773 lines
36 KiB
Python
773 lines
36 KiB
Python
# -*- coding: utf-8 -*-
|
|
from __future__ import unicode_literals
|
|
# text in Western (Windows 1252)
|
|
|
|
import numpy as np
|
|
import h5py
|
|
import math
|
|
import keras.backend as K
|
|
import os.path
|
|
|
|
|
|
class Data:
|
|
def __init__(self, input_type, allow_shuffle_vector_generation=False, save_generated_data=True, shuffle_all_inputs=True,
|
|
additional_letter_attributes=True, reverse_inputs=True, accent_classification=False):
|
|
self._input_type = input_type
|
|
self._save_generated_data = save_generated_data
|
|
self._allow_shuffle_vector_generation = allow_shuffle_vector_generation
|
|
self._shuffle_all_inputs = shuffle_all_inputs
|
|
self._additional_letter_attributes = additional_letter_attributes
|
|
self._reverse_inputs = reverse_inputs
|
|
self._accent_classification = accent_classification
|
|
|
|
self.x_train = None
|
|
self.x_other_features_train = None
|
|
self.y_train = None
|
|
self.x_test = None
|
|
self.x_other_features_test = None
|
|
self.y_test = None
|
|
self.x_validate = None
|
|
self.x_other_features_validate = None
|
|
self.y_validate = None
|
|
|
|
def generate_data(self, train_inputs_name, test_inputs_name, validate_inputs_name, test_and_validation_size=0.1,
|
|
force_override=False, content_name='SlovarIJS_BESEDE_utf8.lex',
|
|
content_shuffle_vector='content_shuffle_vector', shuffle_vector='shuffle_vector',
|
|
inputs_location='../../internal_representations/inputs/', content_location='../../../data/'):
|
|
content_path = '{}{}'.format(content_location, content_name)
|
|
train_path = '{}{}.h5'.format(inputs_location, train_inputs_name)
|
|
test_path = '{}{}.h5'.format(inputs_location, test_inputs_name)
|
|
validate_path = '{}{}.h5'.format(inputs_location, validate_inputs_name)
|
|
if not force_override and os.path.exists(train_path) and os.path.exists(test_path) and os.path.exists(validate_path):
|
|
print('LOADING DATA...')
|
|
self.x_train, self.x_other_features_train, self.y_train = self._load_inputs(train_path)
|
|
self.x_test, self.x_other_features_test, self.y_test = self._load_inputs(test_path)
|
|
self.x_validate, self.x_other_features_validate, self.y_validate = self._load_inputs(validate_path)
|
|
print('LOAD SUCCESSFUL!')
|
|
else:
|
|
content_shuffle_vector_path = '{}{}.h5'.format(inputs_location, content_shuffle_vector)
|
|
shuffle_vector_path = '{}{}'.format(inputs_location, shuffle_vector)
|
|
|
|
# actual generation of inputs
|
|
self._generate_inputs(content_path, content_shuffle_vector_path, shuffle_vector_path, test_and_validation_size)
|
|
|
|
# save inputs
|
|
if self._save_generated_data:
|
|
self._save_inputs(train_path, self.x_train, self.x_other_features_train, self.y_train)
|
|
self._save_inputs(test_path, self.x_test, self.x_other_features_test, self.y_test)
|
|
self._save_inputs(validate_path, self.x_validate, self.x_other_features_validate, self.y_validate)
|
|
|
|
def _generate_inputs(self, content_location, content_shuffle_vector_location, shuffle_vector_location, test_and_validation_size):
|
|
print('READING CONTENT...')
|
|
content = self._read_content(content_location)
|
|
print('CONTENT READ SUCCESSFULLY')
|
|
print('CREATING DICTIONARY...')
|
|
dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
|
|
if self._input_type == 's' or self._input_type == 'sl':
|
|
dictionary = self._create_syllables_dictionary(content, vowels)
|
|
print('DICTIONARY CREATION SUCCESSFUL!')
|
|
# test_and_validation_size = 0.1
|
|
train_content, test_content, validate_content = self._split_content(content, test_and_validation_size, content_shuffle_vector_location)
|
|
feature_dictionary = self._create_feature_dictionary()
|
|
|
|
# Generate X and y
|
|
print('GENERATING X AND y...')
|
|
self.x_train, self.x_other_features_train, self.y_train = self._generate_x_and_y(dictionary, max_word, max_num_vowels, train_content, vowels,
|
|
accented_vowels,
|
|
feature_dictionary, shuffle_vector_location + '_train.h5')
|
|
self.x_test, self.x_other_features_test, self.y_test = self._generate_x_and_y(dictionary, max_word, max_num_vowels, test_content, vowels,
|
|
accented_vowels,
|
|
feature_dictionary, shuffle_vector_location + '_test.h5')
|
|
self.x_validate, self.x_other_features_validate, self.y_validate = self._generate_x_and_y(dictionary, max_word, max_num_vowels,
|
|
validate_content, vowels,
|
|
accented_vowels, feature_dictionary,
|
|
shuffle_vector_location + '_validate.h5')
|
|
print('GENERATION SUCCESSFUL!')
|
|
# return X_train, X_other_features_train, y_train, X_test, X_other_features_test, y_test, X_validate, X_other_features_validate, y_validate
|
|
|
|
# functions for creating X and y from content
|
|
@staticmethod
|
|
def _read_content(content_path):
|
|
with open(content_path) as f:
|
|
content = f.readlines()
|
|
return [x.split('\t') for x in content]
|
|
|
|
def _create_dict(self, content):
|
|
# CREATE dictionary AND max_word
|
|
accented_vowels = self._get_accented_vowels()
|
|
unaccented_vowels = self._get_unaccented_vowels()
|
|
vowels = []
|
|
vowels.extend(accented_vowels)
|
|
vowels.extend(unaccented_vowels)
|
|
|
|
dictionary_input = ['']
|
|
line = 0
|
|
max_word = 0
|
|
# ADD 'EMPTY' VOWEL
|
|
max_num_vowels = 0
|
|
for el in content:
|
|
num_vowels = 0
|
|
try:
|
|
if len(el[3]) > max_word:
|
|
max_word = len(el[3])
|
|
if len(el[0]) > max_word:
|
|
max_word = len(el[0])
|
|
for i in range(len(el[3])):
|
|
if self._is_vowel(list(el[3]), i, vowels):
|
|
num_vowels += 1
|
|
for c in list(el[0]):
|
|
if c not in dictionary_input:
|
|
dictionary_input.append(c)
|
|
if num_vowels > max_num_vowels:
|
|
max_num_vowels = num_vowels
|
|
except Exception:
|
|
print(line - 1)
|
|
print(el)
|
|
break
|
|
line += 1
|
|
dictionary_input = sorted(dictionary_input)
|
|
# max_num_vowels += 1
|
|
return dictionary_input, max_word, max_num_vowels, vowels, accented_vowels
|
|
|
|
# split content so that there is no overfitting
|
|
def _split_content(self, content, test_and_validation_ratio, content_shuffle_vector_location):
|
|
expanded_content = [el[1] if el[1] != '=' else el[0] for el in content]
|
|
# print(len(content))
|
|
unique_content = sorted(set(expanded_content))
|
|
|
|
s = self._load_shuffle_vector(content_shuffle_vector_location, len(unique_content))
|
|
|
|
test_num = math.floor(len(unique_content) * (test_and_validation_ratio * 2))
|
|
validation_num = math.floor(test_num * 0.5)
|
|
shuffled_unique_train_content = [unique_content[i] for i in range(len(s)) if s[i] >= test_num]
|
|
shuffled_unique_train_content_set = set(shuffled_unique_train_content)
|
|
|
|
shuffled_unique_test_content = [unique_content[i] for i in range(len(s)) if test_num > s[i] >= validation_num]
|
|
shuffled_unique_test_content_set = set(shuffled_unique_test_content)
|
|
|
|
shuffled_unique_validate_content = [unique_content[i] for i in range(len(s)) if s[i] < validation_num]
|
|
shuffled_unique_validate_content_set = set(shuffled_unique_validate_content)
|
|
|
|
train_content = [content[i] for i in range(len(content)) if expanded_content[i] in shuffled_unique_train_content_set]
|
|
test_content = [content[i] for i in range(len(content)) if expanded_content[i] in shuffled_unique_test_content_set]
|
|
validate_content = [content[i] for i in range(len(content)) if expanded_content[i] in shuffled_unique_validate_content_set]
|
|
return train_content, test_content, validate_content
|
|
|
|
@staticmethod
|
|
def _create_and_save_shuffle_vector(file_name, length):
|
|
shuffle_vector = np.arange(length)
|
|
np.random.shuffle(shuffle_vector)
|
|
h5f = h5py.File(file_name, 'w')
|
|
adict = dict(shuffle_vector=shuffle_vector)
|
|
for k, v in adict.items():
|
|
h5f.create_dataset(k, data=v)
|
|
h5f.close()
|
|
return shuffle_vector
|
|
|
|
def _x_letter_input(self, content, dictionary, max_word, vowels):
|
|
if self._additional_letter_attributes:
|
|
x = np.zeros((len(content), max_word, len(dictionary) + 6), dtype=int)
|
|
voiced_consonants = self._get_voiced_consonants()
|
|
resonant_silent_consonants = self._get_resonant_silent_consonants()
|
|
nonresonant_silent_consonants = self._get_nonresonant_silent_consonants()
|
|
# print('HERE!!!')
|
|
else:
|
|
# print('HERE!!!')
|
|
x = np.zeros((len(content), max_word, len(dictionary)), dtype=int)
|
|
|
|
i = 0
|
|
for el in content:
|
|
word = el[0]
|
|
if self._reverse_inputs:
|
|
word = word[::-1]
|
|
j = 0
|
|
for c in list(word):
|
|
index = 0
|
|
for d in dictionary:
|
|
if c == d:
|
|
x[i][j][index] = 1
|
|
break
|
|
index += 1
|
|
if self._additional_letter_attributes:
|
|
if self._is_vowel(word, j, vowels):
|
|
x[i][j][len(dictionary)] = 1
|
|
else:
|
|
x[i][j][len(dictionary) + 1] = 1
|
|
if c in voiced_consonants:
|
|
x[i][j][len(dictionary) + 2] = 1
|
|
else:
|
|
x[i][j][len(dictionary) + 3] = 1
|
|
if c in resonant_silent_consonants:
|
|
x[i][j][len(dictionary) + 4] = 1
|
|
elif c in nonresonant_silent_consonants:
|
|
x[i][j][len(dictionary) + 5] = 1
|
|
j += 1
|
|
i += 1
|
|
return x
|
|
|
|
def _x_syllable_input(self, content, dictionary, max_num_vowels, vowels):
|
|
x = np.zeros((len(content), max_num_vowels), dtype=int)
|
|
|
|
i = 0
|
|
for el in content:
|
|
j = 0
|
|
syllables = self._create_syllables(el[0], vowels)
|
|
if self._reverse_inputs:
|
|
syllables = syllables[::-1]
|
|
for syllable in syllables:
|
|
index = dictionary.index(syllable)
|
|
x[i][j] = index
|
|
j += 1
|
|
i += 1
|
|
return x
|
|
|
|
def _y_output(self, content, max_num_vowels, vowels, accentuated_vowels):
|
|
y = np.zeros((len(content), max_num_vowels))
|
|
i = 0
|
|
|
|
for el in content:
|
|
word = el[3]
|
|
if self._reverse_inputs:
|
|
word = word[::-1]
|
|
|
|
j = 0
|
|
# word_accentuations = []
|
|
num_vowels = 0
|
|
for c in list(word):
|
|
index = 0
|
|
for d in accentuated_vowels:
|
|
if c == d:
|
|
if not self._accent_classification:
|
|
y[i][num_vowels] = 1
|
|
else:
|
|
y[i][num_vowels] = index
|
|
# word_accentuations.append(num_vowels)
|
|
break
|
|
index += 1
|
|
if self._is_vowel(word, j, vowels):
|
|
num_vowels += 1
|
|
j += 1
|
|
i += 1
|
|
return y
|
|
|
|
# Generate each y as an array of 11 numbers (with possible values between 0 and 1)
|
|
def _generate_x_and_y(self, dictionary, max_word, max_num_vowels, content, vowels, accentuated_vowels, feature_dictionary,
|
|
shuffle_vector_location):
|
|
if self._input_type == 'l':
|
|
x = self._x_letter_input(content, dictionary, max_word, vowels)
|
|
elif self._input_type == 's' or self._input_type == 'sl':
|
|
x = self._x_syllable_input(content, dictionary, max_num_vowels, vowels)
|
|
else:
|
|
raise ValueError('No input_type provided. It could be \'l\', \'s\' or \'sl\'.')
|
|
y = self._y_output(content, max_num_vowels, vowels, accentuated_vowels)
|
|
|
|
print('CREATING OTHER FEATURES...')
|
|
x_other_features = self._create_x_features(content, feature_dictionary)
|
|
print('OTHER FEATURES CREATED!')
|
|
|
|
if self._shuffle_all_inputs:
|
|
print('SHUFFELING INPUTS...')
|
|
x, x_other_features, y = self._shuffle_inputs(x, x_other_features, y, shuffle_vector_location)
|
|
print('INPUTS SHUFFELED!')
|
|
return x, x_other_features, y
|
|
|
|
def _create_syllables_dictionary(self, content, vowels):
|
|
dictionary = []
|
|
for el in content:
|
|
syllables = self._create_syllables(el[0], vowels)
|
|
for syllable in syllables:
|
|
if syllable not in dictionary:
|
|
dictionary.append(syllable)
|
|
dictionary.append('')
|
|
return sorted(dictionary)
|
|
|
|
def _create_syllables(self, word, vowels):
|
|
word_list = list(word)
|
|
consonants = []
|
|
syllables = []
|
|
for i in range(len(word_list)):
|
|
if self._is_vowel(word_list, i, vowels):
|
|
if syllables == []:
|
|
consonants.append(word_list[i])
|
|
syllables.append(''.join(consonants))
|
|
else:
|
|
left_consonants, right_consonants = self._split_consonants(consonants)
|
|
syllables[-1] += ''.join(left_consonants)
|
|
right_consonants.append(word_list[i])
|
|
syllables.append(''.join(right_consonants))
|
|
consonants = []
|
|
else:
|
|
consonants.append(word_list[i])
|
|
if len(syllables) < 1:
|
|
return word
|
|
syllables[-1] += ''.join(consonants)
|
|
|
|
return syllables
|
|
|
|
def _is_vowel(self, word_list, position, vowels):
|
|
if word_list[position] in vowels:
|
|
return True
|
|
if (word_list[position] == u'r' or word_list[position] == u'R') and (position - 1 < 0 or word_list[position - 1] not in vowels) and (
|
|
position + 1 >= len(word_list) or word_list[position + 1] not in vowels):
|
|
return True
|
|
return False
|
|
|
|
def _split_consonants(self, consonants):
|
|
voiced_consonants = self._get_voiced_consonants()
|
|
resonant_silent_consonants = self._get_resonant_silent_consonants()
|
|
unresonant_silent_consonants = self._get_nonresonant_silent_consonants()
|
|
if len(consonants) == 0:
|
|
return [''], ['']
|
|
elif len(consonants) == 1:
|
|
return [''], consonants
|
|
else:
|
|
split_options = []
|
|
for i in range(len(consonants) - 1):
|
|
if consonants[i] == '-' or consonants[i] == '_':
|
|
split_options.append([i, -1])
|
|
elif consonants[i] == consonants[i + 1]:
|
|
split_options.append([i, 0])
|
|
elif consonants[i] in voiced_consonants:
|
|
if consonants[i + 1] in resonant_silent_consonants or consonants[i + 1] in unresonant_silent_consonants:
|
|
split_options.append([i, 2])
|
|
elif consonants[i] in resonant_silent_consonants:
|
|
if consonants[i + 1] in resonant_silent_consonants:
|
|
split_options.append([i, 1])
|
|
elif consonants[i + 1] in unresonant_silent_consonants:
|
|
split_options.append([i, 3])
|
|
elif consonants[i] in unresonant_silent_consonants:
|
|
if consonants[i + 1] in resonant_silent_consonants:
|
|
split_options.append([i, 4])
|
|
else:
|
|
print(consonants)
|
|
print('UNRECOGNIZED LETTERS!')
|
|
if split_options == []:
|
|
return [''], consonants
|
|
else:
|
|
split = min(split_options, key=lambda x: x[1])
|
|
return consonants[:split[0] + 1], consonants[split[0] + 1:]
|
|
|
|
def _create_x_features(self, content, feature_dictionary):
|
|
content = content
|
|
x_other_features = []
|
|
for el in content:
|
|
x_el_other_features = []
|
|
converted_el = ''.join(self._convert_to_multext_east_v4(list(el[2]), feature_dictionary))
|
|
for feature in feature_dictionary:
|
|
if converted_el[0] == feature[1]:
|
|
x_el_other_features.append(1)
|
|
for i in range(2, len(feature)):
|
|
for j in range(len(feature[i])):
|
|
if i - 1 < len(converted_el) and feature[i][j] == converted_el[i - 1]:
|
|
x_el_other_features.append(1)
|
|
else:
|
|
x_el_other_features.append(0)
|
|
else:
|
|
x_el_other_features.extend([0] * feature[0])
|
|
x_other_features.append(x_el_other_features)
|
|
return np.array(x_other_features)
|
|
|
|
def _shuffle_inputs(self, x, x_other_features, y, shuffle_vector_location):
|
|
s = self._load_shuffle_vector(shuffle_vector_location, x.shape[0])
|
|
x = x[s]
|
|
y = y[s]
|
|
x_other_features = x_other_features[s]
|
|
return x, x_other_features, y
|
|
|
|
# functions for saving, loading and shuffling whole arrays to ram
|
|
@staticmethod
|
|
def _save_inputs(file_name, x, x_other_features, y):
|
|
h5f = h5py.File(file_name, 'w')
|
|
a_dict = dict(X=x, X_other_features=x_other_features, y=y)
|
|
for k, v in a_dict.items():
|
|
h5f.create_dataset(k, data=v)
|
|
h5f.close()
|
|
|
|
@staticmethod
|
|
def _load_inputs(file_name):
|
|
h5f = h5py.File(file_name, 'r')
|
|
x = h5f['X'][:]
|
|
y = h5f['y'][:]
|
|
x_other_features = h5f['X_other_features'][:]
|
|
h5f.close()
|
|
return x, x_other_features, y
|
|
|
|
def _load_shuffle_vector(self, file_path, length=0):
|
|
if os.path.exists(file_path):
|
|
h5f = h5py.File(file_path, 'r')
|
|
shuffle_vector = h5f['shuffle_vector'][:]
|
|
h5f.close()
|
|
else:
|
|
if self._allow_shuffle_vector_generation:
|
|
shuffle_vector = self._create_and_save_shuffle_vector(file_path, length)
|
|
else:
|
|
raise ValueError('Shuffle vector on path: \'{}\' does not exist! Either generate new vector (with initializing new Data object with '
|
|
'parameter allow_shuffle_vector_generation=True or paste one that is already generated!'.format(file_path))
|
|
return shuffle_vector
|
|
|
|
@staticmethod
|
|
def _convert_to_multext_east_v4(old_features, feature_dictionary):
|
|
new_features = ['-'] * 9
|
|
new_features[:len(old_features)] = old_features
|
|
if old_features[0] == 'A':
|
|
if old_features[1] == 'f' or old_features[1] == 'o':
|
|
new_features[1] = 'g'
|
|
return new_features[:len(feature_dictionary[0]) - 1]
|
|
if old_features[0] == 'C':
|
|
return new_features[:len(feature_dictionary[1]) - 1]
|
|
if old_features[0] == 'I':
|
|
return new_features[:len(feature_dictionary[2]) - 1]
|
|
if old_features[0] == 'M':
|
|
new_features[2:6] = old_features[1:5]
|
|
new_features[1] = old_features[5]
|
|
if new_features[2] == 'm':
|
|
new_features[2] = '-'
|
|
return new_features[:len(feature_dictionary[3]) - 1]
|
|
if old_features[0] == 'N':
|
|
if len(old_features) >= 7:
|
|
new_features[5] = old_features[7]
|
|
return new_features[:len(feature_dictionary[4]) - 1]
|
|
if old_features[0] == 'P':
|
|
if new_features[8] == 'n':
|
|
new_features[8] = 'b'
|
|
return new_features[:len(feature_dictionary[5]) - 1]
|
|
if old_features[0] == 'Q':
|
|
return new_features[:len(feature_dictionary[6]) - 1]
|
|
if old_features[0] == 'R':
|
|
return new_features[:len(feature_dictionary[7]) - 1]
|
|
if old_features[0] == 'S':
|
|
if len(old_features) == 4:
|
|
new_features[1] = old_features[3]
|
|
else:
|
|
new_features[1] = '-'
|
|
return new_features[:len(feature_dictionary[8]) - 1]
|
|
if old_features[0] == 'V':
|
|
if old_features[1] == 'o' or old_features[1] == 'c':
|
|
new_features[1] = 'm'
|
|
new_features[3] = old_features[2]
|
|
new_features[2] = '-'
|
|
if old_features[2] == 'i':
|
|
new_features[3] = 'r'
|
|
if len(old_features) > 3 and old_features[3] == 'p':
|
|
new_features[3] = 'r'
|
|
elif len(old_features) > 3 and old_features[3] == 'f':
|
|
new_features[3] = 'f'
|
|
if len(old_features) >= 9:
|
|
new_features[7] = old_features[8]
|
|
else:
|
|
new_features[7] = '-'
|
|
return new_features[:len(feature_dictionary[9]) - 1]
|
|
return ''
|
|
|
|
# generator for inputs for tracking of data fitting
|
|
def generator(self, data_type, batch_size, x=None, x_other_features_validate=None, y_validate=None, content_name='SlovarIJS_BESEDE_utf8.lex',
|
|
content_location='../../../data/'):
|
|
content_path = '{}{}'.format(content_location, content_name)
|
|
if data_type == 'train':
|
|
return self._generator_instance(self.x_train, self.x_other_features_train, self.y_train, batch_size, content_path)
|
|
elif data_type == 'test':
|
|
return self._generator_instance(self.x_test, self.x_other_features_test, self.y_test, batch_size, content_path)
|
|
elif data_type == 'validate':
|
|
return self._generator_instance(self.x_validate, self.x_other_features_validate, self.y_validate, batch_size, content_path)
|
|
else:
|
|
return self._generator_instance(x, x_other_features_validate, y_validate, batch_size)
|
|
|
|
# if self._input_type
|
|
|
|
def _generator_instance(self, orig_x, orig_x_additional, orig_y, batch_size, content_path):
|
|
if self._input_type == 'l':
|
|
content = self._read_content(content_path)
|
|
dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
|
|
return self._letter_generator(orig_x, orig_x_additional, orig_y, batch_size, accented_vowels)
|
|
elif self._input_type == 's':
|
|
content = self._read_content(content_path)
|
|
dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
|
|
syllable_dictionary = self._create_syllables_dictionary(content, vowels)
|
|
eye = np.eye(len(syllable_dictionary), dtype=int)
|
|
return self._syllable_generator(orig_x, orig_x_additional, orig_y, batch_size, eye, accented_vowels)
|
|
elif self._input_type == 'sl':
|
|
content = self._read_content(content_path)
|
|
dictionary, max_word, max_num_vowels, vowels, accented_vowels = self._create_dict(content)
|
|
syllable_dictionary = self._create_syllables_dictionary(content, vowels)
|
|
max_syllable = self._get_max_syllable(syllable_dictionary)
|
|
syllable_letters_translator = self._create_syllable_letters_translator(max_syllable, syllable_dictionary, dictionary, vowels)
|
|
return self._syllable_generator(orig_x, orig_x_additional, orig_y, batch_size, syllable_letters_translator, accented_vowels)
|
|
|
|
# generator for inputs for tracking of data fitting
|
|
def _letter_generator(self, orig_x, orig_x_additional, orig_y, batch_size, accented_vowels):
|
|
size = orig_x.shape[0]
|
|
while 1:
|
|
loc = 0
|
|
if self._accent_classification:
|
|
eye = np.eye(len(accented_vowels), dtype=int)
|
|
eye_input_accent = np.eye(len(orig_y[0]), dtype=int)
|
|
input_x_stack = []
|
|
input_x_other_features_stack = []
|
|
input_y_stack = []
|
|
while loc < size:
|
|
while len(input_x_stack) < batch_size and loc < size:
|
|
accent_loc = 0
|
|
for accent in orig_y[loc]:
|
|
if accent > 0:
|
|
new_orig_x_additional = orig_x_additional[loc]
|
|
new_orig_x_additional = np.concatenate((new_orig_x_additional, eye_input_accent[accent_loc]))
|
|
input_x_stack.append(orig_x[loc])
|
|
input_x_other_features_stack.append(new_orig_x_additional)
|
|
input_y_stack.append(eye[int(accent)])
|
|
accent_loc += 1
|
|
loc += 1
|
|
if len(input_x_stack) > batch_size:
|
|
yield ([np.array(input_x_stack[:batch_size]),
|
|
np.array(input_x_other_features_stack[:batch_size])], np.array(input_y_stack)[:batch_size])
|
|
input_x_stack = input_x_stack[batch_size:]
|
|
input_x_other_features_stack = input_x_other_features_stack[batch_size:]
|
|
input_y_stack = input_y_stack[batch_size:]
|
|
else:
|
|
# print('BBB')
|
|
# print(np.array(input_stack))
|
|
# yield (np.array(input_stack))
|
|
yield ([np.array(input_x_stack), np.array(input_x_other_features_stack)], np.array(input_y_stack))
|
|
input_x_stack = []
|
|
input_x_other_features_stack = []
|
|
input_y_stack = []
|
|
else:
|
|
while loc < size:
|
|
if loc + batch_size >= size:
|
|
yield ([orig_x[loc:size], orig_x_additional[loc:size]], orig_y[loc:size])
|
|
else:
|
|
yield ([orig_x[loc:loc + batch_size], orig_x_additional[loc:loc + batch_size]], orig_y[loc:loc + batch_size])
|
|
loc += batch_size
|
|
|
|
# generator for inputs for tracking of data fitting
|
|
def _syllable_generator(self, orig_x, orig_x_additional, orig_y, batch_size, translator, accented_vowels):
|
|
size = orig_x.shape[0]
|
|
while 1:
|
|
loc = 0
|
|
if self._accent_classification:
|
|
eye = np.eye(len(accented_vowels), dtype=int)
|
|
eye_input_accent = np.eye(len(orig_y[0]), dtype=int)
|
|
input_x_stack = []
|
|
input_x_other_features_stack = []
|
|
input_y_stack = []
|
|
while loc < size:
|
|
while len(input_x_stack) < batch_size and loc < size:
|
|
accent_loc = 0
|
|
for accent in orig_y[loc]:
|
|
if accent > 0:
|
|
new_orig_x_additional = orig_x_additional[loc]
|
|
new_orig_x_additional = np.concatenate((new_orig_x_additional, eye_input_accent[accent_loc]))
|
|
input_x_stack.append(orig_x[loc])
|
|
input_x_other_features_stack.append(new_orig_x_additional)
|
|
input_y_stack.append(eye[int(accent)])
|
|
accent_loc += 1
|
|
loc += 1
|
|
if len(input_x_stack) > batch_size:
|
|
gen_orig_x = translator[np.array(input_x_stack[:batch_size])]
|
|
yield ([gen_orig_x, np.array(input_x_other_features_stack[:batch_size])], np.array(input_y_stack)[:batch_size])
|
|
input_x_stack = input_x_stack[batch_size:]
|
|
input_x_other_features_stack = input_x_other_features_stack[batch_size:]
|
|
input_y_stack = input_y_stack[batch_size:]
|
|
else:
|
|
gen_orig_x = translator[np.array(input_x_stack)]
|
|
yield ([gen_orig_x, np.array(input_x_other_features_stack)], np.array(input_y_stack))
|
|
input_x_stack = []
|
|
input_x_other_features_stack = []
|
|
input_y_stack = []
|
|
else:
|
|
while loc < size:
|
|
if loc + batch_size >= size:
|
|
gen_orig_x = translator[orig_x[loc:size]]
|
|
yield ([gen_orig_x, orig_x_additional[loc:size]], orig_y[loc:size])
|
|
else:
|
|
gen_orig_x = translator[orig_x[loc:loc + batch_size]]
|
|
yield ([gen_orig_x, orig_x_additional[loc:loc + batch_size]], orig_y[loc:loc + batch_size])
|
|
loc += batch_size
|
|
|
|
def _get_max_syllable(self, syllable_dictionary):
|
|
max_len = 0
|
|
for el in syllable_dictionary:
|
|
if len(el) > max_len:
|
|
max_len = len(el)
|
|
return max_len
|
|
|
|
def _create_syllable_letters_translator(self, max_syllable, syllable_dictionary, dictionary, vowels, aditional_letter_attributes=True):
|
|
if aditional_letter_attributes:
|
|
voiced_consonants = self._get_voiced_consonants()
|
|
resonant_silent_consonants = self._get_resonant_silent_consonants()
|
|
nonresonant_silent_consonants = self._get_nonresonant_silent_consonants()
|
|
|
|
syllable_letters_translator = []
|
|
for syllable in syllable_dictionary:
|
|
di_syllable = []
|
|
for let in range(max_syllable):
|
|
# di_let = []
|
|
for a in dictionary:
|
|
if let < len(syllable) and a == list(syllable)[let]:
|
|
di_syllable.append(1)
|
|
else:
|
|
di_syllable.append(0)
|
|
|
|
if aditional_letter_attributes:
|
|
if let >= len(syllable):
|
|
di_syllable.extend([0, 0, 0, 0, 0, 0])
|
|
elif self._is_vowel(list(syllable), let, vowels):
|
|
di_syllable.extend([1, 0, 0, 0, 0, 0])
|
|
else:
|
|
# X[i][j][len(dictionary) + 1] = 1
|
|
if list(syllable)[let] in voiced_consonants:
|
|
# X[i][j][len(dictionary) + 2] = 1
|
|
di_syllable.extend([0, 1, 1, 0, 0, 0])
|
|
else:
|
|
# X[i][j][len(dictionary) + 3] = 1
|
|
if list(syllable)[let] in resonant_silent_consonants:
|
|
# X[i][j][len(dictionary) + 4] = 1
|
|
di_syllable.extend([0, 1, 0, 1, 1, 0])
|
|
elif list(syllable)[let] in nonresonant_silent_consonants:
|
|
# X[i][j][len(dictionary) + 5] = 1
|
|
di_syllable.extend([0, 1, 0, 1, 0, 1])
|
|
else:
|
|
di_syllable.extend([0, 0, 0, 0, 0, 0])
|
|
# di_syllable.append(di_let)
|
|
syllable_letters_translator.append(di_syllable)
|
|
syllable_letters_translator = np.array(syllable_letters_translator, dtype=int)
|
|
return syllable_letters_translator
|
|
|
|
@staticmethod
|
|
def _get_accented_vowels():
|
|
return [u'à', u'á', u'ä', u'é', u'ë', u'ì', u'í', u'î', u'ó', u'ô', u'ö', u'ú', u'ü']
|
|
|
|
@staticmethod
|
|
def _get_unaccented_vowels():
|
|
return [u'a', u'e', u'i', u'o', u'u']
|
|
|
|
@staticmethod
|
|
def _get_voiced_consonants():
|
|
return ['m', 'n', 'v', 'l', 'r', 'j', 'y', 'w']
|
|
|
|
@staticmethod
|
|
def _get_resonant_silent_consonants():
|
|
return ['b', 'd', 'z', 'ž', 'g']
|
|
|
|
@staticmethod
|
|
def _get_nonresonant_silent_consonants():
|
|
return ['p', 't', 's', 'š', 'č', 'k', 'f', 'h', 'c']
|
|
|
|
@staticmethod
|
|
def _create_feature_dictionary():
|
|
# old: http://nl.ijs.si/ME/Vault/V3/msd/html/
|
|
# new: http://nl.ijs.si/ME/V4/msd/html/
|
|
# changes: http://nl.ijs.si/jos/msd/html-en/msd.diffs.html
|
|
return [[21,
|
|
'A',
|
|
['g', 's'],
|
|
['p', 'c', 's'],
|
|
['m', 'f', 'n'],
|
|
['s', 'd', 'p'],
|
|
['n', 'g', 'd', 'a', 'l', 'i'],
|
|
['-', 'n', 'y']],
|
|
[3, 'C', ['c', 's']],
|
|
[1, 'I'],
|
|
[21,
|
|
'M',
|
|
['l'],
|
|
['-', 'c', 'o', 's'],
|
|
['m', 'f', 'n'],
|
|
['s', 'd', 'p'],
|
|
['n', 'g', 'd', 'a', 'l', 'i'],
|
|
['-', 'n', 'y']],
|
|
[17,
|
|
'N',
|
|
['c'],
|
|
['m', 'f', 'n'],
|
|
['s', 'd', 'p'],
|
|
['n', 'g', 'd', 'a', 'l', 'i'],
|
|
['-', 'n', 'y']],
|
|
[40,
|
|
'P',
|
|
['p', 's', 'd', 'r', 'x', 'g', 'q', 'i', 'z'],
|
|
['-', '1', '2', '3'],
|
|
['-', 'm', 'f', 'n'],
|
|
['-', 's', 'd', 'p'],
|
|
['-', 'n', 'g', 'd', 'a', 'l', 'i'],
|
|
['-', 's', 'd', 'p'],
|
|
['-', 'm', 'f', 'n'],
|
|
['-', 'y', 'b']],
|
|
[1, 'Q'],
|
|
[5, 'R', ['g'], ['p', 'c', 's']],
|
|
[7, 'S', ['-', 'g', 'd', 'a', 'l', 'i']],
|
|
[24,
|
|
'V',
|
|
['m'],
|
|
['-'],
|
|
['n', 'u', 'p', 'r', 'f', 'c'],
|
|
['-', '1', '2', '3'],
|
|
['-', 's', 'p', 'd'],
|
|
['-', 'm', 'f', 'n'],
|
|
['-', 'n', 'y']]
|
|
]
|
|
|
|
# Decoders for inputs and outputs
|
|
@staticmethod
|
|
def decode_x(word_encoded, dictionary):
|
|
word = ''
|
|
for el in word_encoded:
|
|
i = 0
|
|
for num in el:
|
|
if num == 1:
|
|
word += dictionary[i]
|
|
break
|
|
i += 1
|
|
return word
|
|
|
|
@staticmethod
|
|
def decode_x_other_features(feature_dictionary, x_other_features):
|
|
final_word = []
|
|
for word in x_other_features:
|
|
final_word = []
|
|
i = 0
|
|
for z in range(len(feature_dictionary)):
|
|
for j in range(1, len(feature_dictionary[z])):
|
|
if j == 1:
|
|
if word[i] == 1:
|
|
final_word.append(feature_dictionary[z][1])
|
|
i += 1
|
|
else:
|
|
for k in range(len(feature_dictionary[z][j])):
|
|
if word[i] == 1:
|
|
final_word.append(feature_dictionary[z][j][k])
|
|
i += 1
|
|
print(u''.join(final_word))
|
|
return u''.join(final_word)
|
|
|
|
@staticmethod
|
|
def decode_y(y):
|
|
i = 0
|
|
res = []
|
|
for el in y:
|
|
if el >= 0.5:
|
|
res.append(i)
|
|
i += 1
|
|
return res
|
|
|
|
|
|
|
|
# def count_vowels(content, vowels):
|
|
# num_all_vowels = 0
|
|
# for el in content:
|
|
# for m in range(len(el[0])):
|
|
# if is_vowel(list(el[0]), m, vowels):
|
|
# num_all_vowels += 1
|
|
# return num_all_vowels
|
|
|
|
|
|
|
|
# metric for calculation of correct results
|
|
# test with:
|
|
# print(mean_pred(y_validate[pos], predictions[pos]).eval())
|
|
# print(mean_pred(np.array([[ 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0.],
|
|
# [ 0., 1., 0., 1., 0., 0., 0., 0., 0., 0., 0.]]),
|
|
# np.array([[ 0., 0.51, 0., 0.51, 0., 0., 0., 0., 0., 0., 0.],
|
|
# [ 0., 0.92, 0., 0.51, 0., 0., 0., 0., 0., 0., 0.]])).eval())
|
|
def actual_accuracy(y_true, y_pred):
|
|
return K.mean(K.equal(K.mean(K.equal(K.round(y_true), K.round(y_pred)), axis=-1), 1.0))
|