def load_whole_input(self):
logger = pocket_logger.get_my_logger()
timer = pocket_timer.GoldenTimer(logger)
csv_io = pocket_file_io.GoldenCsv()
train = csv_io.read_file(path_const.TRAIN1)
test = csv_io.read_file(path_const.TEST1)
use_files = [
path_const.RE_NEW_TRANS1,
path_const.RE_OLD_TRANS1,
path_const.OLD_TRANS3,
path_const.NEW_TRANS6,
path_const.OLD_TRANS6,
path_const.OLD_TRANS9,
# path_const.NEW_TRANS11,
# path_const.OLD_TRANS11,
]
for f in use_files:
train, test = self.load_file_and_merge(train, test, f, csv_io)
pred_train = csv_io.read_file(path_const.NEW_DAY_PRED_OOF)
pred_test = csv_io.read_file(path_const.NEW_DAY_PRED_SUB)
train = pd.merge(train, pred_train, on="card_id", how="left")
test = pd.merge(test, pred_test, on="card_id", how="left")
# train, test = self.load_lda(train, test, csv_io)
print(train.shape)
print(test.shape)
timer.time("load csv in ")
fer = jit_fe.JitFe()
train = fer.do_fe(train)
test = fer.do_fe(test)
return train, test
def load_whole_input(self, use_pred=True):
csv_io = pocket_file_io.GoldenCsv()
train = csv_io.read_file(path_const.ORG_TRAIN)[["card_id"]]
test = csv_io.read_file(path_const.ORG_TEST)[["card_id"]]
train_test_files = [
(path_const.TRAIN1, path_const.TEST1),
(path_const.NEW_DAY_PRED_OOF, path_const.NEW_DAY_PRED_SUB),
(path_const.NEW_PUR_MAX_PRED_OOF, path_const.NEW_PUR_MAX_PRED_SUB),
]
if not use_pred:
train_test_files = [
(path_const.TRAIN1, path_const.TEST1),
]
use_files = [
path_const.RE_NEW_TRANS1,
path_const.RE_OLD_TRANS1,
path_const.OLD_TRANS3,
path_const.NEW_TRANS6,
path_const.OLD_TRANS6,
path_const.OLD_TRANS9,
path_const.NEW_TRANS11,
path_const.OLD_TRANS11,
# path_const.NEW_TRANS13,
# path_const.OLD_TRANS13,
# path_const.FEAT_FROM_TS_NEW,
# path_const.FEAT_FROM_TS_OLD,
# path_const.FEAT_FROM_TS_NEW2,
# path_const.FEAT_FROM_TS_OLD2,
]
for f in train_test_files:
train, test = self.load_train_test_and_merge(
train, test, f[0], f[1], csv_io)
for f in use_files:
train, test = self.load_file_and_merge(train, test, f, csv_io)
print(train.shape)
print(test.shape)
fer = jit_fe.JitFe()
train = fer.do_fe(train)
test = fer.do_fe(test)
return train, test
def load_small_input():
logger = pocket_logger.get_my_logger()
timer = pocket_timer.GoldenTimer(logger)
csv_io = pocket_file_io.GoldenCsv()
train = csv_io.read_file(path_const.TRAIN1)
test = csv_io.read_file(path_const.TEST1)
new_trans = csv_io.read_file(path_const.RE_NEW_TRANS1)
old_trans = csv_io.read_file(path_const.RE_OLD_TRANS1)
new_trans6 = csv_io.read_file(path_const.NEW_TRANS6)
old_trans6 = csv_io.read_file(path_const.OLD_TRANS6)
print(train.shape)
print(test.shape)
timer.time("load csv in ")
train = pd.merge(train, new_trans, on="card_id", how="left")
train = pd.merge(train, old_trans, on="card_id", how="left")
train = pd.merge(train, new_trans6, on="card_id", how="left")
train = pd.merge(train, old_trans6, on="card_id", how="left")
#
test = pd.merge(test, new_trans, on="card_id", how="left")
test = pd.merge(test, old_trans, on="card_id", how="left")
test = pd.merge(test, new_trans6, on="card_id", how="left")
test = pd.merge(test, old_trans6, on="card_id", how="left")
# print(train.shape)
# print(test.shape)
#
fer = jit_fe.JitFe()
train = fer.do_fe(train)
test = fer.do_fe(test)
train_y = train["target"]
# 3.660 - 3.658
use_col = [
"new_trans_elapsed_days_max",
"new_trans_elapsed_days_min",
"new_trans_elapsed_days_mean", # 0.001
"old_trans_elapsed_days_max",
"old_trans_elapsed_days_min",
"old_trans_elapsed_days_mean", # 0.025 mean001
"new_last_day", # 0.005
"old_installments_sum",
"old_installments_mean", # 0.005
"old_month_nunique",
"old_woy_nunique", # 0.010
"old_merchant_id_nunique", # 0.002
"new_month_lag_mean",
"old_month_lag_mean",
"elapsed_days", # 0.010
"new_purchase_amount_max",
"new_purchase_amount_count",
"new_purchase_amount_mean", # 0.020
"old_purchase_amount_max",
"old_purchase_amount_count",
"old_purchase_amount_mean", # 0.002
"old_category_1_mean",
"new_category_1_mean", # 0.006
"old_authorized_flag_sum", # "old_authorized_flag_mean", bad?
"old_no_city_purchase_amount_min", # 0.003
"old_no_city_purchase_amount_max",
"old_no_city_purchase_amount_mean", # 0.002
"rec1_purchase_amount_count", # 0.005
"old_month_lag_max", # 0.002
"new_time_diff_mean",
"new_trans_elapsed_days_std", # 0.002
"old_month_diff_mean",
"old_pa2_month_diff_min", # 0.004
]
train_x = train[use_col]
test_x = test[use_col]
print(train_x.shape)
print(train_y.shape)
print(test_x.shape)
timer.time("prepare train in ")
return train[["card_id",
"target"]], test[["card_id"]], train_x, train_y, test_x
train = pd.merge(train, old_trans3, on="card_id", how="left")
train = pd.merge(train, new_trans6, on="card_id", how="left")
train = pd.merge(train, old_trans6, on="card_id", how="left")
train = pd.merge(train, old_trans9, on="card_id", how="left")
#
test = pd.merge(test, new_trans, on="card_id", how="left")
test = pd.merge(test, old_trans, on="card_id", how="left")
test = pd.merge(test, old_trans3, on="card_id", how="left")
test = pd.merge(test, new_trans6, on="card_id", how="left")
test = pd.merge(test, old_trans6, on="card_id", how="left")
test = pd.merge(test, old_trans9, on="card_id", how="left")
print(train.shape)
print(test.shape)
#
fer = jit_fe.JitFe()
train = fer.do_fe(train)
test = fer.do_fe(test)
pred_train = csv_io.read_file(path_const.NEW_DAY_PRED_OOF)
pred_test = csv_io.read_file(path_const.NEW_DAY_PRED_SUB)
train = pd.merge(train, pred_train, on="card_id", how="left")
train["pred_diff"] = train["pred_new"] - train["new_to_last_day"]
test = pd.merge(test, pred_test, on="card_id", how="left")
test["pred_diff"] = test["pred_new"] - test["new_to_last_day"]
train_y = train["target"]
drop_col = [
"card_id",
"target", # "feature_1", "feature_2", "feature_3",
"old_weekend_mean",