249 lines
8.3 KiB
Python
249 lines
8.3 KiB
Python
#!/usr/bin/python
|
|
import numpy as np
|
|
import pandas as pd
|
|
from csv import reader
|
|
from csv import writer
|
|
from scipy.integrate import solve_ivp
|
|
from scipy.optimize import minimize
|
|
import matplotlib.pyplot as plt
|
|
from datetime import timedelta, datetime
|
|
import argparse
|
|
import sys
|
|
import json
|
|
import ssl
|
|
import urllib.request
|
|
|
|
|
|
def parse_arguments():
|
|
parser = argparse.ArgumentParser()
|
|
|
|
parser.add_argument(
|
|
'--countries',
|
|
action='store',
|
|
dest='countries',
|
|
help='Countries on CSV format. ' +
|
|
'It must exact match the data names or you will get out of bonds error.',
|
|
metavar='COUNTRY_CSV',
|
|
type=str,
|
|
default="")
|
|
|
|
parser.add_argument(
|
|
'--download-data',
|
|
action='store_true',
|
|
dest='download_data',
|
|
help='Download fresh data and then run',
|
|
default=False
|
|
)
|
|
|
|
parser.add_argument(
|
|
'--start-date',
|
|
required=False,
|
|
action='store',
|
|
dest='start_date',
|
|
help='Start date on MM/DD/YY format ... I know ...' +
|
|
'It defaults to first data available 1/22/20',
|
|
metavar='START_DATE',
|
|
type=str,
|
|
default="1/22/20")
|
|
|
|
parser.add_argument(
|
|
'--prediction-days',
|
|
required=False,
|
|
dest='predict_range',
|
|
help='Days to predict with the model. Defaults to 150',
|
|
metavar='PREDICT_RANGE',
|
|
type=int,
|
|
default=150)
|
|
|
|
parser.add_argument(
|
|
'--S_0',
|
|
required=False,
|
|
dest='s_0',
|
|
help='S_0. Defaults to 100000',
|
|
metavar='S_0',
|
|
type=int,
|
|
default=100000)
|
|
|
|
parser.add_argument(
|
|
'--I_0',
|
|
required=False,
|
|
dest='i_0',
|
|
help='I_0. Defaults to 2',
|
|
metavar='I_0',
|
|
type=int,
|
|
default=2)
|
|
|
|
parser.add_argument(
|
|
'--R_0',
|
|
required=False,
|
|
dest='r_0',
|
|
help='R_0. Defaults to 0',
|
|
metavar='R_0',
|
|
type=int,
|
|
default=10)
|
|
|
|
args = parser.parse_args()
|
|
|
|
country_list = []
|
|
if args.countries != "":
|
|
try:
|
|
countries_raw = args.countries
|
|
country_list = countries_raw.split(",")
|
|
except Exception:
|
|
sys.exit("QUIT: countries parameter is not on CSV format")
|
|
else:
|
|
sys.exit("QUIT: You must pass a country list on CSV format.")
|
|
|
|
return (country_list, args.download_data, args.start_date, args.predict_range, args.s_0, args.i_0, args.r_0)
|
|
|
|
|
|
def sumCases_province(input_file, output_file):
|
|
with open(input_file, "r") as read_obj, open(output_file,'w',newline='') as write_obj:
|
|
csv_reader = reader(read_obj)
|
|
csv_writer = writer(write_obj)
|
|
|
|
lines=[]
|
|
for line in csv_reader:
|
|
lines.append(line)
|
|
|
|
i=0
|
|
ix=0
|
|
for i in range(0,len(lines[:])-1):
|
|
if lines[i][1]==lines[i+1][1]:
|
|
if ix==0:
|
|
ix=i
|
|
lines[ix][4:] = np.asfarray(lines[ix][4:],float)+np.asfarray(lines[i+1][4:] ,float)
|
|
else:
|
|
if not ix==0:
|
|
lines[ix][0]=""
|
|
csv_writer.writerow(lines[ix])
|
|
ix=0
|
|
else:
|
|
csv_writer.writerow(lines[i])
|
|
i+=1
|
|
|
|
|
|
def download_data(url_dictionary):
|
|
#Lets download the files
|
|
for url_title in url_dictionary.keys():
|
|
urllib.request.urlretrieve(url_dictionary[url_title], "./data/" + url_title)
|
|
|
|
|
|
def load_json(json_file_str):
|
|
# Loads JSON into a dictionary or quits the program if it cannot.
|
|
try:
|
|
with open(json_file_str, "r") as json_file:
|
|
json_variable = json.load(json_file)
|
|
return json_variable
|
|
except Exception:
|
|
sys.exit("Cannot open JSON file: " + json_file_str)
|
|
|
|
|
|
class Learner(object):
|
|
def __init__(self, country, loss, start_date, predict_range,s_0, i_0, r_0):
|
|
self.country = country
|
|
self.loss = loss
|
|
self.start_date = start_date
|
|
self.predict_range = predict_range
|
|
self.s_0 = s_0
|
|
self.i_0 = i_0
|
|
self.r_0 = r_0
|
|
|
|
|
|
def load_confirmed(self, country):
|
|
df = pd.read_csv('data/time_series_19-covid-Confirmed-country.csv')
|
|
country_df = df[df['Country/Region'] == country]
|
|
return country_df.iloc[0].loc[self.start_date:]
|
|
|
|
|
|
def load_recovered(self, country):
|
|
df = pd.read_csv('data/time_series_19-covid-Recovered-country.csv')
|
|
country_df = df[df['Country/Region'] == country]
|
|
return country_df.iloc[0].loc[self.start_date:]
|
|
|
|
|
|
def load_dead(self, country):
|
|
df = pd.read_csv('data/time_series_19-covid-Deaths-country.csv')
|
|
country_df = df[df['Country/Region'] == country]
|
|
return country_df.iloc[0].loc[self.start_date:]
|
|
|
|
|
|
def extend_index(self, index, new_size):
|
|
values = index.values
|
|
current = datetime.strptime(index[-1], '%m/%d/%y')
|
|
while len(values) < new_size:
|
|
current = current + timedelta(days=1)
|
|
values = np.append(values, datetime.strftime(current, '%m/%d/%y'))
|
|
return values
|
|
|
|
def predict(self, beta, gamma, data, recovered, death, country, s_0, i_0, r_0):
|
|
new_index = self.extend_index(data.index, self.predict_range)
|
|
size = len(new_index)
|
|
def SIR(t, y):
|
|
S = y[0]
|
|
I = y[1]
|
|
R = y[2]
|
|
return [-beta*S*I, beta*S*I-gamma*I, gamma*I]
|
|
extended_actual = np.concatenate((data.values, [None] * (size - len(data.values))))
|
|
extended_recovered = np.concatenate((recovered.values, [None] * (size - len(recovered.values))))
|
|
extended_death = np.concatenate((death.values, [None] * (size - len(death.values))))
|
|
return new_index, extended_actual, extended_recovered, extended_death, solve_ivp(SIR, [0, size], [s_0,i_0,r_0], t_eval=np.arange(0, size, 1))
|
|
|
|
|
|
def train(self):
|
|
recovered = self.load_recovered(self.country)
|
|
death = self.load_dead(self.country)
|
|
data = (self.load_confirmed(self.country) - recovered - death)
|
|
|
|
optimal = minimize(loss, [0.001, 0.001], args=(data, recovered, self.s_0, self.i_0, self.r_0), method='L-BFGS-B', bounds=[(0.00000001, 0.4), (0.00000001, 0.4)])
|
|
print(optimal)
|
|
beta, gamma = optimal.x
|
|
new_index, extended_actual, extended_recovered, extended_death, prediction = self.predict(beta, gamma, data, recovered, death, self.country, self.s_0, self.i_0, self.r_0)
|
|
df = pd.DataFrame({'Infected data': extended_actual, 'Recovered data': extended_recovered, 'Death data': extended_death, 'Susceptible': prediction.y[0], 'Infected': prediction.y[1], 'Recovered': prediction.y[2]}, index=new_index)
|
|
fig, ax = plt.subplots(figsize=(15, 10))
|
|
ax.set_title(self.country)
|
|
df.plot(ax=ax)
|
|
print(f"country={self.country}, beta={beta:.8f}, gamma={gamma:.8f}, r_0:{(beta/gamma):.8f}")
|
|
fig.savefig(f"{self.country}.png")
|
|
|
|
|
|
def loss(point, data, recovered, s_0, i_0, r_0):
|
|
size = len(data)
|
|
beta, gamma = point
|
|
def SIR(t, y):
|
|
S = y[0]
|
|
I = y[1]
|
|
R = y[2]
|
|
return [-beta*S*I, beta*S*I-gamma*I, gamma*I]
|
|
solution = solve_ivp(SIR, [0, size], [s_0,i_0,r_0], t_eval=np.arange(0, size, 1), vectorized=True)
|
|
l1 = np.sqrt(np.mean((solution.y[1] - data)**2))
|
|
l2 = np.sqrt(np.mean((solution.y[2] - recovered)**2))
|
|
alpha = 0.1
|
|
return alpha * l1 + (1 - alpha) * l2
|
|
|
|
|
|
def main():
|
|
|
|
countries, download, startdate, predict_range , s_0, i_0, r_0 = parse_arguments()
|
|
|
|
if download:
|
|
data_d = load_json("./data_url.json")
|
|
download_data(data_d)
|
|
|
|
sumCases_province('data/time_series_19-covid-Confirmed.csv', 'data/time_series_19-covid-Confirmed-country.csv')
|
|
sumCases_province('data/time_series_19-covid-Recovered.csv', 'data/time_series_19-covid-Recovered-country.csv')
|
|
sumCases_province('data/time_series_19-covid-Deaths.csv', 'data/time_series_19-covid-Deaths-country.csv')
|
|
|
|
for country in countries:
|
|
learner = Learner(country, loss, startdate, predict_range, s_0, i_0, r_0)
|
|
#try:
|
|
learner.train()
|
|
#except BaseException:
|
|
# print('WARNING: Problem processing ' + str(country) +
|
|
# '. Be sure it exists in the data exactly as you entry it.' +
|
|
# ' Also check date format if you passed it as parameter.')
|
|
|
|
|
|
if __name__ == '__main__':
|
|
main()
|