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Modified conll18_ud_eval to output JSON

Original script taken from: https://universaldependencies.org/conll18/conll18_ud_eval.py

Show results to STDOUT

python conll18_ud_eval.py \
../data/ud-treebanks-v2.5/UD_English-GUM/en_gum-ud-train.conllu \
spacy.conllu \
-v \
--eval_file - 

Show results to eval.json

python conll18_ud_eval.py \
../data/ud-treebanks-v2.5/UD_English-GUM/en_gum-ud-train.conllu \
spacy.conllu \
-v \
--eval_file eval.json 
#!/usr/bin/env python3
# Compatible with Python 2.7 and 3.2+, can be used either as a module
# or a standalone executable.
#
# Copyright 2017, 2018 Institute of Formal and Applied Linguistics (UFAL),
# Faculty of Mathematics and Physics, Charles University, Czech Republic.
#
# This Source Code Form is subject to the terms of the Mozilla Public
# License, v. 2.0. If a copy of the MPL was not distributed with this
# file, You can obtain one at http://mozilla.org/MPL/2.0/.
#
# Authors: Milan Straka, Martin Popel <surname@ufal.mff.cuni.cz>
#
# Changelog:
# - [12 Apr 2018] Version 0.9: Initial release.
# - [19 Apr 2018] Version 1.0: Fix bug in MLAS (duplicate entries in functional_children).
# Add --counts option.
# - [02 May 2018] Version 1.1: When removing spaces to match gold and system characters,
# consider all Unicode characters of category Zs instead of
# just ASCII space.
# - [25 Jun 2018] Version 1.2: Use python3 in the she-bang (instead of python).
# In Python2, make the whole computation use `unicode` strings.
# Command line usage
# ------------------
# conll18_ud_eval.py [-v] gold_conllu_file system_conllu_file
#
# - if no -v is given, only the official CoNLL18 UD Shared Task evaluation metrics
# are printed
# - if -v is given, more metrics are printed (as precision, recall, F1 score,
# and in case the metric is computed on aligned words also accuracy on these):
# - Tokens: how well do the gold tokens match system tokens
# - Sentences: how well do the gold sentences match system sentences
# - Words: how well can the gold words be aligned to system words
# - UPOS: using aligned words, how well does UPOS match
# - XPOS: using aligned words, how well does XPOS match
# - UFeats: using aligned words, how well does universal FEATS match
# - AllTags: using aligned words, how well does UPOS+XPOS+FEATS match
# - Lemmas: using aligned words, how well does LEMMA match
# - UAS: using aligned words, how well does HEAD match
# - LAS: using aligned words, how well does HEAD+DEPREL(ignoring subtypes) match
# - CLAS: using aligned words with content DEPREL, how well does
# HEAD+DEPREL(ignoring subtypes) match
# - MLAS: using aligned words with content DEPREL, how well does
# HEAD+DEPREL(ignoring subtypes)+UPOS+UFEATS+FunctionalChildren(DEPREL+UPOS+UFEATS) match
# - BLEX: using aligned words with content DEPREL, how well does
# HEAD+DEPREL(ignoring subtypes)+LEMMAS match
# - if -c is given, raw counts of correct/gold_total/system_total/aligned words are printed
# instead of precision/recall/F1/AlignedAccuracy for all metrics.
# API usage
# ---------
# - load_conllu(file)
# - loads CoNLL-U file from given file object to an internal representation
# - the file object should return str in both Python 2 and Python 3
# - raises UDError exception if the given file cannot be loaded
# - evaluate(gold_ud, system_ud)
# - evaluate the given gold and system CoNLL-U files (loaded with load_conllu)
# - raises UDError if the concatenated tokens of gold and system file do not match
# - returns a dictionary with the metrics described above, each metric having
# three fields: precision, recall and f1
# Description of token matching
# -----------------------------
# In order to match tokens of gold file and system file, we consider the text
# resulting from concatenation of gold tokens and text resulting from
# concatenation of system tokens. These texts should match -- if they do not,
# the evaluation fails.
#
# If the texts do match, every token is represented as a range in this original
# text, and tokens are equal only if their range is the same.
# Description of word matching
# ----------------------------
# When matching words of gold file and system file, we first match the tokens.
# The words which are also tokens are matched as tokens, but words in multi-word
# tokens have to be handled differently.
#
# To handle multi-word tokens, we start by finding "multi-word spans".
# Multi-word span is a span in the original text such that
# - it contains at least one multi-word token
# - all multi-word tokens in the span (considering both gold and system ones)
# are completely inside the span (i.e., they do not "stick out")
# - the multi-word span is as small as possible
#
# For every multi-word span, we align the gold and system words completely
# inside this span using LCS on their FORMs. The words not intersecting
# (even partially) any multi-word span are then aligned as tokens.
from __future__ import division
from __future__ import print_function
import argparse
import io
import json
import sys
import unicodedata
import unittest
# CoNLL-U column names
ID, FORM, LEMMA, UPOS, XPOS, FEATS, HEAD, DEPREL, DEPS, MISC = range(10)
# Content and functional relations
CONTENT_DEPRELS = {
"nsubj", "obj", "iobj", "csubj", "ccomp", "xcomp", "obl", "vocative",
"expl", "dislocated", "advcl", "advmod", "discourse", "nmod", "appos",
"nummod", "acl", "amod", "conj", "fixed", "flat", "compound", "list",
"parataxis", "orphan", "goeswith", "reparandum", "root", "dep"
}
FUNCTIONAL_DEPRELS = {
"aux", "cop", "mark", "det", "clf", "case", "cc"
}
UNIVERSAL_FEATURES = {
"PronType", "NumType", "Poss", "Reflex", "Foreign", "Abbr", "Gender",
"Animacy", "Number", "Case", "Definite", "Degree", "VerbForm", "Mood",
"Tense", "Aspect", "Voice", "Evident", "Polarity", "Person", "Polite"
}
# UD Error is used when raising exceptions in this module
class UDError(Exception):
pass
# Conversion methods handling `str` <-> `unicode` conversions in Python2
def _decode(text):
return text if sys.version_info[0] >= 3 or not isinstance(text, str) else text.decode("utf-8")
def _encode(text):
return text if sys.version_info[0] >= 3 or not isinstance(text, unicode) else text.encode("utf-8")
# Load given CoNLL-U file into internal representation
def load_conllu(file):
# Internal representation classes
class UDRepresentation:
def __init__(self):
# Characters of all the tokens in the whole file.
# Whitespace between tokens is not included.
self.characters = []
# List of UDSpan instances with start&end indices into `characters`.
self.tokens = []
# List of UDWord instances.
self.words = []
# List of UDSpan instances with start&end indices into `characters`.
self.sentences = []
def __repr__(self):
return (fr"UDRepresentation(len(chars)={len(self.characters)}, "
fr"len(tokens)={len(self.tokens)}, "
fr"len(words)={len(self.words)}, "
fr"len(sentences)={len(self.sentences)})")
class UDSpan:
def __init__(self, start, end):
self.start = start
# Note that self.end marks the first position **after the end** of span,
# so we can use characters[start:end] or range(start, end).
self.end = end
def __repr__(self):
return fr"UDSpan(start={self.start}, end={self.end})"
class UDWord:
def __init__(self, span, columns, is_multiword):
# Span of this word (or MWT, see below) within ud_representation.characters.
self.span = span
# 10 columns of the CoNLL-U file: ID, FORM, LEMMA,...
self.columns = columns
# is_multiword==True means that this word is part of a multi-word token.
# In that case, self.span marks the span of the whole multi-word token.
self.is_multiword = is_multiword
# Reference to the UDWord instance representing the HEAD (or None if root).
self.parent = None
# List of references to UDWord instances representing functional-deprel children.
self.functional_children = []
# Only consider universal FEATS.
self.columns[FEATS] = "|".join(sorted(feat for feat in columns[FEATS].split("|")
if feat.split("=", 1)[0] in UNIVERSAL_FEATURES))
# Let's ignore language-specific deprel subtypes.
self.columns[DEPREL] = columns[DEPREL].split(":")[0]
# Precompute which deprels are CONTENT_DEPRELS and which FUNCTIONAL_DEPRELS
self.is_content_deprel = self.columns[DEPREL] in CONTENT_DEPRELS
self.is_functional_deprel = self.columns[DEPREL] in FUNCTIONAL_DEPRELS
def __repr__(self):
return fr"UDWord(form={self.columns[FORM]!r}, span={self.span!r}, is_multiword={self.is_multiword})"
ud = UDRepresentation()
# Load the CoNLL-U file
index, sentence_start = 0, None
while True:
line = file.readline()
if not line:
break
line = _decode(line.rstrip("\r\n"))
# Handle sentence start boundaries
if sentence_start is None:
# Skip comments
if line.startswith("#"):
continue
# Start a new sentence
ud.sentences.append(UDSpan(index, 0))
sentence_start = len(ud.words)
if not line:
# Add parent and children UDWord links and check there are no cycles
def process_word(word):
if word.parent == "remapping":
raise UDError("There is a cycle in a sentence")
if word.parent is None:
head = int(word.columns[HEAD])
if head < 0 or head > len(ud.words) - sentence_start:
raise UDError("HEAD '{}' points outside of the sentence".format(_encode(word.columns[HEAD])))
if head:
parent = ud.words[sentence_start + head - 1]
word.parent = "remapping"
process_word(parent)
word.parent = parent
for word in ud.words[sentence_start:]:
process_word(word)
# func_children cannot be assigned within process_word
# because it is called recursively and may result in adding one child twice.
for word in ud.words[sentence_start:]:
if word.parent and word.is_functional_deprel:
word.parent.functional_children.append(word)
# Check there is a single root node
if len([word for word in ud.words[sentence_start:] if word.parent is None]) != 1:
raise UDError("There are multiple roots in a sentence")
# End the sentence
ud.sentences[-1].end = index
sentence_start = None
continue
# Read next token/word
columns = line.split("\t")
if len(columns) != 10:
raise UDError("The CoNLL-U line does not contain 10 tab-separated columns: '{}'".format(_encode(line)))
# Skip empty nodes
if "." in columns[ID]:
continue
# Delete spaces from FORM, so gold.characters == system.characters
# even if one of them tokenizes the space. Use any Unicode character
# with category Zs.
columns[FORM] = "".join(filter(lambda c: unicodedata.category(c) != "Zs", columns[FORM]))
if not columns[FORM]:
raise UDError("There is an empty FORM in the CoNLL-U file")
# Save token
ud.characters.extend(columns[FORM])
ud.tokens.append(UDSpan(index, index + len(columns[FORM])))
index += len(columns[FORM])
# Handle multi-word tokens to save word(s)
if "-" in columns[ID]:
try:
start, end = map(int, columns[ID].split("-"))
except:
raise UDError("Cannot parse multi-word token ID '{}'".format(_encode(columns[ID])))
for _ in range(start, end + 1):
word_line = _decode(file.readline().rstrip("\r\n"))
word_columns = word_line.split("\t")
if len(word_columns) != 10:
raise UDError("The CoNLL-U line does not contain 10 tab-separated columns: '{}'".format(_encode(word_line)))
ud.words.append(UDWord(ud.tokens[-1], word_columns, is_multiword=True))
# Basic tokens/words
else:
try:
word_id = int(columns[ID])
except:
raise UDError("Cannot parse word ID '{}'".format(_encode(columns[ID])))
if word_id != len(ud.words) - sentence_start + 1:
raise UDError("Incorrect word ID '{}' for word '{}', expected '{}'".format(
_encode(columns[ID]), _encode(columns[FORM]), len(ud.words) - sentence_start + 1))
try:
head_id = int(columns[HEAD])
except:
raise UDError("Cannot parse HEAD '{}'".format(_encode(columns[HEAD])))
if head_id < 0:
raise UDError("HEAD cannot be negative")
ud.words.append(UDWord(ud.tokens[-1], columns, is_multiword=False))
if sentence_start is not None:
raise UDError("The CoNLL-U file does not end with empty line")
return ud
class Score(object):
def __init__(self, gold_total, system_total, correct, aligned_total=None):
self.correct = correct
self.gold_total = gold_total
self.system_total = system_total
self.aligned_total = aligned_total
self.precision = correct / system_total if system_total else 0.0
self.recall = correct / gold_total if gold_total else 0.0
self.f1 = 2 * correct / (system_total + gold_total) if system_total + gold_total else 0.0
self.aligned_accuracy = correct / aligned_total if aligned_total else aligned_total
class ScoreEncoder(json.JSONEncoder):
def default(self, obj):
if isinstance(obj, Score):
return {
"correct": obj.correct,
"gold_total": obj.gold_total,
"system_total": obj.system_total,
"aligned_total": obj.aligned_total,
"precision": obj.precision,
"recall": obj.recall,
"f1": obj.f1,
"aligned_accuracy": obj.aligned_accuracy
}
return super().default(obj)
# Evaluate the gold and system treebanks (loaded using load_conllu).
def evaluate(gold_ud, system_ud):
class AlignmentWord:
def __init__(self, gold_word, system_word):
self.gold_word = gold_word
self.system_word = system_word
class Alignment:
def __init__(self, gold_words, system_words):
self.gold_words = gold_words
self.system_words = system_words
self.matched_words = []
self.matched_words_map = {}
def append_aligned_words(self, gold_word, system_word):
self.matched_words.append(AlignmentWord(gold_word, system_word))
self.matched_words_map[system_word] = gold_word
def spans_score(gold_spans, system_spans):
correct, gi, si = 0, 0, 0
while gi < len(gold_spans) and si < len(system_spans):
if system_spans[si].start < gold_spans[gi].start:
si += 1
elif gold_spans[gi].start < system_spans[si].start:
gi += 1
else:
correct += gold_spans[gi].end == system_spans[si].end
si += 1
gi += 1
return Score(len(gold_spans), len(system_spans), correct)
def alignment_score(alignment, key_fn=None, filter_fn=None):
if filter_fn is not None:
gold = sum(1 for gold in alignment.gold_words if filter_fn(gold))
system = sum(1 for system in alignment.system_words if filter_fn(system))
aligned = sum(1 for word in alignment.matched_words if filter_fn(word.gold_word))
else:
gold = len(alignment.gold_words)
system = len(alignment.system_words)
aligned = len(alignment.matched_words)
if key_fn is None:
# Return score for whole aligned words
return Score(gold, system, aligned)
def gold_aligned_gold(word):
return word
def gold_aligned_system(word):
return alignment.matched_words_map.get(word, "NotAligned") if word is not None else None
correct = 0
for words in alignment.matched_words:
if filter_fn is None or filter_fn(words.gold_word):
if key_fn(words.gold_word, gold_aligned_gold) == key_fn(words.system_word, gold_aligned_system):
correct += 1
return Score(gold, system, correct, aligned)
def beyond_end(words, i, multiword_span_end):
if i >= len(words):
return True
if words[i].is_multiword:
return words[i].span.start >= multiword_span_end
return words[i].span.end > multiword_span_end
def extend_end(word, multiword_span_end):
if word.is_multiword and word.span.end > multiword_span_end:
return word.span.end
return multiword_span_end
def find_multiword_span(gold_words, system_words, gi, si):
# We know gold_words[gi].is_multiword or system_words[si].is_multiword.
# Find the start of the multiword span (gs, ss), so the multiword span is minimal.
# Initialize multiword_span_end characters index.
if gold_words[gi].is_multiword:
multiword_span_end = gold_words[gi].span.end
if not system_words[si].is_multiword and system_words[si].span.start < gold_words[gi].span.start:
si += 1
else: # if system_words[si].is_multiword
multiword_span_end = system_words[si].span.end
if not gold_words[gi].is_multiword and gold_words[gi].span.start < system_words[si].span.start:
gi += 1
gs, ss = gi, si
# Find the end of the multiword span
# (so both gi and si are pointing to the word following the multiword span end).
while not beyond_end(gold_words, gi, multiword_span_end) or \
not beyond_end(system_words, si, multiword_span_end):
if gi < len(gold_words) and (si >= len(system_words) or
gold_words[gi].span.start <= system_words[si].span.start):
multiword_span_end = extend_end(gold_words[gi], multiword_span_end)
gi += 1
else:
multiword_span_end = extend_end(system_words[si], multiword_span_end)
si += 1
return gs, ss, gi, si
def compute_lcs(gold_words, system_words, gi, si, gs, ss):
lcs = [[0] * (si - ss) for i in range(gi - gs)]
for g in reversed(range(gi - gs)):
for s in reversed(range(si - ss)):
if gold_words[gs + g].columns[FORM].lower() == system_words[ss + s].columns[FORM].lower():
lcs[g][s] = 1 + (lcs[g+1][s+1] if g+1 < gi-gs and s+1 < si-ss else 0)
lcs[g][s] = max(lcs[g][s], lcs[g+1][s] if g+1 < gi-gs else 0)
lcs[g][s] = max(lcs[g][s], lcs[g][s+1] if s+1 < si-ss else 0)
return lcs
def align_words(gold_words, system_words):
alignment = Alignment(gold_words, system_words)
gi, si = 0, 0
while gi < len(gold_words) and si < len(system_words):
if gold_words[gi].is_multiword or system_words[si].is_multiword:
# A: Multi-word tokens => align via LCS within the whole "multiword span".
gs, ss, gi, si = find_multiword_span(gold_words, system_words, gi, si)
if si > ss and gi > gs:
lcs = compute_lcs(gold_words, system_words, gi, si, gs, ss)
# Store aligned words
s, g = 0, 0
while g < gi - gs and s < si - ss:
if gold_words[gs + g].columns[FORM].lower() == system_words[ss + s].columns[FORM].lower():
alignment.append_aligned_words(gold_words[gs+g], system_words[ss+s])
g += 1
s += 1
elif lcs[g][s] == (lcs[g+1][s] if g+1 < gi-gs else 0):
g += 1
else:
s += 1
else:
# B: No multi-word token => align according to spans.
if (gold_words[gi].span.start, gold_words[gi].span.end) == (system_words[si].span.start, system_words[si].span.end):
alignment.append_aligned_words(gold_words[gi], system_words[si])
gi += 1
si += 1
elif gold_words[gi].span.start <= system_words[si].span.start:
gi += 1
else:
si += 1
return alignment
# Check that the underlying character sequences do match.
if gold_ud.characters != system_ud.characters:
index = 0
while index < len(gold_ud.characters) and index < len(system_ud.characters) and \
gold_ud.characters[index] == system_ud.characters[index]:
index += 1
raise UDError(
"The concatenation of tokens in gold file and in system file differ!\n" +
"First 20 differing characters in gold file: '{}' and system file: '{}'".format(
"".join(map(_encode, gold_ud.characters[index:index + 20])),
"".join(map(_encode, system_ud.characters[index:index + 20]))
)
)
# Align words
alignment = align_words(gold_ud.words, system_ud.words)
# Compute the F1-scores
return {
"Tokens": spans_score(gold_ud.tokens, system_ud.tokens),
"Sentences": spans_score(gold_ud.sentences, system_ud.sentences),
"Words": alignment_score(alignment),
"UPOS": alignment_score(alignment, lambda w, _: w.columns[UPOS]),
"XPOS": alignment_score(alignment, lambda w, _: w.columns[XPOS]),
"UFeats": alignment_score(alignment, lambda w, _: w.columns[FEATS]),
"AllTags": alignment_score(alignment, lambda w, _: (w.columns[UPOS], w.columns[XPOS], w.columns[FEATS])),
"Lemmas": alignment_score(alignment, lambda w, ga: w.columns[LEMMA] if ga(w).columns[LEMMA] != "_" else "_"),
"UAS": alignment_score(alignment, lambda w, ga: ga(w.parent)),
"LAS": alignment_score(alignment, lambda w, ga: (ga(w.parent), w.columns[DEPREL])),
"CLAS": alignment_score(alignment, lambda w, ga: (ga(w.parent), w.columns[DEPREL]),
filter_fn=lambda w: w.is_content_deprel),
"MLAS": alignment_score(alignment, lambda w, ga: (ga(w.parent), w.columns[DEPREL], w.columns[UPOS], w.columns[FEATS],
[(ga(c), c.columns[DEPREL], c.columns[UPOS], c.columns[FEATS])
for c in w.functional_children]),
filter_fn=lambda w: w.is_content_deprel),
"BLEX": alignment_score(alignment, lambda w, ga: (ga(w.parent), w.columns[DEPREL],
w.columns[LEMMA] if ga(w).columns[LEMMA] != "_" else "_"),
filter_fn=lambda w: w.is_content_deprel),
}
def load_conllu_file(path):
_file = open(path, mode="r", **({"encoding": "utf-8"} if sys.version_info >= (3, 0) else {}))
return load_conllu(_file)
def evaluate_wrapper(args):
# Load CoNLL-U files
gold_ud = load_conllu_file(args.gold_file)
system_ud = load_conllu_file(args.system_file)
return evaluate(gold_ud, system_ud)
def generate_parser():
# Parse arguments
parser = argparse.ArgumentParser()
parser.add_argument("gold_file", type=str,
help="Name of the CoNLL-U file with the gold data.")
parser.add_argument("system_file", type=str,
help="Name of the CoNLL-U file with the predicted data.")
parser.add_argument("--verbose", "-v", default=False, action="store_true",
help="Print all metrics.")
parser.add_argument("--counts", "-c", default=False, action="store_true",
help="Print raw counts of correct/gold/system/aligned words instead of prec/rec/F1 for all metrics.")
parser.add_argument("--eval_file", type=argparse.FileType("w"),
help="Saved the evaluation as JSON to the specified file, else prints it to stdout.")
return parser
def main():
parser = generate_parser()
args = parser.parse_args()
# Evaluate
evaluation = evaluate_wrapper(args)
# Print the evaluation
if args.eval_file:
indent = 2
if args.eval_file.name == "<stdout>":
indent=None
json.dump(evaluation, args.eval_file, indent=indent, cls=ScoreEncoder)
elif not args.verbose and not args.counts:
print("LAS F1 Score: {:.2f}".format(100 * evaluation["LAS"].f1))
print("MLAS Score: {:.2f}".format(100 * evaluation["MLAS"].f1))
print("BLEX Score: {:.2f}".format(100 * evaluation["BLEX"].f1))
else:
if args.counts:
print("Metric | Correct | Gold | Predicted | Aligned")
else:
print("Metric | Precision | Recall | F1 Score | AligndAcc")
print("-----------+-----------+-----------+-----------+-----------")
for metric in["Tokens", "Sentences", "Words", "UPOS", "XPOS", "UFeats", "AllTags", "Lemmas", "UAS", "LAS", "CLAS", "MLAS", "BLEX"]:
if args.counts:
print("{:11}|{:10} |{:10} |{:10} |{:10}".format(
metric,
evaluation[metric].correct,
evaluation[metric].gold_total,
evaluation[metric].system_total,
evaluation[metric].aligned_total or (evaluation[metric].correct if metric == "Words" else "")
))
else:
print("{:11}|{:10.2f} |{:10.2f} |{:10.2f} |{}".format(
metric,
100 * evaluation[metric].precision,
100 * evaluation[metric].recall,
100 * evaluation[metric].f1,
"{:10.2f}".format(100 * evaluation[metric].aligned_accuracy) if evaluation[metric].aligned_accuracy is not None else ""
))
if __name__ == "__main__":
main()
# Tests, which can be executed with `python -m unittest conll18_ud_eval`.
class TestAlignment(unittest.TestCase):
@staticmethod
def _load_words(words):
"""Prepare fake CoNLL-U files with fake HEAD to prevent multiple roots errors."""
lines, num_words = [], 0
for w in words:
parts = w.split(" ")
if len(parts) == 1:
num_words += 1
lines.append("{}\t{}\t_\t_\t_\t_\t{}\t_\t_\t_".format(num_words, parts[0], int(num_words>1)))
else:
lines.append("{}-{}\t{}\t_\t_\t_\t_\t_\t_\t_\t_".format(num_words + 1, num_words + len(parts) - 1, parts[0]))
for part in parts[1:]:
num_words += 1
lines.append("{}\t{}\t_\t_\t_\t_\t{}\t_\t_\t_".format(num_words, part, int(num_words>1)))
return load_conllu((io.StringIO if sys.version_info >= (3, 0) else io.BytesIO)("\n".join(lines+["\n"])))
def _test_exception(self, gold, system):
self.assertRaises(UDError, evaluate, self._load_words(gold), self._load_words(system))
def _test_ok(self, gold, system, correct):
metrics = evaluate(self._load_words(gold), self._load_words(system))
gold_words = sum((max(1, len(word.split(" ")) - 1) for word in gold))
system_words = sum((max(1, len(word.split(" ")) - 1) for word in system))
self.assertEqual((metrics["Words"].precision, metrics["Words"].recall, metrics["Words"].f1),
(correct / system_words, correct / gold_words, 2 * correct / (gold_words + system_words)))
def test_exception(self):
self._test_exception(["a"], ["b"])
def test_equal(self):
self._test_ok(["a"], ["a"], 1)
self._test_ok(["a", "b", "c"], ["a", "b", "c"], 3)
def test_equal_with_multiword(self):
self._test_ok(["abc a b c"], ["a", "b", "c"], 3)
self._test_ok(["a", "bc b c", "d"], ["a", "b", "c", "d"], 4)
self._test_ok(["abcd a b c d"], ["ab a b", "cd c d"], 4)
self._test_ok(["abc a b c", "de d e"], ["a", "bcd b c d", "e"], 5)
def test_alignment(self):
self._test_ok(["abcd"], ["a", "b", "c", "d"], 0)
self._test_ok(["abc", "d"], ["a", "b", "c", "d"], 1)
self._test_ok(["a", "bc", "d"], ["a", "b", "c", "d"], 2)
self._test_ok(["a", "bc b c", "d"], ["a", "b", "cd"], 2)
self._test_ok(["abc a BX c", "def d EX f"], ["ab a b", "cd c d", "ef e f"], 4)
self._test_ok(["ab a b", "cd bc d"], ["a", "bc", "d"], 2)
self._test_ok(["a", "bc b c", "d"], ["ab AX BX", "cd CX a"], 1)
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