利用随机森林,xgboost,logistic回归,预测泰坦尼克号上面的乘客的获救概率

数据示例：

,PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked,Embarked_C,Embarked_Q,Embarked_S,Embarked_U
0,1,0,3,"Braund, Mr. Owen Harris",1,22.0,1,0,A/5 21171,7.25,,S,0,0,1,0
1,2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",0,38.0,1,0,PC 17599,71.2833,C85,C,1,0,0,0
2,3,1,3,"Heikkinen, Miss. Laina",0,26.0,0,0,STON/O2. 3101282,7.925,,S,0,0,1,0
3,4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",0,35.0,1,0,113803,53.1,C123,S,0,0,1,0
4,5,0,3,"Allen, Mr. William Henry",1,35.0,0,0,373450,8.05,,S,0,0,1,0
5,6,0,3,"Moran, Mr. James",1,23.8011805916,0,0,330877,8.4583,,Q,0,1,0,0
6,7,0,1,"McCarthy, Mr. Timothy J",1,54.0,0,0,17463,51.8625,E46,S,0,0,1,0
7,8,0,3,"Palsson, Master. Gosta Leonard",1,2.0,3,1,349909,21.075,,S,0,0,1,0
8,9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",0,27.0,0,2,347742,11.1333,,S,0,0,1,0
9,10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",0,14.0,1,0,237736,30.0708,,C,1,0,0,0
10,11,1,3,"Sandstrom, Miss. Marguerite Rut",0,4.0,1,1,PP 9549,16.7,G6,S,0,0,1,0
11,12,1,1,"Bonnell, Miss. Elizabeth",0,58.0,0,0,113783,26.55,C103,S,0,0,1,0
12,13,0,3,"Saundercock, Mr. William Henry",1,20.0,0,0,A/5. 2151,8.05,,S,0,0,1,0
13,14,0,3,"Andersson, Mr. Anders Johan",1,39.0,1,5,347082,31.275,,S,0,0,1,0
14,15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",0,14.0,0,0,350406,7.8542,,S,0,0,1,0
15,16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",0,55.0,0,0,248706,16.0,,S,0,0,1,0
16,17,0,3,"Rice, Master. Eugene",1,2.0,4,1,382652,29.125,,Q,0,1,0,0
17,18,1,2,"Williams, Mr. Charles Eugene",1,33.478692644,0,0,244373,13.0,,S,0,0,1,0
18,19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",0,31.0,1,0,345763,18.0,,S,0,0,1,0
19,20,1,3,"Masselmani, Mrs. Fatima",0,18.4510583333,0,0,2649,7.225,,C,1,0,0,0
20,21,0,2,"Fynney, Mr. Joseph J",1,35.0,0,0,239865,26.0,,S,0,0,1,0
21,22,1,2,"Beesley, Mr. Lawrence",1,34.0,0,0,248698,13.0,D56,S,0,0,1,0
22,23,1,3,"McGowan, Miss. Anna ""Annie""",0,15.0,0,0,330923,8.0292,,Q,0,1,0,0
23,24,1,1,"Sloper, Mr. William Thompson",1,28.0,0,0,113788,35.5,A6,S,0,0,1,0
24,25,0,3,"Palsson, Miss. Torborg Danira",0,8.0,3,1,349909,21.075,,S,0,0,1,0
25,26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",0,38.0,1,5,347077,31.3875,,S,0,0,1,0
26,27,0,3,"Emir, Mr. Farred Chehab",1,34.8922936994,0,0,2631,7.225,,C,1,0,0,0
27,28,0,1,"Fortune, Mr. Charles Alexander",1,19.0,3,2,19950,263.0,C23 C25 C27,S,0,0,1,0
28,29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",0,22.8110194444,0,0,330959,7.8792,,Q,0,1,0,0
29,30,0,3,"Todoroff, Mr. Lalio",1,27.8541556913,0,0,349216,7.8958,,S,0,0,1,0
30,31,0,1,"Uruchurtu, Don. Manuel E",1,40.0,0,0,PC 17601,27.7208,,C,1,0,0,0
31,32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",0,38.0680685714,1,0,PC 17569,146.5208,B78,C,1,0,0,0
32,33,1,3,"Glynn, Miss. Mary Agatha",0,22.2371852543,0,0,335677,7.75,,Q,0,1,0,0
33,34,0,2,"Wheadon, Mr. Edward H",1,66.0,0,0,C.A. 24579,10.5,,S,0,0,1,0
34,35,0,1,"Meyer, Mr. Edgar Joseph",1,28.0,1,0,PC 17604,82.1708,,C,1,0,0,0
35,36,0,1,"Holverson, Mr. Alexander Oskar",1,42.0,1,0,113789,52.0,,S,0,0,1,0

# /usr/bin/python
# -*- encoding:utf-8 -*-

import xgboost as xgb
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import RandomForestClassifier
import pandas as pd
import csv


def show_accuracy(a, b, tip):
    acc = a.ravel() == b.ravel()
    acc_rate = 100 * float(acc.sum()) / a.size
    return acc_rate


def load_data(file_name, is_train):
    data = pd.read_csv(file_name)  # 数据文件路径
    # print 'data.describe() = \n', data.describe()

    # 性别 将性别字段Sex中的值 female用0，male用1代替,类型 int
    data['Sex'] = data['Sex'].map({'female': 0, 'male': 1}).astype(int)

    # 补齐船票价格缺失值
    if len(data.Fare[data.Fare.isnull()]) > 0:
        fare = np.zeros(3)
        for f in range(0, 3):
            fare[f] = data[data.Pclass == f + 1]['Fare'].dropna().median()
        for f in range(0, 3):  # loop 0 to 2
            data.loc[(data.Fare.isnull()) & (data.Pclass == f + 1), 'Fare'] = fare[f]

    # 年龄：使用均值代替缺失值
    # mean_age = data['Age'].dropna().mean()
    # data.loc[(data.Age.isnull()), 'Age'] = mean_age
    if is_train:
        # 年龄：使用随机森林预测年龄缺失值
        print '随机森林预测缺失年龄：--start--'
        data_for_age = data[['Age', 'Survived', 'Fare', 'Parch', 'SibSp', 'Pclass']]
        age_exist = data_for_age.loc[(data.Age.notnull())]   # 年龄不缺失的数据
        age_null = data_for_age.loc[(data.Age.isnull())]
        # print 'data_for_age=\n', data_for_age
        # print 'age_exis=\n', age_exist
        # print 'age_null=\n',age_null
        # print age_exist
        x = age_exist.values[:, 1:]
        y = age_exist.values[:, 0]
        # print 'x = age_exist.values[:, 1:] 中 x=',x
        # print 'y = age_exist.values[:, 0] 中 y=',y
        #n_estimators 决策树的个数，越多越好,值越大，性能就会越差,但至少100
        rfr = RandomForestRegressor(n_estimators=1000)
        rfr.fit(x, y)
        age_hat = rfr.predict(age_null.values[:, 1:])
        # print age_hat
        # print 'age_hat',age_hat
        #填充年龄字段中值为空的
        data.loc[(data.Age.isnull()), 'Age'] = age_hat
        print '随机森林预测缺失年龄：--over--'
    else:
        print '随机森林预测缺失年龄2：--start--'
        data_for_age = data[['Age', 'Fare', 'Parch', 'SibSp', 'Pclass']]
        age_exist = data_for_age.loc[(data.Age.notnull())]  # 年龄不缺失的数据
        age_null = data_for_age.loc[(data.Age.isnull())]
        # print age_exist
        x = age_exist.values[:, 1:]
        y = age_exist.values[:, 0]
        rfr = RandomForestRegressor(n_estimators=1000)
        rfr.fit(x, y)
        age_hat = rfr.predict(age_null.values[:, 1:])
        # print age_hat
        data.loc[(data.Age.isnull()), 'Age'] = age_hat
        print '随机森林预测缺失年龄2：--over--'

    # 起始城市
    data.loc[(data.Embarked.isnull()), 'Embarked'] = 'S'  # 保留缺失出发城市
    # print data['Embarked']
    embarked_data = pd.get_dummies(data.Embarked)
    # print embarked_data
    embarked_data = embarked_data.rename(columns=lambda x: 'Embarked_' + str(x))
    data = pd.concat([data, embarked_data], axis=1)
   # print data.describe()
    data.to_csv('New_Data.csv')

    x = data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked_C', 'Embarked_Q', 'Embarked_S']]
    # x = data[['Pclass', 'Sex', 'Age', 'SibSp', 'Parch', 'Fare', 'Embarked']]
    y = None
    if 'Survived' in data:
        y = data['Survived']

    x = np.array(x)
    y = np.array(y)

    x = np.tile(x, (5, 1))
    y = np.tile(y, (5, ))
    if is_train:
        return x, y
    return x, data['PassengerId']


def write_result(c, c_type):
    file_name = '14.Titanic.test.csv'
    x, passenger_id = load_data(file_name, False)

    if type == 3:
        x = xgb.DMatrix(x)
    y = c.predict(x)
    y[y > 0.5] = 1
    y[~(y > 0.5)] = 0

    predictions_file = open("Prediction_%d.csv" % c_type, "wb")
    open_file_object = csv.writer(predictions_file)
    open_file_object.writerow(["PassengerId", "Survived"])
    open_file_object.writerows(zip(passenger_id, y))
    predictions_file.close()

def totalSurvival(y_hat,tip):
    total=0
    for index,value in enumerate(y_hat):
        if value==1:
            total=total+1
    print tip,'存活：',total
    print '人'

if __name__ == "__main__":
    #加载并完善特征数据
    x, y = load_data('14.Titanic.train.csv', True)
    #划分训练集和测试集x表示样本特征集，y表示样本结果  test_size 样本占比,random_state 随机数的种子
    x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.5, random_state=1)

    #print 'x_train=',x_train,'y_train=',y_train

    #logistic回归
    lr = LogisticRegression(penalty='l2')
    lr.fit(x_train, y_train)
    y_hat = lr.predict(x_test)
    lr_rate = show_accuracy(y_hat, y_test, 'Logistic回归 ')
    totalSurvival(y_hat,'Logistic回归')
    #随机森林 n_estimators：决策树的个数,越多越好,不过值越大，性能就会越差,至少100
    rfc = RandomForestClassifier(n_estimators=100)
    rfc.fit(x_train, y_train)
    y_hat = rfc.predict(x_test)
    rfc_rate = show_accuracy(y_hat, y_test, '随机森林 ')
    totalSurvival(y_hat,'随机森林')
    # write_result(rfc, 2)

    # XGBoost
    data_train = xgb.DMatrix(x_train, label=y_train)
    data_test = xgb.DMatrix(x_test, label=y_test)
    watch_list = [(data_test, 'eval'), (data_train, 'train')]
    param = {'max_depth': 6, 'eta': 0.8, 'silent': 1, 'objective': 'binary:logistic'}
    bst = xgb.train(param, data_train, num_boost_round=100, evals=watch_list)
    y_hat = bst.predict(data_test)
    y_hat[y_hat > 0.5] = 1
    y_hat[~(y_hat > 0.5)] = 0
    xgb_rate = show_accuracy(y_hat, y_test, 'XGBoost ')
    totalSurvival(y_hat,'xgboost')

    print 'Logistic回归：%.3f%%' % lr_rate
    print '随机森林：%.3f%%' % rfc_rate
    print 'XGBoost：%.3f%%' % xgb_rate

结果：

Logistic回归 存活： 813人
随机森林 存活： 859人
xgboost 存活： 872人
准确率:
Logistic回归：78.770%
随机森林：98.160%
XGBoost：97.935%