def normalizer(ver=2, plot=False):
ts, num_req = read_data()
print('-----------------------')
if ver == 2:
min1, max1, min2, max2, num_req_normalize, MaxAbsScalerObj = norm_v2(
num_req)
elif ver == 1:
min1, max1, min2, max2, num_req_normalize, MaxAbsScalerObj = norm_v1(
num_req)
print('min = ', min2, ' max = ', max2)
#num_req_normalize = perform_knn(num_req_normalize)
print('min = ', min(num_req_normalize), ' max = ', max(num_req_normalize))
if plot:
fig = plt.figure(figsize=(8, 6))
plt.subplot(211)
plt.plot(ts, num_req, color='red', label='REQ-data original')
plt.ylabel('Num of REQ original')
plt.legend()
plt.xlabel('Time symbol')
plt.subplot(212)
plt.plot(ts,
num_req_normalize,
color='green',
label='REQ-data normalized')
plt.ylabel('Num of REQ normalized')
plt.legend()
plt.xlabel('Time symbol')
plt.pause(3)
plt.close()
return ts, num_req_normalize, MaxAbsScalerObj
def split_data(plot=False):
ram_data, cpu_data = read_data()
ts = ram_data[:, 0]
ram_values = ram_data[:, 1]
cpu_values = cpu_data[:, 1]
l = len(ts)
print('length of total data are ', len(ts), len(ram_values),
len(cpu_values))
return ts,cpu_values,\
ram_values
def split_data(imf_index, plot=False):
ram_data, cpu_data = read_data(imf_index)
ts = ram_data[:, 0]
ram_values = ram_data[:, 1]
cpu_values = cpu_data[:, 1]
l = len(ts)
print('length of total data are ', len(ts), len(ram_values),
len(cpu_values))
factor1 = 0.8 # train
factor2 = 0.9 # valuation
ts_train = ts[:int(factor1 * l)]
ts_valid = ts[int(factor1 * l) - 1:int(factor2 * l)]
ts_test = ts[int(factor2 * l) - 1:]
cpu_train = cpu_values[:int(factor1 * l)]
cpu_valid = cpu_values[int(factor1 * l) - 1:int(factor2 * l)]
cpu_test = cpu_values[int(factor2 * l) - 1:]
ram_train = ram_values[:int(factor1 * l)]
ram_valid = ram_values[int(factor1 * l) - 1:int(factor2 * l)]
ram_test = ram_values[int(factor2 * l) - 1:]
if plot:
plt.subplot(2, 1, 1)
plt.plot(ts_train, cpu_train, color='red', label='cpu-train-data')
plt.plot(ts_valid,
cpu_valid,
color='green',
label='cpu-validation-data')
plt.plot(ts_test, cpu_test, color='blue', label='cpu-test-data')
plt.ylabel('CPU Req')
plt.xlabel('Time symbol')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(ts_train, ram_train, color='red', label='RAM-train-data')
plt.plot(ts_valid,
ram_valid,
color='green',
label='RAM-validation-data')
plt.plot(ts_test, ram_test, color='blue', label='RAM-test-data')
plt.ylabel('RAM Req')
plt.legend()
plt.xlabel('Time symbol')
plt.show()
return ts,ts_train,ts_valid,ts_test,cpu_values,cpu_train,cpu_valid,cpu_test,\
ram_values,ram_train,ram_valid,ram_test
def normalizer(plot=False):
data = read_data()
ts = data[:, 0]
num_req = data[:, 1]
print('-----------------------')
from sklearn import preprocessing
# max_abs_scaler = preprocessing.StandardScaler()
# num_req_normalize = max_abs_scaler.fit_transform(num_req.reshape(-1, 1))
# print('-----------------------')
minMaxScaler = preprocessing.MinMaxScaler()
num_req_normalize = minMaxScaler.fit_transform(num_req.reshape(-1, 1))
print('-----------------------', len(ts), len(num_req_normalize))
print('min = ', min(num_req_normalize), ' max = ', max(num_req_normalize))
print('-----------------------')
# min1=min(num_req)
# max1=max(num_req)
# #num_req_normalize = num_req / (max1 - min1)
# min2 = min(num_req_normalize)
# max2 = max(num_req_normalize)
# print('min = ', min2, ' max = ', max2)
num_req_normalize = perform_knn(num_req_normalize)
print('min = ', min(num_req_normalize), ' max = ', max(num_req_normalize))
if plot:
fig = plt.figure(figsize=(8, 6))
plt.subplot(211)
plt.plot(ts, num_req, color='red', label='REQ-data original')
plt.ylabel('Num of REQ original')
plt.legend()
plt.xlabel('Time symbol')
plt.subplot(212)
plt.plot(ts,
num_req_normalize,
color='green',
label='REQ-data normalized')
plt.ylabel('Num of REQ normalized')
plt.legend()
plt.xlabel('Time symbol')
plt.pause(3)
plt.close()
return ts, num_req_normalize, minMaxScaler
if save:
plt.savefig(date.strftime("%Y-%m-%d_") +
'COVID-19_Death_Recovered.svg',
transparent=True)
if __name__ == '__main__':
# start_date = datetime.date(2020, 5, 5)
# for i in range(1):
# data_date = start_date + datetime.timedelta(days=14*i)
# world_daily_data_pd = read_data(date=data_date, use_daily=True)
# sorted_world_daily_data_pd = world_daily_data_pd.sort_values(by='Confirmed', ascending=False)
# plot_rose_1(sorted_world_daily_data_pd, date=data_date)
# plot_death_recovered_rate(sorted_world_daily_data_pd, date=data_date)
world_ts_confirmed_data_pd, world_ts_deaths_data_pd, world_ts_recovered_data_pd = read_data(
use_daily=False)
world_sorted_ts_confirmed_data_pd = \
world_ts_confirmed_data_pd.sort_values(by=world_ts_confirmed_data_pd.columns[-1], ascending=False)
world_sorted_ts_deaths_data_pd = \
world_ts_deaths_data_pd.sort_values(by=world_ts_deaths_data_pd.columns[-1], ascending=False)
world_sorted_ts_recovered_data_pd = \
world_ts_recovered_data_pd.sort_values(by=world_ts_recovered_data_pd.columns[-1], ascending=False)
world_sorted_week_confirmed_climb_rate_pd = calc_climb_rate(
world_sorted_ts_confirmed_data_pd)
world_sorted_week_deaths_climb_rate_pd = calc_climb_rate(
world_sorted_ts_deaths_data_pd)
world_sorted_week_recovered_climb_rate_pd = calc_climb_rate(
world_sorted_ts_recovered_data_pd)
plot_climb_rate(world_sorted_week_confirmed_climb_rate_pd,