from sklearn.ensemble import AdaBoostClassifier

 import numpy as np

 import pandas as pd

 from sklearn.linear_model import LogisticRegression

 from sklearn.ensemble import RandomForestClassifier

 from sklearn.feature_selection import SelectKBest, f_classif

 import  matplotlib.pyplot as plt

 train = pd.read_csv("titanic_train.csv", dtype={"Age": np.float64})

 test = pd.read_csv("titanic_test.csv", dtype={"Age": np.float64} )

 print("\n\nTop of the training data:")

 print(train.head())

 print("\n\nSummary statistics of training data")

 print(train.describe())

 #train.to_csv('copy_of_the_training_data.csv', index=False)

 train["Age"]=train["Age"].fillna(-1)

 test["Age"]=test["Age"].fillna(-1)

 train.loc[train["Sex"]=="male","Sex"]=0

 test.loc[test["Sex"]=="male","Sex"]=0

 train.loc[train["Sex"]=="female","Sex"]=1

 test.loc[test["Sex"]=="female","Sex"]=1

 print(train["Embarked"].unique())

 train["Embarked"]=train["Embarked"].fillna("S")

 test["Embarked"]=test["Embarked"].fillna("S")

 train.loc[train["Embarked"]=="S","Embarked"]=0

 train.loc[train["Embarked"]=="C","Embarked"]=1

 train.loc[train["Embarked"]=="Q","Embarked"]=2

 test.loc[test["Embarked"]=="S","Embarked"]=0

 test.loc[test["Embarked"]=="C","Embarked"]=1

 test.loc[test["Embarked"]=="Q","Embarked"]=2

 train["Fare"]=train["Fare"].fillna(train["Fare"].median())

 test["Fare"]=test["Fare"].fillna(test["Fare"].median())

 #Generating a familysize column

 train["FamilySize"]=train["SibSp"]+train["Parch"]

 test["FamilySize"]=train["SibSp"]+test["Parch"]

 train["NameLength"]=train["Name"].apply(lambda x:len(x))

 test["NameLength"]=test["Name"].apply(lambda x:len(x))

 import re 

 def get_title(name):

     # Use a regular expression to search for a title.  Titles always consist of capital and lowercase letters, and end with a period.

     title_search = re.search(' ([A-Za-z]+)\.', name)

     # If the title exists, extract and return it.

     if title_search:

         return title_search.group(1)

     return ""

 # Get all the titles and print how often each one occurs.

 train_titles = train["Name"].apply(get_title)

 test_titles=test["Name"].apply(get_title)

 # Map each title to an integer.  Some titles are very rare, and are compressed into the same codes as other titles.

 title_mapping = {"Mr": 1, "Miss": 2, "Mrs": 3, "Master": 4, "Dr": 5, "Rev": 6, "Major": 7, "Col": 7, "Mlle": 8, "Mme": 8, "Don": 9, "Lady": 10, "Countess": 10, "Jonkheer": 10, "Sir": 9, "Capt": 7, "Ms": 2,"Dona":9}

 for k,v in title_mapping.items():

     train_titles[train_titles == k] =v

     test_titles[test_titles==k]=v

 # Add in the title column.

 train["Title"] = train_titles

 test["Title"]= test_titles

 #print (test["Title"])

 import operator

 # A dictionary mapping family name to id

 family_id_mapping = {}

 # A function to get the id given a row

 def get_family_id(row):

     # Find the last name by splitting on a comma

     last_name = row["Name"].split(",")[0]

     # Create the family id

     family_id = "{0}{1}".format(last_name, row["FamilySize"])

     # Look up the id in the mapping

     if family_id not in family_id_mapping:

         if len(family_id_mapping) == 0:

             current_id = 1

         else:

             # Get the maximum id from the mapping and add one to it if we don't have an id

             current_id = (max(family_id_mapping.items(), key=operator.itemgetter(1))[1] + 1)

         family_id_mapping[family_id] = current_id

     return family_id_mapping[family_id]

 # Get the family ids with the apply method

 train_family_ids = train.apply(get_family_id, axis=1)

 test_family_ids = test.apply(get_family_id,axis=1)

 # There are a lot of family ids, so we'll compress all of the families under 3 members into one code.

 train_family_ids[train["FamilySize"] < 3] = -1

 test_family_ids[test["FamilySize"]<3]=-1

 train["FamilyId"] = train_family_ids

 test["FamilyId"]=test_family_ids

 alg = AdaBoostClassifier()

 #alg=RandomForestClassifier(random_state=1,n_estimators=150,min_samples_split=4,min_samples_leaf=2)

 predictors = ["Pclass", "Sex", "Age", "SibSp", "Parch", "Fare", "Embarked", "FamilySize", "Title", "FamilyId"]

 # Perform feature selection

 selector = SelectKBest(f_classif, k=5)

 selector.fit(train[predictors], train["Survived"])

 # Get the raw p-values for each feature, and transform from p-values into scores

 scores = -np.log10(selector.pvalues_)

 # Plot the scores.  See how "Pclass", "Sex", "Title", and "Fare" are the best?

 plt.bar(range(len(predictors)),scores)

 plt.xticks(range(len(predictors)), predictors, rotation='vertical')

 plt.show()

 print("#######")

 predictors = ["Pclass", "Sex", "Fare","Title"]

 x_train = train[predictors]

 y_train = train['Survived']

 x_test=test[predictors]

 alg.fit(x_train,y_train)

 predictions = alg.predict(x_test)

 submission = pd.DataFrame({

         "PassengerId": test["PassengerId"],

         "Survived": predictions

     })

 submission.to_csv('submission.csv', index=False)

 #coding=utf-8

 #str.format() 函数

 #使用‘｛｝’占位符

 print('what\'s {},{}'.format('wrong','hong!'))

 #使用｛0｝，｛1｝形式的占位符

 print ('{},I\'m {},my qq is {}'.format('hello','hong',''))

 print('{},I\'m {},my E-mail is {}'.format('Hello','Hongten','hongtenzone@foxmail.com'))

 print ('{1},{0}'.format('hello','world'))

 #使用'{name}'形式的占位符

 print('Hi,{name},{message}'.format(name="hongten",message='how are you?'))

 #格式控制：

 import math

 print('The value of PI is approximately {0:.3f}.'.format(math.pi))

 table = {'Sjoerd': 4127, 'Jack': 4098, 'Dcab': 7678}

 for name,phone in table.items():

     print('{0:10}==>{1:10d}'.format(name,phone))

#coding=utf-8

import  re

#re.match()

s='I like hongten! he is so cool!'

m=re.match(r'(\w+)\s',s)

if m:

    print m.group(0)    #print groups()    正则表达式中用 ()表示的是要提取的分组（Group）

else:

    print 'not match'

#re.seach()

text = "JGood is a handsome boy, he is cool, clever, and so on..."

m=re.search(r'\shan(ds)ome\s',text)

if m:

    print m.groups()

else:

    print "No serch"

#re.sub  替换字符串的匹配项

import re

text = "JGood is a handsome boy, he is cool, clever, and so on..."

print re.sub(r'\s+', '-', text)

#re.split

#re.findall

#re.compile

re_telephone=re.compile(r'^(\d{3})-(\d{3,8})$')

print re_telephone.match('010-8086').groups()