from google.colab import files
!pip install -q kaggle

uploaded = files.upload()
#kaggle.json 파일 업로드 (kaggle 로그인 후 MyPage에서 다운로드 받을 수 있음

!mkdir /root/.kaggle
!mv kaggle.json /root/.kaggle/

!kaggle competitions download -c titanic

!pip install --upgrade pandas_profiling

%matplotlib inline
import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
from pandas_profiling import ProfileReport

train = pd.read_csv('./train.csv')
test = pd.read_csv('./test.csv')

print(train.shape)
print(test.shape)

#간단하게 EDA하기 위해 PandasProfiling 사용
rp_train = ProfileReport(train, title='Train', html={'style':{'full_width':True}})

rp_test = ProfileReport(test, title='Test', html={'style':{'full_width':True}})

rp_train.to_file('rp_train.html')
files.download('rp_train.html')

rp_test.to_file('rp_test.html')
files.download('rp_test.html')

#추가 EDA - categorical variables
#Sex
sex_pivot = train.pivot_table(index='Sex', values='Survived')
print(sex_pivot)
sex_pivot.plot.bar(ylim=(0,1))
plt.plot()

#추가 EDA - continuous variable
survived = train[train['Survived']==1]
failed = train[train['Survived']==0]

#Fare
survived['Fare'].plot.hist(alpha=0.3, color='red', bins=100, figsize=(20,10))
failed['Fare'].plot.hist(alpha=0.3, color='blue', bins=100, figsize=(20,10))
plt.legend(['Survived', 'Failed'])
plt.show()

#Age
survived['Age'].plot.hist(alpha=0.3, color='red', bins=100, figsize=(20,10))
failed['Age'].plot.hist(alpha=0.3, color='blue', bins=100, figsize=(20,10))
plt.legend(['Survived', 'Failed'])
plt.show()
#Age를 구간별로 짜르면 class 구분이 가능한 bin이 존재함을 확인(예:0~5살 영유아 구간은 Survived 비율이 높음)

#Cleansing & Mangling
#Processing with Age in training set

#Age는 임의의 구간이 아닌, 의미가 명확한 구간으로 나눌 수 있는 명목변수이다.
#따라서 CAT 변수로 바꿀 수 있다: Age_categories: CAT
#Age는 미싱값이 있다. 미싱값도 하나의 CATEGORY로 변환해서 사용한다

def process_age(df,cut_points,label_names):
    df["Age"] = df["Age"].fillna(-0.5)
    df["Age_categories"] = pd.cut(df["Age"],cut_points,labels=label_names)
    return df


cut_points = [-1,0, 5, 12, 18, 35, 60, 100]
label_names = ["Missing", 'Infant', "Child", 'Teenager', "Young Adult", 'Adult', 'Senior']

train = process_age(train,cut_points,label_names)
test = process_age(test,cut_points,label_names)

age_cat_pivot = train.pivot_table(index="Age_categories",values="Survived")
age_cat_pivot.plot.bar()
plt.show()

#Processing with Fare in test set
#Fare는 Age와 달리 의미 있는 구간으로 나누기 위한 정보가 부족하다. (물가, 소득수준 등)
print(test[test['Fare'].isna()==True])

#동일한 Pclass, Sex, Age_categories 값을 지닌 row들의 mean(Fare)를 사용하기로 하자.
#test셋에는 해당 데이터가 자기 자신밖에 없다. train 셋에서 찾아보자
subset0 = train[(train['Pclass']==3) & (train['Sex']=='male') & (train['Age_categories']=='Senior')]
print(subset0)

#위 subset Fare의 평균값을 na값을 대체하여 사용
test['Fare'].fillna(subset0.Fare.mean(), inplace=True)
print(test.iloc[152])
print()

#잘 replace 되었나 체크
print(test[test['Fare'].isna()==True])

#카테고리컬 variable을 One-hot 인코딩
def create_dummies(df, c_name):
    dummies = pd.get_dummies(df[c_name], prefix=c_name)
    df = pd.concat([df,dummies], axis=1)
    return df

train_dummies = create_dummies(train, "Pclass")
test_dummies = create_dummies(test, "Pclass")

train_dummies = create_dummies(train_dummies, "Sex")
test_dummies = create_dummies(test_dummies, "Sex")

train_dummies = create_dummies(train_dummies, "Age_categories")
test_dummies = create_dummies(test_dummies, "Age_categories")

train_dummies.sample(5)
test_dummies.sample(5)

#Fare 정규화
#트레이닝셋
from sklearn.preprocessing import MinMaxScaler
minmax_scaler = MinMaxScaler()

fitted = minmax_scaler.fit_transform(train_dummies[['Fare']])
train_dummies['Fare_norm']= fitted
print(train_dummies.sample(5))
print();print()

#테스트셋
fitted = minmax_scaler.fit_transform(test_dummies[['Fare']])
test_dummies['Fare_norm']= fitted
print(test_dummies.sample(5))

#Fare 정규화
#트레이닝셋
from sklearn.preprocessing import MinMaxScaler
minmax_scaler = MinMaxScaler()

fitted = minmax_scaler.fit_transform(train_dummies[['Fare']])
train_dummies['Fare_norm']= fitted
print(train_dummies.sample(5))
print();print()

#테스트셋
fitted = minmax_scaler.fit_transform(test_dummies[['Fare']])
test_dummies['Fare_norm']= fitted
print(test_dummies.sample(5))

#머신러닝 모델 만들기
#EDA 결과 독립-종속 간 선형성 확인 가능했다. 로지스틱 회귀 모델(glm)으로 테스트
columns = ['Fare_norm', 'Pclass_1', 'Pclass_2', 'Pclass_3', 'Sex_female', 'Sex_male',
       'Age_categories_Missing','Age_categories_Infant',
       'Age_categories_Child', 'Age_categories_Teenager',
       'Age_categories_Young Adult', 'Age_categories_Adult',
       'Age_categories_Senior']

from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()

from sklearn.model_selection import train_test_split
all_X = train_dummies[columns]; all_y = train_dummies['Survived']
#training, validation셋 분할
train_X, valid_X, train_y, valid_y = train_test_split(all_X, all_y, test_size=0.2, random_state=0)

print(train_X.shape)
print(valid_X.shape)
print(train_y.shape)
print(valid_y.shape)
print()
print(train_X.info())

lr.fit(train_X, train_y)
pred = lr.predict(valid_X)

from sklearn.metrics import accuracy_score, confusion_matrix
acc = accuracy_score(valid_y, pred)
conf_mat = confusion_matrix(valid_y, pred)
print(acc)
print()
print(pd.DataFrame(conf_mat, columns=['Survived_Pred','Died_Pred'], index=[['Survived_True','Died_True']]))
print()

#10-fold validation learning
from sklearn.model_selection import cross_val_score
import numpy as np
scores = cross_val_score(lr, all_X, all_y, cv=10)
print(scores)
print()
print(np.mean(scores))

# test셋 예측
lr_test = LogisticRegression()
lr_test.fit(all_X, all_y)

test_X = test_dummies[columns]
test_pred = lr_test.predict(test_X)
print(test_pred)