这是一个分类任务，特征包含离散特征和连续特征，数据如下：Kaggle地址。目标是根据数据特征预测一个人是否能在泰坦尼克的沉没事故中存活下来。接下来解释下数据的格式：
survival 目标列，是否存活，1代表存活 (0 = No; 1 = Yes)
pclass 乘坐的舱位级别 (1 = 1st; 2 = 2nd; 3 = 3rd)
name 姓名
sex 性别
age 年龄
sibsp 兄弟姐妹的数量（乘客中）
parch 父母的数量（乘客中）
ticket 票号
fare 票价
cabin 客舱
embarked 登船的港口
(C = Cherbourg; Q = Queenstown; S = Southampton)
In [1]:
# -*- coding: UTF-8 -*-
%matplotlib inline
import pandas as pd
import string
import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
In [2]:
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')
def substrings_in_string(big_string, substrings):
for substring in substrings:
if string.find(big_string, substring) != -1:
return substring
return np.nan
def replace_titles(x):
title=x['Title']
if title in ['Mr','Don', 'Major', 'Capt', 'Jonkheer', 'Rev', 'Col']:
return 'Mr'
elif title in ['Master']:
return 'Master'
elif title in ['Countess', 'Mme','Mrs']:
return 'Mrs'
elif title in ['Mlle', 'Ms','Miss']:
return 'Miss'
elif title =='Dr':
if x['Sex']=='Male':
return 'Mr'
else:
return 'Mrs'
elif title =='':
if x['Sex']=='Male':
return 'Master'
else:
return 'Miss'
else:
return title
title_list = ['Mrs', 'Mr', 'Master', 'Miss', 'Major', 'Rev',
'Dr', 'Ms', 'Mlle','Col', 'Capt', 'Mme', 'Countess',
'Don', 'Jonkheer']
In [3]:
label = train['Survived'] # 目标列
In [4]:
# 接下来我们对每个特征进行一下分析：
train.groupby(['Pclass'])['PassengerId'].count().plot(kind='bar')
Out[4]:
In [5]:
train.groupby(['SibSp'])['PassengerId'].count().plot(kind='bar')
Out[5]:
In [6]:
train.groupby(['Parch'])['PassengerId'].count().plot(kind='bar')
Out[6]:
In [7]:
train.groupby(['Embarked'])['PassengerId'].count().plot(kind='bar')
Out[7]:
In [8]:
train.groupby(['Sex'])['PassengerId'].count().plot(kind='bar')
Out[8]:
In [9]:
print '检测是否有缺失值：'
print train[train['Age'].isnull()]['Age'].head()
print train[train['Fare'].isnull()]['Fare'].head()
print train[train['SibSp'].isnull()]['SibSp'].head()
print train[train['Parch'].isnull()]['Parch'].head()
train['Age'] = train['Age'].fillna(train['Age'].mean())
print '填充之后再检测：'
print train[train['Age'].isnull()]['Age'].head()
print train[train['Fare'].isnull()]['Fare'].head()
In [10]:
print '检测测试集是否有缺失值：'
print test[test['Age'].isnull()]['Age'].head()
print test[test['Fare'].isnull()]['Fare'].head()
print test[test['SibSp'].isnull()]['SibSp'].head()
print test[test['Parch'].isnull()]['Parch'].head()
test['Age'] = test['Age'].fillna(test['Age'].mean())
test['Fare'] = test['Fare'].fillna(test['Fare'].mean())
print '填充之后再检测：'
print test[test['Age'].isnull()]['Age'].head()
print test[test['Fare'].isnull()]['Fare'].head()
In [11]:
# 处理Title特征
train['Title'] = train['Name'].map(lambda x: substrings_in_string(x, title_list))
test['Title'] = test['Name'].map(lambda x: substrings_in_string(x, title_list))
train['Title'] = train.apply(replace_titles, axis=1)
test['Title'] = test.apply(replace_titles, axis=1)
# family特征
train['Family_Size'] = train['SibSp'] + train['Parch']
train['Family'] = train['SibSp'] * train['Parch']
test['Family_Size'] = test['SibSp'] + test['Parch']
test['Family'] = test['SibSp'] * test['Parch']
In [12]:
train['AgeFill'] = train['Age']
mean_ages = np.zeros(4)
mean_ages[0] = np.average(train[train['Title'] == 'Miss']['Age'].dropna())
mean_ages[1] = np.average(train[train['Title'] == 'Mrs']['Age'].dropna())
mean_ages[2] = np.average(train[train['Title'] == 'Mr']['Age'].dropna())
mean_ages[3] = np.average(train[train['Title'] == 'Master']['Age'].dropna())
train.loc[ (train.Age.isnull()) & (train.Title == 'Miss') ,'AgeFill'] = mean_ages[0]
train.loc[ (train.Age.isnull()) & (train.Title == 'Mrs') ,'AgeFill'] = mean_ages[1]
train.loc[ (train.Age.isnull()) & (train.Title == 'Mr') ,'AgeFill'] = mean_ages[2]
train.loc[ (train.Age.isnull()) & (train.Title == 'Master') ,'AgeFill'] = mean_ages[3]
train['AgeCat'] = train['AgeFill']
train.loc[ (train.AgeFill<=10), 'AgeCat'] = 'child'
train.loc[ (train.AgeFill>60), 'AgeCat'] = 'aged'
train.loc[ (train.AgeFill>10) & (train.AgeFill <=30) ,'AgeCat'] = 'adult'
train.loc[ (train.AgeFill>30) & (train.AgeFill <=60) ,'AgeCat'] = 'senior'
train['Fare_Per_Person'] = train['Fare'] / (train['Family_Size'] + 1)
In [13]:
test['AgeFill'] = test['Age']
mean_ages = np.zeros(4)
mean_ages[0] = np.average(test[test['Title'] == 'Miss']['Age'].dropna())
mean_ages[1] = np.average(test[test['Title'] == 'Mrs']['Age'].dropna())
mean_ages[2] = np.average(test[test['Title'] == 'Mr']['Age'].dropna())
mean_ages[3] = np.average(test[test['Title'] == 'Master']['Age'].dropna())
test.loc[ (test.Age.isnull()) & (test.Title == 'Miss') ,'AgeFill'] = mean_ages[0]
test.loc[ (test.Age.isnull()) & (test.Title == 'Mrs') ,'AgeFill'] = mean_ages[1]
test.loc[ (test.Age.isnull()) & (test.Title == 'Mr') ,'AgeFill'] = mean_ages[2]
test.loc[ (test.Age.isnull()) & (test.Title == 'Master') ,'AgeFill'] = mean_ages[3]
test['AgeCat'] = test['AgeFill']
test.loc[ (test.AgeFill<=10), 'AgeCat'] = 'child'
test.loc[ (test.AgeFill>60), 'AgeCat'] = 'aged'
test.loc[ (test.AgeFill>10) & (test.AgeFill <=30) ,'AgeCat'] = 'adult'
test.loc[ (test.AgeFill>30) & (test.AgeFill <=60) ,'AgeCat'] = 'senior'
test['Fare_Per_Person'] = test['Fare'] / (test['Family_Size'] + 1)
In [14]:
train.Embarked = train.Embarked.fillna('S')
test.Embarked = test.Embarked.fillna('S')
train.loc[ train.Cabin.isnull() == True, 'Cabin'] = 0.2
train.loc[ train.Cabin.isnull() == False, 'Cabin'] = 1
test.loc[ test.Cabin.isnull() == True, 'Cabin'] = 0.2
test.loc[ test.Cabin.isnull() == False, 'Cabin'] = 1
In [15]:
#Age times class
train['AgeClass'] = train['AgeFill'] * train['Pclass']
train['ClassFare'] = train['Pclass'] * train['Fare_Per_Person']
train['HighLow'] = train['Pclass']
train.loc[ (train.Fare_Per_Person < 8) ,'HighLow'] = 'Low'
train.loc[ (train.Fare_Per_Person >= 8) ,'HighLow'] = 'High'
#Age times class
test['AgeClass'] = test['AgeFill'] * test['Pclass']
test['ClassFare'] = test['Pclass'] * test['Fare_Per_Person']
test['HighLow'] = test['Pclass']
test.loc[ (test.Fare_Per_Person < 8) ,'HighLow'] = 'Low'
test.loc[ (test.Fare_Per_Person >= 8) ,'HighLow'] = 'High'
In [16]:
print train.head(1)
# print test.head()
In [17]:
# 处理训练集
Pclass = pd.get_dummies(train.Pclass)
Sex = pd.get_dummies(train.Sex)
Embarked = pd.get_dummies(train.Embarked)
Title = pd.get_dummies(train.Title)
AgeCat = pd.get_dummies(train.AgeCat)
HighLow = pd.get_dummies(train.HighLow)
train_data = pd.concat([Pclass, Sex, Embarked, Title, AgeCat, HighLow], axis=1)
train_data['Age'] = train['Age']
train_data['Fare'] = train['Fare']
train_data['SibSp'] = train['SibSp']
train_data['Parch'] = train['Parch']
train_data['Family_Size'] = train['Family_Size']
train_data['Family'] = train['Family']
train_data['AgeFill'] = train['AgeFill']
train_data['Fare_Per_Person'] = train['Fare_Per_Person']
train_data['Cabin'] = train['Cabin']
train_data['AgeClass'] = train['AgeClass']
train_data['ClassFare'] = train['ClassFare']
cols = ['Age', 'Fare', 'SibSp', 'Parch', 'Family_Size', 'Family', 'AgeFill', 'Fare_Per_Person', 'AgeClass', 'ClassFare']
train_data[cols] = train_data[cols].apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))
print train_data.head()
# 处理测试集
Pclass = pd.get_dummies(test.Pclass)
Sex = pd.get_dummies(test.Sex)
Embarked = pd.get_dummies(test.Embarked)
Title = pd.get_dummies(test.Title)
AgeCat = pd.get_dummies(test.AgeCat)
HighLow = pd.get_dummies(test.HighLow)
test_data = pd.concat([Pclass, Sex, Embarked, Title, AgeCat, HighLow], axis=1)
test_data['Age'] = test['Age']
test_data['Fare'] = test['Fare']
test_data['SibSp'] = test['SibSp']
test_data['Parch'] = test['Parch']
test_data['Family_Size'] = test['Family_Size']
test_data['Family'] = test['Family']
test_data['AgeFill'] = test['AgeFill']
test_data['Fare_Per_Person'] = test['Fare_Per_Person']
test_data['Cabin'] = test['Cabin']
test_data['AgeClass'] = test['AgeClass']
test_data['ClassFare'] = test['ClassFare']
test_data[cols] = test_data[cols].apply(lambda x: (x - np.min(x)) / (np.max(x) - np.min(x)))
print test_data.head()
In [18]:
from sklearn.linear_model import LogisticRegression as LR
from sklearn.cross_validation import cross_val_score
from sklearn.naive_bayes import GaussianNB as GNB
from sklearn.ensemble import RandomForestClassifier
import numpy as np
In [19]:
model_lr = LR(penalty = 'l2', dual = True, random_state = 0)
model_lr.fit(train_data, label)
print "逻辑回归10折交叉验证得分: ", np.mean(cross_val_score(model_lr, train_data, label, cv=10, scoring='roc_auc'))
result = model_lr.predict( test_data )
output = pd.DataFrame( data={"PassengerId":test["PassengerId"], "Survived":result} )
output.to_csv( "lr.csv", index=False, quoting=3 )
In [20]:
model_GNB = GNB()
model_GNB.fit(train_data, label)
print "高斯贝叶斯分类器10折交叉验证得分: ", np.mean(cross_val_score(model_GNB, train_data, label, cv=10, scoring='roc_auc'))
result = model_GNB.predict( test_data )
output = pd.DataFrame( data={"PassengerId":test["PassengerId"], "Survived":result} )
output.to_csv( "gnb.csv", index=False, quoting=3 )
In [21]:
forest = RandomForestClassifier( n_estimators=500, criterion='entropy', max_depth=5, min_samples_split=1,
min_samples_leaf=1, max_features='auto', bootstrap=False, oob_score=False, n_jobs=4,
verbose=0)
%time forest = forest.fit( train_data, label )
print "随机森林分类器10折交叉验证得分: ", np.mean(cross_val_score(forest, train_data, label, cv=10, scoring='roc_auc'))
result = forest.predict( test_data )
output = pd.DataFrame( data={"PassengerId":test["PassengerId"], "Survived":result} )
output.to_csv( "rf.csv", index=False, quoting=3 )
In [22]:
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV
from sklearn.cross_validation import train_test_split,StratifiedShuffleSplit,StratifiedKFold
param_grid = dict( )
pipeline=Pipeline([ ('clf', forest) ])
grid_search = GridSearchCV(pipeline, param_grid=param_grid, verbose=3, scoring='accuracy',
cv=StratifiedShuffleSplit(label, n_iter=10, test_size=0.2, train_size=None)).fit(train_data, label)
print("Best score: %0.3f" % grid_search.best_score_)