def main():
event_file, event_title, nuggets_tsv, odir = parse_args()
event = load_event(event_title, event_file)
nuggets = read_nuggets_tsv(nuggets_tsv, filter_query_id=event.query_id)
hours = [dth for dth in gen_dates(event.start, event.end)]
updates_file = os.path.join(odir, u"updates.txt")
write_updates(nuggets, updates_file)
sum_sents = []
for hour in hours:
while len(nuggets) > 0:
if nuggets[0].timestamp.strftime(u"%Y-%m-%d-%H") <= hour:
sum_sents.append(nuggets[0].text)
nuggets.pop(0)
else:
break
if len(sum_sents) > 0:
ofile = os.path.join(odir, u"{}.txt".format(hour))
write_summary(sum_sents, ofile)
if len(nuggets) > 0:
sum_sents.extend([nugget.text for nugget in nuggets])
ofile = os.path.join(odir, u"final.txt")
write_summary(sum_sents, ofile)
def main():
event_file, event_title, nuggets_tsv, odir = parse_args()
event = load_event(event_title, event_file)
nuggets = read_nuggets_tsv(nuggets_tsv, filter_query_id=event.query_id)
hours = [dth for dth in gen_dates(event.start, event.end)]
updates_file = os.path.join(odir, u'updates.txt')
write_updates(nuggets, updates_file)
sum_sents = []
for hour in hours:
while len(nuggets) > 0:
if nuggets[0].timestamp.strftime(u'%Y-%m-%d-%H') <= hour:
sum_sents.append(nuggets[0].text)
nuggets.pop(0)
else:
break
if len(sum_sents) > 0:
ofile = os.path.join(odir, u'{}.txt'.format(hour))
write_summary(sum_sents, ofile)
if len(nuggets) > 0:
sum_sents.extend([nugget.text for nugget in nuggets])
ofile = os.path.join(odir, u'final.txt')
write_summary(sum_sents, ofile)
def main():
event_file, rc_dir, event_title, nuggets_tsv, ss_params, ofile = parse_args()
ss_model, ss_vocab, ss_dims = ss_params
event = load_event(event_title, event_file)
nuggets = read_nuggets_tsv(nuggets_tsv, filter_query_id=event.query_id)
hours = [dth for dth in gen_dates(event.start, event.end)]
print u"Found", len(nuggets), u"nuggets."
print u"Loading sentence-sim model..."
wmat_model = cuttsum.wtmf.load_model(ss_model, ss_vocab, latent_dims=ss_dims)
nugget_lvecs = wmat_model.factor_unicode([n.text for n in nuggets])
meta_data = []
unicodes = []
print u"Loading sentence data..."
nhours = len(hours)
for h, hour in enumerate(hours, 1):
chunk = os.path.join(rc_dir, u'{}.sc.gz'.format(hour))
for si_idx, si in enumerate(sc.Chunk(path=chunk)):
if u'article-clf' not in si.body.sentences:
continue
sent_idx_map = {}
for idx, sent in enumerate(si.body.sentences[u'serif']):
sent_idx_map[sentence_uni(sent)] = idx
for sent in si.body.sentences[u'article-clf']:
uni = sentence_uni(sent)
meta_data.append((hour, si.stream_id, sent_idx_map[uni]))
unicodes.append(uni)
print u"Computing similarities..."
sent_lvecs = wmat_model.factor_unicode(unicodes)
S = cosine_similarity(sent_lvecs, nugget_lvecs)
S = np.ma.masked_array(S, np.isnan(S))
Szmuv = (S - S.mean(axis=0)) / S.std(axis=0)
M = np.amax(Szmuv, axis=1)
m = np.amin(Szmuv, axis=1)
U = np.mean(Szmuv, axis=1)
T = np.sum(Szmuv, axis=1)
### WRITE TSV HEADER AND DATA ###
print u"Writing to", ofile
header = 'date-hour\tstream-id\tsent-id\tmax-sim\tmin-sim' + \
'\tmean-sim\ttotal-sim'
for i in range(ss_dims):
header += '\tlv{}'.format(i)
with open(ofile, 'w') as f:
f.write(header)
f.write('\n')
for idx, meta_datum in enumerate(meta_data):
f.write('{}\t{}\t{}\t{}\t{}\t{}\t{}'.format(meta_datum[0], meta_datum[1],
meta_datum[2], M[idx], m[idx], U[idx]))
for c in range(ss_dims):
f.write('\t{}'.format(sent_lvecs[idx,c]))
f.write('\n')
f.flush()
def main():
args = parse_args()
event_file, rc_dir, event_title, nuggets_tsv = args[0:4]
doc_freqs, word_freqs = args[4:]
print 'Generating Regression Features'
print '=============================='
event = load_event(event_title, event_file)
nuggets = read_nuggets_tsv(nuggets_tsv, filter_query_id=event.query_id)
hours = [dth for dth in gen_dates(event.start, event.end)]
worker((rc_dir, nuggets, hours, event, doc_freqs, word_freqs))
def main():
args = parse_args()
event_file, rc_dir, event_title, nuggets_tsv = args[0:4]
doc_freqs, word_freqs = args[4:]
print 'Generating Regression Features'
print '=============================='
event = load_event(event_title, event_file)
nuggets = read_nuggets_tsv(nuggets_tsv, filter_query_id=event.query_id)
hours = [dth for dth in gen_dates(event.start, event.end)]
worker((rc_dir, nuggets, hours, event, doc_freqs, word_freqs))
def main():
event_file, rc_dir, event_title, nuggets_tsv, ss_params, ofile = parse_args(
)
ss_model, ss_vocab, ss_dims = ss_params
event = load_event(event_title, event_file)
nuggets = read_nuggets_tsv(nuggets_tsv, filter_query_id=event.query_id)
hours = [dth for dth in gen_dates(event.start, event.end)]
print u"Found", len(nuggets), u"nuggets."
print u"Loading sentence-sim model..."
wmat_model = cuttsum.wtmf.load_model(ss_model,
ss_vocab,
latent_dims=ss_dims)
nugget_lvecs = wmat_model.factor_unicode([n.text for n in nuggets])
meta_data = []
unicodes = []
print u"Loading sentence data..."
nhours = len(hours)
for h, hour in enumerate(hours, 1):
chunk = os.path.join(rc_dir, u'{}.sc.gz'.format(hour))
for si_idx, si in enumerate(sc.Chunk(path=chunk)):
if u'article-clf' not in si.body.sentences:
continue
sent_idx_map = {}
for idx, sent in enumerate(si.body.sentences[u'serif']):
sent_idx_map[sentence_uni(sent)] = idx
for sent in si.body.sentences[u'article-clf']:
uni = sentence_uni(sent)
meta_data.append((hour, si.stream_id, sent_idx_map[uni]))
unicodes.append(uni)
print u"Computing similarities..."
sent_lvecs = wmat_model.factor_unicode(unicodes)
S = cosine_similarity(sent_lvecs, nugget_lvecs)
S = np.ma.masked_array(S, np.isnan(S))
Szmuv = (S - S.mean(axis=0)) / S.std(axis=0)
M = np.amax(Szmuv, axis=1)
m = np.amin(Szmuv, axis=1)
U = np.mean(Szmuv, axis=1)
T = np.sum(Szmuv, axis=1)
### WRITE TSV HEADER AND DATA ###
print u"Writing to", ofile
header = 'date-hour\tstream-id\tsent-id\tmax-sim\tmin-sim' + \
'\tmean-sim\ttotal-sim'
for i in range(ss_dims):
header += '\tlv{}'.format(i)
with open(ofile, 'w') as f:
f.write(header)
f.write('\n')
for idx, meta_datum in enumerate(meta_data):
f.write('{}\t{}\t{}\t{}\t{}\t{}\t{}'.format(
meta_datum[0], meta_datum[1], meta_datum[2], M[idx], m[idx],
U[idx]))
for c in range(ss_dims):
f.write('\t{}'.format(sent_lvecs[idx, c]))
f.write('\n')
f.flush()
