def similar_top_opt3(vec, words, topn=200, nthreads=4, freq=None):
vec.init_sims()
indices = [vec.vocab[w].index for w in words if w in vec.vocab]
vecs = vec.syn0norm[indices]
dists = np.dot(vecs, vec.syn0norm.T)
if freq is not None:
dists = dists * np.log(freq)
if nthreads == 1:
res = dists2neighbours(vec, dists, indices, topn)
else:
batchsize = int(ceil(1. * len(indices) / nthreads))
print >> stderr, "dists2neighbours for %d words in %d threads, batchsize=%d" % (
len(indices), nthreads, batchsize)
def ppp(i):
return dists2neighbours(vec, dists[i:i + batchsize],
indices[i:i + batchsize], topn)
lres = parallel_map(ppp,
range(0, len(indices), batchsize),
threads=nthreads)
res = OrderedDict()
for lr in lres:
res.update(lr)
return res
def similar_top_opt3(vec, words, topn=200, nthreads=4, freq=None):
vec.init_sims()
indices = [vec.vocab[w].index for w in words if w in vec.vocab]
vecs = vec.syn0norm[indices]
dists = np.dot(vecs, vec.syn0norm.T)
print "Shape before freq: ", dists.shape
if freq is not None:
print "Using freq weighting"
dists = dists * np.log(freq)
print "Shape after freq: ", dists.shape
if nthreads==1:
res = dists2neighbours(vec, dists, indices, topn)
else:
batchsize = int(ceil(1. * len(indices) / nthreads))
print >> stderr, "dists2neighbours for %d words in %d threads, batchsize=%d" % (len(indices), nthreads, batchsize)
def ppp(i):
return dists2neighbours(vec, dists[i:i+batchsize], indices[i:i+batchsize], topn)
lres = parallel_map(ppp, range(0,len(indices),batchsize), threads=nthreads)
res = OrderedDict()
for lr in lres:
res.update(lr)
return res
def similar_top_opt3(wvectors, cvectors, words, topn=200, nthreads=4):
wvectors.init_sims()
cvectors.init_sims()
indices = [wvectors.vocab[w].index for w in words if w in wvectors.vocab]
wvecs = wvectors.syn0norm[indices]
dists = np.dot(wvecs, cvectors.syn0norm.T)
if nthreads==1:
res = dists2neighbours(wvectors, cvectors, dists, indices, topn)
else:
batchsize = int(ceil(1. * len(indices) / nthreads))
print >> stderr, "dists2neighbours for %d words in %d threads, batchsize=%d" % (len(indices), nthreads, batchsize)
def ppp(i):
return dists2neighbours(wvectors, cvectors, dists[i:i+batchsize], indices[i:i+batchsize], topn)
lres = parallel_map(ppp, range(0,len(indices),batchsize), threads=nthreads)
res = OrderedDict()
for lr in lres:
res.update(lr)
return res
print(pmid, colored("ya descargado", "yellow"))
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("entrada", help="Archivo de entrada", type=open)
parser.add_argument("-p",
"--parallel",
help="Ejecuta en forma paralela",
action="store_true")
parser.add_argument("--super1",
help="Usa el Super Scraper 1 (Sci-hub)",
action="store_true")
parser.add_argument("--super2",
help="Usa el Super Scraper 2 (Libgen)",
action="store_true")
args = parser.parse_args()
downloaded = load_downloaded()
in_parallel = args.parallel
use_super1 = args.super1
use_super2 = args.super2
f = args.entrada
if in_parallel:
parallel.parallel_map(process_line, f.readlines())
else:
for line in f:
process_line(line)
socket.setdefaulttimeout(timeout)
def _do_crawl(url, args):
try:
import urllib2
data = urllib2.urlopen(url).read()
return True, data
except:
return False, None
def callback(url, ret, args, data):
if ret:
fp = args['output']
data = ' '.join(data.split('\n'))
print >>fp, url + '\t' + data
if __name__ == '__main__':
import sys
if len(sys.argv) != 4:
print 'usage: prog urllist outputfile threadnum'
sys.exit(1)
urllist = sys.argv[1]
outputfile = sys.argv[2]
threadnum = int(sys.argv[3])
l = [x.strip() for x in open(urllist)]
fp = open(outputfile, 'w')
args = {}
args['output'] = fp
parallel_map(l, _do_crawl, callback, args, threadnum)
fp.close()