def preprocess_qa(source, nlp, w2vec_model, row_num=None, test=False):
answer_n = len(source.answer_names)
if row_num is None:
quest_n_train = source.train_data.shape[0]
quest_n_test = source.test_data.shape[0]
else:
quest_n_train, quest_n_test = row_num, row_num
_helpScripts.print_msg('generate tree representations for qa')
train_questions = source.train_data['question'].values[:row_num]
train_answers = source.train_data[source.answer_names].values[:row_num]
if test:
test_questions = source.test_data['question'].values[:row_num]
test_answers = source.test_data[source.answer_names].values[:row_num]
sents_all = np.concatenate((train_questions, train_answers.flatten(),
test_questions, test_answers.flatten()))
else:
sents_all = np.concatenate((train_questions, train_answers.flatten()))
words, weights_trees, vocab, root_inds, weights_ = create_trees_all(sents_all, nlp)
word_n = np.shape(weights_trees)[1]
wc_num = len(weights_)
mask_root_ind = get_root_ind_mask(root_inds, length=word_n)
mask = np.zeros_like(mask_root_ind)
for i, ri in enumerate(root_inds):
mask[i, range(ri[-1] + 1)] = 1
input_ = np.array([sents_to_w2v(sent, w2vec_model, word_n) for sent in words], dtype='float32')
train_q, train_a = unflaffen_qa(input_[:quest_n_train*(1 + answer_n)],
quest_n_train, answer_n)
mask_train_ri_q, mask_train_ri_a = unflaffen_qa(mask_root_ind[:quest_n_train*(1 + answer_n)],
quest_n_train, answer_n)
mask_train_q, mask_train_a = unflaffen_qa(mask[:quest_n_train*(1 + answer_n)],
quest_n_train, answer_n)
wt_train_q, wt_train_a = unflaffen_qa(weights_trees[:quest_n_train*(1 + answer_n)],
quest_n_train, answer_n)
if test is None:
test_q, test_a = None, None
mask_test_ri_q, mask_test_ri_a = None, None
mask_test_q, mask_test_a = None, None
wt_test_q, wt_test_a = None, None
else:
test_q, test_a = unflaffen_qa(input_[quest_n_train*(1 + answer_n):],
quest_n_train, answer_n)
mask_test_ri_q, mask_test_ri_a = unflaffen_qa(mask_root_ind[quest_n_train*(1 + answer_n):],
quest_n_train, answer_n)
mask_test_q, mask_test_a = unflaffen_qa(mask[quest_n_train*(1 + answer_n):],
quest_n_train, answer_n)
wt_test_q, wt_test_a = unflaffen_qa(weights_trees[quest_n_train*(1 + answer_n):],
quest_n_train, answer_n)
correct_answ_ = source.train_data['correctAnswer'].values[:row_num]
targets = int32([np.where(answ == np.array(source.answer_sym))[0][0] for answ in correct_answ_])
return (train_q, train_a, test_q, test_a,
wt_train_q, wt_train_a, wt_test_q, wt_test_a,
mask_train_q, mask_train_a, mask_test_q, mask_test_a,
mask_train_ri_q, mask_train_ri_a, mask_test_ri_q, mask_test_ri_a,
vocab, word_n, wc_num, targets)
def pre_proc_quest(source, vocab, quest_data_file, row_num=None):
#-----------------------------------------------tokenize question / answer data
_helpScripts.print_msg('generate question / answer data')
quest_n_train = source.train_data.shape[0]
quest_n_test = source.test_data.shape[0]
answer_sym = ['A', 'B', 'C', 'D']
answer_names = ['answer' + s for s in answer_sym]
train_quest_tok = np.array([nltk.word_tokenize(sent) for sent in source.train_data['question'].values])
test_quest_tok = np.array([nltk.word_tokenize(sent) for sent in source.test_data['question'].values])
train_quest_sl = [len(t) for t in train_quest_tok]
test_quest_sl = [len(t) for t in test_quest_tok]
QUEST_SENT_L = np.max(train_quest_sl + test_quest_sl)
train_answ_tok = np.transpose(np.array([[nltk.word_tokenize(sent) for sent in source.train_data[a].values] for a in answer_names]))
test_answ_tok = np.transpose(np.array([[nltk.word_tokenize(sent) for sent in source.test_data[a].values] for a in answer_names]).reshape((-1, 4)))
train_answ_sl = [len(t) for t in train_answ_tok.flatten()]
test_answ_sl = [len(t) for t in test_answ_tok.flatten()]
ANSW_SENT_L = np.max(train_answ_sl + test_answ_sl)
print 'max question length is {}, answer length: {}'.format(QUEST_SENT_L, ANSW_SENT_L)
#-------------------------------------------------------generate the input data
_helpScripts.print_msg('generate indexing for questions', allign='left')
PAD_TOKEN_IND = len(vocab) - 1
train_quest = np.expand_dims(int16(sents2indPar(train_quest_tok, vocab, proc=8, max_l=QUEST_SENT_L)), 1)
test_quest = np.expand_dims(int16(sents2indPar(test_quest_tok, vocab, proc=8, max_l=QUEST_SENT_L)), 1)
_helpScripts.print_msg('generate indexing for answers', allign='left')
train_answ = int16(sents2indPar(train_answ_tok.reshape((-1)), vocab, proc=8, max_l=ANSW_SENT_L)).reshape((-1, 4, ANSW_SENT_L))
test_answ = int16(sents2indPar(test_answ_tok.reshape((-1)), vocab, proc=8, max_l=ANSW_SENT_L)).reshape((-1, 4, ANSW_SENT_L))
correct_answ_ = correct_answ=source.train_data['correctAnswer'].values
train_correct_answ = float32([np.where(answ == np.array(answer_sym))[0][0] for answ in correct_answ_])
#-------------------------------------------------------------save data to file
_helpScripts.print_msg('save data to file ' + quest_data_file, allign='left')
with open(quest_data_file, 'wb') as f:
cPickle.dump((train_quest, train_answ, train_correct_answ,
test_quest, test_answ), f, protocol=2)
_helpScripts.print_msg('completed')
return (train_quest, train_answ, train_correct_answ,
test_quest, test_answ)
def pre_process_context(context_data_file, vocab_size=VOCAB_SIZE, row_num=None):
#-----------------------------------------------------------load whoosh results
_helpScripts.print_msg('load whoosh results')
with open(XAPIAN_DIR + 'train.pickle', 'rb') as f:
wh_pred_answ_train, wh_scores_train, wh_themes_train = pickle.load(f)
with open(XAPIAN_DIR + 'test.pickle', 'rb') as f:
wh_pred_answ_test, wh_scores_test, wh_themes_test = pickle.load(f)
if row_num is not None:
wh_pred_answ_train, wh_scores_train, wh_themes_train = \
wh_pred_answ_train[:row_num], wh_scores_train[:row_num], wh_themes_train[:row_num]
wh_pred_answ_test, wh_scores_test, wh_themes_test = \
wh_pred_answ_test[:row_num], wh_scores_test[:row_num], wh_themes_test[:row_num]
themes_train_ = [wh_th.flatten() for wh_th in wh_themes_train]
themes_train = set([item for sublist in themes_train_ for item in sublist])
themes_test_ = [wh_th.flatten() for wh_th in wh_themes_test]
themes_test = set([item for sublist in themes_test_ for item in sublist])
themes_all = themes_train | themes_test
themes_all -= set([0])
themes_name_all = set([os.path.splitext(os.path.basename(th))[0] for th in list(themes_all)])
print '{} articles are used from whoosh results'.format(len(themes_name_all))
#--------------------------------------------------------------tokenize results
_helpScripts.print_msg('tokenize results')
sentences_, art_names = tokenize_docs(list(themes_all), mode='append',
add_sep=False,
max_sent_length=MAX_SENT_LENGTH,
min_sent_length=MIN_SENT_LENGTH)
sentences = []
sentences.extend(list(itertools.chain(*sentences_)))
sent_l = [len(s) for s in sentences]
print '{} sentences with sentence length: min = {}, max = {}, median = {}'.format(len(sent_l), np.min(sent_l), np.max(sent_l), np.median(sent_l))
#-------------------------------------------------------------------make source
_helpScripts.print_msg('make source')
source = _aai_data.Source(lower=True, create_sents=False)
sentences.extend(source.sent_train_tokens)
sentences.extend(source.sent_test_tokens)
quest_n_train = source.train_data.shape[0]
quest_n_test = source.test_data.shape[0]
print 'there are {} train questions and {} test questions'.format(quest_n_train, quest_n_test)
#-----------------------------------------------------------generate vocabulary
_helpScripts.print_msg('generate vocabulary')
VOCAB = generate_vocab(sentences, vocab_size=vocab_size)
vocab_size = len(VOCAB)
PAD_TOKEN_IND = len(VOCAB) - 1
#-------------------------------------------------------------generate indexies
_helpScripts.print_msg('select themes')
_helpScripts.print_msg('for train', allign='left')
themes_train = select_themes(wh_scores_train, wh_themes_train, themes_quest=ARTICLES_QUEST)
themes_train_names = [[os.path.splitext(os.path.basename(th))[0] for th in th_q] for th_q in themes_train]
_helpScripts.print_msg('for test', allign='left')
themes_test = select_themes(wh_scores_test, wh_themes_test, themes_quest=ARTICLES_QUEST)
themes_test_names = [[os.path.splitext(os.path.basename(th))[0] for th in th_q] for th_q in themes_test]
#-------------------------------------------------------------generate indexies
_helpScripts.print_msg('generate indexies')
_helpScripts.print_msg('for whole context', allign='left')
sent_count = [[len(sents) for sents in sentences_[i]] for i in range(len(sentences_)) if art_names[i] in themes_all]
sent_count = list(itertools.chain(*sent_count))
# MAX_ART_THEME = np.median(sent_count)
sentences_ = np.array(sentences_)
# themes_all = list(themes_all)
article_inds = get_art_word_ind(sentences_,
VOCAB, MAX_SENT_ART,
MAX_SENT_LENGTH, PAD_TOKEN_IND)
#-------------------------------------------------------------save data to file
_helpScripts.print_msg('save data to file ' + context_data_file, allign='left')
with open(context_data_file, 'wb') as f:
cPickle.dump((VOCAB, sentences_, art_names, source,
article_inds,
themes_train, themes_test), f, protocol=2)
_helpScripts.print_msg('completed')
return (VOCAB, sentences_, art_names, source,
article_inds,
themes_train, themes_test)
"""
return LL.get_output(encode_layer, inputs=X).eval()
def get_layer_by_name(net, name):
"""
from https://github.com/mikesj-public/convolutional_autoencoder/blob/master/mnist_conv_autoencode.ipynb
"""
for i, layer in enumerate(net.get_all_layers()):
if layer.name == name:
return layer, i
return None, None
if __name__ == "__main__":
_helpScripts.print_msg('start at time ' + strftime("%Y-%m-%d %H:%M:%S"))
# print strftime("%Y-%m-%d %H:%M:%S")
startTime = datetime.datetime.now()
MAX_SAMPLES = None
os.chdir('/Users/lexx/Documents/Work/Kaggle/AllenAIScience/')
if not os.path.isdir(saveDir):
os.makedirs(saveDir)
#--------------------------------------------------generate / load context data
context_data_file = saveDir + 'context_data.cpickle'
if os.path.isfile(context_data_file):
_helpScripts.print_msg('load context data from file: ' + context_data_file)
with open(context_data_file, 'rb') as f:
(VOCAB, sentences_, art_names, source,
article_inds, themes_train, themes_test) = cPickle.load(f)
else:
(VOCAB, sentences_, art_names, source,
# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logger = logging.getLogger()
fhandler = logging.FileHandler(filename='./data/w2v_models/mylog.log',
mode='a')
formatter = logging.Formatter('%(asctime)s : %(levelname)s : %(message)s')
fhandler.setFormatter(formatter)
logger.addHandler(fhandler)
logger.setLevel(logging.INFO)
answer_sym = ['A', 'B', 'C', 'D']
answer_names = ['answer' + s for s in answer_sym]
# quest_num_train = source.train_data.shape[0]
if not os.path.isfile(w2vec_model_name):
_helpScripts.print_msg('prepare the model')
w2vec_model = Word2Vec(size=500,
alpha=0.1,
window=5,
min_count=5,
max_vocab_size=None,
sample=1e-5,
seed=1,
workers=16,
min_alpha=0.0001,
sg=1,
hs=1,
negative=5,
cbow_mean=0,
iter=2,
null_word=0,
return scores__
def predict_answer(answer_sym, scores):
pred_answ_ = np.argmax(scores, axis=1)
pred_answ = [answer_sym[i] for i in pred_answ_]
return pred_answ
if __name__ == "__main__":
# os.chdir('/Users/lexx/Documents/Work/Kaggle/AllenAIScience/')
# corpus_tokens = get_all_corpus(search_dir)
xapian_dir = './data/xapian/{0}/'.format(XAPIAN_VERSION)
pmi_dir = './data/pmi/{0}/'.format(PMI_VERSION)
# ------------------------------------------------------------------load results
_helpScripts.print_msg('load results')
_helpScripts.print_msg('pmi version {}'.format(PMI_VERSION), allign='left')
with open(pmi_dir + 'train.pickle', 'rb') as f:
(pmi_pred_answ_train, pmi_train, pmi_colls_train) = cPickle.load(f)
with open(pmi_dir + 'test.pickle', 'rb') as f:
(pmi_pred_answ_test, pmi_test, pmi_colls_test) = cPickle.load(f)
_helpScripts.print_msg('xapian version {}'.format(XAPIAN_VERSION), allign='left')
with open(xapian_dir + 'train.pickle', 'rb') as f:
(x_pred_answ_train, x_train_scores, x_train_paths) = pickle.load(f)
with open(xapian_dir + 'test.pickle', 'rb') as f:
(x_pred_answ_test, x_test_scores, x_test_paths) = pickle.load(f)
# ------------------------------------------------------------------------source
source = _aai_data.Source(create_sents=False)
# ---------------------------------------------------------------combine results
x_train_scores_ = combine_scores_x(x_train_scores, mode='sum',
return scores__
def predict_answer(answer_sym, scores):
pred_answ_ = np.argmax(scores, axis=1)
pred_answ = [answer_sym[i] for i in pred_answ_]
return pred_answ
if __name__ == "__main__":
# os.chdir('/Users/lexx/Documents/Work/Kaggle/AllenAIScience/')
# corpus_tokens = get_all_corpus(search_dir)
xapian_dir = './data/xapian/{0}/'.format(XAPIAN_VERSION)
pmi_dir = './data/pmi/{0}/'.format(PMI_VERSION)
# ------------------------------------------------------------------load results
_helpScripts.print_msg('load results')
_helpScripts.print_msg('pmi version {}'.format(PMI_VERSION), allign='left')
with open(pmi_dir + 'train.pickle', 'rb') as f:
(pmi_pred_answ_train, pmi_train, pmi_colls_train) = cPickle.load(f)
with open(pmi_dir + 'test.pickle', 'rb') as f:
(pmi_pred_answ_test, pmi_test, pmi_colls_test) = cPickle.load(f)
_helpScripts.print_msg('xapian version {}'.format(XAPIAN_VERSION),
allign='left')
with open(xapian_dir + 'train.pickle', 'rb') as f:
(x_pred_answ_train, x_train_scores, x_train_paths) = pickle.load(f)
with open(xapian_dir + 'test.pickle', 'rb') as f:
(x_pred_answ_test, x_test_scores, x_test_paths) = pickle.load(f)
# ------------------------------------------------------------------------source
source = _aai_data.Source(create_sents=False)
# ---------------------------------------------------------------combine results
return leave_idx
if __name__ == "__main__":
os.chdir("/Users/lexx/Documents/Work/Kaggle/AllenAIScience/")
save_dir = "./data/generated_sents/"
source = _aai_data.Source()
art_dirs_pre = u"./data/corpus/"
wiki_dir = u"wiki_text_mod_spl_50/"
ck12_full_spl_dir = u"ck12_full_themes_mod_spl_50/"
ck12_wiki_spl_dir = u"wiki_text_ck12_themes_mod_spl_50/"
art_dirs = [wiki_dir, ck12_full_spl_dir, ck12_wiki_spl_dir]
_helpScripts.print_msg("Generate sentences")
(sents_true, sents_false) = generate_sents_par(
art_dirs_pre + art_dirs[2],
pos_count=1,
neg_count=3,
rng=np.random.RandomState(156),
save_dir=save_dir + art_dirs[2],
)
sents_true = np.array(sents_true)
sents_false = np.array(sents_false)
full_idx = get_full_sents(sents_true, sents_false)
sents_true = sents_true[full_idx]
sents_false = sents_false[full_idx]
def temp():
# logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO)
logger = logging.getLogger()
fhandler = logging.FileHandler(filename='./data/w2v_models/mylog.log', mode='a')
formatter = logging.Formatter('%(asctime)s : %(levelname)s : %(message)s')
fhandler.setFormatter(formatter)
logger.addHandler(fhandler)
logger.setLevel(logging.INFO)
answer_sym = ['A', 'B', 'C', 'D']
answer_names = ['answer' + s for s in answer_sym]
# quest_num_train = source.train_data.shape[0]
if not os.path.isfile(w2vec_model_name):
_helpScripts.print_msg('prepare the model')
w2vec_model = Word2Vec(size=500, alpha=0.1, window=5, min_count=5,
max_vocab_size=None, sample=1e-5, seed=1, workers=16,
min_alpha=0.0001, sg=1, hs=1, negative=5,
cbow_mean=0, iter=2, null_word=0, trim_rule=None)
# if os.path.isfile('./data/w2v_models/sentences'):
# with open(sent_file, 'rb') as f:
# sentences = pickle.load(f)
# else:
wiki_dir = './data/corpus/wiki_text_mod/'
ck12_full_spl_dir = u'./data/corpus/ck12_full_themes_mod/'
ck12_wiki_spl_dir = u'./data/corpus/wiki_text_ck12_themes_mod/'
_helpScripts.print_msg('tokenize wiki', allign='left')
def pre_proc_quest(source, vocab, quest_data_file, row_num=None):
#-----------------------------------------------tokenize question / answer data
_helpScripts.print_msg('generate question / answer data')
quest_n_train = source.train_data.shape[0]
quest_n_test = source.test_data.shape[0]
answer_sym = ['A', 'B', 'C', 'D']
answer_names = ['answer' + s for s in answer_sym]
train_quest_tok = np.array([
nltk.word_tokenize(sent)
for sent in source.train_data['question'].values
])
test_quest_tok = np.array([
nltk.word_tokenize(sent)
for sent in source.test_data['question'].values
])
train_quest_sl = [len(t) for t in train_quest_tok]
test_quest_sl = [len(t) for t in test_quest_tok]
QUEST_SENT_L = np.max(train_quest_sl + test_quest_sl)
train_answ_tok = np.transpose(
np.array([[
nltk.word_tokenize(sent) for sent in source.train_data[a].values
] for a in answer_names]))
test_answ_tok = np.transpose(
np.array(
[[nltk.word_tokenize(sent) for sent in source.test_data[a].values]
for a in answer_names]).reshape((-1, 4)))
train_answ_sl = [len(t) for t in train_answ_tok.flatten()]
test_answ_sl = [len(t) for t in test_answ_tok.flatten()]
ANSW_SENT_L = np.max(train_answ_sl + test_answ_sl)
print 'max question length is {}, answer length: {}'.format(
QUEST_SENT_L, ANSW_SENT_L)
#-------------------------------------------------------generate the input data
_helpScripts.print_msg('generate indexing for questions', allign='left')
PAD_TOKEN_IND = len(vocab) - 1
train_quest = np.expand_dims(
int16(sents2indPar(train_quest_tok, vocab, proc=8,
max_l=QUEST_SENT_L)), 1)
test_quest = np.expand_dims(
int16(sents2indPar(test_quest_tok, vocab, proc=8, max_l=QUEST_SENT_L)),
1)
_helpScripts.print_msg('generate indexing for answers', allign='left')
train_answ = int16(
sents2indPar(train_answ_tok.reshape((-1)),
vocab,
proc=8,
max_l=ANSW_SENT_L)).reshape((-1, 4, ANSW_SENT_L))
test_answ = int16(
sents2indPar(test_answ_tok.reshape((-1)),
vocab,
proc=8,
max_l=ANSW_SENT_L)).reshape((-1, 4, ANSW_SENT_L))
correct_answ_ = correct_answ = source.train_data['correctAnswer'].values
train_correct_answ = float32([
np.where(answ == np.array(answer_sym))[0][0] for answ in correct_answ_
])
#-------------------------------------------------------------save data to file
_helpScripts.print_msg('save data to file ' + quest_data_file,
allign='left')
with open(quest_data_file, 'wb') as f:
cPickle.dump((train_quest, train_answ, train_correct_answ, test_quest,
test_answ),
f,
protocol=2)
_helpScripts.print_msg('completed')
return (train_quest, train_answ, train_correct_answ, test_quest, test_answ)
def pre_process_context(context_data_file,
vocab_size=VOCAB_SIZE,
row_num=None):
#-----------------------------------------------------------load whoosh results
_helpScripts.print_msg('load whoosh results')
with open(XAPIAN_DIR + 'train.pickle', 'rb') as f:
wh_pred_answ_train, wh_scores_train, wh_themes_train = pickle.load(f)
with open(XAPIAN_DIR + 'test.pickle', 'rb') as f:
wh_pred_answ_test, wh_scores_test, wh_themes_test = pickle.load(f)
if row_num is not None:
wh_pred_answ_train, wh_scores_train, wh_themes_train = \
wh_pred_answ_train[:row_num], wh_scores_train[:row_num], wh_themes_train[:row_num]
wh_pred_answ_test, wh_scores_test, wh_themes_test = \
wh_pred_answ_test[:row_num], wh_scores_test[:row_num], wh_themes_test[:row_num]
themes_train_ = [wh_th.flatten() for wh_th in wh_themes_train]
themes_train = set([item for sublist in themes_train_ for item in sublist])
themes_test_ = [wh_th.flatten() for wh_th in wh_themes_test]
themes_test = set([item for sublist in themes_test_ for item in sublist])
themes_all = themes_train | themes_test
themes_all -= set([0])
themes_name_all = set(
[os.path.splitext(os.path.basename(th))[0] for th in list(themes_all)])
print '{} articles are used from whoosh results'.format(
len(themes_name_all))
#--------------------------------------------------------------tokenize results
_helpScripts.print_msg('tokenize results')
sentences_, art_names = tokenize_docs(list(themes_all),
mode='append',
add_sep=False,
max_sent_length=MAX_SENT_LENGTH,
min_sent_length=MIN_SENT_LENGTH)
sentences = []
sentences.extend(list(itertools.chain(*sentences_)))
sent_l = [len(s) for s in sentences]
print '{} sentences with sentence length: min = {}, max = {}, median = {}'.format(
len(sent_l), np.min(sent_l), np.max(sent_l), np.median(sent_l))
#-------------------------------------------------------------------make source
_helpScripts.print_msg('make source')
source = _aai_data.Source(lower=True, create_sents=False)
sentences.extend(source.sent_train_tokens)
sentences.extend(source.sent_test_tokens)
quest_n_train = source.train_data.shape[0]
quest_n_test = source.test_data.shape[0]
print 'there are {} train questions and {} test questions'.format(
quest_n_train, quest_n_test)
#-----------------------------------------------------------generate vocabulary
_helpScripts.print_msg('generate vocabulary')
VOCAB = generate_vocab(sentences, vocab_size=vocab_size)
vocab_size = len(VOCAB)
PAD_TOKEN_IND = len(VOCAB) - 1
#-------------------------------------------------------------generate indexies
_helpScripts.print_msg('select themes')
_helpScripts.print_msg('for train', allign='left')
themes_train = select_themes(wh_scores_train,
wh_themes_train,
themes_quest=ARTICLES_QUEST)
themes_train_names = [[
os.path.splitext(os.path.basename(th))[0] for th in th_q
] for th_q in themes_train]
_helpScripts.print_msg('for test', allign='left')
themes_test = select_themes(wh_scores_test,
wh_themes_test,
themes_quest=ARTICLES_QUEST)
themes_test_names = [[
os.path.splitext(os.path.basename(th))[0] for th in th_q
] for th_q in themes_test]
#-------------------------------------------------------------generate indexies
_helpScripts.print_msg('generate indexies')
_helpScripts.print_msg('for whole context', allign='left')
sent_count = [[len(sents) for sents in sentences_[i]]
for i in range(len(sentences_))
if art_names[i] in themes_all]
sent_count = list(itertools.chain(*sent_count))
# MAX_ART_THEME = np.median(sent_count)
sentences_ = np.array(sentences_)
# themes_all = list(themes_all)
article_inds = get_art_word_ind(sentences_, VOCAB, MAX_SENT_ART,
MAX_SENT_LENGTH, PAD_TOKEN_IND)
#-------------------------------------------------------------save data to file
_helpScripts.print_msg('save data to file ' + context_data_file,
allign='left')
with open(context_data_file, 'wb') as f:
cPickle.dump((VOCAB, sentences_, art_names, source, article_inds,
themes_train, themes_test),
f,
protocol=2)
_helpScripts.print_msg('completed')
return (VOCAB, sentences_, art_names, source, article_inds, themes_train,
themes_test)
"""
return LL.get_output(encode_layer, inputs=X).eval()
def get_layer_by_name(net, name):
"""
from https://github.com/mikesj-public/convolutional_autoencoder/blob/master/mnist_conv_autoencode.ipynb
"""
for i, layer in enumerate(net.get_all_layers()):
if layer.name == name:
return layer, i
return None, None
if __name__ == "__main__":
_helpScripts.print_msg('start at time ' + strftime("%Y-%m-%d %H:%M:%S"))
# print strftime("%Y-%m-%d %H:%M:%S")
startTime = datetime.datetime.now()
MAX_SAMPLES = None
os.chdir('/Users/lexx/Documents/Work/Kaggle/AllenAIScience/')
if not os.path.isdir(saveDir):
os.makedirs(saveDir)
#--------------------------------------------------generate / load context data
context_data_file = saveDir + 'context_data.cpickle'
if os.path.isfile(context_data_file):
_helpScripts.print_msg('load context data from file: ' +
context_data_file)
with open(context_data_file, 'rb') as f:
(VOCAB, sentences_, art_names, source, article_inds, themes_train,
themes_test) = cPickle.load(f)
else:
return leave_idx
if __name__ == '__main__':
os.chdir('/Users/lexx/Documents/Work/Kaggle/AllenAIScience/')
save_dir = './data/generated_sents/'
source = _aai_data.Source()
art_dirs_pre = u'./data/corpus/'
wiki_dir = u'wiki_text_mod_spl_50/'
ck12_full_spl_dir = u'ck12_full_themes_mod_spl_50/'
ck12_wiki_spl_dir = u'wiki_text_ck12_themes_mod_spl_50/'
art_dirs = [wiki_dir, ck12_full_spl_dir, ck12_wiki_spl_dir]
_helpScripts.print_msg('Generate sentences')
(sents_true, sents_false) = generate_sents_par(art_dirs_pre+art_dirs[2],
pos_count=1, neg_count=3,
rng=np.random.RandomState(156),
save_dir=save_dir+art_dirs[2])
sents_true = np.array(sents_true)
sents_false = np.array(sents_false)
full_idx = get_full_sents(sents_true, sents_false)
sents_true = sents_true[full_idx]
sents_false = sents_false[full_idx]
def temp():
art_files = [glob.glob(art_dirs_pre + art_dir + '*.txt') for art_dir in art_dirs]
"""
return LL.get_output(encode_layer, inputs=X).eval()
def get_layer_by_name(net, name):
"""
from https://github.com/mikesj-public/convolutional_autoencoder/blob/master/mnist_conv_autoencode.ipynb
"""
for i, layer in enumerate(net.get_all_layers()):
if layer.name == name:
return layer, i
return None, None
if __name__ == '__main__':
_helpScripts.print_msg('start at time ' + strftime("%Y-%m-%d %H:%M:%S"))
# print strftime("%Y-%m-%d %H:%M:%S")
startTime = datetime.datetime.now()
# os.chdir('/Users/lexx/Documents/Work/Kaggle/AllenAIScience/')
if not os.path.isdir(saveDir):
os.makedirs(saveDir)
nlp = _spEnglish(data_dir=os.environ.get('SPACY_DATA', _spLOCAL_DATA_DIR))
#--------------------------------------------------------------load w2vec model
w2vec_model_name = W2VEC_DIR + 'm{}'.format(W2VEC_VERSION)
_helpScripts.print_msg('load the word2vec model from', w2vec_model_name)
w2vec_model = Word2Vec.load(w2vec_model_name)
w2vec_model.init_sims(replace=True)
repr_length = w2vec_model.syn0.shape[1]
#--------------------------------------------------generate / load context data
#-------------------------------------------------------------------make source
def preprocess_qa(source, nlp, w2vec_model, row_num=None, test=False):
answer_n = len(source.answer_names)
if row_num is None:
quest_n_train = source.train_data.shape[0]
quest_n_test = source.test_data.shape[0]
else:
quest_n_train, quest_n_test = row_num, row_num
_helpScripts.print_msg('generate tree representations for qa')
train_questions = source.train_data['question'].values[:row_num]
train_answers = source.train_data[source.answer_names].values[:row_num]
if test:
test_questions = source.test_data['question'].values[:row_num]
test_answers = source.test_data[source.answer_names].values[:row_num]
sents_all = np.concatenate(
(train_questions, train_answers.flatten(), test_questions,
test_answers.flatten()))
else:
sents_all = np.concatenate((train_questions, train_answers.flatten()))
words, weights_trees, vocab, root_inds, weights_ = create_trees_all(
sents_all, nlp)
word_n = np.shape(weights_trees)[1]
wc_num = len(weights_)
mask_root_ind = get_root_ind_mask(root_inds, length=word_n)
mask = np.zeros_like(mask_root_ind)
for i, ri in enumerate(root_inds):
mask[i, range(ri[-1] + 1)] = 1
input_ = np.array(
[sents_to_w2v(sent, w2vec_model, word_n) for sent in words],
dtype='float32')
train_q, train_a = unflaffen_qa(input_[:quest_n_train * (1 + answer_n)],
quest_n_train, answer_n)
mask_train_ri_q, mask_train_ri_a = unflaffen_qa(
mask_root_ind[:quest_n_train * (1 + answer_n)], quest_n_train,
answer_n)
mask_train_q, mask_train_a = unflaffen_qa(
mask[:quest_n_train * (1 + answer_n)], quest_n_train, answer_n)
wt_train_q, wt_train_a = unflaffen_qa(
weights_trees[:quest_n_train * (1 + answer_n)], quest_n_train,
answer_n)
if test is None:
test_q, test_a = None, None
mask_test_ri_q, mask_test_ri_a = None, None
mask_test_q, mask_test_a = None, None
wt_test_q, wt_test_a = None, None
else:
test_q, test_a = unflaffen_qa(input_[quest_n_train * (1 + answer_n):],
quest_n_train, answer_n)
mask_test_ri_q, mask_test_ri_a = unflaffen_qa(
mask_root_ind[quest_n_train * (1 + answer_n):], quest_n_train,
answer_n)
mask_test_q, mask_test_a = unflaffen_qa(
mask[quest_n_train * (1 + answer_n):], quest_n_train, answer_n)
wt_test_q, wt_test_a = unflaffen_qa(
weights_trees[quest_n_train * (1 + answer_n):], quest_n_train,
answer_n)
correct_answ_ = source.train_data['correctAnswer'].values[:row_num]
targets = int32([
np.where(answ == np.array(source.answer_sym))[0][0]
for answ in correct_answ_
])
return (train_q, train_a, test_q, test_a, wt_train_q, wt_train_a,
wt_test_q, wt_test_a, mask_train_q, mask_train_a, mask_test_q,
mask_test_a, mask_train_ri_q, mask_train_ri_a, mask_test_ri_q,
mask_test_ri_a, vocab, word_n, wc_num, targets)
"""
return LL.get_output(encode_layer, inputs=X).eval()
def get_layer_by_name(net, name):
"""
from https://github.com/mikesj-public/convolutional_autoencoder/blob/master/mnist_conv_autoencode.ipynb
"""
for i, layer in enumerate(net.get_all_layers()):
if layer.name == name:
return layer, i
return None, None
if __name__ == '__main__':
_helpScripts.print_msg('start at time ' + strftime("%Y-%m-%d %H:%M:%S"))
# print strftime("%Y-%m-%d %H:%M:%S")
startTime = datetime.datetime.now()
# os.chdir('/Users/lexx/Documents/Work/Kaggle/AllenAIScience/')
if not os.path.isdir(saveDir):
os.makedirs(saveDir)
nlp = _spEnglish(data_dir=os.environ.get('SPACY_DATA', _spLOCAL_DATA_DIR))
#--------------------------------------------------------------load w2vec model
w2vec_model_name = W2VEC_DIR + 'm{}'.format(W2VEC_VERSION)
_helpScripts.print_msg('load the word2vec model from', w2vec_model_name)
w2vec_model = Word2Vec.load(w2vec_model_name)
w2vec_model.init_sims(replace=True)
repr_length = w2vec_model.syn0.shape[1]
#--------------------------------------------------generate / load context data
#-------------------------------------------------------------------make source
