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utils.py
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utils.py
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import os
import matplotlib as mpl
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
import math
from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
from statsmodels.tsa.arima_model import ARIMA
from sklearn.svm import SVR
from sklearn.linear_model import *
from sklearn.tree import *
from sklearn.ensemble import *
from sklearn.preprocessing import StandardScaler
from datetime import datetime, timedelta
def update_data():
os.system("cd COVID-19 && git pull origin master")
def get_predict_by_step(ml_model, data, start, current, end=None, day_lag=10, return_param=False):
if end == None:
end = data.shape[1]
predict_data = data[:,:current]
list_param_byu = []
for day in range(predict_data.shape[1]-day_lag, end-day_lag):
return_result = ml_model.predict([predict_data[0][day:day+day_lag], predict_data[1][day:day+day_lag], predict_data[2][day:day+day_lag]], return_param=return_param)
if return_param:
next_day, param_byu = return_result
list_param_byu.append(param_byu[0])
else:
next_day = return_result
next_day = next_day[0][-1]
next_day[1] += next_day[2] + next_day[3]
predict_data = np.append(predict_data, next_day[1:].reshape((-1, 1)), axis=1)
if return_param and current >= start - day_lag:
for k in range(current - start + day_lag):
s = current-k-day_lag
e = current-k
return_result = ml_model.predict([data[0][s:e], data[1][s:e], data[2][s:e]], return_param=return_param)
next_day, param_byu = return_result
list_param_byu.insert(0, param_byu[0])
return predict_data, list_param_byu
def get_predict_result(ml_model, data, start, end=None, step=31, day_lag=10):
predict_data = data[:,:start]
if end == None: end = data.shape[1]
confirmed = [0 for _ in range(end-start)]
recovered = [0 for _ in range(end-start)]
deceased = [0 for _ in range(end-start)]
list_div = [0 for _ in range(end-start)]
for ste in range(start, end-step+1):
tem_predict = data[:,:ste]
for day in range(ste, ste+step):
next_day = ml_model.predict([tem_predict[0][day-day_lag:day], tem_predict[1][day-day_lag:day], tem_predict[2][day-day_lag:day]])[0][-1]
next_day[1] += next_day[2] + next_day[3]
tem_predict = np.append(tem_predict, next_day[1:].reshape((-1, 1)), axis=1)
confirmed[day-start] += next_day[1]
recovered[day-start] += next_day[2]
deceased[day-start] += next_day[3]
list_div[day-start] += 1
confirmed = np.array(confirmed) / np.array(list_div)
recovered = np.array(recovered) / np.array(list_div)
deceased = np.array(deceased) / np.array(list_div)
return np.append(predict_data, [confirmed, recovered, deceased], axis=1)
def get_compare_metric(data, predict_data, start, end):
r_value_0 = r2_score(data[0][start:end],predict_data[0][start:end])
r_value_1 = r2_score(data[1][start:end],predict_data[1][start:end])
r_value_2 = r2_score(data[2][start:end],predict_data[2][start:end])
r_value_3 = r2_score(data[0][start:end] - data[1][start:end] - data[2][start:end],
predict_data[0][start:end] - predict_data[1][start:end] - predict_data[2][start:end])
print("R2:")
print("Total infectious:", r_value_0)
print("Daily infectious", r_value_3)
print("Recovered: ", r_value_1)
print("Deceased: ", r_value_2)
mae_0 = mean_absolute_error(data[0][start:end],predict_data[0][start:end])
mae_1 = mean_absolute_error(data[1][start:end],predict_data[1][start:end])
mae_2 = mean_absolute_error(data[2][start:end],predict_data[2][start:end])
mae_3 = mean_absolute_error(data[0][start:end] - data[1][start:end] - data[2][start:end],
predict_data[0][start:end] - predict_data[1][start:end] - predict_data[2][start:end])
print("MAE:")
print("Total infectious:", mae_0)
print("Daily infectious", mae_3)
print("Recovered: ", mae_1)
print("Deceased: ", mae_2)
rmse_0 = math.sqrt(mean_squared_error(data[0][start:end],predict_data[0][start:end]))
rmse_1 = math.sqrt(mean_squared_error(data[1][start:end],predict_data[1][start:end]))
rmse_2 = math.sqrt(mean_squared_error(data[2][start:end],predict_data[2][start:end]))
rmse_3 = math.sqrt(mean_squared_error(data[0][start:end] - data[1][start:end] - data[2][start:end],
predict_data[0][start:end] - predict_data[1][start:end] - predict_data[2][start:end]))
print("RMSE:")
print("Total infectious:", rmse_0)
print("Daily infectious", rmse_3)
print("Recovered: ", rmse_1)
print("Deceased: ", rmse_2)
mpe_0 = np.mean(1-(predict_data[0][start:end] / data[0][start:end]))
mpe_3 = np.mean(1-((predict_data[0][start:end]-predict_data[1][start:end]-predict_data[2][start:end]) / (data[0][start:end]-predict_data[1][start:end]-predict_data[2][start:end])))
mpe_1 = np.mean(1-(predict_data[1][start:end] / data[1][start:end]))
mpe_2 = np.mean(1-(predict_data[2][start:end] / data[2][start:end]))
print("MAPE:")
print("Total infectious:", abs(mpe_0))
print("Daily infectious", abs(mpe_3))
print("Recovered: ", abs(mpe_1))
print("Deceased: ", abs(mpe_2))
return np.array([[r_value_0, r_value_3, r_value_1, r_value_2],
[mae_0, mae_3, mae_1, mae_2],
[rmse_0, rmse_3, rmse_1, rmse_2],
[abs(mpe_0), abs(mpe_3), abs(mpe_1), abs(mpe_2)]])
def plotParam(list_param_byu, start, end, country="world", idx=""):
list_param_byu = np.array(list_param_byu)
figure, axes = plt.subplots(nrows=3, ncols=1, sharex=True)
axes.ravel()[2].set_xlabel("Days")
axes.ravel()[0].plot(list(range(start, end)), list_param_byu[:, 0], label="beta", color="red")
axes.ravel()[0].set_title(r"$\beta$", fontdict={'fontsize': mpl.rcParams['axes.titlesize'],
'fontweight': mpl.rcParams['axes.titleweight'],
'color': 'red',
'verticalalignment': 'baseline',
'horizontalalignment': 'center'})
axes.ravel()[1].plot(list(range(start, end)), list_param_byu[:, 1], label="gamma", color="blue")
axes.ravel()[1].set_title(r"$\gamma$", fontdict={'fontsize': mpl.rcParams['axes.titlesize'],
'fontweight': mpl.rcParams['axes.titleweight'],
'color': 'blue',
'verticalalignment': 'baseline',
'horizontalalignment': 'center'})
axes.ravel()[2].plot(list(range(start, end)), list_param_byu[:, 2], label="muy", color="gray")
axes.ravel()[2].set_title(r"$\mu$", fontdict={'fontsize': mpl.rcParams['axes.titlesize'],
'fontweight': mpl.rcParams['axes.titleweight'],
'color': 'gray',
'verticalalignment': 'baseline',
'horizontalalignment': 'center'})
plt.tight_layout()
if country:
country += "/"
plt.savefig('images/%splot_param%s.png'%(country,idx))
plt.close()
def plot(data, predict_data, start, end, country="world", idx=""):
fig, ax1 = plt.subplots(1,1)
fig.suptitle('Comparison of real and predicted daily infectious cases')
ax1.set_xlabel("Days")
ax1.set_ylabel("People")
real_plot = data[0][start:end] - data[1][start:end] - data[2][start:end]
ax1.plot(list(range(start, end)), real_plot, label="Real daily infectious")
predict_plot = predict_data[0][start:end] - predict_data[1][start:end] - predict_data[2][start:end]
ax1.plot(list(range(start, end)), predict_plot, label="Predicted daily infectious")
plt.legend()
if country:
if not os.path.exists('images/%s'%country):
os.makedirs('images/%s'%country)
country += "/"
plt.savefig('images/%splot_daily_infectious%s.png'%(country,idx))
plt.close()
#################################################
fig2, ax2 = plt.subplots(1,1)
fig2.suptitle('Comparison of real and predicted total infectious cases')
ax2.set_xlabel("Days")
ax2.set_ylabel("People")
real_plot = data[0][start:end]
ax2.plot(list(range(start, end)), real_plot, label="Real total infectious")
predict_plot = predict_data[0][start:end]
ax2.plot(list(range(start, end)), predict_plot, label="Predicted total infectious")
plt.legend()
plt.savefig('images/%splot_total_infectious%s.png'%(country,idx))
plt.close()
#################################################
fig3, ax3 = plt.subplots(1,1)
fig3.suptitle('Comparison of real and predicted recovered cases')
ax3.set_xlabel("Days")
ax3.set_ylabel("People")
real_plot = data[1][start:end]
ax3.plot(list(range(start, end)), data[1][start:end], label="Real recovered")
predict_plot = predict_data[1][start:end]
ax3.plot(list(range(start, end)), predict_data[1][start:end], label="Predicted recovered")
plt.legend()
plt.savefig('images/%splot_recovered%s.png'%(country,idx))
plt.close()
#################################################
fig4, ax4 = plt.subplots(1,1)
fig4.suptitle('Comparison of real and predicted deceased cases')
ax4.set_xlabel("Days")
ax4.set_ylabel("People")
real_plot = data[2][start:end]
ax4.plot(list(range(start, end)), data[2][start:end], label="Real deceased")
predict_plot = predict_data[2][start:end]
ax4.plot(list(range(start, end)), predict_data[2][start:end], label="Predicted deceased")
plt.legend()
plt.savefig('images/%splot_deceased%s.png'%(country,idx))
plt.close()
def get_all_compare(data, ml_model, start, end, step=1, day_lag=10):
print("****** Our Model ******")
predict_data = get_predict_result(ml_model, data, start, end=end, step=step, day_lag=day_lag)
get_compare_metric(data, predict_data, start, end)
print("****** ARIMA ******")
predict_data = predict_arima(data, start, end, step=step)
get_compare_metric(data, predict_data, start, end)
print("****** Ridge ******")
predict_data = predict_ridge(data, start, end, step=step, day_lag=day_lag)
get_compare_metric(data, predict_data, start, end)
print("****** Lasso ******")
predict_data = predict_lasso(data, start, end, step=step, day_lag=day_lag)
get_compare_metric(data, predict_data, start, end)
print("****** SVR ******")
predict_data = predict_svr(data, start, end, step=step, day_lag=day_lag)
get_compare_metric(data, predict_data, start, end)
print("****** Decision Tree Regressor ******")
predict_data = predict_dtr(data, start, end, step=step, day_lag=day_lag)
get_compare_metric(data, predict_data, start, end)
print("****** Random Forest Regressor ******")
predict_data = predict_rfr(data, start, end, step=step, day_lag=day_lag)
get_compare_metric(data, predict_data, start, end)
print("****** Gradient Boosting Regressor ******")
predict_data = predict_gbr(data, start, end, step=step, day_lag=day_lag)
get_compare_metric(data, predict_data, start, end)
def predict_arima(data, start, end, step):
if step > (end-start): return []
sd = datetime.strptime("2020-01-22", "%Y-%m-%d")
date_series = []
for i in range(start):
date_series.append(sd+timedelta(days=i))
confirmed = [0 for _ in range(end-start)]
recovered = [0 for _ in range(end-start)]
deceased = [0 for _ in range(end-start)]
list_div = [0 for _ in range(end-start)]
for ste in range(start, end-step+1):
arima_0 = ARIMA(data[0][:ste], order=(1,0,0), dates=date_series[:ste], freq="D")
arima_1 = ARIMA(data[1][:ste], order=(1,0,0), dates=date_series[:ste], freq="D")
arima_2 = ARIMA(data[2][:ste], order=(1,0,0), dates=date_series[:ste], freq="D")
arima_result_0 = arima_0.fit(disp=0)
arima_result_1 = arima_1.fit(disp=0)
arima_result_2 = arima_2.fit(disp=0)
a = arima_result_0.predict(start=(ste), end=(ste+step-1), dynamic=False)
b = arima_result_1.predict(start=(ste), end=(ste+step-1), dynamic=False)
c = arima_result_2.predict(start=(ste), end=(ste+step-1), dynamic=False)
for i, x in enumerate(a):
confirmed[ste-start+i] += x
list_div[ste-start+i] += 1
for i, x in enumerate(b):
recovered[ste-start+i] += x
for i, x in enumerate(c):
deceased[ste-start+i] += x
confirmed = np.array(confirmed) / np.array(list_div)
recovered = np.array(recovered) / np.array(list_div)
deceased = np.array(deceased) / np.array(list_div)
return np.array([
np.append(data[0][:start], confirmed),
np.append(data[1][:start], recovered),
np.append(data[2][:start], deceased)
])
def predict_ridge(data, start, end, step, day_lag):
if step > (end-start): return []
predict_data = []
Ridge_model = Ridge(solver="svd")
for f in range(data.shape[0]):
train_data = data[f]
res = [0 for _ in range(end-start)]
list_div = [0 for _ in range(end-start)]
for ste in range(start, end-step+1):
X = []
y = []
for i in range(ste-day_lag):
X.append(train_data[i:i+day_lag])
y.append([train_data[i+day_lag]])
Ridge_model.fit(X, y)
tem_predict = data[f,:ste]
for day in range(ste, ste+step):
next_day = Ridge_model.predict([tem_predict[day-day_lag:day]])
tem_predict = np.append(tem_predict, next_day)
for i, x in enumerate(tem_predict[ste:]):
res[ste-start+i] += x
list_div[ste-start+i] += 1
predict_data.append(np.array(res) / np.array(list_div))
return np.append(data[:,:start], predict_data, axis=1)
def predict_lasso(data, start, end, step, day_lag):
if step > (end-start): return []
predict_data = []
Lasso_model = Lasso()
for f in range(data.shape[0]):
train_data = data[f]
res = [0 for _ in range(end-start)]
list_div = [0 for _ in range(end-start)]
for ste in range(start, end-step+1):
X = []
y = []
for i in range(ste-day_lag):
X.append(train_data[i:i+day_lag])
y.append([train_data[i+day_lag]])
Lasso_model.fit(X, y)
tem_predict = data[f,:ste]
for day in range(ste, ste+step):
next_day = Lasso_model.predict([tem_predict[day-day_lag:day]])
tem_predict = np.append(tem_predict, next_day)
for i, x in enumerate(tem_predict[ste:]):
res[ste-start+i] += x
list_div[ste-start+i] += 1
predict_data.append(np.array(res) / np.array(list_div))
return np.append(data[:,:start], predict_data, axis=1)
def predict_svr(data, start, end, step, day_lag):
if step > (end-start): return []
predict_data = []
SVR_model = SVR(kernel="rbf")
for f in range(data.shape[0]):
train_data = data[f]
res = [0 for _ in range(end-start)]
list_div = [0 for _ in range(end-start)]
for ste in range(start, end-step+1):
X = []
y = []
for i in range(ste-day_lag):
X.append(train_data[i:i+day_lag])
y.append([train_data[i+day_lag]])
sc_X = StandardScaler()
sc_y = StandardScaler()
X = sc_X.fit_transform(X)
y = sc_y.fit_transform(y)
SVR_model.fit(X, y)
tem_predict = data[f,:ste]
for day in range(ste, ste+step):
next_day = SVR_model.predict([tem_predict[day-day_lag:day]])
next_day = sc_y.inverse_transform(next_day)
tem_predict = np.append(tem_predict, next_day)
for i, x in enumerate(tem_predict[ste:]):
res[ste-start+i] += x
list_div[ste-start+i] += 1
predict_data.append(np.array(res) / np.array(list_div))
return np.append(data[:,:start], predict_data, axis=1)
def predict_dtr(data, start, end, step, day_lag):
if step > (end-start): return []
predict_data = []
DTR_model = DecisionTreeRegressor()
for f in range(data.shape[0]):
train_data = data[f]
res = [0 for _ in range(end-start)]
list_div = [0 for _ in range(end-start)]
for ste in range(start, end-step+1):
X = []
y = []
for i in range(ste-day_lag):
X.append(train_data[i:i+day_lag])
y.append([train_data[i+day_lag]])
DTR_model.fit(X, y)
tem_predict = data[f,:ste]
for day in range(ste, ste+step):
next_day = DTR_model.predict([tem_predict[day-day_lag:day]])
tem_predict = np.append(tem_predict, next_day)
for i, x in enumerate(tem_predict[ste:]):
res[ste-start+i] += x
list_div[ste-start+i] += 1
predict_data.append(np.array(res) / np.array(list_div))
return np.append(data[:,:start], predict_data, axis=1)
def predict_rfr(data, start, end, step, day_lag):
if step > (end-start): return []
predict_data = []
RFR_model = RandomForestRegressor()
for f in range(data.shape[0]):
train_data = data[f]
res = [0 for _ in range(end-start)]
list_div = [0 for _ in range(end-start)]
for ste in range(start, end-step+1):
X = []
y = []
for i in range(ste-day_lag):
X.append(train_data[i:i+day_lag])
y.append([train_data[i+day_lag]])
RFR_model.fit(X, y)
tem_predict = data[f,:ste]
for day in range(ste, ste+step):
next_day = RFR_model.predict([tem_predict[day-day_lag:day]])
tem_predict = np.append(tem_predict, next_day)
for i, x in enumerate(tem_predict[ste:]):
res[ste-start+i] += x
list_div[ste-start+i] += 1
predict_data.append(np.array(res) / np.array(list_div))
return np.append(data[:,:start], predict_data, axis=1)
def predict_gbr(data, start, end, step, day_lag):
if step > (end-start): return []
predict_data = []
GBR_model = GradientBoostingRegressor()
for f in range(data.shape[0]):
train_data = data[f]
res = [0 for _ in range(end-start)]
list_div = [0 for _ in range(end-start)]
for ste in range(start, end-step+1):
X = []
y = []
for i in range(ste-day_lag):
X.append(train_data[i:i+day_lag])
y.append([train_data[i+day_lag]])
GBR_model.fit(X, y)
tem_predict = data[f,:ste]
for day in range(ste, ste+step):
next_day = GBR_model.predict([tem_predict[day-day_lag:day]])
tem_predict = np.append(tem_predict, next_day)
for i, x in enumerate(tem_predict[ste:]):
res[ste-start+i] += x
list_div[ste-start+i] += 1
predict_data.append(np.array(res) / np.array(list_div))
return np.append(data[:,:start], predict_data, axis=1)