def load_document_as_bos(input_file,
language="en",
normalization="stemming",
stoplist=[]):
"""Load a document as a bag of words/stems/lemmas.
Args:
input_file (str): path to input file.
language (str): language of the input documents, used for stop_words
in sklearn CountVectorizer, defaults to 'en'.
normalization (str): word normalization method, defaults to 'stemming'.
Other possible values are 'lemmatization' or 'None' for using word
surface forms instead of stems/lemmas.
stoplist (list): the stop words for filtering tokens, default to [].
"""
# initialize load file object
doc = LoadFile()
# read the input file
doc.load_document(input=input_file,
language=language,
normalization=normalization)
# initialize document vector
vector = defaultdict(int)
# loop through the sentences and add the stems to the vector
for i, sentence in enumerate(doc.sentences):
for j, stem in enumerate(sentence.stems):
if stem in stoplist:
continue
vector[stem] += 1
return vector
def expand_word_graph(self,
input_file,
similarity,
window=10,
pos=None):
"""Expands the word graph using the given document.
Args:
input_file (str): path to the input file.
similarity (float): similarity for weighting edges.
window (int): the window within the sentence for connecting two
words in the graph, defaults to 10.
pos (set): the set of valid pos for words to be considered as nodes
in the graph, defaults to ('NOUN', 'PROPN', 'ADJ').
"""
# define default pos tags set
if pos is None:
pos = {'NOUN', 'PROPN', 'ADJ'}
# initialize document loader
doc = LoadFile()
# read document
doc.load_document(input=input_file,
language=self.language,
normalization=self.normalization)
# flatten document and initialize nodes
sequence = []
for sentence in doc.sentences:
for j, node in enumerate(sentence.stems):
if node not in self.graph and sentence.pos[j] in pos:
self.graph.add_node(node)
sequence.append((node, sentence.pos[j]))
# loop through sequence to build the edges in the graph
for j, node_1 in enumerate(sequence):
for k in range(j + 1, min(j + window, len(sequence))):
node_2 = sequence[k]
if node_1[1] in pos and node_2[1] in pos \
and node_1[0] != node_2[0]:
if not self.graph.has_edge(node_1[0], node_2[0]):
self.graph.add_edge(node_1[0], node_2[0], weight=0)
self.graph[node_1[0]][node_2[0]]['weight'] += similarity
def compute_document_frequency(
documents,
output_file,
language='en',
stoplist=None,
normalization='stemming',
delimiter='\t',
# TODO: What is the use case for changing this ?
n=3):
"""Compute the n-gram document frequencies from a set of input documents.
An extra row is added to the output file for specifying the number of
documents from which the document frequencies were computed
(--NB_DOC-- tab XXX). The output file is compressed using gzip.
Args:
documents (list): list of pke-readable documents.
output_file (str): the output file.
language (str): language of the input documents (used for computing the
n-stem or n-lemma forms), defaults to 'en' (english).
stoplist (list): the stop words for filtering n-grams, default to
pke.lang.stopwords[language].
normalization (str): word normalization method, defaults to
'stemming'. Other possible value is 'none' for using word surface
forms instead of stems/lemmas.
delimiter (str): the delimiter between n-grams and document
frequencies, defaults to tabulation (\t).
n (int): the size of the n-grams, defaults to 3.
"""
# document frequency container
frequencies = defaultdict(int)
# initialize number of documents
nb_documents = 0
# loop through the documents
for document in documents:
# initialize load file object
doc = LoadFile()
# read the input file
doc.load_document(input=document,
language=language,
stoplist=stoplist,
normalization=normalization)
# candidate selection
doc.ngram_selection(n=n)
# filter candidates containing punctuation marks
doc.candidate_filtering()
# loop through candidates
for lexical_form in doc.candidates:
frequencies[lexical_form] += 1
nb_documents += 1
if nb_documents % 1000 == 0:
logging.info("{} docs, memory used: {} mb".format(
nb_documents,
sys.getsizeof(frequencies) / 1024 / 1024))
# create directories from path if not exists
if os.path.dirname(output_file):
os.makedirs(os.path.dirname(output_file), exist_ok=True)
# dump the df container
with gzip.open(output_file, 'wt', encoding='utf-8') as f:
# add the number of documents as special token
first_line = '--NB_DOC--' + delimiter + str(nb_documents)
f.write(first_line + '\n')
for ngram in frequencies:
line = ngram + delimiter + str(frequencies[ngram])
f.write(line + '\n')
def compute_lda_model(documents,
output_file,
n_topics=500,
language="en",
stoplist=None,
normalization="stemming"):
"""Compute a LDA model from a collection of documents. Latent Dirichlet
Allocation is computed using sklearn module.
Args:
documents (str): list fo pke-readable documents.
output_file (str): the output file.
n_topics (int): number of topics for the LDA model, defaults to 500.
language (str): language of the input documents, used for stop_words
in sklearn CountVectorizer, defaults to 'en'.
stoplist (list): the stop words for filtering words, default to
pke.lang.stopwords[language].
normalization (str): word normalization method, defaults to
'stemming'. Other possible value is 'none'
for using word surface forms instead of stems/lemmas.
"""
# texts container
texts = []
# loop throught the documents
for document in documents:
# initialize load file object
doc = LoadFile()
# read the input file
doc.load_document(input=document,
language=language,
normalization=normalization)
# container for current document
text = []
# loop through sentences
for sentence in doc.sentences:
# get the tokens (stems) from the sentence if they are not
# punctuation marks
text.extend([
sentence.stems[i] for i in range(sentence.length)
if sentence.pos[i] != 'PUNCT' and sentence.pos[i].isalpha()
])
# add the document to the texts container
texts.append(' '.join(text))
# vectorize dataset
# get the stoplist from pke.lang because CountVectorizer only contains
# english stopwords atm
if stoplist is None:
stoplist = stopwords.get(language)
tf_vectorizer = CountVectorizer(stop_words=stoplist)
tf = tf_vectorizer.fit_transform(texts)
# extract vocabulary
vocabulary = tf_vectorizer.get_feature_names()
# TODO: deprecation warning: use get_feature_names_out
# create LDA model and train
lda_model = LatentDirichletAllocation(n_components=n_topics,
random_state=0,
learning_method='batch')
lda_model.fit(tf)
# save all data necessary for later prediction
saved_model = (vocabulary, lda_model.components_,
lda_model.exp_dirichlet_component_,
lda_model.doc_topic_prior_)
# Dump the df container
logging.info('writing LDA model to {}'.format(output_file))
# create directories from path if not exists
if os.path.dirname(output_file):
os.makedirs(os.path.dirname(output_file), exist_ok=True)
# dump the LDA model
with gzip.open(output_file, 'wb') as fp:
pickle.dump(saved_model, fp)
def compute_document_frequency(input_dir,
output_file,
extension='xml',
language='en',
normalization="stemming",
stoplist=None,
delimiter='\t',
n=3,
max_length=10**6,
encoding=None):
"""Compute the n-gram document frequencies from a set of input documents. An
extra row is added to the output file for specifying the number of
documents from which the document frequencies were computed
(--NB_DOC-- tab XXX). The output file is compressed using gzip.
Args:
input_dir (str): the input directory.
output_file (str): the output file.
extension (str): file extension for input documents, defaults to xml.
language (str): language of the input documents (used for computing the
n-stem or n-lemma forms), defaults to 'en' (english).
normalization (str): word normalization method, defaults to 'stemming'.
Other possible values are 'lemmatization' or 'None' for using word
surface forms instead of stems/lemmas.
stoplist (list): the stop words for filtering n-grams, default to None.
delimiter (str): the delimiter between n-grams and document frequencies,
defaults to tabulation (\t).
n (int): the size of the n-grams, defaults to 3.
encoding (str): encoding of files in input_dir, default to None.
"""
# document frequency container
frequencies = defaultdict(int)
# initialize number of documents
nb_documents = 0
# loop through the documents
for input_file in glob.iglob(input_dir + os.sep + '*.' + extension):
#logging.info('reading file {}'.format(input_file))
# initialize load file object
doc = LoadFile()
# read the input file
doc.load_document(input=input_file,
language=language,
normalization=normalization,
max_length=max_length,
encoding=encoding)
# candidate selection
doc.ngram_selection(n=n)
# filter candidates containing punctuation marks
doc.candidate_filtering(stoplist=stoplist)
# loop through candidates
for lexical_form in doc.candidates:
frequencies[lexical_form] += 1
nb_documents += 1
if nb_documents % 1000 == 0:
logging.info("{} docs, memory used: {} mb".format(
nb_documents,
sys.getsizeof(frequencies) / 1024 / 1024))
# create directories from path if not exists
if os.path.dirname(output_file):
os.makedirs(os.path.dirname(output_file), exist_ok=True)
# dump the df container
with gzip.open(output_file, 'wt', encoding='utf-8') as f:
# add the number of documents as special token
first_line = '--NB_DOC--' + delimiter + str(nb_documents)
f.write(first_line + '\n')
for ngram in frequencies:
line = ngram + delimiter + str(frequencies[ngram])
f.write(line + '\n')
def compute_lda_model(input_dir,
output_file,
n_topics=500,
extension="xml",
language="en",
normalization="stemming",
max_length=10**6,
encoding=None):
"""Compute a LDA model from a collection of documents. Latent Dirichlet
Allocation is computed using sklearn module.
Args:
input_dir (str): the input directory.
output_file (str): the output file.
n_topics (int): number of topics for the LDA model, defaults to 500.
extension (str): file extension for input documents, defaults to xml.
language (str): language of the input documents, used for stop_words
in sklearn CountVectorizer, defaults to 'en'.
normalization (str): word normalization method, defaults to 'stemming'.
Other possible values are 'lemmatization' or 'None' for using word
surface forms instead of stems/lemmas.
encoding (str): encoding of files in `input_dir`, default to None.
"""
# texts container
texts = []
# loop throught the documents
for input_file in glob.iglob(input_dir + os.sep + '*.' + extension):
logging.info('reading file {}'.format(input_file))
# initialize load file object
doc = LoadFile()
# read the input file
doc.load_document(input=input_file,
language=language,
normalization=normalization,
max_length=max_length,
encoding=encoding)
# container for current document
text = []
# loop through sentences
for sentence in doc.sentences:
# get the tokens (stems) from the sentence if they are not
# punctuation marks
text.extend([
sentence.stems[i] for i in range(sentence.length)
if sentence.pos[i] != 'PUNCT' and sentence.pos[i].isalpha()
])
# add the document to the texts container
texts.append(' '.join(text))
# vectorize dataset
# get the stoplist from nltk because CountVectorizer only contains english
# stopwords atm
tf_vectorizer = CountVectorizer(stop_words=get_stopwords(language))
tf = tf_vectorizer.fit_transform(texts)
# extract vocabulary
vocabulary = tf_vectorizer.get_feature_names()
# create LDA model and train
lda_model = LatentDirichletAllocation(n_components=n_topics,
random_state=0,
learning_method='batch')
lda_model.fit(tf)
# save all data necessary for later prediction
saved_model = (vocabulary, lda_model.components_,
lda_model.exp_dirichlet_component_,
lda_model.doc_topic_prior_)
# Dump the df container
logging.info('writing LDA model to {}'.format(output_file))
# create directories from path if not exists
if os.path.dirname(output_file):
os.makedirs(os.path.dirname(output_file), exist_ok=True)
# dump the LDA model
with gzip.open(output_file, 'wb') as fp:
pickle.dump(saved_model, fp)
