8000 Fixing some issues when we have a vector-valued time-varying parameter in the state-space class + started working on multivariate time series in the structural class. by sangrey · Pull Request #490 · pymc-devs/pymc-extras · GitHub
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Fixing some issues when we have a vector-valued time-varying parameter in the state-space class + started working on multivariate time series in the structural class. #490

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Fixing some issues when we have a vector-valued time-varying parameter in the state-space class + started working on multivariate time series in the structural class. #490

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248ad25
Updated the representation file to handle obs_intercept with a time i…
sangrey May 22, 2025
972a821
Merge branch 'main' into sangrey_20250522a
sangrey May 30, 2025
3ea0f4c
Brought the attempt to make structural.py work with multivariate time…
sangrey May 30, 2025
93b244c
Fixed a few other minor syntax errors.
sangrey May 30, 2025
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Fixed a few other minor syntax errors.
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@sangrey
sangrey committed May 30, 2025
commit 93b244c553dae38e8894e9c549c9c807a1331144
42 changes: 19 additions & 23 deletions pymc_extras/statespace/models/structural.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,8 @@
ALL_STATE_DIM,
AR_PARAM_DIM,
LONG_MATRIX_NAMES,
OBS_STATE_AUX_DIM,
OBS_STATE_DIM,
POSITION_DERIVATIVE_NAMES,
TIME_DIM,
)
Expand Down Expand Up @@ -353,7 +355,7 @@ def __init__(
k_endog,
k_states,
k_posdef,
endog_names=None:
endog_names=None,
state_names=None,
data_names=None,
shock_names=None,
Expand Down Expand Up @@ -601,7 +603,6 @@ def _make_combined_name(self):
return name

def __add__(self, other):

if self.endog_names != other.endog_names:
raise ValueError("The endog names must be the same.")

Expand Down Expand Up @@ -859,7 +860,7 @@ def __init__(
measurement_error=False,
combine_hidden_states=False,
obs_state_idxs=np.array([1.0] + [0.0] * (k_states - 1)),
**kwargs
**kwargs,
)

@property
Expand All @@ -870,7 +871,6 @@ def observed_states(self):
def k_endog(self):
return len(self.endog_names)


def populate_component_properties(self):
name_slice = POSITION_DERIVATIVE_NAMES[: self.k_states]
self.param_names = ["initial_trend"]
Expand Down Expand Up @@ -901,7 +901,9 @@ def make_symbolic_graph(self) -> None:
R = R[:, self.innovations_order]
self.ssm["selection", :, :] = R

self.ssm["design", :, :] = np.tile(np.array([1.0] + [0.0] * (self.k_states - 1)), (self.k_endog, 1))
self.ssm["design", :, :] = np.tile(
np.array([1.0] + [0.0] * (self.k_states - 1)), (self.k_endog, 1)
)

if self.k_posdef > 0:
sigma_trend = self.make_and_register_variable("sigma_trend", shape=(self.k_posdef,))
Expand Down Expand Up @@ -949,36 +951,36 @@ class MeasurementError(Component):
"""

def __init__(self, endog_names, time_dim, name: str = "MeasurementError", **kwargs):

k_states = 0
k_posdef = 0
self._time_dim = time_dim

super().__init__(
name,
endog_names=endog_names,
k_endog=len(endog_names),
k_states,
k_posdef,
endog_names=endog_names,
k_endog=len(endog_names),
measurement_error=True,
combine_hidden_states=False,
**kwargs,
)


def populate_component_properties(self):
self.param_info = {
f"{self.name}_covariance": {
"shape": (self.k_endog, self.k_endog),
"constraints": "Positive semi-definite",
"dims": ( OBS_STATE_DIM, OBS_STATE_AUX_DIM,),
"dims": (
OBS_STATE_DIM,
OBS_STATE_AUX_DIM,
),
}
}
self.param_names = list(self.param_info.keys())
self.param_dims = {name: val["dims"] for name, val in self.param_info.items()}

def make_symbolic_graph(self) -> None:

sigma_shape = (self.k_endog, self.k_endog)
obs_covariance = self.make_and_register_variable(
f"{self.name}_covariance", shape=sigma_shape
Expand Down Expand Up @@ -1043,7 +1045,7 @@ class AutoregressiveComponent(Component):

"""

def __init__( self, endog_names, order: int = 1, name: str = "AutoRegressive"):
def __init__(self, endog_names, order: int = 1, name: str = "AutoRegressive"):
order = order_to_mask(order)
ar_lags = np.flatnonzero(order).ravel().astype(int) + 1
k_states = len(order)
Expand Down Expand Up @@ -1351,9 +1353,7 @@ class FrequencySeasonality(Component):
to isolate and identify a "Monday" effect, for instance.
"""

def __init__(
self, endog_names, season_length, n=None, name=None, innovations=True, **kwargs
):
def __init__(self, endog_names, season_length, n=None, name=None, innovations=True, **kwargs):
if n is None:
n = int(season_length // 2)
if name is None:
Expand Down Expand Up @@ -1382,7 +1382,7 @@ def __init__(
measurement_error=False,
combine_hidden_states=True,
obs_state_idxs=obs_state_idx,
**kwargs
**kwargs,
)

def make_symbolic_graph(self) -> None:
Expand Down Expand Up @@ -1718,11 +1718,9 @@ def populate_component_properties(self) -> None:
"dims": ("exog_state",),
}

class ObsIntercept(Component):


class ObsIntercept(Component):
def __init__(self, endog_names, time_dim, name: str = "y"):

self._time_dim = time_dim
super().__init__(
name=name,
Expand All @@ -1735,9 +1733,8 @@ def __init__(self, endog_names, time_dim, name: str = "y"):
)

def populate_component_properties(self):

self.param_info = {
f"mu": {
"mu": {
"shape": (
# TODO This should not be necessary.
self._time_dim,
Expand All @@ -1754,9 +1751,8 @@ def populate_component_properties(self):
self.param_names = list(self.param_info.keys())

def make_symbolic_graph(self) -> None:

mu = self.make_and_register_variable(
f"mu",
"mu",
shape=(
self._time_dim,
self.k_endog,
Expand Down
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