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conll18_ud_eval_lax.py
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#!/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 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 = [] | |
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 | |
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 | |
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 | |
# Evaluate the gold and system treebanks (loaded using load_conllu). | |
def evaluate(gold_ud, system_ud): | |
class Score: | |
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 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 main(): | |
# 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.") | |
args = parser.parse_args() | |
# Evaluate | |
evaluation = evaluate_wrapper(args) | |
# Print the evaluation | |
if 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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