def nproph_check_consistent_usage(y: Y_TYPE, s, k, a):
if y is not None:
assert dimension(y) - 1 == s['immutable']['dim_exog']
if k is not None:
assert k == s['immutable']['k']
if a is not None:
assert dimension(a) == s['immutable']['dim_a']
def pmd_set_immutable(y: Y_TYPE, k: int, a: A_TYPE = None, n_warm: int = 20):
""" Set on the first invocation, when s={} is passed """
return {
'k': k,
'alpha': 0.25, # Determines confidence interval
'n_fit': 250,
'n_warm': n_warm,
'dim_exog': dimension(y) - 1,
'dim_a': dimension(a)
}
def dlm_exogenous_r3(y, s, k, a, t, e, r):
""" One way to use dlm
:returns: x, s', w
"""
if not s:
s = dict()
s['dim'] = dimension(y)
s = dlm_set_exog_hyperparams(s=s, r=r)
y0, exog = split_exogenous(y=y)
s['n_obs'] = 0
s['model'] = quietDlm([], printInfo=False) + trend(
s['trend_degree'], s['discount']) + seasonality(
s['period'], s['discount'])
s['model'] = s['model'] + fixedAutoReg(
degree=s['auto_degree'], name='ar', w=1.0)
if exog:
exog_wrapped = [[None if np.isnan(ex0) else ex0 for ex0 in exog]]
s['model'] = s['model'] + dynamic(features=exog_wrapped,
discount=0.99,
name='exog') # Set's first exog
if y is not None:
y = wrap(y)
assert dimension(y) == s['dim'], 'Cannot change dimension of data sent'
s['n_obs'] += 1
y0, exog = split_exogenous(y=y)
y0_passed_in = None if np.isnan(
y0) else y0 # pydlm uses None for missing values
s['model'].append([y0_passed_in])
if exog:
exog_wrapped = [[None if np.isnan(ex0) else ex0 for ex0 in exog]]
if s['n_obs'] > 1:
s['model'].append(
data=exog_wrapped,
component='exog') # Don't get first exog twice
num_obs = len(s['model'].data) if s.get('model') else 0
if num_obs % s['n_fit'] == s['n_fit'] - 1:
_, _, s = dlm_exogenous_r3(y=None, s=s, k=k, a=a, t=t, e=10, r=r)
s['model'].fitForwardFilter()
return _dlm_exog_prediction_helper(s=s, k=k, y=y)
if y is None:
if dimension(y) == 1:
s['model'].tune(maxit=20)
# Don't tune if exogenous ... haven't got this to work
s['model'].fit()
return None, None, s
def dlm_univariate_r3(y, s: dict, k: int, a=None, t=None, e=None, r=None):
""" Univariate filter
- Uses the discounting method of H/W so, doesn't need to be fit as often
- Discount factors are periodically tuned
- The hyper-parameter controls 'auto_degree', 'trend_degree', 'period'
:returns: x, x_std, s
"""
assert r is not None, 'Requires hyper-parameter (interpreted in dimension 3) '
if not s:
s = dict()
s = dlm_set_univariate_params(s=s, r=r)
s['dim'] = dimension(y)
s['n_obs'] = 0
s['model'] = dlm([], printInfo=False) + trend(
s['trend_degree'], s['discount']) + seasonality(
s['period'], s['discount'])
s['model'] = s['model'] + fixedAutoReg(
degree=s['auto_degree'], name='ar', w=1.0)
if y is not None:
s['n_obs'] += 1
assert isinstance(y, float) or len(
y) == s['dim'], ' Cannot change dimension of input in flight '
y0, exog = split_exogenous(y=y)
y0_passed_in = None if np.isnan(
y0) else y0 # pydlm uses None for missing values
s['model'].append([y0_passed_in])
num_obs = len(s['model'].data) if s.get('model') else 0
if num_obs % s['n_fit'] == s['n_fit'] - 1:
# Perform periodic tuning of discount factors
_, _, s = dlm_univariate_r3(y=None,
s=s,
k=k,
a=a,
t=t,
e=1000,
r=r)
s['model'].fitForwardFilter()
return _dlm_prediction_helper(s=s, k=k, y=y)
if y is None and e > 60:
s['model'].tune() # Tunes discount factors
s['model'].fit()
return None, None, s