1-3.文本数据建模流程范例

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import os#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" 

!pip install gensim 
!pip install torchkeras

import torch 
import gensim
import torchkeras 
print("torch.__version__ = ", torch.__version__)
print("gensim.__version__ = ", gensim.__version__) 
print("torchkeras.__version__ = ", torchkeras.__version__) 

torch.__version__ =  2.0.1
gensim.__version__ =  4.3.1
torchkeras.__version__ =  3.9.3

公众号 算法美食屋 回复关键词：pytorch， 获取本项目源码和所用数据集百度云盘下载链接。

一，准备数据

imdb数据集的目标是根据电影评论的文本内容预测评论的情感标签。

训练集有20000条电影评论文本，测试集有5000条电影评论文本，其中正面评论和负面评论都各占一半。

文本数据预处理较为繁琐，包括文本切词，构建词典，编码转换，序列填充，构建数据管道等等。

此处使用gensim中的词典工具并自定义Dataset。

下面进行演示。

import numpy as np 
import pandas as pd 
import torch MAX_LEN = 200           #每个样本保留200个词的长度
BATCH_SIZE = 20 dftrain = pd.read_csv("./eat_pytorch_datasets/imdb/train.tsv",sep="\t",header = None,names = ["label","text"])
dfval = pd.read_csv("./eat_pytorch_datasets/imdb/test.tsv",sep="\t",header = None,names = ["label","text"])

from gensim import corpora
import string#1，文本切词
def textsplit(text):translator = str.maketrans('', '', string.punctuation)words = text.translate(translator).split(' ')return words#2，构建词典
vocab = corpora.Dictionary((textsplit(text) for text in dftrain['text']))
vocab.filter_extremes(no_below=5,no_above=5000)
special_tokens = {'<pad>': 0, '<unk>': 1}
vocab.patch_with_special_tokens(special_tokens)
vocab_size = len(vocab.token2id) 
print('vocab_size = ',vocab_size)#3，序列填充
def pad(seq,max_length,pad_value=0):n = len(seq)result = seq+[pad_value]*max_lengthreturn result[:max_length]#4，编码转换
def text_pipeline(text):tokens = vocab.doc2idx(textsplit(text))tokens = [x if x>0 else special_tokens['<unk>']  for x in tokens ]result = pad(tokens,MAX_LEN,special_tokens['<pad>'])return result print(text_pipeline("this is an example!")) 

vocab_size =  29924
[145, 77, 569, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]

#5，构建管道
from torch.utils.data import Dataset,DataLoaderclass ImdbDataset(Dataset):def __init__(self,df):self.df = dfdef __len__(self):return len(self.df)def __getitem__(self,index):text = self.df["text"].iloc[index]label = torch.tensor([self.df["label"].iloc[index]]).float()tokens = torch.tensor(text_pipeline(text)).int() return tokens,labelds_train = ImdbDataset(dftrain)
ds_val = ImdbDataset(dfval)

dl_train = DataLoader(ds_train,batch_size = 50,shuffle = True)
dl_val = DataLoader(ds_val,batch_size = 50,shuffle = False)

for features,labels in dl_train:break 

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(nn.Sequential,nn.ModuleList,nn.ModuleDict)进行封装。

此处选择使用第三种方式进行构建。

import torch
from torch import nn 
torch.manual_seed(42)

<torch._C.Generator at 0x142700950>

class Net(nn.Module):def __init__(self):super(Net, self).__init__()#设置padding_idx参数后将在训练过程中将填充的token始终赋值为0向量self.embedding = nn.Embedding(num_embeddings = vocab_size,embedding_dim = 3,padding_idx = 0)self.conv = nn.Sequential()self.conv.add_module("conv_1",nn.Conv1d(in_channels = 3,out_channels = 16,kernel_size = 5))self.conv.add_module("pool_1",nn.MaxPool1d(kernel_size = 2))self.conv.add_module("relu_1",nn.ReLU())self.conv.add_module("conv_2",nn.Conv1d(in_channels = 16,out_channels = 128,kernel_size = 2))self.conv.add_module("pool_2",nn.MaxPool1d(kernel_size = 2))self.conv.add_module("relu_2",nn.ReLU())self.dense = nn.Sequential()self.dense.add_module("flatten",nn.Flatten())self.dense.add_module("linear",nn.Linear(6144,1))def forward(self,x):x = self.embedding(x).transpose(1,2)x = self.conv(x)y = self.dense(x)return ynet = Net() 
print(net)

Net((embedding): Embedding(29924, 3, padding_idx=0)(conv): Sequential((conv_1): Conv1d(3, 16, kernel_size=(5,), stride=(1,))(pool_1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(relu_1): ReLU()(conv_2): Conv1d(16, 128, kernel_size=(2,), stride=(1,))(pool_2): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(relu_2): ReLU())(dense): Sequential((flatten): Flatten(start_dim=1, end_dim=-1)(linear): Linear(in_features=6144, out_features=1, bias=True))
)

Net((embedding): Embedding(8813, 3, padding_idx=0)(conv): Sequential((conv_1): Conv1d(3, 16, kernel_size=(5,), stride=(1,))(pool_1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(relu_1): ReLU()(conv_2): Conv1d(16, 128, kernel_size=(2,), stride=(1,))(pool_2): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)(relu_2): ReLU())(dense): Sequential((flatten): Flatten(start_dim=1, end_dim=-1)(linear): Linear(in_features=6144, out_features=1, bias=True))
)

from torchkeras import summary 
summary(net,input_data=features);

--------------------------------------------------------------------------
Layer (type)                            Output Shape              Param #
==========================================================================
Embedding-1                             [-1, 200, 3]               89,772
Conv1d-2                               [-1, 16, 196]                  256
MaxPool1d-3                             [-1, 16, 98]                    0
ReLU-4                                  [-1, 16, 98]                    0
Conv1d-5                               [-1, 128, 97]                4,224
MaxPool1d-6                            [-1, 128, 48]                    0
ReLU-7                                 [-1, 128, 48]                    0
Flatten-8                                 [-1, 6144]                    0
Linear-9                                     [-1, 1]                6,145
==========================================================================
Total params: 100,397
Trainable params: 100,397
Non-trainable params: 0
--------------------------------------------------------------------------
Input size (MB): 0.000069
Forward/backward pass size (MB): 0.287788
Params size (MB): 0.382984
Estimated Total Size (MB): 0.670841
--------------------------------------------------------------------------

三，训练模型

训练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仿照Keras风格的类形式的训练循环。

该训练循环的代码也是torchkeras库的核心代码。

torchkeras详情: https://github.com/lyhue1991/torchkeras

import os,sys,time
import numpy as np
import pandas as pd
import datetime 
from tqdm import tqdm import torch
from torch import nn 
from copy import deepcopydef printlog(info):nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')print("\n"+"=========="*8 + "%s"%nowtime)print(str(info)+"\n")class StepRunner:def __init__(self, net, loss_fn,stage = "train", metrics_dict = None, optimizer = None, lr_scheduler = None):self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stageself.optimizer,self.lr_scheduler = optimizer,lr_schedulerdef __call__(self, features, labels):#losspreds = self.net(features)loss = self.loss_fn(preds,labels)#backward()if self.optimizer is not None and self.stage=="train":loss.backward()self.optimizer.step()if self.lr_scheduler is not None:self.lr_scheduler.step()self.optimizer.zero_grad()#metricsstep_metrics = {self.stage+"_"+name:metric_fn(preds, labels).item() for name,metric_fn in self.metrics_dict.items()}return loss.item(),step_metricsclass EpochRunner:def __init__(self,steprunner):self.steprunner = steprunnerself.stage = steprunner.stageself.steprunner.net.train() if self.stage=="train" else self.steprunner.net.eval()def __call__(self,dataloader):total_loss,step = 0,0loop = tqdm(enumerate(dataloader), total =len(dataloader))for i, batch in loop: if self.stage=="train":loss, step_metrics = self.steprunner(*batch)else:with torch.no_grad():loss, step_metrics = self.steprunner(*batch)step_log = dict({self.stage+"_loss":loss},**step_metrics)total_loss += lossstep+=1if i!=len(dataloader)-1:loop.set_postfix(**step_log)else:epoch_loss = total_loss/stepepoch_metrics = {self.stage+"_"+name:metric_fn.compute().item() for name,metric_fn in self.steprunner.metrics_dict.items()}epoch_log = dict({self.stage+"_loss":epoch_loss},**epoch_metrics)loop.set_postfix(**epoch_log)for name,metric_fn in self.steprunner.metrics_dict.items():metric_fn.reset()return epoch_logclass KerasModel(torch.nn.Module):def __init__(self,net,loss_fn,metrics_dict=None,optimizer=None,lr_scheduler = None):super().__init__()self.history = {}self.net = netself.loss_fn = loss_fnself.metrics_dict = nn.ModuleDict(metrics_dict) self.optimizer = optimizer if optimizer is not None else torch.optim.Adam(self.parameters(), lr=1e-2)self.lr_scheduler = lr_schedulerdef forward(self, x):if self.net:return self.net.forward(x)else:raise NotImplementedErrordef fit(self, train_data, val_data=None, epochs=10, ckpt_path='checkpoint.pt', patience=5, monitor="val_loss", mode="min"):for epoch in range(1, epochs+1):printlog("Epoch {0} / {1}".format(epoch, epochs))# 1，train -------------------------------------------------  train_step_runner = StepRunner(net = self.net,stage="train",loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),optimizer = self.optimizer, lr_scheduler = self.lr_scheduler)train_epoch_runner = EpochRunner(train_step_runner)train_metrics = train_epoch_runner(train_data)for name, metric in train_metrics.items():self.history[name] = self.history.get(name, []) + [metric]# 2，validate -------------------------------------------------if val_data:val_step_runner = StepRunner(net = self.net,stage="val",loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict))val_epoch_runner = EpochRunner(val_step_runner)with torch.no_grad():val_metrics = val_epoch_runner(val_data)val_metrics["epoch"] = epochfor name, metric in val_metrics.items():self.history[name] = self.history.get(name, []) + [metric]# 3，early-stopping -------------------------------------------------if not val_data:continuearr_scores = self.history[monitor]best_score_idx = np.argmax(arr_scores) if mode=="max" else np.argmin(arr_scores)if best_score_idx==len(arr_scores)-1:torch.save(self.net.state_dict(),ckpt_path)print("<<<<<< reach best {0} : {1} >>>>>>".format(monitor,arr_scores[best_score_idx]),file=sys.stderr)if len(arr_scores)-best_score_idx>patience:print("<<<<<< {} without improvement in {} epoch, early stopping >>>>>>".format(monitor,patience),file=sys.stderr)break self.net.load_state_dict(torch.load(ckpt_path))  return pd.DataFrame(self.history)@torch.no_grad()def evaluate(self, val_data):val_step_runner = StepRunner(net = self.net,stage="val",loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict))val_epoch_runner = EpochRunner(val_step_runner)val_metrics = val_epoch_runner(val_data)return [e-mail]_grad()def predict(self, dataloader):self.net.eval()result = torch.cat([self.forward(t[0]) for t in dataloader])return result.data

from torchmetrics import Accuracynet = Net() 
model = KerasModel(net,loss_fn = nn.BCEWithLogitsLoss(),optimizer= torch.optim.Adam(net.parameters(),lr = 0.01),  metrics_dict = {"acc":Accuracy(task='binary')})

model.fit(dl_train,val_data=dl_val,epochs=10,ckpt_path='checkpoint',patience=3,monitor='val_acc',mode='max')

================================================================================2023-08-02 14:20:21
Epoch 1 / 10100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:10<00:00, 39.28it/s, train_acc=0.496, train_loss=0.701]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 51.21it/s, val_acc=0.518, val_loss=0.693]
<<<<<< reach best val_acc : 0.5180000066757202 >>>>>>================================================================================2023-08-02 14:20:33
Epoch 2 / 10100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:09<00:00, 40.14it/s, train_acc=0.503, train_loss=0.693]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 54.22it/s, val_acc=0.58, val_loss=0.689]
<<<<<< reach best val_acc : 0.5803999900817871 >>>>>>================================================================================2023-08-02 14:20:45
Epoch 3 / 10100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:10<00:00, 39.46it/s, train_acc=0.69, train_loss=0.58]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 53.84it/s, val_acc=0.781, val_loss=0.47]
<<<<<< reach best val_acc : 0.7807999849319458 >>>>>>================================================================================2023-08-02 14:20:57
Epoch 4 / 10100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:09<00:00, 40.33it/s, train_acc=0.83, train_loss=0.386]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 54.18it/s, val_acc=0.819, val_loss=0.408]
<<<<<< reach best val_acc : 0.8194000124931335 >>>>>>================================================================================2023-08-02 14:21:09
Epoch 5 / 10100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:09<00:00, 40.63it/s, train_acc=0.893, train_loss=0.262]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 55.69it/s, val_acc=0.836, val_loss=0.395]
<<<<<< reach best val_acc : 0.8357999920845032 >>>>>>================================================================================2023-08-02 14:21:21
Epoch 6 / 10100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:09<00:00, 40.58it/s, train_acc=0.932, train_loss=0.176]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 50.93it/s, val_acc=0.828, val_loss=0.456]================================================================================2023-08-02 14:21:33
Epoch 7 / 10100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:10<00:00, 39.62it/s, train_acc=0.956, train_loss=0.119]
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 55.26it/s, val_acc=0.829, val_loss=0.558]================================================================================2023-08-02 14:21:44
Epoch 8 / 10100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:09<00:00, 40.58it/s, train_acc=0.973, train_loss=0.0754]
100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 52.91it/s, val_acc=0.823, val_loss=0.67]
<<<<<< val_acc without improvement in 3 epoch, early stopping >>>>>>

	train_loss	train_acc	val_loss	val_acc	epoch
0	0.701064	0.49580	0.693045	0.5180	1
1	0.693060	0.50335	0.688656	0.5804	2
2	0.579867	0.69010	0.469574	0.7808	3
3	0.385625	0.82990	0.407633	0.8194	4
4	0.261653	0.89260	0.394901	0.8358	5
5	0.175921	0.93210	0.455604	0.8284	6
6	0.119178	0.95610	0.558430	0.8286	7
7	0.075409	0.97330	0.670172	0.8232	8

四，评估模型

import pandas as pd history = model.history
dfhistory = pd.DataFrame(history) 
dfhistory 

	train_loss	train_acc	val_loss	val_acc	epoch
0	0.701064	0.49580	0.693045	0.5180	1
1	0.693060	0.50335	0.688656	0.5804	2
2	0.579867	0.69010	0.469574	0.7808	3
3	0.385625	0.82990	0.407633	0.8194	4
4	0.261653	0.89260	0.394901	0.8358	5
5	0.175921	0.93210	0.455604	0.8284	6
6	0.119178	0.95610	0.558430	0.8286	7
7	0.075409	0.97330	0.670172	0.8232	8

%matplotlib inline
%config InlineBackend.figure_format = 'svg'import matplotlib.pyplot as pltdef plot_metric(dfhistory, metric):train_metrics = dfhistory["train_"+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,"acc")

# 评估
model.evaluate(dl_val)

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 50.26it/s, val_acc=0.836, val_loss=0.395]{'val_loss': 0.39490113019943235, 'val_acc': 0.8357999920845032}

五，使用模型

def predict(net,dl):net.eval()with torch.no_grad():result = nn.Sigmoid()(torch.cat([net.forward(t[0]) for t in dl]))return(result.data)

y_pred_probs = predict(net,dl_val)
y_pred_probs

tensor([[0.9372],[1.0000],[0.8672],...,[0.5141],[0.4756],[0.9998]])

六，保存模型

#模型权重已经被保存在了ckpt_path='checkpoint.'
net_clone = Net()
net_clone.load_state_dict(torch.load('checkpoint'))

<All keys matched successfully>

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