先附全代码预览

import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
# 使用RandomForestClassifier,填补缺失的年龄
from sklearn.ensemble import RandomForestRegressor
train = pd.read_csv("C:/Users/10158565/Desktop/kaggle/train.csv")
test= pd.read_csv("C:/Users/10158565/Desktop/kaggle/test.csv")
PassengerId=test['PassengerId']
all_data = pd.concat([train, test], ignore_index = True)

all_data['Title'] = all_data['Name'].apply(lambda x:x.split(',')[1].split('.')[0].strip())
Title_Dict = {}
Title_Dict.update(dict.fromkeys(['Capt', 'Col', 'Major', 'Dr', 'Rev'], 'Officer'))
Title_Dict.update(dict.fromkeys(['Don', 'Sir', 'the Countess', 'Dona', 'Lady'], 'Royalty'))
Title_Dict.update(dict.fromkeys(['Mme', 'Ms', 'Mrs'], 'Mrs'))
Title_Dict.update(dict.fromkeys(['Mlle', 'Miss'], 'Miss'))
Title_Dict.update(dict.fromkeys(['Mr'], 'Mr'))
Title_Dict.update(dict.fromkeys(['Master','Jonkheer'], 'Master'))


all_data['FamilySize']=all_data['SibSp']+all_data['Parch']+1
def Fam_label(s):
    if (s >= 2) & (s <= 4):
        return 2
    elif ((s > 4) & (s <= 7)) | (s == 1):
        return 1
    elif (s > 7):
        return 0
all_data['FamilyLabel']=all_data['FamilySize'].apply(Fam_label)
all_data['Cabin'] = all_data['Cabin'].fillna('Unknown')
all_data['Deck']=all_data['Cabin'].str.get(0)
Ticket_Count = dict(all_data['Ticket'].value_counts())
all_data['TicketGroup'] = all_data['Ticket'].apply(lambda x:Ticket_Count[x])

# 把已有的数据特征值取出，放入到Random Forest Regressor中
age_df = all_data[['Age', 'Pclass','Sex','Title']]
# 把年龄做特征值转化，即根据上述放进来的特征值，把对应的转换为0、1两个数
age_df=pd.get_dummies(age_df)
# 分为已知年龄和未知年龄
known_age = age_df[age_df.Age.notnull()].as_matrix()
unknown_age = age_df[age_df.Age.isnull()].as_matrix()
# 目标年龄值
y = known_age[:, 0]
# 特征属性值
X = known_age[:, 1:]
# fit到RandomForestRegressor之中
rfr = RandomForestRegressor(random_state=0, n_estimators=100, n_jobs=-1)
rfr.fit(X, y)
# 用得到的模型进行未知年龄结果预测
predictedAges = rfr.predict(unknown_age[:, 1::])
# 用得到的预测结果填补缺失数据
all_data.loc[ (all_data.Age.isnull()), 'Age' ] = predictedAges 

# Embarked字段缺失量填充
all_data['Embarked'] = all_data['Embarked'].fillna('C')
# Fare字段缺失量填充
fare=all_data[(all_data['Embarked'] == "S") & (all_data['Pclass'] == 3)].Fare.median()
all_data['Fare']=all_data['Fare'].fillna(fare)

#同组识别
all_data['Surname']=all_data['Name'].apply(lambda x:x.split(',')[0].strip())
Surname_Count = dict(all_data['Surname'].value_counts())
all_data['FamilyGroup'] = all_data['Surname'].apply(lambda x:Surname_Count[x])
Female_Child_Group=all_data.loc[(all_data['FamilyGroup']>=2) & ((all_data['Age']<=12) | (all_data['Sex']=='female'))]
Male_Adult_Group=all_data.loc[(all_data['FamilyGroup']>=2) & (all_data['Age']>12) & (all_data['Sex']=='male')]


Female_Child_Group=Female_Child_Group.groupby('Surname')['Survived'].mean()
Dead_List=set(Female_Child_Group[Female_Child_Group.apply(lambda x:x==0)].index)
print(Dead_List)
Male_Adult_List=Male_Adult_Group.groupby('Surname')['Survived'].mean()
Survived_List=set(Male_Adult_List[Male_Adult_List.apply(lambda x:x==1)].index)
print(Survived_List)


#惩罚修改
train=all_data.loc[all_data['Survived'].notnull()]
test=all_data.loc[all_data['Survived'].isnull()]
test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Sex'] = 'male'
test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Age'] = 60
test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Title'] = 'Mr'
test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Sex'] = 'female'
test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Age'] = 5
test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Title'] = 'Miss'


all_data=pd.concat([train, test])
all_data=all_data[['Survived','Pclass','Sex','Age','Fare','Embarked','Title','FamilyLabel','Deck','TicketGroup']]
all_data=pd.get_dummies(all_data)
train=all_data[all_data['Survived'].notnull()]
test=all_data[all_data['Survived'].isnull()].drop('Survived',axis=1)
X = train.as_matrix()[:,1:]
y = train.as_matrix()[:,0]



from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.feature_selection import SelectKBest

pipe=Pipeline([('select',SelectKBest(k=20)), 
               ('classify', RandomForestClassifier(random_state = 10, max_features = 'sqrt'))])

param_test = {'classify__n_estimators':list(range(20,50,2)), 
              'classify__max_depth':list(range(3,60,3))}
gsearch = GridSearchCV(estimator = pipe, param_grid = param_test, scoring='roc_auc', cv=10)
gsearch.fit(X,y)
print(gsearch.best_params_, gsearch.best_score_)



from sklearn.pipeline import make_pipeline
select = SelectKBest(k = 20)
clf = RandomForestClassifier(random_state = 10, warm_start = True, 
                                  n_estimators = 26,
                                  max_depth = 6, 
                                  max_features = 'sqrt')
pipeline = make_pipeline(select, clf)
pipeline.fit(X, y)


from sklearn import cross_validation, metrics
cv_score = cross_validation.cross_val_score(pipeline, X, y, cv= 10)
print("CV Score : Mean - %.7g | Std - %.7g " % (np.mean(cv_score), np.std(cv_score)))


predictions = pipeline.predict(test)
submission = pd.DataFrame({"PassengerId": PassengerId, "Survived": predictions.astype(np.int32)})
submission.to_csv(r"C:/Users/10158565/Desktop/kaggle_submit.csv", index=False)

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详解来了_详解来了详解来了_详解来了详解来了_详解来了详解来了_详解来了

数据概览

官方给出了三个文档，其中train是训练数据，我们将其导入到工作中

此时我们观察到，数据中有几个字段存在空值，并且Survived字段中的0,1分别代表死亡和存活，也正是我们想探究的量

•PassengerID（ID）
•Survived(是否存活)
•Pclass（客舱等级）
•Name（姓名）
•Sex（性别）
•Age（年龄）
•Parch（直系亲友）
•SibSp（旁系亲友）
•Ticket（票编号）
•Fare（票价）
•Cabin（客舱编号）
•Embarked（港口）

data_train.info()

由于缺失的数据会影响到分析结果，使用info()查看统计字段的结果（Age,Cabin,Embarked都是缺失不满891的字段，使用时需注意）

各字段与获救关联性分析

按照数据给出的所有字段，分析字段和Survived字段的联系
PassengerId: 从1-n往后排肯定是没什么关系了

Pclass：

import pandas as pd 
data_train = pd.read_csv("C:/Users/10158565/Desktop/kaggle/train.csv")
survived=data_train.Pclass[data_train.Survived == 1].value_counts()
nsurvived=data_train.Pclass[data_train.Survived == 0].value_counts()
df=pd.DataFrame({'suivived':survived,'nsurvived':nsurvived})
df.plot(kind='bar',stacked=True,title='Pclass')

可以看出，不同的客舱等级会影响到幸存率，一等舱的幸存率明显高

Name：
至于名称这个变量，博主一开始是想到可能会有什么血统影响（遗传跑得快），或者是什么皇室血统优先跑啥的，然后展开了一下…
emmm这个姓氏整理的有点不规则，等后续有时间再数据治理吧，先假装没关系…

Sex：

import pandas as pd 
data_train = pd.read_csv("C:/Users/10158565/Desktop/kaggle/train.csv")
survived=data_train.Sex[data_train.Survived == 1].value_counts()
nsurvived=data_train.Sex[data_train.Survived == 0].value_counts()
df=pd.DataFrame({'suivived':survived,'nsurvived':nsurvived})
df.plot(kind='bar',stacked=True,title='Name')

可以看出，不同的性别也会影响幸存率，女士幸存率更高

Age：

import pandas as pd 
import matplotlib.pyplot as plt
data_train = pd.read_csv("C:/Users/10158565/Desktop/kaggle/train.csv")
survived=data_train.Age[data_train.Survived == 1].value_counts()
nsurvived=data_train.Age[data_train.Survived == 0].value_counts()
df=pd.DataFrame({'suivived':survived,'nsurvived':nsurvived})
df.plot(stacked=True,title='Age')

plt.figure()
ss=survived/(survived+nsurvived)
plt.plot(ss)

Age是有缺失值的
但从第一幅图是看不出来特别大的区别的，但从第二幅中的幸存率来看，0~15的区间内幸存率会普遍高（尊老爱幼吧），为什么排除尊老呢，因为如果用以上方法探究，舱位的分布随年龄递增，可能是因为年龄大头等舱多才收到影响导致幸存率高吧（其实就是取两段年龄段有点麻烦,有空回来再优化吧哈哈）。
那我们可以考虑年龄偏小幸存率高这个是拥有可信度的~

Sibsp:

import pandas as pd 
data_train = pd.read_csv("C:/Users/10158565/Desktop/kaggle/train.csv")
survived=data_train.SibSp[data_train.Survived == 1].value_counts()
nsurvived=data_train.SibSp[data_train.Survived == 0].value_counts()
df=pd.DataFrame({'suivived':survived,'nsurvived':nsurvived})
df.plot(kind='bar',stacked=True,title='SibSp')

旁系亲友数量在1~两个间的获救率更高一些

Parch:

import pandas as pd 
import matplotlib.pyplot as plt
data_train = pd.read_csv("C:/Users/10158565/Desktop/kaggle/train.csv")
survived=data_train.Parch[data_train.Survived == 1].value_counts()
nsurvived=data_train.Parch[data_train.Survived == 0].value_counts()
df=pd.DataFrame({'suivived':survived,'nsurvived':nsurvived})
df.plot(kind='bar',stacked=True,title='Parch')

plt.figure()
ss=survived/(survived+nsurvived)
plt.plot(ss)

直系亲属在1、3的获救概率比较高

Ticket:

因为票也是随机生成的，同理乘客ID 所以不考虑做影响因素（实际已画图验证，毫无规律）

Fare:

import pandas as pd 
import matplotlib.pyplot as plt
data_train = pd.read_csv("C:/Users/10158565/Desktop/kaggle/train.csv")
survived=data_train.Fare[data_train.Survived == 1].value_counts()
nsurvived=data_train.Fare[data_train.Survived == 0].value_counts()
df=pd.DataFrame({'suivived':survived,'nsurvived':nsurvived})
df.plot(stacked=True,title='Fare')

plt.figure()
ss=survived/(survived+nsurvived)
plt.plot(ss)

票价就相当于舱位，票价高的舱位好，之前也验证了获救率高

Cabin:

首先观察Cabin的信息缺失了很多，并且按照有道翻译理解是不同类别的甲板位置（A,B,C,D），所以根据信息取首字母来判别一下。

import pandas as pd 
import matplotlib.pyplot as plt
data_train = pd.read_csv("C:/Users/10158565/Desktop/kaggle/train.csv")
survived=data_train.Cabin.str.get(0)[data_train.Survived == 1].value_counts()
nsurvived=data_train.Cabin.str.get(0)[data_train.Survived == 0].value_counts()
df=pd.DataFrame({'suivived':survived,'nsurvived':nsurvived})
df.plot(stacked=True,title='Cabin')

plt.figure()
ss=survived/(survived+nsurvived)
plt.plot(ss)

可以看出来，B\D\E的幸存率高一些

Embarked:

import pandas as pd 
import matplotlib.pyplot as plt
data_train = pd.read_csv("C:/Users/10158565/Desktop/kaggle/train.csv")
survived=data_train.Embarked[data_train.Survived == 1].value_counts()
nsurvived=data_train.Embarked[data_train.Survived == 0].value_counts()
df=pd.DataFrame({'suivived':survived,'nsurvived':nsurvived})
df.plot(stacked=True,title='Embarked')

plt.figure()
ss=survived/(survived+nsurvived)
plt.plot(ss)

可以看出来，C港口的幸存率明显高一些

数据预处理

缺失值填充

Age：
由上一部分我们发现的Age缺失：
用scikit-learn中的RandomForest来拟合一下缺失的年龄数据(注：RandomForest是一个用在原始数据中做不同采样，建立多颗DecisionTree，再进行average等等来降低过拟合现象，提高结果的机器学习算法)
(这一段是博主度娘来的处理方法，后续深入研究了RandomForest会附一篇教程链接在这个位置，此处立一个flag)

import pandas as pd 

# 使用RandomForestClassifier,填补缺失的年龄
from sklearn.ensemble import RandomForestRegressor

data_train = pd.read_csv("C:/Users/10158565/Desktop/kaggle/train.csv")

# 把已有的数据特征值取出，放入到Random Forest Regressor中
age_df = data_train[['Age', 'Pclass','Fare', 'Parch', 'SibSp']]

# 把年龄做特征值转化，即根据上述放进来的特征值，把对应的转换为0、1两个数
age_df=pd.get_dummies(age_df)

# 分为已知年龄和未知年龄
known_age = age_df[age_df.Age.notnull()].as_matrix()
unknown_age = age_df[age_df.Age.isnull()].as_matrix()

# 目标年龄值
y = known_age[:, 0]

# 特征属性值
X = known_age[:, 1:]

# fit到RandomForestRegressor之中
rfr = RandomForestRegressor(random_state=0, n_estimators=100, n_jobs=-1)
rfr.fit(X, y)

# 用得到的模型进行未知年龄结果预测
predictedAges = rfr.predict(unknown_age[:, 1::])

# 用得到的预测结果填补缺失数据
data_train.loc[ (data_train.Age.isnull()), 'Age' ] = predictedAges 

此时Age列已经被处理完毕。

说明一下get_dummies：

因为逻辑回归建模时，需要输入的特征都是数值型特征，我们通常会先对类目型的特征因子化。
什么叫做因子化呢？举个例子：
以Cabin为例，原本一个属性维度，因为其取值可以是[‘yes’,‘no’]，而将其平展开为’Cabin_yes’,'Cabin_no’两个属性
原本Cabin取值为yes的，在此处的"Cabin_yes"下取值为1，在"Cabin_no"下取值为0
原本Cabin取值为no的，在此处的"Cabin_yes"下取值为0，在"Cabin_no"下取值为1
我们使用pandas的"get_dummies"来完成这个工作，并拼接在原来的"data_train"之上

Embarked：

Embarked缺失量为2，很少，缺失Embarked信息的乘客的Pclass均为1，且Fare均为80，因为Embarked为C且Pclass为1的乘客的Fare中位数为80，所以缺失值填充为C。

data_train['Embarked'] = data_train['Embarked'].fillna('C')

同组识别

按照对于数据的理解，对能够组合起来的项目进行分组查看
本思想为：按照理解，年龄和性别都是导致幸存率差异的问题，那么同一家庭中，女性和儿童与男性对比，幸存率会更高，那么就需要调整违反规律的组别（家庭）（从分组后人数大于1的组中对比）

data_train['Surname']=data_train['Name'].apply(lambda x:x.split(',')[0].strip())
Surname_Count = dict(data_train['Surname'].value_counts())
data_train['FamilyGroup'] = data_train['Surname'].apply(lambda x:Surname_Count[x])
Female_Child_Group=data_train.loc[(data_train['FamilyGroup']>=2) & ((data_train['Age']<=15) | (data_train['Sex']=='female'))]
Male_Adult_Group=data_train.loc[(data_train['FamilyGroup']>=2) & (data_train['Age']>15) & (data_train['Sex']=='male')]

查看结果：

Female_Child=pd.DataFrame(Female_Child_Group.groupby('Surname')['Survived'].mean().value_counts())
Female_Child.columns=['GroupCount']
Female_Child

发现绝大部分女性和儿童组的平均存活率都为1或0，即同组（同一家庭中）的女性和儿童要么全部幸存，要么全部遇难。

再来看一下男性

Male_Adult=pd.DataFrame(Male_Adult_Group.groupby('Surname')['Survived'].mean().value_counts())
Male_Adult.columns=['GroupCount']
Male_Adult

绝大部分成年男性组的平均存活率也为1或0。

因为普遍规律是女性和儿童幸存率高，成年男性幸存较低，所以我们把不符合普遍规律的反常组选出来单独处理。把女性和儿童组中幸存率为0的组设置为遇难组，把成年男性组中存活率为1的设置为幸存组，推测处于遇难组的女性和儿童幸存的可能性较低，处于幸存组的成年男性幸存的可能性较高。

Female_Child_Group=Female_Child_Group.groupby('Surname')['Survived'].mean()
Dead_List=set(Female_Child_Group[Female_Child_Group.apply(lambda x:x==0)].index)
print(Dead_List)
Male_Adult_List=Male_Adult_Group.groupby('Surname')['Survived'].mean()
Survived_List=set(Male_Adult_List[Male_Adult_List.apply(lambda x:x==1)].index)
print(Survived_List)

为了使处于这两种反常组中的样本能够被正确分类，对测试集中处于反常组中的样本的Age，Sex进行惩罚修改。

train=all_data.loc[all_data['Survived'].notnull()]
test=all_data.loc[all_data['Survived'].isnull()]
test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Sex'] = 'male'
test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Age'] = 60
test.loc[(test['Surname'].apply(lambda x:x in Dead_List)),'Title'] = 'Mr'
test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Sex'] = 'female'
test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Age'] = 5
test.loc[(test['Surname'].apply(lambda x:x in Survived_List)),'Title'] = 'Miss'

特征转换

选取特征，转换为数值变量，划分训练集和测试集

all_data=pd.concat([train, test])
all_data=all_data[['Survived','Pclass','Sex','Age','Fare','Embarked','Title','FamilyLabel','Deck','TicketGroup']]
all_data=pd.get_dummies(all_data)
train=all_data[all_data['Survived'].notnull()]
test=all_data[all_data['Survived'].isnull()].drop('Survived',axis=1)
X = train.as_matrix()[:,1:]
y = train.as_matrix()[:,0]

建模和优化

参数优化

用网格搜索自动化选取最优参数（事实上网上有很多更加优化的参数…）

from sklearn.pipeline import Pipeline
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.feature_selection import SelectKBest

pipe=Pipeline([('select',SelectKBest(k=20)), 
               ('classify', RandomForestClassifier(random_state = 10, max_features = 'sqrt'))])

param_test = {'classify__n_estimators':list(range(20,50,2)), 
              'classify__max_depth':list(range(3,60,3))}
gsearch = GridSearchCV(estimator = pipe, param_grid = param_test, scoring='roc_auc', cv=10)
gsearch.fit(X,y)
print(gsearch.best_params_, gsearch.best_score_)

训练模型

from sklearn.pipeline import make_pipeline
select = SelectKBest(k = 20)
clf = RandomForestClassifier(random_state = 10, warm_start = True, 
                                  n_estimators = 26,
                                  max_depth = 6, 
                                  max_features = 'sqrt')
pipeline = make_pipeline(select, clf)
pipeline.fit(X, y)

交叉验证

from sklearn import cross_validation, metrics
cv_score = cross_validation.cross_val_score(pipeline, X, y, cv= 10)
print("CV Score : Mean - %.7g | Std - %.7g " % (np.mean(cv_score), np.std(cv_score)))

预测

predictions = pipeline.predict(test)
submission = pd.DataFrame({"PassengerId": PassengerId, "Survived": predictions.astype(np.int32)})
submission.to_csv(r"C:/Users/10158565/Desktop/kaggle_submit.csv", index=False)

当前分数

排名亮点，哎嘿嘿