utf-8") as fin:
+        for ii, line in enumerate(fin):
+            if line.strip() in sent2line:
+                raise Exception('got multiple embeddings for the same line')
+            sent2line[line.strip()] = ii
+
+    line_embeddings = np.fromfile(embed_file, dtype=np.float32, count=-1)
+    if line_embeddings.size == 0:
+        raise Exception('Got empty embedding file')
+
+    embedding_size = line_embeddings.size // len(sent2line)
+    line_embeddings.resize(line_embeddings.shape[0] // embedding_size, embedding_size)
+    return sent2line, line_embeddings
+
+def make_alignment_types(max_alignment_size):
+    # Return list of all (n,m) where n+m <= this
+    alignment_types = []
+    for x in range(1, max_alignment_size):
+        for y in range(1, max_alignment_size):
+            if x + y <= max_alignment_size:
+                alignment_types.append([x, y])
+    alignment_types = [[0,1], [1,0]] + alignment_types
+    return np.array(alignment_types)
+
+def read_job(file):
+    job = []
+    with open(file, 'r', encoding="utf-8") as f:
+        for line in f:
+            if not line.startswith("#"):
+                job.append(line.strip())
+    return job
+    
+if __name__ == '__main__':
+    _main()

bin/gale_align.py

@@ -0,0 +1,193 @@
+import os
+import sys
+import argparse
+import time
+import math
+import numba as nb
+import numpy as np
+
+def _main():
+  # user-defined parameters
+  parser = argparse.ArgumentParser('Sentence alignment using Gale-Church Algrorithm',
+                                   formatter_class = argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument('--job', type=str, required=True, help='Job file for alignment task.')
+  args = parser.parse_args()
+  
+  # fixed parameters to determine the
+  # window size for alignment  
+  min_win_size = 10
+  max_win_size = 600
+  win_per_100 = 8
+  
+  # alignment types
+  align_types = np.array([
+    [0,1],
+    [1,0],
+    [1,1],
+    [1,2],
+    [2,1],
+    [2,2]
+  ], dtype=np.int)
+  
+  # prior probability
+  priors = np.array([0, 0.0099, 0.89, 0.089, 0.011])
+  
+  # mean and variance
+  c = 1
+  s2 = 6.8
+  
+  # gale church align
+  job = read_job(args.job)
+  for rec in job:
+    src_file, tgt_file, align_file = rec.split("\t")
+    print("Aligning {} to {}".format(src_file, tgt_file))
+    src_lines = open(src_file, 'rt', encoding="utf-8").readlines() # UTF-8 byte length
+    tgt_lines = open(tgt_file, 'rt', encoding="utf-8").readlines()
+    src_len = calculate_txt_len(src_lines)
+    tgt_len = calculate_txt_len(tgt_lines)
+    
+    m = src_len.shape[0] - 1
+    n = tgt_len.shape[0] - 1
+  
+    # find search path
+    w, search_path = \
+      find_search_path(m, n, min_win_size, max_win_size, win_per_100)
+    
+    cost, back = align(src_len, tgt_len, w, search_path, align_types, priors, c, s2)
+    alignment = back_track(m, n, back, search_path, align_types)
+    #print(alignment)
+    
+    # save alignment
+    f = open(align_file, 'w', encoding="utf-8")
+    print_alignments(alignment, file=f)
+    
+def print_alignments(alignments, file=sys.stdout):
+  for x, y in alignments:
+    print('%s:%s' % (x, y), file=file)
+    
+def back_track(i, j, b, search_path, a_types):
+    #i = b.shape[0] - 1
+    #j = b.shape[1] - 1
+    alignment = []
+    while ( i !=0  and j != 0 ):
+        j_offset = j - search_path[i][0]
+        a = b[i][j_offset]
+        s = a_types[a][0]
+        t = a_types[a][1]
+        src_range = [i - offset - 1 for offset in range(s)][::-1]
+        tgt_range = [j - offset - 1 for offset in range(t)][::-1]
+        alignment.append((src_range, tgt_range))
+
+        i = i-s
+        j = j-t
+        
+    return alignment[::-1]
+
+@nb.jit(nopython=True, fastmath=True, cache=True)
+def align(src_len, tgt_len, w, search_path, align_types, priors, c, s2):
+
+  #initialize cost and backpointer matrix
+  m = src_len.shape[0] - 1
+  cost = np.zeros((m + 1, 2 * w + 1))
+  back = np.zeros((m + 1, 2 * w + 1), dtype=nb.int64)
+  cost[0][0] = 0
+  back[0][0] = -1
+
+  for i in range(m + 1):
+    i_start = search_path[i][0]
+    i_end = search_path[i][1]
+
+    for j in range(i_start, i_end + 1):
+      if i + j == 0:
+        continue
+   
+      best_score = np.inf
+      best_a = -1
+      for a in range(align_types.shape[0]):
+        a_1 = align_types[a][0]
+        a_2 = align_types[a][1]
+        prev_i = i - a_1
+        prev_j = j - a_2
+        
+        if prev_i < 0 or prev_j < 0 :  # no previous cell 
+          continue
+        
+        prev_i_start = search_path[prev_i][0]
+        prev_i_end =  search_path[prev_i][1]
+        
+        if prev_j < prev_i_start or prev_j > prev_i_end: # out of bound of cost matrix
+            continue
+            
+        prev_j_offset = prev_j - prev_i_start
+
+        score = cost[prev_i][prev_j_offset] - math.log(priors[a_1 + a_2]) + \
+          get_score(src_len[i] - src_len[i - a_1], tgt_len[j] - tgt_len[j - a_2], c, s2)
+        
+        if score < best_score:
+          best_score = score
+          best_a = a
+      
+      j_offset = j - i_start
+      cost[i][j_offset] = best_score
+      back[i][j_offset] = best_a
+   
+  return cost, back
+  
+@nb.jit(nopython=True, fastmath=True, cache=True)
+def get_score(len_s, len_t, c, s2):
+  
+  mean = (len_s + len_t / c) / 2
+  z = (len_t - len_s * c) / math.sqrt(mean * s2)
+  
+  pd = 2 * (1 - norm_cdf(abs(z)))
+  if pd > 0:
+    return -math.log(pd)
+    
+  return 25
+  
+@nb.jit(nopython=True, fastmath=True, cache=True)
+def find_search_path(src_len, tgt_len, min_win_size, max_win_size, win_per_100):
+  yx_ratio = tgt_len / src_len
+  win_size_1 = int(yx_ratio * tgt_len * win_per_100 / 100)
+  win_size_2 = int(abs(tgt_len - src_len) * 3/4)
+  w_1 = min(max(min_win_size, max(win_size_1, win_size_2)), max_win_size)
+  #w_2 = int(max(src_len, tgt_len) * 0.05)
+  w_2 = int(max(src_len, tgt_len) * 0.06)
+  w = max(w_1, w_2)
+  search_path = np.zeros((src_len + 1, 2), dtype=nb.int64)
+  for i in range(0, src_len + 1):
+    center = int(yx_ratio * i)
+    w_start = max(0, center - w)
+    w_end = min(center + w, tgt_len)
+    search_path[i] = [w_start, w_end]
+  return w, search_path
+  
+@nb.jit(nopython=True, fastmath=True, cache=True)
+def norm_cdf(z):
+  t = 1/float(1+0.2316419*z) # t = 1/(1+pz) , z=0.2316419
+  p_norm = 1 - 0.3989423*math.exp(-z*z/2) * ((0.319381530 * t)+ \
+                                         (-0.356563782 * t)+ \
+                                         (1.781477937 * t) + \
+                                         (-1.821255978* t) + \
+                                         (1.330274429 * t))
+  return p_norm
+  
+def calculate_txt_len(lines):
+    txt_len = []
+    txt_len.append(0)
+    for i, line in enumerate(lines):
+        txt_len.append(txt_len[i] + len(line.strip().encode("utf-8")))
+    return np.array(txt_len)
+
+def read_job(file):
+    job = []
+    with open(file, 'r', encoding="utf-8") as f:
+        for line in f:
+            if not line.startswith("#"):
+                job.append(line.strip())
+    return job
+
+if __name__ == '__main__':
+    t_0 = time.time()
+    _main()
+    print("It takes {}".format(time.time() - t_0))

bin/hunalign/null.dic

@@ -0,0 +1 @@
+NULL @ NULL

bin/vecalign/LICENSE

@@ -0,0 +1,202 @@
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bin/vecalign/README.md

@@ -0,0 +1,158 @@
+# Vecalign
+
+Vecalign is an accurate sentence alignment algorithm which is fast even for very long documents.
+In conjunction with [LASER](https://github.com/facebookresearch/LASER), Vecalign 
+works in about 100 languages (i.e. 100^2 language pairs), 
+without the need for a machine translation system or lexicon. 
+
+Vecalign uses similarity of multilingual sentence embeddings to judge the similarity of sentences.
+
+![multilingual_sentence_embedding image](media/multilingual_sentence_embedding.png)
+[image based on [this Facebook AI post](https://engineering.fb.com/ai-research/laser-multilingual-sentence-embeddings/)]
+
+Vecalign uses an approximation to Dynamic Programming based on 
+[Fast Dynamic Time Warping](https://content.iospress.com/articles/intelligent-data-analysis/ida00303)
+which is linear in time and space with respect to the number of sentences being aligned. 
+
+![dynamic_programing_approximation visualization](media/dynamic_programing_approximation.gif)
+
+### License 
+
+Copyright 2019 Brian Thompson
+
+Vecalign is released under the [Apache License, Version 2.0](LICENSE).
+For convenience, the dev and test datasets from Bleualign are provided. Bleualign is Copyright 2010 Rico Sennrich and is released under the [GNU General Public License Version 2](bleualign_data/LICENSE)
+
+### Build Vecalign
+
+You will need python 3.6+ with numpy and cython. You can build an environment using conda as follows:
+
+```
+# Use latest conda
+conda update conda -y
+# Create conda environment
+conda create  --force -y --name vecalign python=3.7
+# Activate new environment
+source `conda info --base`/etc/profile.d/conda.sh # See: https://github.com/conda/conda/issues/7980
+conda activate vecalign
+# Install required packages
+conda install -y -c anaconda cython
+conda install -y -c anaconda numpy
+```
+
+Note that Vecalign contains cython code, but there is no need to build it manually as it is compiled automatically by [pyximport](https://github.com/cython/cython/tree/master/pyximport).
+
+### Run Vecalign (using provided embeddings)
+```
+./vecalign.py --alignment_max_size 8 --src bleualign_data/dev.de --tgt bleualign_data/dev.fr \
+   --src_embed bleualign_data/overlaps.de bleualign_data/overlaps.de.emb  \
+   --tgt_embed bleualign_data/overlaps.fr bleualign_data/overlaps.fr.emb
+```
+
+Alignments are written to stdout:
+```
+[0]:[0]:0.156006
+[1]:[1]:0.160997
+[2]:[2]:0.217155
+[3]:[3]:0.361439
+[4]:[4]:0.346332
+[5]:[5]:0.211873
+[6]:[6, 7, 8]:0.507506
+[7]:[9]:0.252747
+[8, 9]:[10, 11, 12]:0.139594
+[10, 11]:[13]:0.273751
+[12]:[14]:0.165397
+[13]:[15, 16, 17]:0.436312
+[14]:[18, 19, 20, 21]:0.734142
+[]:[22]:0.000000
+[]:[23]:0.000000
+[]:[24]:0.000000
+[]:[25]:0.000000
+[15]:[26, 27, 28]:0.840094
+...
+```
+
+The first two entries are the source and target sentence indexes for each alignment, respectively. 
+The third entry in each line is the sentence alignment cost computed by Vecalign. 
+Note that this cost includes normalization but does *not* include the penalties terms for containing more than one sentence. 
+Note that the alignment cost is set to zero for insertions/deletions. 
+Also note that the results may vary slightly due to randomness in the normalization.
+
+To score against a gold alignment, use the "-g" flag.
+Flags "-s", "-t", and "-g" can accept multiple arguments. This is primarily useful for scoring, as the output alignments will all be concatenated together in stdout. For example, to align and score the bleualign test set: 
+```
+./vecalign.py --alignment_max_size 8 --src bleualign_data/test*.de --tgt bleualign_data/test*.fr \
+   --gold bleualign_data/test*.defr  \
+   --src_embed bleualign_data/overlaps.de bleualign_data/overlaps.de.emb  \
+   --tgt_embed bleualign_data/overlaps.fr bleualign_data/overlaps.fr.emb > /dev/null
+```
+Which should give you results that approximately match the Vecalign paper:
+
+```
+
+ ---------------------------------
+|             |  Strict |    Lax  |
+| Precision   |   0.899 |   0.985 |
+| Recall      |   0.904 |   0.987 |
+| F1          |   0.902 |   0.986 |
+ ---------------------------------
+```
+
+Note: Run `./vecalign.py -h` for full sentence alignment usage and options. 
+For stand-alone scoring against a gold reference, see [score.py](score.py)
+
+### Embed your own documents
+
+The Vecalign repository contains overlap and embedding files for the Bluealign dev/test files. 
+This section shows how those files were made, as an example for running on new data.
+
+Vecalign requires not only embeddings of sentences in each document, 
+but also embeddings of *concatenations* of consecutive sentences.
+The embeddings of multiple, consecutive sentences are needed to consider 1-many, many-1, and many-many alignments.
+
+
+To create a file containing all the sentence combinations in the dev and test files from Bleualign:
+```
+./overlap.py -i bleualign_data/dev.fr bleualign_data/test*.fr -o bleualign_data/overlaps.fr -n 10
+./overlap.py -i bleualign_data/dev.de bleualign_data/test*.de -o bleualign_data/overlaps.de -n 10
+```
+
+Note: Run `./overlap.py -h` to see full set of embedding options. 
+
+`bleualign_data/overlaps.fr` and `bleualign_data/overlaps.de` are text files containing one or more sentences per line. 
+
+These files must then be embedded using a multilingual sentence embedder.
+
+We recommend the [Language-Agnostic SEntence Representations (LASER)](https://github.com/facebookresearch/LASER) 
+toolkit from Facebook, as it has strong performance and comes with a pretrained model which works well in about 100 languages. 
+However, Vecalign should also work with other embedding methods as well. Embeddings should be provided as a binary file containing float32 values.
+
+The following assumes LASER is installed and the LASER environmental variable has been set.
+
+To embed the Bleualign files using LASER:
+```
+$LASER/tasks/embed/embed.sh bleualign_data/overlaps.fr fr bleualign_data/overlaps.fr.emb
+$LASER/tasks/embed/embed.sh bleualign_data/overlaps.de de bleualign_data/overlaps.de.emb
+```
+
+Note that LASER will not overwrite an embedding file if it exsts, so you may need to run first `rm bleualign_data/overlaps.fr.emb bleualign_data/overlaps.de.emb`.
+
+### Publications
+
+If you use Vecalign, please cite our [paper](https://www.aclweb.org/anthology/D19-1136.pdf):
+
+```
+@inproceedings{thompson-koehn-2019-vecalign,
+    title = "{V}ecalign: Improved Sentence Alignment in Linear Time and Space",
+    author = "Thompson, Brian and Koehn, Philipp",
+    booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
+    month = nov,
+    year = "2019",
+    address = "Hong Kong, China",
+    publisher = "Association for Computational Linguistics",
+    url = "https://www.aclweb.org/anthology/D19-1136",
+    doi = "10.18653/v1/D19-1136",
+    pages = "1342--1348",
+}
+```
+

bin/vecalign/dp_core.pyx

@@ -0,0 +1,411 @@
+# cython: language_level=3
+
+"""
+Copyright 2019 Brian Thompson
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    https://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import numpy as np
+
+cimport numpy as np
+cimport cython
+
+
+def make_x_y_offsets(alignment_types):
+    # alignment types for which we will precompute costs
+
+    # deletion/insertion is added later
+    for x, y in alignment_types:
+        assert (x > 0)
+        assert (y > 0)
+
+    x_offsets = np.array([x for x, y in alignment_types], dtype=np.int32)  # MUST **NOT** INCLUDE (0,1), (1,0)
+    y_offsets = np.array([y for x, y in alignment_types], dtype=np.int32)  # MUST **NOT** INCLUDE (0,1), (1,0)
+    return x_offsets, y_offsets
+
+
+def make_dense_costs(np.ndarray[float, ndim=3] vecs0,  # itput
+                     np.ndarray[float, ndim=3] vecs1,  # input
+                     np.ndarray[float, ndim=2] norm0,  # input
+                     np.ndarray[float, ndim=2] norm1,  # input
+                     int offset0 = 0,  # index into vecs0/norms0
+                     int offset1 = 0,  # index into vecs1/norms1
+                     ):
+    """
+    Make a full N*M feature matrix. By default, makes 1-1 alignments, 
+       can build others by specifying offset0, offset1 to index into
+       vecs0, norms0 and vecs1, norms1 respectivly. 
+    """
+    assert vecs0.shape[0] > offset0
+    assert vecs1.shape[0] > offset1
+    assert norm0.shape[0] > offset0
+    assert norm1.shape[0] > offset1
+
+    cdef int size0 = np.shape(vecs0)[1]
+    assert norm0.shape[1] == size0
+
+    cdef int size1 = np.shape(vecs1)[1]
+    assert norm1.shape[1] == size1
+
+    cdef int vecsize = np.shape(vecs0)[2]
+    assert vecs1.shape[2] == vecsize
+
+    cdef int xi, yi
+    cdef float sumx
+
+    cdef np.ndarray[float, ndim=2] costs = np.empty((size0, size1), dtype=np.float32)
+
+    for xi in range(size0):
+        for yi in range(size1):
+            sumx = 0.0
+            for jj in range(vecsize):
+                sumx += vecs0[offset0, xi, jj] * vecs1[offset1, yi, jj]
+
+            costs[xi, yi] = 2.0 * (1.0 - sumx) / (1e-6 + norm0[offset0, xi] + norm1[offset1, yi])
+            # normalize by alignment type  
+            costs[xi, yi] = costs[xi, yi] * (offset0 + 1) * (offset1 + 1)
+
+    return costs
+
+
+def dense_dp(np.ndarray[float, ndim=2] alignment_cost, float pen):
+    """
+    Compute cost matrix (csum) and backpointers (bp) 
+    from full 2-D 1-1 alignment costs matrix (alignment_cost) 
+    """
+
+    size0 = alignment_cost.shape[0]
+    size1 = alignment_cost.shape[1]
+    # csum and traceback matrix are both on nodes
+    #   so they are +1 in each dimension compared to the jump costs matrix
+    # For anything being used in accumulation, use float64
+    cdef np.ndarray[double, ndim=2] csum = np.empty((size0 + 1, size1 + 1), dtype=np.float64)
+    cdef np.ndarray[int, ndim=2] bp = np.empty((size0 + 1, size1 + 1), dtype=np.int32)
+
+    # bp and csum are nodes, 
+    #   while alignment_cost is the cost of going between the nodes
+    # Size of nodes should be one larger than alignment costs
+    b0, b1 = np.shape(bp)
+    c0, c1 = np.shape(csum)
+    j0, j1 = np.shape(alignment_cost)
+    assert (b0 == c0 == j0 + 1)
+    assert (b1 == c1 == j1 + 1)
+
+    cdef int cmax = np.shape(csum)[1]
+    cdef int rmax = np.shape(csum)[0]
+    cdef int c, r
+    cdef double cost0, cost1, cost2
+
+    # initialize the all c-direction deletion path
+    for c in range(cmax):
+        csum[0, c] = c * pen
+        bp[0, c] = 1
+
+    # initialize the all r-direction deletion path
+    for r in range(rmax):
+        csum[r, 0] = r * pen
+        bp[r, 0] = 2
+
+    # Initial cost is 0.0
+    csum[0, 0] = 0.0  # noop
+    bp[0, 0] = 4  # should not matter
+
+    # Calculate the rest recursively
+    for c in range(1, cmax):
+        for r in range(1, rmax):
+
+            # alignment_cost indexes are off by 1 wrt
+            #   csum/bp, since csum/bp are nodes
+            cost0 = csum[r - 1, c - 1] + alignment_cost[r - 1, c - 1]
+            cost1 = csum[r, c - 1] + pen
+            cost2 = csum[r - 1, c] + pen
+
+            csum[r, c] = cost0
+            bp[r, c] = 0
+
+            if cost1 < csum[r, c]:
+                csum[r, c] = cost1
+                bp[r, c] = 1
+            if cost2 < csum[r, c]:
+                csum[r, c] = cost2
+                bp[r, c] = 2
+
+    return csum, bp
+
+
+def score_path(np.ndarray[int, ndim=1] xx,
+               np.ndarray[int, ndim=1] yy,
+               np.ndarray[float, ndim=1] norm1,
+               np.ndarray[float, ndim=1] norm2,
+               np.ndarray[float, ndim=2] vecs1,
+               np.ndarray[float, ndim=2] vecs2,
+               np.ndarray[float, ndim=1] out):
+    cdef int xi, yi, ii, jj
+    cdef float outx
+    cdef int lenxy = xx.shape[0]
+    cdef int vecsize = vecs1.shape[1]
+
+    for ii in range(lenxy):
+        xi = xx[ii]
+        yi = yy[ii]
+        outx = 0.0
+        for jj in range(vecsize):
+            outx += vecs1[xi, jj] * vecs2[yi, jj]
+        out[ii] = 2.0 * (1.0 - outx) / (norm1[xi] + norm2[yi])
+
+
+# Bounds checking and wraparound slow things down by about 2x
+# Division by 0 checking has minimal speed impact
+@cython.boundscheck(False)  # turn off bounds-checking for entire function
+@cython.wraparound(False)  # turn off negative index wrapping for entire function
+@cython.cdivision(True)  # use c-style division (no division-by-zero check)
+def make_sparse_costs(np.ndarray[float, ndim=3] vecs0,  # intput: num aligns X num sents X dim
+                      np.ndarray[float, ndim=3] vecs1,  # input
+                      np.ndarray[float, ndim=2] norms0,  # intput: num aligns X num sents
+                      np.ndarray[float, ndim=2] norms1,  # input
+                      x_y_path,
+                      alignment_types,
+                      int width_over2):
+    """
+    Make features for DP, *for lines running across approximate path*, *for each alignment type*
+    x_offsets, y_offsets should not include (0,1), (1,0)
+
+    Basically, we take the feature matrix, rotate it 45 degress, 
+       and compute a "wavy" matrix for the features.
+    It's like the diagonal but it moves around to hopefully always include the true path.
+    """
+
+    cdef np.ndarray[int, ndim=2] x_y_path_ = np.array(x_y_path).astype(np.int32)
+
+    assert (vecs0.shape[0] == norms0.shape[0])
+    assert (vecs1.shape[0] == norms1.shape[0])
+
+    assert (vecs0.shape[1] == norms0.shape[1])
+    assert (vecs1.shape[1] == norms1.shape[1])
+
+    # check how many overlaps vectors were passed in
+    num_overlaps_in_vecs0 = vecs0.shape[0]
+    num_overlaps_in_vecs1 = vecs1.shape[0]
+
+    # check how many overlaps were requested
+    # edge case: alignment_types could be empty
+    #    In that case, we should just return insertions/deletions
+    #    and max_x_overlap == max_y_overlap == 0
+    max_x_overlap = max([0] + [x for x, y in alignment_types])  # add [0] in case alignment_types is empty
+    max_y_overlap = max([0] + [y for x, y in alignment_types])  # add [0] in case alignment_types is empty
+
+    # note: alignment types are specified 1-based, but vectors are stored 0-based
+    if max_x_overlap > num_overlaps_in_vecs0:
+        raise Exception('%d x overlaps requrested (via alignment_types), but vecs0 only has %d' % (
+            max_x_overlap, num_overlaps_in_vecs0))
+    if max_y_overlap > num_overlaps_in_vecs1:
+        raise Exception('%d y overlaps requrested (via alignment_types), but vecs1 only has %d' % (
+            max_y_overlap, num_overlaps_in_vecs1))
+
+    # number of sentences in each document
+    cdef int xsize = vecs0.shape[1]
+    cdef int ysize = vecs1.shape[1]
+
+    # vector diminsions should match
+    assert (vecs0.shape[2] == vecs1.shape[2])
+
+    cdef np.ndarray[int, ndim=1] x_offsets, y_offsets
+    x_offsets, y_offsets = make_x_y_offsets(alignment_types)
+
+    # reserve outputs
+    a_len = x_y_path_.shape[0]
+    b_len = 2 * width_over2
+    cdef np.ndarray[float, ndim=3] a_b_feats = np.empty((len(alignment_types), a_len, b_len), dtype=np.float32)
+    cdef np.ndarray[int, ndim=1] b_offset = np.empty(a_len).astype(np.int32)
+
+    cdef int x, y, aa, bb, xx, yy, a_idx, b_idx, bb2, x_offset, y_offset, ii_align, x_offset_idx, y_offset_idx
+    cdef int vecsize = vecs0.shape[2]
+    cdef int num_alignments = x_offsets.shape[0]
+
+    cdef float sumx, feat
+    cdef float inf = np.inf
+
+    for ii in range(x_y_path_.shape[0]):
+        x = x_y_path_[ii, 0]
+        y = x_y_path_[ii, 1]
+
+        # convert xy to ab cords
+        aa = x + y
+        bb = y
+
+        a_idx = aa
+        b_offset[aa] = bb - width_over2
+        for b_idx, bb2 in enumerate(range(bb - width_over2, bb + width_over2)):
+            # convert ab to xy cords
+            xx = aa - bb2
+            yy = bb2
+
+            for ii_align in range(num_alignments):
+                x_offset = x_offsets[ii_align]
+                x_offset_idx = x_offset - 1  # overlaps start at 1, vectors stored 0-based
+                y_offset = y_offsets[ii_align]
+                y_offset_idx = y_offset - 1
+
+                if 0 <= xx < xsize and 0 <= yy < ysize:
+                    sumx = 0.0
+                    for jj in range(vecsize):
+                        sumx += vecs0[x_offset_idx, xx, jj] * vecs1[y_offset_idx, yy, jj]
+                    feat = 2.0 * x_offset * y_offset * (1.0 - sumx) / (
+                            1e-6 + norms0[x_offset_idx, xx] + norms1[y_offset_idx, yy])
+
+                else:
+                    feat = inf
+
+                a_b_feats[ii_align, a_idx, b_idx] = feat
+
+    return a_b_feats, b_offset
+
+
+def sparse_dp(np.ndarray[float, ndim=3] a_b_costs,
+              np.ndarray[int, ndim=1] b_offset_in,
+              alignment_types,
+              double del_penalty,
+              int x_in_size,
+              int y_in_size):
+    """
+    Do DP along a path, using features saved off along path.
+    x_offsets, y_offsets should not include (0,1), (1,0)
+
+    xsize, ysize refer to the costs a_b_csum, but in x/y space
+
+    As in the simpler full-DP case, 
+       we compute cumulative costs and backpointers on notes,
+       and there are COSTS associated with moving between them.
+
+    This means the size of the notes  +1,+1 larger (in x,y) than the COSTS.
+    
+    So the size of a_b_csum, a_b_xp, a_b_yp are all one larger in x and y compared to the costs
+
+    In order to save memory (and time, vs a sparse matrix with hashes to look up values), let:
+             a = x + y
+             b = x - y
+
+    b_offsets tells us how far from the left edge the features are computed for.
+         basically it's like we are computing along the diagonal, 
+         but we shift the diagonal around based on our belief
+         about where the alignments are. 
+
+    b_offsets is used for both costs AND csum, backpointers, so it needs to be 
+        +2 longer (it is in the a-direction) than the costs (in the a direction)
+
+    """
+    cdef np.ndarray[int, ndim=1] x_offsets, y_offsets
+    x_offsets, y_offsets = make_x_y_offsets(alignment_types)
+
+    # make x/y offsets, including (0,1), (1,), i.e. including deletion and insertion
+    x_offsets = np.concatenate([x_offsets, np.array([0, 1], dtype=np.int32)])
+    y_offsets = np.concatenate([y_offsets, np.array([1, 0], dtype=np.int32)])
+
+    cdef int a_in_size = a_b_costs.shape[1]
+    cdef int b_in_size = a_b_costs.shape[2]
+
+    cdef int a_out_size = a_in_size + 2
+    cdef int b_out_size = b_in_size
+
+    cdef int x_out_size = x_in_size + 1
+    cdef int y_out_size = y_in_size + 1
+
+    # costs are the costs of going between nodes.
+    # in x,y for the nodes, we basically add a buffer 
+    #   at x=0 and y=0, and shift the cost by (x=+1,y=+1)
+    # In a,b space, this means adding two points (for the buffer)
+    #      at the beginning, and shifting by (a=+0,b=+1) since
+    #      a=x+y and b=y
+    # for the first two points, we can simply replicate the
+    #    original b_offset, since it should be -width_over2
+    # i.e. b_offset_in[0] == -width_over2
+    extra_two_points = np.array([b_offset_in[0], b_offset_in[0]], dtype=np.int32)
+    cdef np.ndarray[int, ndim=1] b_offset_out = np.concatenate([extra_two_points, b_offset_in + 1])
+
+    # outputs
+    # For anything being used in accumulation, use float64
+    cdef np.ndarray[double, ndim=2] a_b_csum = np.zeros((a_in_size + 2, b_in_size),
+                                                        dtype=np.float64) + np.inf  # error cumulative sum
+    cdef np.ndarray[int, ndim=2] a_b_xp = np.zeros((a_in_size + 2, b_in_size), dtype=np.int32) - 2  # backpointer for x
+    cdef np.ndarray[int, ndim=2] a_b_yp = np.zeros((a_in_size + 2, b_in_size), dtype=np.int32) - 2  # backpointer for y
+
+    cdef int num_alignments = x_offsets.shape[0]
+    cdef double inf = np.inf
+    cdef int xx_out, yy_out, ii_align, x_offset, y_offset
+    cdef int aa_in_cost, bb_in_cost, aa_out, bb_out, aa_out_prev, bb_out_prev, xx_in_cost, yy_in_cost, xx_out_prev, yy_out_prev
+
+    cdef double alignment_cost, total_cost, prev_cost
+
+    # increasing in a is the same as going along diagonals in x/y, so DP order works
+    #  (and any ordering is fine in b - nothing depends on values adjacent on diagonal in x/y)
+    for aa_out in range(a_in_size + 2):
+        for bb_out in range(b_in_size):
+            #xx_out, yy_out = ab2xy_w_offset(aa_out, bb_out, b_offset_out)
+            yy_out = bb_out + b_offset_out[aa_out]
+            xx_out = aa_out - yy_out
+
+            # edge case: all deletions in y-direction
+            if xx_out == 0 and 0 <= yy_out < y_out_size:
+                a_b_csum[aa_out, bb_out] = del_penalty * yy_out
+                a_b_xp[aa_out, bb_out] = 0
+                a_b_yp[aa_out, bb_out] = 1
+
+            # edge case: all deletions in x-direction
+            elif yy_out == 0 and 0 <= xx_out < x_out_size:
+                a_b_csum[aa_out, bb_out] = del_penalty * xx_out
+                a_b_xp[aa_out, bb_out] = 1
+                a_b_yp[aa_out, bb_out] = 0
+
+            else:
+                # initialize output to inf
+                a_b_csum[aa_out, bb_out] = inf
+                a_b_xp[aa_out, bb_out] = -42
+                a_b_yp[aa_out, bb_out] = -42
+
+                for ii_align in range(num_alignments):
+                    x_offset = x_offsets[ii_align]
+                    y_offset = y_offsets[ii_align]
+
+                    # coords of location of alignment cost, in input x/y space
+                    xx_in_cost = xx_out - 1  # features were front padded,
+                    yy_in_cost = yy_out - 1  #   so offset is always 1
+
+                    # the coords of location of previous cumsum cost, in input x/y space
+                    xx_out_prev = xx_out - x_offset
+                    yy_out_prev = yy_out - y_offset
+
+                    if 0 <= xx_in_cost < x_in_size and 0 <= yy_in_cost < y_in_size and 0 <= xx_out_prev < x_out_size and 0 <= yy_out_prev < y_out_size:
+                        # convert x,y to a,b
+                        aa_in_cost = xx_in_cost + yy_in_cost
+                        bb_in_cost = yy_in_cost - b_offset_in[aa_in_cost]
+
+                        aa_out_prev = xx_out_prev + yy_out_prev
+                        bb_out_prev = yy_out_prev - b_offset_out[aa_out_prev]
+
+                        if 0 <= aa_in_cost < a_in_size and 0 <= bb_in_cost < b_in_size and 0 <= aa_out_prev < a_out_size and 0 <= bb_out_prev < b_out_size:
+                            if x_offset == 0 or y_offset == 0:
+                                alignment_cost = del_penalty
+                            else:
+                                alignment_cost = a_b_costs[ii_align, aa_in_cost, bb_in_cost]
+
+                            prev_cost = a_b_csum[aa_out_prev, bb_out_prev]
+
+                            total_cost = prev_cost + alignment_cost
+
+                            if total_cost < a_b_csum[aa_out, bb_out]:
+                                a_b_csum[aa_out, bb_out] = total_cost
+                                a_b_xp[aa_out, bb_out] = x_offset
+                                a_b_yp[aa_out, bb_out] = y_offset
+
+    return a_b_csum, a_b_xp, a_b_yp, b_offset_out

bin/vecalign/dp_utils.py

@@ -0,0 +1,665 @@
+"""
+Copyright 2019 Brian Thompson
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    https://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import logging
+import sys
+from ast import literal_eval
+from collections import OrderedDict
+from math import ceil
+from time import time
+
+import numpy as np
+
+import pyximport
+pyximport.install(setup_args={'include_dirs':np.get_include()}, inplace=True, reload_support=True)
+
+from dp_core import make_dense_costs, score_path, sparse_dp, make_sparse_costs, dense_dp
+
+logger = logging.getLogger('vecalign')  # set up in vecalign.py
+
+
+def preprocess_line(line):
+    line = line.strip()
+    if len(line) == 0:
+        line = 'BLANK_LINE'
+    return line
+
+
+def yield_overlaps(lines, num_overlaps):
+    lines = [preprocess_line(line) for line in lines]
+    for overlap in range(1, num_overlaps + 1):
+        for out_line in layer(lines, overlap):
+            # check must be here so all outputs are unique
+            out_line2 = out_line[:10000]  # limit line so dont encode arbitrarily long sentences
+            yield out_line2
+
+
+def read_in_embeddings(text_file, embed_file):
+    """
+    Given a text file with candidate sentences and a corresponing embedding file,
+       make a maping from candidate sentence to embedding index, 
+       and a numpy array of the embeddings
+    """
+    sent2line = dict()
+    with open(text_file, 'rt', encoding="utf-8") as fin:
+        for ii, line in enumerate(fin):
+            if line.strip() in sent2line:
+                raise Exception('got multiple embeddings for the same line')
+            sent2line[line.strip()] = ii
+
+    line_embeddings = np.fromfile(embed_file, dtype=np.float32, count=-1)
+    if line_embeddings.size == 0:
+        raise Exception('Got empty embedding file')
+
+    laser_embedding_size = line_embeddings.size // len(sent2line)  # currently hardcoded to 1024
+    if laser_embedding_size != 1024:
+        logger.warning('expected an embedding size of 1024, got %s', laser_embedding_size)
+    logger.info('laser_embedding_size determined to be %d', laser_embedding_size)
+    line_embeddings.resize(line_embeddings.shape[0] // laser_embedding_size, laser_embedding_size)
+    return sent2line, line_embeddings
+
+
+def make_doc_embedding(sent2line, line_embeddings, lines, num_overlaps):
+    """
+    lines: sentences in input document to embed
+    sent2line, line_embeddings: precomputed embeddings for lines (and overlaps of lines)
+    """
+
+    lines = [preprocess_line(line) for line in lines]
+
+    vecsize = line_embeddings.shape[1]
+
+    vecs0 = np.empty((num_overlaps, len(lines), vecsize), dtype=np.float32)
+
+    for ii, overlap in enumerate(range(1, num_overlaps + 1)):
+        for jj, out_line in enumerate(layer(lines, overlap)):
+            try:
+                line_id = sent2line[out_line]
+            except KeyError:
+                logger.warning('Failed to find overlap=%d line "%s". Will use random vector.', overlap, out_line)
+                line_id = None
+
+            if line_id is not None:
+                vec = line_embeddings[line_id]
+            else:
+                vec = np.random.random(vecsize) - 0.5
+                vec = vec / np.linalg.norm(vec)
+
+            vecs0[ii, jj, :] = vec
+
+    return vecs0
+
+
+def make_norm1(vecs0):
+    """
+    make vectors norm==1 so that cosine distance can be computed via dot product
+    """
+    for ii in range(vecs0.shape[0]):
+        for jj in range(vecs0.shape[1]):
+            norm = np.sqrt(np.square(vecs0[ii, jj, :]).sum())
+            vecs0[ii, jj, :] = vecs0[ii, jj, :] / (norm + 1e-5)
+
+
+def layer(lines, num_overlaps, comb=' '):
+    """
+    make front-padded overlapping sentences
+    """
+    if num_overlaps < 1:
+        raise Exception('num_overlaps must be >= 1')
+    out = ['PAD', ] * min(num_overlaps - 1, len(lines))
+    for ii in range(len(lines) - num_overlaps + 1):
+        out.append(comb.join(lines[ii:ii + num_overlaps]))
+    return out
+
+
+def read_alignments(fin):
+    alignments = []
+    with open(fin, 'rt', encoding="utf-8") as infile:
+        for line in infile:
+            fields = [x.strip() for x in line.split(':') if len(x.strip())]
+            if len(fields) < 2:
+                raise Exception('Got line "%s", which does not have at least two ":" separated fields' % line.strip())
+            try:
+                src = literal_eval(fields[0])
+                tgt = literal_eval(fields[1])
+            except:
+                raise Exception('Failed to parse line "%s"' % line.strip())
+            alignments.append((src, tgt))
+
+    # I know bluealign files have a few entries entries missing,
+    #   but I don't fix them in order to be consistent previous reported scores
+    return alignments
+
+
+def print_alignments(alignments, scores=None, file=sys.stdout):
+    if scores is not None:
+        for (x, y), s in zip(alignments, scores):
+            print('%s:%s:%.6f' % (x, y, s), file=file)
+    else:
+        for x, y in alignments:
+            print('%s:%s' % (x, y), file=file)
+
+
+class DeletionKnob(object):
+    """
+    A good deletion penalty is dependent on normalization, and probably language, domain, etc, etc
+    I want a way to control deletion penalty that generalizes well...
+    Sampling costs and use percentile seems to work fairly well.
+    """
+    def __init__(self, samp, res_min, res_max):
+
+        self.res_min = res_min
+        self.res_max = res_max
+
+        if self.res_min >= self.res_max:
+            logger.warning('res_max <= res_min, increasing it')
+            self.res_max = self.res_min + 1e-4
+
+        num_bins = 1000
+        num_pts = 30
+
+        self.hist, self.bin_edges = np.histogram(samp, bins=num_bins,
+                                                 range=[self.res_min, self.res_max],
+                                                 density=True)
+
+        dx = self.bin_edges[1] - self.bin_edges[0]
+        self.cdf = np.cumsum(self.hist) * dx
+
+        interp_points = [(0, self.res_min), ]
+        for knob_val in np.linspace(0, 1, num_pts - 1)[1:-1]:
+            cdf_idx = np.searchsorted(self.cdf, knob_val)
+            cdf_val = self.res_min + cdf_idx / float(num_bins) * (self.res_max - self.res_min)
+            interp_points.append((knob_val, cdf_val))
+        interp_points.append((1, self.res_max))
+        self.x, self.y = zip(*interp_points)
+
+    def percentile_frac_to_del_penalty(self, knob_val):
+        del_pen = np.interp([knob_val], self.x, self.y)[0]
+        return del_pen
+
+
+def make_alignment_types(max_alignment_size):
+    # return list of all (n,m) where n+m <= this
+    alignment_types = []
+    for x in range(1, max_alignment_size):
+        for y in range(1, max_alignment_size):
+            if x + y <= max_alignment_size:
+                alignment_types.append((x, y))
+    return alignment_types
+
+
+def ab2xy_w_offset(aa, bb_idx, bb_offset):
+    bb_from_side = bb_idx + bb_offset[aa]
+    xx = aa - bb_from_side
+    yy = bb_from_side
+    return (xx, yy)
+
+
+def xy2ab_w_offset(xx, yy, bb_offset):
+    aa = xx + yy
+    bb_from_side = yy
+    bb = bb_from_side - bb_offset[aa]
+    return aa, bb
+
+
+def process_scores(scores, alignments):
+    # floating point sometimes gives negative numbers, which is a little unnerving ...
+    scores = np.clip(scores, a_min=0, a_max=None)
+
+    for ii, (x_algn, y_algn) in enumerate(alignments):
+        # deletion penalty is pretty arbitrary, just report 0
+        if len(x_algn) == 0 or len(y_algn) == 0:
+            scores[ii] = 0.0
+        # report sores un-normalized by alignment sizes
+        #    (still normalized with random vectors, though)
+        else:
+            scores[ii] = scores[ii] / len(x_algn) / len(y_algn)
+
+    return scores
+
+
+def sparse_traceback(a_b_csum, a_b_xp, a_b_yp, b_offset, xsize, ysize):
+    alignments = []
+    xx = xsize
+    yy = ysize
+
+    cum_costs = []
+
+    while True:
+        aa, bb = xy2ab_w_offset(xx, yy, b_offset)
+
+        cum_costs.append(a_b_csum[aa, bb])
+
+        xp = a_b_xp[aa, bb]
+        yp = a_b_yp[aa, bb]
+
+        if xx == yy == 0:
+            break
+
+        if xx < 0 or yy < 0:
+            raise Exception('traceback bug')
+
+        x_side = list(range(xx - xp, xx))
+        y_side = list(range(yy - yp, yy))
+        alignments.append((x_side, y_side))
+
+        xx = xx - xp
+        yy = yy - yp
+
+    alignments.reverse()
+    cum_costs.reverse()
+    costs = np.array(cum_costs[1:]) - np.array(cum_costs[:-1])
+    # "costs" are scaled by x_alignment_size * y_alignment_size
+    #     and the cost of a deletion is del_penalty
+    # "scores": 0 for deletion/insertion, 
+    #    and cosine distance, *not* scaled 
+    #    by len(x_alignment)*len(y_alignment)
+    scores = process_scores(scores=costs, alignments=alignments)
+
+    return alignments, scores
+
+
+def dense_traceback(x_y_tb):
+    xsize, ysize = x_y_tb.shape
+
+    xx = xsize - 1
+    yy = ysize - 1
+
+    alignments = []
+    while True:
+        if xx == yy == 0:
+            break
+        bp = x_y_tb[xx, yy]
+        if bp == 0:
+            xp, yp = 1, 1
+            alignments.append(([xx - 1], [yy - 1]))
+        elif bp == 1:
+            xp, yp = 0, 1
+            alignments.append(([], [yy - 1]))
+        elif bp == 2:
+            xp, yp = 1, 0
+            alignments.append(([xx - 1], []))
+        else:
+            raise Exception('got unknown value')
+
+        xx = xx - xp
+        yy = yy - yp
+
+    alignments.reverse()
+
+    return alignments
+
+
+def append_slant(path, xwidth, ywidth):
+    """
+    Append quantized approximation to a straight line
+       from current x,y to a point at (x+xwidth, y+ywidth)
+    """
+    NN = xwidth + ywidth
+    xstart, ystart = path[-1]
+    for ii in range(1, NN + 1):
+        x = xstart + round(xwidth * ii / NN)
+        y = ystart + round(ywidth * ii / NN)
+        # In the case of ties we want them to round differently,
+        #   so explicitly make sure we take a step of 1, not 0 or 2
+        lastx, lasty = path[-1]
+        delta = x + y - lastx - lasty
+        if delta == 1:
+            path.append((x, y))
+        elif delta == 2:
+            path.append((x - 1, y))
+        elif delta == 0:
+            path.append((x + 1, y))
+
+
+def alignment_to_search_path(algn):
+    """
+    Given an alignment, make searchpath.
+    Searchpath must step exactly one position in x XOR y at each time step.
+    
+    In the case of a block of deletions, the order found by DP is not meaningful.
+    To make things consistent and to improve the probability of recovering 
+       from search errors, we search an approximately straight line
+       through a block of deletions. We do the same through a many-many 
+       alignment, even though we currently don't refine a many-many alignment...
+    """
+    path = [(0, 0), ]
+    xdel, ydel = 0, 0
+    ydel = 0
+    for x, y in algn:
+        if len(x) and len(y):
+            append_slant(path, xdel, ydel)
+            xdel, ydel = 0, 0
+            append_slant(path, len(x), len(y))
+        elif len(x):
+            xdel += len(x)
+        elif len(y):
+            ydel += len(y)
+
+    append_slant(path, xdel, ydel)
+
+    return path
+
+
+def extend_alignments(course_alignments, size0, size1):
+    """
+    extend alignments to include new endpoints size0, size1
+    if alignments are larger than size0/size1, raise exception
+    """
+    # could be a string of deletions or insertions at end, so cannot just grab last one
+    xmax = 0  # maximum x value in course_alignments
+    ymax = 0  # maximum y value in course_alignments
+    for x, y in course_alignments:
+        for xval in x:
+            xmax = max(xmax, xval)
+        for yval in y:
+            ymax = max(ymax, yval)
+
+    if xmax > size0 or ymax > size1:
+        raise Exception('asked to extend alignments but already bigger than requested')
+
+    # do not duplicate xmax/ymax, do include size0/size1 
+    extra_x = list(range(xmax + 1, size0 + 1))
+    extra_y = list(range(ymax + 1, size1 + 1))
+
+    logger.debug('extending alignments in x by %d and y by %d', len(extra_x), len(extra_y))
+
+    if len(extra_x) == 0:
+        for yval in extra_y:
+            course_alignments.append(([], [yval]))
+    elif len(extra_y) == 0:
+        for xval in extra_x:
+            course_alignments.append(([xval], []))
+    else:
+        course_alignments.append((extra_x, extra_y))
+
+
+def upsample_alignment(algn):
+    def upsample_one_alignment(xx):
+        return list(range(min(xx) * 2, (max(xx) + 1) * 2))
+
+    new_algn = []
+    for xx, yy in algn:
+        if len(xx) == 0:
+            for yyy in upsample_one_alignment(yy):
+                new_algn.append(([], [yyy]))
+        elif len(yy) == 0:
+            for xxx in upsample_one_alignment(xx):
+                new_algn.append(([xxx], []))
+        else:
+            new_algn.append((upsample_one_alignment(xx), upsample_one_alignment(yy)))
+    return new_algn
+
+
+def make_del_knob(e_laser,
+                  f_laser,
+                  e_laser_norms,
+                  f_laser_norms,
+                  sample_size):
+    e_size = e_laser.shape[0]
+    f_size = f_laser.shape[0]
+
+    if e_size > 0 and f_size > 0 and sample_size > 0:
+
+        if e_size * f_size < sample_size:
+            # dont sample, just compute full matrix
+            sample_size = e_size * f_size
+            x_idxs = np.zeros(sample_size, dtype=np.int32)
+            y_idxs = np.zeros(sample_size, dtype=np.int32)
+            c = 0
+            for ii in range(e_size):
+                for jj in range(f_size):
+                    x_idxs[c] = ii
+                    y_idxs[c] = jj
+                    c += 1
+        else:
+            # get random samples
+            x_idxs = np.random.choice(range(e_size), size=sample_size, replace=True).astype(np.int32)
+            y_idxs = np.random.choice(range(f_size), size=sample_size, replace=True).astype(np.int32)
+
+        # output
+        random_scores = np.empty(sample_size, dtype=np.float32)
+
+        score_path(x_idxs, y_idxs,
+                   e_laser_norms, f_laser_norms,
+                   e_laser, f_laser,
+                   random_scores, )
+
+        min_score = 0
+        max_score = max(random_scores)  # could bump this up... but its probably fine
+
+    else:
+        # Not much we can do here...
+        random_scores = np.array([0.0, 0.5, 1.0])  # ???
+        min_score = 0
+        max_score = 1  # ????
+
+    del_knob = DeletionKnob(random_scores, min_score, max_score)
+
+    return del_knob
+
+
+def compute_norms(vecs0, vecs1, num_samples, overlaps_to_use=None):
+    # overlaps_to_use = 10  # 10 matches before
+
+    overlaps1, size1, dim = vecs1.shape
+    overlaps0, size0, dim0 = vecs0.shape
+    assert (dim == dim0)
+
+    if overlaps_to_use is not None:
+        if overlaps_to_use > overlaps1:
+            raise Exception('Cannot use more overlaps than provided. You may want to re-run make_verlaps.py with a larger -n value')
+    else:
+        overlaps_to_use = overlaps1
+
+    samps_per_overlap = ceil(num_samples / overlaps_to_use)
+
+    if size1 and samps_per_overlap:
+        # sample other size (from all overlaps) to compre to this side
+        vecs1_rand_sample = np.empty((samps_per_overlap * overlaps_to_use, dim), dtype=np.float32)
+        for overlap_ii in range(overlaps_to_use):
+            idxs = np.random.choice(range(size1), size=samps_per_overlap, replace=True)
+            random_vecs = vecs1[overlap_ii, idxs, :]
+            vecs1_rand_sample[overlap_ii * samps_per_overlap:(overlap_ii + 1) * samps_per_overlap, :] = random_vecs
+
+        norms0 = np.empty((overlaps0, size0), dtype=np.float32)
+        for overlap_ii in range(overlaps0):
+            e_laser = vecs0[overlap_ii, :, :]
+            sim = np.matmul(e_laser, vecs1_rand_sample.T)
+            norms0[overlap_ii, :] = 1.0 - sim.mean(axis=1)
+
+    else:  # no samples, no normalization
+        norms0 = np.ones((overlaps0, size0)).astype(np.float32)
+
+    return norms0
+
+
+def downsample_vectors(vecs1):
+    a, b, c = vecs1.shape
+    half = np.empty((a, b // 2, c), dtype=np.float32)
+    for ii in range(a):
+        # average consecutive vectors
+        for jj in range(0, b - b % 2, 2):
+            v1 = vecs1[ii, jj, :]
+            v2 = vecs1[ii, jj + 1, :]
+            half[ii, jj // 2, :] = v1 + v2
+        # compute mean for all vectors
+        mean = np.mean(half[ii, :, :], axis=0)
+        for jj in range(0, b - b % 2, 2):
+            # remove mean
+            half[ii, jj // 2, :] = half[ii, jj // 2, :] - mean
+    # make vectors norm==1 so dot product is cosine distance
+    make_norm1(half)
+    return half
+
+
+def vecalign(vecs0,
+             vecs1,
+             final_alignment_types,
+             del_percentile_frac,
+             width_over2,
+             max_size_full_dp,
+             costs_sample_size,
+             num_samps_for_norm,
+             norms0=None,
+             norms1=None):
+    if width_over2 < 3:
+        logger.warning('width_over2 was set to %d, which does not make sense. increasing to 3.', width_over2)
+        width_over2 = 3
+
+    # make sure input embeddings are norm==1
+    make_norm1(vecs0)
+    make_norm1(vecs1)
+
+    # save off runtime stats for summary
+    runtimes = OrderedDict()
+
+    # Determine stack depth
+    s0, s1 = vecs0.shape[1], vecs1.shape[1]
+    max_depth = 0
+    while s0 * s1 > max_size_full_dp ** 2:
+        max_depth += 1
+        s0 = s0 // 2
+        s1 = s1 // 2
+
+    # init recursion stack
+    # depth is 0-based (full size is 0, 1 is half, 2 is quarter, etc)
+    stack = {0: {'v0': vecs0, 'v1': vecs1}}
+
+    # downsample sentence vectors
+    t0 = time()
+    for depth in range(1, max_depth + 1):
+        stack[depth] = {'v0': downsample_vectors(stack[depth - 1]['v0']),
+                        'v1': downsample_vectors(stack[depth - 1]['v1'])}
+    runtimes['Downsample embeddings'] = time() - t0
+
+    # compute norms for all depths, add sizes, add alignment types
+    t0 = time()
+    for depth in stack:
+        stack[depth]['size0'] = stack[depth]['v0'].shape[1]
+        stack[depth]['size1'] = stack[depth]['v1'].shape[1]
+        stack[depth]['alignment_types'] = final_alignment_types if depth == 0 else [(1, 1)]
+
+        if depth == 0 and norms0 is not None:
+            if norms0.shape != vecs0.shape[:2]:
+                print('norms0.shape:', norms0.shape)
+                print('vecs0.shape[:2]:', vecs0.shape[:2])
+                raise Exception('norms0 wrong shape')
+            stack[depth]['n0'] = norms0
+        else:
+            stack[depth]['n0'] = compute_norms(stack[depth]['v0'], stack[depth]['v1'], num_samps_for_norm)
+
+        if depth == 0 and norms1 is not None:
+            if norms1.shape != vecs1.shape[:2]:
+                print('norms1.shape:', norms1.shape)
+                print('vecs1.shape[:2]:', vecs1.shape[:2])
+                raise Exception('norms1 wrong shape')
+            stack[depth]['n1'] = norms1
+        else:
+            stack[depth]['n1'] = compute_norms(stack[depth]['v1'], stack[depth]['v0'], num_samps_for_norm)
+
+    runtimes['Normalize embeddings'] = time() - t0
+
+    # Compute deletion penalty for all depths
+    t0 = time()
+    for depth in stack:
+        stack[depth]['del_knob'] = make_del_knob(e_laser=stack[depth]['v0'][0, :, :],
+                                                 f_laser=stack[depth]['v1'][0, :, :],
+                                                 e_laser_norms=stack[depth]['n0'][0, :],
+                                                 f_laser_norms=stack[depth]['n1'][0, :],
+                                                 sample_size=costs_sample_size)
+        stack[depth]['del_penalty'] = stack[depth]['del_knob'].percentile_frac_to_del_penalty(del_percentile_frac)
+        logger.debug('del_penalty at depth %d: %f', depth, stack[depth]['del_penalty'])
+    runtimes['Compute deletion penalties'] = time() - t0
+    tt = time() - t0
+    logger.debug('%d x %d full DP make features: %.6fs (%.3e per dot product)',
+                 stack[max_depth]['size0'], stack[max_depth]['size1'], tt,
+                 tt / (stack[max_depth]['size0'] + 1e-6) / (stack[max_depth]['size1'] + 1e-6))
+    # full DP at maximum recursion depth
+    t0 = time()
+    stack[max_depth]['costs_1to1'] = make_dense_costs(stack[max_depth]['v0'],
+                                                      stack[max_depth]['v1'],
+                                                      stack[max_depth]['n0'],
+                                                      stack[max_depth]['n1'])
+
+    runtimes['Full DP make features'] = time() - t0
+    t0 = time()
+    _, stack[max_depth]['x_y_tb'] = dense_dp(stack[max_depth]['costs_1to1'], stack[max_depth]['del_penalty'])
+    stack[max_depth]['alignments'] = dense_traceback(stack[max_depth]['x_y_tb'])
+    runtimes['Full DP'] = time() - t0
+
+    # upsample the path up to the top resolution
+    compute_costs_times = []
+    dp_times = []
+    upsample_depths = [0, ] if max_depth == 0 else list(reversed(range(0, max_depth)))
+    for depth in upsample_depths:
+        if max_depth > 0:  # upsample previoius alignment to current resolution
+            course_alignments = upsample_alignment(stack[depth + 1]['alignments'])
+            # features may have been truncated when downsampleing, so alignment may need extended
+            extend_alignments(course_alignments, stack[depth]['size0'], stack[depth]['size1'])  # in-place
+        else:  # We did a full size 1-1 search, so search same size with more alignment types
+            course_alignments = stack[0]['alignments']
+
+        # convert couse alignments to a searchpath
+        stack[depth]['searchpath'] = alignment_to_search_path(course_alignments)
+
+        # compute ccosts for sparse DP
+        t0 = time()
+        stack[depth]['a_b_costs'], stack[depth]['b_offset'] = make_sparse_costs(stack[depth]['v0'], stack[depth]['v1'],
+                                                                                stack[depth]['n0'], stack[depth]['n1'],
+                                                                                stack[depth]['searchpath'],
+                                                                                stack[depth]['alignment_types'],
+                                                                                width_over2)
+
+        tt = time() - t0
+        num_dot_products = len(stack[depth]['b_offset']) * len(stack[depth]['alignment_types']) * width_over2 * 2
+        logger.debug('%d x %d sparse DP (%d alignment types, %d window) make features: %.6fs (%.3e per dot product)',
+                     stack[max_depth]['size0'], stack[max_depth]['size1'],
+                     len(stack[depth]['alignment_types']), width_over2 * 2,
+                     tt, tt / (num_dot_products + 1e6))
+
+        compute_costs_times.append(time() - t0)
+        t0 = time()
+        # perform sparse DP
+        stack[depth]['a_b_csum'], stack[depth]['a_b_xp'], stack[depth]['a_b_yp'], \
+        stack[depth]['new_b_offset'] = sparse_dp(stack[depth]['a_b_costs'], stack[depth]['b_offset'],
+                                                 stack[depth]['alignment_types'], stack[depth]['del_penalty'],
+                                                 stack[depth]['size0'], stack[depth]['size1'])
+
+        # performace traceback to get alignments and alignment scores
+        # for debugging, avoid overwriting stack[depth]['alignments']
+        akey = 'final_alignments' if depth == 0 else 'alignments'
+        stack[depth][akey], stack[depth]['alignment_scores'] = sparse_traceback(stack[depth]['a_b_csum'],
+                                                                                stack[depth]['a_b_xp'],
+                                                                                stack[depth]['a_b_yp'],
+                                                                                stack[depth]['new_b_offset'],
+                                                                                stack[depth]['size0'],
+                                                                                stack[depth]['size1'])
+        dp_times.append(time() - t0)
+
+    runtimes['Upsample DP compute costs'] = sum(compute_costs_times[:-1])
+    runtimes['Upsample DP'] = sum(dp_times[:-1])
+
+    runtimes['Final DP compute costs'] = compute_costs_times[-1]
+    runtimes['Final DP'] = dp_times[-1]
+
+    # log time stats
+    max_key_str_len = max([len(key) for key in runtimes])
+    for key in runtimes:
+        if runtimes[key] > 5e-5:
+            logger.info(key + ' took ' + '.' * (max_key_str_len + 5 - len(key)) + ('%.4fs' % runtimes[key]).rjust(7))
+
+    return stack

bin/vecalign/overlap.py

@@ -0,0 +1,61 @@
+#!/usr/bin/env python3
+
+"""
+Copyright 2019 Brian Thompson
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    https://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+
+import argparse
+
+from dp_utils import yield_overlaps
+
+
+def go(output_file, input_files, num_overlaps):
+    output = set()
+    for fin in input_files:
+        lines = open(fin, 'rt', encoding="utf-8").readlines()
+        for out_line in yield_overlaps(lines, num_overlaps):
+            output.add(out_line)
+
+    # for reproducibility
+    output = list(output)
+    output.sort()
+
+    with open(output_file, 'wt', encoding="utf-8") as fout:
+        for line in output:
+            fout.write(line + '\n')
+
+
+def _main():
+    parser = argparse.ArgumentParser('Create text file containing overlapping sentences.',
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    parser.add_argument('-i', '--inputs', type=str, nargs='+',
+                        help='input text file(s).')
+
+    parser.add_argument('-o', '--output', type=str,
+                        help='output text file containing overlapping sentneces')
+
+    parser.add_argument('-n', '--num_overlaps', type=int, default=4,
+                        help='Maximum number of allowed overlaps.')
+
+    args = parser.parse_args()
+    go(output_file=args.output,
+       num_overlaps=args.num_overlaps,
+       input_files=args.inputs)
+
+
+if __name__ == '__main__':
+    _main()

bin/vecalign/score.py

@@ -0,0 +1,170 @@
+#!/usr/bin/env python3
+
+"""
+Copyright 2019 Brian Thompson
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    https://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+"""
+
+import argparse
+import sys
+from collections import defaultdict
+
+import numpy as np
+
+from dp_utils import read_alignments
+
+"""
+Faster implementation of lax and strict precision and recall, based on
+   https://www.aclweb.org/anthology/W11-4624/.
+
+"""
+
+
+def _precision(goldalign, testalign):
+    """
+    Computes tpstrict, fpstrict, tplax, fplax for gold/test alignments
+    """
+    tpstrict = 0  # true positive strict counter
+    tplax = 0     # true positive lax counter
+    fpstrict = 0  # false positive strict counter
+    fplax = 0     # false positive lax counter
+
+    # convert to sets, remove alignments empty on both sides
+    testalign = set([(tuple(x), tuple(y)) for x, y in testalign if len(x) or len(y)])
+    goldalign = set([(tuple(x), tuple(y)) for x, y in goldalign if len(x) or len(y)])
+
+    # mappings from source test sentence idxs to
+    #    target gold sentence idxs for which the source test sentence 
+    #    was found in corresponding source gold alignment
+    src_id_to_gold_tgt_ids = defaultdict(set)
+    for gold_src, gold_tgt in goldalign:
+        for gold_src_id in gold_src:
+            for gold_tgt_id in gold_tgt:
+                src_id_to_gold_tgt_ids[gold_src_id].add(gold_tgt_id)
+
+    for (test_src, test_target) in testalign:
+        if (test_src, test_target) == ((), ()):
+            continue
+        if (test_src, test_target) in goldalign:
+            # strict match
+            tpstrict += 1
+            tplax += 1
+        else:
+            # For anything with partial gold/test overlap on the source,
+            #   see if there is also partial overlap on the gold/test target
+            # If so, its a lax match
+            target_ids = set()
+            for src_test_id in test_src:
+                for tgt_id in src_id_to_gold_tgt_ids[src_test_id]:
+                    target_ids.add(tgt_id)
+            if set(test_target).intersection(target_ids):
+                fpstrict += 1
+                tplax += 1
+            else:
+                fpstrict += 1
+                fplax += 1
+
+    return np.array([tpstrict, fpstrict, tplax, fplax], dtype=np.int32)
+
+
+def score_multiple(gold_list, test_list, value_for_div_by_0=0.0):
+    # accumulate counts for all gold/test files
+    pcounts = np.array([0, 0, 0, 0], dtype=np.int32)
+    rcounts = np.array([0, 0, 0, 0], dtype=np.int32)
+    for goldalign, testalign in zip(gold_list, test_list):
+        pcounts += _precision(goldalign=goldalign, testalign=testalign)
+        # recall is precision with no insertion/deletion and swap args
+        test_no_del = [(x, y) for x, y in testalign if len(x) and len(y)]
+        gold_no_del = [(x, y) for x, y in goldalign if len(x) and len(y)]
+        rcounts += _precision(goldalign=test_no_del, testalign=gold_no_del)
+
+    # Compute results
+    # pcounts: tpstrict,fnstrict,tplax,fnlax
+    # rcounts: tpstrict,fpstrict,tplax,fplax
+
+    if pcounts[0] + pcounts[1] == 0:
+        pstrict = value_for_div_by_0
+    else:
+        pstrict = pcounts[0] / float(pcounts[0] + pcounts[1])
+
+    if pcounts[2] + pcounts[3] == 0:
+        plax = value_for_div_by_0
+    else:
+        plax = pcounts[2] / float(pcounts[2] + pcounts[3])
+
+    if rcounts[0] + rcounts[1] == 0:
+        rstrict = value_for_div_by_0
+    else:
+        rstrict = rcounts[0] / float(rcounts[0] + rcounts[1])
+
+    if rcounts[2] + rcounts[3] == 0:
+        rlax = value_for_div_by_0
+    else:
+        rlax = rcounts[2] / float(rcounts[2] + rcounts[3])
+
+    if (pstrict + rstrict) == 0:
+        fstrict = value_for_div_by_0
+    else:
+        fstrict = 2 * (pstrict * rstrict) / (pstrict + rstrict)
+
+    if (plax + rlax) == 0:
+        flax = value_for_div_by_0
+    else:
+        flax = 2 * (plax * rlax) / (plax + rlax)
+
+    result = dict(recall_strict=rstrict,
+                  recall_lax=rlax,
+                  precision_strict=pstrict,
+                  precision_lax=plax,
+                  f1_strict=fstrict,
+                  f1_lax=flax)
+
+    return result
+
+
+def log_final_scores(res):
+    print(' ---------------------------------', file=sys.stderr)
+    print('|             |  Strict |    Lax  |', file=sys.stderr)
+    print('| Precision   |   {precision_strict:.3f} |   {precision_lax:.3f} |'.format(**res), file=sys.stderr)
+    print('| Recall      |   {recall_strict:.3f} |   {recall_lax:.3f} |'.format(**res), file=sys.stderr)
+    print('| F1          |   {f1_strict:.3f} |   {f1_lax:.3f} |'.format(**res), file=sys.stderr)
+    print(' ---------------------------------', file=sys.stderr)
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        'Compute strict/lax precision and recall for one or more pairs of gold/test alignments',
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    parser.add_argument('-t', '--test', type=str, nargs='+', required=True,
+                        help='one or more test alignment files')
+
+    parser.add_argument('-g', '--gold', type=str, nargs='+', required=True,
+                        help='one or more gold alignment files')
+
+    args = parser.parse_args()
+
+    if len(args.test) != len(args.gold):
+        raise Exception('number of gold/test files must be the same')
+
+    gold_list = [read_alignments(x) for x in args.gold]
+    test_list = [read_alignments(x) for x in args.test]
+
+    res = score_multiple(gold_list=gold_list, test_list=test_list)
+    log_final_scores(res)
+
+
+if __name__ == '__main__':
+    main()

bin/vecalign/vecalign.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+
+"""
+Copyright 2019 Brian Thompson
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    https://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+import argparse
+import logging
+import pickle
+from math import ceil
+from random import seed as seed
+
+import numpy as np
+
+logger = logging.getLogger('vecalign')
+logger.setLevel(logging.WARNING)
+logFormatter = logging.Formatter("%(asctime)s  %(levelname)-5.5s  %(message)s")
+consoleHandler = logging.StreamHandler()
+consoleHandler.setFormatter(logFormatter)
+logger.addHandler(consoleHandler)
+
+from dp_utils import make_alignment_types, print_alignments, read_alignments, \
+    read_in_embeddings, make_doc_embedding, vecalign
+
+from score import score_multiple, log_final_scores
+
+
+def _main():
+    # make runs consistent
+    seed(42)
+    np.random.seed(42)
+
+    parser = argparse.ArgumentParser('Sentence alignment using sentence embeddings and FastDTW',
+                                     formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+    #parser.add_argument('-s', '--src', type=str, nargs='+', required=True,
+    #                    help='preprocessed source file to align')
+
+    #parser.add_argument('-t', '--tgt', type=str, nargs='+', required=True,
+    #                    help='preprocessed target file to align')
+
+    parser.add_argument('--job', type=str, required=True, help='Job file for alignment task.')
+
+    parser.add_argument('-g', '--gold_alignment', type=str, nargs='+', required=False,
+                        help='preprocessed target file to align')
+
+    parser.add_argument('--src_embed', type=str, nargs=2, required=True,
+                        help='Source embeddings. Requires two arguments: first is a text file, sencond is a binary embeddings file. ')
+
+    parser.add_argument('--tgt_embed', type=str, nargs=2, required=True,
+                        help='Target embeddings. Requires two arguments: first is a text file, sencond is a binary embeddings file. ')
+
+    parser.add_argument('-a', '--alignment_max_size', type=int, default=5,
+                        help='Searches for alignments up to size N-M, where N+M <= this value. Note that the the embeddings must support the requested number of overlaps')
+
+    parser.add_argument('-d', '--del_percentile_frac', type=float, default=0.2,
+                        help='Deletion penalty is set to this percentile (as a fraction) of the cost matrix distribution. Should be between 0 and 1.')
+
+    parser.add_argument('-v', '--verbose', help='sets consle to logging.DEBUG instead of logging.WARN',
+                        action='store_true')
+
+    parser.add_argument('--max_size_full_dp', type=int, default=300,
+                        help='Maximum size N for which is is acceptable to run full N^2 dynamic programming.')
+
+    parser.add_argument('--costs_sample_size', type=int, default=20000,
+                        help='Sample size to estimate costs distribution, used to set deletion penalty in conjunction with deletion_percentile.')
+
+    parser.add_argument('--num_samps_for_norm', type=int, default=100,
+                        help='Number of samples used for normalizing embeddings')
+
+    parser.add_argument('--search_buffer_size', type=int, default=5,
+                        help='Width (one side) of search buffer. Larger values makes search more likely to recover from errors but increases runtime.')
+
+    parser.add_argument('--debug_save_stack', type=str,
+                        help='Write stack to pickle file for debug purposes')
+
+    args = parser.parse_args()
+
+    #if len(args.src) != len(args.tgt):
+    #    raise Exception('number of source files must match number of target files')
+
+    #if args.gold_alignment is not None:
+    #    if len(args.gold_alignment) != len(args.src):
+    #        raise Exception('number of gold alignment files, if provided, must match number of source and target files')
+
+    if args.verbose:
+        import logging
+        logger.setLevel(logging.INFO)
+
+    if args.alignment_max_size < 2:
+        logger.warning('Alignment_max_size < 2. Increasing to 2 so that 1-1 alignments will be considered')
+        args.alignment_max_size = 2
+
+    src_sent2line, src_line_embeddings = read_in_embeddings(args.src_embed[0], args.src_embed[1])
+    tgt_sent2line, tgt_line_embeddings = read_in_embeddings(args.tgt_embed[0], args.tgt_embed[1])
+
+    width_over2 = ceil(args.alignment_max_size / 2.0) + args.search_buffer_size
+
+    test_alignments = []
+    stack_list = []
+
+    # read in alignment jobs
+    job = read_job(args.job)
+    #for src_file, tgt_file in zip(args.src, args.tgt):
+    for rec in job:
+        #logger.info('Aligning src="%s" to tgt="%s"', src_file, tgt_file)
+        src_file, tgt_file, align_file = rec.split("\t")
+        print("Aligning {} to {}".format(src_file, tgt_file))
+        src_lines = open(src_file, 'rt', encoding="utf-8").readlines()
+        vecs0 = make_doc_embedding(src_sent2line, src_line_embeddings, src_lines, args.alignment_max_size)
+
+        tgt_lines = open(tgt_file, 'rt', encoding="utf-8").readlines()
+        vecs1 = make_doc_embedding(tgt_sent2line, tgt_line_embeddings, tgt_lines, args.alignment_max_size)
+
+        final_alignment_types = make_alignment_types(args.alignment_max_size)
+        logger.debug('Considering alignment types %s', final_alignment_types)
+
+        stack = vecalign(vecs0=vecs0,
+                         vecs1=vecs1,
+                         final_alignment_types=final_alignment_types,
+                         del_percentile_frac=args.del_percentile_frac,
+                         width_over2=width_over2,
+                         max_size_full_dp=args.max_size_full_dp,
+                         costs_sample_size=args.costs_sample_size,
+                         num_samps_for_norm=args.num_samps_for_norm)
+
+        # write final alignments to stdout
+        #print_alignments(stack[0]['final_alignments'], stack[0]['alignment_scores'])
+        out_f = open(align_file, 'w', encoding="utf-8")
+        #print_alignments(stack[0]['final_alignments'], stack[0]['alignment_scores'],file=out_f)
+        print_alignments(stack[0]['final_alignments'],file=out_f)
+
+        #test_alignments.append(stack[0]['final_alignments'])
+        #stack_list.append(stack)
+
+    #if args.gold_alignment is not None:
+    #    gold_list = [read_alignments(x) for x in args.gold_alignment]
+    #    res = score_multiple(gold_list=gold_list, test_list=test_alignments)
+    #   log_final_scores(res)
+
+    #if args.debug_save_stack:
+    #  pickle.dump(stack_list, open(args.debug_save_stack, 'wb'))
+
+def read_job(file):
+    job = []
+    with open(file, 'r', encoding="utf-8") as f:
+        for line in f:
+            if not line.startswith("#"):
+                job.append(line.strip())
+    return job
+
+if __name__ == '__main__':
+    _main()