kaggle Titanic

# coding: utf-8

# In[19]:

# 0.78468


# In[20]:


import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings(‘ignore‘)
from sklearn import preprocessing


# In[21]:


train_path = r‘C:\Users\cbattle\Desktop\train.csv‘ # r‘/home/adminn/桌面/train.csv‘ 
test_path = r‘C:\Users\cbattle\Desktop\test.csv‘ # r‘/home/adminn/桌面/test.csv‘
out_path = r‘C:\Users\cbattle\Desktop\out.csv‘ # r‘/home/adminn/桌面/out.csv‘

train = pd.read_csv(train_path)
test = pd.read_csv(test_path)

print(‘train:‘,train.shape)
print(‘test:‘,test.shape)
# train.info()
# test.info()
# print(train.head())

# 属性列
# print([col for col in train])
# print([col for col in test])

# 策略
# [‘PassengerId‘, ‘Pclass‘, ‘Name‘, ‘Sex‘, ‘Age‘, ‘SibSp‘, ‘Parch‘, ‘Ticket‘, ‘Fare‘, ‘Cabin‘, ‘Embarked‘]
#     drop          onehot   drop    0/1    num    num       num      drop     num      0/1    用S补空，onehot



# In[22]:


X = train.drop([‘Survived‘,‘PassengerId‘,‘Name‘], axis=1)
y = train[‘Survived‘]
Xtest = test.drop([‘PassengerId‘,‘Name‘], axis=1)
# print(‘X:‘,X.shape)
# print(‘y:‘,y.shape)
# print(‘Xtest:‘,Xtest.shape)


# In[23]:


key = [col for col in X if X[col].dtype != ‘object‘ # numberic [‘Pclass‘, ‘Age‘, ‘SibSp‘, ‘Parch‘, ‘Fare‘]
       or col == ‘Sex‘
       or col == ‘Embarked‘
       or col == ‘Cabin‘
      ] 
X = X[key]
Xtest = Xtest[key]
# print(key)

def showNullNum(a,b):
    print(a.isnull().sum())
    print()
    print(b.isnull().sum())
    print(‘------------------------------------‘)

showNullNum(X,Xtest)

# Xtest[‘Fare‘][Xtest[‘Fare‘].isnull()] = Xtest[‘Fare‘].median() # replace nan with median
# X = X.dropna(axis=0) # drop X and y in the same row

#-------------------------------------------------------------------------------
# Pclass    Ticket class
# 1 = 1st, 2 = 2nd, 3 = 3rd  onehot
# for i in X[‘Pclass‘].unique():
#     X[‘Pclass_‘+str(i)] = (X[‘Pclass‘]==i).astype(int)
#     Xtest[‘Pclass_‘+str(i)] = (Xtest[‘Pclass‘]==i).astype(int)

# X = X.drop([‘Pclass‘],axis=1)
# Xtest = Xtest.drop([‘Pclass‘],axis=1)

#-------------------------------------------------------------------------------
# Sex
X[‘Sex‘] = X[‘Sex‘].apply(lambda i:1 if i==‘female‘ else 0)
Xtest[‘Sex‘] = Xtest[‘Sex‘].apply(lambda i:1 if i==‘female‘ else 0)

#-------------------------------------------------------------------------------
# Embarked

# 1 label encoding
X[‘Embarked‘][X[‘Embarked‘].isnull()] = ‘S‘
X[‘Embarked‘] = X[‘Embarked‘].map({‘S‘:0,‘C‘:1,‘Q‘:2}).astype(int)
Xtest[‘Embarked‘] = Xtest[‘Embarked‘].map({‘S‘:0,‘C‘:1,‘Q‘:2}).astype(int)
# or use sklearn.preprocessing.LabelEncoder




# print(X.head())
# print(Xtest.head())

# X[‘Embarked‘][X[‘Embarked‘].isnull()] = ‘S‘
# from sklearn import preprocessing
# le = preprocessing.LabelEncoder()
# X[‘Embarked‘] = le.fit_transform(X[‘Embarked‘])
# Xtest[‘Embarked‘] = le.transform(Xtest[‘Embarked‘])

# print(X.head())
# print(Xtest.head())




# 2 onehot
# for i in X[‘Embarked‘].unique():
#     print(i, ‘sum:‘, sum(X[‘Embarked‘]==i))

# X[‘Embarked‘][X[‘Embarked‘].isnull()] = ‘S‘ # most_frequent
# for i in X[‘Embarked‘].unique():
#     X[‘Embarked_type_‘+i] = (X[‘Embarked‘]==i).astype(int)
#     Xtest[‘Embarked_type_‘+i] = (Xtest[‘Embarked‘]==i).astype(int)
    
# X = X.drop([‘Embarked‘],axis=1)
# Xtest = Xtest.drop([‘Embarked‘],axis=1)
# print(X.head(10))

#-------------------------------------------------------------------------------
# Cabin
# has a cabin or not
# print(X.head(5))
Xtest[‘Cabin‘] = Xtest[‘Cabin‘].apply(lambda i:1 if isinstance(i,str) else 0)
X[‘Cabin‘] = X[‘Cabin‘].apply(lambda i:1 if isinstance(i,str) else 0)
# print(X.head(5))


#-------------------------------------------------------------------------------
# age and fare
# use median to replace nan 
from sklearn.preprocessing import Imputer
ip = Imputer(strategy=‘median‘)
X = ip.fit_transform(X)
Xtest = ip.transform(Xtest)
print(np.isnan(X).sum(),np.isnan(Xtest).sum())



# In[24]:


from xgboost import XGBClassifier
xgb = XGBClassifier()
xgb.fit(X,y)
ans = xgb.predict(Xtest)

# from sklearn.tree import DecisionTreeClassifier
# from sklearn.ensemble import ExtraTreesClassifier
# from sklearn.svm import LinearSVC



# In[25]:


out = pd.DataFrame({‘PassengerId‘:test[‘PassengerId‘],‘Survived‘:ans})
out.to_csv(out_path,index = False)
print(‘ok‘)


# In[26]:


from sklearn import preprocessing
le = preprocessing.LabelEncoder()
le.fit([‘a‘,‘b‘,‘c‘])
ans = le.transform([‘a‘,‘a‘,‘c‘])
print(ans)