Merge remote-tracking branch 'origin/master'

bitext_align.py

@@ -22,21 +22,26 @@ translate_client = translate.Client()
 
 
 def master_align(text0, text1, lang0, lang1): 
-	""" Takes two equivalent texts (original and trnslation) and returns 
-	    aligned texts. """
-	df0 = frame_from_text(text0, source=lang0, target=lang1)
-	df1 = frame_from_text(text1, source=lang1, target=lang0, is1=True)
+    """ Takes two equivalent texts (original and trnslation) and returns 
+        aligned texts. """
+    df0 = frame_from_text(text0, source=lang0, target=lang1)
+    print('A')
+    df1 = frame_from_text(text1, source=lang1, target=lang0, is1=True)
+    print('B')
     # returns dfs with ['sent', 'trans', 'rellen', 'relpos']
     anchors = anchors_from_frames(df0, df1, score_funct, score_threshold, window=2)
+    print('C')
     alignments = intermediate_align(df0, df1, anchors, lookahead=4)
+    print('D')
     textdict0, textdict1 = textdicts_from_alignments(df0, df1, alignments)
+    print('E')
     return textdict0, textdict1
 
 
-def frame_from_text(text, source='ru', target='en', is0=True): # 
+def frame_from_text(text, source='ru', target='en', is1=False): # 
     """  """ # 
     cols = [c+str(int(is1)) for c in ['sent','trans','rellen','relpos']]
-    frame = pd.DataFrame(colmns=cols)
+    frame = pd.DataFrame(columns=cols)
     frame[cols[0]] = sent_tokenize(text)
     frame[cols[1]] = frame[cols[0]].apply(lambda x: translate_client.translate(x, source_language=source, target_language=target, model='nmt')['translatedText'])
     frame[cols[2]] = frame[cols[0]].apply(lambda x: len(x))
@@ -44,7 +49,7 @@ def frame_from_text(text, source='ru', target='en', is0=True): #
     cumul_b = list(np.cumsum(frame[cols[2]]))
     cumul_a = [0]+cumul_b[:-1]
     frame[cols[3]] = pd.Series(list(zip(cumul_a, cumul_b)))
-	return frame
+    return frame
 
 
 def anchors_from_frames(frame0, frame1, window): # 
@@ -55,8 +60,8 @@ def anchors_from_frames(frame0, frame1, window): #
     pairdf['rellen_ratio'] = (pairdf.rellen0/pairdf.rellen1).apply(gr1)
     pairdf['minlev'] = pairdf[['lev0', 'lev1']].min(axis=1)
     pairdf['maxlev'] = pairdf[['lev0', 'lev1']].min(axis=1)
-    pairdf['isanchor'] = pairdf.minlev<045 & pairdf.maxlev<0.6 & pairdf.rellen_ratio<1.3
-	return pairdf[pairdf.isanchor][['index0','index1']]
+    pairdf['isanchor'] = pairdf.minlev<0.45 & pairdf.maxlev<0.6 & pairdf.rellen_ratio<1.3
+    return pairdf[pairdf.isanchor][['index0','index1']]
 
 
 def intermediate_align(frame0, frame1, anchs, lookahead): # 
@@ -68,7 +73,7 @@ def intermediate_align(frame0, frame1, anchs, lookahead): #
         interaligns =  get_interalign(frame0, frame1, *rang, lookahead)
         aligns.append(rang[0])
         aligns.extend(interaligns)
-	return aligns[1:] # format [((i_start, i_end),(j_start, j_end))]
+    return aligns[1:] # format [((i_start, i_end),(j_start, j_end))]
 
 
 def get_interalign(df0, df1, anchors_init, anchors_next, lookahead): # 
@@ -79,20 +84,20 @@ def get_interalign(df0, df1, anchors_init, anchors_next, lookahead): #
     j+=1
     end0, end1 = anchors_next
     while i<end0 and j<end1:
-    	room0, room1 = min(end0-i,lookahead), min(end1-j,lookahead)
+        room0, room1 = min(end0-i,lookahead), min(end1-j,lookahead)
         best_alignment = min([(x,y) for x,y in cp(range(i,i+room0),range(j+room1)) if x==i or y==j], key=score(df0, df1, start0, start1, end0, end1))
         interaligns.append((best_alignment))
     return interaligns
 
 
 def score(frame0, frame1, start0, start1, end0, end1): # 
-	s0 = ' '.join(frame0.loc[start0:end0+1, 'sent0'])
-	s1 = ' '.join(frame0.loc[start1:end1+1, 'sent1'])
+    s0 = ' '.join(frame0.loc[start0:end0+1, 'sent0'])
+    s1 = ' '.join(frame0.loc[start1:end1+1, 'sent1'])
     t0 = ' '.join(frame0.loc[start0:end0+1, 'trans0'])
-	t1 = ' '.join(frame0.loc[start1:end1+1, 'trans1'])
-	l0 = sum(frame0.loc[start0:end0+1, 'rellen0'])
-	l1 = sum(frame1.loc[start1:end1+1, 'rellen1'])
-	return (lev(s0,t1)+lev(s1,t0))*gr1(l0/l1)/2
+    t1 = ' '.join(frame0.loc[start1:end1+1, 'trans1'])
+    l0 = sum(frame0.loc[start0:end0+1, 'rellen0'])
+    l1 = sum(frame1.loc[start1:end1+1, 'rellen1'])
+    return (lev(s0,t1)+lev(s1,t0))*gr1(l0/l1)/2
 
 
 
@@ -105,7 +110,7 @@ def textdicts_from_alignments(frame0, frame1, aligns): #
         t1 = ' '.join(frame0.loc[a1:b1+1, 'sent0'])
         textdict0.update({i:t0})
         textdict1.update({i:t1})
-	return textdict0, textdict1
+    return textdict0, textdict1
 
 
 def generate_pairdf(frame0, frame1, window): 
@@ -113,19 +118,19 @@ def generate_pairdf(frame0, frame1, window):
     pairdf = pd.DataFrame(columns=['index0', 'index1'])
     ranges0 = frame0.relpos0
     ranges1 = frame1.relpos1
-    overlap = [i,j for (i,(a,b)),(j,(c,d)) in cp(enumerate(ranges0), enumerate(ranges1)) if get_overlap(a,b,c,d)>0]
+    overlap = [(i,j) for (i,(a,b)),(j,(c,d)) in cp(enumerate(ranges0), enumerate(ranges1)) if get_overlap(a,b,c,d)>0]
     allpairs = []
     for i,j in overlap:
-    	for k in range(-window:window+1):
-    		for l in range(-window:window+1):
+        for k in range(-window, window+1):
+            for l in range(-window, window+1):
                 allpairs.append()
     allpairs = sorted(list(set(allpairs)))
     pairdf[pairdf.columns] = pd.DataFrame(allpairs).values
-	return pairdf
+    return pairdf
 
 
 def get_overlap(a,b,c,d): 
-	#print(a0,b0,a1,b1)
+    #print(a0,b0,a1,b1)
     if b0>a1 and b0<=b1:
         return b0-max(a0,a1)
     elif a0>=a1 and a0<b1: