神经网络 -- 天气预测
- 本文为365天深度学习训练营 中的学习记录博客
- 参考文章地址： 365天深度学习训练营-第R3周：天气预测
- 作者：K同学啊
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。
一、导入数据
import 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 MinMaxScaler
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Activation,Dropout
from tensorflow.keras.callbacks import EarlyStopping
from tensorflow.keras.layers import Dropout
from sklearn.metrics import classification_report,confusion_matrix
from sklearn.metrics import r2_score
from sklearn.metrics import mean_absolute_error , mean_absolute_percentage_error , mean_squared_errordata = pd.read_csv("weatherAUS.csv")
df = data.copy()
data.head()
data.describe()
data.dtypesDate 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
data['Date'] = pd.to_datetime(data['Date'])
data['Date']0 2008-12-01
1 2008-12-02
2 2008-12-03
3 2008-12-04
4 2008-12-05
...
145455 2017-06-21
145456 2017-06-22
145457 2017-06-23
145458 2017-06-24
145459 2017-06-25
Name: Date, Length: 145460, dtype: datetime64[ns]
data['year'] = data['Date'].dt.year
data['Month'] = data['Date'].dt.month
data['day'] = data['Date'].dt.daydata.head()
data.drop('Date',axis=1,inplace=True)data.columnsIndex(['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
plt.figure(figsize=(15,13))
# data.corr()表示了data中的两个变量之间的相关性
ax = sns.heatmap(data.corr(), square=True, annot=True, fmt='.2f')
ax.set_xticklabels(ax.get_xticklabels(), rotation=90)
plt.show()
2. 是否会下雨
sns.set(style="darkgrid")
plt.figure(figsize=(4,3))
sns.countplot(x='RainTomorrow',data=data)
plt.figure(figsize=(4,3))
sns.countplot(x='RainToday',data=data)
x=pd.crosstab(data['RainTomorrow'],data['RainToday'])
x
y=x/x.transpose().sum().values.reshape(2,1)*100
y
● 如果今天不下雨，那么明天下雨的机会 = 15%
● 如果今天下雨明天下雨的机会 = 46%
y.plot(kind="bar",figsize=(4,3),color=['#006666','#d279a6']);
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)
位置影响下雨，对于 Portland 来说，有 36% 的时间在下雨，而对于 Woomers 来说，只有6%的时间在下雨
4. 湿度和压力对下雨的影响
data.columnsIndex(['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');
plt.figure(figsize=(8,6))
sns.scatterplot(data=data,x='Humidity9am',y='Humidity3pm',hue='RainTomorrow');
低压与高湿度会增加第二天下雨的概率，尤其是下午 3 点的空气湿度。
5. 气温对下雨的影响
plt.figure(figsize=(8,6))
sns.scatterplot(x='MaxTemp', y='MinTemp', data=data, hue='RainTomorrow');
结论：当一天的最高气温和最低气温接近时，第二天下雨的概率会增加。
三、数据预处理
1. 处理缺损值
# 每列中缺失数据的百分比
data.isnull().sum()/data.shape[0]*100Location 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['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['MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine', 'WindGustSpeed', 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm']
# .median(), 中位数
for i in num_cols:
data[i].fillna(data[i].median(), inplace=True)data.isnull().sum()Location 0 MinTemp 0 MaxTemp 0 Rainfall 0 Evaporation 0 Sunshine 0 WindGustDir 0 WindGustSpeed 0 WindDir9am 0 WindDir3pm 0 WindSpeed9am 0 WindSpeed3pm 0 Humidity9am 0 Humidity3pm 0 Pressure9am 0 Pressure3pm 0 Cloud9am 0 Cloud3pm 0 Temp9am 0 Temp3pm 0 RainToday 0 RainTomorrow 0 year 0 Month 0 day 0 dtype: int64
2. 构建数据集
from sklearn.preprocessing import LabelEncoder
label_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)四、预测是否下雨
1. 搭建神经网络
from tensorflow.keras.optimizers import Adam
model = Sequential()
model.add(Dense(units=24,activation='tanh',))
model.add(Dense(units=18,activation='tanh'))
model.add(Dense(units=23,activation='tanh'))
model.add(Dropout(0.5))
model.add(Dense(units=12,activation='tanh'))
model.add(Dropout(0.2))
model.add(Dense(units=1,activation='sigmoid'))
optimizer = tf.keras.optimizers.Adam(learning_rate=1e-4)
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics="accuracy")early_stop = EarlyStopping(monitor='val_loss',
mode='min',
min_delta=0.001,
verbose=1,
patience=25,
restore_best_weights=True)2. 模型训练
model.fit(x=X_train,
y=y_train,
validation_data=(X_test, y_test), verbose=1,
callbacks=[early_stop],
epochs = 10,
batch_size = 32
)Epoch 1/10 3410/3410 [==============================] - 12s 3ms/step - loss: 0.5305 - accuracy: 0.7447 - val_loss: 0.3942 - val_accuracy: 0.8265 Epoch 2/10 3410/3410 [==============================] - 9s 3ms/step - loss: 0.4031 - accuracy: 0.8285 - val_loss: 0.3772 - val_accuracy: 0.8365 Epoch 3/10 3410/3410 [==============================] - 10s 3ms/step - loss: 0.3906 - accuracy: 0.8336 - val_loss: 0.3743 - val_accuracy: 0.8394 Epoch 4/10 3410/3410 [==============================] - 9s 3ms/step - loss: 0.3877 - accuracy: 0.8357 - val_loss: 0.3730 - val_accuracy: 0.8405 Epoch 5/10 3410/3410 [==============================] - 10s 3ms/step - loss: 0.3848 - accuracy: 0.8364 - val_loss: 0.3719 - val_accuracy: 0.8401 Epoch 6/10 3410/3410 [==============================] - 9s 3ms/step - loss: 0.3799 - accuracy: 0.8398 - val_loss: 0.3713 - val_accuracy: 0.8399 Epoch 7/10 3410/3410 [==============================] - 9s 3ms/step - loss: 0.3815 - accuracy: 0.8380 - val_loss: 0.3706 - val_accuracy: 0.8401 Epoch 8/10 3410/3410 [==============================] - 10s 3ms/step - loss: 0.3763 - accuracy: 0.8381 - val_loss: 0.3707 - val_accuracy: 0.8388 Epoch 9/10 3410/3410 [==============================] - 9s 3ms/step - loss: 0.3817 - accuracy: 0.8381 - val_loss: 0.3700 - val_accuracy: 0.8395 Epoch 10/10 3410/3410 [==============================] - 9s 3ms/step - loss: 0.3780 - accuracy: 0.8387 - val_loss: 0.3697 - val_accuracy: 0.8408
3. 结果可视化
import matplotlib.pyplot as plt
acc = model.history.history['accuracy']
val_acc = model.history.history['val_accuracy']
loss = model.history.history['loss']
val_loss = model.history.history['val_loss']
epochs_range = range(10)
plt.figure(figsize=(14, 4))
plt.subplot(1, 2, 1)
plt.plot(epochs_range, acc, label='Training Accuracy')
plt.plot(epochs_range, val_acc, label='Validation Accuracy')
plt.legend(loc='lower right')
plt.title('Training and Validation Accuracy')
plt.subplot(1, 2, 2)
plt.plot(epochs_range, loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')
plt.legend(loc='upper right')
plt.title('Training and Validation Loss')
plt.show()