def __init__(self, model, filename=None, num_rows=None):
self.logger = pocket_logger.get_my_logger()
self.model = model
use_col = feature_engineerer.get_necessary_col()
dtypes = csv_loader.get_featured_dtypes()
local_filename = HOLDOUT_DATA
if filename is None:
filename = HOLDOUT_DATA
if num_rows is None:
self.holdout_df = dd.read_csv(filename, dtype=dtypes, usecols=use_col).compute()
else:
self.holdout_df = dd.read_csv(filename, dtype=dtypes, nrows=num_rows, usecols=use_col).compute()
print(self.holdout_df.info())
print("Initialized validator.")
OUTPUT_DATA8 = os.path.join(OUTPUT_DIR, "long_train_day8.feather")
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()
def __init__(self, model, df, predict_col):
self.logger = pocket_logger.get_my_logger()
self.model = model
self.holdout_df = df
self.predict_col = predict_col
print("Initialized validator.")
