def example_pmd():
s = {}
y, a = hospital_with_exog(k=3)
x = [pmd_exogenous(y=yj, s=s, k=3, a=aj) for yj, aj in zip(y[:500], a)]
return s
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
t: T_TYPE = None,
e: E_TYPE = None,
r: R_TYPE = None):
""" Exponential moving average
r weight to place on existing anchor point
"""
assert r is not None
y0 = wrap(y)[0]
if not s.get('rho'):
s = {'x': y0, 'rho': r}
assert 0 <= s['rho'] <= 1, 'Expecting rho=r to be between 0 and 1'
else:
assert abs(r - s['rho']) < 1e-6, 'rho=r is immutable'
if y0 is None:
return None, s, None
else:
s['x'] = s['rho'] * s['x'] + (1 - s['rho']) * y0 # Make me better !
x = [s['x'] * k]
return x, [1.0] * k, s
if __name__ == '__main__':
from timemachines.skatertools.data import hospital_with_exog
from timemachines.skatertools.evaluation.evaluators import evaluate_mean_absolute_error
k = 3
y, a = hospital_with_exog(k=k, n=500)
f = trivial_ema_r1
err1 = evaluate_mean_absolute_error(f=f, k=k, y=y, a=a, r=0.9, n_burn=50)