from datetime import datetime
from csv import DictReader
from math import exp,log,sqrt
train = 'data/train.csv'
test = 'data/test.csv'
alpha = .1 # learning rate
beta = 1. # smoothing parameter for adaptive learning rate
L1 = 1. # L1 regularization, larger value means more regularized
L2 = 1. # L2 regularization, larger value means more regularized
# C, feature/hash trick
D = 2 ** 20 # number of weights to use
interaction = False # whether to enable poly2 feature interactions
# D, training/validation
epoch = 1 # learn training data for N passes
holdafter = 29 # data after date N (exclusive) are used as validation
holdout = None # use every N training instance for holdout validation
class ftrl_proximal(object):
"""
''' Our main algorithm: Follow the regularized leader - proximal
In short,
this is an adaptive-learning-rate sparse logistic-regression with
efficient L1-L2-regularization
Reference:
http://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf
'''
"""
pass
def data(path,D):
''' GENERATOR: Apply hash-trick to the original csv row
and for simplicity, we one-hot-encode everything
INPUT:
path: path to training or testing file
D: the max index that we can hash to
YIELDS:
ID: id of the instance, mainly useless
x: a list of hashed and one-hot-encoded 'indices'
we only need the index since all values are either 0 or 1
y: y = 1 if we have a click, else we have y = 0
'''
for t,row in enumerate(DictReader(open(path))):
ID = row['id']
del row['id']
y = 0.
##############################################################################
# start training #############################################################
##############################################################################
start = datetime.now()
learner = ftrl_proximal(alpha,beta,L1,L2,D,interaction)
for e in range(epoch):
loss = 0.
count = 0
for t,date,ID,x,y in data(train,D):
