def evaluate_sklearn_metric(f, y, k:int, a=None, t=None, e=None, r=None, metric=None, n_burn=None)->float:
""" Compute prior for skater and evaluate an sklearn metric """
assert metric is not None
assert n_burn is not None
x, x_std = prior(f=f, y=y, k=k, a=a, t=t, r=r, e=e )
yt = targets(y)
xk = [ xt[-1] for xt in x ] # k-steps ahead
return metric(yt[n_burn:], xk[n_burn:] )
def prior_plot(f,
y=None,
k=None,
t=None,
e=None,
r=None,
x0=np.nan,
n=150,
n_plot=25):
"""
Apply state machine to univariate series,
Show observations and out of sample predictions predictions
"""
if y is None:
y = brownian_with_noise(n=n)
if t is None:
t = range(len(y))
x, x_std = prior(f=f, y=y, k=k, a=t, t=t, e=e, r=r, x0=x0)
ysf = [[y_] for y_ in y]
xk = [xt[-1] for xt in x]
plot_with_last_value(t=t, x=xk, y=ysf, k=k, n_plot=n_plot)
def prior_plot_exogenous(f,
y=None,
k=None,
a=None,
t=None,
e=None,
r=None,
x0=np.nan,
n=150,
n_plot=25):
"""
Apply state machine to univariate series,
Show observations, out of sample predictions predictions, and exogenous variables
"""
if y is None:
y = brownian_with_exogenous(n)
if t is None:
t = range(len(y))
x, x_std = prior(f=f, y=y, k=k, a=a, t=t, e=e, r=r, x0=x0)
xk = [xt[-1] for xt in x]
plot_with_last_value(t=t, x=xk, y=y, k=k, n_plot=n_plot)