def generate_feature_matrix(data, stemmer, **prune_params):
config = Configuration()
config.enable_image_fetching = False
config.use_meta_language = False
goose = Goose(config)
_parser = HTMLParser()
sr_index = HashedIndex()
for url_path, label in data.items():
if os.path.exists(url_path):
with open(url_path, 'r') as html_file:
html_text = html_file.read()
text = unicode(goose.extract(raw_html=html_text).cleaned_text)
text = _parser.unescape(text)
for token in word_tokenize(text, stemmer=stemmer):
sr_index.add_term_occurrence(token, url_path)
sr_index.prune(**prune_params)
X = sr_index.generate_feature_matrix(mode='tfidf')
y = np.zeros(len(sr_index.documents()))
for index, doc in enumerate(sr_index.documents()):
y[index] = 0 if data[doc] is None else 1
return X, y
def cmd_readstream(args, t, active_events):
import textwrap
from goose import Goose, Configuration
config = Configuration()
config.enable_image_fetching = False
g = Goose(config)
raw_stream = True
for arg in args:
if arg == "articles":
raw_stream = False
for event in active_events:
print event
if event.query_id.startswith("TS13"):
corpus = cuttsum.corpora.EnglishAndUnknown2013()
elif event.query_id.startswith("TS14"):
corpus = cuttsum.corpora.SerifOnly2014()
else:
raise Exception("Bad query id: {}".format(event.query_id))
if raw_stream is True:
from cuttsum.trecdata import SCChunkResource
si_iter = SCChunkResource().streamitem_iter(event, corpus)
else:
from cuttsum.pipeline import ArticlesResource
si_iter = ArticlesResource().streamitem_iter(event, corpus)
for hour, path, si in si_iter:
if si.body.clean_visible is not None:
print si.stream_id
try:
text_height = t.height - 4
#n_chars = t.
article = g.extract(raw_html=si.body.clean_html)
lines = textwrap.wrap(article.cleaned_text)
idx = 0
while 1:
print t.clear
print "hour:", hour
print "title:", article.title
print "article:"
print "\n".join(lines[idx:idx + text_height])
#print article.cleaned_text
with t.cbreak():
char = t.inkey()
if char == "i" and idx > 0:
idx -= 1 #idx - 1 if idx > 0 else 0
elif char == "k" and idx + text_height < len(
lines):
idx += 1
elif char == "l":
break
except Exception, e:
print e
continue
def cmd_readstream(args, t, active_events):
import textwrap
from goose import Goose, Configuration
config = Configuration()
config.enable_image_fetching = False
g = Goose(config)
raw_stream = True
for arg in args:
if arg == "articles":
raw_stream = False
for event in active_events:
print event
if event.query_id.startswith("TS13"):
corpus = cuttsum.corpora.EnglishAndUnknown2013()
elif event.query_id.startswith("TS14"):
corpus = cuttsum.corpora.SerifOnly2014()
else:
raise Exception("Bad query id: {}".format(event.query_id))
if raw_stream is True:
from cuttsum.trecdata import SCChunkResource
si_iter = SCChunkResource().streamitem_iter(event, corpus)
else:
from cuttsum.pipeline import ArticlesResource
si_iter = ArticlesResource().streamitem_iter(event, corpus)
for hour, path, si in si_iter:
if si.body.clean_visible is not None:
print si.stream_id
try:
text_height = t.height-4
#n_chars = t.
article = g.extract(raw_html=si.body.clean_html)
lines = textwrap.wrap(article.cleaned_text)
idx = 0
while 1:
print t.clear
print "hour:", hour
print "title:", article.title
print "article:"
print "\n".join(lines[idx:idx+text_height])
#print article.cleaned_text
with t.cbreak():
char = t.inkey()
if char == "i" and idx > 0:
idx -= 1 #idx - 1 if idx > 0 else 0
elif char == "k" and idx + text_height < len(lines):
idx += 1
elif char == "l":
break
except Exception, e:
print e
continue
def generate_feature_matrix(wiki, data, n_concepts=10, **word_concept_params):
"""
Transforms a given data source to a corresponding feature matrix and label
vector based on the "Bag of Concepts" model which uses Wikipedia as an
exogenous knowledge source for Word Sense Disambiguation and as additional
domain knowledge.
Contains logging code which is displayed depending on the currently set
logging level of the root logger.
:param wiki: WikiIndex instance to some database index
:param data: data labels loaded using a load_data_source method
:param n_concepts: number of concepts to use per page.
:param word_concept_params: word concept parameters to use for generation of concepts.
:return: Numpy Feature Matrix and Label Vector.
"""
config = Configuration()
config.enable_image_fetching = False
config.use_meta_language = False
goose = Goose(config)
results = {}
concepts = set()
# Iterate through the data and perform training
for index, (abs_path, label) in enumerate(data.items()):
if not os.path.exists(abs_path):
continue
with open(abs_path, 'r') as fp:
html_text = fp.read()
# Determine relative path using a simple heuristic
cutoff = abs_path.find('pages/')
rel_path = abs_path[cutoff + 6:]
logging.info('\n%d: http://%s' % (index, rel_path[:-3]))
article = goose.extract(raw_html=html_text)
if len(article.cleaned_text) > 500:
logging.info('%s (%s)', article.title, label)
search_results, terms, query_vector = wiki.word_concepts(
article.cleaned_text, article.title, **word_concept_params)
if search_results:
results[abs_path] = [(sr.page_id, sr.weight)
for sr in search_results[:n_concepts]]
# Remove any concepts which have a weight of 0
results[abs_path] = filter(lambda x: x[1] > 0,
results[abs_path])
for search_result in search_results[:n_concepts]:
concepts.add(search_result.page_id)
logging.info(search_results[:n_concepts])
else:
logging.warn('No word concepts returned')
else:
logging.info('Document is of insufficient length')
shape = (len(results), len(concepts))
concepts_index = dict([(b, a) for (a, b) in enumerate(concepts)])
feature_matrix = np.zeros(shape=shape)
label_vector = np.zeros(len(results))
for i, (abs_path, page_list) in enumerate(results.iteritems()):
label_vector[i] = 1 if data[abs_path] is not None else 0
for page_id, weight in page_list:
j = concepts_index[page_id]
feature_matrix[i, j] = weight
return feature_matrix, label_vector
def __init__(self,need_stem):
#set up goose
config = Configuration()
config.enable_image_fetching = False
self._g = Goose(config)
self._need_stem = need_stem
from goose import Configuration, Goose
from HTMLParser import HTMLParser
from datasource import load_data_source
from index.hashedindex import HashedIndex, load_meta
from utils import search_files
if __name__ == '__main__':
import time
t0 = time.time()
_parser = HTMLParser()
_config = Configuration()
_config.enable_image_fetching = False
_config.use_meta_language = False
_goose = Goose(_config)
# Lancaster Stemmer is very very slow
_stemmer = textparser.NullStemmer()
data_path = '/home/michaela/Development/Reddit-Testing-Data'
# Set the parameters to the program over here
force_reindex = False
parameters = {
'samples': 800,
'subreddit': 'python',
import cuttsum.judgements
import cuttsum.events
import cuttsum.corpora
from cuttsum.misc import stringify_streamcorpus_sentence
event = cuttsum.events.get_events(by_query_ids=["TS13.1"])[0]
corpus = cuttsum.corpora.EnglishAndUnknown2013()
example_path = "/scratch/t-chkedz/trec-data/articles/gold/2012_buenos_aires_rail_disaster/2012-02-23-01.sc.gz"
example_id = "1329959700-18d497cf08e3500f195066be60e6a201"
matches = cuttsum.judgements.get_merged_dataframe()
from goose import Goose, Configuration
config = Configuration()
config.enable_image_fetching = False
g = Goose(config)
def si_to_df(si):
sents = []
for s, sent in enumerate(si.body.sentences["lingpipe"]):
sents.append(
with sc.Chunk(path=example_path, mode="rb", message=corpus.sc_msg()) as chunk:
for si in chunk:
if si.stream_id == example_id:
for s, sent in enumerate(si.body.sentences["lingpipe"]):
def __init__(self, need_stem):
#set up goose
config = Configuration()
config.enable_image_fetching = False
self._g = Goose(config)
self._need_stem = need_stem
def do_job_unit(self, event, corpus, unit, **kwargs):
extractor = kwargs.get("extractor", "gold")
data_dir = os.path.join(self.dir_, extractor, event.fs_name())
chunks_resource = SCChunkResource()
if not os.path.exists(data_dir):
try:
os.makedirs(data_dir)
except OSError as exc:
if exc.errno == errno.EEXIST and os.path.isdir(data_dir):
pass
if extractor == "gold":
import cuttsum.judgements
all_matches = cuttsum.judgements.get_matches()
matches = all_matches[all_matches["query id"] == event.query_id]
stream_ids = set(
matches["update id"].apply(
lambda x: "-".join(x.split("-")[:-1])).tolist())
hours = set([datetime.utcfromtimestamp(
int(update_id.split("-")[0])).replace(
minute=0, second=0)
for update_id in matches["update id"].tolist()])
hours = sorted(list(hours))
hour = hours[unit]
output_path = self.get_chunk_path(event, extractor, hour, corpus)
gold_si = []
for path in chunks_resource.get_chunks_for_hour(hour, corpus, event):
with sc.Chunk(path=path, mode="rb",
message=corpus.sc_msg()) as chunk:
for si in chunk:
if si.stream_id in stream_ids:
gold_si.append(si)
gold_si.sort(key=lambda x: x.stream_id)
for si in gold_si:
print si.stream_id
if os.path.exists(output_path):
os.remove(path)
with sc.Chunk(path=output_path, mode="wb",
message=corpus.sc_msg()) as chunk:
for si in gold_si:
chunk.add(si)
elif extractor == "goose":
import nltk
from nltk.tokenize import WordPunctTokenizer
sent_tok = nltk.data.load('tokenizers/punkt/english.pickle')
word_tok = WordPunctTokenizer()
from goose import Goose, Configuration
config = Configuration()
config.enable_image_fetching = False
g = Goose(config)
hour = event.list_event_hours()[unit]
output_path_tmp = self.get_chunk_template(event, extractor, hour, corpus)
good_si = []
for path in chunks_resource.get_chunks_for_hour(hour, corpus, event):
try:
with sc.Chunk(path=path, mode="rb",
message=corpus.sc_msg()) as chunk:
for si in chunk:
if si.body.clean_visible is None:
continue
article_text = self._get_goose_text(g, si)
if article_text is None:
continue
if not self._contains_query(event, article_text):
continue
art_pretty = sent_tok.tokenize(article_text)
art_sents = [word_tok.tokenize(sent)
for sent in art_pretty]
df = si2df(si)
I = self._map_goose2streamitem(
art_sents, df["words"].tolist())
if "serif" in si.body.sentences:
si_sentences = si.body.sentences["serif"]
elif "lingpipe" in si.body.sentences:
si_sentences = si.body.sentences["lingpipe"]
else:
raise Exception("Bad sentence annotator.")
ann = sc.Annotator()
ann.annotator_id = "goose"
si.body.sentences["goose"] = [sc.Sentence()
for _ in si_sentences]
for i_goose, i_si in enumerate(I):
#print art_pretty[i_goose]
#print df.loc[i_si, "sent text"]
#print
tokens = [sc.Token(token=token.encode("utf-8"))
for token in art_sents[i_goose]]
si.body.sentences["goose"][i_si].tokens.extend(
tokens)
good_si.append(si)
except TypeError:
continue
#if len(good_si) == 0:
# print "Nothing in hour:", hour
# return
output_path = output_path_tmp.format(len(good_si))
odir = os.path.dirname(output_path)
if not os.path.exists(odir):
os.makedirs(odir)
if os.path.exists(output_path):
os.remove(path)
good_si.sort(key=lambda x: x.stream_id)
for si in good_si:
print si.stream_id
if os.path.exists(output_path):
os.remove(output_path)
print "Writing to", output_path
with sc.Chunk(path=output_path, mode="wb",
message=corpus.sc_msg()) as chunk:
for si in good_si:
chunk.add(si)
else:
raise Exception("extractor: {} not implemented!".format(extractor))
def generate_feature_matrix(wiki, data, n_concepts=10, **word_concept_params):
"""
Transforms a given data source to a corresponding feature matrix and label
vector based on the "Bag of Concepts" model which uses Wikipedia as an
exogenous knowledge source for Word Sense Disambiguation and as additional
domain knowledge.
Contains logging code which is displayed depending on the currently set
logging level of the root logger.
:param wiki: WikiIndex instance to some database index
:param data: data labels loaded using a load_data_source method
:param n_concepts: number of concepts to use per page.
:param word_concept_params: word concept parameters to use for generation of concepts.
:return: Numpy Feature Matrix and Label Vector.
"""
config = Configuration()
config.enable_image_fetching = False
config.use_meta_language = False
goose = Goose(config)
results = {}
concepts = set()
# Iterate through the data and perform training
for index, (abs_path, label) in enumerate(data.items()):
if not os.path.exists(abs_path):
continue
with open(abs_path, 'r') as fp:
html_text = fp.read()
# Determine relative path using a simple heuristic
cutoff = abs_path.find('pages/')
rel_path = abs_path[cutoff + 6:]
logging.info('\n%d: http://%s' % (index, rel_path[:-3]))
article = goose.extract(raw_html=html_text)
if len(article.cleaned_text) > 500:
logging.info('%s (%s)', article.title, label)
search_results, terms, query_vector = wiki.word_concepts(article.cleaned_text, article.title, **word_concept_params)
if search_results:
results[abs_path] = [(sr.page_id, sr.weight) for sr in search_results[:n_concepts]]
# Remove any concepts which have a weight of 0
results[abs_path] = filter(lambda x: x[1] > 0, results[abs_path])
for search_result in search_results[:n_concepts]:
concepts.add(search_result.page_id)
logging.info(search_results[:n_concepts])
else:
logging.warn('No word concepts returned')
else:
logging.info('Document is of insufficient length')
shape = (len(results), len(concepts))
concepts_index = dict([(b, a) for (a, b) in enumerate(concepts)])
feature_matrix = np.zeros(shape=shape)
label_vector = np.zeros(len(results))
for i, (abs_path, page_list) in enumerate(results.iteritems()):
label_vector[i] = 1 if data[abs_path] is not None else 0
for page_id, weight in page_list:
j = concepts_index[page_id]
feature_matrix[i, j] = weight
return feature_matrix, label_vector
