1.1准备数据
本项目使用的数据可在作者公众号‘算法美食屋 ’获取
import numpy as np import pandas as pd import matplotlib.pyplot as pltimport torch from torch import nn from torch.utils.data import Dataset,DataLoader,TensorDatasetdftrain_raw = pd.read_csv('./eat_pytorch_in_20_days-master/eat_pytorch_datasets/titanic/train.csv')dftest_raw = pd.read_csv('./eat_pytorch_in_20_days-master/eat_pytorch_datasets/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】
1.2.数据预处理
def preprocessing(dfdata):dfresult= pd.DataFrame()#PclassdfPclass = pd.get_dummies(dfdata['Pclass'])dfPclass.columns = ['Pclass_' +str(x) for x in dfPclass.columns ]dfresult = pd.concat([dfresult,dfPclass],axis = 1)#SexdfSex = pd.get_dummies(dfdata['Sex'])dfresult = pd.concat([dfresult,dfSex],axis = 1)#Agedfresult['Age'] = dfdata['Age'].fillna(0)dfresult['Age_null'] = pd.isna(dfdata['Age']).astype('int32')#SibSp,Parch,Faredfresult['SibSp'] = dfdata['SibSp']dfresult['Parch'] = dfdata['Parch']dfresult['Fare'] = dfdata['Fare']#Carbindfresult['Cabin_null'] = pd.isna(dfdata['Cabin']).astype('int32')#EmbarkeddfEmbarked = 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).valuesy_train = dftrain_raw[['Survived']].valuesx_test = preprocessing(dftest_raw).valuesy_test = dftest_raw[['Survived']].valuesprint("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 )
1.3.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_val = DataLoader(TensorDataset(torch.tensor(x_test).float(),torch.tensor(y_test).float()),shuffle = False, batch_size = 8)
2.定义模型
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))return netnet = create_net()print(net)
3.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仿照Keras风格的脚本形式的训练循环。
该脚本形式的训练代码与 torchkeras 库的核心代码基本一致。
torchkeras详情: /lyhue1991/torchkeras
import os,sys,timeimport numpy as npimport pandas as pdimport datetime from tqdm import tqdm import torchfrom torch import nn from copy import deepcopyfrom torchkeras.metrics import Accuracydef printlog(info):nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')print("\n"+"=========="*8 + "%s"%nowtime)print(str(info)+"\n")loss_fn = nn.BCEWithLogitsLoss()optimizer= torch.optim.Adam(net.parameters(),lr = 0.01) metrics_dict = {"acc":Accuracy()}epochs = 20 ckpt_path='checkpoint.pt'#early_stopping相关设置monitor="val_acc"patience=5mode="max"history = {}for epoch in range(1, epochs+1):printlog("Epoch {0} / {1}".format(epoch, epochs))# 1，train ------------------------------------------------- net.train()total_loss,step = 0,0loop = tqdm(enumerate(dl_train), total =len(dl_train))train_metrics_dict = deepcopy(metrics_dict) for i, batch in loop: features,labels = batch#forwardpreds = net(features)loss = loss_fn(preds,labels)#backwardloss.backward()optimizer.step()optimizer.zero_grad()#metricsstep_metrics = {"train_"+name:metric_fn(preds, labels).item() for name,metric_fn in train_metrics_dict.items()}step_log = dict({"train_loss":loss.item()},**step_metrics)total_loss += loss.item()step+=1if i!=len(dl_train)-1:loop.set_postfix(**step_log)else:epoch_loss = total_loss/stepepoch_metrics = {"train_"+name:pute().item() for name,metric_fn in train_metrics_dict.items()}epoch_log = dict({"train_loss":epoch_loss},**epoch_metrics)loop.set_postfix(**epoch_log)for name,metric_fn in train_metrics_dict.items():metric_fn.reset()for name, metric in epoch_log.items():history[name] = history.get(name, []) + [metric]# 2，validate -------------------------------------------------net.eval()total_loss,step = 0,0loop = tqdm(enumerate(dl_val), total =len(dl_val))val_metrics_dict = deepcopy(metrics_dict) with torch.no_grad():for i, batch in loop: features,labels = batch#forwardpreds = net(features)loss = loss_fn(preds,labels)#metricsstep_metrics = {"val_"+name:metric_fn(preds, labels).item() for name,metric_fn in val_metrics_dict.items()}step_log = dict({"val_loss":loss.item()},**step_metrics)total_loss += loss.item()step+=1if i!=len(dl_val)-1:loop.set_postfix(**step_log)else:epoch_loss = (total_loss/step)epoch_metrics = {"val_"+name:pute().item() for name,metric_fn in val_metrics_dict.items()}epoch_log = dict({"val_loss":epoch_loss},**epoch_metrics)loop.set_postfix(**epoch_log)for name,metric_fn in val_metrics_dict.items():metric_fn.reset()epoch_log["epoch"] = epoch for name, metric in epoch_log.items():history[name] = history.get(name, []) + [metric]# 3，early-stopping -------------------------------------------------arr_scores = 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(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 net.load_state_dict(torch.load(ckpt_path))dfhistory = pd.DataFrame(history)
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