In [1]:

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__

Out[1]:

'1.4.0'

5.2 Pytorch处理结构化数据¶

简介¶

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

数据预处理¶

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

In [2]:

#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()

Out[2]:

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

In [3]:

#查看数据类型
df.head()

Out[3]:

	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

In [4]:

#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()

Out[4]:

	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

In [5]:

#查看一共有多少数据
len(df)

Out[5]:

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

In [6]:

#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

In [7]:

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

In [8]:

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。

In [9]:

df.head()

Out[9]:

	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.

In [10]:

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

Out[10]:

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

In [11]:

X=df.drop(columns=result_var)
X

Out[11]:

	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。

定义数据集¶

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

In [12]:

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])

In [13]:

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

In [14]:

train_ds[0]

Out[14]:

(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)

定义模型¶

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

In [15]:

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:

训练¶

In [16]:

#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

In [17]:

#损失函数
criterion =nn.CrossEntropyLoss()

In [18]:

#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)
)

In [19]:

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

Out[19]:

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

In [20]:

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

In [21]:

#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

In [22]:

%%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

In [23]:

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

学习资源站

019-Pytorch处理结构化数据

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¶