pytorch泰坦尼克号幸存者预测（二分类）

文章目录

一、前言

1. 任务目标：根据《泰坦尼克号登船人员名单》上的个人信息预测其是否生还
2. 数据集：《泰坦尼克号登船人员名单》，自取https://download.csdn.net/download/weixin_43721000/87740848
3. 数据集解释： 第一列age ，表示的是年龄（数值数据） 第二列cabin，表示客舱号（分类数据 ：字符串类型） 第三列embarked表示登船港口，S是Southampton南安普顿，C是Cherbourg法国瑟堡，Q是Queenstown爱尔兰昆士敦（分类数据：直接类别） 第四列fare，表示船票价格（数值数据） 第五列name，表示的是名字（分类数据：字符串类型） 第六列parch，船上父母数/子女数，不同代的直系亲属数，比如某人和他的女儿及父亲同在这个船上，则他的这个数值就是父母数（1）+子女数（1）=2 第七列passengerId，表示乘船编号 第八列pclass，表示客舱等级，这里有三级，1为一等舱，2为二等舱，3为三等舱（分类数据：直接类别） 第九列sex，表示性别male为男性，famale为女性（分类数据） 第十列sibsp，表示兄弟姐妹数/配偶数，同一代的直系亲属人数，比如某人和他的弟弟及妻子同在这个船上，则他的这个数值就是兄妹数（1）+配偶数（1）=2 第十一列surverved，表示是否存活，1为生存，2为死亡（分类数据：直接类别） 第十二列ticket，表示船票编号(数值数据)

二、实现方法

1.读取数据集

import numpy as np
import pandas as pd

dataset = pd.read_csv('/kaggle/input/titanic/train.csv')
X_test = pd.read_csv('/kaggle/input/titanic/test.csv')print(dataset)#      PassengerId  Survived  Pclass  \# 0              1         0       3   # 1              2         1       1   # 2              3         1       3   # 3              4         1       1   # 4              5         0       3   # ..           ...       ...     ...   # 886          887         0       2   # 887          888         1       1   # 888          889         0       3   # 889          890         1       1   # 890          891         0       3   # #                                                   Name     Sex   Age  SibSp  \# 0                              Braund, Mr. Owen Harris    male  22.0      1   # 1    Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   # 2                               Heikkinen, Miss. Laina  female  26.0      0   # 3         Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   # 4                             Allen, Mr. William Henry    male  35.0      0   # ..                                                 ...     ...   ...    ...   # 886                              Montvila, Rev. Juozas    male  27.0      0   # 887                       Graham, Miss. Margaret Edith  female  19.0      0   # 888           Johnston, Miss. Catherine Helen "Carrie"  female   NaN      1   # 889                              Behr, Mr. Karl Howell    male  26.0      0   # 890                                Dooley, Mr. Patrick    male  32.0      0   # #      Parch            Ticket     Fare Cabin Embarked  # 0        0         A/5 21171   7.2500   NaN        S  # 1        0          PC 17599  71.2833   C85        C  # 2        0  STON/O2. 3101282   7.9250   NaN        S  # 3        0            113803  53.1000  C123        S  # 4        0            373450   8.0500   NaN        S  # ..     ...               ...      ...   ...      ...  # 886      0            211536  13.0000   NaN        S  # 887      0            112053  30.0000   B42        S  # 888      2        W./C. 6607  23.4500   NaN        S  # 889      0            111369  30.0000  C148        C  # 890      0            370376   7.7500   NaN        Q#  # [891 rows x 12 columns]print(X_test)#      PassengerId  Pclass                                          Name  \# 0            892       3                              Kelly, Mr. James   # 1            893       3              Wilkes, Mrs. James (Ellen Needs)   # 2            894       2                     Myles, Mr. Thomas Francis   # 3            895       3                              Wirz, Mr. Albert   # 4            896       3  Hirvonen, Mrs. Alexander (Helga E Lindqvist)   # ..           ...     ...                                           ...   # 413         1305       3                            Spector, Mr. Woolf   # 414         1306       1                  Oliva y Ocana, Dona. Fermina   # 415         1307       3                  Saether, Mr. Simon Sivertsen   # 416         1308       3                           Ware, Mr. Frederick   # 417         1309       3                      Peter, Master. Michael J   # #         Sex   Age  SibSp  Parch              Ticket      Fare Cabin Embarked  # 0      male  34.5      0      0              330911    7.8292   NaN        Q  # 1    female  47.0      1      0              363272    7.0000   NaN        S  # 2      male  62.0      0      0              240276    9.6875   NaN        Q  # 3      male  27.0      0      0              315154    8.6625   NaN        S  # 4    female  22.0      1      1             3101298   12.2875   NaN        S  # ..      ...   ...    ...    ...                 ...       ...   ...      ...  # 413    male   NaN      0      0           A.5. 3236    8.0500   NaN        S  # 414  female  39.0      0      0            PC 17758  108.9000  C105        C  # 415    male  38.5      0      0  SOTON/O.Q. 3101262    7.2500   NaN        S  # 416    male   NaN      0      0              359309    8.0500   NaN        S  # 417    male   NaN      1      1                2668   22.3583   NaN        C  # # [418 rows x 11 columns]

2.数据清洗

1. 清洗称谓

# 从 Name 字段中提取称谓，并将较少的称谓统一替换为 ‘rare’ 减少数据噪声
dataset_title =[i.split(',')[1].split('.')[0].strip()for i in dataset['Name']]# 提取称谓
dataset['Title']= pd.Series(dataset_title)# 插入新列print(dataset['Title'].value_counts())# # Mr              517# Miss            182# Mrs             125# Master           40# Dr                7# Rev               6# Mlle              2# Major             2# Col               2# the Countess      1# Capt              1# Ms                1# Sir               1# Lady              1# Mme               1# Don               1# Jonkheer          1# Name: Title, dtype: int64# 查看称谓数量分布
dataset['Title']= dataset['Title'].replace(['Lady','the Countess','Countess','Capt','Col','Don','Dr','Major','Rev','Sir','Jonkheer','Dona','Ms','Mme','Mlle'],'Rare')# 替换数量较少的称谓为 ‘rare’print(dataset['Title'].value_counts())# Mr        517# Miss      182# Mrs       125# Master     40# Rare       27# Name: Title, dtype: int64

dataset_title =[i.split(',')[1].split('.')[0].strip()for i in X_test['Name']]
X_test['Title']= pd.Series(dataset_title)print(X_test['Title'].value_counts())# Mr        240# Miss       78# Mrs        72# Master     21# Col         2# Rev         2# Ms          1# Dr          1# Dona        1# Name: Title, dtype: int64

X_test['Title']= X_test['Title'].replace(['Lady','the Countess','Countess','Capt','Col','Don','Dr','Major','Rev','Sir','Jonkheer','Dona','Ms','Mme','Mlle'],'Rare')print(X_test['Title'].value_counts())# Mr        240# Miss       78# Mrs        72# Master     21# Rare        7# Name: Title, dtype: int64

1. 家庭成员数量清洗

# 从 SibSp（同代家属：兄弟姐妹或配偶）和 Parch（不同代家属：父母或子女）中提取上船的家庭人数=SibSp+Parch+1，并将家庭人数标签化
dataset['FamilyS']= dataset['SibSp']+ dataset['Parch']+1
X_test['FamilyS']= X_test['SibSp']+ X_test['Parch']+1deffamily(x):if x <2:return'Single'elif x ==2:return'Couple'elif x <=4:return'InterM'else:return'Large'
    
dataset['FamilyS']= dataset['FamilyS'].apply(family)
X_test['FamilyS']= X_test['FamilyS'].apply(family)print(dataset['FamilyS'].value_counts())# Single    537# Couple    161# InterM    131# Large      62# Name: FamilyS, dtype: int64

1. 缺失数据填充

# 将 登船港口列 缺失的值替换为该列的众数（如有多个众数取第一个）
dataset['Embarked'].fillna(dataset['Embarked'].mode()[0], inplace=True)
X_test['Embarked'].fillna(X_test['Embarked'].mode()[0], inplace=True)# 将 年龄列 缺失的值替换为该列的平均数
dataset['Age'].fillna(dataset['Age'].median(), inplace=True)
X_test['Age'].fillna(X_test['Age'].median(), inplace=True)# 将 船票价格 缺失的值替换为该列的平均数
dataset['Fare'].fillna(dataset['Fare'].median(), inplace=True)
X_test['Fare'].fillna(X_test['Fare'].median(), inplace=True)

1. 不使用数据清除

# 删掉不使用的列
dataset = dataset.drop(['PassengerId','Cabin','Name','SibSp','Parch','Ticket'], axis=1)
X_test_passengers = X_test['PassengerId']
X_test = X_test.drop(['PassengerId','Cabin','Name','SibSp','Parch','Ticket'], axis=1)print(dataset)#      Survived  Pclass     Sex   Age     Fare Embarked Title FamilyS# 0           0       3    male  22.0   7.2500        S    Mr  Couple# 1           1       1  female  38.0  71.2833        C   Mrs  Couple# 2           1       3  female  26.0   7.9250        S  Miss  Single# 3           1       1  female  35.0  53.1000        S   Mrs  Couple# 4           0       3    male  35.0   8.0500        S    Mr  Single# ..        ...     ...     ...   ...      ...      ...   ...     ...# 886         0       2    male  27.0  13.0000        S  Rare  Single# 887         1       1  female  19.0  30.0000        S  Miss  Single# 888         0       3  female  28.0  23.4500        S  Miss  InterM# 889         1       1    male  26.0  30.0000        C    Mr  Single# 890         0       3    male  32.0   7.7500        Q    Mr  Single

3.划分训练集、验证集 和 测试集

1. 样本和标签分离

# 拆分训练集的样本和标签
X_train = dataset.iloc[:,1:9].values
Y_train = dataset.iloc[:,0].values
# 测试集只有样本没有标签
X_test = X_test.values

1. 文本标签转为 one-hot 编码

# 文本标签直接转为 one-hot 编码from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer

print(X_train[0], X_train[0].shape)# [3 'male' 22.0 7.25 'S' 'Mr' 'Couple'] (7,)

one_hot_encoder = ColumnTransformer([('one_hot_encoder',# 转换器名称（任意）
        OneHotEncoder(categories='auto'),# 编码类型[0,1,4,5,6]# 对哪些列进行编码)],   
    remainder='passthrough'# 保留未编码的列)
X_train = one_hot_encoder.fit_transform(X_train).tolist()
X_test = one_hot_encoder.fit_transform(X_test).tolist()print(X_train[0],len(X_train[0]))# [0.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 22.0, 7.25] 19# 7列变为19列的稀疏矩阵

1. 从训练集中分离 1/10 的数据作为验证集

# 拆分训练集和验证集from sklearn.model_selection import train_test_split

x_train, x_val, y_train, y_val = train_test_split(X_train, Y_train, test_size =0.1)print(len(x_train))# 801print(len(x_val))# 90print(len(y_train))# 801print(len(y_val))# 90

4.创建模型

# 创建神经网络import torch
import torch.nn as nn
import torch.nn.functional as F

classNet(nn.Module):def__init__(self):super(Net, self).__init__()
        self.fc1 = nn.Linear(19,270)# 上采样到270
        self.fc2 = nn.Linear(270,2)# 2分类defforward(self, x):
        x = self.fc1(x)
        x = F.dropout(x, p=0.1)
        x = F.elu(x)
        x = self.fc2(x)#         x = torch.sigmoid(x)              # 输出结果映射到（0,1）之间return x
    
net = Net()

5.指定训练参数

# 训练参数
batch_size =50
num_epochs =50
learning_rate =0.01
batch_no =len(x_train)// batch_size

6.定义损失函数、优化器

# 损失函数、优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(net.parameters(), lr=learning_rate)

7.训练

# 训练from sklearn.utils import shuffle
from torch.autograd import Variable

for epoch inrange(num_epochs):if epoch %5==0:print('Epoch {}'.format(epoch+1))
    x_train, y_train = shuffle(x_train, y_train)# 乱序 # Mini batch learningfor i inrange(batch_no):
        start = i * batch_size
        end = start + batch_size
        x_var = Variable(torch.FloatTensor(x_train[start:end]))
        y_var = Variable(torch.LongTensor(y_train[start:end]))# Forward + Backward + Optimize
        optimizer.zero_grad()
        ypred_var = net(x_var)
        loss =criterion(ypred_var, y_var)
        loss.backward()
        optimizer.step()

8.验证准确率

# 验证准确率
test_var = Variable(torch.FloatTensor(x_val), requires_grad=True)with torch.no_grad():
    result = net(test_var)# values, labels = torch.max(result, 1)# print(values, labels)
labels = torch.argmax(result, dim=1)print(labels)# tensor([1, 1, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0, 1, 0, 1, 1, 0,#        0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0,#        0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0,#        0, 0, 1, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1])
num_right = np.sum(labels.data.numpy()== y_val)print('Accuracy {:.2f}'.format(num_right /len(y_val)))# Accuracy 0.81

9.预测

# 预测
X_test_var = Variable(torch.FloatTensor(X_test), requires_grad=True)with torch.no_grad():
    test_result = net(X_test_var)#     print(test_result)
values, labels = torch.max(test_result,1)
survived = labels.data.numpy()print(f"预测结果：{survived}")# 预测结果：[0 1 0 0 1 0 1 0 1 0 0 0 1 0 1 1 0 0 0 1 0 1 1 0 1 0 1 0 0 0 0 0 1 1 0 0 1#  1 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 0 0 0 1 1 1 1 0 0 1 1 0 1 0#  1 1 0 1 0 1 1 0 0 0 0 0 1 1 1 1 1 0 1 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 0 0 0#  1 1 1 1 0 0 1 0 1 1 0 1 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 1 0 0 1 1 0 0 0 0 0#  1 0 1 0 0 1 0 0 1 1 0 1 1 1 1 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 0 1#  0 1 0 0 0 0 0 1 0 1 0 1 1 0 1 1 1 1 1 0 0 1 0 1 0 0 0 0 1 0 0 1 0 1 0 1 0#  1 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 1 0 1 0 1 1 1 0 0 0 0 0 0 0 1#  0 0 0 1 1 0 0 0 0 1 0 0 0 1 1 0 1 0 0 0 0 1 1 1 1 1 0 0 0 0 0 0 1 0 1 0 0#  1 0 0 0 0 0 0 0 1 1 1 1 0 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 0 0#  1 0 0 0 0 0 1 0 0 0 1 1 1 0 1 0 1 1 0 0 0 1 0 1 0 0 1 0 1 1 0 1 0 0 1 1 0#  0 1 0 0 1 1 0 0 0 0 0 0 1 1 0 1 0 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0 0 0 0#  1 1 1 1 1 1 0 1 0 0 1]