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utils.py
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utils.py
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import os
import re
import numpy as np
models_path = "./models"
eval_path = "./evaluation"
eval_temp = os.path.join(eval_path, "temp")
eval_script = os.path.join(eval_path, "conlleval")
def get_name(parameters):
"""
Generate a model name from its parameters.
"""
l = []
for k, v in list(parameters.items()):
if type(v) is str 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 set_values(name, param, pretrained):
"""
Initialize a network parameter with pretrained values.
We check that sizes are compatible.
"""
param_value = param.get_value()
if pretrained.size != param_value.size:
raise Exception(
"Size mismatch for parameter %s. Expected %i, found %i."
% (name, param_value.size, pretrained.size)
)
param.set_value(np.reshape(
pretrained, param_value.shape
).astype(np.float32))
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(list(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 list(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['char_bidirect']:
input.append(char_rev)
input.append(char_pos)
if parameters['cap_dim']:
input.append(caps)
if add_label:
input.append(data['tags'])
return input
import torch.nn as nn
from torch.nn import init
def init_embedding(input_embedding):
"""
Initialize embedding
"""
bias = np.sqrt(3.0 / input_embedding.size(1))
nn.init.uniform_(input_embedding, -bias, bias)
def init_linear(input_linear):
"""
Initialize linear transformation
"""
bias = np.sqrt(6.0 / (input_linear.weight.size(0) + input_linear.weight.size(1)))
nn.init.uniform_(input_linear.weight, -bias, bias)
if input_linear.bias is not None:
input_linear.bias.data.zero_()
def adjust_learning_rate(optimizer, lr):
"""
shrink learning rate for pytorch
"""
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def init_lstm(input_lstm):
"""
Initialize lstm
"""
for ind in range(0, input_lstm.num_layers):
weight = eval('input_lstm.weight_ih_l' + str(ind))
bias = np.sqrt(6.0 / (weight.size(0) / 4 + weight.size(1)))
nn.init.uniform_(weight, -bias, bias)
weight = eval('input_lstm.weight_hh_l' + str(ind))
bias = np.sqrt(6.0 / (weight.size(0) / 4 + weight.size(1)))
nn.init.uniform_(weight, -bias, bias)
if input_lstm.bidirectional:
for ind in range(0, input_lstm.num_layers):
weight = eval('input_lstm.weight_ih_l' + str(ind) + '_reverse')
bias = np.sqrt(6.0 / (weight.size(0) / 4 + weight.size(1)))
nn.init.uniform_(weight, -bias, bias)
weight = eval('input_lstm.weight_hh_l' + str(ind) + '_reverse')
bias = np.sqrt(6.0 / (weight.size(0) / 4 + weight.size(1)))
nn.init.uniform_(weight, -bias, bias)
if input_lstm.bias:
for ind in range(0, input_lstm.num_layers):
weight = eval('input_lstm.bias_ih_l' + str(ind))
weight.data.zero_()
weight.data[input_lstm.hidden_size: 2 * input_lstm.hidden_size] = 1
weight = eval('input_lstm.bias_hh_l' + str(ind))
weight.data.zero_()
weight.data[input_lstm.hidden_size: 2 * input_lstm.hidden_size] = 1
if input_lstm.bidirectional:
for ind in range(0, input_lstm.num_layers):
weight = eval('input_lstm.bias_ih_l' + str(ind) + '_reverse')
weight.data.zero_()
weight.data[input_lstm.hidden_size: 2 * input_lstm.hidden_size] = 1
weight = eval('input_lstm.bias_hh_l' + str(ind) + '_reverse')
weight.data.zero_()
weight.data[input_lstm.hidden_size: 2 * input_lstm.hidden_size] = 1