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from __future__ import absolute_import, division, unicode_literals

import io

import numpy as np

import logging

import cmath

import random

import math

import os

from keras.preprocessing.sequence import pad_sequences

def load_complex_embedding(embedding_dir):

word2id = np.load(os.path.join(embedding_dir,'word2id.npy')).item()

phase_embedding = np.load(os.path.join(embedding_dir,'phase_embedding.npy'))

amplitude_embedding = np.load(os.path.join(embedding_dir,'amplitude_embedding.npy'))

complex_embedding_params = {'word2id':word2id, 'phase_embedding': phase_embedding, 'amplitude_embedding': amplitude_embedding}

print(phase_embedding.shape)

print(amplitude_embedding.shape)

print(len(word2id))

return complex_embedding_params

# Create dictionary

def create_dictionary(sentences, threshold=0):

words = {}

for s in sentences:

for word in s:

words[word] = words.get(word, 0) + 1

if threshold > 0:

newwords = {}

for word in words:

if words[word] >= threshold:

newwords[word] = words[word]

words = newwords

# words['<s>'] = 1e9 + 4

# words['</s>'] = 1e9 + 3

# words['<p>'] = 1e9 + 2

sorted_words = sorted(words.items(), key=lambda x: -x[1]) # inverse sort

id2word = []

word2id = {}

for i, (w, _) in enumerate(sorted_words):

id2word.append(w)

word2id[w] = i+1

return id2word, word2id

def form_matrix(file_name):

word_list = []

ll = []

with io.open(file_name, 'r',encoding='utf-8') as f:

for line in f:

word, vec = line.split(' ', 1)

ll.append(np.fromstring(vec, sep=' '))

word_list.append(word)

matrix = np.asarray(ll)

return matrix, word_list

def orthonormalized_word_embeddings(word_embeddings_file):

matrix, word_list = form_matrix(word_embeddings_file)

print('Initial matrix constructed!')

matrix_norm = np.zeros((matrix.shape[0], matrix.shape[1]))

matrix = matrix.astype(np.float)

matrix_sum = np.sqrt(np.sum(np.square(matrix), axis=1))

for i in range(np.shape(matrix)[0]):

matrix_norm[i] = matrix[i]/matrix_sum[i]

print('Matrix normalized')

##q - basis vectors(num_words x dimension).

##r - coefficients of each word in the basis(dimension x num_words)

q, r = np.linalg.qr(np.transpose(matrix_norm), mode = 'complete')

print('qr factorization completed. Matrix orthogonalized!')

## Dot product of king and prince vectors same as in the original embeddings (0.76823)

# king = word_list.index('king')

# prince = word_list.index('prince')

# print (np.dot(r[:, king], r[:, prince]))

return r, word_list

# Get word vectors from vocabulary (glove, word2vec, fasttext ..)

def get_wordvec(path_to_vec, word2id=None, orthonormalized=True):

if orthonormalized:

coefficients_matrix, word_list = orthonormalized_word_embeddings(path_to_vec)

else:

matrix, word_list = form_matrix(path_to_vec)

coefficients_matrix = np.transpose(matrix)

word_vec = {}

with io.open(path_to_vec, 'r', encoding='utf-8') as f:

# if word2vec or fasttext file : skip first line "next(f)"

if word2id == None:

print('program goes here!')

for word in word_list:

word_vec[word] = coefficients_matrix[:, word_list.index(word)]

else:

for line in f:

word, vec = line.split(' ', 1)

if word in word2id:

word_vec[word] = coefficients_matrix[:, word_list.index(word)]

logging.info('Found {0} words with word vectors, out of \

{1} words'.format(len(word_vec), len(word2id)))

return word_vec

# Set word phases

# Currently only using random phase

def set_wordphase(word2id):

word2phase = {}

for word in word2id.keys():

word2phase[word] = random.random()*2*math.pi

return word2phase

def get_index_batch(embedding_params, batch):

batch = [sent if sent != [] else ['.'] for sent in batch]

embeddings = []

word2id = embedding_params['word2id']

for sent in batch:

sentvec = []

for word in sent:

if word in word2id:

assert word2id[word] > 0

sentvec.append(word2id[word])

if not sentvec:

vec = np.zeros(embedding_params['wvec_dim'])

sentvec.append(vec)

# word_count = len(sentvec)

# sentvec = np.mean(sentvec, 0)*math.sqrt(word_count)

embeddings.append(sentvec)

# embeddings = np.vstack(embeddings)

return embeddings

def get_vector_batch(embedding_params, batch):

batch = [sent if sent != [] else ['.'] for sent in batch]

embeddings = []

for sent in batch:

sentvec = []

for word in sent:

if word in embedding_params['word_vec']:

wordvec = embedding_params['word_vec'][word]

if word in embedding_params['word_complex_phase']:

complex_phase = embedding_params['word_complex_phase'][word]

wordvec = [x * cmath.exp(1j*complex_phase) for x in wordvec]

sentvec.append(wordvec)

if not sentvec:

vec = np.zeros(embedding_params['wvec_dim'])

sentvec.append(vec)

word_count = len(sentvec)

sentvec = np.mean(sentvec, 0)*math.sqrt(word_count)

embeddings.append(sentvec)

embeddings = np.vstack(embeddings)

return embeddings

def get_lookup_table(embedding_params):

id2word = embedding_params['id2word']

word_vec = embedding_params['word_vec']

lookup_table = []

# Index 0 corresponds to nothing

lookup_table.append([0]* embedding_params['wvec_dim'])

for i in range(0, len(id2word)):

word = id2word[i]

wvec = [0]* embedding_params['wvec_dim']

if word in word_vec:

wvec = word_vec[word]

# print(wvec)

lookup_table.append(wvec)

lookup_table = np.asarray(lookup_table)

return(lookup_table)

def batch_gen(data, max_sequence_length):

sentences = data['X']

labels = data['y']

# print(labels)

for batch, label in zip(sentences, labels):

padded_batch = pad_sequences(batch, maxlen=max_sequence_length, dtype='int32',

padding='post', truncating='post', value=0.)

yield np.asarray(padded_batch), np.asarray(label)

def data_gen(data, max_sequence_length):

sentences = data['X']

labels = data['y']

# print(labels)

# batch_list = []

# for batch, label in zip(sentences, labels):

# padded_batch = pad_sequences(batch, maxlen=max_sequence_length, dtype='int32',

# padding='post', truncating='post', value=0.)

# batch_list=padded_batch

padded_sentences = pad_sequences(sentences[0], maxlen=max_sequence_length, dtype='int32',padding='post', truncating='post', value=0.)

return np.asarray(padded_sentences), np.transpose(np.asarray(labels))

def main():

complex_embedding_dir = 'eval/eval_CR/embedding'

load_complex_embedding(complex_embedding_dir)

if __name__ == '__main__':

main()