685 lines
23 KiB
Python
685 lines
23 KiB
Python
# -*- coding: utf-8 -*-
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from __future__ import unicode_literals
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# text in Western (Windows 1252)
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import numpy as np
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import h5py
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import gc
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import math
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# functions for saving, loading and shuffling whole arrays to ram
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def save_inputs(file_name, X, y, other_features=[]):
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h5f = h5py.File(file_name, 'w')
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if other_features == []:
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adict = dict(X=X, y=y)
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else:
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adict = dict(X=X, X_other_features=other_features, y=y)
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for k, v in adict.items():
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h5f.create_dataset(k, data=v)
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h5f.close()
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def load_inputs(file_name, other_features=False):
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h5f = h5py.File(file_name,'r')
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X = h5f['X'][:]
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y = h5f['y'][:]
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if other_features:
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X_other_features = h5f['X_other_features'][:]
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h5f.close()
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return X, X_other_features, y
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h5f.close()
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return X, y
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def shuffle_inputs(X, y, X_pure=[]):
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s = np.arange(X.shape[0])
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np.random.shuffle(s)
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X = X[s]
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y = y[s]
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if X_pure != []:
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X_pure = X_pure[s]
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return X, y, X_pure
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else:
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return X, y
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# functions for saving and loading partial arrays to ram
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def create_and_save_inputs(file_name, part, X, y, X_pure):
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# X, y, X_pure = generate_full_vowel_matrix_inputs()
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h5f = h5py.File(file_name + part + '.h5', 'w')
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adict=dict(X=X, y=y, X_pure=X_pure)
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for k, v in adict.items():
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h5f.create_dataset(k,data=v)
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h5f.close()
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def load_extended_inputs(file_name, obtain_range):
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h5f = h5py.File(file_name,'r')
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X = h5f['X'][obtain_range[0]:obtain_range[1]]
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y = h5f['y'][obtain_range[0]:obtain_range[1]]
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X_pure = h5f['X_pure'][obtain_range[0]:obtain_range[1]]
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h5f.close()
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return X, y, X_pure
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# functions for creating and loading shuffle vector
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def create_and_save_shuffle_vector(file_name, shuffle_vector):
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# X, y, X_pure = generate_full_vowel_matrix_inputs()
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h5f = h5py.File(file_name + '_shuffle_vector.h5', 'w')
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adict=dict(shuffle_vector=shuffle_vector)
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for k, v in adict.items():
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h5f.create_dataset(k,data=v)
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h5f.close()
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def load_shuffle_vector(file_name):
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h5f = h5py.File(file_name,'r')
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shuffle_vector = h5f['shuffle_vector'][[179859, 385513, 893430]]
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h5f.close()
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return shuffle_vector
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# functions for saving and loading model - ONLY WHERE KERAS IS NOT NEEDED
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# def save_model(model, file_name):
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# h5f = h5py.File(file_name, 'w')
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# adict = dict(W1=model['W1'], b1=model['b1'], W2=model['W2'], b2=model['b2'])
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# for k,v in adict.items():
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# h5f.create_dataset(k,data=v)
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#
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# h5f.close()
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#
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#
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# def load_model(file_name):
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# h5f = h5py.File(file_name,'r')
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# model = {}
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# W1.set_value(h5f['W1'][:])
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# b1.set_value(h5f['b1'][:])
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# W2.set_value(h5f['W2'][:])
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# b2.set_value(h5f['b2'][:])
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# h5f.close()
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# return model
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# functions for creating X and y from content
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def read_content():
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print('READING CONTENT...')
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with open('../../../data/SlovarIJS_BESEDE_utf8.lex') as f:
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content = f.readlines()
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print('CONTENT READ SUCCESSFULY')
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return [x.split('\t') for x in content]
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def is_vowel(word_list, position, vowels):
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if word_list[position] in vowels:
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return True
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if 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):
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return True
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return False
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def is_accetuated_vowel(word_list, position, accetuated_vowels):
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if word_list[position] in accetuated_vowels:
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return True
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return False
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def create_dict():
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content = read_content()
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print('CREATING DICTIONARY...')
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# CREATE dictionary AND max_word
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accetuated_vowels = [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']
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default_vowels = [u'a', u'e', u'i', u'o', u'u']
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vowels = []
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vowels.extend(accetuated_vowels)
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vowels.extend(default_vowels)
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dictionary = ['']
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line = 0
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max_word = 0
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# ADD 'EMPTY' VOWEL
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max_num_vowels = 0
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for el in content:
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num_vowels = 0
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i = 0
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try:
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if len(el[3]) > max_word:
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max_word = len(el[3])
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if len(el[0]) > max_word:
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max_word = len(el[0])
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for c in list(el[3]):
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if is_vowel(list(el[3]), i, vowels):
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num_vowels += 1
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if c not in dictionary:
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dictionary.append(c)
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i += 1
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for c in list(el[0]):
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if c not in dictionary:
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dictionary.append(c)
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if num_vowels > max_num_vowels:
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max_num_vowels = num_vowels
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except Exception:
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print(line - 1)
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print(el)
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break
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line += 1
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dictionary = sorted(dictionary)
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max_num_vowels += 1
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print('DICTIONARY CREATION SUCCESSFUL!')
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return dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels
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# GENERATE X and y
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def generate_presentable_y(accetuations_list, word_list, max_num_vowels):
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while len(accetuations_list) < 2:
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accetuations_list.append(0)
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if len(accetuations_list) > 2:
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accetuations_list = accetuations_list[:2]
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accetuations_list = np.array(accetuations_list)
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final_position = accetuations_list[0] + max_num_vowels * accetuations_list[1]
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return final_position
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# def generate_inputs():
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# dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels = create_dict()
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#
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# print('GENERATING X AND y...')
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# X = np.zeros((len(content), max_word*len(dictionary)))
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# y = np.zeros((len(content), max_num_vowels * max_num_vowels ))
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#
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# i = 0
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# for el in content:
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# j = 0
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# for c in list(el[0]):
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# index = 0
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# for d in dictionary:
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# if c == d:
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# X[i][index + j * max_word] = 1
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# break
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# index += 1
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# j += 1
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# j = 0
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# word_accetuations = []
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# num_vowels = 0
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# for c in list(el[3]):
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# index = 0
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# if is_vowel(el[3], j, vowels):
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# num_vowels += 1
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# for d in accetuated_vowels:
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# if c == d:
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# word_accetuations.append(num_vowels)
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# break
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# index += 1
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# j += 1
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# y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
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# i += 1
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# print('GENERATION SUCCESSFUL!')
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# print('SHUFFELING INPUTS...')
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# X, y = shuffle_inputs(X, y)
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# print('INPUTS SHUFFELED!')
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# return X, y
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#
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#
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# def generate_matrix_inputs():
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# dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels = create_dict()
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#
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# print('GENERATING X AND y...')
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# # X = np.zeros((len(content), max_word*len(dictionary)))
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# y = np.zeros((len(content), max_num_vowels * max_num_vowels ))
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#
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# X = []
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#
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# i = 0
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# for el in content:
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# # j = 0
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# word = []
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# for c in list(el[0]):
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# index = 0
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# character = np.zeros(len(dictionary))
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# for d in dictionary:
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# if c == d:
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# # X[i][index + j * max_word] = 1
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# character[index] = 1
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# break
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# index += 1
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# word.append(character)
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# # j += 1
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# j = 0
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# X.append(word)
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# word_accetuations = []
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# num_vowels = 0
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# for c in list(el[3]):
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# index = 0
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# if is_vowel(el[3], j, vowels):
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# num_vowels += 1
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# for d in accetuated_vowels:
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# if c == d:
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# word_accetuations.append(num_vowels)
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# break
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# index += 1
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# j += 1
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# y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
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# i += 1
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# X = np.array(X)
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# print('GENERATION SUCCESSFUL!')
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# print('SHUFFELING INPUTS...')
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# X, y = shuffle_inputs(X, y)
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# print('INPUTS SHUFFELED!')
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# return X, y
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def generate_full_matrix_inputs():
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dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels = create_dict()
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train_content, validate_content = split_content(content, 0.2)
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feature_dictionary = create_feature_dictionary(content)
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# Generate X and y
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print('GENERATING X AND y...')
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X_train, X_other_features_train, y_train = generate_X_and_y(dictionary, max_word, max_num_vowels, train_content, vowels, accetuated_vowels, feature_dictionary)
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X_validate, X_other_features_validate, y_validate = generate_X_and_y(dictionary, max_word, max_num_vowels, validate_content, vowels, accetuated_vowels, feature_dictionary)
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print('GENERATION SUCCESSFUL!')
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return X_train, X_other_features_train, y_train, X_validate, X_other_features_validate, y_validate
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# Generate each y as an array of 11 numbers (with possible values between 0 and 1)
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def generate_X_and_y(dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels, feature_dictionary):
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y = np.zeros((len(content), max_num_vowels))
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X = np.zeros((len(content), max_word, len(dictionary)))
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print('CREATING OTHER FEATURES...')
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X_other_features = create_X_features(content, feature_dictionary)
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print('OTHER FEATURES CREATED!')
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i = 0
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for el in content:
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j = 0
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for c in list(el[0]):
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index = 0
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for d in dictionary:
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if c == d:
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X[i][j][index] = 1
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break
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index += 1
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j += 1
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j = 0
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word_accetuations = []
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num_vowels = 0
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for c in list(el[3]):
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index = 0
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if is_vowel(el[3], j, vowels):
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num_vowels += 1
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for d in accetuated_vowels:
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if c == d:
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word_accetuations.append(num_vowels)
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break
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index += 1
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j += 1
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if len(word_accetuations) > 0:
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y_value = 1/len(word_accetuations)
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for el in word_accetuations:
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y[i][el] = y_value
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else:
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y[i][0] = 1
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# y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
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i += 1
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print('SHUFFELING INPUTS...')
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X, y, X_other_features = shuffle_inputs(X, y, X_pure=X_other_features)
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print('INPUTS SHUFFELED!')
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return X, X_other_features, y
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# Generate each y as an array of 121 numbers (with one 1 per line and the rest zeros)
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def generate_X_and_y_one_classification(dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels, feature_dictionary):
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y = np.zeros((len(content), max_num_vowels * max_num_vowels ))
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X = np.zeros((len(content), max_word, len(dictionary)))
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print('CREATING OTHER FEATURES...')
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X_other_features = create_X_features(content, feature_dictionary)
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print('OTHER FEATURES CREATED!')
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i = 0
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for el in content:
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j = 0
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for c in list(el[0]):
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index = 0
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for d in dictionary:
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if c == d:
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X[i][j][index] = 1
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break
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index += 1
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j += 1
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j = 0
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word_accetuations = []
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num_vowels = 0
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for c in list(el[3]):
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index = 0
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if is_vowel(el[3], j, vowels):
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num_vowels += 1
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for d in accetuated_vowels:
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if c == d:
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word_accetuations.append(num_vowels)
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break
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index += 1
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j += 1
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y[i][generate_presentable_y(word_accetuations, list(el[3]), max_num_vowels)] = 1
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i += 1
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print('SHUFFELING INPUTS...')
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X, y, X_other_features = shuffle_inputs(X, y, X_pure=X_other_features)
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print('INPUTS SHUFFELED!')
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return X, X_other_features, y
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def count_vowels(content, vowels):
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num_all_vowels = 0
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for el in content:
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for m in range(len(el[0])):
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if is_vowel(list(el[0]), m, vowels):
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num_all_vowels += 1
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return num_all_vowels
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# Data generation for generator inputs
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def generate_X_and_y_RAM_efficient(name, split_number):
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h5f = h5py.File(name + '.h5', 'w')
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dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels = create_dict()
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num_all_vowels = count_vowels(content, vowels)
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data_X = h5f.create_dataset('X', (num_all_vowels, max_word, len(dictionary)),
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maxshape=(num_all_vowels, max_word, len(dictionary)),
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dtype=np.uint8)
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data_y = h5f.create_dataset('y', (num_all_vowels,),
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maxshape=(num_all_vowels,),
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dtype=np.uint8)
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data_X_pure = h5f.create_dataset('X_pure', (num_all_vowels,),
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maxshape=(num_all_vowels,),
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dtype=np.uint8)
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gc.collect()
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print('GENERATING X AND y...')
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X_pure = []
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X = []
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y = []
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part_len = len(content)/float(split_number)
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current_part_generation = 1
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i = 0
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num_all_vowels = 0
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old_num_all_vowels = 0
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for el in content:
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j = 0
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X_el = np.zeros((max_word, len(dictionary)))
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for c in list(el[0]):
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index = 0
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for d in dictionary:
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if c == d:
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X_el[j][index] = 1
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break
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index += 1
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j += 1
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vowel_i = 0
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for m in range(len(el[0])):
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if is_vowel(list(el[0]), m, vowels):
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X.append(X_el)
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X_pure.append(vowel_i)
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vowel_i += 1
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if is_accetuated_vowel(list(el[3]), m, accetuated_vowels):
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y.append(1)
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else:
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y.append(0)
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if current_part_generation * part_len <= i:
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print('Saving part '+ str(current_part_generation))
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data_X[old_num_all_vowels:num_all_vowels + 1] = np.array(X)
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data_y[old_num_all_vowels:num_all_vowels + 1] = np.array(y)
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data_X_pure[old_num_all_vowels:num_all_vowels + 1] = np.array(X_pure)
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old_num_all_vowels = num_all_vowels + 1
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X_pure = []
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X = []
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y = []
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current_part_generation += 1
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num_all_vowels += 1
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if i%10000 == 0:
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print(i)
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i += 1
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print('Saving part ' + str(current_part_generation))
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data_X[old_num_all_vowels:num_all_vowels] = np.array(X)
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data_y[old_num_all_vowels:num_all_vowels] = np.array(y)
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data_X_pure[old_num_all_vowels:num_all_vowels] = np.array(X_pure)
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h5f.close()
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# generator for inputs
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def generate_arrays_from_file(path, batch_size):
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h5f = h5py.File(path, 'r')
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X = h5f['X'][:]
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y = h5f['y'][:]
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X_pure = h5f['X_pure'][:]
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yield (X, y, X_pure)
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# while 1:
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# f = open(path)
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# for line in f:
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# # create Numpy arrays of input data
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# # and labels, from each line in the file
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# x, y = process_line(line)
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# yield (x, y)
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# # f.close()
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h5f.close()
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# shuffle inputs for generator
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def shuffle_full_vowel_inputs(name, orderd_name, parts):
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dictionary, max_word, max_num_vowels, content, vowels, accetuated_vowels = create_dict()
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num_all_vowels = count_vowels(content, vowels)
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# num_all_vowels = 12
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s = np.arange(num_all_vowels)
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np.random.shuffle(s)
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h5f = h5py.File(name, 'w')
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data_X = h5f.create_dataset('X', (num_all_vowels, max_word, len(dictionary)),
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maxshape=(num_all_vowels, max_word, len(dictionary)),
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dtype=np.uint8)
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data_y = h5f.create_dataset('y', (num_all_vowels,),
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maxshape=(num_all_vowels,),
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dtype=np.uint8)
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data_X_pure = h5f.create_dataset('X_pure', (num_all_vowels,),
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maxshape=(num_all_vowels,),
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dtype=np.uint8)
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gc.collect()
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print('Shuffled vector loaded!')
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section_range = [0, (num_all_vowels + 1)/parts]
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for h in range(1, parts+1):
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gc.collect()
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new_X = np.zeros((section_range[1] - section_range[0], max_word, len(dictionary)))
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new_X_pure = np.zeros(section_range[1] - section_range[0])
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new_y = np.zeros(section_range[1] - section_range[0])
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targeted_range = [0, (num_all_vowels + 1)/parts]
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for i in range(1, parts+1):
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X, y, X_pure = load_extended_inputs(orderd_name, targeted_range)
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for j in range(X.shape[0]):
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if s[j + targeted_range[0]] >= section_range[0] and s[j + targeted_range[0]] < section_range[1]:
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# print 's[j] ' + str(s[j + targeted_range[0]]) + ' section_range[0] ' + str(section_range[0]) + ' section_range[1] ' + str(section_range[1])
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new_X[s[j + targeted_range[0]] - section_range[0]] = X[j]
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new_y[s[j + targeted_range[0]] - section_range[0]] = y[j]
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new_X_pure[s[j + targeted_range[0]] - section_range[0]] = X_pure[j]
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targeted_range[0] = targeted_range[1]
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if targeted_range[1] + (num_all_vowels + 1) / parts < num_all_vowels:
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targeted_range[1] += (num_all_vowels + 1) / parts
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else:
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targeted_range[1] = num_all_vowels
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del X, y, X_pure
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print('CREATED ' + str(h) + '. PART OF SHUFFLED MATRIX')
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data_X[section_range[0]:section_range[1]] = new_X
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data_y[section_range[0]:section_range[1]] = new_y
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data_X_pure[section_range[0]:section_range[1]] = new_X_pure
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section_range[0] = section_range[1]
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if section_range[1] + (num_all_vowels + 1)/parts < num_all_vowels:
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section_range[1] += (num_all_vowels + 1)/parts
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else:
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section_range[1] = num_all_vowels
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del new_X, new_X_pure, new_y
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h5f.close()
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# Decoders for inputs and outputs
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def decode_X_features(feature_dictionary, X_other_features):
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for word in X_other_features:
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final_word = []
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i = 0
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for z in range(len(feature_dictionary)):
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for j in range(1, len(feature_dictionary[z])):
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if j == 1:
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if word[i] == 1:
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final_word.append(feature_dictionary[z][1])
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i += 1
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else:
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for k in range(len(feature_dictionary[z][j])):
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if word[i] == 1:
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final_word.append(feature_dictionary[z][j][k])
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i += 1
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print(u''.join(final_word))
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def decode_position(y, max_num_vowels):
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max_el = 0
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i = 0
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pos = -1
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for el in y:
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if el > max_el:
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max_el = el
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pos = i
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i += 1
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return [pos % max_num_vowels, pos / max_num_vowels]
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|
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def decode_input(word_encoded, dictionary):
|
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word = ''
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for el in word_encoded:
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i = 0
|
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for num in el:
|
|
if num == 1:
|
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word += dictionary[i]
|
|
break
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|
i += 1
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return word
|
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|
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|
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def decode_position_from_number(y, max_num_vowels):
|
|
return [y % max_num_vowels, y / max_num_vowels]
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|
|
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|
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def generate_input_from_word(word, max_word, dictionary):
|
|
x = np.zeros(max_word*len(dictionary))
|
|
j = 0
|
|
for c in list(word):
|
|
index = 0
|
|
for d in dictionary:
|
|
if c == d:
|
|
x[index + j * max_word] = 1
|
|
break
|
|
index += 1
|
|
j += 1
|
|
return x
|
|
|
|
|
|
def generate_input_per_vowel_from_word(word, max_word, dictionary, vowels):
|
|
X_el = np.zeros((max_word, len(dictionary)))
|
|
j = 0
|
|
for c in list(word):
|
|
index = 0
|
|
for d in dictionary:
|
|
if c == d:
|
|
X_el[j][index] = 1
|
|
break
|
|
index += 1
|
|
j += 1
|
|
|
|
X = []
|
|
X_pure = []
|
|
vowel_i = 0
|
|
for i in range(len(word)):
|
|
if is_vowel(list(word), i, vowels):
|
|
X.append(X_el)
|
|
X_pure.append(vowel_i)
|
|
vowel_i += 1
|
|
return np.array(X), np.array(X_pure)
|
|
|
|
|
|
def decode_position_from_vowel_to_final_number(y):
|
|
res = []
|
|
for i in range(len(y)):
|
|
if y[i][0] > 0.5:
|
|
res.append(i + 1)
|
|
return res
|
|
|
|
|
|
# split content so that there is no overfitting
|
|
def split_content(content, ratio):
|
|
expanded_content = [el[1] if el[1] != '=' else el[0] for el in content]
|
|
# print(len(content))
|
|
unique_content = sorted(set(expanded_content))
|
|
|
|
s = np.arange(len(unique_content))
|
|
np.random.shuffle(s)
|
|
|
|
split_num = math.floor(len(unique_content) * ratio)
|
|
shuffled_unique_train_content = [unique_content[i] for i in range(len(s)) if s[i] >= split_num]
|
|
|
|
shuffled_unique_train_content_set = set(shuffled_unique_train_content)
|
|
shuffled_unique_validate_content = [unique_content[i] for i in range(len(s)) if s[i] < split_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]
|
|
validate_content = [content[i] for i in range(len(content)) if expanded_content[i] in shuffled_unique_validate_content_set]
|
|
return train_content, validate_content
|
|
|
|
|
|
# create feature dictionary
|
|
def create_feature_dictionary(content):
|
|
additional_data = [el[2] for el in content]
|
|
possible_variants = sorted(set(additional_data))
|
|
categories = sorted(set([el[0] for el in possible_variants]))
|
|
|
|
feature_dictionary = []
|
|
for category in categories:
|
|
category_features = [1, category]
|
|
examples_per_category = [el for el in possible_variants if el[0] == category]
|
|
longest_element = max(examples_per_category, key=len)
|
|
for i in range(1, len(longest_element)):
|
|
possibilities_per_el = sorted(set([el[i] for el in examples_per_category if i < len(el)]))
|
|
category_features[0] += len(possibilities_per_el)
|
|
category_features.append(possibilities_per_el)
|
|
feature_dictionary.append(category_features)
|
|
return feature_dictionary
|
|
|
|
|
|
def create_X_features(content, feature_dictionary):
|
|
content = content
|
|
X_other_features = []
|
|
for el in content:
|
|
X_el_other_features = []
|
|
for feature in feature_dictionary:
|
|
if el[2][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(el[2]) and feature[i][j] == el[2][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) |