class KerasTokenizer():
def __init__(self, vocab_size=None, oov_token="<OOV>"):
self.vocab_size = vocab_size
self.oov_token = oov_token
@property
def vocab(self):
return self.tokenizer.word_index
def fit(self, texts):
self.tokenizer = Tokenizer(num_words=self.vocab_size,
oov_token=self.oov_token)
self.tokenizer.fit_on_texts(texts)
def encode(self, text):
if type(text) == str:
return self.tokenizer.texts_to_sequences([text])[0]
return self.tokenizer.texts_to_sequences(text)
def decode(self, encoded_text):
if not encoded_text:
return ""
if type(encoded_text[0]) == int:
return self.tokenizer.sequences_to_texts([encoded_text])[0]
return self.tokenizer.sequences_to_texts(encoded_text)
def tokenize(self, text):
if type(text) == str:
return text.split()
else:
return [t.split() for t in text]
class Model:
def __init__(self, text):
self.sliding_token_size = 15
self.path = None
self.model = None
self.tokens = text.split()
self.tokenizer = Tokenizer(filters='')
self.tokenizer.fit_on_texts(self.tokens)
self.x = numpy.zeros((len(self.tokens) - self.sliding_token_size, self.sliding_token_size))
self.y = numpy.zeros((len(self.tokens) - self.sliding_token_size, 1))
sequences = self.tokenizer.texts_to_sequences(self.tokens)
for token_n in range(len(self.tokens) - self.sliding_token_size):
for sliding_token_n in range(self.sliding_token_size):
self.x[token_n][sliding_token_n] = sequences[token_n + sliding_token_n][0]
self.y[token_n] = sequences[token_n + self.sliding_token_size][0]
def save(self):
pickle.dump(self.model, open(self.path + 'model.bin', 'wb'))
pickle.dump(self.tokens, open(self.path + 'tokens.bin', 'wb'))
pickle.dump(self.tokenizer, open(self.path + 'tokenizer.bin', 'wb'))
def load(self):
self.model = pickle.load(open(self.path + 'model.bin', 'rb'))
self.tokens = pickle.load(open(self.path + 'tokens.bin', 'rb'))
self.tokenizer = pickle.load(open(self.path + 'tokenizer.bin', 'rb'))
def generate(self):
text = random.choice(self.tokens)
for _ in range(100):
sequences = pad_sequences([self.tokenizer.texts_to_sequences([text])[0]], self.sliding_token_size)
text += ' ' + self.tokenizer.sequences_to_texts([self.model.predict_classes(sequences)])[0]
return text
def get_result(max_length):
x_test = get_text("data/processed" + "/X_test.txt")
x_train = get_text("data/processed" + "/X_train.txt")
y_test = np.loadtxt("data/processed" + "/y_test.txt", dtype=int)
y_train = np.loadtxt("data/processed" + "/y_train.txt", dtype=int)
# word index from 1
tokenizer = Tokenizer(num_words=5000)
tokenizer.fit_on_texts(x_train)
x_train_cut_num = tokenizer.texts_to_sequences(x_train)
x_test_cut_num = tokenizer.texts_to_sequences(x_test)
x_train_cut_num_pad = pad_sequences(x_train_cut_num,
padding="post",
maxlen=max_length,
value=4)
x_test_cut_num_pad = pad_sequences(x_test_cut_num,
padding="post",
maxlen=max_length,
value=4)
x_train_cut_text = tokenizer.sequences_to_texts(x_train_cut_num_pad)
x_test_cut_text = tokenizer.sequences_to_texts(x_test_cut_num_pad)
nb_pipeline = Pipeline([('tfidf', TfidfVectorizer()),
('clf',
MultinomialNB(fit_prior=True, class_prior=None))])
nb_total = 0
nb_result_list = []
for i in range(y_test.shape[1]):
nb_pipeline.fit(x_train_cut_text, y_train[:, i])
nb_predict = nb_pipeline.predict(x_test_cut_text)
nb_result_list.append(nb_predict)
nb_total += np.sum(
[y_test[j, i] == nb_predict[j] for j in range(y_test.shape[0])])
nb_result_reshape = np.array(nb_result_list).reshape(
y_test.shape[0], y_test.shape[1])
total_num = y_test.shape[0] * y_test.shape[1]
print("navie bayes accuracy: ")
print(nb_total / total_num)
print("F1 score: ")
print(
precision_recall_fscore_support(y_test,
nb_result_reshape,
average='macro'))
print("roc score: ")
print(roc_auc_score(y_test, nb_result_reshape))
class Preprocessor:
def __init__(self, cache_path=None, stop_words=None, **extra):
if cache_path and os.path.exists(cache_path):
with open(cache_path, 'r') as f:
self._tk = tokenizer_from_json(f.read())
else:
self._tk = Tokenizer(lower=True, **extra)
self._cache_path = cache_path
def fit(self, data):
self._tk.fit_on_texts(data)
def save(self):
filename = self._cache_path
with open(filename, 'w') as f:
f.write(self._tk.to_json())
def transform(self, data: pd.Series, truncate: Union[str, int] = 'median'):
"""Transform a list of Series of texts into a list of Series of vectors"""
seq = self._tk.texts_to_sequences(data)
lens = [len(vec) for vec in seq]
logging.info(
f'median {np.median(lens)}, mean {np.mean(lens)}, max {np.max(lens)}, min {np.min(lens)}'
)
if truncate == 'median':
text_len = int(np.median(lens))
else:
text_len = truncate
logging.info(f'Transforming texts into vectors with {text_len} size')
return pd.Series(
pad_sequences(seq, padding='post', maxlen=text_len).tolist())
def to_text(self, data):
"""Transform a vector back to text
Arguments:
data {list} -- ndarray or pd.Series
"""
return self._tk.sequences_to_texts(data)
def build_co_occurence(self, word_index, corpus, window_size):
# Cleaning the corpus
tk = Tokenizer()
tk.fit_on_texts(corpus)
corpus = tk.texts_to_sequences(corpus)
corpus = tk.sequences_to_texts(corpus)
vocab_size = len(word_index) + 1
idx_to_word = {word_index[word]:word for word in word_index if word in word_index}
# Collecting indices as a sparse matrix
self.cooccurences = sparse.lil_matrix((vocab_size, vocab_size), dtype = np.float64)
print(f"Shape of coocc = {self.cooccurences.shape}")
# Get the tokenized sequence * TODO implement with tokenizer
for i, line in enumerate(corpus):
# TODO add progress bar
print(f"\rForming the Co-Occurence Matrix : {(100*(i+1 )/len(corpus)):0.2f}%", end = "")
sys.stdout.flush()
tokens = line.strip().split()
token_ids = [word_index[word.lower()] for word in tokens if word.lower() in word_index]
# extracting context words to the left
for center_i, center_id in enumerate(token_ids):
context_ids = token_ids[max(0, center_i - window_size): center_i]
contexts_len = len(context_ids)
# Adding to the coocc matrix
for left_i, left_id in enumerate(context_ids):
dist = contexts_len - left_i
inc = 1/float(dist)
self.cooccurences[center_id, left_id] += inc
self.cooccurences[left_id, center_id] += inc
print()
print(f"Generated co-occurence matrix of shape {self.cooccurences.shape}")
return self.cooccurences
seed_text_rtl = " ".join(word for word in ngram[1])
print(seed_text_ltr, "->", current_word, "->", seed_text_rtl)
token_list = tokenizer.texts_to_sequences([seed_text_ltr])[0]
token_list = pad_sequences([token_list],
maxlen=max_sequence_len - 1,
padding='pre')
token_list_rev = tokenizer.texts_to_sequences([seed_text_rtl])[0]
token_list_rev = pad_sequences([token_list_rev],
maxlen=max_sequence_len - 1,
padding='pre')
predicted_id = np.argmax(model.predict([token_list, token_list_rev]),
axis=-1)
predicted_word = tokenizer.sequences_to_texts([predicted_id])[0]
print(predicted_word)
predicted_probs = model.predict([token_list, token_list_rev])
predicted_best = np.argsort(-predicted_probs, axis=-1)[0][:4500]
suggestions = []
correct = None
for prob in predicted_best:
output_word = tokenizer.sequences_to_texts([[prob]])[0]
ed = nltk.edit_distance(current_word, output_word)
if ed == 0:
print("I got this one; it seems correct -->", current_word,
"=", output_word)
def map_to_string(cls, input_vector: List, tokenizer: Tokenizer) -> List:
'''
Map a given vector to an unpadded string.
'''
return tokenizer.sequences_to_texts(input_vector)
nlp = spacy.load('en_core_web_lg')
with open('./data/great_expectation.txt', 'r', encoding='utf-8-sig') as f:
text_chunks = f.read().replace('\n', ' ').split('.')
LINES = 1000
LINES = len(text_chunks)
for i in tqdm(range(LINES)):
#line = tokenize_line(text_chunks[i])
line = text_chunks[i].strip().lower()
line = re.sub(F, ' ', line)
if len(line) <= 0:
continue
line = tokenize_line(line)
tokenizer.fit_on_texts(line)
seq = tokenizer.texts_to_sequences(line)
txt = tokenizer.sequences_to_texts(seq)
toks = nlp(' '.join(txt))
for s, t in zip(seq, toks):
print(t, t.pos, s[0])
exit()
with open('./tokenizer.pickle', 'wb') as f:
pickle.dump(tok, f)
print('word count: ', len(tok.word_counts))
print(tok.word_counts)
print('count = ', count)
filters='!"#$%&()*+,-./:;<=>?@[\\]^_`{|}~\t\n' + additional_filters,
lower=True,
split=" ",
char_level=False,
oov_token="UNK",
document_count=0)
token.fit_on_texts(sentence_data)
tokenizer_config = token.get_config()
print(tokenizer_config.keys())
#print(tokenizer_config["word_index"])
#print("\n\n\n\n\n\n\n")
import json
word_counts = json.loads(tokenizer_config['word_counts'])
#print(word_counts)
print(word_counts["the"])
index_word = json.loads(tokenizer_config['index_word'])
word_index=json.loads(tokenizer_config["word_index"])
#print(sentence_data)
print(sentence_data[:5])
sentence_seq = token.texts_to_sequences(sentence_data)
print(sentence_seq[0:5])
senetn = token.sequences_to_texts(sentence_seq)
print(senetn[:5])
class Pungen:
def __init__(self, **kwargs):
self.filepath = kwargs.get('filepath')
self.embedding_layer = None
def _parse_corpus(self, min_seq_len, filepath):
print('Indexing word vectors.')
self.texts = []
with open(filepath, encoding='utf-8') as fp:
for line in fp:
if line == "\n":
continue
self.texts.append(line)
self.tokenizer = Tokenizer(num_words=MAX_NUM_WORDS,
filters=TOKEN_FILTER)
self.tokenizer.fit_on_texts(self.texts)
self.sequences = self.tokenizer.texts_to_sequences(self.texts)
self.sequences = [x for x in self.sequences if len(x) >= min_seq_len]
self.word_index = self.tokenizer.word_index
print('Found %s unique tokens.' % len(self.word_index))
print('Found %s texts.' % len(self.sequences))
def prepare_emb(self, emb_dim, input_length):
print('Indexing word vectors.')
emb_name = 'glove.6B.' + str(emb_dim) + "d.txt"
self.embeddings_index = {}
with open(os.path.join(GLOVE_DIR, emb_name), encoding='utf-8') as f:
for line in f:
word, coefs = line.split(maxsplit=1)
coefs = np.fromstring(coefs, 'f', sep=' ')
self.embeddings_index[word] = coefs
print('Found %s word vectors.' % len(self.embeddings_index))
# prepare embedding matrix
num_words = MAX_NUM_WORDS
self.embedding_matrix = np.zeros((num_words, emb_dim))
for word, i in self.word_index.items():
if i >= num_words:
continue
embedding_vector = self.embeddings_index.get(word)
if embedding_vector is not None:
# words not found in embedding index will be all-zeros.
self.embedding_matrix[i] = embedding_vector
# load pre-trained word embeddings into an Embedding layer
# note that we set trainable = False so as to keep the embeddings fixed
self.embedding_layer = Embedding(num_words,
emb_dim,
embeddings_initializer=Constant(
self.embedding_matrix),
input_length=input_length,
trainable=False)
def check_generator(self):
texts = self.tokenizer.sequences_to_texts(self.sequences)
if len(texts) != len(self.texts):
print("Different sizes of texts")
return
filter = set(TOKEN_FILTER)
for i in range(len(texts)):
if texts[i].lower() != self.texts[i][:-1].lower():
if any((c in filter) for c in self.texts[i][:-1].lower()):
continue
print(texts[i], self.texts[i][:-1])
print(self.texts[i][:-1].lower())
print("Tokenizer failed to tokenize properly!")
return
print("Tokenizer check was succesfull!")
def form_pun(self, eval_path):
retrieve = Retrieve(sentence_path=TEXT_DATA_DIR + TEXT_DATA,
pun_path=PUN_DATA_DIR + PUN_DATA)
(pun, sentence, score) = retrieve.retrieve()
if not sentence:
print("No sentence with word {} was found. Exiting...".format(
pun[1]))
raise Exception()
text = word_tokenize(sentence)
tokenized = nltk.pos_tag(text)
print(tokenized)
print(sentence, pun[0], pun[1])
pre = self.tokenizer.texts_to_sequences([sentence])
wp = self.tokenizer.texts_to_sequences([pun[0]])
wa = self.tokenizer.texts_to_sequences([pun[1]])
if (not wa[0]) or (not wp[0]):
print(
"The pair of pun and word does not exist in the parsed corpus. Exit..."
)
raise Exception()
index_wa = -1
for seq in pre[0]:
index_wa = index_wa + 1
if seq == wa[0][0]:
pre[0][index_wa] = wp[0][0]
break
wordsimilarity = WordSimilarity()
wordsimilarity.word2vec()
wordsimilarity.load()
try_limit = 5
try_count = 0
index_topic = 0
while True:
try:
topic_word = None
for i in range(index_topic, len(tokenized)):
(word, pos) = tokenized[i]
if (pos == 'NNP'):
topic_word = "man"
print(word, pos)
index_topic = index_topic + 1
break
if (pos == 'NN') or (pos == 'PRP') or (pos == 'NNS') or (
pos == 'PRP$'):
topic_word = word
print(word, pos)
index_topic = index_topic + 1
break
index_topic = index_topic + 1
result = wordsimilarity.getSimilar([topic_word, pun[0]],
[pun[1]], 10)
other_result = wordsimilarity.getSimilar([pun[0]], [], 10)
break
except KeyError:
print("Word {} is not in vocabulary, try with the next one".
format(topic_word))
try_count = try_count + 1
if try_limit == try_count:
print("Limit of trys has been reached. Exit...")
raise Exception()
eval_surprisal = Evaluate()
eval_surprisal.load_model(eval_path)
finals = []
mean_amalgam = 0
for (word, prob) in result:
swap = self.tokenizer.texts_to_sequences([word])
context_window = 2
surprise = eval_surprisal.compute_surpisal(
sentence=pre[0],
pun_word=wa[0][0],
pun_alternative=wp[0][0],
context_window=context_window)
mean_amalgam = mean_amalgam + surprise
print(surprise)
pre[0][index_topic] = swap[0][0]
post_simple = self.tokenizer.sequences_to_texts([pre[0]])
print(post_simple)
pre[0][index_topic + 1] = 0
if index_topic >= 2:
pre[0][index_topic - 1] = 0
post_smoothing = self.dac.inference(pre[0])
post_smoothing = self.tokenizer.sequences_to_texts(
post_smoothing.tolist())
finals.append(post_smoothing)
print(post_smoothing)
print(finals)
print(mean_amalgam / 10)
other_finals = []
mean_similar = 0
for (word, prob) in other_result:
swap = self.tokenizer.texts_to_sequences([word])
context_window = 2
surprise = eval_surprisal.compute_surpisal(
sentence=pre[0],
pun_word=wa[0][0],
pun_alternative=wp[0][0],
context_window=context_window)
mean_similar = mean_similar + surprise
print(surprise)
pre[0][index_topic] = swap[0][0]
post_simple = self.tokenizer.sequences_to_texts([pre[0]])
print(post_simple)
pre[0][index_topic + 1] = 0
if index_topic >= 2:
pre[0][index_topic - 1] = 0
post_smoothing = self.dac.inference(pre[0])
post_smoothing = self.tokenizer.sequences_to_texts(
post_smoothing.tolist())
other_finals.append(post_smoothing)
print(post_smoothing)
print(other_finals)
print(mean_similar / 10)
return finals.extend(other_finals)
def train_predict_model(self, model_params):
predict_word = WordPredict(max_len=MAX_LEN,
max_words=MAX_NUM_WORDS,
emb_layer=self.embedding_layer)
predict_word.build_model(**model_params)
predict_word.compile_model(model_params)
generator = Generator(sequences=self.sequences,
batch_size=PREDICT_BS,
max_words=MAX_NUM_WORDS,
max_len=MAX_LEN,
split=PREDICT_SPLIT)
predict_word.train(generator, PREDICT_BS, PREDICT_SPLIT,
PREDICT_EPOCHS)
return predict_word
def load_predict_model(self, path):
predict_word = load_model(path)
return predict_word
def train_dac_model(self, model_params):
dac = DAC()
smoother_model = dac.build_model(hidden_sizes=[64, 64],
seq_len=50,
no_words=40000,
emb_layer=self.embedding_layer,
lr=0.01)
generator = Generator(sequences=self.sequences,
batch_size=SMOOTH_BS,
max_words=MAX_NUM_WORDS,
max_len=MAX_LEN,
split=SMOOTH_SPLIT)
smoother_model = dac.train(generator,
full_model=smoother_model,
model_params=model_params,
bs=SMOOTH_BS,
split=SMOOTH_SPLIT,
pretrain_epochs=4,
epochs=SMOOTH_EPOCHS)
def run(self, predict_path, smoother_path, eval_path):
self._parse_corpus(MIN_SEQ_LEN, TEXT_DATA_DIR + TEXT_DATA)
self.prepare_emb(EMBEDDING_DIM, MAX_LEN)
predict_model = None
if predict_path is None:
model_params = {
'lstm': [16],
'merge_layer': 'concat',
'dense': {
'size': [64, 32],
'act': 'elu',
'dropout': 0
},
'optimizer': 'adam',
'lr': 0.0005
}
predict_model = self.train_predict_model(model_params)
else:
pass
#predict_model = self.load_predict_model(predict_path)
#smoother_model = None
if smoother_path is None:
model_params = {'size': [64, 64], 'lr': 0.01}
#smoother_model = self.train_dac_model(model_params)
else:
self.dac = DAC()
self.dac.load_model(smoother_path)
#GENERATE PUN
while True:
try:
final = pungen.form_pun(eval_path)
break
except Exception:
pass
print(final)
class DDTokenizer:
def __init__(self, num_words, oov_token='<UNK>'):
self.tokenizer = Tokenizer(num_words=num_words,
oov_token=oov_token,
filters='!"#$%&*+,-./:;<>?\\^_`{|}~\t\n',
char_level=True,
lower=False)
self.has_trained = False
self.pad_type = 'post'
self.trunc_type = 'post'
# The encoded data
self.word_index = {}
def fit(self, train_data):
# Get max training sequence length
print("Training Tokenizer...")
self.tokenizer.fit_on_texts(train_data)
self.has_trained = True
print("Done training...")
# Get our training data word index
self.word_index = self.tokenizer.word_index
def encode(self,
data,
use_padding=True,
padding_size=None,
normalize=False):
# Encode training data sentences into sequences
train_sequences = self.tokenizer.texts_to_sequences(data)
# Get max training sequence length if there is none passed
if padding_size is None:
maxlen = max([len(x) for x in train_sequences])
else:
maxlen = padding_size
if use_padding:
train_sequences = pad_sequences(train_sequences,
padding=self.pad_type,
truncating=self.trunc_type,
maxlen=maxlen)
if normalize:
train_sequences = np.multiply(1 / len(self.tokenizer.word_index),
train_sequences)
return train_sequences
def pad(self, data, padding_size=None):
# Get max training sequence length if there is none passed
if padding_size is None:
padding_size = max([len(x) for x in data])
padded_sequence = pad_sequences(data,
padding=self.pad_type,
truncating=self.trunc_type,
maxlen=padding_size)
return padded_sequence
def decode(self, array):
assert self.has_trained, "Train this tokenizer before decoding a string."
return self.tokenizer.sequences_to_texts(array)
def test(self, string):
encoded = list(self.encode(string)[0])
decoded = self.decode(self.encode(string))
print("\nEncoding:")
print("{original} -> {encoded}".format(original=string[0],
encoded=encoded))
print("\nDecoding:")
print("{original} -> {encoded}".format(original=encoded,
encoded=decoded[0].replace(
" ", "")))
def get_info(self):
return self.tokenizer.index_word
if __name__ == '__main__':
shakespeare_url = "https://homl.info/shakespeare"
filepath = keras.utils.get_file("shakespeare.txt", shakespeare_url)
with open(filepath) as f:
shakespeare_text = f.read()
# Then Encode char to integer
# Use Tokenizer class: allow to vectorize text corpus by tuning each text to sequence of integer
# or into a vector
tokenizer = keras.preprocessing.text.Tokenizer(char_level=True)
tokenizer.fit_on_texts([shakespeare_text])
tokenizer.texts_to_sequences(["First"])
tokenizer.sequences_to_texts([[20, 6, 9, 8, 3]])
max_id = len(tokenizer.word_index) # number of distinct chars
# dataset_size = tokenizer.document_count # total number of chars
[encoded] = np.array(tokenizer.texts_to_sequences([shakespeare_text])) - 1
dataset_size = encoded.shape[0]
train_size = dataset_size * 90 // 100
# Slice for get 90% to dataset
a = encoded[:train_size]
dataset = tf.data.Dataset.from_tensor_slices(encoded[:train_size])
n_steps = 100
window_length = n_steps + 1 # target = input shifted 1 character ahead
# Window method create several windows with length = 101, 1st window contain 0 -> 100
# second one contain 1-> 101, then flatten all of window
class CrimData:
def __init__(self, args):
# embeddings model
self.w2v = args['w2v']
# training tuples
train = args['train']
# test tuples
test = args['test']
# validation tuples
validation = args['validation']
# synonyms
self.synonyms = args['synonyms']
# if set to -1 then we will use full vector space vocab
# otherwise use indicated size
self.limited_vocab_n = args['limited_vocab_n']
if self.limited_vocab_n > -1:
print("Creating limited vocabulary of %d" % (self.limited_vocab_n))
# collect words for exercise
flat_synonym = [word for v in self.synonyms.values() for word in v]
hyponyms = list(set([x for x, y in train + test + validation]))
hypernyms = list(set([y for x, y in train + test + validation]))
# dataset set vocab
vocab = list(set(hyponyms + hypernyms + flat_synonym))
vocab_len = len(vocab)
print("Dataset vocabulary size is %d" % (vocab_len))
model_words = list(self.w2v.vocab.keys())
# sample words from vector space; sample more words than requested to handle collisions with dataset words
random_words = np.random.choice(model_words,
(self.limited_vocab_n + 10000),
replace=False)
vocab = vocab + [
w for w in random_words.tolist() if w not in vocab
][:self.limited_vocab_n - vocab_len]
print("Truncated vocab length is %d" % (len(vocab)))
else:
# choose all words in vector space
vocab = list(self.w2v.vocab.keys())
# create tokenizer from embeddings model
self.tokenizer = Tokenizer(filters='', lower=False)
# fit on vocab
self.tokenizer.fit_on_texts(vocab)
print("Vocab size is %d words" % (len(self.tokenizer.index_word)))
# initialise negative word sampler
print("Initialising negative sampler")
self.negative_sampler = make_sampler(
list(self.tokenizer.word_index.values()))
print("Tokenising all dataset tuples")
# tokenize dataset -> convert to numbers which will serve as embeddings lookup keys
self.all_data_token = self.tokenizer.texts_to_sequences(
[[x, y] for x, y in train + test + validation])
# create hypernym dictionary lookup
self.hypernym_id_lookup = defaultdict(list)
for x, y in self.all_data_token:
self.hypernym_id_lookup[x].append(y)
# disable default factory
self.hypernym_id_lookup.default_factory = None
print("Creating embeddings matrix")
# create embeddings matrix
self.embeddings_matrix = np.zeros(
(len(self.tokenizer.index_word) + 1, 300))
for k, v in self.tokenizer.index_word.items():
self.embeddings_matrix[k] = self.w2v[v]
#vectors should already by nornalised
#self.embeddings_matrix[k] /= np.linalg.norm(emb_matrix[k])
print("Done!")
# get list of padded synonyms
def sample_synonyms(self, word_id, sample_length):
# convert word_id to word to look for in synyony dictionary
word = self.tokenizer.index_word[word_id]
if word in self.synonyms:
_syn = self.synonyms[word]
else:
_syn = []
# convert list to embeddings index array
syn_list = np.asarray(self.tokenizer.texts_to_sequences([_syn])[0])
result = np.asarray([])
# if we have enough synonyms, we can randomly sample length-1 from list and add the hyponym itself to
# the list
if (sample_length > 1 and len(syn_list) >= (sample_length - 1)):
result = np.random.choice(syn_list,
sample_length - 1,
replace=False)
result = np.append(result, word_id)
# otherwise, we pick all synyonyms and pad the sequences to match model fixed-input
else:
result = np.append(syn_list, word_id)
result = pad_sequences([result],
sample_length,
padding='post',
value=word_id)
# we're expecting 1-D vector
return result.flatten()
def get_negative_random(self, word_id, neg_count):
neg_samples = []
while len(neg_samples) < neg_count:
tmp_neg = next(self.negative_sampler)
if tmp_neg not in self.hypernym_id_lookup[word_id]:
neg_samples.append(tmp_neg)
return neg_samples
def get_augmented_batch(self, query_batch, neg_count, syn_count):
# create synonym equivalent in ids, prepending the hyponym to the list of synonyms
query_input = np.zeros((len(query_batch) * (neg_count + 1), 1),
dtype='int32')
hyper_input = np.zeros((len(query_batch) * (neg_count + 1), 1),
dtype='int32')
synonym_input = np.zeros(
(len(query_batch) * (neg_count + 1), syn_count), dtype='int32')
y_input = np.zeros(len(query_batch) * (neg_count + 1))
for idx, (query, hyper) in enumerate(query_batch):
query_input[idx * (neg_count + 1)] = np.asarray(query)
hyper_input[idx * (neg_count + 1)] = np.asarray(hyper)
synonym_input[idx * (neg_count + 1)] = self.sample_synonyms(
query, syn_count)
y_input[idx * (neg_count + 1)] = 1
if neg_count > 0:
negatives = self.get_negative_random(word_id=query,
neg_count=neg_count)
for m, neg in enumerate(negatives):
query_input[(idx * (neg_count + 1)) +
(m + 1)] = np.asarray(query)
hyper_input[(idx * (neg_count + 1)) +
(m + 1)] = np.asarray(neg)
synonym_input[(idx * (neg_count + 1)) +
(m + 1)] = self.sample_synonyms(
query, syn_count)
return query_input, hyper_input, synonym_input, y_input
def token_to_words(self, dataset):
_q = self.tokenizer.sequences_to_texts(dataset[:, 0].reshape(-1, 1))
_h = self.tokenizer.sequences_to_texts(dataset[:, 1].reshape(-1, 1))
return list(zip(_q, _h))
# Tokenizer
X_tokenizer = Tokenizer(filters=args.filters,
lower=args.lower,
char_level=args.char_level,
oov_token='<UNK>')
X_tokenizer.fit_on_texts(X_train)
vocab_size = len(X_tokenizer.word_index) + 1 # +1 for padding token
config.logger.info(f"→ vocab_size: {vocab_size}")
# Convert texts to sequences of indices
original_text = X_train[0]
X_train = np.array(X_tokenizer.texts_to_sequences(X_train))
X_val = np.array(X_tokenizer.texts_to_sequences(X_val))
X_test = np.array(X_tokenizer.texts_to_sequences(X_test))
preprocessed_text = X_tokenizer.sequences_to_texts([X_train[0]])[0]
config.logger.info("→ Text to indices:\n"
f" (raw) → {original_text}\n"
f" (preprocessed) → {preprocessed_text}\n"
f" (tokenized) → {X_train[0]}")
# Label encoder
y_tokenizer = LabelEncoder()
y_tokenizer = y_tokenizer.fit(y_train)
classes = y_tokenizer.classes_
config.logger.info("→ classes:\n" f" {classes}")
# Convert labels to tokens
class_ = y_train[0]
y_train = y_tokenizer.transform(y_train)
y_val = y_tokenizer.transform(y_val)
# sequences greater than 100 in length will be truncated
MAX_SEQ_LENGTH = 100
X_padded = pad_sequences(X_encoded,
maxlen=MAX_SEQ_LENGTH,
padding="pre",
truncating="post")
Y_padded = pad_sequences(Y_encoded,
maxlen=MAX_SEQ_LENGTH,
padding="pre",
truncating="post")
# print the first sequence
print(X_padded[0], "\n" * 3)
print(Y_padded[0])
X, Y = X_padded, Y_padded
Y = to_categorical(Y)
TEST_SIZE = 0.10
X_train, X_test, Y_train, Y_test = train_test_split(X,
Y,
test_size=TEST_SIZE,
random_state=4)
# Re-evaluate the model
# loss, acc = model.evaluate(X_test,Y_test, verbose=2)
input = X_padded[0].reshape((1, 100))
print(tag_tokenizer.sequences_to_texts(np.argmax(model.predict(input),
axis=2)))
print(tag_tokenizer.sequences_to_texts(Y_padded)[0])
def vschat_service(request):
# text 입력 받은 후
if request.method == 'POST':
# input1 받아옴 + 모델 탑재하고 라벨과 쿼리 받아오기
input1 = request.POST['input1']
okt = Okt()
max_len = 40
vocab_size = 515
tokenizer = Tokenizer()
with open('./static/word_dict_ver03.json') as json_file:
word_index = json.load(json_file)
tokenizer.word_index = word_index
# print(tokenizer.word_index)
tokenized_sentence = []
temp_X = okt.morphs(input1, stem=True) # 토큰화
tokenized_sentence.append(temp_X)
print(tokenized_sentence)
input_data = tokenizer.texts_to_sequences(tokenized_sentence)
print(input_data)
input_data = pad_sequences(input_data, maxlen=max_len) # padding
loaded_model = load_model('./static/best_model_ver_relu_epc500.h5')
prediction = loaded_model.predict(input_data)
print(prediction)
print("label: ", np.argmax(prediction[0]))
label = str(np.argmax(prediction[0]))
if label == '1':
query = "select * from stepcountData where saved_time BETWEEN date('now', '-7 days', 'localtime') AND date('now', 'localtime');"
elif label == '2':
query = "select * from stepcountData where saved_time BETWEEN date('now', '-35 days', 'localtime') AND date('now', 'localtime');"
elif label == '3':
query = "select * from stepcountData where saved_time BETWEEN date('now', '-4 months','start of month', 'localtime') AND date('now', '+1 days', 'localtime');"
else:
with open('./static/tokenizer_for_attention.json') as f:
data = json.load(f)
tokenizer = tokenizer_from_json(data)
# 모델 생성
model = Seq2seq(sos=tokenizer.word_index['\t'],
eos=tokenizer.word_index['\n'])
model.load_weights("./static/attention_ckpt/attention_ckpt")
# Implement algorithm test
@tf.function
def test_step(model, inputs):
return model(inputs, training=False)
tmp_seq = [" ".join(okt.morphs(input1))]
print("tmp_seq : ", tmp_seq)
test_data = list()
test_data = tokenizer.texts_to_sequences(tmp_seq)
print("tokenized data : ", test_data)
prd_data = tf.keras.preprocessing.sequence.pad_sequences(
test_data, value=0, padding='pre', maxlen=128)
prd_data = tf.data.Dataset.from_tensor_slices(prd_data).batch(
1).prefetch(1024)
for seq in prd_data:
prediction = test_step(model, seq)
predicted_seq = tokenizer.sequences_to_texts(
prediction.numpy())
print(predicted_seq)
print("predict tokens : ", prediction.numpy())
predicted_seq = str(predicted_seq[0]).replace(" _ ", "_")
predicted_seq = predicted_seq.replace("e (", "e(")
predicted_seq = predicted_seq.replace("' ", "'")
predicted_seq = predicted_seq.replace(" '", "'")
predicted_seq = predicted_seq.replace(" - ", "-")
predicted_seq = predicted_seq.replace("+ ", "+")
predicted_seq = predicted_seq.replace("- ", "-")
print(predicted_seq)
query = "select * from stepcountData where " + predicted_seq + ";"
# if not legend_value or xValue or yValue or response:
legend_value.clear()
xValue.clear()
yValue.clear()
response.clear()
x1.clear()
x2.clear()
y1.clear()
y2.clear()
print(legend_value, xValue, yValue, response, x1, x2, y1, y2)
try:
if label == "2":
show_weeks_avg(query)
print("주별 평균")
elif label == "3":
show_months_avg(query)
print("월별 평균")
elif label == "6":
if check_week(query) == True:
show_by_week(query)
print('주별비교')
else:
show_by_month(query)
print('월별비교')
else:
show_barchart(query)
print('바차트')
except HTTPError as e:
print("httperror")
print("데이터를 불러올 수 없습니다. 텍스트를 다시 입력하세요")
except IndexError as e:
print("indexerror")
print("데이터를 불러올 수 없습니다. 텍스트를 다시 입력하세요")
# 예외처리에 대한 알림 메세지 어떻게 출력하는지 보기
# 딕셔너리에 저장(응답, 쿼리 결과 저장 변수, 라벨)
output = dict()
if not output:
# output['response'] = response
output['response'] = "그래프가 출력되었습니다"
output['xValues'] = xValue
output['yValues'] = yValue
output['label'] = label
output['legend_value'] = legend_value
print(output)
else:
del output
output['response'] = response
utput['response'] = "그래프가 출력되었습니다"
output['xValues'] = xValue
output['yValues'] = yValue
output['label'] = label
output['legend_value'] = legend_value
print(output)
print("-----------------------------------------")
print("-----------------------------------------")
return HttpResponse(json.dumps(output), status=200)
else:
return render(request, 'chat.html')
# Procesamos data con la red neuronal y decodifcamos el intents
try:
# PROCESAMIENTO DE LA RED NEURONAL
# tokenizar por palabras
token = text_to_word_sequence(data)
# obtener la secuencia
seq = tok.texts_to_sequences(token)
# encode la secuencia
encoded = np.add.reduce(to_categorical(seq, size))
pred = model.predict(np.array([encoded]))
seq2 = np.argmax(pred, axis=None, out=None)
intent_get = tok2.sequences_to_texts(np.array([[seq2]]))
intent_get = intent_get[-1]
if spread:
print("#" * 20 + " SUMMARY " + "#" * 20)
print("\t Context: {0}".format(context))
print("\t Hope: {0}".format(hope))
print("\t Data: {0}".format(data))
print("\t Bag: {0}".format(bag))
print("\t Intent: {0}".format(intent_get))
print("#" * 49)
time.sleep(5)
# CONTEXTO CONVERSATION
if hope and context in ["conversation"] and data not in [""]:
hope = False
class Preprocessor:
def __init__(self, dataset_name):
self.name = dataset_name
self.path = os.path.dirname(__file__) + '\\prepared-datasets\\' + self.name + '.pkl'
self.eos_token = '<eos>'
self.sos_token = '<sos>'
self.tokenizer = None
if not os.path.exists(self.path):
print('loading dataset to disk. this may take a minute or two...')
raw_dataset = self._load_dataset_from_tensorflow()
ready_to_save_data = self._prepare_dataset_for_saving(raw_dataset)
self._save_prepared_data(ready_to_save_data)
print('dataset loaded to disk.')
def _load_dataset_from_tensorflow(self):
dataset = tfds.load(self.name, as_supervised=True, split=['train', 'test', 'validation'])
return dataset
def _prepare_dataset_for_saving(self, raw_data):
# raw_data is a tuple (train_data, test_data, validation_data)
train_data = self._prepare_data_as_supervised(raw_data[0])
test_data = self._prepare_data_as_supervised(raw_data[1])
validation_data = self._prepare_data_as_supervised(raw_data[2])
return train_data, test_data, validation_data
def _prepare_data_as_supervised(self, data):
# data is a list_like of (input_text, target_text) elements
# this function returns a list of (input_text, target_text) elements
to_be_returned = []
for sample in data:
input_word_sequence = (self.sos_token + ' ' + sample[0] + ' ' + self.eos_token).numpy().decode('ASCII', 'ignore')
target_word_sequence = (self.sos_token + ' ' + sample[1] + ' ' + self.eos_token).numpy().decode('ASCII', 'ignore')
to_be_returned.append((input_word_sequence, target_word_sequence))
return to_be_returned
def _save_prepared_data(self, data):
data_holder = self._DataHolder(data)
self._save(data_holder)
def _save(self, data_holder):
with open(self.path, 'wb') as f:
pickle.dump(data_holder, f, protocol=pickle.HIGHEST_PROTOCOL)
f.close()
def _load(self, split):
data_holder = None
with open(self.path, 'rb') as f:
data_holder = pickle.load(f)
f.close()
return data_holder.get_data(split)
def load_preprocessed_data(self, split, vocab_size, max_input_len=400, max_target_len=150,
filters='!"#$%&()*+,-./:;=?@[\\]^_`{|}~\t\n',
to_lower=True, padding='post'):
'''
split: a string with value 'train' or 'test' or 'validation'
vocab_size: the size of vocabulary to be used for tokenization we use the most frequent vocab_size - 1 words.
batch_size: batch size to be used by the DataLoaders during trainning.
max_input_len: the max length of input sequences, all sequences that are
longer will be excluded from the dataset (along with thier targets!!), default = 400.
max_target_len: the max length of target sequences, all sequences that are
longer will be excluded from the dataset (along with thier inputs!!), default = 150.
filters: a string of characters to be filtered out from texts, default = '!"#$%&()*+,-./:;=?@[\\]^_`{|}~\\t\\n'.
to_lower: whether to convert all text to lower during tokenization or not, default = True.
padding: whether to use padding at the end ('post') or at the beggining of sequence ('pre'),
or not use padding at all (None), default = 'post'.
'''
assert type(split) == str
if (split == 'train'):
self.tokenizer = Tokenizer(num_words=vocab_size, filters=filters, lower=to_lower,
oov_token='<unk>', split=' ')
data = self._load(split)
raw_data = data
# ._load() returns either train, test or validation data depending on split
# we also need the raw data for experiments, comparison and visualization later
data = self._to_2_lists(data)#: (List, List)
# each data set is converted from list of 2-elements tuples to a tuple of 2 lists
if (split == 'train'):
self.tokenizer.fit_on_texts(data[0])
''' we only fit the tokenizer on training inputs
by 'fit' we mean contructing the vocab and initializing the
tokenizer for later use '''
assert not (self.tokenizer == None)
data = (self.tokenizer.texts_to_sequences(data[0]),
self.tokenizer.texts_to_sequences(data[1]))#: a tuple of 2 lists of sequences
# using the tokenizer, we convert raw text samples into integer sequences
data = self._filter_by_lengths(data, max_input_len, max_target_len)#: tuple of 2 lists of sequences
''' we removed long inputs and targets based on:
max_input_len, max_target_len '''
if not (padding == None):
data = self._as_supervised_pad_list_of_sequences(data, padding=padding)#: a list of 5 tenors
''' we replaced the 2 lists of inputs targets with 5 tensors:
(x_encoder, x_decoder, y, encoder_lengths, decoder_lengths)
where x_encoder is the input of the encoder, x_decoder is the input
of the decoder, y is the target. encoder_lengths and decoder_lengths
are the lengths of non-padded input sequences for encoder and decoder.
we need them for training later. they are used with pack_padded_sequence()
and pad_packed_sequence() methods in pytorch '''
# finally we create data loaders for training and evaluation loops
return data, raw_data
def _to_2_lists(self, data):
inputs = []
targets = []
for sample in data:
inputs.append(sample[0])
targets.append(sample[1])
return inputs, targets
def _filter_by_lengths(self, data, max_input_len, max_target_len):
length = len(data[0])
i = 0
while i < length:
if (len(data[0][i]) > max_input_len) or (len(data[1][i]) > max_target_len):
data[0].pop(i)
data[1].pop(i)
length -= 1
i -= 1
i += 1
return data
def _as_supervised_pad_list_of_sequences(self, data, padding='post'):
inputs = data[0]
decoder_inputs = [t[:-1] for t in data[1]]
# we excluded the end of string token from the decoder_inputs
targets = [t[1:] for t in data[1]]
# we excluded the start of string token from the targets
encoder_lengths = torch.LongTensor(self._get_lengths_from_sequences(inputs))
decoder_lengths = torch.LongTensor(self._get_lengths_from_sequences(targets))
inputs = torch.from_numpy(pad_sequences(inputs, padding=padding)).type(torch.LongTensor)
targets = torch.from_numpy(pad_sequences(targets, padding=padding)).type(torch.LongTensor)
decoder_inputs = torch.from_numpy(pad_sequences(decoder_inputs, padding=padding)).type(torch.LongTensor)
to_be_returned = [inputs, decoder_inputs, targets, encoder_lengths, decoder_lengths]
return to_be_returned
def _get_lengths_from_sequences(self, sequences):
to_be_returned = []
for seq in sequences:
to_be_returned.append(len(seq))
return to_be_returned
def _create_data_loader(self, split, vocab_size, batch_size, max_input_len=400, max_target_len=150,
filters='!"#$%&()*+,-./:;=?@[\\]^_`{|}~\t\n', to_lower=True,
padding='post', shuffle=True, num_workers=6):
data = self._SummarizationDataset(self, split, vocab_size, max_input_len=max_input_len,
max_target_len=max_target_len,
filters='!"#$%&()*+,-./:;=?@[\\]^_`{|}~\t\n',
to_lower=True, padding=padding)
data = DataLoader(data, batch_size=batch_size, pin_memory=True,
shuffle=shuffle, num_workers=num_workers)
return data
def create_data_loaders(self, vocab_size, batch_size, num_workers=6, max_input_len=400,
max_target_len=150, filters='!"#$%&()*+,-./:;=?@[\\]^_`{|}~\t\n',
to_lower=True, padding='post'):
start = timer()
train = self._create_data_loader('train', vocab_size, batch_size, num_workers=num_workers,
max_input_len=max_input_len, max_target_len=max_target_len,
filters='!"#$%&()*+,-./:;=?@[\\]^_`{|}~\t\n',
to_lower=True, padding=padding)
test = self._create_data_loader('test', vocab_size, batch_size, num_workers=num_workers,
max_input_len=max_input_len, max_target_len=max_target_len,
filters='!"#$%&()*+,-./:;=?@[\\]^_`{|}~\t\n',
to_lower=True, padding=padding)
validation = self._create_data_loader('validation', vocab_size, batch_size, num_workers=num_workers,
max_input_len=max_input_len, max_target_len=max_target_len,
filters='!"#$%&()*+,-./:;=?@[\\]^_`{|}~\t\n',
to_lower=True, padding=padding)
print('preprocessing time: ', timer() - start)
return train, test, validation
def sequences_to_texts(self, sequences):
return self.tokenizer.sequences_to_texts(sequences)
def texts_to_sequences(self, texts):
return self.tokenizer.texts_to_sequences(texts)
def get_eos_token(self):
return self.texts_to_sequences([[self.eos_token]])
def get_sos_token(self):
return self.texts_to_sequences([[self.sos_token]])
def filters(self):
return self.tokenizer.filters
class _DataHolder:
def __init__(self, data):
self.data = data
def get_data(self, split):
assert type(split) == str
if split == 'train':
return self.data[0]
if split == 'test':
return self.data[1]
if split == 'validation':
return self.data[2]
else:
raise ValueError('valid values are: "train", "test", "validation".')
class _SummarizationDataset(Dataset):
def __init__(self, preprocessor, split, vocab_size, max_input_len=400,
max_target_len=150, filters='!"#$%&()*+,-./:;=?@[\\]^_`{|}~\t\n',
to_lower=True, padding='post'):
self.data, self.raw_data = preprocessor.load_preprocessed_data(split, vocab_size,
max_input_len=max_input_len, max_target_len=max_target_len,
filters=filters, to_lower=to_lower, padding=padding)
self.len = self.data[0].size(0)
def __getitem__(self, index):
data = (self.data[0][index], self.data[1][index], self.data[2][index],
self.data[3][index], self.data[4][index])
raw_data = self.raw_data[index]
return data, raw_data
def __len__(self):
return self.len
# Obtenemos los datos de salida para el entrenamiento
clases = list(sorted(set(t)))
size_s = len(clases) + 1
tok2 = Tokenizer()
tok2.fit_on_texts(clases)
tokens_s = tok2.texts_to_sequences(t)
y_train = [np.add.reduce(to_categorical(Y, size_s)) for Y in tokens_s]
y_train = np.array(y_train)
# CREAMOS LA RED NEURONAL CON KERAS
model = keras.models.load_model('model.h5')
model.summary()
data = "I need to create a new account"
data = text_to_word_sequence(data)
secuence = tok.texts_to_sequences(data)
encode = np.add.reduce(to_categorical(secuence, size))
print("secuence -> {0}".format(secuence))
print("-" * 50)
print(np.argmax(to_categorical(secuence, size), axis=1))
print("-" * 50)
a = np.rint(model.predict(np.array([encode])))
print(a)
i = np.argmax(a, axis=None, out=None)
print(tok2.sequences_to_texts(np.array([[i]])))
plt.show()
# generate the next 10 words in a sentence
best_model = load_model(model_file)
start = 'Today as i was leaving for work'
test_seqs = tokenizer.texts_to_sequences([start])
for i in range(10):
test_seqs_padded = pad_sequences(test_seqs, maxlen=sequence_len,
padding='pre', truncating='pre')
# use best_model to generate the next word
# (remember to convert from categorical to ordinal)
# TODO: Replace _ANS_ with your answers
next_word = best_model.predict([test_seqs_padded]).argmax(axis=1)
test_seqs[0].append(next_word[0])
print(tokenizer.sequences_to_texts(test_seqs))
def next_char(text, temperature=1):
x_new = preprocess([text])
y_proba = model.predict(x_new)[0, -1:, :]
rescaled_logit = tf.math.log(y_proba) / temperature
char_id = tf.random.categorical(rescaled_logit, num_samples=1) + 1
return tokenizer.sequences_to_texts(char_id.numpy())[0]
model.compile(
loss='categorical_crossentropy',
optimizer='adam',
metrics=['acc'])
model.summary()
# *******
# train the model on X (values) and Y (labels)
# *******
history = model.fit(X, Y, epochs=num_epochs, verbose=0) # use verbose= 1 or 2 for output on training
# ***************************************************************
#
# predict
#
# ***************************************************************
sentences = ["i have a cute fluffy cat", "the cat is fluffy", "i like to dance"]
sequences = tokenizer.texts_to_sequences(sentences)
decoded_sentences = tokenizer.sequences_to_texts(sequences)
padded_sequences = pad_sequences(sequences, maxlen=max_sequence_len)
predictions = model.predict(padded_sequences)
for i, prediction in enumerate(predictions):
category = category_tags[np.argmax(prediction)]
print(f"----- sekntence {i} -----")
print(f"decoded text: {decoded_sentences[i]}")
print(f"sentence: {sentences[i]} | tag: {category} | prediction values: {prediction} | max prediction index: {np.argmax(prediction)}")
LSTM.fit(x_train,
y_train,
batch_size=64,
epochs=30,
validation_split=0.1,
callbacks=[early_stopping_cb, reduce_learing_cb])
#prediction
string = "I really want to go to a beautiful place"
index_list = tokenizer.texts_to_sequences([string])[0]
for i in range(4):
input_ = np.array(index_list[i:i + max_len - 1]).reshape(1, max_len - 1)
predicted_results = LSTM.predict(input_)
index_list.append(np.argmax(predicted_results))
word_list = tokenizer.sequences_to_texts([index_list])
output = " ".join(word_list)
print(output)
dump(tokenizer, open("tokenizer.pkl", "wb"))
#creating training samples
max_len = 40
x = []
y = []
for i in range(0, len(clean_tokens) - max_len - 1, 8):
x_temp = clean_tokens[i:i + max_len]
x_temp2 = [wordindex_dic.get(word, 0) for word in x_temp]
y_temp = clean_tokens[i + 1:i + max_len + 1]
y_temp2 = [wordindex_dic.get(word, 0) for word in y_temp]
x.append(x_temp2)
# generated_sequence = generated_sequence.reshape(1,308)
for word in range(max_len):
#ip_one_hot = one_hot(generated_sequence, num_classes)
generated_sequence = generated_sequence.reshape(1,308)
prediction = model.predict(
generated_sequence[None], verbose = 0)[0]
sampled_token = np.random.choice(
np.arange(num_classes), p=prediction)
#print(generated_sequence)
generated_sequence = np.append(
generated_sequence[0,1:],sampled_token)
print(generated_sequence)
#print(generated_sequence)
#generated_sequence = generated_sequence.reshape(1,308)
#generated_sequence = generated_sequence.astype(int)
generated_txt = tk.sequences_to_texts([generated_sequence])[0]
print("Sample {}: {}".format(i, generated_txt))
plt.plot(history.history['accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train accuracy'], loc='upper left')
plt.show()
# summarize history for loss
plt.plot(history.history['loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')