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svmlitegen.py
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svmlitegen.py
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"""
usage: python svmlitegen.py
"""
from __future__ import division
import csv
import datetime
import database
import numpy as np
import re
import sys
LDA_TOPICS_FNAME = "debates.doc.topics"
DEBATE_DATA_FNAME = "oct3.labeled"
COUNT_QUERY = 'SELECT Count(*) FROM reactions,users WHERE reactions.userid=users.userid AND reactions.reaction="%s" %s AND reactions.time BETWEEN "%s" AND "%s"'
COUNT_QUERY_ALL = 'SELECT Count(*) FROM reactions,users WHERE reactions.userid=users.userid %s AND reactions.time BETWEEN "%s" AND "%s"'
BARE_COUNT_QUERY = 'SELECT Count(*) FROM reactions,users WHERE reactions.userid=users.userid AND reactions.time BETWEEN "%s" AND "%s"'
def strptime(x, y):
return datetime.datetime.strptime(x, y)
def svmlite_lines(react_type, user_attrs, label_fn, times, feats_list):
if user_attrs:
sql_attrs = ' AND ' + ' AND '.join(['users.%s="%s"' % x
for x in user_attrs])
else:
sql_attrs = ''
for (start, end), feats in zip(times, feats_list):
if react_type == "all":
query = COUNT_QUERY_ALL % (sql_attrs, start, end)
else:
query = COUNT_QUERY % (react_type, sql_attrs, start, end)
num_reacts = database.fetch(query)[0][0]
feat_str = ' '.join(["%s:%s" % (idx, feat) for idx, feat in feats
+ [('100', str((strptime(end, '%H:%M:%S') - strptime(start, '%H:%M:%S')).seconds))]])
yield "%s %s\n" % (label_fn(num_reacts), feat_str)
def svmlite_lines_wlabels(labels, times, feats_list):
#for (start, end), feats in zip(times, feats_list):
for label, feats in zip(labels, feats_list):
#len_feat = ' 100:%d' % ((strptime(end, '%H:%M:%S') - strptime(start, '%H:%M:%S')).seconds)
len_feat = ''
feat_str = ' '.join(["%s:%s" % (idx, feat) for idx, feat in feats]) + len_feat
yield "%s %s\n" % (label, feat_str)
def counts_per_turn(react_type, user_attrs, per_second=True, curr_speaker=True):
speakers = {'0': 'Moderator', '1': 'Romney', '2': 'Obama'}
if user_attrs:
user_attr_sql = ' AND ' + ' AND '.join(['users.%s="%s"' % x
for x in user_attrs])
else:
user_attr_sql = ''
with open(DEBATE_DATA_FNAME) as debate_data:
reader = csv.reader(debate_data, delimiter=',', quotechar='"')
react_counts = []
for row in reader:
query = BARE_COUNT_QUERY % (row[2], row[3])
query += user_attr_sql
if curr_speaker:
print row
query += ' AND reactions.reaction="%s:%s"' % (speakers[row[1]], react_type)
react_count = database.fetch(query)[0][0]
react_counts.append(react_count)
return react_counts
def task_1(party):
with open('task1%s.train' % (party), 'w') as f:
attrs = [('candidate', party)]
counts = counts_per_turn(None, attrs, curr_speaker=False)
median = np.median(counts)
labels = [1 if x > median else 0 for x in counts]
feats = lda_topic_feats()
for line in svmlite_lines_wlabels(labels, None, feats):
f.write(line + '\n')
def task_2(party):
with open('task2%s.train' % (party), 'w') as f:
attrs = [('candidate', party)]
agree_counts = counts_per_turn('Agree', attrs, per_second=False)
disagree_counts = counts_per_turn('Disagree', attrs, per_second=False)
ratios = [x / (y + 1) for x, y in zip(agree_counts, disagree_counts)]
median = np.median(ratios)
labels = [1 if x > median else 0 for x in ratios]
feats = lda_topic_feats()
for line in svmlite_lines_wlabels(labels, None, feats):
f.write(line + '\n')
def task_3(party):
with open('task3%s.train' % (party), 'w') as f:
attrs = [('candidate', party)]
spin_counts = counts_per_turn('Spin', attrs)
dodge_counts = counts_per_turn('Dodge', attrs)
totals = [x + y for x, y in zip(spin_counts, dodge_counts)]
median = np.median(totals)
labels = [1 if x > median else 0 for x in totals]
feats = lda_topic_feats()
for line in svmlite_lines_wlabels(labels, None, feats):
f.write(line + '\n')
def binned_labels(react_type, user_attrs, times):
if user_attrs:
sql_attrs = ' AND ' + ' AND '.join(['users.%s="%s"' % x
for x in user_attrs])
else:
sql_attrs = ''
react_counts = []
for start, end in times:
if react_type == "all":
query = COUNT_QUERY_ALL % (sql_attrs, start, end)
else:
query = COUNT_QUERY % (react_type.split(":")[0] + ":Agree",
sql_attrs, start, end)
#print query
st = strptime(start, '%H:%M:%S')
nd = strptime(end, '%H:%M:%S')
turn_time = (nd - st).seconds + 1
num_reacts = database.fetch(query)[0][0]
#print num_reacts
react_counts.append(num_reacts / turn_time)
median = np.median(react_counts)
return [1 if x > median else 0 for x in react_counts]
def ratio_labels(react_type, user_attrs, times):
if user_attrs:
sql_attrs = ' AND ' + ' AND '.join(['users.%s="%s"' % x
for x in user_attrs])
else:
sql_attrs = ''
ratios = []
for start, end in times:
query = COUNT_QUERY % (react_type.split(":")[0] + ":Agree",
sql_attrs, start, end)
num_agrees = database.fetch(query)[0][0]
query = COUNT_QUERY % (react_type.split(":")[0] + ":Disagree",
sql_attrs, start, end)
num_disagrees = database.fetch(query)[0][0]
if num_disagrees == 0:
ratios.append(num_agrees)
else:
ratios.append(num_agrees / num_disagrees)
median = np.median(ratios)
return [1 if x > median else 0 for x in ratios]
def identity_label(count):
return count
def binned_label(count):
ranges = [(0, 5), (5, 100), (100, 500), (500, float("inf"))]
for idx, bounds in enumerate(ranges):
if count >= bounds[0] and count < bounds[1]:
return idx
def lda_topic_feats():
with open(LDA_TOPICS_FNAME) as topics_file:
for line_num, line in enumerate(topics_file):
if line_num >= 197:
break
topics = re.findall(r'(\d+)\s+(\d\.\d+(E\-\d+)?)', line)
topics.sort(key=lambda(x): int(x[0]))
yield [tup[0:2] for tup in topics]
def turn_times():
with open(DEBATE_DATA_FNAME) as debate_file:
reader = csv.reader(debate_file, delimiter=',', quotechar='"')
for row in reader:
start = row[2]
end = row[3]
yield (start, end)
def svmgen_test():
react_type = "Obama:Agree"
user_attrs = {"gender": "female", "preferred_candidate": "obama"}.items()
lines = svmlite_lines(react_type, user_attrs, binned_label, turn_times(),
lda_topic_feats())
with open('test.train', 'w') as train:
for line in lines:
train.write(line)
def main(argv):
task_1('obama')
task_1('romney')
task_2('obama')
task_2('romney')
task_3('obama')
task_3('romney')
if __name__ == "__main__":
main(sys.argv[1:])