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covid.py
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covid.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
import codecs
import os
import re
from concurrent.futures import ProcessPoolExecutor
import matplotlib.pyplot as plt
import pandas as pd
from pmdarima import arima
from pmdarima.model_selection import train_test_split
from sklearn.metrics import r2_score
province_name = {
"Anhui": "安徽",
"Beijing": "北京",
"Chongqing": "重庆",
"Fujian": "福建",
"Gansu": "甘肃",
"Guangdong": "广东",
"Guangxi": "广西",
"Guizhou": "贵州",
"Hainan": "海南",
"Hebei": "河北",
"Heilongjiang": "黑龙江",
"Henan": "河南",
"Hong Kong": "香港",
"Hubei": "湖北",
"Hunan": "湖南",
"Inner Mongolia": "内蒙古",
"Jiangsu": "江苏",
"Jiangxi": "江西",
"Jilin": "吉林",
"Liaoning": "辽宁",
"Macau": "澳门",
"Ningxia": "宁夏",
"Qinghai": "青海",
"Shaanxi": "陕西",
"Shandong": "山东",
"Shanghai": "上海",
"Shanxi": "山西",
"Sichuan": "四川",
"Tianjin": "天津",
"Tibet": "西藏",
"Xinjiang": "新疆",
"Yunnan": "云南",
"Zhejiang": "浙江",
"Taiwan": "台湾",
}
def adjust_date(s):
t = s.split("/")
return f"20{t[2]}-{int(t[0]):02d}-{int(t[1]):02d}"
def adjust_name(s):
return re.sub(r"\*|\,|\(|\)|\ |\'", "_", s)
def draw(province):
draw_(province, True)
draw_(province, False)
def draw_(province, isDaily):
# 模型训练
model = arima.AutoARIMA(
start_p=0,
max_p=4,
d=None,
start_q=0,
max_q=1,
start_P=0,
max_P=1,
D=None,
start_Q=0,
max_Q=1,
m=7,
seasonal=True,
test="kpss",
trace=True,
error_action="ignore",
suppress_warnings=True,
stepwise=True,
)
if isDaily:
data = df[province].diff().dropna()
model.fit(data)
else:
data = df[province]
model.fit(data)
# 模型验证
train, test = train_test_split(data, train_size=0.8)
pred_test = model.predict_in_sample(start=train.shape[0] + 1, end=data.shape[0], dynamic=False)
validating = pd.Series(pred_test, index=test.index)
r2 = r2_score(test, pred_test)
# 开始预测
pred, pred_ci = model.predict(n_periods=14, return_conf_int=True)
idx = pd.date_range(data.index.max(), periods=14, freq="D")
forecasting = pd.Series(pred, index=idx)
# 绘图呈现
plt.figure(figsize=(24, 6))
plt.plot(data.index, data, label="实际值", color="blue")
plt.plot(validating.index, validating, label="校验值", color="orange")
plt.plot(forecasting.index, forecasting, label="预测值", color="red")
# plt.fill_between(forecasting.index, pred_ci[:, 0], pred_ci[:, 1], color="black", alpha=.25)
plt.legend()
plt.ticklabel_format(style="plain", axis="y")
plt.rcParams["font.sans-serif"] = ["WenQuanYi Zen Hei"]
if isDaily:
plt.title(
f"每日新增预测 - {province_name[province]}\nARIMA {model.model_.order}x{model.model_.seasonal_order} (R2 = {r2:.6f})"
)
plt.savefig(
os.path.join("figures", f"{adjust_name(province)}-daily.svg"),
bbox_inches="tight",
)
plt.close()
else:
plt.title(
f"累计确诊预测 - {province_name[province]}\nARIMA {model.model_.order}x{model.model_.seasonal_order} (R2 = {r2:.6f})"
)
plt.savefig(
os.path.join("figures", f"{adjust_name(province)}.svg"), bbox_inches="tight"
)
plt.close()
if __name__ == "__main__":
# 准备数据
df = pd.read_csv(
"https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv",
index_col="Province/State",
).drop(columns=["Lat", "Long"])
df = (
df[(df["Country/Region"] == "China") | (df["Country/Region"] == "Taiwan*")]
.transpose()
.drop("Country/Region")
.rename(columns=str)
.rename(columns={"nan": "Taiwan"})
.drop(columns=["Unknown"])
.sort_index(axis=1)
)
df.index = pd.DatetimeIndex(df.index.map(adjust_date))
provinces = df.columns.to_list()
# 线程池
with ProcessPoolExecutor() as pool:
pool.map(draw, provinces)
pool.shutdown(wait=True)
# 编制索引
with codecs.open("README.md", "w", "utf-8") as f:
f.write("# COVID-19 Forecasting\n\n")
f.write(
"[](https://github.com/winsphinx/covid-cn/actions/workflows/build.yml)\n"
)
f.write(
"[](https://github.com/winsphinx/covid-cn/actions/workflows/check.yml)\n"
)
f.write(
"[](https://github.com/CSSEGISandData/COVID-19)\n"
)
for province in provinces:
f.write(f"## {province_name.get(province, province)}\n\n")
f.write(f"}.svg)\n\n")
f.write(f"}-daily.svg)\n\n")