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数据来源
澳大利亚降雨预测数据集来自于Kaggle网站： https://www.kaggle.com/jsphyg/weather-dataset-rattle-package 该数据集该数据集包含来自许多澳大利亚气象站的大约10年的每日天气观测以及天气预报，“RainTomorrow”是要预测的目标变量，这意味着-第二天下雨了，如果当天的降雨量为1mm或更大，则此列为“是”。气象信息包括日期，城市，最低温度，最高温度，降雨量，蒸发量，阳光（一天中阳光明媚的小时数），一天中最强阵风、9am、3pm的风向和风速，一天中9am、3pm的湿度、气压、云层（云层遮盖的天空比例）、温度、当日是否下雨。 该数据集的主要任务目标是根据今日的气象信息训练分类模型，根据该模型预测澳大利亚第二天的降雨。数据总量为145460行，23列。
数据预处理
# -*- coding: utf-8 -*-
import pandas as pd
from sklearn.model_selection import train_test_split
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_graphviz
from sklearn.neighbors import KNeighborsClassifier
import graphviz
import pydotplus
import io
pd.set_option('display.max_columns', None)
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_rows', 10)
pd.set_option('display.width', None)
def preprocessing(df, index):
labels = set([])
for label in df[index]:
labels.add(label)
labels = list(labels)
# print(labels)
print('总类别数：', len(labels))
column = df[index]
df.drop(axis=1, columns=index, inplace=True)
if 'RainT' in index:
temp = column.map(lambda x: labels.index(x))
elif index == 'Date':
temp = column.map(lambda x: x[5] if x[6] == '/' else x[5:7])
else:
temp = column.map(lambda x: labels.index(x)+1)
df.insert(0, index, temp)
return df
def get_dataset(location): # 获取数据集
df = pd.read_csv(location)
# df = df[['Location', 'MinTemp', 'MaxTemp', 'Rainfall', 'Evaporation', 'Sunshine', 'WindGustDir', 'WindGustSpeed',
# 'WindSpeed9am', 'WindSpeed3pm', 'Humidity9am', 'Humidity3pm', 'Pressure9am', 'Pressure3pm',
# 'Cloud9am', 'Cloud3pm', 'Temp9am', 'Temp3pm', 'RainToday', 'RainTomorrow']]
print(df)
print(df.info()) # 查看缺失值
df = df.dropna() # 去除包含缺失值的行
# 用整数标签代替字符串型数据
for label in ['Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'Date', 'RainToday', 'RainTomorrow']:
df = preprocessing(df, label)
print(df)
# Date列格式转换
date = df['Date'].astype(float)
df.drop(axis=1, columns='Date', inplace=True)
df.insert(2, 'Date', date)
print(df.dtypes)
print(df.describe())
print(df.corr()['RainTomorrow']) # 打印相关系数矩阵
# 删除相关系数过低的数据
for key in df.corr()['RainTomorrow'].keys():
if (df.corr()['RainTomorrow'][key] -0.1):
df.drop(axis=1, columns=key, inplace=True)
# 画相关性热力图
# sns.heatmap(df.corr()[['RainToday', 'RainTomorrow']])
plt.show()
return df
def test(resultset, rightset): # 计算测试集的正确率
print('预测结果为', resultset)
print('正确结果为', rightset)
if len(resultset) != len(rightset):
print("数据集长度不同, 错误！")
return 0
total = len(resultset)
right = 0
for r1, r2 in zip(resultset, rightset):
if r1 == r2:
right += 1
rightrate = right/total
print(rightrate)
KNN
from tools import *
if __name__ == '__main__':
df = get_dataset('E:\\X\\DATA\\weatherAUS.csv')
df_list = df.values.tolist()
feature = df.drop(['RainTomorrow'], axis=1).values.tolist()
label = df['RainTomorrow'].values.tolist()
feature_train, feature_test, label_train, label_test = train_test_split(feature, label, test_size=0.1)
knn = KNeighborsClassifier()
knn.fit(feature_test, label_test)
result = knn.predict(feature_test)
test(result, label_test)
决策树
from tools import *
if __name__ == '__main__':
df = get_dataset('E:\\X\\DATA\\weatherAUS.csv')
print(df)
df_list = df.values.tolist()
feature = df.drop(['RainTomorrow'], axis=1).values.tolist()
label = df['RainTomorrow'].values.tolist()
feature_train, feature_test, label_train, label_test = train_test_split(feature, label, test_size=0.1)
clf = DecisionTreeClassifier(max_depth=3)
clf = clf.fit(feature_train, label_train)
test(clf.predict(feature_test), label_test)
# 可视化
dot_data = io.StringIO()
export_graphviz(clf, out_file=dot_data)
graph = pydotplus.graph_from_dot_data(dot_data.getvalue())
graph.write_pdf("tree.pdf")
随机森林
from tools import *
if __name__ == '__main__':
df = get_dataset('E:\\X\\DATA\\weatherAUS.csv')
df_list = df.values.tolist()
feature = df.drop(['RainTomorrow'], axis=1).values.tolist()
label = df['RainTomorrow'].values.tolist()
feature_train, feature_test, label_train, label_test = train_test_split(feature, label, test_size=0.1)
clf = RandomForestClassifier()
clf = clf.fit(feature_train, label_train)
print("随机森林分类")
test(clf.predict(feature_test), label_test)
贝叶斯
from tools import *
if __name__ == '__main__':
df = get_dataset('E:\\X\\DATA\\weatherAUS.csv')
df_list = df.values.tolist()
print(df)
feature = df.drop(['RainTomorrow'], axis=1).values.tolist()
label = df['RainTomorrow'].values.tolist()
feature_train, feature_test, label_train, label_test = train_test_split(feature, label, test_size=0.1)
clf = GaussianNB()
clf.fit(feature_train, label_train)
test(clf.predict(feature_test), label_test)
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