def __init__(self):
self.aan = aanmeta()
self.cnw = CitationNetwork()
self.prestige_features = {}
self.position_features = {}
self.content_features = {}
self.style_features = {}
self.load_features()
def main(self):
n = int(sys.argv[1])
diff = sys.argv[2] if len(sys.argv) > 2 else 3
init = 1980
last = 2006
aan = aanmeta()
all_papers = aan.get_restricted_papers(init, last)
till_n = [p for p in all_papers if p.year <= n]
training = random.sample(till_n, int(math.ceil(0.8 * len(till_n))))
model_test = [i for i in till_n if i not in training]
test = [i for i in all_papers if i.year == (n + diff)]
print "Total files: %d" % (len(training) + len(model_test) + len(test))
self.feats = {}
featfile = open("1980_2006.pruned_feats", "r")
for line in featfile:
line = line.strip()
[pid, featstr] = line.split("\t")
self.feats[pid] = featstr.split("<>")
training_fname = "experiment_files/1980_%s.train.txt" % n
model_test_fname = "experiment_files/1980_%s.modeltest.txt" % n
test_fname = "experiment_files/%s.test.txt" % (n + diff)
self.write_data(training, training_fname)
self.write_data(model_test, model_test_fname)
self.write_data(test, test_fname)
# creating the response files
self.cnw = CitationNetwork()
training_resp_file = open("experiment_files/1980_%s.train.resp.txt" % n, "w")
model_test_resp_file = open("experiment_files/1980_%s.modeltest.resp.txt" % n, "w")
test_resp_file = open("experiment_files/%s.test.resp.txt" % (n + diff), "w")
self.write_response(training, training_resp_file, n)
self.write_response(model_test, model_test_resp_file, n)
self.write_response(test, test_resp_file, n)
# write the time step files
ts_file = open("experiment_files/%s_%s_timesteps.txt" % (init, n), "w")
for pid in [i.pid for i in all_papers if i.year >= init and i.year <= n]:
ts_file.write("%s\t%d\n" % (pid, get_year_from_id(pid)))
from nltk.corpus import stopwords
from utils import get_ft_ngrams
import time
from aanmeta import aanmeta
from citation_nw import CitationNetwork
# Given a reference year and forecast year
# start from 1980 and get all titles till reference
# Extract 1,2 and 3 grams from the titles and create feature files for training data
lyear = 1980
hyear = 2006
aan = aanmeta()
refpapers = aan.get_restricted_papers(1980, 2006)
# # refpapers.sort(key=lambda x: x.year)
ngram_sep = "_"
feat_file_name = "%s_%s.pruned_feats" % (lyear, hyear)
feat_file = open(feat_file_name, 'w')
# resp_file = open(resp_file_name, 'w')
stopwords = stopwords.words('english')
featfreq = {}
initfeats = {}
print "Total %d papers" % len(refpapers)
