def load_annotation(gold_file):
source_sentences = []
gold_edits = []
fgold = smart_open(gold_file, 'r')
puffer = fgold.read()
fgold.close()
puffer = puffer.decode('utf8')
for item in paragraphs(puffer.splitlines(True)):
item = item.splitlines(False)
sentence = [line[2:].strip() for line in item if line.startswith('S ')]
assert sentence != []
annotation = []
for line in item[1:]:
if line.startswith('I ') or line.startswith('S '):
continue
assert line.startswith('A ')
line = line[2:]
fields = line.split('|||')
start_offset = int(fields[0].split()[0])
end_offset = int(fields[0].split()[1])
etype = fields[1]
corrections = [c.strip() if c != '-NONE-' else '' for c in fields[2].split('||')]
# NOTE: start and end are *token* offsets
original = ' '.join(' '.join(sentence).split()[start_offset:end_offset])
annotation.append((start_offset, end_offset, original, corrections))
tok_offset = 0
for this_sentence in sentence:
tok_offset += len(this_sentence.split())
this_edits = [edit for edit in annotation if edit[0] <= tok_offset and edit[1] <= tok_offset]
source_sentences.append(this_sentence)
gold_edits.append(this_edits)
return (source_sentences, gold_edits)
def load_annotation(gold_file):
source_sentences = []
gold_edits = []
fgold = smart_open(gold_file, 'r')
puffer = fgold.read()
fgold.close()
#puffer = puffer.decode('utf8')
for item in paragraphs(puffer.splitlines(True)):
item = item.splitlines(False)
sentence = [line[2:].strip() for line in item if line.startswith('S ')]
#print(sentence)
assert sentence != []
annotations = {}
for line in item[1:]:
if line.startswith('I ') or line.startswith('S '):
continue
assert line.startswith('A ')
line = line[2:]
fields = line.split('|||')
start_offset = int(fields[0].split()[0])
end_offset = int(fields[0].split()[1])
etype = fields[1]
if etype == 'noop':
start_offset = -1
end_offset = -1
corrections = [
c.strip() if c != '-NONE-' else ''
for c in fields[2].split('||')
]
# NOTE: start and end are *token* offsets
original = ' '.join(
' '.join(sentence).split()[start_offset:end_offset])
annotator = int(fields[5])
if annotator not in list(annotations.keys()):
annotations[annotator] = []
# print(etype, original,corrections)
annotations[annotator].append(
(start_offset, end_offset, original, corrections, etype))
tok_offset = 0
for this_sentence in sentence:
tok_offset += len(this_sentence.split())
source_sentences.append(this_sentence)
this_edits = {}
for annotator, annotation in annotations.items():
this_edits[annotator] = [
edit for edit in annotation if edit[0] <= tok_offset
and edit[1] <= tok_offset and edit[0] >= 0 and edit[1] >= 0
]
if len(this_edits) == 0:
this_edits[0] = []
#print(this_edits)
gold_edits.append(this_edits)
return (source_sentences, gold_edits)
def load_annotation(gold_file):
source_sentences = []
gold_edits = []
fgold = smart_open(gold_file, "r")
puffer = fgold.read()
fgold.close()
puffer = puffer.decode("utf8")
for item in paragraphs(puffer.splitlines(True)):
item = item.splitlines(False)
sentence = [line[2:].strip() for line in item if line.startswith("S ")]
assert sentence != []
annotations = {}
for line in item[1:]:
if line.startswith("I ") or line.startswith("S "):
continue
assert line.startswith("A ")
line = line[2:]
fields = line.split("|||")
start_offset = int(fields[0].split()[0])
end_offset = int(fields[0].split()[1])
etype = fields[1]
if etype == "noop":
start_offset = -1
end_offset = -1
corrections = [c.strip() if c != "-NONE-" else "" for c in fields[2].split("||")]
# NOTE: start and end are *token* offsets
original = " ".join(" ".join(sentence).split()[start_offset:end_offset])
annotator = int(fields[5])
if annotator not in annotations.keys():
annotations[annotator] = []
annotations[annotator].append((start_offset, end_offset, original, corrections))
tok_offset = 0
for this_sentence in sentence:
tok_offset += len(this_sentence.split())
source_sentences.append(this_sentence)
this_edits = {}
for annotator, annotation in annotations.iteritems():
this_edits[annotator] = [
edit
for edit in annotation
if edit[0] <= tok_offset and edit[1] <= tok_offset and edit[0] >= 0 and edit[1] >= 0
]
if len(this_edits) == 0:
this_edits[0] = []
gold_edits.append(this_edits)
return (source_sentences, gold_edits)
def evaluateIt(system_file,gold_file,verbose=False):
max_unchanged_words=2
ignore_whitespace_casing= False
very_verbose = False
# opts, args = getopt(sys.argv[1:], "v", ["max_unchanged_words=", "verbose", "ignore_whitespace_casing", "very_verbose"])
# for o, v in opts:
# if o in ('-v', '--verbose'):
# verbose = True
# elif o == '--very_verbose':
# very_verbose = True
# elif o == '--max_unchanged_words':
# max_unchanged_words = int(v)
# elif o == '--ignore_whitespace_casing':
# ignore_whitespace_casing = True
# else:
# print >> sys.stderr, "Unknown option :", o
# print_usage()
# sys.exit(-1)
#
# # starting point
# if len(args) != 2:
# print_usage()
# sys.exit(-1)
#system_file = args[0]
#gold_file = args[1]
# load source sentences and gold edits
source_sentences, gold_edits = load_annotation(gold_file)
# load system hypotheses
fin = smart_open(system_file, 'r')
system_sentences = [line.decode("utf8").strip() for line in fin.readlines()]
fin.close()
p, r, f1 = levenshtein.batch_multi_pre_rec_f1(system_sentences, source_sentences, gold_edits, max_unchanged_words, ignore_whitespace_casing, verbose, very_verbose)
#print "Precision : %.4f" % p
#print "Recall : %.4f" % r
#print "F1 : %.4f" % f1
return p,r,f1
max_unchanged_words = int(v)
elif o == '--ignore_whitespace_casing':
ignore_whitespace_casing = True
else:
print >> sys.stderr, "Unknown option :", o
print_usage()
sys.exit(-1)
# starting point
if len(args) != 2:
print_usage()
sys.exit(-1)
system_file = args[0]
gold_file = args[1]
# load source sentences and gold edits
source_sentences, gold_edits = load_annotation(gold_file)
# load system hypotheses
fin = smart_open(system_file, 'r')
system_sentences = [line.decode("utf8").strip() for line in fin.readlines()]
fin.close()
p, r, f1 = levenshtein.batch_pre_rec_f1(system_sentences, source_sentences, gold_edits, max_unchanged_words, ignore_whitespace_casing, verbose, very_verbose)
print "Precision : %.4f" % p
print "Recall : %.4f" % r
print "F1 : %.4f" % f1
elif o == "--ignore_whitespace_casing":
ignore_whitespace_casing = True
else:
print >> sys.stderr, "Unknown option :", o
print_usage()
sys.exit(-1)
# starting point
if len(args) != 2:
print_usage()
sys.exit(-1)
system_file = args[0]
gold_file = args[1]
# load source sentences and gold edits
source_sentences, gold_edits = load_annotation(gold_file)
# load system hypotheses
fin = smart_open(system_file, "r")
system_sentences = [line.decode("utf8").strip() for line in fin.readlines()]
fin.close()
p, r, f1 = levenshtein.batch_multi_pre_rec_f1(
system_sentences, source_sentences, gold_edits, max_unchanged_words, ignore_whitespace_casing, verbose, very_verbose
)
print "Precision : %.4f" % p
print "Recall : %.4f" % r
print "F1 : %.4f" % f1
else:
print("Unknown option :", o, file=sys.stderr)
print_usage()
sys.exit(-1)
# starting point
if len(args) != 2:
print_usage()
sys.exit(-1)
system_file = args[0]
gold_file = args[1]
# load source sentences and gold edits
source_sentences, gold_edits = load_annotation(gold_file)
# load system hypotheses
fin = smart_open(system_file, mode='r')
system_sentences = [line.strip() for line in fin.readlines()]
fin.close()
p, r, f1 = levenshtein.batch_multi_pre_rec_f1(system_sentences,
source_sentences, gold_edits,
max_unchanged_words, beta,
ignore_whitespace_casing,
verbose, very_verbose)
print(("Precision : %.4f" % p))
print(("Recall : %.4f" % r))
print(("F_%.1f : %.4f" % (beta, f1)))
