def test_random_skater(show=False):
k = 3
n = 100
f = random.choice(MERLION_SKATERS)
print(f.__name__)
y = hospital(n=n)
neg_y = [-yt for yt in y]
e = [-1] * 90 + [100] + [-1] * 90
x, x_std = prior(f=f, y=neg_y, k=k, e=e)
def test_univariate_without_time(show=False):
k = 3
n = 100
y = hospital(n=n)
x, x_std, forecast, m = nprophet_iskater_factory(y=y, k=k)
assert len(x) == k
x1, x_std1, forecast1, m1 = nprophet_fit_and_predict_simple(y=y, k=k)
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 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)
model = NeuralProphet(**used_params)
model.set_log_level(log_level='CRITICAL')
df = pd.DataFrame(columns=['y'], data=y)
df['ds'] = pd.date_range(start='2021-01-01',
periods=len(y),
freq=freq)
metrics = model.fit(df, freq=freq, epochs=40, use_tqdm=False)
future = model.make_future_dataframe(df)
forecast = model.predict(future)
x = [
forecast['yhat' + str(j + 1)].values[-k + j] for j in range(k)
]
x_std = [1.0] * k
return x, x_std, forecast, model
if __name__ == '__main__':
from timemachines.skatertools.data.real import hospital
k = 3
n = 500
y = hospital(n=n)[-200:]
x, x_std, forecast, m = nprophet_iskater_factory(y=y, k=k)
print(x)
assert len(x) == k
x1, x_std1, forecast1, m1 = nprophet_fit_and_predict_simple(y=y, k=k)
if True:
m.plot(forecast)
m1.plot(forecast1)
import matplotlib.pyplot as plt
plt.show()
model_params=model_params)
s['m'] = True # Flag indicating a model has been fit (there is no point keeping the model itself, however)
else:
x = [y[0]] * k
x_std = None
# Get running mean prediction errors from the prediction parade
x_resid, x_resid_std, s['p'] = parade(p=s['p'], x=x, y=y[0])
x_resid = nonecast(x_resid, y[0])
x_resid_std = nonecast(x_resid_std, 1.0)
# Compute center of mass between bias-corrected and uncorrected predictions
x_corrected = np.array(x_resid) + np.array(x)
x_center = nonecenter(m=[emp_mass, 1 - emp_mass],
x=[x_corrected, x])
x_std_center = nonecenter(m=[emp_std_mass, 1 - emp_std_mass],
x=[x_resid_std, x_std])
return x_center, x_std_center, s
if __name__ == '__main__':
from timemachines.skatertools.data.real import hospital
from timemachines.skatertools.evaluation.evaluators import evaluate_mean_absolute_error
k = 5
y = hospital(n=420)
f = nprophet_skater_factory
err2 = evaluate_mean_absolute_error(f=f, k=k, y=y, n_burn=400)
print(err2)
