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### SMOTE
from imblearn.over_sampling import SMOTE
X_resampled, y_resampled = SMOTE().fit_sample(X_train, y_train)

### ROC_AUC_SCORE
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import roc_auc_score

logreg = LogisticRegression()
logreg.fit(X_train, y_train) 

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import numpy as np
import pandas as pd
from sklearn.model_selection import StratifiedShuffleSplit

train = pd.read_csv("data/application_train.csv")
test = pd.read_csv("data/application_test.csv")


# common fuction
def error(actual, predicted):

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package ssw;

import java.util.*;

/**
 * Created by sunu.park on 2018. 11. 10.
 */

    class Point{
        int x, y;

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##!pip3 install summa

#Step1. textrank를 활용 문서에서 중요문장 추출

from summa.summarizer import summarize
f = open("wiki_en/chosun.txt", 'r')
data = f.read()
summary = summarize(data, ratio=0.2)

f = open("wiki_en/chosun_min.txt", "w")

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##!pip3 install textrankr

# Step1. 문서요약으로 중요문장 찾기 (여기선 3문장)

from __future__ import print_function
from textrankr import TextRank

f = open("wiki/chosun.txt", 'r')
data = f.read()
textrank = TextRank(data)

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'''
Source code for an attention based image caption generation system described
in:
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
International Conference for Machine Learning (2015)
http://arxiv.org/abs/1502.03044
'''

import torch
import torch.nn as nn

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channels:
  - defaults
dependencies:
  - ipython
  - ipywidgets
  - matplotlib
  - numpy
  - scipy