李宏毅机器学习作业1——预测COVID-19人数

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已于 2022-11-11 11:02:36 修改 · 2.5k 阅读

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#人工智能 #深度学习

于 2022-11-08 18:34:31 首次发布

这篇博客介绍了一项使用深度学习预测COVID-19感染人数的任务。作者通过数据集划分、特征选择和模型训练，展示了如何利用TensorDataset和nn.DataParallel进行并行计算。文中提到特征选择的重要性，并探讨了模型层数对结果的影响。

数据集

训练集中给出美国某些州五天COVID-19的感染人数（及相关特征数据），测试集中给出前四天的相关数据，预测第五天的感染人数。下载地址：ML2022Spring-hw1 | Kaggle

特征包括：
● States (37, 独热编码)
● COVID-like illness (4)
        ○ cli、ili …
● Behavior Indicators (8)
        ○ wearing_mask、travel_outside_state …
● Mental Health Indicators (3)
        ○ anxious、depressed …
● Tested Positive Cases (1)
        ○ tested_positive (this is what we want to predict)

训练集有2699行， 118列 (id + 37 states + 16 features x 5 days)
测试集有1078，117列 (without last day's positive rate)

导包

# Numerical Operations
import math
import numpy as np
from sklearn.model_selection import train_test_split

# Reading/Writing Data
import pandas as pd
import os
import csv

# For Progress Bar
from tqdm import tqdm
from d2l import torch as d2l

# Pytorch
import torch 
import torch.nn as nn
from torch.utils.data import Dataset, DataLoader, random_split, TensorDataset

# For plotting learning curve
from torch.utils.tensorboard import SummaryWriter

辅助函数

设定种子

我的理解是，模型初始化和验证集划分都要用到seed，这里固定下来

def same_seed(seed): 
    '''Fixes random number generator seeds for reproducibility.'''
    torch.backends.cudnn.deterministic = True
    torch.backends.cudnn.benchmark = False
    np.random.seed(seed)
    torch.manual_seed(seed)
    if torch.cuda.is_available():
        torch.cuda.manual_seed_all(seed)

划分数据集

可以使用random_split函数

def train_valid_split(data_set, valid_ratio, seed):
    '''Split provided training data into training set and validation set'''
    valid_set_size = int(valid_ratio * len(data_set)) 
    train_set_size = len(data_set) - valid_set_size
    train_set, valid_set = random_split(data_set, [train_set_size, valid_set_size],
                                        generator=torch.Generator().manual_seed(seed))
    return np.array(train_set), np.array(valid_set)

也可以采取train_test_split函数

def train_valid_split(data_set, valid_ratio, seed):
    '''Split provided training data into training set and validation set'''    
    train_set, valid_set =  train_test_split(data_set, test_size=valid_ratio, random_state=seed)
    return np.array(train_set), np.array(valid_set)

模型

整个作业最重要的就是模型和特征选择，还有超参数设置。这里放原代码

class My_Model(nn.Module):
    def __init__(self, input_dim):
        super(My_Model, self).__init__()
        # TODO: modify model's structure, be aware of dimensions. 
        self.layers = nn.Sequential(
            nn.Linear(input_dim, 16),
            nn.ReLU(),
            nn.Linear(16, 8),
            nn.ReLU(),
            nn.Linear(8, 1)
        )

    def forward(self, x):
        x = self.layers(x)
        x = x.squeeze(1) # (B, 1) -> (B)
        return x

特征选择

对原代码进行了改动，因为使用了TensorDataset，它的自变量应该是tensor，所以要把train_data, valid_data, test_data变为tensor格式

def select_feat(train_data, valid_data, test_data, select_all=True):
    '''Selects useful features to perform regression'''  
    
    train_data = torch.FloatTensor(train_data)
    valid_data = torch.FloatTensor(valid_data)
    test_data = torch.FloatTensor(test_data)

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