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泰州网站建设服务公司,东城专业网站建设公司,wordpress页面多打开空白,网站开发iis怎么配置🍨 本文为🔗365天深度学习训练营 中的学习记录博客🍖 原作者：K同学啊 本周任务： 任务说明：该数据集提供了来自澳大利亚许多地点的大约 10 年的每日天气观测数据。你需要做的是根据这些数据对RainTomorrow进…
  • 🍨 本文为🔗365天深度学习训练营 中的学习记录博客
  • 🍖 原作者：K同学啊

本周任务：
任务说明：该数据集提供了来自澳大利亚许多地点的大约 10 年的每日天气观测数据。你需要做的是根据这些数据对RainTomorrow进行一个预测，这次任务任务与以往的不同，我增加了探索式数据分析（EDA），希望这部分内容可以帮助到大家。

一、导入数据

from datetime import dateimport numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import warnings
warnings.filterwarnings('ignore')from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScalerimport torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as Ffile_path = 'D:/OneDrive/code learning(python and matlab and latex)/365camp/data/weatherAUS.csv'
data = pd.read_csv(file_path)
df = data.copy()
data.head(10)

代码输出：

         Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \
0  2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   
1  2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   
2  2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   
3  2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   
4  2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   
5  2008-12-06   Albury     14.6     29.7       0.2          NaN       NaN   
6  2008-12-07   Albury     14.3     25.0       0.0          NaN       NaN   
7  2008-12-08   Albury      7.7     26.7       0.0          NaN       NaN   
8  2008-12-09   Albury      9.7     31.9       0.0          NaN       NaN   
9  2008-12-10   Albury     13.1     30.1       1.4          NaN       NaN   WindGustDir  WindGustSpeed WindDir9am  ... Humidity9am  Humidity3pm  \
0           W           44.0          W  ...        71.0         22.0   
1         WNW           44.0        NNW  ...        44.0         25.0   
2         WSW           46.0          W  ...        38.0         30.0   
3          NE           24.0         SE  ...        45.0         16.0   
4           W           41.0        ENE  ...        82.0         33.0   
5         WNW           56.0          W  ...        55.0         23.0   
6           W           50.0         SW  ...        49.0         19.0   
7           W           35.0        SSE  ...        48.0         19.0   
8         NNW           80.0         SE  ...        42.0          9.0   
9           W           28.0          S  ...        58.0         27.0   Pressure9am  Pressure3pm  Cloud9am  Cloud3pm  Temp9am  Temp3pm  RainToday  \
0       1007.7       1007.1       8.0       NaN     16.9     21.8         No   
1       1010.6       1007.8       NaN       NaN     17.2     24.3         No   
2       1007.6       1008.7       NaN       2.0     21.0     23.2         No   
3       1017.6       1012.8       NaN       NaN     18.1     26.5         No   
4       1010.8       1006.0       7.0       8.0     17.8     29.7         No   
5       1009.2       1005.4       NaN       NaN     20.6     28.9         No   
6       1009.6       1008.2       1.0       NaN     18.1     24.6         No   
7       1013.4       1010.1       NaN       NaN     16.3     25.5         No   
8       1008.9       1003.6       NaN       NaN     18.3     30.2         No   
9       1007.0       1005.7       NaN       NaN     20.1     28.2        Yes   RainTomorrow  
0            No  
1            No  
2            No  
3            No  
4            No  
5            No  
6            No  
7            No  
8           Yes  
9            No  [10 rows x 23 columns]

我们接下来查看数据的基本特征

data.describe()

代码输出：

             MinTemp        MaxTemp       Rainfall   Evaporation  \
count  143975.000000  144199.000000  142199.000000  82670.000000   
mean       12.194034      23.221348       2.360918      5.468232   
std         6.398495       7.119049       8.478060      4.193704   
min        -8.500000      -4.800000       0.000000      0.000000   
25%         7.600000      17.900000       0.000000      2.600000   
50%        12.000000      22.600000       0.000000      4.800000   
75%        16.900000      28.200000       0.800000      7.400000   
max        33.900000      48.100000     371.000000    145.000000   Sunshine  WindGustSpeed   WindSpeed9am   WindSpeed3pm  \
count  75625.000000  135197.000000  143693.000000  142398.000000   
mean       7.611178      40.035230      14.043426      18.662657   
std        3.785483      13.607062       8.915375       8.809800   
min        0.000000       6.000000       0.000000       0.000000   
25%        4.800000      31.000000       7.000000      13.000000   
50%        8.400000      39.000000      13.000000      19.000000   
75%       10.600000      48.000000      19.000000      24.000000   
max       14.500000     135.000000     130.000000      87.000000   Humidity9am    Humidity3pm   Pressure9am    Pressure3pm  \
count  142806.000000  140953.000000  130395.00000  130432.000000   
mean       68.880831      51.539116    1017.64994    1015.255889   
std        19.029164      20.795902       7.10653       7.037414   
min         0.000000       0.000000     980.50000     977.100000   
25%        57.000000      37.000000    1012.90000    1010.400000   
50%        70.000000      52.000000    1017.60000    1015.200000   
75%        83.000000      66.000000    1022.40000    1020.000000   
max       100.000000     100.000000    1041.00000    1039.600000   Cloud9am      Cloud3pm        Temp9am       Temp3pm  
count  89572.000000  86102.000000  143693.000000  141851.00000  
mean       4.447461      4.509930      16.990631      21.68339  
std        2.887159      2.720357       6.488753       6.93665  
min        0.000000      0.000000      -7.200000      -5.40000  
25%        1.000000      2.000000      12.300000      16.60000  
50%        5.000000      5.000000      16.700000      21.10000  
75%        7.000000      7.000000      21.600000      26.40000  
max        9.000000      9.000000      40.200000      46.70000

随后我们查看数据类型：

data.dtypes

代码输出：

Date              object
Location          object
MinTemp          float64
MaxTemp          float64
Rainfall         float64
Evaporation      float64
Sunshine         float64
WindGustDir       object
WindGustSpeed    float64
WindDir9am        object
WindDir3pm        object
WindSpeed9am     float64
WindSpeed3pm     float64
Humidity9am      float64
Humidity3pm      float64
Pressure9am      float64
Pressure3pm      float64
Cloud9am         float64
Cloud3pm         float64
Temp9am          float64
Temp3pm          float64
RainToday         object
RainTomorrow      object
dtype: object

将日期转换为日期时间格式：

data['Date'] = pd.to_datetime(data['Date'])data['year'] = data['Date'].dt.year
data['month'] = data['Date'].dt.month
data['day'] = data['Date'].dt.daydata.head()

代码输出：

        Date Location  MinTemp  MaxTemp  Rainfall  Evaporation  Sunshine  \
0 2008-12-01   Albury     13.4     22.9       0.6          NaN       NaN   
1 2008-12-02   Albury      7.4     25.1       0.0          NaN       NaN   
2 2008-12-03   Albury     12.9     25.7       0.0          NaN       NaN   
3 2008-12-04   Albury      9.2     28.0       0.0          NaN       NaN   
4 2008-12-05   Albury     17.5     32.3       1.0          NaN       NaN   WindGustDir  WindGustSpeed WindDir9am  ... Pressure3pm  Cloud9am  Cloud3pm  \
0           W           44.0          W  ...      1007.1       8.0       NaN   
1         WNW           44.0        NNW  ...      1007.8       NaN       NaN   
2         WSW           46.0          W  ...      1008.7       NaN       2.0   
3          NE           24.0         SE  ...      1012.8       NaN       NaN   
4           W           41.0        ENE  ...      1006.0       7.0       8.0   Temp9am  Temp3pm  RainToday  RainTomorrow  year  month  day  
0     16.9     21.8         No            No  2008     12    1  
1     17.2     24.3         No            No  2008     12    2  
2     21.0     23.2         No            No  2008     12    3  
3     18.1     26.5         No            No  2008     12    4  
4     17.8     29.7         No            No  2008     12    5  [5 rows x 26 columns]

data.drop('Date', axis=1, inplace=True)
data.columns

代码输出：

Index(['Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine','WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm','WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm','Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am','Temp3pm', 'RainToday', 'RainTomorrow', 'year', 'month', 'day'],dtype='object')

二、探索式数据分析（EDA）

1、数据相关性探索

numeric_data = data.select_dtypes(include=['number'])
plt.figure(figsize=(15,13))
ax = sns.heatmap(numeric_data.corr(), square=True, annot=True, fmt='.2f')
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
plt.show()

代码输出：
在这里插入图片描述

2、是否会下雨

# 设置样式和调色板
sns.set(style="whitegrid", palette="Set2")# 创建一个 1 行 2 列的图像布局
fig, axes = plt.subplots(1, 2, figsize=(10, 4))  # 图形尺寸调大 (10, 4)# 图表标题样式
title_font = {'fontsize': 14, 'fontweight': 'bold', 'color': 'darkblue'}# 第一张图：RainTomorrow
sns.countplot(x='RainTomorrow', data=data, ax=axes[0], edgecolor='black',palette={'Yes': 'green', 'No': 'red'})  # 添加边框
axes[0].set_title('Rain Tomorrow', fontdict=title_font)  # 设置标题
axes[0].set_xlabel('Will it Rain Tomorrow?', fontsize=12)  # X轴标签
axes[0].set_ylabel('Count', fontsize=12)  # Y轴标签
axes[0].tick_params(axis='x', labelsize=11)  # X轴刻度字体大小
axes[0].tick_params(axis='y', labelsize=11)  # Y轴刻度字体大小# 第二张图：RainToday
sns.countplot(x='RainToday', data=data, ax=axes[1], edgecolor='black',palette={'Yes': 'green', 'No': 'red'})  # 添加边框
axes[1].set_title('Rain Today', fontdict=title_font)  # 设置标题
axes[1].set_xlabel('Did it Rain Today?', fontsize=12)  # X轴标签
axes[1].set_ylabel('Count', fontsize=12)  # Y轴标签
axes[1].tick_params(axis='x', labelsize=11)  # X轴刻度字体大小
axes[1].tick_params(axis='y', labelsize=11)  # Y轴刻度字体大小sns.despine()      # 去除图表顶部和右侧的边框
plt.tight_layout() # 调整布局，避免图形之间的重叠
plt.show()

代码输出：
在这里插入图片描述

x=pd.crosstab(data['RainTomorrow'],data['RainToday'])
x

代码输出：

RainToday        No    Yes
RainTomorrow              
No            92728  16858
Yes           16604  14597

y=x/x.transpose().sum().values.reshape(2,1)*100
y

代码输出：

RainToday            No        Yes
RainTomorrow                      
No            84.616648  15.383352
Yes           53.216243  46.783757

y.plot(kind="bar",figsize=(4,3),color=['#006666','#d279a6']);

代码输出：
在这里插入图片描述

3、地理位置与下雨的关系

x=pd.crosstab(data['Location'],data['RainToday']) 
# 获取每个城市下雨天数和非下雨天数的百分比
y=x/x.transpose().sum().values.reshape((-1, 1))*100
# 按每个城市的雨天百分比排序
y=y.sort_values(by='Yes',ascending=True )color=['#cc6699','#006699','#006666','#862d86','#ff9966'  ]
y.Yes.plot(kind="barh",figsize=(15,20),color=color)

代码输出：
在这里插入图片描述

4、湿度和压力对下雨的影响

data.columns

代码输出：

Index(['Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine','WindGustDir', 'WindGustSpeed', 'WindDir9am', 'WindDir3pm','WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm','Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am','Temp3pm', 'RainToday', 'RainTomorrow', 'year', 'month', 'day'],dtype='object')

plt.figure(figsize=(8,6))
sns.scatterplot(data=data,x='Pressure9am',y='Pressure3pm',hue='RainTomorrow');

代码输出：
在这里插入图片描述

plt.figure(figsize=(8,6))
sns.scatterplot(data=data,x='Humidity9am',y='Humidity3pm',hue='RainTomorrow');

代码输出：
在这里插入图片描述

5、气温对下雨的影响

plt.figure(figsize=(8,6))
sns.scatterplot(x='MaxTemp', y='MinTemp', data=data, hue='RainTomorrow');

代码输出：
在这里插入图片描述

三、数据预处理

# 每列中缺失数据的百分比
data.isnull().sum()/data.shape[0]*100

代码输出：

Location          0.000000
MinTemp           1.020899
MaxTemp           0.866905
Rainfall          2.241853
Evaporation      43.166506
Sunshine         48.009762
WindGustDir       7.098859
WindGustSpeed     7.055548
WindDir9am        7.263853
WindDir3pm        2.906641
WindSpeed9am      1.214767
WindSpeed3pm      2.105046
Humidity9am       1.824557
Humidity3pm       3.098446
Pressure9am      10.356799
Pressure3pm      10.331363
Cloud9am         38.421559
Cloud3pm         40.807095
Temp9am           1.214767
Temp3pm           2.481094
RainToday         2.241853
RainTomorrow      2.245978
year              0.000000
month             0.000000
day               0.000000
dtype: float64

# 在该列中随机选择数进行填充
lst=['Evaporation','Sunshine','Cloud9am','Cloud3pm']
for col in lst:fill_list = data[col].dropna()data[col] = data[col].fillna(pd.Series(np.random.choice(fill_list, size=len(data.index))))s = (data.dtypes == "object")
object_cols = list(s[s].index)
object_cols

代码输出：

['Location','WindGustDir','WindDir9am','WindDir3pm','RainToday','RainTomorrow']

# inplace=True：直接修改原对象，不创建副本
# data[i].mode()[0] 返回频率出现最高的选项，众数for i in object_cols:data[i].fillna(data[i].mode()[0], inplace=True)t = (data.dtypes == "float64")
num_cols = list(t[t].index)
num_cols

代码输出：

['MinTemp','MaxTemp','Rainfall','Evaporation','Sunshine','WindGustSpeed','WindSpeed9am','WindSpeed3pm','Humidity9am','Humidity3pm','Pressure9am','Pressure3pm','Cloud9am','Cloud3pm','Temp9am','Temp3pm']

中位数进行填充：

# .median(), 中位数
for i in num_cols:data[i].fillna(data[i].median(), inplace=True)data.isnull().sum()

代码输出：

Date                 0
Location             0
MinTemp              0
MaxTemp              0
Rainfall             0
Evaporation          0
Sunshine             0
WindGustDir      10326
WindGustSpeed        0
WindDir9am       10566
WindDir3pm        4228
WindSpeed9am         0
WindSpeed3pm         0
Humidity9am          0
Humidity3pm          0
Pressure9am          0
Pressure3pm          0
Cloud9am             0
Cloud3pm             0
Temp9am              0
Temp3pm              0
RainToday         3261
RainTomorrow      3267
dtype: int64

2、构建数据集

from sklearn.preprocessing import LabelEncoderlabel_encoder = LabelEncoder()
for i in object_cols:data[i] = label_encoder.fit_transform(data[i])X = data.drop(['RainTomorrow','day'],axis=1).values
y = data['RainTomorrow'].valuesX_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.25,random_state=101)scaler = MinMaxScaler()
scaler.fit(X_train)
X_train = scaler.transform(X_train)
X_test  = scaler.transform(X_test)

3、网络构建与训练

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
import numpy as np# 检查 GPU 是否可用
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
print("Using device:", device)# 1. 定义模型结构
class MyModel(nn.Module):def __init__(self, input_dim):super(MyModel, self).__init__()self.fc1 = nn.Linear(input_dim, 24)       # 第一层：输入 -> 24self.fc2 = nn.Linear(24, 18)               # 第二层：24 -> 18self.fc3 = nn.Linear(18, 23)               # 第三层：18 -> 23self.dropout1 = nn.Dropout(0.5)            # dropout 0.5self.fc4 = nn.Linear(23, 12)               # 第四层：23 -> 12self.dropout2 = nn.Dropout(0.2)            # dropout 0.2self.fc5 = nn.Linear(12, 1)                # 输出层：12 -> 1def forward(self, x):x = torch.tanh(self.fc1(x))               # 使用 tanh 激活x = torch.tanh(self.fc2(x))x = torch.tanh(self.fc3(x))x = self.dropout1(x)x = torch.tanh(self.fc4(x))x = self.dropout2(x)x = torch.sigmoid(self.fc5(x))            # 输出层用 sigmoid，输出概率return x# 2. 数据准备
# 假设 X_train, y_train, X_test, y_test 是 NumPy 数组
X_train_tensor = torch.tensor(X_train, dtype=torch.float32)
y_train_tensor = torch.tensor(y_train, dtype=torch.float32).unsqueeze(1)  # 调整为 (N,1)
X_test_tensor  = torch.tensor(X_test, dtype=torch.float32)
y_test_tensor  = torch.tensor(y_test, dtype=torch.float32).unsqueeze(1)# 创建数据集和 DataLoader
train_dataset = TensorDataset(X_train_tensor, y_train_tensor)
val_dataset   = TensorDataset(X_test_tensor, y_test_tensor)batch_size = 32
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
val_loader   = DataLoader(val_dataset, batch_size=batch_size, shuffle=False)# 3. 实例化模型，并移动到 GPU（如果可用）
input_dim = X_train.shape[1]  # 特征个数
model = MyModel(input_dim).to(device)# 4. 定义优化器和损失函数
optimizer = optim.Adam(model.parameters(), lr=1e-4)
criterion = nn.BCELoss()  # 二元交叉熵损失# 5. 设置早停参数
patience = 25          # 最多等待 25 个 epoch
min_delta = 0.001      # 验证损失改善阈值
best_val_loss = float('inf')
patience_counter = 0import matplotlib.pyplot as plt# 初始化记录指标的列表
train_loss_history = []
val_loss_history = []
train_acc_history = []
val_acc_history = []num_epochs = 10  # 或你实际的训练周期数for epoch in range(num_epochs):model.train()train_loss = 0.0correct_train = 0total_train = 0# 遍历训练集for batch_x, batch_y in train_loader:# 将数据移动到设备（GPU/CPU）batch_x = batch_x.to(device)batch_y = batch_y.to(device)optimizer.zero_grad()            # 清除梯度outputs = model(batch_x)         # 前向传播loss = criterion(outputs, batch_y) # 计算损失loss.backward()                  # 反向传播optimizer.step()                 # 更新参数train_loss += loss.item() * batch_x.size(0)# 二分类：将输出概率大于 0.5 的判为 1，否则为 0preds = (outputs >= 0.5).float()correct_train += (preds == batch_y).sum().item()total_train += batch_y.size(0)epoch_train_loss = train_loss / total_trainepoch_train_acc = correct_train / total_traintrain_loss_history.append(epoch_train_loss)train_acc_history.append(epoch_train_acc)# 验证阶段model.eval()val_loss = 0.0correct_val = 0total_val = 0with torch.no_grad():for batch_x, batch_y in val_loader:batch_x = batch_x.to(device)batch_y = batch_y.to(device)outputs = model(batch_x)loss = criterion(outputs, batch_y)val_loss += loss.item() * batch_x.size(0)preds = (outputs >= 0.5).float()correct_val += (preds == batch_y).sum().item()total_val += batch_y.size(0)epoch_val_loss = val_loss / total_valepoch_val_acc = correct_val / total_valval_loss_history.append(epoch_val_loss)val_acc_history.append(epoch_val_acc)print(f"Epoch {epoch+1}/{num_epochs}, Training Loss: {epoch_train_loss:.4f}, Training Acc: {epoch_train_acc:.4f}, "f"Validation Loss: {epoch_val_loss:.4f}, Validation Acc: {epoch_val_acc:.4f}")# 绘图：训练和验证的准确率与损失
epochs_range = range(1, num_epochs + 1)plt.figure(figsize=(14, 4))plt.subplot(1, 2, 1)
plt.plot(epochs_range, train_acc_history, label='Training Accuracy')
plt.plot(epochs_range, val_acc_history, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')plt.subplot(1, 2, 2)
plt.plot(epochs_range, train_loss_history, label='Training Loss')
plt.plot(epochs_range, val_loss_history, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()

代码输出：

Using device: cuda
Epoch 1/10, Training Loss: 0.4736, Training Acc: 0.7941, Validation Loss: 0.3953, Validation Acc: 0.8288
Epoch 2/10, Training Loss: 0.3899, Training Acc: 0.8356, Validation Loss: 0.3775, Validation Acc: 0.8381
Epoch 3/10, Training Loss: 0.3836, Training Acc: 0.8391, Validation Loss: 0.3746, Validation Acc: 0.8385
Epoch 4/10, Training Loss: 0.3812, Training Acc: 0.8400, Validation Loss: 0.3730, Validation Acc: 0.8392
Epoch 5/10, Training Loss: 0.3797, Training Acc: 0.8392, Validation Loss: 0.3719, Validation Acc: 0.8398
Epoch 6/10, Training Loss: 0.3776, Training Acc: 0.8400, Validation Loss: 0.3711, Validation Acc: 0.8404
Epoch 7/10, Training Loss: 0.3776, Training Acc: 0.8398, Validation Loss: 0.3707, Validation Acc: 0.8405
Epoch 8/10, Training Loss: 0.3771, Training Acc: 0.8400, Validation Loss: 0.3715, Validation Acc: 0.8400
Epoch 9/10, Training Loss: 0.3769, Training Acc: 0.8402, Validation Loss: 0.3708, Validation Acc: 0.8405
Epoch 10/10, Training Loss: 0.3770, Training Acc: 0.8395, Validation Loss: 0.3698, Validation Acc: 0.8398<Figure size 1400x400 with 2 Axes>

4、数据可视化

在这里插入图片描述


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