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utils.py
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utils.py
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import optparse
import sys
from collections import OrderedDict
import codecs
import numpy as np
import os
import re
#import theano
models_path = "./models"
eval_path = "./evaluation"
eval_temp = os.path.join(eval_path, "temp")
eval_script = os.path.join(eval_path, "conlleval")
def lock_file(f):
import fcntl, errno, time
while True:
try:
fcntl.flock(f, fcntl.LOCK_EX | fcntl.LOCK_NB)
break
except IOError as e:
# raise on unrelated IOErrors
if e.errno != errno.EAGAIN:
raise
else:
time.sleep(0.1)
return True
def unlock_file(f):
import fcntl
fcntl.flock(f, fcntl.LOCK_UN)
def create_a_model_subpath(models_path):
current_model_paths = read_model_paths_database(models_path)
if len(current_model_paths) > 0:
last_model_path_id_part = int(current_model_paths[-1][0].split("-")[1])
else:
last_model_path_id_part = -1
return os.path.join(models_path, "model-%08d" % (last_model_path_id_part+1)), (last_model_path_id_part+1)
def add_a_model_path_to_the_model_paths_database(models_path, model_subpath, model_params_string):
f = codecs.open(os.path.join(models_path, "model_paths_database.dat"), "a+")
lock_file(f)
f.write("%s %s\n" % (model_subpath, model_params_string))
unlock_file(f)
f.close()
def read_model_paths_database(models_path):
try:
f = codecs.open(os.path.join(models_path, "model_paths_database.dat"), "r")
lock_file(f)
lines = f.readlines()
sorted_model_subpaths = sorted([line.strip().split() for line in lines if len(line.strip()) > 0], key=lambda x: x[0])
# current_model_paths = {model_path: model_params for model_path, model_params in sorted_model_paths}
unlock_file(f)
f.close()
except IOError as e:
return []
return sorted_model_subpaths
def get_model_subpath(parameters):
model_parameters_string = get_name(parameters)
sorted_model_subpaths = read_model_paths_database("models")
for cur_model_subpath, cur_model_parameters_string in sorted_model_subpaths[::-1]:
if cur_model_parameters_string == model_parameters_string:
return cur_model_subpath
def get_name(parameters):
"""
Generate a model name from its parameters.
"""
l = []
for k, v in parameters.items():
if (type(v) is str or type(v) is unicode) and "/" in v:
l.append((k, v[::-1][:v[::-1].index('/')][::-1]))
else:
l.append((k, v))
name = ",".join(["%s=%s" % (k, str(v).replace(',', '')) for k, v in l])
return "".join(i for i in name if i not in "\/:*?<>|")
def create_dico(item_list):
"""
Create a dictionary of items from a list of list of items.
"""
assert type(item_list) is list
dico = {}
for items in item_list:
for item in items:
if item not in dico:
dico[item] = 1
else:
dico[item] += 1
return dico
def create_mapping(dico):
"""
Create a mapping (item to ID / ID to item) from a dictionary.
Items are ordered by decreasing frequency.
"""
sorted_items = sorted(dico.items(), key=lambda x: (-x[1], x[0]))
id_to_item = {i: v[0] for i, v in enumerate(sorted_items)}
item_to_id = {v: k for k, v in id_to_item.items()}
return item_to_id, id_to_item
def zero_digits(s):
"""
Replace every digit in a string by a zero.
"""
return re.sub('\d', '0', s)
def iob2(tags):
"""
Check that tags have a valid IOB format.
Tags in IOB1 format are converted to IOB2.
"""
for i, tag in enumerate(tags):
if tag == 'O':
continue
split = tag.split('-')
if len(split) != 2 or split[0] not in ['I', 'B']:
return False
if split[0] == 'B':
continue
elif i == 0 or tags[i - 1] == 'O': # conversion IOB1 to IOB2
tags[i] = 'B' + tag[1:]
elif tags[i - 1][1:] == tag[1:]:
continue
else: # conversion IOB1 to IOB2
tags[i] = 'B' + tag[1:]
return True
def iob_iobes(tags):
"""
IOB -> IOBES
"""
new_tags = []
for i, tag in enumerate(tags):
if tag == 'O':
new_tags.append(tag)
elif tag.split('-')[0] == 'B':
if i + 1 != len(tags) and \
tags[i + 1].split('-')[0] == 'I':
new_tags.append(tag)
else:
new_tags.append(tag.replace('B-', 'S-'))
elif tag.split('-')[0] == 'I':
if i + 1 < len(tags) and \
tags[i + 1].split('-')[0] == 'I':
new_tags.append(tag)
else:
new_tags.append(tag.replace('I-', 'E-'))
else:
raise Exception('Invalid IOB format!')
return new_tags
def iobes_iob(tags):
"""
IOBES -> IOB
"""
new_tags = []
for i, tag in enumerate(tags):
if tag.split('-')[0] == 'B':
new_tags.append(tag)
elif tag.split('-')[0] == 'I':
new_tags.append(tag)
elif tag.split('-')[0] == 'S':
new_tags.append(tag.replace('S-', 'B-'))
elif tag.split('-')[0] == 'E':
new_tags.append(tag.replace('E-', 'I-'))
elif tag.split('-')[0] == 'O':
new_tags.append(tag)
else:
raise Exception('Invalid format!')
return new_tags
def insert_singletons(words, singletons, p=0.5):
"""
Replace singletons by the unknown word with a probability p.
"""
new_words = []
for word in words:
if word in singletons and np.random.uniform() < p:
new_words.append(0)
else:
new_words.append(word)
return new_words
def pad_word_chars(words):
"""
Pad the characters of the words in a sentence.
Input:
- list of lists of ints (list of words, a word being a list of char indexes)
Output:
- padded list of lists of ints
- padded list of lists of ints (where chars are reversed)
- list of ints corresponding to the index of the last character of each word
"""
max_length = max([len(word) for word in words])
char_for = []
char_rev = []
char_pos = []
for word in words:
padding = [0] * (max_length - len(word))
char_for.append(word + padding)
char_rev.append(word[::-1] + padding)
char_pos.append(len(word) - 1)
return char_for, char_rev, char_pos
def create_input(data, parameters, add_label, singletons=None):
"""
Take sentence data and return an input for
the training or the evaluation function.
"""
words = data['words']
chars = data['chars']
if singletons is not None:
words = insert_singletons(words, singletons)
if parameters['cap_dim']:
caps = data['caps']
char_for, char_rev, char_pos = pad_word_chars(chars)
input = []
if parameters['word_dim']:
input.append(words)
if parameters['char_dim']:
input.append(char_for)
if parameters['ch_b']:
input.append(char_rev)
input.append(char_pos)
if parameters['cap_dim']:
input.append(caps)
# print input
if add_label:
input.append(data['tags'])
# print input
return input
def evaluate(parameters, f_eval, raw_sentences, parsed_sentences,
id_to_tag, dictionary_tags, predict_and_exit_filename=""):
"""
Evaluate current model using CoNLL script.
"""
n_tags = len(id_to_tag)
predictions = []
count = np.zeros((n_tags, n_tags), dtype=np.int32)
activation_values_dump_files = dict()
if predict_and_exit_filename:
activation_values_dump_files['char_lstm_for'] =\
open(os.path.join("./models/", get_name(parameters), "activation_values_char_lstm_for.dat"), "w")
for raw_sentence, parsed_sentence in zip(raw_sentences, parsed_sentences):
input = create_input(parsed_sentence, parameters, False)
eval_result_for_input = f_eval(*input)
if parameters['crf']:
# y_preds = np.array(eval_result_for_input[0])[1:-1]
y_preds = np.array(eval_result_for_input)[1:-1]
else:
y_preds = eval_result_for_input.argmax(axis=1)
y_reals = np.array(parsed_sentence['tags']).astype(np.int32)
assert len(y_preds) == len(y_reals)
with open("erratic-predictions.txt", "a") as f:
if np.any((y_preds >= n_tags) * (y_preds < 0)):
f.write(str(y_preds) + "\n")
y_preds[(y_preds >= n_tags) * (y_preds < 0)] = 0
p_tags = [id_to_tag[y_pred] for y_pred in y_preds]
r_tags = [id_to_tag[y_real] for y_real in y_reals]
if parameters['t_s'] == 'iobes':
p_tags = iobes_iob(p_tags)
r_tags = iobes_iob(r_tags)
for i, (y_pred, y_real) in enumerate(zip(y_preds, y_reals)):
new_line = " ".join(raw_sentence[i][:-1] + [r_tags[i], p_tags[i]])
predictions.append(new_line)
count[y_real, y_pred] += 1
predictions.append("")
# Write predictions to disk and run CoNLL script externally
eval_id = np.random.randint(1000000, 2000000)
output_path = os.path.join(eval_temp, "eval.%i.output" % eval_id)
scores_path = os.path.join(eval_temp, "eval.%i.scores" % eval_id)
with codecs.open(output_path, 'w', 'utf8') as f:
f.write("\n".join(predictions))
if predict_and_exit_filename:
with codecs.open(predict_and_exit_filename, 'w', 'utf8') as f:
f.write("\n".join(predictions))
os.system("%s < %s > %s" % (eval_script, output_path, predict_and_exit_filename+".scores"))
# CoNLL evaluation results
eval_lines = [l.rstrip() for l in codecs.open(predict_and_exit_filename+".scores", 'r', 'utf8')]
for line in eval_lines:
print line
else:
os.system("%s < %s > %s" % (eval_script, output_path, scores_path))
# CoNLL evaluation results
eval_lines = [l.rstrip() for l in codecs.open(scores_path, 'r', 'utf8')]
for line in eval_lines:
print line
# Remove temp files
# os.remove(output_path)
# os.remove(scores_path)
# Confusion matrix with accuracy for each tag
print ("{: >2}{: >15}{: >7}%s{: >9}" % ("{: >15}" * n_tags)).format(
"ID", "NE", "Total",
*([id_to_tag[i] for i in xrange(n_tags)] + ["Percent"])
)
for i in xrange(n_tags):
print ("{: >2}{: >15}{: >7}%s{: >9}" % ("{: >15}" * n_tags)).format(
str(i), id_to_tag[i], str(count[i].sum()),
*([count[i][j] for j in xrange(n_tags)] +
["%.3f" % (count[i][i] * 100. / max(1, count[i].sum()))])
)
# Global accuracy
print "%i/%i (%.5f%%)" % (
count.trace(), count.sum(), 100. * count.trace() / max(1, count.sum())
)
# F1 on all entities
return float(eval_lines[1].strip().split()[-1])
def read_args(evaluation=False, args_as_a_list=sys.argv[1:]):
optparser = optparse.OptionParser()
optparser.add_option(
"-T", "--train", default="",
help="Train set location"
)
optparser.add_option(
"-d", "--dev", default="",
help="Dev set location"
)
optparser.add_option(
"-t", "--test", default="",
help="Test set location"
)
optparser.add_option(
"--yuret_train", default="",
help="yuret train set location"
)
optparser.add_option(
"--yuret_test", default="",
help="yuret test set location"
)
optparser.add_option(
"--train_with_yuret", default=False, action="store_true",
help="train with yuret training set"
)
optparser.add_option(
"--test_with_yuret", default=False, action="store_true",
help="test with yuret training set"
)
optparser.add_option(
"--use_golden_morpho_analysis_in_word_representation", default=False, action="store_true",
help="use golden morpho analysis when representing words"
)
optparser.add_option(
"-s", "--tag_scheme", default="iobes",
help="Tagging scheme (IOB or IOBES)"
)
optparser.add_option(
"-l", "--lower", default="0",
type='int', help="Lowercase words (this will not affect character inputs)"
)
optparser.add_option(
"-z", "--zeros", default="0",
type='int', help="Replace digits with 0"
)
optparser.add_option(
"-c", "--char_dim", default="25",
type='int', help="Char embedding dimension"
)
optparser.add_option(
"-C", "--char_lstm_dim", default="25",
type='int', help="Char LSTM hidden layer size"
)
optparser.add_option(
"-b", "--char_bidirect", default="1",
type='int', help="Use a bidirectional LSTM for chars"
)
# morpho_tag section
optparser.add_option(
"--morpho_tag_dim", default="100",
type='int', help="Morpho tag embedding dimension"
)
optparser.add_option(
"--morpho_tag_lstm_dim", default="100",
type='int', help="Morpho tag LSTM hidden layer size"
)
optparser.add_option(
"--morpho_tag_bidirect", default="1",
type='int', help="Use a bidirectional LSTM for morpho tags"
)
optparser.add_option(
"--morpho_tag_type", default="char",
help="Mode of morphological tag extraction"
)
optparser.add_option(
"--morpho-tag-column-index", default="1",
type='int', help="the index of the column which contains the morphological tags in the conll format"
)
optparser.add_option(
"--integration_mode", default="0",
type='int', help="integration mode"
)
optparser.add_option(
"--active_models", default="0",
type='int', help="active models: 0: NER, 1: MD, 2: JOINT"
)
optparser.add_option(
"--multilayer", default="0",
type='int', help="use a multilayered sentence level Bi-LSTM"
)
optparser.add_option(
"--shortcut_connections", default="0",
type='int', help="use shortcut connections in the multilayered scheme"
)
optparser.add_option(
"--tying_method", default="",
help="tying method"
)
optparser.add_option(
"-w", "--word_dim", default="100",
type='int', help="Token embedding dimension"
)
optparser.add_option(
"-W", "--word_lstm_dim", default="100",
type='int', help="Token LSTM hidden layer size"
)
optparser.add_option(
"-B", "--word_bidirect", default="1",
type='int', help="Use a bidirectional LSTM for words"
)
optparser.add_option(
"-p", "--pre_emb", default="",
help="Location of pretrained embeddings"
)
optparser.add_option(
"-A", "--all_emb", default="0",
type='int', help="Load all embeddings"
)
optparser.add_option(
"-a", "--cap_dim", default="0",
type='int', help="Capitalization feature dimension (0 to disable)"
)
optparser.add_option(
"-f", "--crf", default="1",
type='int', help="Use CRF (0 to disable)"
)
optparser.add_option(
"-D", "--dropout", default="0.5",
type='float', help="Droupout on the input (0 = no dropout)"
)
optparser.add_option(
"-L", "--lr_method", default="sgd-lr_.005",
help="Learning method (SGD, Adadelta, Adam..)"
)
optparser.add_option(
"--disable_sparse_updates", default=True, action="store_false",
dest="sparse_updates_enabled",
help="Sparse updates enabled"
)
optparser.add_option(
"-r", "--reload", default="0",
type='int', help="Reload the last saved model"
)
optparser.add_option(
"--model_path", default="",
type='str', help="Model path must be given when a reload is requested"
)
optparser.add_option(
"--skip-testing", default="0",
type='int',
help="Skip the evaluation on test set (because dev and test sets are the same and thus testing is irrelevant)"
)
optparser.add_option(
"--predict-and-exit-filename", default="",
help="Used with '--reload 1', the loaded model is used for predicting on the test set and the results are written to the filename"
)
optparser.add_option(
"--overwrite-mappings", default="0",
type='int', help="Explicitly state to overwrite mappings"
)
optparser.add_option(
"--maximum-epochs", default="100",
type='int', help="Maximum number of epochs"
)
optparser.add_option(
"--batch-size", default="5",
type='int', help="Number of samples in one epoch"
)
optparser.add_option(
"--use-buckets", action="store_true", default=False,
help="whether to use buckets"
)
optparser.add_option(
"--dynet-gpu", default="1",
type='int', help="Use gpu or not"
)
if evaluation:
optparser.add_option(
"--run-for-all-checkpoints", default="0",
type='int', help="run evaluation for all checkpoints"
)
opts = optparser.parse_args(args_as_a_list)[0]
return opts
def form_parameters_dict(opts):
parameters = OrderedDict()
parameters['t_s'] = opts.tag_scheme
parameters['lower'] = opts.lower == 1
parameters['zeros'] = opts.zeros == 1
parameters['char_dim'] = opts.char_dim
parameters['char_lstm_dim'] = opts.char_lstm_dim
parameters['ch_b'] = opts.char_bidirect == 1
# morpho_tag section
parameters['mt_d'] = opts.morpho_tag_dim
parameters['mt_t'] = opts.morpho_tag_type
parameters['mt_ci'] = opts.morpho_tag_column_index
parameters['integration_mode'] = opts.integration_mode
parameters['active_models'] = opts.active_models
parameters['multilayer'] = opts.multilayer
parameters['shortcut_connections'] = opts.shortcut_connections
parameters['tying_method'] = opts.tying_method
parameters['train_with_yuret'] = opts.train_with_yuret
parameters['test_with_yuret'] = opts.test_with_yuret
parameters['use_golden_morpho_analysis_in_word_representation'] = opts.use_golden_morpho_analysis_in_word_representation
parameters['word_dim'] = opts.word_dim
parameters['word_lstm_dim'] = opts.word_lstm_dim
parameters['w_b'] = opts.word_bidirect == 1
parameters['pre_emb'] = opts.pre_emb
parameters['all_emb'] = opts.all_emb == 1
parameters['cap_dim'] = opts.cap_dim
parameters['crf'] = opts.crf == 1
parameters['dropout'] = opts.dropout
parameters['lr_method'] = opts.lr_method
parameters['sparse_updates_enabled'] = opts.sparse_updates_enabled
parameters['use_buckets'] = opts.use_buckets
return parameters