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1-1,结构化数据建模流程范例

import os
import datetime

#打印时间
def printbar():
    nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    print("\n"+"=========="*8 + "%s"%nowtime)

#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"

一，准备数据

titanic数据集的目标是根据乘客信息预测他们在Titanic号撞击冰山沉没后能否生存。

结构化数据一般会使用Pandas中的DataFrame进行预处理。

import numpy as np 
import pandas as pd 
import matplotlib.pyplot as plt
import torch 
from torch import nn 
from torch.utils.data import Dataset,DataLoader,TensorDataset

dftrain_raw = pd.read_csv('./data/titanic/train.csv')
dftest_raw = pd.read_csv('./data/titanic/test.csv')
dftrain_raw.head(10)

字段说明：

  • Survived:0代表死亡，1代表存活【y标签】
  • Pclass:乘客所持票类，有三种值(1,2,3) 【转换成onehot编码】
  • Name:乘客姓名 【舍去】
  • Sex:乘客性别 【转换成bool特征】
  • Age:乘客年龄(有缺失) 【数值特征，添加“年龄是否缺失”作为辅助特征】
  • SibSp:乘客兄弟姐妹/配偶的个数(整数值) 【数值特征】
  • Parch:乘客父母/孩子的个数(整数值)【数值特征】
  • Ticket:票号(字符串)【舍去】
  • Fare:乘客所持票的价格(浮点数，0-500不等) 【数值特征】
  • Cabin:乘客所在船舱(有缺失) 【添加“所在船舱是否缺失”作为辅助特征】
  • Embarked:乘客登船港口:S、C、Q(有缺失)【转换成onehot编码，四维度 S,C,Q,nan】

利用Pandas的数据可视化功能我们可以简单地进行探索性数据分析EDA（Exploratory Data Analysis）。

label分布情况

%matplotlib inline
%config InlineBackend.figure_format = 'png'
ax = dftrain_raw['Survived'].value_counts().plot(kind = 'bar',
     figsize = (12,8),fontsize=15,rot = 0)
ax.set_ylabel('Counts',fontsize = 15)
ax.set_xlabel('Survived',fontsize = 15)
plt.show()

年龄分布情况

%matplotlib inline
%config InlineBackend.figure_format = 'png'
ax = dftrain_raw['Age'].plot(kind = 'hist',bins = 20,color= 'purple',
                    figsize = (12,8),fontsize=15)

ax.set_ylabel('Frequency',fontsize = 15)
ax.set_xlabel('Age',fontsize = 15)
plt.show()

年龄和label的相关性

%matplotlib inline
%config InlineBackend.figure_format = 'png'
ax = dftrain_raw.query('Survived == 0')['Age'].plot(kind = 'density',
                      figsize = (12,8),fontsize=15)
dftrain_raw.query('Survived == 1')['Age'].plot(kind = 'density',
                      figsize = (12,8),fontsize=15)
ax.legend(['Survived==0','Survived==1'],fontsize = 12)
ax.set_ylabel('Density',fontsize = 15)
ax.set_xlabel('Age',fontsize = 15)
plt.show()

下面为正式的数据预处理

def preprocessing(dfdata):

    dfresult= pd.DataFrame()

    #Pclass
    dfPclass = pd.get_dummies(dfdata['Pclass'])
    dfPclass.columns = ['Pclass_' +str(x) for x in dfPclass.columns ]
    dfresult = pd.concat([dfresult,dfPclass],axis = 1)

    #Sex
    dfSex = pd.get_dummies(dfdata['Sex'])
    dfresult = pd.concat([dfresult,dfSex],axis = 1)

    #Age
    dfresult['Age'] = dfdata['Age'].fillna(0)
    dfresult['Age_null'] = pd.isna(dfdata['Age']).astype('int32')

    #SibSp,Parch,Fare
    dfresult['SibSp'] = dfdata['SibSp']
    dfresult['Parch'] = dfdata['Parch']
    dfresult['Fare'] = dfdata['Fare']

    #Carbin
    dfresult['Cabin_null'] =  pd.isna(dfdata['Cabin']).astype('int32')

    #Embarked
    dfEmbarked = pd.get_dummies(dfdata['Embarked'],dummy_na=True)
    dfEmbarked.columns = ['Embarked_' + str(x) for x in dfEmbarked.columns]
    dfresult = pd.concat([dfresult,dfEmbarked],axis = 1)

    return(dfresult)

x_train = preprocessing(dftrain_raw).values
y_train = dftrain_raw[['Survived']].values

x_test = preprocessing(dftest_raw).values
y_test = dftest_raw[['Survived']].values

print("x_train.shape =", x_train.shape )
print("x_test.shape =", x_test.shape )

print("y_train.shape =", y_train.shape )
print("y_test.shape =", y_test.shape )
x_train.shape = (712, 15)
x_test.shape = (179, 15)
y_train.shape = (712, 1)
y_test.shape = (179, 1)

进一步使用DataLoader和TensorDataset封装成可以迭代的数据管道。

dl_train = DataLoader(TensorDataset(torch.tensor(x_train).float(),torch.tensor(y_train).float()),
                     shuffle = True, batch_size = 8)
dl_valid = DataLoader(TensorDataset(torch.tensor(x_test).float(),torch.tensor(y_test).float()),
                     shuffle = False, batch_size = 8)
# 测试数据管道
for features,labels in dl_train:
    print(features,labels)
    break
tensor([[  0.0000,   0.0000,   1.0000,   0.0000,   1.0000,   0.0000,   1.0000,
           0.0000,   0.0000,   7.8958,   1.0000,   0.0000,   0.0000,   1.0000,
           0.0000],
        [  1.0000,   0.0000,   0.0000,   0.0000,   1.0000,   0.0000,   1.0000,
           0.0000,   0.0000,  30.5000,   0.0000,   0.0000,   0.0000,   1.0000,
           0.0000],
        [  1.0000,   0.0000,   0.0000,   1.0000,   0.0000,  31.0000,   0.0000,
           1.0000,   0.0000, 113.2750,   0.0000,   1.0000,   0.0000,   0.0000,
           0.0000],
        [  1.0000,   0.0000,   0.0000,   0.0000,   1.0000,  60.0000,   0.0000,
           0.0000,   0.0000,  26.5500,   1.0000,   0.0000,   0.0000,   1.0000,
           0.0000],
        [  0.0000,   0.0000,   1.0000,   0.0000,   1.0000,  28.0000,   0.0000,
           0.0000,   0.0000,  22.5250,   1.0000,   0.0000,   0.0000,   1.0000,
           0.0000],
        [  0.0000,   0.0000,   1.0000,   0.0000,   1.0000,  32.0000,   0.0000,
           0.0000,   0.0000,   8.3625,   1.0000,   0.0000,   0.0000,   1.0000,
           0.0000],
        [  0.0000,   1.0000,   0.0000,   1.0000,   0.0000,  28.0000,   0.0000,
           0.0000,   0.0000,  13.0000,   1.0000,   0.0000,   0.0000,   1.0000,
           0.0000],
        [  1.0000,   0.0000,   0.0000,   0.0000,   1.0000,  36.0000,   0.0000,
           0.0000,   1.0000, 512.3292,   0.0000,   1.0000,   0.0000,   0.0000,
           0.0000]]) tensor([[0.],
        [1.],
        [1.],
        [0.],
        [0.],
        [0.],
        [1.],
        [1.]])



二，定义模型


使用Pytorch通常有三种方式构建模型：使用nn.Sequential按层顺序构建模型，继承nn.Module基类构建自定义模型，继承nn.Module基类构建模型并辅助应用模型容器进行封装。


此处选择使用最简单的nn.Sequential，按层顺序模型。


def create_net():
    net = nn.Sequential()
    net.add_module("linear1",nn.Linear(15,20))
    net.add_module("relu1",nn.ReLU())
    net.add_module("linear2",nn.Linear(20,15))
    net.add_module("relu2",nn.ReLU())
    net.add_module("linear3",nn.Linear(15,1))
    net.add_module("sigmoid",nn.Sigmoid())
    return net
    
net = create_net()
print(net)


Sequential(
  (linear1): Linear(in_features=15, out_features=20, bias=True)
  (relu1): ReLU()
  (linear2): Linear(in_features=20, out_features=15, bias=True)
  (relu2): ReLU()
  (linear3): Linear(in_features=15, out_features=1, bias=True)
  (sigmoid): Sigmoid()
)



from torchkeras import summary
summary(net,input_shape=(15,))


----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Linear-1                   [-1, 20]             320
              ReLU-2                   [-1, 20]               0
            Linear-3                   [-1, 15]             315
              ReLU-4                   [-1, 15]               0
            Linear-5                    [-1, 1]              16
           Sigmoid-6                    [-1, 1]               0
================================================================
Total params: 651
Trainable params: 651
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.000057
Forward/backward pass size (MB): 0.000549
Params size (MB): 0.002483
Estimated Total Size (MB): 0.003090
----------------------------------------------------------------



三，训练模型


Pytorch通常需要用户编写自定义训练循环，训练循环的代码风格因人而异。


有3类典型的训练循环代码风格：脚本形式训练循环，函数形式训练循环，类形式训练循环。


此处介绍一种较通用的脚本形式。


from sklearn.metrics import accuracy_score

loss_func = nn.BCELoss()
optimizer = torch.optim.Adam(params=net.parameters(),lr = 0.01)
metric_func = lambda y_pred,y_true: accuracy_score(y_true.data.numpy(),y_pred.data.numpy()>0.5)
metric_name = "accuracy"


epochs = 10
log_step_freq = 30

dfhistory = pd.DataFrame(columns = ["epoch","loss",metric_name,"val_loss","val_"+metric_name]) 
print("Start Training...")
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("=========="*8 + "%s"%nowtime)

for epoch in range(1,epochs+1):  

    # 1，训练循环-------------------------------------------------
    net.train()
    loss_sum = 0.0
    metric_sum = 0.0
    step = 1
    
    for step, (features,labels) in enumerate(dl_train, 1):
    
        # 梯度清零
        optimizer.zero_grad()

        # 正向传播求损失
        predictions = net(features)
        loss = loss_func(predictions,labels)
        metric = metric_func(predictions,labels)
        
        # 反向传播求梯度
        loss.backward()
        optimizer.step()

        # 打印batch级别日志
        loss_sum += loss.item()
        metric_sum += metric.item()
        if step%log_step_freq == 0:   
            print(("[step = %d] loss: %.3f, "+metric_name+": %.3f") %
                  (step, loss_sum/step, metric_sum/step))
            
    # 2，验证循环-------------------------------------------------
    net.eval()
    val_loss_sum = 0.0
    val_metric_sum = 0.0
    val_step = 1

    for val_step, (features,labels) in enumerate(dl_valid, 1):
        # 关闭梯度计算
        with torch.no_grad():
            predictions = net(features)
            val_loss = loss_func(predictions,labels)
            val_metric = metric_func(predictions,labels)
        val_loss_sum += val_loss.item()
        val_metric_sum += val_metric.item()

    # 3，记录日志-------------------------------------------------
    info = (epoch, loss_sum/step, metric_sum/step, 
            val_loss_sum/val_step, val_metric_sum/val_step)
    dfhistory.loc[epoch-1] = info
    
    # 打印epoch级别日志
    print(("\nEPOCH = %d, loss = %.3f,"+ metric_name + \
          "  = %.3f, val_loss = %.3f, "+"val_"+ metric_name+" = %.3f") 
          %info)
    nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
    print("\n"+"=========="*8 + "%s"%nowtime)
        
print('Finished Training...')


Start Training...
================================================================================2020-06-17 20:53:49
[step = 30] loss: 0.703, accuracy: 0.583
[step = 60] loss: 0.629, accuracy: 0.675

EPOCH = 1, loss = 0.643,accuracy  = 0.673, val_loss = 0.621, val_accuracy = 0.725

================================================================================2020-06-17 20:53:49
[step = 30] loss: 0.653, accuracy: 0.662
[step = 60] loss: 0.624, accuracy: 0.673

EPOCH = 2, loss = 0.621,accuracy  = 0.669, val_loss = 0.519, val_accuracy = 0.708

================================================================================2020-06-17 20:53:49
[step = 30] loss: 0.582, accuracy: 0.688
[step = 60] loss: 0.555, accuracy: 0.723

EPOCH = 3, loss = 0.543,accuracy  = 0.740, val_loss = 0.516, val_accuracy = 0.741

================================================================================2020-06-17 20:53:49
[step = 30] loss: 0.563, accuracy: 0.721
[step = 60] loss: 0.528, accuracy: 0.752

EPOCH = 4, loss = 0.515,accuracy  = 0.764, val_loss = 0.471, val_accuracy = 0.777

================================================================================2020-06-17 20:53:50
[step = 30] loss: 0.433, accuracy: 0.783
[step = 60] loss: 0.477, accuracy: 0.785

EPOCH = 5, loss = 0.489,accuracy  = 0.785, val_loss = 0.447, val_accuracy = 0.804

================================================================================2020-06-17 20:53:50
[step = 30] loss: 0.460, accuracy: 0.812
[step = 60] loss: 0.477, accuracy: 0.798

EPOCH = 6, loss = 0.474,accuracy  = 0.798, val_loss = 0.451, val_accuracy = 0.772

================================================================================2020-06-17 20:53:50
[step = 30] loss: 0.516, accuracy: 0.792
[step = 60] loss: 0.496, accuracy: 0.779

EPOCH = 7, loss = 0.473,accuracy  = 0.794, val_loss = 0.485, val_accuracy = 0.783

================================================================================2020-06-17 20:53:50
[step = 30] loss: 0.472, accuracy: 0.779
[step = 60] loss: 0.487, accuracy: 0.794

EPOCH = 8, loss = 0.474,accuracy  = 0.791, val_loss = 0.446, val_accuracy = 0.788

================================================================================2020-06-17 20:53:50
[step = 30] loss: 0.492, accuracy: 0.771
[step = 60] loss: 0.445, accuracy: 0.800

EPOCH = 9, loss = 0.464,accuracy  = 0.796, val_loss = 0.519, val_accuracy = 0.746

================================================================================2020-06-17 20:53:50
[step = 30] loss: 0.436, accuracy: 0.796
[step = 60] loss: 0.460, accuracy: 0.794

EPOCH = 10, loss = 0.462,accuracy  = 0.787, val_loss = 0.415, val_accuracy = 0.810

================================================================================2020-06-17 20:53:51
Finished Training...



四，评估模型


我们首先评估一下模型在训练集和验证集上的效果。


dfhistory 




%matplotlib inline
%config InlineBackend.figure_format = 'svg'

import matplotlib.pyplot as plt

def plot_metric(dfhistory, metric):
    train_metrics = dfhistory[metric]
    val_metrics = dfhistory['val_'+metric]
    epochs = range(1, len(train_metrics) + 1)
    plt.plot(epochs, train_metrics, 'bo--')
    plt.plot(epochs, val_metrics, 'ro-')
    plt.title('Training and validation '+ metric)
    plt.xlabel("Epochs")
    plt.ylabel(metric)
    plt.legend(["train_"+metric, 'val_'+metric])
    plt.show()


plot_metric(dfhistory,"loss")






plot_metric(dfhistory,"accuracy")






五，使用模型


#预测概率
y_pred_probs = net(torch.tensor(x_test[0:10]).float()).data
y_pred_probs


tensor([[0.0119],
        [0.6029],
        [0.2970],
        [0.5717],
        [0.5034],
        [0.8655],
        [0.0572],
        [0.9182],
        [0.5038],
        [0.1739]])



#预测类别
y_pred = torch.where(y_pred_probs>0.5,
        torch.ones_like(y_pred_probs),torch.zeros_like(y_pred_probs))
y_pred


tensor([[0.],
        [1.],
        [0.],
        [1.],
        [1.],
        [1.],
        [0.],
        [1.],
        [1.],
        [0.]])





六，保存模型


Pytorch 有两种保存模型的方式，都是通过调用pickle序列化方法实现的。


第一种方法只保存模型参数。


第二种方法保存完整模型。


推荐使用第一种，第二种方法可能在切换设备和目录的时候出现各种问题。


1，保存模型参数(推荐)


print(net.state_dict().keys())


odict_keys(['linear1.weight', 'linear1.bias', 'linear2.weight', 'linear2.bias', 'linear3.weight', 'linear3.bias'])



# 保存模型参数

torch.save(net.state_dict(), "./data/net_parameter.pkl")

net_clone = create_net()
net_clone.load_state_dict(torch.load("./data/net_parameter.pkl"))

net_clone.forward(torch.tensor(x_test[0:10]).float()).data


tensor([[0.0119],
        [0.6029],
        [0.2970],
        [0.5717],
        [0.5034],
        [0.8655],
        [0.0572],
        [0.9182],
        [0.5038],
        [0.1739]])





2，保存完整模型(不推荐)


torch.save(net, './data/net_model.pkl')
net_loaded = torch.load('./data/net_model.pkl')
net_loaded(torch.tensor(x_test[0:10]).float()).data


tensor([[0.0119],
        [0.6029],
        [0.2970],
        [0.5717],
        [0.5034],
        [0.8655],
        [0.0572],
        [0.9182],
        [0.5038],
        [0.1739]])





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