机器学习日志 泰坦尼克号获救预测 Titanic sklearn 决策树/随机森林

我是链接

第一次做机器学习的题目

题目要求：给定一堆已知的泰坦尼克号船员信息，每个人的信息包括

PassengerId => 乘客ID
Pclass => 客舱等级(1/2/3等舱位)
Name => 乘客姓名
Sex => 性别
Age => 年龄
SibSp => 兄弟姐妹数/配偶数
Parch => 父母数/子女数
Ticket => 船票编号
Fare => 船票价格
Cabin => 客舱号
Embarked => 登船港口

其中一部分还知道是否获救

现在让你推测剩余的人是否获救？

做法是利用决策树来做

刚入门，决策树做的非常简陋

利用信息增益函数(ent)来找用哪个属性来划分决策树

因为年龄有部分船员确实所以我就忽略了

为了使属性值是离散的，我让属性值和平均值作了比较来划分

正确率77.511%，以后慢慢改进

import pandas as pd
import math

cnt = 0#树的id编号
bestA = []#树上当前位置最好的a(属性)划分
ye = []#是否是叶子节点(是否得到了分类结果)
ping = []


# 判断A是否都一样
def allSame(nowD, nowA):
    for i in range(len(nowA)):
        for j in range(len(nowD)):
            if nowD[j][i] != nowD[1][i]:
                return 0
    return 1


def ent(nowD):
    sum = 0
    flag = [0, 0];
    p = [0, 0]
    for i in range(len(nowD)):
        flag[nowD[i][0]] = flag[nowD[i][0]] + 1
    p[0] = flag[0] / (flag[0] + flag[1])
    p[1] = flag[1] / (flag[0] + flag[1])
    return -p[0] * math.log(p[0] + 0.0001, 2) - p[1] * math.log(p[1] + 0.0001, 2)


def calcGain(nowD, a):
    tmpD = [[], []]
    for i in range(len(nowD)):
        if nowD[i][a] < ping[a]:
            tmpD[0].append(nowD[i])
        else:
            tmpD[1].append(nowD[i])
    return ent(nowD) - len(tmpD[0]) / len(nowD) * ent(tmpD[0]) - len(tmpD[1]) / len(nowD) * ent(tmpD[1])


# 建决策树
def build(nowId, nowD, nowA):
    flag = [0, 0];
    tmpD = [[], []]
    global ye, cnt, bestA
    for i in range(len(nowD)):
        flag[nowD[i][0]] = flag[nowD[i][0]] + 1
    if flag[0] == 0:
        ye[nowId] = 1
        return
    if flag[1] == 0:
        ye[nowId] = 0
        return
    if len(nowA) == 0 or allSame(nowD, nowA):
        ye[nowId] = 1 if flag[1] > flag[0] else 0
        return
    bestA[nowId] = nowA[0];
    bestGain = calcGain(nowD, nowA[0])
    for i in range(1, len(nowA)):
        nowGain = calcGain(nowD, nowA[i])
        if nowGain > bestGain:
            bestA[nowId] = nowA[i];
            bestGain = nowGain
    for i in range(len(nowD)):
        if nowD[i][bestA[nowId]] < ping[bestA[nowId]]:
            tmpD[0].append(nowD[i])
        else:
            tmpD[1].append(nowD[i])
    nowA.remove(bestA[nowId])
    for i in [0, 1]:
        if len(tmpD[i]) == 0:
            ye[nowId * 2 + i] = 1 if flag[1] > flag[0] else 0
        else:
            build(nowId * 2 + i, tmpD[i], nowA)

def ask(nowId,D):
    if ye[nowId]!=-1:
        return ye[nowId]
    if D[bestA[nowId]]<ping[bestA[nowId]]:
        return ask(nowId*2,D)
    else:
        return ask(nowId*2+1,D)

myTrain = pd.read_csv('train.csv')
myTest = pd.read_csv('test.csv')

D = [];
A = [1, 2, 3, 4, 5]
PassengerId=[]
Survived=[]
# 预处理
for i in range(10000):
    ye.append(-1)
for i in range(10000):
    bestA.append(-1)
# 读入
for i in range(myTrain.shape[0]):
    D.append(
        [myTrain.values[i][1], myTrain.values[i][2], 1 if (myTrain.values[i][4] == 'male') else 0, myTrain.values[i][6],
         myTrain.values[i][7], myTrain.values[i][9]])
# 计算各个属性平均值
for i in range(len(D[1])):
    sum = 0
    for j in range(len(D)):
        sum = sum + D[j][i]
    ping.append(sum / len(D))

build(1, D, A)
for i in range(len(myTest)):
    PassengerId.append(myTest.values[i][0])
    Survived.append(ask(1,[0,myTest.values[i][1], 1 if (myTest.values[i][3] == 'male') else 0, myTest.values[i][5],myTest.values[i][6], myTest.values[i][8]]))
myAns = pd.DataFrame({'PassengerId': PassengerId, 'Survived': Survived})
myAns.to_csv("myAns.csv", index = False, sep=',')

再放一个用sklearn做的随机森林版本，正确率差不多， 这里把nan的年龄用平均值代替了

import pandas as pd
from sklearn.ensemble import RandomForestClassifier

train_data = pd.read_csv("train.csv")
test_data = pd.read_csv("test.csv")
y = train_data["Survived"]
features = ["Pclass", "Sex", "Age", "SibSp", "Parch"]#利用这些特征来做决策树
x = pd.get_dummies(train_data[features])

# print(train_data["Age"].sum()/len(train_data["Age"]))#23.79
x = x.fillna(24)#用平均值来填充

model = RandomForestClassifier(n_estimators=100, max_depth=5, random_state=233)
model.fit(x, y)#训练模型

myIn = pd.get_dummies(test_data[features])
myIn = myIn.fillna(24)
predictions = model.predict(myIn)

myAns = pd.DataFrame({'PassengerId': test_data.PassengerId, 'Survived': predictions})
myAns.to_csv('myAns.csv', index=False)