ML-SVR.py

#!venv/bin/python3

'''
Author:		Massimo Clementi
Created:	2021-02-20

'''

import os
from datetime import datetime
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.svm import SVR

df = pd.read_csv("data_time_series.csv")

'''
 FEATURES THAT CAN BE SELECTED:
	totale_positivi		tamponi
	nuovi_positivi		terapia_intensiva
'''
selected_feature = "nuovi_positivi"


dates = df['data'].values
# pd.to_datetime(dates[0]) + pd.Timedelta(1,'day')
x = np.arange(0,dates.shape[0]).reshape(-1,1)
y = df[[selected_feature]].values.squeeze()

svr_model = SVR(kernel='rbf', C=5e7)
print("Training started at", datetime.now())
svr_model.fit(x,y)
print("        finished at", datetime.now())
print("  sv = ",svr_model.n_support_)

print("Generating plot...")
plt.figure(figsize=(14,5), dpi=100)
plt.plot(pd.to_datetime(dates),y, marker='x', markersize=3, linewidth=0, label=selected_feature+" - actual data")
plt.plot(pd.to_datetime(dates[:-3]), \
    	 np.convolve(np.pad(y,3,mode='edge'), np.ones(7)/7, mode='valid')[:-3], \
    		alpha=0.5, label=selected_feature+" - 7 days moving average")
#plt.plot(pd.to_datetime(dates), np.convolve(np.pad(y,7,mode='edge'), np.ones(15)/15, mode='valid'), \
#    		alpha=0.5, label=selected_feature+" - 15-days window mean")
plt.plot(pd.to_datetime(dates)[:-15], \
    	 np.convolve(np.pad(y,15,mode='edge'), np.ones(31)/31, mode='valid')[:-15], \
    		alpha=0.5, label=selected_feature+" - 31 days moving average")
plt.plot(pd.to_datetime(dates),svr_model.predict(x), color='red', label=selected_feature+" - SVR model")

days_to_predict = 14
x_pred = np.arange(x[-1][0],x[-1][0]+days_to_predict).reshape(-1,1)
x_pred_dates = [pd.to_datetime(dates)[-1] + pd.Timedelta(i,'day') for i in range(0,days_to_predict)]
plt.plot(x_pred_dates,svr_model.predict(x_pred), ':', color='red', label=selected_feature+" - SVR prediction")

plt.legend()

plt.gcf().savefig('Figura-4-SVR.pdf')
print("Saving and opening export folder...")
os.system("open ./")
#plt.show()
plt.close()