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multi_label_ner_performance.py
188 lines (147 loc) · 5.95 KB
/
multi_label_ner_performance.py
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from collections import defaultdict
import pickle
import os
import numpy as np
def pickle_load_large_file(filepath):
max_bytes = 2**31 - 1
input_size = os.path.getsize(filepath)
bytes_in = bytearray(0)
with open(filepath, 'rb') as f_in:
for _ in range(0, input_size, max_bytes):
bytes_in += f_in.read(max_bytes)
obj = pickle.loads(bytes_in)
return obj
def get_entities(seq, suffix=False):
if any(isinstance(s, list) for s in seq):
seq = [item for sublist in seq for item in sublist + [['O']]]
#print('seq: ',seq[0:100])
prev_tag = ['O']
prev_type = ['']
previous_begin_offset = {}
chunks = []
for i, chunk in enumerate(seq + [['O']]):
if chunk == []:
chunk = ['O']
tag = [singlechunk[0] for singlechunk in chunk]
type_ = [singlechunk.split('-')[-1] for singlechunk in chunk]
for idx, prev_tag_temp in enumerate(prev_tag):
prev_type_temp = prev_type[idx]
if end_of_chunk(prev_tag_temp, tag, prev_type_temp, type_):
chunks.append((prev_type_temp, begin_offset[prev_type_temp], i - 1))
begin_offset = {}
for idx, singlechunk in enumerate(chunk):
tag_temp = tag[idx]
type__temp = type_[idx]
if start_of_chunk(tag_temp, type__temp):
begin_offset[type__temp] = i
else:
if type__temp not in previous_begin_offset.keys():
begin_offset[type__temp] = i
else:
begin_offset[type__temp]=previous_begin_offset[type__temp]
previous_begin_offset=begin_offset.copy()
prev_tag = tag
prev_type = type_
return chunks
def end_of_chunk(prev_tag, tag, prev_type, type_):
chunk_end = False
if prev_tag == 'E': chunk_end = True
if prev_tag == 'S': chunk_end = True
if prev_tag == 'B' and 'I' not in tag: chunk_end = True
if prev_tag == 'B' and 'I' in tag:
for idx, tag_temp in enumerate(tag):
if tag_temp == 'I':
if type_[idx] == prev_type:
chunk_end = False
break
chunk_end = True
if prev_tag == 'I' and 'I' not in tag: chunk_end = True
if prev_tag == 'I' and 'I' in tag:
for idx, tag_temp in enumerate(tag):
if tag_temp == 'I':
if type_[idx] == prev_type:
chunk_end = False
break
chunk_end = True
return chunk_end
def start_of_chunk(tag, type_):
chunk_start = False
if tag == 'B': chunk_start = True
if tag == 'S': chunk_start = True
return chunk_start
def f1_score(y_true, y_pred, average='micro', suffix=False):
true_entities = set(get_entities(y_true, suffix))
pred_entities = set(get_entities(y_pred, suffix))
nb_correct = len(true_entities & pred_entities)
nb_pred = len(pred_entities)
nb_true = len(true_entities)
p = nb_correct / nb_pred if nb_pred > 0 else 0
r = nb_correct / nb_true if nb_true > 0 else 0
score = 2 * p * r / (p + r) if p + r > 0 else 0
return score
def accuracy_score(y_true, y_pred):
if any(isinstance(s, list) for s in y_true):
y_true = [item for sublist in y_true for item in sublist]
y_pred = [item for sublist in y_pred for item in sublist]
nb_correct = sum(y_t==y_p for y_t, y_p in zip(y_true, y_pred))
nb_true = len(y_true)
score = nb_correct / nb_true
return score
def precision_score(y_true, y_pred, average='micro', suffix=False):
true_entities = set(get_entities(y_true, suffix))
pred_entities = set(get_entities(y_pred, suffix))
nb_correct = len(true_entities & pred_entities)
nb_pred = len(pred_entities)
score = nb_correct / nb_pred if nb_pred > 0 else 0
return score
def recall_score(y_true, y_pred, average='micro', suffix=False):
true_entities = set(get_entities(y_true, suffix))
pred_entities = set(get_entities(y_pred, suffix))
nb_correct = len(true_entities & pred_entities)
nb_true = len(true_entities)
score = nb_correct / nb_true if nb_true > 0 else 0
return score
def classification_report(y_true, y_pred, digits=2, suffix=False):
true_entities = set(get_entities(y_true, suffix))
pred_entities = set(get_entities(y_pred, suffix))
name_width = 0
d1 = defaultdict(set)
d2 = defaultdict(set)
for e in true_entities:
d1[e[0]].add((e[1], e[2]))
name_width = max(name_width, len(e[0]))
for e in pred_entities:
d2[e[0]].add((e[1], e[2]))
last_line_heading = 'avg / total'
width = max(name_width, len(last_line_heading), digits)
headers = ["precision", "recall", "f1-score", "support"]
head_fmt = u'{:>{width}s} ' + u' {:>9}' * len(headers)
report = head_fmt.format(u'', *headers, width=width)
report += u'\n\n'
row_fmt = u'{:>{width}s} ' + u' {:>9.{digits}f}' * 3 + u' {:>9}\n'
ps, rs, f1s, s = [], [], [], []
for type_name, true_entities in d1.items():
pred_entities = d2[type_name]
nb_correct = len(true_entities & pred_entities)
nb_pred = len(pred_entities)
nb_true = len(true_entities)
p = nb_correct / nb_pred if nb_pred > 0 else 0
r = nb_correct / nb_true if nb_true > 0 else 0
f1 = 2 * p * r / (p + r) if p + r > 0 else 0
report += row_fmt.format(*[type_name, p, r, f1, nb_true], width=width, digits=digits)
ps.append(p)
rs.append(r)
f1s.append(f1)
s.append(nb_true)
report += u'\n'
# compute averages
report += row_fmt.format(last_line_heading,
np.average(ps, weights=s),
np.average(rs, weights=s),
np.average(f1s, weights=s),
np.sum(s),
width=width, digits=digits)
return report