文章目录

• 5.2 Pytorch处理结构化数据

• * 简介

  • 数据预处理
  • 定义数据集
  • 定义模型
  • 训练

  import numpy as np
  import pandas as pd
  import torch
  from sklearn.preprocessing import LabelEncoder
  from torch.utils.data import Dataset, DataLoader
  import torch.nn.functional as F
  import torch.nn as nn
  from collections import Counter
  torch.version

  '1.4.0'

5.2 Pytorch处理结构化数据

在介绍之前，我们首先要明确下什么是结构化的数据。结构化数据，可以从名称中看出，是高度组织和整齐格式化的数据。它是可以放入表格和电子表格中的数据类型。对我们来说，结构化数据可以理解为就是一张2维的表格，例如一个csv文件，就是结构化数据，在英文一般被称作Tabular Data或者叫 structured data，下面我们来看一下结构化数据的例子。

一下文件来自于fastai的自带数据集：
https://github.com/fastai/fastai/blob/master/examples/tabular.ipynb
fastai样例在这里

我们拿到的结构化数据，一般都是一个csv文件或者数据库中的一张表格，所以对于结构化的数据，我们直接使用pasdas库处理就可以了

#读入文件
df = pd.read_csv('./data/adult.csv')
#salary是这个数据集最后要分类的结果
df['salary'].unique()


array(['>=50k', '<50k'], dtype=object)


#查看数据类型
df.head()

age

workclass

fnlwgt

education

education-num

marital-status

occupation

relationship

race

sex

capital-gain

capital-loss

hours-per-week

native-country

salary

0

49

Private

101320

Assoc-acdm

12.0

Married-civ-spouse

NaN

Wife

White

Female

0

1902

40

United-States

>=50k

1

44

Private

236746

Masters

14.0

Divorced

Exec-managerial

Not-in-family

White

Male

10520

0

45

United-States

>=50k

2

38

Private

96185

HS-grad

NaN

Divorced

NaN

Unmarried

Black

Female

0

0

32

United-States

<50k

3

38

Self-emp-inc

112847

Prof-school

15.0

Married-civ-spouse

Prof-specialty

Husband

Asian-Pac-Islander

Male

0

0

40

United-States

>=50k

4

42

Self-emp-not-inc

82297

7th-8th

NaN

Married-civ-spouse

Other-service

Wife

Black

Female

0

0

50

United-States

<50k

#pandas的describe可以告诉我们整个数据集的大概结构，是非常有用的
df.describe()

age

fnlwgt

education-num

capital-gain

capital-loss

hours-per-week

count

32561.000000

3.256100e+04

32074.000000

32561.000000

32561.000000

32561.000000

mean

38.581647

1.897784e+05

10.079815

1077.648844

87.303830

40.437456

std

13.640433

1.055500e+05

2.572999

7385.292085

402.960219

12.347429

min

17.000000

1.228500e+04

1.000000

0.000000

0.000000

1.000000

25%

28.000000

1.178270e+05

9.000000

0.000000

0.000000

40.000000

50%

37.000000

1.783560e+05

10.000000

0.000000

0.000000

40.000000

75%

48.000000

2.370510e+05

12.000000

0.000000

0.000000

45.000000

max

90.000000

1.484705e+06

16.000000

99999.000000

4356.000000

99.000000

#查看一共有多少数据
len(df)


32561

对于模型的训练，只能够处理数字类型的数据，所以这里面我们首先要将数据分成三个类别

• 训练的结果标签：即训练的结果，通过这个结果我们就能够明确的知道我们这次训练的任务是什么，是分类的任务，还是回归的任务。

• 分类数据：这类的数据是离散的，无法通过直接输入到模型中进行训练，所以我们在预处理的时候需要优先对这部分进行处理，这也是数据预处理的主要工作之一

• 数值型数据：这类数据是直接可以输入到模型中的，但是这部分数据有可能还是离散的，所以如果需要也可以对其进行处理，并且处理后会对训练的精度有很大的提升，这里暂且不讨论

  #训练结果
  result_var = 'salary'
  #分类型数据
  cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race','sex','native-country']
  #数值型数据
  cont_names = ['age', 'fnlwgt', 'education-num','capital-gain','capital-loss','hours-per-week']

人工确认完数据类型后，我们可以看一下分类类型数据的数量和分布情况

for col in df.columns:
    if col in cat_names:
        ccol=Counter(df[col])
        print(col,len(ccol),ccol)
        print("\r\n")


workclass 9 Counter({' Private': 22696, ' Self-emp-not-inc': 2541, ' Local-gov': 2093, ' ?': 1836, ' State-gov': 1298, ' Self-emp-inc': 1116, ' Federal-gov': 960, ' Without-pay': 14, ' Never-worked': 7})

​
education 16 Counter({’ HS-grad’: 10501, ’ Some-college’: 7291, ’ Bachelors’: 5355, ’ Masters’: 1723, ’ Assoc-voc’: 1382, ’ 11th’: 1175, ’ Assoc-acdm’: 1067, ’ 10th’: 933, ’ 7th-8th’: 646, ’ Prof-school’: 576, ’ 9th’: 514, ’ 12th’: 433, ’ Doctorate’: 413, ’ 5th-6th’: 333, ’ 1st-4th’: 168, ’ Preschool’: 51})

​
marital-status 7 Counter({’ Married-civ-spouse’: 14976, ’ Never-married’: 10683, ’ Divorced’: 4443, ’ Separated’: 1025, ’ Widowed’: 993, ’ Married-spouse-absent’: 418, ’ Married-AF-spouse’: 23})

​
occupation 16 Counter({’ Prof-specialty’: 4073, ’ Craft-repair’: 4028, ’ Exec-managerial’: 4009, ’ Adm-clerical’: 3720, ’ Sales’: 3590, ’ Other-service’: 3247, ’ Machine-op-inspct’: 1968, ’ ?’: 1820, ’ Transport-moving’: 1566, ’ Handlers-cleaners’: 1347, ’ Farming-fishing’: 977, ’ Tech-support’: 905, ’ Protective-serv’: 643, nan: 512, ’ Priv-house-serv’: 147, ’ Armed-Forces’: 9})

​
relationship 6 Counter({’ Husband’: 13193, ’ Not-in-family’: 8305, ’ Own-child’: 5068, ’ Unmarried’: 3446, ’ Wife’: 1568, ’ Other-relative’: 981})

​
race 5 Counter({’ White’: 27816, ’ Black’: 3124, ’ Asian-Pac-Islander’: 1039, ’ Amer-Indian-Eskimo’: 311, ’ Other’: 271})

​
sex 2 Counter({’ Male’: 21790, ’ Female’: 10771})

​
native-country 42 Counter({’ United-States’: 29170, ’ Mexico’: 643, ’ ?’: 583, ’ Philippines’: 198, ’ Germany’: 137, ’ Canada’: 121, ’ Puerto-Rico’: 114, ’ El-Salvador’: 106, ’ India’: 100, ’ Cuba’: 95, ’ England’: 90, ’ Jamaica’: 81, ’ South’: 80, ’ China’: 75, ’ Italy’: 73, ’ Dominican-Republic’: 70, ’ Vietnam’: 67, ’ Guatemala’: 64, ’ Japan’: 62, ’ Poland’: 60, ’ Columbia’: 59, ’ Taiwan’: 51, ’ Haiti’: 44, ’ Iran’: 43, ’ Portugal’: 37, ’ Nicaragua’: 34, ’ Peru’: 31, ’ Greece’: 29, ’ France’: 29, ’ Ecuador’: 28, ’ Ireland’: 24, ’ Hong’: 20, ’ Trinadad&Tobago’: 19, ’ Cambodia’: 19, ’ Thailand’: 18, ’ Laos’: 18, ’ Yugoslavia’: 16, ’ Outlying-US(Guam-USVI-etc)’: 14, ’ Hungary’: 13, ’ Honduras’: 13, ’ Scotland’: 12, ’ Holand-Netherlands’: 1})

​
​

下一步就是要将分类型数据转成数字型数据，在这部分里面，我们还做了对于缺失数据的填充

for col in df.columns:
    if col in cat_names:
        df[col].fillna('---')
        df[col] = LabelEncoder().fit_transform(df[col].astype(str))
    if col in cont_names:
        df[col]=df[col].fillna(0)

上面的代码中：

我们首先使用了pandas的fillna函数对分类的数据做了空值的填充，这里面标识成一个与其他现有值不一样的值就可以，这里面我使用的三个中划线 — 作为标记，然后就是使用了sklearn的LabelEncoder函数进行了数据的处理

然后有对我们的数值型的数据做了0填充的处理，对于数值型数据的填充，也可以使用平均值，或者其他方式填充，这个不是我们的重点，就不详细说明了。

df.head()

age

workclass

fnlwgt

education

education-num

marital-status

occupation

relationship

race

sex

capital-gain

capital-loss

hours-per-week

native-country

salary

0

49

4

101320

7

12.0

2

15

5

4

0

0

1902

40

39

>=50k

1

44

4

236746

12

14.0

0

4

1

4

1

10520

0

45

39

>=50k

2

38

4

96185

11

0.0

0

15

4

2

0

0

0

32

39

<50k

3

38

5

112847

14

15.0

2

10

0

1

1

0

0

40

39

>=50k

4

42

6

82297

5

0.0

2

8

5

2

0

0

0

50

39

<50k

数据处理完成后可以看到，现在所有的数据都是数字类型的了，可以直接输入到模型进行训练了.

#分割下训练数据和标签
Y = df['salary']
Y_label = LabelEncoder()
Y=Y_label.fit_transform(Y)
Y


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


X=df.drop(columns=result_var)
X

age

workclass

fnlwgt

education

education-num

marital-status

occupation

relationship

race

sex

capital-gain

capital-loss

hours-per-week

native-country

0

49

4

101320

7

12.0

2

15

5

4

0

0

1902

40

39

1

44

4

236746

12

14.0

0

4

1

4

1

10520

0

45

39

2

38

4

96185

11

0.0

0

15

4

2

0

0

0

32

39

3

38

5

112847

14

15.0

2

10

0

1

1

0

0

40

39

4

42

6

82297

5

0.0

2

8

5

2

0

0

0

50

39

…

…

…

…

…

…

…

…

…

…

…

…

…

…

…

32556

36

4

297449

9

13.0

0

10

1

4

1

14084

0

40

39

32557

23

0

123983

9

13.0

4

0

3

3

1

0

0

40

39

32558

53

4

157069

7

12.0

2

7

0

4

1

0

0

40

39

32559

32

2

217296

11

9.0

2

14

5

4

0

4064

0

22

39

32560

26

4

182308

15

10.0

2

10

0

4

1

0

0

40

39

32561 rows × 14 columns

以上，基本的数据预处理已经完成了，上面展示的只是一些必要的处理，如果要提高训练准确率还有很多技巧，这里就不详细说明了。

要使用pytorch处理数据，肯定要使用Dataset进行数据集的定义，下面定义一个简单的数据集

class tabularDataset(Dataset):
    def __init__(self, X, Y):
        self.x = X.values
        self.y = Y

    def __len__(self):
        return len(self.y)

    def __getitem__(self, idx):
        return (self.x[idx], self.y[idx])


train_ds = tabularDataset(X, Y)

可以直接使用索引访问定义好的数据集中的数据

train_ds[0]


(array([4.9000e+01, 4.0000e+00, 1.0132e+05, 7.0000e+00, 1.2000e+01,
        2.0000e+00, 1.5000e+01, 5.0000e+00, 4.0000e+00, 0.0000e+00,
        0.0000e+00, 1.9020e+03, 4.0000e+01, 3.9000e+01]),
 1)

数据已经准备完毕了，下一步就是要定义我们的模型了，这里使用了3层线性层的简单模型作为处理

class tabularModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.lin1 = nn.Linear(14, 500)
        self.lin2 = nn.Linear(500, 100)
        self.lin3 = nn.Linear(100, 2)
        self.bn_in = nn.BatchNorm1d(14)
        self.bn1 = nn.BatchNorm1d(500)
        self.bn2 = nn.BatchNorm1d(100)

    def forward(self,x_in):
        #print(x_in.shape)
        x = self.bn_in(x_in)
        x = F.relu(self.lin1(x))
        x = self.bn1(x)
        #print(x)

        x = F.relu(self.lin2(x))
        x = self.bn2(x)
        #print(x)

        x = self.lin3(x)
        x=torch.sigmoid(x)
        return x

在定义模型的时候看到了我们加入了Batch Normalization来做批量的归一化：
批量归一化的内容请见这篇文章：https://mp.weixin.qq.com/s/FFLQBocTZGqnyN79JbSYcQ

或者扫描这个二维码，在微信中查看:
[外链图片转存失败,源站可能有防盗链机制,建议将图片保存下来直接上传(img-nmttIjq2-1618641614650)(https://raw.githubusercontent.com/zergtant/pytorch-handbook/master/deephub.jpg)]

#训练前指定使用的设备
DEVICE=torch.device("cpu")
if torch.cuda.is_available():
        DEVICE=torch.device("cuda")
print(DEVICE)


cuda


#损失函数
criterion =nn.CrossEntropyLoss()


#实例化模型
model = tabularModel().to(DEVICE)
print(model)


tabularModel(
  (lin1): Linear(in_features=14, out_features=500, bias=True)
  (lin2): Linear(in_features=500, out_features=100, bias=True)
  (lin3): Linear(in_features=100, out_features=2, bias=True)
  (bn_in): BatchNorm1d(14, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (bn1): BatchNorm1d(500, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
  (bn2): BatchNorm1d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
)


#测试模型是否没问题
rn=torch.rand(3,14).to(DEVICE)
model(rn)


tensor([[0.5110, 0.1931],
        [0.4274, 0.5801],
        [0.5549, 0.7322]], device='cuda:0', grad_fn=<SigmoidBackward>)


#学习率
LEARNING_RATE=0.01
#BS
batch_size = 1024
#优化器
optimizer = torch.optim.Adam(model.parameters(), lr=LEARNING_RATE)



#DataLoader加载数据
train_dl = DataLoader(train_ds, batch_size=batch_size,shuffle=True)

以上的基本步骤是每个训练过程都需要的，所以就不多介绍了，下面开始进行模型的训练

%%time
model.train()
#训练10轮
TOTAL_EPOCHS=100
#记录损失函数
losses = [];
for epoch in range(TOTAL_EPOCHS):
    for i, (x, y) in enumerate(train_dl):
        x = x.float().to(DEVICE) #输入必须未float类型
        y = y.long().to(DEVICE) #结果标签必须未long类型
        #清零
        optimizer.zero_grad()
        outputs = model(x)
        #计算损失函数
        loss = criterion(outputs, y)
        loss.backward()
        optimizer.step()
        losses.append(loss.cpu().data.item())
    print ('Epoch : %d/%d,   Loss: %.4f'%(epoch+1, TOTAL_EPOCHS, np.mean(losses)))


Epoch : 1/100,   Loss: 0.4936
Epoch : 2/100,   Loss: 0.4766
Epoch : 3/100,   Loss: 0.4693
Epoch : 4/100,   Loss: 0.4653
Epoch : 5/100,   Loss: 0.4627
Epoch : 6/100,   Loss: 0.4606
Epoch : 7/100,   Loss: 0.4591
Epoch : 8/100,   Loss: 0.4582
Epoch : 9/100,   Loss: 0.4573
Epoch : 10/100,   Loss: 0.4565
Epoch : 11/100,   Loss: 0.4557
Epoch : 12/100,   Loss: 0.4551
Epoch : 13/100,   Loss: 0.4546
Epoch : 14/100,   Loss: 0.4540
Epoch : 15/100,   Loss: 0.4535
Epoch : 16/100,   Loss: 0.4530
Epoch : 17/100,   Loss: 0.4526
Epoch : 18/100,   Loss: 0.4522
Epoch : 19/100,   Loss: 0.4519
Epoch : 20/100,   Loss: 0.4515
Epoch : 21/100,   Loss: 0.4511
Epoch : 22/100,   Loss: 0.4508
Epoch : 23/100,   Loss: 0.4504
Epoch : 24/100,   Loss: 0.4502
Epoch : 25/100,   Loss: 0.4499
Epoch : 26/100,   Loss: 0.4496
Epoch : 27/100,   Loss: 0.4492
Epoch : 28/100,   Loss: 0.4489
Epoch : 29/100,   Loss: 0.4486
Epoch : 30/100,   Loss: 0.4483
Epoch : 31/100,   Loss: 0.4480
Epoch : 32/100,   Loss: 0.4477
Epoch : 33/100,   Loss: 0.4474
Epoch : 34/100,   Loss: 0.4471
Epoch : 35/100,   Loss: 0.4469
Epoch : 36/100,   Loss: 0.4466
Epoch : 37/100,   Loss: 0.4463
Epoch : 38/100,   Loss: 0.4460
Epoch : 39/100,   Loss: 0.4458
Epoch : 40/100,   Loss: 0.4455
Epoch : 41/100,   Loss: 0.4452
Epoch : 42/100,   Loss: 0.4449
Epoch : 43/100,   Loss: 0.4447
Epoch : 44/100,   Loss: 0.4445
Epoch : 45/100,   Loss: 0.4442
Epoch : 46/100,   Loss: 0.4439
Epoch : 47/100,   Loss: 0.4437
Epoch : 48/100,   Loss: 0.4434
Epoch : 49/100,   Loss: 0.4432
Epoch : 50/100,   Loss: 0.4429
Epoch : 51/100,   Loss: 0.4426
Epoch : 52/100,   Loss: 0.4424
Epoch : 53/100,   Loss: 0.4421
Epoch : 54/100,   Loss: 0.4419
Epoch : 55/100,   Loss: 0.4417
Epoch : 56/100,   Loss: 0.4414
Epoch : 57/100,   Loss: 0.4411
Epoch : 58/100,   Loss: 0.4409
Epoch : 59/100,   Loss: 0.4406
Epoch : 60/100,   Loss: 0.4404
Epoch : 61/100,   Loss: 0.4402
Epoch : 62/100,   Loss: 0.4399
Epoch : 63/100,   Loss: 0.4397
Epoch : 64/100,   Loss: 0.4394
Epoch : 65/100,   Loss: 0.4392
Epoch : 66/100,   Loss: 0.4390
Epoch : 67/100,   Loss: 0.4387
Epoch : 68/100,   Loss: 0.4384
Epoch : 69/100,   Loss: 0.4382
Epoch : 70/100,   Loss: 0.4380
Epoch : 71/100,   Loss: 0.4377
Epoch : 72/100,   Loss: 0.4375
Epoch : 73/100,   Loss: 0.4373
Epoch : 74/100,   Loss: 0.4371
Epoch : 75/100,   Loss: 0.4368
Epoch : 76/100,   Loss: 0.4366
Epoch : 77/100,   Loss: 0.4364
Epoch : 78/100,   Loss: 0.4362
Epoch : 79/100,   Loss: 0.4360
Epoch : 80/100,   Loss: 0.4358
Epoch : 81/100,   Loss: 0.4356
Epoch : 82/100,   Loss: 0.4353
Epoch : 83/100,   Loss: 0.4351
Epoch : 84/100,   Loss: 0.4348
Epoch : 85/100,   Loss: 0.4346
Epoch : 86/100,   Loss: 0.4344
Epoch : 87/100,   Loss: 0.4342
Epoch : 88/100,   Loss: 0.4340
Epoch : 89/100,   Loss: 0.4338
Epoch : 90/100,   Loss: 0.4336
Epoch : 91/100,   Loss: 0.4333
Epoch : 92/100,   Loss: 0.4331
Epoch : 93/100,   Loss: 0.4329
Epoch : 94/100,   Loss: 0.4328
Epoch : 95/100,   Loss: 0.4326
Epoch : 96/100,   Loss: 0.4324
Epoch : 97/100,   Loss: 0.4322
Epoch : 98/100,   Loss: 0.4320
Epoch : 99/100,   Loss: 0.4318
Epoch : 100/100,   Loss: 0.4316
Wall time: 49.6 s

训练完成后我们可以看一下模型的准确率

model.eval()
correct = 0
total = 0
for i,(x, y) in enumerate(train_dl):
    x = x.float().to(DEVICE)
    y = y.long()
    outputs = model(x).cpu()
    _, predicted = torch.max(outputs.data, 1)
    total += y.size(0)
    correct += (predicted == y).sum()
print('准确率: %.4f %%' % (100 * correct / total))


准确率: 89.0000 %

以上就是基本的流程了