def update_model(subreddit, text_array):
debug = False
#Path to the dictionary
path_to_dict = r'../models/wikidump_wordids/wikidump_wordids.txt'
#Path to the folder full of models pertaining to certain subreddits
path_to_models_folder = '../models/' + subreddit + '/'
#Path to where we're gonna save the model
path_to_model = path_to_models_folder + 'model.model'
path_to_load = path_to_models_folder + 'model.model'
#Load model
model = gensim.models.LdaModel.load(path_to_load)
#Loading in dictionary
dct = Dictionary()
dct.add_documents([text_array])
id2word = dct
#Cleaning up the text
common_corpus = [clean_text(text) for text in text_array]
#Debug Print
if debug:
print(common_corpus)
#Training the new model
model.update(common_corpus)
#Save the new model
model.save(path_to_model)
def extract_dictionary(document_paths, max_words):
"""
Extracts a gensim Dictionary object from a set of documents.
Parameters
----------
document_paths : [str]
List of document paths that make up the corpus.
Returns
-------
dictionary : gensim.corpora.Dictionary
Extracted dictionary (or tokenizer).
"""
print("Extracting dictionary from corpus")
dictionary = Dictionary(prune_at=None)
preprocessor = TextPreprocessor()
for document_path in tqdm(document_paths):
with open(document_path, "r") as f:
document = f.read()
document = preprocessor.clean_sentence(document,
alphabetic_only=True)
words = preprocessor.tokenize_text(document)
dictionary.add_documents([words])
if len(dictionary) > max_words:
start = time()
dictionary.filter_extremes(no_below=10,
no_above=0.5,
keep_n=int(max_words * 0.9))
print("Dictionary filtered in {} seconds".format(time() -
start))
return dictionary
class MyCorpus(object):
def __init__(self, input_file, K):
self.K = K
self.input_file = input_file
self.dictionary = Dictionary()
with open(input_file, "rt") as f:
for line in f:
self.dictionary.add_documents([line.split()])
self.dictionary.filter_extremes(no_below = 2, no_above = 0.5, keep_n = K)
def __iter__(self):
count = 1
with open(self.input_file, "rt") as f:
count += 1
for line in f:
yield self.dictionary.doc2bow(line.rstrip().split())
def __str__(self):
s = "MyCorpus(" + str(self.dictionary.num_docs) + " documents, "
s += str(len(self.dictionary.keys())) + " features, "
s += str(corpus.dictionary.num_nnz) + " non-zero entries)"
return s
def __repr__(self):
return "MyCorpus('" + self.input_file + "', " + str(self.K) + ")"
class CorpusMUC(corpora.TextCorpus):
def __init__(self):
super(TextCorpus, self).__init__()
self.stopwords = NLTKStopwords.words('english')
self.stopwords.extend(['``', ',', '(', ')', '.'])
self.msgs = MUCmessages()
self.dictionary = Dictionary()
self.dictionary.add_documents(self.get_texts())
def get_texts(self):
"""
Parse documents from the .cor file provided in the constructor. Lowercase
each document and ignore some stopwords.
.cor format: one document per line, words separated by whitespace.
"""
for doc in self.msgs:
document = [
word for word in [
word_tokenize(sentence) for sentence in wordpunct_tokenize(
doc[1]['content'].lower())
]
]
yield [
str(word[0]) for word in document
if str(word[0]) not in self.stopwords
]
def __len__(self):
"""Define this so we can use `len(corpus)`"""
if 'length' not in self.__dict__:
logger.info(
"caching corpus size (calculating number of documents)")
self.length = sum(1 for doc in self.get_texts())
return self.length
def __init__(self, movie_tags):
"""
movie_tags: dict {item_id => tags}
"""
self.item_vectors = {}
self.id_to_idx = {}
self.idx_to_id = []
dictionary = Dictionary()
for item, tags in movie_tags.items():
dictionary.add_documents([tags])
for item, tags in movie_tags.items():
self.item_vectors[item] = dictionary.doc2bow(tags)
data = []
row_ind = []
col_ind = []
i = 0
for item, tags in self.item_vectors.items():
for (col, count) in tags:
data.append(count)
row_ind.append(i)
col_ind.append(col)
self.id_to_idx[item] = i
self.idx_to_id.append(item)
i += 1
self.item_vectors = csr_matrix((data, (row_ind, col_ind)),
shape=(i, dictionary.num_pos))
def create_dictionary(doc_dict):
# create gensim dictionary by using python dictionary as input
dic = Dictionary()
for doc in doc_dict:
dic.add_documents([[word for (word, _) in doc_dict[doc]]])
dic.save(dic_path)
return dic
def _generate_vocabulary(self):
vocab = Dictionary()
session = DBSession()
i = 0
for question in session.query(Question).yield_per(self.yield_per):
i += 1
if i % self.print_per == 0:
logger.info('Processed %d / %d questions :: %d unique tokens' % (i, self.n_questions, vocab.num_docs))
strings = [question.title, question.content] if question.content is not None else [question.title]
vocab.add_documents([CorpusDictionary.tokenize(s) for s in strings])
i = 0
for answer in session.query(Answer).yield_per(self.yield_per):
i += 1
if i % self.print_per == 0:
logger.info('Processed %d / %d answers :: %d unique tokens' % (i, self.n_answers, vocab.num_docs))
vocab.add_documents([CorpusDictionary.tokenize(answer.content)])
# commit and close the session
session.commit()
session.close()
return vocab
def dic_creation(ids):
for phase in ['train', 'val']:
print(phase)
with open('data/preprocessed_{}.pickle'.format(phase),
'rb') as preprocessed:
preprocessed_dict = pickle.load(preprocessed)
article_txts = []
for id_ in tqdm(ids[phase]):
article_txts.append(preprocessed_dict[id_]['article_text'])
# article_txts = list(map(lambda id_: preprocessed_dict[id_]['article_text'], ids[phase]))
print("loaded text")
article_tokens = []
for txt in tqdm(article_txts):
article_tokens.append(word_tokenize(txt))
# article_tokens = list(map(lambda txt: word_tokenize(txt), article_txts))
print("tokenized")
if phase == 'train':
dct = Dictionary(article_tokens)
else:
with open('data/dict.pickle', 'rb') as handle:
dct = pickle.load(handle)
dct.add_documents(article_tokens)
print("dict processed")
# save dict
with open('data/dict.pickle', 'wb') as handle:
pickle.dump(dct, handle, protocol=pickle.HIGHEST_PROTOCOL)
print("saved")
def create_dic():
txt_in = get_in()
txt_out = get_out()
dic = Dictionary(txt_in+txt_out+[[EXTRA]])
dic.add_documents([[START,STOP,EXTRA]])
dic.save(DIC_NAME)
return dic
def trainModel():
""" Train a model
"""
if args.mode == 'Random':
return args.topics, 0
# need to train on dump
files = [
f"{args.input}/{f}" for f in os.listdir(args.input)
if os.path.isfile(os.path.join(args.input, f))
]
if args.mode == 'LDA':
# create dictionary
with open(files[0], "r", encoding='utf-8') as f:
dct = Dictionary([' '.join(f.readlines()).split()])
for filename in files[1:]:
with open(filename, "r", encoding='utf-8') as f:
dct.add_documents([' '.join(f.readlines()).split()])
# create corpus
corpus = []
for filename in files:
with open(filename, "r", encoding='utf-8') as f:
corpus.append(dct.doc2bow(' '.join(f.readlines()).split()))
lda = LdaModel(corpus, num_topics=args.topics)
lda.save("./models/LDAdump.model")
dct.save("./models/LDAdump.dct")
return lda, dct
if args.mode == 'loadLDA':
return LdaModel.load("./models/LDAdump.model"), Dictionary.load(
"./models/LDAdump.dct")
def preprocess(segments, dct=None, bigram=None):
processed_segments = []
for seg in segments:
processed_seg = []
for word in seg:
if True in [word.is_space, word.is_stop, word.is_punct]:
continue
word = word.lemma_
word = word.lower()
processed_seg.append(word)
processed_segments.append(processed_seg)
if bigram is None:
phrases = Phrases(processed_segments, min_count=3, threshold=3)
bigram = Phraser(phrases)
processed_segments = bigram[processed_segments]
if dct is None:
dct = Dictionary(processed_segments)
else:
dct.add_documents(processed_segments)
return [dct.doc2bow(line)
for line in processed_segments], dct, processed_segments, bigram
def build_tag_vectors(tag_directory_path):
"""Loads tag files, builds sparse vectors for each song
Parameters
----------
tag_directory_path : String, path of directory containing tags
Returns
-------
id_vec_mapping : dict (song id => list[tuple(tagId, count)])
dictionary : gensim Dictionary containing all tags and ids
"""
dictionary = Dictionary()
for f in listdir(tag_directory_path):
with open(tag_directory_path+"/"+f, 'r') as tags:
tokens = tags.read().split(sep=' ')
dictionary.add_documents([tokens])
dictionary.filter_extremes(no_below=2, no_above=0.5)
dictionary.compactify()
id_vec_mapping = {}
for f in listdir(tag_directory_path):
song_id = f[0:-4]
with open(tag_directory_path+"/"+f, 'r') as tags:
tokens = tags.read().split(sep=' ')
sparse_vec = dictionary.doc2bow(tokens)
add_to_dictionary(id_vec_mapping, (song_id, sparse_vec))
return id_vec_mapping, dictionary
class KeywordDict():
# dictionary.bin 있으면 로드하고 없으면 새로 만들고 저장
def __init__(self, name="default", phraser=None):
self.name = "dictionary_" + name + ".bin"
self.phraser = phraser
if self.name in os.listdir(dir_dictionary):
with open(dir_dictionary + self.name, 'rb') as dic:
self.get_dict = pickle.load(dic)
print("keyword dictionary loaded")
else:
print("dictionary not exists")
print("start building...")
self.build_dictionary()
self.save()
# 사전 만드는 함수
def build_dictionary(self):
self.get_dict = Dictionary()
tickers = [
i for i in os.listdir(dir_cleaned_news) if i.endswith(".csv")
]
for ticker in tickers:
df = pd.read_csv(dir_cleaned_news + ticker, index_col=0)
self.get_dict.add_documents(tokenizer(df['content'], self.phraser))
print(ticker + " added")
print("done")
# 저장
def save(self):
with open(dir_dictionary + self.name, "wb") as dic:
pickle.dump(self.get_dict, dic)
def preprocess(documents,
stem=False,
vocab_size=10000,
oov_token="<OOV>",
oov_id=-1):
"""Preprocess documents.
Args:
documents: An array of strings, each string representing a document.
stem: (bool) Whether to use a stemmer. Defaults to False.
Returns:
(gensim Dictionary, tokenized documents)
"""
porter_stemmer = PorterStemmer()
def process_document(doc):
tokens = word_tokenize(doc)
tokens = [token.lower() for token in tokens if token.isalpha()]
if stem:
tokens = [porter_stemmer.stem(token) for token in tokens]
return tokens
tokenized_docs = list(map(process_document, documents))
dictionary = Dictionary(tokenized_docs)
dictionary.filter_extremes(no_below=5, no_above=0.8, keep_n=vocab_size)
# Add OOV to dictionary
dictionary.add_documents([["<OOV>"]])
return dictionary, tokenized_docs
def _generate_vocabulary(self):
vocab = Dictionary()
session = DBSession()
i = 0
for question in session.query(Question).yield_per(self.yield_per):
i += 1
if i % self.print_per == 0:
logger.info('Processed %d / %d questions :: %d unique tokens' %
(i, self.n_questions, vocab.num_docs))
strings = [question.title, question.content
] if question.content is not None else [question.title]
vocab.add_documents(
[CorpusDictionary.tokenize(s) for s in strings])
i = 0
for answer in session.query(Answer).yield_per(self.yield_per):
i += 1
if i % self.print_per == 0:
logger.info('Processed %d / %d answers :: %d unique tokens' %
(i, self.n_answers, vocab.num_docs))
vocab.add_documents([CorpusDictionary.tokenize(answer.content)])
# commit and close the session
session.commit()
session.close()
return vocab
def testFilterTokens(self):
self.maxDiff = 10000
d = Dictionary(self.texts)
removed_word = d[0]
d.filter_tokens([0])
expected = {
'computer': 0,
'eps': 8,
'graph': 10,
'human': 1,
'interface': 2,
'minors': 11,
'response': 3,
'survey': 4,
'system': 5,
'time': 6,
'trees': 9,
'user': 7
}
del expected[removed_word]
self.assertEqual(sorted(d.token2id.keys()), sorted(expected.keys()))
expected[removed_word] = len(expected)
d.add_documents([[removed_word]])
self.assertEqual(sorted(d.token2id.keys()), sorted(expected.keys()))
def create_dictionary(dataset, texts):
dict = Dictionary([])
for text in texts:
dict.add_documents([text])
dict.save_as_text('../dataset_files/dictionary.txt')
return dict
def _bow(table,
input_col,
add_words=None,
no_below=1,
no_above=0.8,
keep_n=10000):
word_list = table[input_col].tolist()
dictionary = Dictionary(word_list)
if add_words != None:
dictionary.add_documents([add_words])
dictionary.filter_extremes(no_below=no_below,
no_above=no_above,
keep_n=keep_n,
keep_tokens=None)
params = {
'Input Column': input_col,
'Minimum Number of Occurrence': no_below,
'Maximum Fraction of Occurrence': no_above,
'Keep N most Frequent': keep_n
}
empty_description = ''
if len(list(dictionary.dfs.values())) == 0:
out_table = pd.DataFrame([], columns=['token', 'document_frequency'])
empty_description = 'Out table is empty since parameter \"Minimum Number of Occurrence\" is greater than the maximum of document frequency.'
else:
out_table = pd.DataFrame.from_dict(dictionary.token2id,
orient='index').drop([0], axis=1)
out_table.insert(loc=0,
column='token',
value=dictionary.token2id.keys())
token_cnt = sorted(dictionary.dfs.items(), key=operator.itemgetter(0))
dfs_list = []
for i in range(len(dictionary.dfs)):
dfs_list.append(token_cnt[i][1])
out_table['document_frequency'] = dfs_list
rb = BrtcReprBuilder()
rb.addMD(
strip_margin("""
|# Bag of Words Result
|### Parameters
|
| {display_params}
|
| {description}
|
""".format(display_params=dict2MD(params),
description=empty_description)))
model = _model_dict('bow')
model['dict_table'] = out_table
model['dictionary'] = dictionary
model['add_words'] = add_words
model['_repr_brtc_'] = rb.get()
return {'model': model, 'out_table': out_table}
def load_dictionary(self, filepath):
dictionary = Dictionary()
with open(filepath, "rb") as f:
for line in f.readlines():
# example = SampleTrainingExample(line)
# context = example.context
dictionary.add_documents([[word.lower() for word in line.split()]])
return dictionary
def get_coherence(config, topicvec, docwords, glove_vectors):
"""Calculate UMass and w2v (GloVe) coherence scores."""
# Clean docwords
docs = []
for word_list in docwords:
doc = word_list[0]
docs.append(doc)
# Clean topics
byte_topics = topicvec.printTopWordsInTopics(topicvec.docs_theta, True)
word_topics = []
one_word = []
for topic in byte_topics:
new_topic = []
for word in topic:
if type(word) != str:
word = word.decode()
new_topic.append(word)
one = [word]
one_word.append(one)
word_topics.append(new_topic)
# Get dictionary
vocab_dict = Dictionary(docs[1:])
# Make sure words in topics are in the dictionary
vocab_dict.add_documents(one_word)
# Get corpus
corpus = [vocab_dict.doc2bow(doc) for doc in docs]
# Calculate UMass coherence score
# The closer to 0, the more coherent
cm = CoherenceModel(topics=word_topics,
corpus=corpus,
dictionary=vocab_dict,
coherence='u_mass')
umass_coherence = cm.get_coherence()
# Calculate GloVe coherence score
# Ranges between 0 and 1
# The closer to 1, the better
cm = CoherenceModel(topics=word_topics,
corpus=corpus,
dictionary=vocab_dict,
coherence='c_w2v',
keyed_vectors=glove_vectors)
glove_coherence = cm.get_coherence()
# Log coherence score and other metrics
results_dict = OrderedDict([('num_topics', config['K']),
('alpha0', config['alpha0']),
('alpha1', config['alpha1']),
('delta', config['iniDelta']),
('umass', umass_coherence),
('glove', glove_coherence)])
return results_dict
def build_vocabulary_and_corpus():
'''
Build the vocabularies and stem sequences for each type of entities.
'''
# Vocabulary (same for question and answers)
v = Dictionary()
# Stemmer.
stemmer = PorterStemmer()
# Tokenizer.
tokenizer = TweetTokenizer()
# Read indexes
user_index, question_index, answer_index, comment_index = read_indexes()
# Question, answer
q = {}
a = {}
# Read entities.
with open(entity_path, 'rb') as obj:
entities = pickle.load(obj)
# Browse question and answers to first build vocabulary.
for e in entities:
# Question or answer.
if e['type'] == 'Q' or e['type'] == 'A':
# String content.
title = str(e['title']).encode('utf-8').lower()
content = str(e['content']).encode('utf-8').lower()
# Tokenize
d = tokenizer.tokenize(title + content)
# Stem word
d = [stemmer.stem(s) for s in d]
# Process vocabulary.
v.add_documents([d])
# Question
if e['type'] == 'Q':
q[question_index[e['id']]] = d
# Answer
if e['type'] == 'A':
a[answer_index[e['id']]] = d
# Write question corpus.
with open(os.path.join(data_path, 'q.corpus'), 'wb') as f:
pickle.dump(q, f)
# Write answer corpus.
with open(os.path.join(data_path, 'a.corpus'), 'wb') as f:
pickle.dump(a, f)
# Write to analyse.
v.filter_extremes(no_below=1000, keep_n=10000)
v.compactify()
v.save(os.path.join(data_path, "raw_vocabulary.gensim"))
class LoadCorpora(Component, TextCorpus):
"""
Load corpus:
input is an array with a list of json files
"""
def __init__(self, input_files=None):
"""Redefine the gensim's TextCorpus init method"""
super().__init__()
self.input = input_files
self.dictionary = Dictionary(prune_at=5000000)
self.metadata = False
if input_files is not None:
self.dictionary.add_documents(self.get_texts(), prune_at=5000000)
else:
self.logger.warning(
"No input document stream provided; assuming "
"dictionary will be initialized some other way.")
def get_texts(self):
"""
Iterate through documents:
yield each token on each document
"""
if not isinstance(self.input, list):
raise ConfigError('Input argument is not a List')
for filename in self.input: # each file
with open(filename, 'r') as stream:
for line in stream: # each line
doc = json.loads(line)
yield doc['content'].split() # split on each word
def __iter__(self):
"""
Iterate through documents:
yield the bow representation of each document
"""
if not isinstance(self.input, list):
raise ConfigError('Input argument is not a List')
for filename in self.input: # each file
with open(filename, 'r') as stream:
for line in stream: # each line
doc = json.loads(line)
yield self.dictionary.doc2bow(doc['content'].split())
def save(self):
"""Override abstract method"""
return
def save_corpus(self, fname, corpus, id2word=None, metadata=False):
"""Override abstract method"""
return
def create_dict(self, corpus_file):
dictionary = Dictionary();
with open(corpus_file,"rb") as infile:
lines = infile.readlines() #reads single line from file
for line in lines:
doc = line #.split() #doc as bag of words (bow) of tokens in this line
dictionary.add_documents([doc])
#infile.close()
return dictionary
def test_save_load():
dct = Dictionary()
docs = ['一种 大头菜 自然风', '风 主要 包括 大头菜 风', '架 主要 包括 底座 支柱']
docs_token_list = get_token_lists_of_docs(docs)
dct.add_documents(docs_token_list)
corpus = [dct.doc2bow(['大头菜', '风', '底座'])]
print('corpus to save is {}'.format(corpus))
save_path = 'resources/corpus/test_corpus.mm'
save2disk(save_path, corpus)
load_corpus = load_from_disk(save_path)
print('load corpus is {}'.format(load_corpus))
def get_dictionary(documents: Dict[int, List[str]]) -> Dictionary:
if os.path.exists(DICTIONARY_FILE_NAME):
print(f"loading dictionary from {DICTIONARY_FILE_NAME}")
gensim_dict = Dictionary.load(DICTIONARY_FILE_NAME)
else:
print("creating dictionary")
gensim_dict = Dictionary()
gensim_dict.add_documents(documents.values())
gensim_dict.compactify()
print(f"saving dictionary to {DICTIONARY_FILE_NAME}")
gensim_dict.save(DICTIONARY_FILE_NAME)
return gensim_dict
def testFilterKeepTokens_keepn(self):
# keep_tokens should also work if the keep_n parameter is used, but only
# to keep a maximum of n (so if keep_n < len(keep_n) the tokens to keep are
# still getting removed to reduce the size to keep_n!)
d = Dictionary(self.texts)
# Note: there are four tokens with freq 3, all the others have frequence 2
# in self.texts. In order to make the test result deterministic, we add
# 2 tokens of frequency one
d.add_documents([['worda'], ['wordb']])
# this should keep the 3 tokens with freq 3 and the one we want to keep
d.filter_extremes(keep_n=5, no_below=0, no_above=1.0, keep_tokens=['worda'])
expected = {'graph', 'trees', 'system', 'user', 'worda'}
self.assertEqual(set(d.token2id.keys()), expected)
def testFilterKeepTokens_keepn(self):
# keep_tokens should also work if the keep_n parameter is used, but only
# to keep a maximum of n (so if keep_n < len(keep_n) the tokens to keep are
# still getting removed to reduce the size to keep_n!)
d = Dictionary(self.texts)
# Note: there are four tokens with freq 3, all the others have frequence 2
# in self.texts. In order to make the test result deterministic, we add
# 2 tokens of frequency one
d.add_documents([['worda'], ['wordb']])
# this should keep the 3 tokens with freq 3 and the one we want to keep
d.filter_extremes(keep_n=5, no_below=0, no_above=1.0, keep_tokens=['worda'])
expected = {'graph', 'trees', 'system', 'user', 'worda'}
self.assertEqual(set(d.token2id.keys()), expected)
class Text2BowTransformer(TransformerMixin, BaseEstimator):
"""
Base Text2Bow module
"""
def __init__(self, prune_at=2000000, tokenizer=tokenize):
"""
Sklearn wrapper for Text2Bow model.
"""
self.gensim_model = None
self.prune_at = prune_at
self.tokenizer = tokenizer
def fit(self, X, y=None):
"""
Fit the model according to the given training data.
"""
tokenized_docs = list(map(lambda x: list(self.tokenizer(x)), X))
self.gensim_model = Dictionary(documents=tokenized_docs,
prune_at=self.prune_at)
return self
def transform(self, docs):
"""
Return the BOW format for the input documents.
"""
if self.gensim_model is None:
raise NotFittedError(
"This model has not been fitted yet. Call 'fit' with appropriate arguments before using this method."
)
# input as python lists
check = lambda x: [x] if isinstance(x, string_types) else x
docs = check(docs)
tokenized_docs = list(map(lambda x: list(self.tokenizer(x)), docs))
X = [[] for _ in range(0, len(tokenized_docs))]
for k, v in enumerate(tokenized_docs):
bow_val = self.gensim_model.doc2bow(v)
X[k] = bow_val
return X
def partial_fit(self, X):
if self.gensim_model is None:
self.gensim_model = Dictionary(prune_at=self.prune_at)
tokenized_docs = list(map(lambda x: list(self.tokenizer(x)), X))
self.gensim_model.add_documents(tokenized_docs)
return self
class Text2BowTransformer(TransformerMixin, BaseEstimator):
"""
Base Text2Bow module
"""
def __init__(self, prune_at=2000000, tokenizer=tokenize):
"""
Sklearn wrapper for Text2Bow model.
"""
self.gensim_model = None
self.prune_at = prune_at
self.tokenizer = tokenizer
def fit(self, X, y=None):
"""
Fit the model according to the given training data.
"""
tokenized_docs = [list(self.tokenizer(x)) for x in X]
self.gensim_model = Dictionary(documents=tokenized_docs, prune_at=self.prune_at)
return self
def transform(self, docs):
"""
Return the BOW format for the input documents.
"""
if self.gensim_model is None:
raise NotFittedError(
"This model has not been fitted yet. Call 'fit' with appropriate arguments before using this method."
)
# input as python lists
check = lambda x: [x] if isinstance(x, string_types) else x
docs = check(docs)
tokenized_docs = [list(self.tokenizer(x)) for x in docs]
X = [[] for _ in range(0, len(tokenized_docs))]
for k, v in enumerate(tokenized_docs):
bow_val = self.gensim_model.doc2bow(v)
X[k] = bow_val
return X
def partial_fit(self, X):
if self.gensim_model is None:
self.gensim_model = Dictionary(prune_at=self.prune_at)
tokenized_docs = [list(self.tokenizer(x)) for x in X]
self.gensim_model.add_documents(tokenized_docs)
return self
def create_comment_dictionary(filter_every_n=1000,
max_iter=20,
from_db=True,
get_data_func=None):
comment_dict = Dictionary()
text_gen = data_preprocessor(max_iter=max_iter,
from_db=from_db,
get_data_func=get_data_func)
n_iter = 0
for _, stemmed_text, _ in text_gen:
comment_dict.add_documents([stemmed_text])
n_iter += 1
if n_iter % filter_every_n == 0:
comment_dict.filter_extremes()
return comment_dict
def build_vocab():
start = time.time()
test_path = os.path.join(config.DATA_PATH, 'test.csv')
train_path = os.path.join(config.DATA_PATH, 'train.csv')
normalized_text_path = os.path.join(config.PROCESSED_PATH, 'normalized_comments.txt')
bigram_path = os.path.join(config.PROCESSED_PATH, 'bigram')
bigram_comments_path = os.path.join(config.PROCESSED_PATH, 'bigram_commnets.txt')
if config.PROCESSED_PATH not in os.listdir(config.DATA_PATH):
try:
os.mkdir(config.PROCESSED_PATH)
except OSError:
pass
vocab = {}
train_df = read_file(train_path)
test_df = read_file(test_path)
print('tokenizing vocab file')
texts = np.concatenate([train_df.comment_text.fillna('N/A').values,
test_df.comment_text.fillna('N/A').values])
with open(normalized_text_path, 'w') as f:
processed_text = parallelize_dataframe(texts, tokenizer)
for line in processed_text:
f.write(line + '\n')
gc.collect()
lines = LineSentence(normalized_text_path)
bigram = Phrases(lines)
bigram.save(bigram_path)
phraser = Phraser(bigram)
with open(bigram_comments_path, 'w', encoding='utf_8') as f:
for comment in lines:
comm = u' '.join(phraser[comment])
f.write(comm + '\n')
commnets = LineSentence(bigram_comments_path)
bigram_dict = Dictionary(commnets)
bigram_dict.filter_extremes(no_below=config.THRESHOLD)
bigram_dict.save_as_text(config.VOCAB_PATH)
bigram_dict.add_documents([['<pad>']])
with open(os.path.join(config.ROOT, 'src', 'config.py'), 'a') as f:
f.write('VOCAB_SIZE = {}'.format(len(bigram_dict)))
print('time passed: {} minutes'.format((time.time() - start) / 60))
def build_hdp_vec(docs, targets, dct=None, hdp=None):
docs = [[str(o) for o in one] for one in docs]
if dct is None: # train set
dct = Dictionary(docs)
for one in docs:
dct.add_documents([[str(o) for o in one]])
copus = [dct.doc2bow(o) for o in docs]
if hdp is None: # train
hdp = HdpModel(copus, dct)
v = [hdp[o] for o in copus]
v_d = matutils.corpus2dense(v, num_terms=len(dct.token2id)).T
return copus, v_d, targets, dct, hdp
def testFilterTokens(self):
self.maxDiff = 10000
d = Dictionary(self.texts)
removed_word = d[0]
d.filter_tokens([0])
expected = {'computer': 0, 'eps': 8, 'graph': 10, 'human': 1,
'interface': 2, 'minors': 11, 'response': 3, 'survey': 4,
'system': 5, 'time': 6, 'trees': 9, 'user': 7}
del expected[removed_word]
self.assertEqual(sorted(d.token2id.keys()), sorted(expected.keys()))
expected[removed_word] = len(expected)
d.add_documents([[removed_word]])
self.assertEqual(sorted(d.token2id.keys()), sorted(expected.keys()))
def get_data_tokenizer(fromdate, todate):
print 'Starting get and save data from mysql-server into local folder....'
fromdate = fromdate + ' 00:00:00'
todate = todate + ' 23:59:59'
connection = my_connection.getConnection()
cursor = connection.cursor()
query = 'SELECT id, vntokenizer, catid FROM news WHERE create_time BETWEEN ' + '\'' + fromdate + '\' AND \'' + todate + '\';'
print query
cursor.execute(query)
rows = cursor.fetchall()
count = 0
token_dictionary = Dictionary()
data = dict()
for row in rows:
id = row[0]
tokenizer = row[1]
catid = row[2]
if tokenizer != None:
tokenizer = tokenizer.lower()
count += 1
print count
print tokenizer
token_list = tokenizer.split(' ')
valid_token_list = list()
for token in token_list:
if my_util.check_valid_token(token):
valid_token_list.append(token)
token_dictionary.add_documents([valid_token_list])
if catid == my_catid:
data[id] = valid_token_list
my_connection.closeConnection(connection)
# save dictionary and data into text file
token_dictionary.save_as_text('..' + parameter.FILE_DICTIONARY)
fb = open('..' + parameter.FILE_DATA, 'wb')
pickle.dump(data, fb)
fb.close()
print 'Done get and save data from mysql-server!'
class MyCorpus(object):
def __init__(self, filename, max_vocab_size = 2000000):
self.filename = filename
self.max_vocab_size = max_vocab_size
self.dictionary = Dictionary()
self._build_dict()
def _build_dict(self):
with open(self.filename, "rt") as f:
for line in f:
doc = line.rstrip().split()
self.dictionary.add_documents([doc])
def __iter__(self):
with open(self.filename, "rt") as f:
for sentence in f:
yield sentence.rstrip().split()
def main(path, epochs):
with open(path) as f:
ds = csv.DictReader(f)
tweets = (d['tweet_text'] for d in ds)
tweets = list(tweets)
dct = Dictionary()
with Pool() as pool:
dct.add_documents(pool.map(preprocess, tweets))
dct.filter_extremes(no_below=10, no_above=0.5)
model = SimpleEmbedder(dct, dims=100)
model.load_docs(tweets[-100000:])
for i in range(int(epochs)):
loss = model._epoch()
print(f'Epoch {i} loss: {loss[1]}')
np.save('embeddings', model.embeddings)
def compile_vocab(docs,
limit=1e6,
verbose=0,
tokenizer=Tokenizer(stem=None, lower=None, strip=None)):
"""Get the set of words used anywhere in a sequence of documents and assign an integer id
This vectorizer is much faster than the scikit-learn version (and only requires low/constant RAM ?).
>>> gen = ('label: ' + chr(ord('A') + i % 3)*3 for i in range(11))
>>> d = compile_vocab(gen, verbose=0)
>>> d
<gensim.corpora.dictionary.Dictionary ...>
>>> print(d)
Dictionary(4 unique tokens: [u'AAA', u'BBB', u'CCC', u'label'])
>>> sorted(d.token2id.values())
[0, 1, 2, 3]
>>> sorted(d.token2id.keys())
[u'AAA', u'BBB', u'CCC', u'label']
"""
tokenizer = make_tokenizer(tokenizer)
d = Dictionary()
try:
limit = min(limit, docs.count())
docs = docs.iterator()
except (AttributeError, TypeError):
pass
for i, doc in enumerate(docs):
# if isinstance(doc, (tuple, list)) and len(doc) == 2 and isinstance(doc[1], int):
# doc, score = docs
try:
# in case docs is a values() queryset (dicts of records in a DB table)
doc = doc.values()
except AttributeError: # doc already is a values_list
if not isinstance(doc, str):
doc = ' '.join([str(v) for v in doc])
else:
doc = str(doc)
if i >= limit:
break
d.add_documents([list(tokenizer(doc))])
if verbose and not i % 100:
log.info('{}: {}'.format(i, repr(d)[:120]))
return d
def __buildVectors(self, dataset_file):
lines = 0
dct = Dictionary()
tmp_file = TemporaryFile(mode='w+t', encoding='utf-8')
for doc_idx, (document,
lines) in enumerate(self.__buildDocument(dataset_file)):
dct.add_documents([document])
tmp_file.write(' '.join(document) + '\n')
if doc_idx % 500 == 499:
G.log.debug('%d', doc_idx)
if dct.num_docs < self.__LeastDocuments: # 字典字数太少或文档数太少，没必要聚类
tmp_file.close()
raise UserWarning('Too few records[%d]' % dct.num_docs)
# 去掉低频词，压缩字典
num_token = len(dct)
no_below = int(min(self.__NoBelow, int(dct.num_docs / 50)))
dct.filter_extremes(no_below=no_below,
no_above=0.999,
keep_n=self.__KeepN)
dct.compactify()
G.log.info(
'Dictionary[%d tokens, reduced from %d] built with [%s]. '
'[%d]records(%d lines, %d words) in %s', len(dct), num_token,
self.__ruleSet[0], dct.num_docs, lines, dct.num_pos, dataset_file)
if len(dct) < self.__LeastTokens: # 字典字数太少,重新采样
G.log.info('Too few tokens[%d], Re-sample with next RuleSet].' %
(len(dct)))
tmp_file.close()
return None, None
# 构造tf-idf词袋和文档向量
tfidf_model = TfidfModel(dictionary=dct, normalize=False)
vectors = np.zeros((dct.num_docs, len(dct)))
tmp_file.seek(0)
for doc_idx, new_line in enumerate(tmp_file):
for (word_idx, tf_idf_value) in tfidf_model[dct.doc2bow(
new_line.split())]: # [(id,tf-idf)...], id是升序
vectors[doc_idx, word_idx] = tf_idf_value
G.log.info('[%d*%d]Vectors built, %.2f%% non-zeros.' %
(dct.num_docs, len(dct),
dct.num_nnz * 100 / len(dct) / dct.num_docs))
tmp_file.close()
return dct, vectors
class DictionaryLearner(object):
'''Learn a gensim dictionary from all available documents.'''
def __init__(self, n=4):
'''Initialize a DictionaryLearner instance using vocabulary of ngrams of size `n`.'''
self._ngram = NgramTransformer(n)
self._dictionary = Dictionary()
def fit(self, documentstorage, filter_extremes=True):
'''Fit a dictonary using documents from given documentstorage.'''
for document in documentstorage.load_iterator(u''):
text_document = document.text
ngrams = self._ngram.transform([text_document])
self._dictionary.add_documents(ngrams)
if filter_extremes:
self._dictionary.filter_extremes()
def get(self):
return self._dictionary
class Text2BowTransformer(TransformerMixin, BaseEstimator):
"""
Base Text2Bow module
"""
def __init__(self, prune_at=2000000, tokenizer=tokenize):
"""
Sklearn wrapper for Text2Bow model.
"""
self.gensim_model = None
self.prune_at = prune_at
self.tokenizer = tokenizer
def fit(self, X, y=None):
"""
Fit the model according to the given training data.
"""
tokenized_docs = [list(self.tokenizer(x)) for x in X]
self.gensim_model = Dictionary(documents=tokenized_docs, prune_at=self.prune_at)
return self
def transform(self, docs):
"""
Return the BOW format for the input documents.
"""
if self.gensim_model is None:
raise NotFittedError(
"This model has not been fitted yet. Call 'fit' with appropriate arguments before using this method."
)
# input as python lists
if isinstance(docs, string_types):
docs = [docs]
tokenized_docs = (list(self.tokenizer(doc)) for doc in docs)
return [self.gensim_model.doc2bow(doc) for doc in tokenized_docs]
def partial_fit(self, X):
if self.gensim_model is None:
self.gensim_model = Dictionary(prune_at=self.prune_at)
tokenized_docs = [list(self.tokenizer(x)) for x in X]
self.gensim_model.add_documents(tokenized_docs)
return self
class SeriesCorpus(TextCorpus):
def __init__(self, series, vocab=None, stem=False, bigram=None,
labels=True):
""" Create a corpus that returns one row at a time out
of a Pandas Series"""
self.series = series
self.metadata = False
if vocab is not None:
vocab = set(vocab)
self.vocab = vocab
self.labels = labels
self.kwargs = dict(stem=stem, bigram=bigram)
logging.info("Building SeriesCorpus")
self.dictionary = Dictionary()
self.dictionary.add_documents(self.get_texts())
def __iter__(self):
if self.labels:
for index, line in zip(self.series.index, self.series.values):
label = ['SENT_%s' % str(index)]
ls = LabeledSentence(line.split(' '), label)
yield ls
else:
for index, line in self.series.index, self.series.values:
yield line.split(' ')
def line_iter(self, line):
if self.vocab is not None:
for word in line.split(' '):
if word in self.vocab:
yield word
else:
for word in line.split(' '):
yield word
def get_texts(self):
logging.info("Iterating SeriesCorpus")
for lineno, line in enumerate(self.series.values):
if self.metadata:
yield self.line_iter(line), (lineno,)
else:
yield self.line_iter(line)
def compile_vocab(docs, limit=1e6, verbose=0, tokenizer=Tokenizer(stem=None, lower=None, strip=None)):
"""Get the set of words used anywhere in a sequence of documents and assign an integer id
This vectorizer is much faster than the scikit-learn version (and only requires low/constant RAM ?).
>>> gen = ('label: ' + chr(ord('A') + i % 3)*3 for i in range(11))
>>> d = compile_vocab(gen, verbose=0)
>>> d
<gensim.corpora.dictionary.Dictionary ...>
>>> print(d)
Dictionary(4 unique tokens: [u'AAA', u'BBB', u'CCC', u'label'])
>>> sorted(d.token2id.values())
[0, 1, 2, 3]
>>> sorted(d.token2id.keys())
[u'AAA', u'BBB', u'CCC', u'label']
"""
tokenizer = make_tokenizer(tokenizer)
d = Dictionary()
try:
limit = min(limit, docs.count())
docs = docs.iterator()
except (AttributeError, TypeError):
pass
for i, doc in enumerate(docs):
# if isinstance(doc, (tuple, list)) and len(doc) == 2 and isinstance(doc[1], int):
# doc, score = docs
try:
# in case docs is a values() queryset (dicts of records in a DB table)
doc = doc.values()
except AttributeError: # doc already is a values_list
if not isinstance(doc, str):
doc = ' '.join([str(v) for v in doc])
else:
doc = str(doc)
if i >= limit:
break
d.add_documents([list(tokenizer(doc))])
if verbose and not i % 100:
log.info('{}: {}'.format(i, repr(d)[:120]))
return d
class Text2BowTransformer(TransformerMixin, BaseEstimator):
"""Base Text2Bow module , wraps :class:`~gensim.corpora.dictionary.Dictionary`.
For more information please have a look to `Bag-of-words model <https://en.wikipedia.org/wiki/Bag-of-words_model>`_.
"""
def __init__(self, prune_at=2000000, tokenizer=tokenize):
"""
Parameters
----------
prune_at : int, optional
Total number of unique words. Dictionary will keep not more than `prune_at` words.
tokenizer : callable (str -> list of str), optional
A callable to split a document into a list of each terms, default is :func:`gensim.utils.tokenize`.
"""
self.gensim_model = None
self.prune_at = prune_at
self.tokenizer = tokenizer
def fit(self, X, y=None):
"""Fit the model according to the given training data.
Parameters
----------
X : iterable of str
A collection of documents used for training the model.
Returns
-------
:class:`~gensim.sklearn_api.text2bow.Text2BowTransformer`
The trained model.
"""
tokenized_docs = [list(self.tokenizer(x)) for x in X]
self.gensim_model = Dictionary(documents=tokenized_docs, prune_at=self.prune_at)
return self
def transform(self, docs):
"""Get the BOW format for the `docs`.
Parameters
----------
docs : {iterable of str, str}
A collection of documents to be transformed.
Returns
-------
iterable of list (int, int) 2-tuples.
The BOW representation of each document.
"""
if self.gensim_model is None:
raise NotFittedError(
"This model has not been fitted yet. Call 'fit' with appropriate arguments before using this method."
)
# input as python lists
if isinstance(docs, string_types):
docs = [docs]
tokenized_docs = (list(self.tokenizer(doc)) for doc in docs)
return [self.gensim_model.doc2bow(doc) for doc in tokenized_docs]
def partial_fit(self, X):
"""Train model over a potentially incomplete set of documents.
This method can be used in two ways:
1. On an unfitted model in which case the dictionary is initialized and trained on `X`.
2. On an already fitted model in which case the dictionary is **expanded** by `X`.
Parameters
----------
X : iterable of str
A collection of documents used to train the model.
Returns
-------
:class:`~gensim.sklearn_api.text2bow.Text2BowTransformer`
The trained model.
"""
if self.gensim_model is None:
self.gensim_model = Dictionary(prune_at=self.prune_at)
tokenized_docs = [list(self.tokenizer(x)) for x in X]
self.gensim_model.add_documents(tokenized_docs)
return self
class MyCorpus(object):
'''
Corpus class for streaming review documents
'''
def __init__(self, file_list, file_dir, dictionary = None, mindf = MINDF, maxdf = MAXDF, \
maxwords = MAXWORDS, cluster_words = CLUSTER_WORDS, cluster_ul = CLUSTER_UL):
self.file_list = file_list # list of cuisine text files
self.file_dir = file_dir # directory of cuisine text files
self.maxwords = maxwords # maximum number of words to keep after building dictionary from clusters
self.cluster_words = cluster_words # maximum number of words to keep from each cluster
self.cluster_ul = cluster_ul # upper proportion of reviews to limit for cluster processing
self.mindf = mindf # minimum number of documents to keep word
self.maxdf = maxdf # max proportion of documents to keep word
self.agglomerate = True # return clusters as single documents (True) or return single reviews (False)
if dictionary:
self.dictionary = dictionary
else:
self.dictionary = Dictionary()
self._build_dict()
def __str__(self):
return "<MyCorpus at " + str(hex(id(self))) + ">"
def __repr__(self):
return self.__str__()
def _build_dict(self):
for filename in self.file_list:
dictionary = dict()
num_reviews = 0
with open(os.path.join(self.file_dir, filename), "rt") as f:
for line in f:
num_reviews += 1
words = line[REVIEW_INDEX:].split()
for word in set(words):
if word not in dictionary:
dictionary[word] = 1
else:
dictionary[word] += 1
doc = [item for item in dictionary.items() if dictionary[item[0]] > 2 and dictionary[item[0]] / num_reviews < self.cluster_ul]
doc.sort(key = lambda x: -x[1])
doc = [word for word, f in doc]
self.dictionary.add_documents([doc[:self.cluster_words]])
print("%s added to corpus dictionary!" % (filename,))
self.dictionary.filter_extremes(self.mindf, self.maxdf, self.maxwords)
self.dictionary.save("cuisine_dictionary.gensimDict")
def __iter__(self):
'''
Iterates through cuisines by combining all reviews for each cuisine into a single
processed document. Also stores the length of each processed document
'''
if self.agglomerate:
for filename in self.file_list:
with open(os.path.join(self.file_dir, filename), "rt") as f:
doc = " ".join([line[REVIEW_INDEX:].rstrip() for line in f])
yield self.dictionary.doc2bow(doc.split())
else:
reviewIDs = set()
for filename in self.file_list:
with open(os.path.join(self.file_dir, filename), "rt") as f:
for line in f:
id = line[:RATING_INDEX - 1]
if id not in reviewIDs:
reviewIDs.update([id])
doc = line[REVIEW_INDEX:].rstrip()
yield self.dictionary.doc2bow(doc.split())
def main():
parser = ArgumentParser(
description="wrapper script for churning datasets of wiki or elasticsearch kind through gensim to produce topic models please see gensim documentation for more information"
)
parser.add_argument("-ds", "--dataset", default="wiki", help="What kind of dataset to use. (wiki,es,file)")
parser.add_argument("-d", "--dump-file", help="Wiki: bz2 dump file with wiki in it")
parser.add_argument("-l", "--limit", help="Wiki: How many documents to extract from wiki")
parser.add_argument("--model-id", default="model", help="Filename for created model.")
parser.add_argument("--model-type", default="lsi", help="Model type (lsi, lda, word2vec, hdp, vocabulary).")
parser.add_argument("--n-topics", default=10, help="Number of topics to model.")
parser.add_argument("--n-passes", default=1, help="Number of passes for LDA model.")
parser.add_argument("--w2v-size", default=100, help="size of Word2Vec context.")
parser.add_argument("--w2v-window", default=5, help="window for Word2Vec.")
parser.add_argument("-q", "--query", default=None, help="Elasticsearch: Query to use to fetch documents")
parser.add_argument("--index", help="Elasticsearch: index to read from.")
parser.add_argument("--doc_type", default="doc", help="Elasticsearch: data type in index.")
parser.add_argument("--data-dir", help="Directory to save the generated models and vocabularies into.")
parser.add_argument("--vocab", help="Prebuilt Vocabulary file. Use this to avoid having to generate one.")
opts = parser.parse_args()
model_type = opts.model_type.lower()
if model_type not in ["lsi", "lda", "word2vec", "hdp", "vocabulary"]:
logging.error("Invalid model type %s" % model_type)
parser.print_usage()
exit(-1)
logging.info("Using model type %s" % model_type)
dump_fn = opts.dump_file
limit = int(opts.limit) if opts.limit else None
data_type = opts.dataset.lower()
if data_type not in ["es", "wiki", "file"]:
logging.error("Invalid dataset type %s" % data_type)
parser.print_usage()
exit(-1)
limit = None
if opts.limit:
limit = int(opts.limit)
if not dump_fn and data_type in ["wiki"]:
logging.error("--dump-file required for wiki dataset")
sys.exit(1)
query = opts.query
index = opts.index
doc_type = opts.doc_type
if data_type == "es" and index is None:
logging.error(
"Please be kind to at least specify the index you want to fetch from elasticsearch using the --index parameter"
)
sys.exit(1)
n_topics = int(opts.n_topics)
n_passes = int(opts.n_passes)
logging.info("Using %d topics." % n_topics)
data_dir = opts.data_dir
model_id = opts.model_id
model_fn = "%s_%s_%d" % (model_id, model_type, n_topics)
if data_dir:
model_fn = "%s/%s" % (data_dir, model_fn)
if model_type == "word2vec":
w2v_size = int(opts.w2v_size)
w2v_window = int(opts.w2v_window)
model_fn = "%s_w_%s_s_%s" % (model_fn, w2v_window, w2v_size)
logging.info("Writing models to %s." % model_fn)
if data_type == "es":
logging.info("Using data type %s with index %s, doc_type %s query %s" % (data_type, index, doc_type, query))
dataset = ElasticsearchDataset(
read_index=index, read_doc_type=doc_type, query=query, normalize_func=normalize_es
)
elif data_type == "wiki":
logging.info("Using data type %s with dump_file %s and limit %s" % (data_type, dump_fn, limit))
dataset = WikipediaDataset(dump_fn=dump_fn, num_articles=limit, normalize_func=normalize_wiki)
elif data_type == "file":
logging.info("Using data type %s with dump_file %s and limit %s" % (data_type, dump_fn, limit))
dataset = FileDataset(dump_fn=dump_fn, num_articles=limit, normalize_func=normalize_file)
vocab_file = opts.vocab
vocab = Dictionary()
sw = set(stopwords.words("norwegian"))
if not vocab_file or model_type == "vocabulary":
vocab.add_documents([get_tokenized(page, sw) for page in dataset])
vocab.filter_extremes()
vocab.compactify()
vocab.save(model_fn + ".vocab")
else:
vocab = Dictionary.load(vocab_file)
if model_type == "vocabulary":
return
tfidf = TfidfModel(dictionary=vocab)
if model_type == "lsi":
corpus = IterableDataset(dataset, sw, vocab)
model = LsiModel(corpus=tfidf[corpus], num_topics=n_topics, id2word=vocab)
elif model_type == "lda":
corpus = IterableDataset(dataset, sw, vocab)
model = LdaModel(corpus=tfidf[corpus], num_topics=n_topics, passes=n_passes, id2word=vocab)
elif model_type == "word2vec":
corpus = IterableDataset(dataset, sw, vocab, doc2bow=False)
corpus.dictionary = vocab
model = Word2Vec(sentences=corpus, window=w2v_window, size=w2v_size)
elif model_type == "hdp":
corpus = IterableDataset(dataset, sw, vocab)
model = HdpModel(corpus=tfidf[corpus], id2word=vocab)
logging.info(model)
model.save(model_fn)
class YahooDictionary:
def __init__(self, source_file, vocab_size=20000, max_ans_len=1000, max_sub_len=100, max_cont_len=500, dict_file_name=''):
assert os.path.exists(source_file), 'The file "%s" was not found' % source_file
self.source_file = source_file
self.vocab = Dictionary()
self.vocab_size = vocab_size
print('Creating XML tree...')
tree = ET.parse(source_file)
self.root = tree.getroot()
# maximum lengths for everything
self.max_ans_len = max_ans_len
self.max_sub_len = max_sub_len
self.max_cont_len = max_cont_len
print('Creating dictionary...')
self._create_dictionary()
@staticmethod
def tokenize(text):
return gensim.utils.tokenize(text, to_lower=True)
def _create_dictionary(self):
categories = set()
# create dictionary
for vespaadd in self.root.iter('vespaadd'):
doc = vespaadd.find('document')
subject_text = YahooDictionary.tokenize(doc.find('subject').text)
content_text = YahooDictionary.tokenize(doc.find('content').text)
self.vocab.add_documents([subject_text, content_text], prune_at=self.vocab_size)
# category
categories.add(doc.find('cat').text)
# answers
answers = [YahooDictionary.tokenize(answer.text) for answer in doc.find('nbestanswers').getchildren()]
self.vocab.add_documents(answers, prune_at=self.vocab_size)
self.cat_to_idx = dict((c, i+1) for i, c in enumerate(categories))
self.idx_to_cat = dict((i+1, c) for i, c in enumerate(categories))
def get_docs(self):
all_answers = []
all_subjects = []
all_contents = []
all_categories = []
# create dictionary
for vespaadd in self.root.iter('vespaadd'):
doc = vespaadd.find('document')
# subject and content
subject_text_iter = YahooDictionary.tokenize(doc.find('subject').text)
content_text_iter = YahooDictionary.tokenize(doc.find('content').text)
subject_enc = [self.vocab.token2id[x] for x in itertools.islice(subject_text_iter, self.max_sub_len)]
content_enc = [self.vocab.token2id[x] for x in itertools.islice(content_text_iter, self.max_cont_len)]
# category index
category = self.cat_to_idx[doc.find('cat').text]
# answers
answers = [YahooDictionary.tokenize(answer.text) for answer in doc.find('nbestanswers').getchildren()]
for answer in answers:
answer_enc = [self.vocab.token2id[x] for x in itertools.islice(answer, self.max_ans_len)]
all_categories.append(category)
all_subjects.append(subject_enc)
all_contents.append(content_enc)
all_answers.append(answer_enc)
return pad_sequences(all_answers, self.max_ans_len),\
pad_sequences(all_subjects, self.max_sub_len),\
pad_sequences(all_contents, self.max_cont_len),\
np.array(all_categories)