def train_vw(filename, output):
with open(filename, 'r') as f:
xx = f.readlines()
y = [int(x[0]) - 1 for x in xx]
y = np.asarray(y)
xx = np.asarray(xx)
cv = StratifiedKFold(y, 4)
os.popen('rm -f /home/rodion/facebids/tmp/*')
with open(output, 'w') as fw:
fw.write('bidder_id,proba\n')
for a, b in cv:
y_a, y_b = y[a], y[b]
xx_a, xx_b = xx[a], xx[b]
vw = VW('/home/' + os.getenv('USER') + '/bin/vw', '/home/rodion/facebids/tmp/',
{'loss_function': 'hinge', 'passes': '10', 'holdout_off': False,
'learning_rate': '0.9961190941783473', 'b': '24'})
vw.fit(xx_a, y_a)
tags, probas = vw.predict_tag_proba(xx_b)
for tag, proba in itertools.izip(tags, probas):
fw.write(tag + ',' + str(proba[1]) + '\n')
def classify_test_vw(train_filename, test_filename, proba_filename):
vw = VW('/home/' + os.getenv('USER') + '/bin/vw', '/home/rodion/facebids/tmp/',
{'loss_function': 'hinge', 'passes': '10', 'holdout_off': False,
'learning_rate': '0.9961190941783473', 'b': '24'})
with open(train_filename, 'r') as f:
xx = f.readlines()
y = [int(x[0]) - 1 for x in xx]
vw.fit(xx, y)
with open(test_filename, 'r') as f, open(proba_filename, 'w') as fw:
xx = f.readlines()
tags, probas = vw.predict_tag_proba(xx)
fw.write('bidder_id,proba\n')
for tag, proba in itertools.izip(tags, probas):
fw.write(tag + ',' + str(proba[1]) + '\n')
