def drop_multiple(feature_names, month):
col_names = np.genfromtxt(paths.my_path + 'col_names{0}.csv'.format(month),
delimiter=',',
dtype=str)
col_number = col_names.shape[1]
X = Matrix(
np.genfromtxt(paths.my_path + 'X{0}.csv'.format(month),
delimiter=',',
dtype=str), list(col_names[0, :]),
["%s" for i in xrange(col_names.shape[1])])
for name in feature_names:
X.drop(name)
X.check_point(month)
def drop(feature_name, month, checkpoint=True):
col_names = np.genfromtxt(paths.my_path + 'col_names{0}.csv'.format(month),
delimiter=',',
dtype=str)
col_number = col_names.shape[1]
X = Matrix(
np.genfromtxt(paths.my_path + 'X{0}.csv'.format(month),
delimiter=',',
dtype=str), list(col_names[0, :]),
["%s" for i in xrange(col_names.shape[1])])
X.drop(feature_name)
if checkpoint:
X.check_point(month)
def append_feature_by_month(func, month, since, to):
col_names = np.genfromtxt(
paths.my_path + 'col_names_{0}_{1}{2}.csv'.format(month, since, to),
delimiter=',',
dtype=str)
col_number = col_names.shape[1]
X = Matrix(
np.genfromtxt(paths.my_path +
'X_{0}_{1}{2}.csv'.format(month, since, to),
delimiter=',',
dtype=str), list(col_names[0, :]),
["%s" for i in xrange(col_names.shape[1])])
func(X, month)
X.check_point(month, since, to)
def gen_by_month(offline_source,
online_source,
target_file,
month,
since=None):
offline = Matrix(
np.genfromtxt(paths.ccf_path + offline_source,
delimiter=',',
dtype=str),
["uid", "mid", "cid", "dis_rate", "dist", "date_rec", "date"],
["%s" for i in xrange(7)])
online = Matrix(
np.genfromtxt(paths.ccf_path + online_source, delimiter=',',
dtype=str),
["uid", "mid", "act", "cid", "dis_rate", "date_rec", "date"],
["%s" for i in xrange(7)])
if month < 7:
X = Matrix(
np.genfromtxt(paths.ccf_path + 'offline_train_test_' + str(month) +
'.csv',
delimiter=',',
dtype=str),
["uid", "mid", "cid", "dis_rate", "dist", "date_rec", "date"],
["%s" for i in xrange(7)])
else:
X = Matrix(
np.genfromtxt(paths.ccf_path + 'offline_train_test_' + str(month) +
'.csv',
delimiter=',',
dtype=str),
["uid", "mid", "cid", "dis_rate", "dist", "date_rec"],
["%s" for i in xrange(6)])
print "generating month {0}".format(month)
print "gen_basic_features" #11
gen_basic_features(X)
print "gen_user_buy_with_coupon"
gen_user_buy_with_coupon(offline, online, X) #14
print "gen_user_shop_features"
#X.join("uid", feature_names, features, formats, dft=0.0)
gen_user_shop_features(
paths.my_path + "u_features_10_{0}.csv".format(month),
paths.my_path + "i_features_10_{0}.csv".format(month), X) #35
print "gen_user_buy_in_shop"
gen_user_buy_in_shop(offline, online, X) #37
print "gen_user_buy_coupon_in_shop"
gen_user_buy_coupon_in_shop(offline, online, X)
if month < 7:
print "gen_label"
gen_label(X)
print "gen_user_get_shop_coupon"
gen_user_get_shop_coupon(offline_source, online_source, X)
print "gen_unused_shop_coupon"
gen_unused_shop_coupon(X)
print "gen_no_penalty_user_shop_features"
gen_no_penalty_user_shop_features(X, month)
X.check_point(target_file)