-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathcovid.py
135 lines (115 loc) · 4.23 KB
/
covid.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
#!/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
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(country):
draw_(country, True)
draw_(country, False)
def draw_(country, 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[country].diff().dropna()
model.fit(data)
else:
data = df[country]
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="Actual Value", color="blue")
plt.plot(validating.index, validating, label="Check Value", color="orange")
plt.plot(forecasting.index, forecasting, label="Predict Value", 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"] = ["Microsoft YaHei"]
if isDaily:
plt.title(
f"Daily Confirmed Cases Forecasting - {country}\nARIMA {model.model_.order}x{model.model_.seasonal_order} (R2 = {r2:.6f})"
)
plt.savefig(
os.path.join("figures", f"covid-{adjust_name(country)}-daily.svg"),
bbox_inches="tight",
)
plt.close()
else:
plt.title(
f"Accumulative Confirmed Cases Forecasting - {country}\nARIMA {model.model_.order}x{model.model_.seasonal_order} (R2 = {r2:.6f})"
)
plt.savefig(
os.path.join("figures", f"covid-{adjust_name(country)}.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"
)
.drop(columns=["Province/State", "Lat", "Long"])
.groupby("Country/Region")
.sum()
.transpose()
)
df.index = pd.DatetimeIndex(df.index.map(adjust_date))
countries = df.columns.to_list()
# 线程池
with ProcessPoolExecutor() as pool:
pool.map(draw, countries)
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/actions/workflows/build.yml)\n"
)
f.write(
"[](https://github.com/winsphinx/covid/actions/workflows/check.yml)\n"
)
f.write(
"[](https://github.com/CSSEGISandData/COVID-19)\n"
)
for country in countries:
f.write(f"## {country}\n\n")
f.write(f"}.svg)\n\n")
f.write(f"}-daily.svg)\n\n")