def output_html():
target_file = get_target_file()
print "processing:", target_file
fi = file(target_file)
from lr import learn, make_feature_matrix
lr = learn()
lines = fi.readlines()
X = make_feature_matrix(lines)
ps = lr.predict_proba(X)[:, 1]
data = []
for line, p in zip(lines, ps):
if line.startswith("RT "):
continue
if p < args.threshold:
continue
if p > args.upper_limit:
continue
items = line.split("\t")
url = "https://twitter.com/{1}/status/{2}".format(*items)
data.append(dict(url=url, score=p, text=items[0]))
print len(data)
render(data, target_file)
def add_train_data():
target_file = get_target_file()
print "processing:", target_file
fi = file(target_file)
from lr import learn, make_feature_matrix
lr = learn()
lines = fi.readlines()
X = make_feature_matrix(lines)
ps = lr.predict_proba(X)[:, 1]
data = []
for line, p in zip(lines, ps):
if line.startswith("RT "):
continue
if p < args.threshold:
continue
if p > args.upper_limit:
continue
print line
print p
items = line.split("\t")
url = "https://twitter.com/{1}/status/{2}".format(*items)
print url
ret = raw_input("negative(z), neutral(x), positive(c)>")
if ret == "c":
fo = file("positive.txt", "a")
fo.write(line)
fo.close()
elif ret == "z":
fo = file("negative.txt", "a")
fo.write(line)
fo.close()
print
def main():
lr = learn()
latest_id = crawl()
output_html(lr)
while True:
time.sleep(60)
try:
latest_id = crawl(previous_latest=latest_id)
finally:
print 'latest:', latest_id
print 'rendering'
output_html(lr)
print 'rendered'
