OUTPUT_DATA9 = os.path.join(OUTPUT_DIR, "long_train_day9.feather")
OUTPUT_TEST = os.path.join(OUTPUT_DIR, "long_test.feather")
MAMAS_INDEX = os.path.join(INPUT_DIR, "last_test_idx.npy")
OUTPUT_FILE = os.path.join(OUTPUT_DIR, "sub_long.csv")
import pandas as pd
import numpy as np
from sklearn import model_selection
import gc
from dask import dataframe as dd
from talkingdata.fe import column_selector, runtime_fe
from talkingdata.common import csv_loader, pocket_lgb, pocket_timer, pocket_logger
logger = pocket_logger.get_my_logger()
timer = pocket_timer.GoldenTimer(logger)
predict_col = column_selector.get_predict_col()
dtypes = csv_loader.get_featured_dtypes()
train7 = pd.read_feather(OUTPUT_DATA7)
train8 = pd.read_feather(OUTPUT_DATA8)
train9 = pd.read_feather(OUTPUT_DATA9)
test = pd.read_feather(OUTPUT_TEST)
timer.time("load csv in ")
train = train7.append(train8).append(train9)
print(train.info())
print(test.info())
del train7, train8, train9
gc.collect()
sys.path.append(ROOT)
APP_ROOT = os.path.join(ROOT, "talkingdata")
INPUT_DIR = os.path.join(APP_ROOT, "input")
OUTPUT_DIR = os.path.join(APP_ROOT, "output")
TRAIN_DATA = os.path.join(OUTPUT_DIR, "full_train_day3_featured.csv")
TEST_DATA = os.path.join(OUTPUT_DIR, "full_test_featured.csv")
OUTPUT_FILE = os.path.join(OUTPUT_DIR, "submission_full.csv")
import pandas as pd
import numpy as np
from sklearn import model_selection
import gc
import time
from talkingdata.common import csv_loader, feature_engineerer, pocket_lgb, pocket_timer
timer = pocket_timer.GoldenTimer()
dtypes = csv_loader.get_featured_dtypes()
train = pd.read_csv(TRAIN_DATA, dtype=dtypes)
train = train[feature_engineerer.get_necessary_col()]
print(train.info())
train_y = train["is_attributed"]
train_x = train.drop("is_attributed", axis=1)
X_train, X_valid, y_train, y_valid = model_selection.train_test_split(train_x, train_y, test_size=0.2, random_state=99)
timer.time("prepare train in ")
lgb = pocket_lgb.GoldenLgb()
model = lgb.do_train_sk(X_train, X_valid, y_train, y_valid)
lgb.show_feature_importance(model)