def pmd_exog_compare(f, k=1):
from timemachines.skatertools.evaluation.evaluators import evaluate_mean_absolute_error
from timemachines.skatertools.evaluation.evaluators import hospital_with_exog
y, a = hospital_with_exog(k=k)
y0 = [yi[0] for yi in y]
r = 0.1 # Doesn't matter?
err1 = evaluate_mean_absolute_error(f=f, k=k, y=y0, r=r, n_burn=250)
err2 = evaluate_mean_absolute_error(f=f, k=k, y=y, r=r, n_burn=250)
err3 = evaluate_mean_absolute_error(f=f, k=k, y=y, r=r, a=a, n_burn=250)
errlv = evaluate_mean_absolute_error(f=empirical_last_value,
k=k,
y=y,
r=r,
a=a,
n_burn=250)
print('----------------')
print("Error w/o exogenous = " + str(err1))
print("Error w exogenous = " + str(err2))
print("Error w exo + known = " + str(err3))
print("Error last val cache = " + str(errlv))
if __name__ == '__main__':
f = pmd_exogenous
if True:
prior_plot_exogenous(f=f, k=1, n=200)
if True:
prior_plot(f=f, k=1, n=200)
param_names = ['changepoint_prior_scale', 'seasonality_prior_scale']
return fbprophet_hyperparam_skater_factory(y=y0,
s=s,
k=k,
a=None,
t=t,
e=e,
r=r,
param_names=param_names,
recursive=False)
PROPHET_R2_SKATERS = [
fbprophet_exogenous_r2, fbprophet_known_r2, fbprophet_univariate_r2,
fbprophet_recursive_r2
]
else:
PROPHET_SKATERS_SINGULAR = []
PROPHET_R2_SKATERS = []
if __name__ == '__main__':
from timemachines.skatertools.data import hospital_with_exog
from timemachines.skatertools.visualization.priorplot import prior_plot
import matplotlib.pyplot as plt
k = 1
y, a = hospital_with_exog(k=k, n=450, offset=True)
f = fbprophet_exogenous
err2 = prior_plot(f=f, k=k, y=y, n=450, n_plot=50)
print(err2)
plt.show()
pass
if not s:
s = dict(y=[])
if y0 is None:
return None, None, s # Ignore suggestion to fit offline
# Update buffer
s['y'].append(y0)
if len(s['y']) > max_buffer_len + 1000:
s['y'] = s['y'][-max_buffer_len:]
# Fit and predict, if warm, or just last value
if len(s['y']) < max(n_warm, MIN_N_WARM):
return [y0] * k, [abs(y0)] * k, s
else:
with no_stdout_stderr():
kwargs = deepcopy(DIVINE_MODEL)
if model_params:
kwargs.update(**model_params)
model = dv.divinity(forecast_length=k, **kwargs)
model.fit(np.array(s['y']))
x = list(model.predict())
x_std = [1.0] * k # TODO: fixme
return x, x_std, s
if __name__ == '__main__':
err = prior_plot(f=divinity_univariate_factory, k=5, n=500)
pass