def hospital_mean_square_error(f,
k: int = 1,
n=120,
n_burn=30,
r=None) -> float:
""" Useful for a quick test of a skater, univariate and random hyper-param """
y = hospital()[:n]
return evaluate_mean_squared_error(f=f, y=y, k=k, r=r, n_burn=n_burn)
def test_univariate_without_time(show=False):
k = 3
n = 100
y = hospital(n=n)
x, x_std, forecast, m = prophet_iskater_factory(y=y, k=k)
assert len(x) == k
x1, x_std1, forecast1, m1 = prophet_fit_and_predict_simple(y=y, k=k)
assert nearlysame(x1, x, 0.0001)
if show:
m.plot(forecast)
m1.plot(forecast1)
import matplotlib.pyplot as plt
plt.show()
def test_univariate_with_time_and_advance_time(show=False):
k = 3
n = 100
y = hospital(n=n)
t = [i * 15 * 50 for i in range(len(y) + k)]
x, x_std, forecast, m = prophet_iskater_factory(y=y, k=k, t=t)
assert len(x) == k
x1, x_std1, forecast1, m1 = prophet_fit_and_predict_with_time_and_advance_time(
y=y, k=k, t=t)
assert nearlysame(x1, x, 0.0001)
if show:
m.plot(forecast)
m1.plot(forecast1)
import matplotlib.pyplot as plt
plt.show()
def next_opinionated_forecast(n_train, k, n_recent, multiple, name=None):
while True:
try:
from microprediction import MicroReader
n_obs = 0
while n_obs < 1000:
mr = MicroReader()
if name is None:
names = mr.get_stream_names()
random_name = random.choice(names)
else:
random_name = name
lag_values, lag_times = mr.get_lagged_values_and_times(
name=random_name, count=2000)
y = list(reversed(lag_values))
t = list(reversed(lag_times))
n_obs = len(y)
except ImportError:
from timemachines.data.real import hospital
y = hospital()
t = [15 * 60 * i for i in range(len(y))]
name = 'hospital'
for i in [100 * j for j in range(10)]:
print('Looking at ' + random_name + ' ' + str(i) + '/1000')
if len(y) > i + 2 * k + n_train:
y_fit = y[i:i + n_train]
t_fit = t[i:i + n_train]
y_hats, _, forecast, m = prophet_iskater_factory(y=y_fit,
k=k,
t=t_fit)
if is_opinonated(y=y_fit,
forecast=forecast,
k=k,
n_recent=n_recent,
multiple=multiple):
y_3avg = np.mean(y[i + n_train - 3:i +
n_train]) # avg of last three points
y_true_mean = np.mean(y[i + n_train + k - 1:i + n_train +
k + 1]) # avg of 3 future points
error = (y_hats[-1] - y_true_mean) / abs(0.01 + y_3avg)
avg_error = (y_3avg - y_true_mean) / abs(0.01 + y_3avg)
return forecast, m, random_name, error, avg_error, y[
i + n_train:i + n_train + k]
print(random_name + ' is okay.')
return None, None, None