From a70b7337782a852be7f8e69a1134d0f3fa1283ca Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Tue, 24 Jun 2025 21:00:39 +0200 Subject: [PATCH 01/21] Reorganize structural model module --- pymc_extras/statespace/models/structural.py | 1679 ----------------- .../statespace/models/structural/__init__.py | 21 + .../models/structural/components/__init__.py | 0 .../structural/components/autoregressive.py | 122 ++ .../models/structural/components/cycle.py | 201 ++ .../structural/components/level_trend.py | 196 ++ .../components/measurement_error.py | 80 + .../structural/components/regression.py | 111 ++ .../structural/components/seasonality.py | 353 ++++ .../statespace/models/structural/core.py | 697 +++++++ .../statespace/models/structural/utils.py | 16 + .../statespace/models/structural/__init__.py | 0 .../models/structural/components/__init__.py | 0 .../components/test_autoregressive.py | 28 + .../structural/components/test_cycle.py | 52 + .../structural/components/test_level_trend.py | 24 + .../components/test_measurement_error.py | 10 + .../structural/components/test_regression.py | 69 + .../structural/components/test_seasonality.py | 83 + .../statespace/models/structural/conftest.py | 27 + .../test_against_statsmodels.py} | 305 --- .../statespace/models/structural/test_core.py | 102 + 22 files changed, 2192 insertions(+), 1984 deletions(-) delete mode 100644 pymc_extras/statespace/models/structural.py create mode 100644 pymc_extras/statespace/models/structural/__init__.py create mode 100644 pymc_extras/statespace/models/structural/components/__init__.py create mode 100644 pymc_extras/statespace/models/structural/components/autoregressive.py create mode 100644 pymc_extras/statespace/models/structural/components/cycle.py create mode 100644 pymc_extras/statespace/models/structural/components/level_trend.py create mode 100644 pymc_extras/statespace/models/structural/components/measurement_error.py create mode 100644 pymc_extras/statespace/models/structural/components/regression.py create mode 100644 pymc_extras/statespace/models/structural/components/seasonality.py create mode 100644 pymc_extras/statespace/models/structural/core.py create mode 100644 pymc_extras/statespace/models/structural/utils.py create mode 100644 tests/statespace/models/structural/__init__.py create mode 100644 tests/statespace/models/structural/components/__init__.py create mode 100644 tests/statespace/models/structural/components/test_autoregressive.py create mode 100644 tests/statespace/models/structural/components/test_cycle.py create mode 100644 tests/statespace/models/structural/components/test_level_trend.py create mode 100644 tests/statespace/models/structural/components/test_measurement_error.py create mode 100644 tests/statespace/models/structural/components/test_regression.py create mode 100644 tests/statespace/models/structural/components/test_seasonality.py create mode 100644 tests/statespace/models/structural/conftest.py rename tests/statespace/models/{test_structural.py => structural/test_against_statsmodels.py} (62%) create mode 100644 tests/statespace/models/structural/test_core.py diff --git a/pymc_extras/statespace/models/structural.py b/pymc_extras/statespace/models/structural.py deleted file mode 100644 index a982366c3..000000000 --- a/pymc_extras/statespace/models/structural.py +++ /dev/null @@ -1,1679 +0,0 @@ -import functools as ft -import logging - -from abc import ABC -from collections.abc import Sequence -from itertools import pairwise -from typing import Any - -import numpy as np -import pytensor -import pytensor.tensor as pt -import xarray as xr - -from pytensor import Variable -from pytensor.compile.mode import Mode - -from pymc_extras.statespace.core import PytensorRepresentation -from pymc_extras.statespace.core.statespace import PyMCStateSpace -from pymc_extras.statespace.models.utilities import ( - conform_time_varying_and_time_invariant_matrices, - make_default_coords, -) -from pymc_extras.statespace.utils.constants import ( - ALL_STATE_AUX_DIM, - ALL_STATE_DIM, - AR_PARAM_DIM, - LONG_MATRIX_NAMES, - POSITION_DERIVATIVE_NAMES, - TIME_DIM, -) - -_log = logging.getLogger("pymc.experimental.statespace") - -floatX = pytensor.config.floatX - - -def order_to_mask(order): - if isinstance(order, int): - return np.ones(order).astype(bool) - else: - return np.array(order).astype(bool) - - -def _frequency_transition_block(s, j): - lam = 2 * np.pi * j / s - - return pt.stack([[pt.cos(lam), pt.sin(lam)], [-pt.sin(lam), pt.cos(lam)]]) - - -class StructuralTimeSeries(PyMCStateSpace): - r""" - Structural Time Series Model - - The structural time series model, named by [1] and presented in statespace form in [2], is a framework for - decomposing a univariate time series into level, trend, seasonal, and cycle components. It also admits the - possibility of exogenous regressors. Unlike the SARIMAX framework, the time series is not assumed to be stationary. - - Notes - ----- - - .. math:: - y_t = \mu_t + \gamma_t + c_t + \varepsilon_t - - """ - - def __init__( - self, - ssm: PytensorRepresentation, - state_names: list[str], - data_names: list[str], - shock_names: list[str], - param_names: list[str], - exog_names: list[str], - param_dims: dict[str, tuple[int]], - coords: dict[str, Sequence], - param_info: dict[str, dict[str, Any]], - data_info: dict[str, dict[str, Any]], - component_info: dict[str, dict[str, Any]], - measurement_error: bool, - name_to_variable: dict[str, Variable], - name_to_data: dict[str, Variable] | None = None, - name: str | None = None, - verbose: bool = True, - filter_type: str = "standard", - mode: str | Mode | None = None, - ): - # Add the initial state covariance to the parameters - if name is None: - name = "data" - self._name = name - - k_states, k_posdef, k_endog = ssm.k_states, ssm.k_posdef, ssm.k_endog - param_names, param_dims, param_info = self._add_inital_state_cov_to_properties( - param_names, param_dims, param_info, k_states - ) - self._state_names = state_names.copy() - self._data_names = data_names.copy() - self._shock_names = shock_names.copy() - self._param_names = param_names.copy() - self._param_dims = param_dims.copy() - - default_coords = make_default_coords(self) - coords.update(default_coords) - - self._coords = coords - self._param_info = param_info.copy() - self._data_info = data_info.copy() - self.measurement_error = measurement_error - - super().__init__( - k_endog, - k_states, - max(1, k_posdef), - filter_type=filter_type, - verbose=verbose, - measurement_error=measurement_error, - mode=mode, - ) - self.ssm = ssm.copy() - - if k_posdef == 0: - # If there is no randomness in the model, add dummy matrices to the representation to avoid errors - # when we go to construct random variables from the matrices - self.ssm.k_posdef = self.k_posdef - self.ssm.shapes["state_cov"] = (1, 1, 1) - self.ssm["state_cov"] = pt.zeros((1, 1, 1)) - - self.ssm.shapes["selection"] = (1, self.k_states, 1) - self.ssm["selection"] = pt.zeros((1, self.k_states, 1)) - - self._component_info = component_info.copy() - - self._name_to_variable = name_to_variable.copy() - self._name_to_data = name_to_data.copy() - - self._exog_names = exog_names.copy() - self._needs_exog_data = len(exog_names) > 0 - - P0 = self.make_and_register_variable("P0", shape=(self.k_states, self.k_states)) - self.ssm["initial_state_cov"] = P0 - - @staticmethod - def _add_inital_state_cov_to_properties(param_names, param_dims, param_info, k_states): - param_names += ["P0"] - param_dims["P0"] = (ALL_STATE_DIM, ALL_STATE_AUX_DIM) - param_info["P0"] = { - "shape": (k_states, k_states), - "constraints": "Positive semi-definite", - "dims": param_dims["P0"], - } - - return param_names, param_dims, param_info - - @property - def param_names(self): - return self._param_names - - @property - def data_names(self) -> list[str]: - return self._data_names - - @property - def state_names(self): - return self._state_names - - @property - def observed_states(self): - return [self._name] - - @property - def shock_names(self): - return self._shock_names - - @property - def param_dims(self): - return self._param_dims - - @property - def coords(self) -> dict[str, Sequence]: - return self._coords - - @property - def param_info(self) -> dict[str, dict[str, Any]]: - return self._param_info - - @property - def data_info(self) -> dict[str, dict[str, Any]]: - return self._data_info - - def make_symbolic_graph(self) -> None: - """ - Assign placeholder pytensor variables among statespace matrices in positions where PyMC variables will go. - - Notes - ----- - This assignment is handled by the components, so this function is implemented only to avoid the - NotImplementedError raised by the base class. - """ - - pass - - def _state_slices_from_info(self): - info = self._component_info.copy() - comp_states = np.cumsum([0] + [info["k_states"] for info in info.values()]) - state_slices = [slice(i, j) for i, j in pairwise(comp_states)] - - return state_slices - - def _hidden_states_from_data(self, data): - state_slices = self._state_slices_from_info() - info = self._component_info - names = info.keys() - result = [] - - for i, (name, s) in enumerate(zip(names, state_slices)): - obs_idx = info[name]["obs_state_idx"] - if obs_idx is None: - continue - - X = data[..., s] - if info[name]["combine_hidden_states"]: - sum_idx = np.flatnonzero(obs_idx) - result.append(X[..., sum_idx].sum(axis=-1)[..., None]) - else: - comp_names = self.state_names[s] - for j, state_name in enumerate(comp_names): - result.append(X[..., j, None]) - - return np.concatenate(result, axis=-1) - - def _get_subcomponent_names(self): - state_slices = self._state_slices_from_info() - info = self._component_info - names = info.keys() - result = [] - - for i, (name, s) in enumerate(zip(names, state_slices)): - if info[name]["combine_hidden_states"]: - result.append(name) - else: - comp_names = self.state_names[s] - result.extend([f"{name}[{comp_name}]" for comp_name in comp_names]) - return result - - def extract_components_from_idata(self, idata: xr.Dataset) -> xr.Dataset: - r""" - Extract interpretable hidden states from an InferenceData returned by a PyMCStateSpace sampling method - - Parameters - ---------- - idata: Dataset - A Dataset object, returned by a PyMCStateSpace sampling method - - Returns - ------- - idata: Dataset - An Dataset object with hidden states transformed to represent only the "interpretable" subcomponents - of the structural model. - - Notes - ----- - In general, a structural statespace model can be represented as: - - .. math:: - y_t = \mu_t + \nu_t + \cdots + \gamma_t + c_t + \xi_t + \epsilon_t \tag{1} - - Where: - - - :math:`\mu_t` is the level of the data at time t - - :math:`\nu_t` is the slope of the data at time t - - :math:`\cdots` are higher time derivatives of the position (acceleration, jerk, etc) at time t - - :math:`\gamma_t` is the seasonal component at time t - - :math:`c_t` is the cycle component at time t - - :math:`\xi_t` is the autoregressive error at time t - - :math:`\varepsilon_t` is the measurement error at time t - - In state space form, some or all of these components are represented as linear combinations of other - subcomponents, making interpretation of the outputs of the outputs difficult. The purpose of this function is - to take the expended statespace representation and return a "reduced form" of only the components shown in - equation (1). - """ - - def _extract_and_transform_variable(idata, new_state_names): - *_, time_dim, state_dim = idata.dims - state_func = ft.partial(self._hidden_states_from_data) - new_idata = xr.apply_ufunc( - state_func, - idata, - input_core_dims=[[time_dim, state_dim]], - output_core_dims=[[time_dim, state_dim]], - exclude_dims={state_dim}, - ) - new_idata.coords.update({state_dim: new_state_names}) - return new_idata - - var_names = list(idata.data_vars.keys()) - is_latent = [idata[name].shape[-1] == self.k_states for name in var_names] - new_state_names = self._get_subcomponent_names() - - latent_names = [name for latent, name in zip(is_latent, var_names) if latent] - dropped_vars = set(var_names) - set(latent_names) - if len(dropped_vars) > 0: - _log.warning( - f'Variables {", ".join(dropped_vars)} do not contain all hidden states (their last dimension ' - f"is not {self.k_states}). They will not be present in the modified idata." - ) - if len(dropped_vars) == len(var_names): - raise ValueError( - "Provided idata had no variables with all hidden states; cannot extract components." - ) - - idata_new = xr.Dataset( - { - name: _extract_and_transform_variable(idata[name], new_state_names) - for name in latent_names - } - ) - return idata_new - - -class Component(ABC): - r""" - Base class for a component of a structural timeseries model. - - This base class contains a subset of the class attributes of the PyMCStateSpace class, and none of the class - methods. The purpose of a component is to allow the partial definition of a structural model. Components are - assembled into a full model by the StructuralTimeSeries class. - - Parameters - ---------- - name: str - The name of the component - k_endog: int - Number of endogenous variables being modeled. Currently, must be one because structural models only support - univariate data. - k_states: int - Number of hidden states in the component model - k_posdef: int - Rank of the state covariance matrix, or the number of sources of innovations in the component model - measurement_error: bool - Whether the observation associated with the component has measurement error. Default is False. - combine_hidden_states: bool - Flag for the ``extract_hidden_states_from_data`` method. When ``True``, hidden states from the component model - are extracted as ``hidden_states[:, np.flatnonzero(Z)]``. Should be True in models where hidden states - individually have no interpretation, such as seasonal or autoregressive components. - """ - - def __init__( - self, - name, - k_endog, - k_states, - k_posdef, - state_names=None, - data_names=None, - shock_names=None, - param_names=None, - exog_names=None, - representation: PytensorRepresentation | None = None, - measurement_error=False, - combine_hidden_states=True, - component_from_sum=False, - obs_state_idxs=None, - ): - self.name = name - self.k_endog = k_endog - self.k_states = k_states - self.k_posdef = k_posdef - self.measurement_error = measurement_error - - self.state_names = state_names if state_names is not None else [] - self.data_names = data_names if data_names is not None else [] - self.shock_names = shock_names if shock_names is not None else [] - self.param_names = param_names if param_names is not None else [] - self.exog_names = exog_names if exog_names is not None else [] - - self.needs_exog_data = len(self.exog_names) > 0 - self.coords = {} - self.param_dims = {} - - self.param_info = {} - self.data_info = {} - - self.param_counts = {} - - if representation is None: - self.ssm = PytensorRepresentation(k_endog=k_endog, k_states=k_states, k_posdef=k_posdef) - else: - self.ssm = representation - - self._name_to_variable = {} - self._name_to_data = {} - - if not component_from_sum: - self.populate_component_properties() - self.make_symbolic_graph() - - self._component_info = { - self.name: { - "k_states": self.k_states, - "k_enodg": self.k_endog, - "k_posdef": self.k_posdef, - "combine_hidden_states": combine_hidden_states, - "obs_state_idx": obs_state_idxs, - } - } - - def make_and_register_variable(self, name, shape, dtype=floatX) -> Variable: - r""" - Helper function to create a pytensor symbolic variable and register it in the _name_to_variable dictionary - - Parameters - ---------- - name : str - The name of the placeholder variable. Must be the name of a model parameter. - shape : int or tuple of int - Shape of the parameter - dtype : str, default pytensor.config.floatX - dtype of the parameter - - Notes - ----- - Symbolic pytensor variables are used in the ``make_symbolic_graph`` method as placeholders for PyMC random - variables. The change is made in the ``_insert_random_variables`` method via ``pytensor.graph_replace``. To - make the change, a dictionary mapping pytensor variables to PyMC random variables needs to be constructed. - - The purpose of this method is to: - 1. Create the placeholder symbolic variables - 2. Register the placeholder variable in the ``_name_to_variable`` dictionary - - The shape provided here will define the shape of the prior that will need to be provided by the user. - - An error is raised if the provided name has already been registered, or if the name is not present in the - ``param_names`` property. - """ - if name not in self.param_names: - raise ValueError( - f"{name} is not a model parameter. All placeholder variables should correspond to model " - f"parameters." - ) - - if name in self._name_to_variable.keys(): - raise ValueError( - f"{name} is already a registered placeholder variable with shape " - f"{self._name_to_variable[name].type.shape}" - ) - - placeholder = pt.tensor(name, shape=shape, dtype=dtype) - self._name_to_variable[name] = placeholder - return placeholder - - def make_and_register_data(self, name, shape, dtype=floatX) -> Variable: - r""" - Helper function to create a pytensor symbolic variable and register it in the _name_to_data dictionary - - Parameters - ---------- - name : str - The name of the placeholder data. Must be the name of an expected data variable. - shape : int or tuple of int - Shape of the parameter - dtype : str, default pytensor.config.floatX - dtype of the parameter - - Notes - ----- - See docstring for make_and_register_variable for more details. This function is similar, but handles data - inputs instead of model parameters. - - An error is raised if the provided name has already been registered, or if the name is not present in the - ``data_names`` property. - """ - if name not in self.data_names: - raise ValueError( - f"{name} is not a model parameter. All placeholder variables should correspond to model " - f"parameters." - ) - - if name in self._name_to_data.keys(): - raise ValueError( - f"{name} is already a registered placeholder variable with shape " - f"{self._name_to_data[name].type.shape}" - ) - - placeholder = pt.tensor(name, shape=shape, dtype=dtype) - self._name_to_data[name] = placeholder - return placeholder - - def make_symbolic_graph(self) -> None: - raise NotImplementedError - - def populate_component_properties(self): - raise NotImplementedError - - def _get_combined_shapes(self, other): - k_states = self.k_states + other.k_states - k_posdef = self.k_posdef + other.k_posdef - if self.k_endog != other.k_endog: - raise NotImplementedError( - "Merging elements with different numbers of observed states is not supported.>" - ) - k_endog = self.k_endog - - return k_states, k_posdef, k_endog - - def _combine_statespace_representations(self, other): - def make_slice(name, x, o_x): - ndim = max(x.ndim, o_x.ndim) - return (name,) + (slice(None, None, None),) * ndim - - k_states, k_posdef, k_endog = self._get_combined_shapes(other) - - self_matrices = [self.ssm[name] for name in LONG_MATRIX_NAMES] - other_matrices = [other.ssm[name] for name in LONG_MATRIX_NAMES] - - x0, P0, c, d, T, Z, R, H, Q = ( - self.ssm[make_slice(name, x, o_x)] - for name, x, o_x in zip(LONG_MATRIX_NAMES, self_matrices, other_matrices) - ) - o_x0, o_P0, o_c, o_d, o_T, o_Z, o_R, o_H, o_Q = ( - other.ssm[make_slice(name, x, o_x)] - for name, x, o_x in zip(LONG_MATRIX_NAMES, self_matrices, other_matrices) - ) - - initial_state = pt.concatenate(conform_time_varying_and_time_invariant_matrices(x0, o_x0)) - initial_state.name = x0.name - - initial_state_cov = pt.linalg.block_diag(P0, o_P0) - initial_state_cov.name = P0.name - - state_intercept = pt.concatenate(conform_time_varying_and_time_invariant_matrices(c, o_c)) - state_intercept.name = c.name - - obs_intercept = d + o_d - obs_intercept.name = d.name - - transition = pt.linalg.block_diag(T, o_T) - transition.name = T.name - - design = pt.concatenate(conform_time_varying_and_time_invariant_matrices(Z, o_Z), axis=-1) - design.name = Z.name - - selection = pt.linalg.block_diag(R, o_R) - selection.name = R.name - - obs_cov = H + o_H - obs_cov.name = H.name - - state_cov = pt.linalg.block_diag(Q, o_Q) - state_cov.name = Q.name - - new_ssm = PytensorRepresentation( - k_endog=k_endog, - k_states=k_states, - k_posdef=k_posdef, - initial_state=initial_state, - initial_state_cov=initial_state_cov, - state_intercept=state_intercept, - obs_intercept=obs_intercept, - transition=transition, - design=design, - selection=selection, - obs_cov=obs_cov, - state_cov=state_cov, - ) - - return new_ssm - - def _combine_property(self, other, name): - self_prop = getattr(self, name) - if isinstance(self_prop, list): - return self_prop + getattr(other, name) - elif isinstance(self_prop, dict): - new_prop = self_prop.copy() - new_prop.update(getattr(other, name)) - return new_prop - - def _combine_component_info(self, other): - combined_info = {} - for key, value in self._component_info.items(): - if not key.startswith("StateSpace"): - if key in combined_info.keys(): - raise ValueError(f"Found duplicate component named {key}") - combined_info[key] = value - - for key, value in other._component_info.items(): - if not key.startswith("StateSpace"): - if key in combined_info.keys(): - raise ValueError(f"Found duplicate component named {key}") - combined_info[key] = value - - return combined_info - - def _make_combined_name(self): - components = self._component_info.keys() - name = f'StateSpace[{", ".join(components)}]' - return name - - def __add__(self, other): - state_names = self._combine_property(other, "state_names") - data_names = self._combine_property(other, "data_names") - param_names = self._combine_property(other, "param_names") - shock_names = self._combine_property(other, "shock_names") - param_info = self._combine_property(other, "param_info") - data_info = self._combine_property(other, "data_info") - param_dims = self._combine_property(other, "param_dims") - coords = self._combine_property(other, "coords") - exog_names = self._combine_property(other, "exog_names") - - _name_to_variable = self._combine_property(other, "_name_to_variable") - _name_to_data = self._combine_property(other, "_name_to_data") - - measurement_error = any([self.measurement_error, other.measurement_error]) - - k_states, k_posdef, k_endog = self._get_combined_shapes(other) - ssm = self._combine_statespace_representations(other) - - new_comp = Component( - name="", - k_endog=1, - k_states=k_states, - k_posdef=k_posdef, - measurement_error=measurement_error, - representation=ssm, - component_from_sum=True, - ) - new_comp._component_info = self._combine_component_info(other) - new_comp.name = new_comp._make_combined_name() - - names_and_props = [ - ("state_names", state_names), - ("data_names", data_names), - ("param_names", param_names), - ("shock_names", shock_names), - ("param_dims", param_dims), - ("coords", coords), - ("param_dims", param_dims), - ("param_info", param_info), - ("data_info", data_info), - ("exog_names", exog_names), - ("_name_to_variable", _name_to_variable), - ("_name_to_data", _name_to_data), - ] - - for prop, value in names_and_props: - setattr(new_comp, prop, value) - - return new_comp - - def build( - self, name=None, filter_type="standard", verbose=True, mode: str | Mode | None = None - ): - """ - Build a StructuralTimeSeries statespace model from the current component(s) - - Parameters - ---------- - name: str, optional - Name of the exogenous data being modeled. Default is "data" - - filter_type : str, optional - The type of Kalman filter to use. Valid options are "standard", "univariate", "single", "cholesky", and - "steady_state". For more information, see the docs for each filter. Default is "standard". - - verbose : bool, optional - If True, displays information about the initialized model. Defaults to True. - - mode: str or Mode, optional - Pytensor compile mode, used in auxiliary sampling methods such as ``sample_conditional_posterior`` and - ``forecast``. The mode does **not** effect calls to ``pm.sample``. - - Regardless of whether a mode is specified, it can always be overwritten via the ``compile_kwargs`` argument - to all sampling methods. - - Returns - ------- - PyMCStateSpace - An initialized instance of a PyMCStateSpace, constructed using the system matrices contained in the - components. - """ - - return StructuralTimeSeries( - self.ssm, - name=name, - state_names=self.state_names, - data_names=self.data_names, - shock_names=self.shock_names, - param_names=self.param_names, - param_dims=self.param_dims, - coords=self.coords, - param_info=self.param_info, - data_info=self.data_info, - component_info=self._component_info, - measurement_error=self.measurement_error, - exog_names=self.exog_names, - name_to_variable=self._name_to_variable, - name_to_data=self._name_to_data, - filter_type=filter_type, - verbose=verbose, - mode=mode, - ) - - -class LevelTrendComponent(Component): - r""" - Level and trend component of a structural time series model - - Parameters - ---------- - __________ - order : int - - Number of time derivatives of the trend to include in the model. For example, when order=3, the trend will - be of the form ``y = a + b * t + c * t ** 2``, where the coefficients ``a, b, c`` come from the initial - state values. - - innovations_order : int or sequence of int, optional - - The number of stochastic innovations to include in the model. By default, ``innovations_order = order`` - - Notes - ----- - This class implements the level and trend components of the general structural time series model. In the most - general form, the level and trend is described by a system of two time-varying equations. - - .. math:: - \begin{align} - \mu_{t+1} &= \mu_t + \nu_t + \zeta_t \\ - \nu_{t+1} &= \nu_t + \xi_t - \zeta_t &\sim N(0, \sigma_\zeta) \\ - \xi_t &\sim N(0, \sigma_\xi) - \end{align} - - Where :math:`\mu_{t+1}` is the mean of the timeseries at time t, and :math:`\nu_t` is the drift or the slope of - the process. When both innovations :math:`\zeta_t` and :math:`\xi_t` are included in the model, it is known as a - *local linear trend* model. This system of two equations, corresponding to ``order=2``, can be expanded or - contracted by adding or removing equations. ``order=3`` would add an acceleration term to the sytsem: - - .. math:: - \begin{align} - \mu_{t+1} &= \mu_t + \nu_t + \zeta_t \\ - \nu_{t+1} &= \nu_t + \eta_t + \xi_t \\ - \eta_{t+1} &= \eta_{t-1} + \omega_t \\ - \zeta_t &\sim N(0, \sigma_\zeta) \\ - \xi_t &\sim N(0, \sigma_\xi) \\ - \omega_t &\sim N(0, \sigma_\omega) - \end{align} - - After setting all innovation terms to zero and defining initial states :math:`\mu_0, \nu_0, \eta_0`, these equations - can be collapsed to: - - .. math:: - \mu_t = \mu_0 + \nu_0 \cdot t + \eta_0 \cdot t^2 - - Which clarifies how the order and initial states influence the model. In particular, the initial states are the - coefficients on the intercept, slope, acceleration, and so on. - - In this light, allowing for innovations can be understood as allowing these coefficients to vary over time. Each - component can be individually selected for time variation by passing a list to the ``innovations_order`` argument. - For example, a constant intercept with time varying trend and acceleration is specified as ``order=3, - innovations_order=[0, 1, 1]``. - - By choosing the ``order`` and ``innovations_order``, a large variety of models can be obtained. Notable - models include: - - * Constant intercept, ``order=1, innovations_order=0`` - - .. math:: - \mu_t = \mu - - * Constant linear slope, ``order=2, innovations_order=0`` - - .. math:: - \mu_t = \mu_{t-1} + \nu - - * Gaussian Random Walk, ``order=1, innovations_order=1`` - - .. math:: - \mu_t = \mu_{t-1} + \zeta_t - - * Gaussian Random Walk with Drift, ``order=2, innovations_order=1`` - - .. math:: - \mu_t = \mu_{t-1} + \nu + \zeta_t - - * Smooth Trend, ``order=2, innovations_order=[0, 1]`` - - .. math:: - \begin{align} - \mu_t &= \mu_{t-1} + \nu_{t-1} \\ - \nu_t &= \nu_{t-1} + \xi_t - \end{align} - - * Local Level, ``order=2, innovations_order=2`` - - [1] notes that the smooth trend model produces more gradually changing slopes than the full local linear trend - model, and is equivalent to an "integrated trend model". - - References - ---------- - .. [1] Durbin, James, and Siem Jan Koopman. 2012. - Time Series Analysis by State Space Methods: Second Edition. - Oxford University Press. - - """ - - def __init__( - self, - order: int | list[int] = 2, - innovations_order: int | list[int] | None = None, - name: str = "LevelTrend", - ): - if innovations_order is None: - innovations_order = order - - self._order_mask = order_to_mask(order) - max_state = np.flatnonzero(self._order_mask)[-1].item() + 1 - - # If the user passes excess zeros, raise an error. The alternative is to prune them, but this would cause - # the shape of the state to be different to what the user expects. - if len(self._order_mask) > max_state: - raise ValueError( - f"order={order} is invalid. The highest derivative should not be set to zero. If you want a " - f"lower order model, explicitly omit the zeros." - ) - k_states = max_state - - if isinstance(innovations_order, int): - n = innovations_order - innovations_order = order_to_mask(k_states) - if n > 0: - innovations_order[n:] = False - else: - innovations_order[:] = False - else: - innovations_order = order_to_mask(innovations_order) - - self.innovations_order = innovations_order[:max_state] - k_posdef = int(sum(innovations_order)) - - super().__init__( - name, - k_endog=1, - k_states=k_states, - k_posdef=k_posdef, - measurement_error=False, - combine_hidden_states=False, - obs_state_idxs=np.array([1.0] + [0.0] * (k_states - 1)), - ) - - def populate_component_properties(self): - name_slice = POSITION_DERIVATIVE_NAMES[: self.k_states] - self.param_names = ["initial_trend"] - self.state_names = [name for name, mask in zip(name_slice, self._order_mask) if mask] - self.param_dims = {"initial_trend": ("trend_state",)} - self.coords = {"trend_state": self.state_names} - self.param_info = {"initial_trend": {"shape": (self.k_states,), "constraints": None}} - - if self.k_posdef > 0: - self.param_names += ["sigma_trend"] - self.shock_names = [ - name for name, mask in zip(name_slice, self.innovations_order) if mask - ] - self.param_dims["sigma_trend"] = ("trend_shock",) - self.coords["trend_shock"] = self.shock_names - self.param_info["sigma_trend"] = {"shape": (self.k_posdef,), "constraints": "Positive"} - - for name in self.param_names: - self.param_info[name]["dims"] = self.param_dims[name] - - def make_symbolic_graph(self) -> None: - initial_trend = self.make_and_register_variable("initial_trend", shape=(self.k_states,)) - self.ssm["initial_state", :] = initial_trend - triu_idx = np.triu_indices(self.k_states) - self.ssm[np.s_["transition", triu_idx[0], triu_idx[1]]] = 1 - - R = np.eye(self.k_states) - R = R[:, self.innovations_order] - self.ssm["selection", :, :] = R - - self.ssm["design", 0, :] = np.array([1.0] + [0.0] * (self.k_states - 1)) - - if self.k_posdef > 0: - sigma_trend = self.make_and_register_variable("sigma_trend", shape=(self.k_posdef,)) - diag_idx = np.diag_indices(self.k_posdef) - idx = np.s_["state_cov", diag_idx[0], diag_idx[1]] - self.ssm[idx] = sigma_trend**2 - - -class MeasurementError(Component): - r""" - Measurement error term for a structural timeseries model - - Parameters - ---------- - name: str, optional - - Name of the observed data. Default is "obs". - - Notes - ----- - This component should only be used in combination with other components, because it has no states. It's only use - is to add a variance parameter to the model, associated with the observation noise matrix H. - - Examples - -------- - Create and estimate a deterministic linear trend with measurement error - - .. code:: python - - from pymc_extras.statespace import structural as st - import pymc as pm - import pytensor.tensor as pt - - trend = st.LevelTrendComponent(order=2, innovations_order=0) - error = st.MeasurementError() - ss_mod = (trend + error).build() - - with pm.Model(coords=ss_mod.coords) as model: - P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0']) - intitial_trend = pm.Normal('initial_trend', sigma=10, dims=ss_mod.param_dims['initial_trend']) - sigma_obs = pm.Exponential('sigma_obs', 1, dims=ss_mod.param_dims['sigma_obs']) - - ss_mod.build_statespace_graph(data) - idata = pm.sample(nuts_sampler='numpyro') - """ - - def __init__(self, name: str = "MeasurementError"): - k_endog = 1 - k_states = 0 - k_posdef = 0 - - super().__init__( - name, k_endog, k_states, k_posdef, measurement_error=True, combine_hidden_states=False - ) - - def populate_component_properties(self): - self.param_names = [f"sigma_{self.name}"] - self.param_dims = {} - self.param_info = { - f"sigma_{self.name}": { - "shape": (), - "constraints": "Positive", - "dims": None, - } - } - - def make_symbolic_graph(self) -> None: - sigma_shape = () - error_sigma = self.make_and_register_variable(f"sigma_{self.name}", shape=sigma_shape) - diag_idx = np.diag_indices(self.k_endog) - idx = np.s_["obs_cov", diag_idx[0], diag_idx[1]] - self.ssm[idx] = error_sigma**2 - - -class AutoregressiveComponent(Component): - r""" - Autoregressive timeseries component - - Parameters - ---------- - order: int or sequence of int - - If int, the number of lags to include in the model. - If a sequence, an array-like of zeros and ones indicating which lags to include in the model. - - Notes - ----- - An autoregressive component can be thought of as a way o introducing serially correlated errors into the model. - The process is modeled: - - .. math:: - x_t = \sum_{i=1}^p \rho_i x_{t-i} - - Where ``p``, the number of autoregressive terms to model, is the order of the process. By default, all lags up to - ``p`` are included in the model. To disable lags, pass a list of zeros and ones to the ``order`` argumnet. For - example, ``order=[1, 1, 0, 1]`` would become: - - .. math:: - x_t = \rho_1 x_{t-1} + \rho_2 x_{t-1} + \rho_4 x_{t-1} - - The coefficient :math:`\rho_3` has been constrained to zero. - - .. warning:: This class is meant to be used as a component in a structural time series model. For modeling of - stationary processes with ARIMA, use ``statespace.BayesianSARIMA``. - - Examples - -------- - Model a timeseries as an AR(2) process with non-zero mean: - - .. code:: python - - from pymc_extras.statespace import structural as st - import pymc as pm - import pytensor.tensor as pt - - trend = st.LevelTrendComponent(order=1, innovations_order=0) - ar = st.AutoregressiveComponent(2) - ss_mod = (trend + ar).build() - - with pm.Model(coords=ss_mod.coords) as model: - P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0']) - intitial_trend = pm.Normal('initial_trend', sigma=10, dims=ss_mod.param_dims['initial_trend']) - ar_params = pm.Normal('ar_params', dims=ss_mod.param_dims['ar_params']) - sigma_ar = pm.Exponential('sigma_ar', 1, dims=ss_mod.param_dims['sigma_ar']) - - ss_mod.build_statespace_graph(data) - idata = pm.sample(nuts_sampler='numpyro') - - """ - - def __init__(self, order: int = 1, name: str = "AutoRegressive"): - order = order_to_mask(order) - ar_lags = np.flatnonzero(order).ravel().astype(int) + 1 - k_states = len(order) - - self.order = order - self.ar_lags = ar_lags - - super().__init__( - name=name, - k_endog=1, - k_states=k_states, - k_posdef=1, - measurement_error=True, - combine_hidden_states=True, - obs_state_idxs=np.r_[[1.0], np.zeros(k_states - 1)], - ) - - def populate_component_properties(self): - self.state_names = [f"L{i + 1}.data" for i in range(self.k_states)] - self.shock_names = [f"{self.name}_innovation"] - self.param_names = ["ar_params", "sigma_ar"] - self.param_dims = {"ar_params": (AR_PARAM_DIM,)} - self.coords = {AR_PARAM_DIM: self.ar_lags.tolist()} - - self.param_info = { - "ar_params": { - "shape": (self.k_states,), - "constraints": None, - "dims": (AR_PARAM_DIM,), - }, - "sigma_ar": {"shape": (), "constraints": "Positive", "dims": None}, - } - - def make_symbolic_graph(self) -> None: - k_nonzero = int(sum(self.order)) - ar_params = self.make_and_register_variable("ar_params", shape=(k_nonzero,)) - sigma_ar = self.make_and_register_variable("sigma_ar", shape=()) - - T = np.eye(self.k_states, k=-1) - self.ssm["transition", :, :] = T - self.ssm["selection", 0, 0] = 1 - self.ssm["design", 0, 0] = 1 - - ar_idx = ("transition", np.zeros(k_nonzero, dtype="int"), np.nonzero(self.order)[0]) - self.ssm[ar_idx] = ar_params - - cov_idx = ("state_cov", *np.diag_indices(1)) - self.ssm[cov_idx] = sigma_ar**2 - - -class TimeSeasonality(Component): - r""" - Seasonal component, modeled in the time domain - - Parameters - ---------- - season_length: int - The number of periods in a single seasonal cycle, e.g. 12 for monthly data with annual seasonal pattern, 7 for - daily data with weekly seasonal pattern, etc. - - innovations: bool, default True - Whether to include stochastic innovations in the strength of the seasonal effect - - name: str, default None - A name for this seasonal component. Used to label dimensions and coordinates. Useful when multiple seasonal - components are included in the same model. Default is ``f"Seasonal[s={season_length}]"`` - - state_names: list of str, default None - List of strings for seasonal effect labels. If provided, it must be of length ``season_length``. An example - would be ``state_names = ['Mon', 'Tue', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun']`` when data is daily with a weekly - seasonal pattern (``season_length = 7``). - - If None, states will be numbered ``[State_0, ..., State_s]`` - - remove_first_state: bool, default True - If True, the first state will be removed from the model. This is done because there are only n-1 degrees of - freedom in the seasonal component, and one state is not identified. If False, the first state will be - included in the model, but it will not be identified -- you will need to handle this in the priors (e.g. with - ZeroSumNormal). - - Notes - ----- - A seasonal effect is any pattern that repeats every fixed interval. Although there are many possible ways to - model seasonal effects, the implementation used here is the one described by [1] as the "canonical" time domain - representation. The seasonal component can be expressed: - - .. math:: - \gamma_t = -\sum_{i=1}^{s-1} \gamma_{t-i} + \omega_t, \quad \omega_t \sim N(0, \sigma_\gamma) - - Where :math:`s` is the ``seasonal_length`` parameter and :math:`\omega_t` is the (optional) stochastic innovation. - To give interpretation to the :math:`\gamma` terms, it is helpful to work through the algebra for a simple - example. Let :math:`s=4`, and omit the shock term. Define initial conditions :math:`\gamma_0, \gamma_{-1}, - \gamma_{-2}`. The value of the seasonal component for the first 5 timesteps will be: - - .. math:: - \begin{align} - \gamma_1 &= -\gamma_0 - \gamma_{-1} - \gamma_{-2} \\ - \gamma_2 &= -\gamma_1 - \gamma_0 - \gamma_{-1} \\ - &= -(-\gamma_0 - \gamma_{-1} - \gamma_{-2}) - \gamma_0 - \gamma_{-1} \\ - &= (\gamma_0 - \gamma_0 )+ (\gamma_{-1} - \gamma_{-1}) + \gamma_{-2} \\ - &= \gamma_{-2} \\ - \gamma_3 &= -\gamma_2 - \gamma_1 - \gamma_0 \\ - &= -\gamma_{-2} - (-\gamma_0 - \gamma_{-1} - \gamma_{-2}) - \gamma_0 \\ - &= (\gamma_{-2} - \gamma_{-2}) + \gamma_{-1} + (\gamma_0 - \gamma_0) \\ - &= \gamma_{-1} \\ - \gamma_4 &= -\gamma_3 - \gamma_2 - \gamma_1 \\ - &= -\gamma_{-1} - \gamma_{-2} -(-\gamma_0 - \gamma_{-1} - \gamma_{-2}) \\ - &= (\gamma_{-2} - \gamma_{-2}) + (\gamma_{-1} - \gamma_{-1}) + \gamma_0 \\ - &= \gamma_0 \\ - \gamma_5 &= -\gamma_4 - \gamma_3 - \gamma_2 \\ - &= -\gamma_0 - \gamma_{-1} - \gamma_{-2} \\ - &= \gamma_1 - \end{align} - - This exercise shows that, given a list ``initial_conditions`` of length ``s-1``, the effects of this model will be: - - - Period 1: ``-sum(initial_conditions)`` - - Period 2: ``initial_conditions[-1]`` - - Period 3: ``initial_conditions[-2]`` - - ... - - Period s: ``initial_conditions[0]`` - - Period s+1: ``-sum(initial_condition)`` - - And so on. So for interpretation, the ``season_length - 1`` initial states are, when reversed, the coefficients - associated with ``state_names[1:]``. - - .. warning:: - Although the ``state_names`` argument expects a list of length ``season_length``, only ``state_names[1:]`` - will be saved as model dimensions, since the 1st coefficient is not identified (it is defined as - :math:`-\sum_{i=1}^{s} \gamma_{t-i}`). - - Examples - -------- - Estimate monthly with a model with a gaussian random walk trend and monthly seasonality: - - .. code:: python - - from pymc_extras.statespace import structural as st - import pymc as pm - import pytensor.tensor as pt - import pandas as pd - - # Get month names - state_names = pd.date_range('1900-01-01', '1900-12-31', freq='MS').month_name().tolist() - - # Build the structural model - grw = st.LevelTrendComponent(order=1, innovations_order=1) - annual_season = st.TimeSeasonality(season_length=12, name='annual', state_names=state_names, innovations=False) - ss_mod = (grw + annual_season).build() - - # Estimate with PyMC - with pm.Model(coords=ss_mod.coords) as model: - P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0']) - intitial_trend = pm.Deterministic('initial_trend', pt.zeros(1), dims=ss_mod.param_dims['initial_trend']) - annual_coefs = pm.Normal('annual_coefs', sigma=1e-2, dims=ss_mod.param_dims['annual_coefs']) - trend_sigmas = pm.HalfNormal('trend_sigmas', sigma=1e-6, dims=ss_mod.param_dims['trend_sigmas']) - ss_mod.build_statespace_graph(data) - idata = pm.sample(nuts_sampler='numpyro') - - References - ---------- - .. [1] Durbin, James, and Siem Jan Koopman. 2012. - Time Series Analysis by State Space Methods: Second Edition. - Oxford University Press. - """ - - def __init__( - self, - season_length: int, - innovations: bool = True, - name: str | None = None, - state_names: list | None = None, - remove_first_state: bool = True, - ): - if name is None: - name = f"Seasonal[s={season_length}]" - if state_names is None: - state_names = [f"{name}_{i}" for i in range(season_length)] - else: - if len(state_names) != season_length: - raise ValueError( - f"state_names must be a list of length season_length, got {len(state_names)}" - ) - state_names = state_names.copy() - self.innovations = innovations - self.remove_first_state = remove_first_state - - if self.remove_first_state: - # In traditional models, the first state isn't identified, so we can help out the user by automatically - # discarding it. - # TODO: Can this be stashed and reconstructed automatically somehow? - state_names.pop(0) - - k_states = season_length - int(self.remove_first_state) - - super().__init__( - name=name, - k_endog=1, - k_states=k_states, - k_posdef=int(innovations), - state_names=state_names, - measurement_error=False, - combine_hidden_states=True, - obs_state_idxs=np.r_[[1.0], np.zeros(k_states - 1)], - ) - - def populate_component_properties(self): - self.param_names = [f"{self.name}_coefs"] - self.param_info = { - f"{self.name}_coefs": { - "shape": (self.k_states,), - "constraints": None, - "dims": (f"{self.name}_state",), - } - } - self.param_dims = {f"{self.name}_coefs": (f"{self.name}_state",)} - self.coords = {f"{self.name}_state": self.state_names} - - if self.innovations: - self.param_names += [f"sigma_{self.name}"] - self.param_info[f"sigma_{self.name}"] = { - "shape": (), - "constraints": "Positive", - "dims": None, - } - self.shock_names = [f"{self.name}"] - - def make_symbolic_graph(self) -> None: - if self.remove_first_state: - # In this case, parameters are normalized to sum to zero, so the current state is the negative sum of - # all previous states. - T = np.eye(self.k_states, k=-1) - T[0, :] = -1 - else: - # In this case we assume the user to be responsible for ensuring the states sum to zero, so T is just a - # circulant matrix that cycles between the states. - T = np.eye(self.k_states, k=1) - T[-1, 0] = 1 - - self.ssm["transition", :, :] = T - self.ssm["design", 0, 0] = 1 - - initial_states = self.make_and_register_variable( - f"{self.name}_coefs", shape=(self.k_states,) - ) - self.ssm["initial_state", np.arange(self.k_states, dtype=int)] = initial_states - - if self.innovations: - self.ssm["selection", 0, 0] = 1 - season_sigma = self.make_and_register_variable(f"sigma_{self.name}", shape=()) - cov_idx = ("state_cov", *np.diag_indices(1)) - self.ssm[cov_idx] = season_sigma**2 - - -class FrequencySeasonality(Component): - r""" - Seasonal component, modeled in the frequency domain - - Parameters - ---------- - season_length: float - The number of periods in a single seasonal cycle, e.g. 12 for monthly data with annual seasonal pattern, 7 for - daily data with weekly seasonal pattern, etc. Non-integer seasonal_length is also permitted, for example - 365.2422 days in a (solar) year. - - n: int - Number of fourier features to include in the seasonal component. Default is ``season_length // 2``, which - is the maximum possible. A smaller number can be used for a more wave-like seasonal pattern. - - name: str, default None - A name for this seasonal component. Used to label dimensions and coordinates. Useful when multiple seasonal - components are included in the same model. Default is ``f"Seasonal[s={season_length}, n={n}]"`` - - innovations: bool, default True - Whether to include stochastic innovations in the strength of the seasonal effect - - Notes - ----- - A seasonal effect is any pattern that repeats every fixed interval. Although there are many possible ways to - model seasonal effects, the implementation used here is the one described by [1] as the "canonical" frequency domain - representation. The seasonal component can be expressed: - - .. math:: - \begin{align} - \gamma_t &= \sum_{j=1}^{2n} \gamma_{j,t} \\ - \gamma_{j, t+1} &= \gamma_{j,t} \cos \lambda_j + \gamma_{j,t}^\star \sin \lambda_j + \omega_{j, t} \\ - \gamma_{j, t}^\star &= -\gamma_{j,t} \sin \lambda_j + \gamma_{j,t}^\star \cos \lambda_j + \omega_{j,t}^\star - \lambda_j &= \frac{2\pi j}{s} - \end{align} - - Where :math:`s` is the ``seasonal_length``. - - Unlike a ``TimeSeasonality`` component, a ``FrequencySeasonality`` component does not require integer season - length. In addition, for long seasonal periods, it is possible to obtain a more compact state space representation - by choosing ``n << s // 2``. Using ``TimeSeasonality``, an annual seasonal pattern in daily data requires 364 - states, whereas ``FrequencySeasonality`` always requires ``2 * n`` states, regardless of the ``seasonal_length``. - The price of this compactness is less representational power. At ``n = 1``, the seasonal pattern will be a pure - sine wave. At ``n = s // 2``, any arbitrary pattern can be represented. - - One cost of the added flexibility of ``FrequencySeasonality`` is reduced interpretability. States of this model are - coefficients :math:`\gamma_1, \gamma^\star_1, \gamma_2, \gamma_2^\star ..., \gamma_n, \gamma^\star_n` associated - with different frequencies in the fourier representation of the seasonal pattern. As a result, it is not possible - to isolate and identify a "Monday" effect, for instance. - """ - - def __init__(self, season_length, n=None, name=None, innovations=True): - if n is None: - n = int(season_length // 2) - if name is None: - name = f"Frequency[s={season_length}, n={n}]" - - k_states = n * 2 - self.n = n - self.season_length = season_length - self.innovations = innovations - - # If the model is completely saturated (n = s // 2), the last state will not be identified, so it shouldn't - # get a parameter assigned to it and should just be fixed to zero. - # Test this way (rather than n == s // 2) to catch cases when n is non-integer. - self.last_state_not_identified = self.season_length / self.n == 2.0 - self.n_coefs = k_states - int(self.last_state_not_identified) - - obs_state_idx = np.zeros(k_states) - obs_state_idx[slice(0, k_states, 2)] = 1 - - super().__init__( - name=name, - k_endog=1, - k_states=k_states, - k_posdef=k_states * int(self.innovations), - measurement_error=False, - combine_hidden_states=True, - obs_state_idxs=obs_state_idx, - ) - - def make_symbolic_graph(self) -> None: - self.ssm["design", 0, slice(0, self.k_states, 2)] = 1 - - init_state = self.make_and_register_variable(f"{self.name}", shape=(self.n_coefs,)) - - init_state_idx = np.arange(self.n_coefs, dtype=int) - self.ssm["initial_state", init_state_idx] = init_state - - T_mats = [_frequency_transition_block(self.season_length, j + 1) for j in range(self.n)] - T = pt.linalg.block_diag(*T_mats) - self.ssm["transition", :, :] = T - - if self.innovations: - sigma_season = self.make_and_register_variable(f"sigma_{self.name}", shape=()) - self.ssm["state_cov", :, :] = pt.eye(self.k_posdef) * sigma_season**2 - self.ssm["selection", :, :] = np.eye(self.k_states) - - def populate_component_properties(self): - self.state_names = [f"{self.name}_{f}_{i}" for i in range(self.n) for f in ["Cos", "Sin"]] - self.param_names = [f"{self.name}"] - - self.param_dims = {self.name: (f"{self.name}_state",)} - self.param_info = { - f"{self.name}": { - "shape": (self.k_states - int(self.last_state_not_identified),), - "constraints": None, - "dims": (f"{self.name}_state",), - } - } - - init_state_idx = np.arange(self.k_states, dtype=int) - if self.last_state_not_identified: - init_state_idx = init_state_idx[:-1] - self.coords = {f"{self.name}_state": [self.state_names[i] for i in init_state_idx]} - - if self.innovations: - self.shock_names = self.state_names.copy() - self.param_names += [f"sigma_{self.name}"] - self.param_info[f"sigma_{self.name}"] = { - "shape": (), - "constraints": "Positive", - "dims": None, - } - - -class CycleComponent(Component): - r""" - A component for modeling longer-term cyclical effects - - Parameters - ---------- - name: str - Name of the component. Used in generated coordinates and state names. If None, a descriptive name will be - used. - - cycle_length: int, optional - The length of the cycle, in the calendar units of your data. For example, if your data is monthly, and you - want to model a 12-month cycle, use ``cycle_length=12``. You cannot specify both ``cycle_length`` and - ``estimate_cycle_length``. - - estimate_cycle_length: bool, default False - Whether to estimate the cycle length. If True, an additional parameter, ``cycle_length`` will be added to the - model. You cannot specify both ``cycle_length`` and ``estimate_cycle_length``. - - dampen: bool, default False - Whether to dampen the cycle by multiplying by a dampening factor :math:`\rho` at every timestep. If true, - an additional parameter, ``dampening_factor`` will be added to the model. - - innovations: bool, default True - Whether to include stochastic innovations in the strength of the seasonal effect. If True, an additional - parameter, ``sigma_{name}`` will be added to the model. - - Notes - ----- - The cycle component is very similar in implementation to the frequency domain seasonal component, expect that it - is restricted to n=1. The cycle component can be expressed: - - .. math:: - \begin{align} - \gamma_t &= \rho \gamma_{t-1} \cos \lambda + \rho \gamma_{t-1}^\star \sin \lambda + \omega_{t} \\ - \gamma_{t}^\star &= -\rho \gamma_{t-1} \sin \lambda + \rho \gamma_{t-1}^\star \cos \lambda + \omega_{t}^\star \\ - \lambda &= \frac{2\pi}{s} - \end{align} - - Where :math:`s` is the ``cycle_length``. [1] recommend that this component be used for longer term cyclical - effects, such as business cycles, and that the seasonal component be used for shorter term effects, such as - weekly or monthly seasonality. - - Unlike a FrequencySeasonality component, the length of a CycleComponent can be estimated. - - Examples - -------- - Estimate a business cycle with length between 6 and 12 years: - - .. code:: python - - from pymc_extras.statespace import structural as st - import pymc as pm - import pytensor.tensor as pt - import pandas as pd - import numpy as np - - data = np.random.normal(size=(100, 1)) - - # Build the structural model - grw = st.LevelTrendComponent(order=1, innovations_order=1) - cycle = st.CycleComponent('business_cycle', estimate_cycle_length=True, dampen=False) - ss_mod = (grw + cycle).build() - - # Estimate with PyMC - with pm.Model(coords=ss_mod.coords) as model: - P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states), dims=ss_mod.param_dims['P0']) - intitial_trend = pm.Normal('initial_trend', dims=ss_mod.param_dims['initial_trend']) - sigma_trend = pm.HalfNormal('sigma_trend', dims=ss_mod.param_dims['sigma_trend']) - - cycle_strength = pm.Normal('business_cycle') - cycle_length = pm.Uniform('business_cycle_length', lower=6, upper=12) - - sigma_cycle = pm.HalfNormal('sigma_business_cycle', sigma=1) - ss_mod.build_statespace_graph(data) - - idata = pm.sample(nuts_sampler='numpyro') - - References - ---------- - .. [1] Durbin, James, and Siem Jan Koopman. 2012. - Time Series Analysis by State Space Methods: Second Edition. - Oxford University Press. - """ - - def __init__( - self, - name: str | None = None, - cycle_length: int | None = None, - estimate_cycle_length: bool = False, - dampen: bool = False, - innovations: bool = True, - ): - if cycle_length is None and not estimate_cycle_length: - raise ValueError("Must specify cycle_length if estimate_cycle_length is False") - if cycle_length is not None and estimate_cycle_length: - raise ValueError("Cannot specify cycle_length if estimate_cycle_length is True") - if name is None: - cycle = int(cycle_length) if cycle_length is not None else "Estimate" - name = f"Cycle[s={cycle}, dampen={dampen}, innovations={innovations}]" - - self.estimate_cycle_length = estimate_cycle_length - self.cycle_length = cycle_length - self.innovations = innovations - self.dampen = dampen - self.n_coefs = 1 - - k_states = 2 - k_endog = 1 - k_posdef = 2 - - obs_state_idx = np.zeros(k_states) - obs_state_idx[slice(0, k_states, 2)] = 1 - - super().__init__( - name=name, - k_endog=k_endog, - k_states=k_states, - k_posdef=k_posdef, - measurement_error=False, - combine_hidden_states=True, - obs_state_idxs=obs_state_idx, - ) - - def make_symbolic_graph(self) -> None: - self.ssm["design", 0, slice(0, self.k_states, 2)] = 1 - self.ssm["selection", :, :] = np.eye(self.k_states) - self.param_dims = {self.name: (f"{self.name}_state",)} - self.coords = {f"{self.name}_state": self.state_names} - - init_state = self.make_and_register_variable(f"{self.name}", shape=(self.k_states,)) - - self.ssm["initial_state", :] = init_state - - if self.estimate_cycle_length: - lamb = self.make_and_register_variable(f"{self.name}_length", shape=()) - else: - lamb = self.cycle_length - - if self.dampen: - rho = self.make_and_register_variable(f"{self.name}_dampening_factor", shape=()) - else: - rho = 1 - - T = rho * _frequency_transition_block(lamb, j=1) - self.ssm["transition", :, :] = T - - if self.innovations: - sigma_cycle = self.make_and_register_variable(f"sigma_{self.name}", shape=()) - self.ssm["state_cov", :, :] = pt.eye(self.k_posdef) * sigma_cycle**2 - - def populate_component_properties(self): - self.state_names = [f"{self.name}_{f}" for f in ["Cos", "Sin"]] - self.param_names = [f"{self.name}"] - - self.param_info = { - f"{self.name}": { - "shape": (2,), - "constraints": None, - "dims": (f"{self.name}_state",), - } - } - - if self.estimate_cycle_length: - self.param_names += [f"{self.name}_length"] - self.param_info[f"{self.name}_length"] = { - "shape": (), - "constraints": "Positive, non-zero", - "dims": None, - } - - if self.dampen: - self.param_names += [f"{self.name}_dampening_factor"] - self.param_info[f"{self.name}_dampening_factor"] = { - "shape": (), - "constraints": "0 < x ≤ 1", - "dims": None, - } - - if self.innovations: - self.param_names += [f"sigma_{self.name}"] - self.param_info[f"sigma_{self.name}"] = { - "shape": (), - "constraints": "Positive", - "dims": None, - } - self.shock_names = self.state_names.copy() - - -class RegressionComponent(Component): - def __init__( - self, - k_exog: int | None = None, - name: str | None = "Exogenous", - state_names: list[str] | None = None, - innovations=False, - ): - self.innovations = innovations - k_exog = self._handle_input_data(k_exog, state_names, name) - - k_states = k_exog - k_endog = 1 - k_posdef = k_exog - - super().__init__( - name=name, - k_endog=k_endog, - k_states=k_states, - k_posdef=k_posdef, - state_names=self.state_names, - measurement_error=False, - combine_hidden_states=False, - exog_names=[f"data_{name}"], - obs_state_idxs=np.ones(k_states), - ) - - @staticmethod - def _get_state_names(k_exog: int | None, state_names: list[str] | None, name: str): - if k_exog is None and state_names is None: - raise ValueError("Must specify at least one of k_exog or state_names") - if state_names is not None and k_exog is not None: - if len(state_names) != k_exog: - raise ValueError(f"Expected {k_exog} state names, found {len(state_names)}") - elif k_exog is None: - k_exog = len(state_names) - else: - state_names = [f"{name}_{i + 1}" for i in range(k_exog)] - - return k_exog, state_names - - def _handle_input_data(self, k_exog: int, state_names: list[str] | None, name) -> int: - k_exog, state_names = self._get_state_names(k_exog, state_names, name) - self.state_names = state_names - - return k_exog - - def make_symbolic_graph(self) -> None: - betas = self.make_and_register_variable(f"beta_{self.name}", shape=(self.k_states,)) - regression_data = self.make_and_register_data( - f"data_{self.name}", shape=(None, self.k_states) - ) - - self.ssm["initial_state", :] = betas - self.ssm["transition", :, :] = np.eye(self.k_states) - self.ssm["selection", :, :] = np.eye(self.k_states) - self.ssm["design"] = pt.expand_dims(regression_data, 1) - - if self.innovations: - sigma_beta = self.make_and_register_variable( - f"sigma_beta_{self.name}", (self.k_states,) - ) - row_idx, col_idx = np.diag_indices(self.k_states) - self.ssm["state_cov", row_idx, col_idx] = sigma_beta**2 - - def populate_component_properties(self) -> None: - self.shock_names = self.state_names - - self.param_names = [f"beta_{self.name}"] - self.data_names = [f"data_{self.name}"] - self.param_dims = { - f"beta_{self.name}": ("exog_state",), - } - - self.param_info = { - f"beta_{self.name}": { - "shape": (self.k_states,), - "constraints": None, - "dims": ("exog_state",), - }, - } - - self.data_info = { - f"data_{self.name}": { - "shape": (None, self.k_states), - "dims": (TIME_DIM, "exog_state"), - }, - } - self.coords = {"exog_state": self.state_names} - - if self.innovations: - self.param_names += [f"sigma_beta_{self.name}"] - self.param_dims[f"sigma_beta_{self.name}"] = "exog_state" - self.param_info[f"sigma_beta_{self.name}"] = { - "shape": (), - "constraints": "Positive", - "dims": ("exog_state",), - } diff --git a/pymc_extras/statespace/models/structural/__init__.py b/pymc_extras/statespace/models/structural/__init__.py new file mode 100644 index 000000000..57cb6d7ac --- /dev/null +++ b/pymc_extras/statespace/models/structural/__init__.py @@ -0,0 +1,21 @@ +from pymc_extras.statespace.models.structural.components.autoregressive import ( + AutoregressiveComponent, +) +from pymc_extras.statespace.models.structural.components.cycle import CycleComponent +from pymc_extras.statespace.models.structural.components.level_trend import LevelTrendComponent +from pymc_extras.statespace.models.structural.components.measurement_error import MeasurementError +from pymc_extras.statespace.models.structural.components.regression import RegressionComponent +from pymc_extras.statespace.models.structural.components.seasonality import ( + FrequencySeasonality, + TimeSeasonality, +) + +__all__ = [ + "LevelTrendComponent", + "MeasurementError", + "AutoregressiveComponent", + "TimeSeasonality", + "FrequencySeasonality", + "RegressionComponent", + "CycleComponent", +] diff --git a/pymc_extras/statespace/models/structural/components/__init__.py b/pymc_extras/statespace/models/structural/components/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/pymc_extras/statespace/models/structural/components/autoregressive.py b/pymc_extras/statespace/models/structural/components/autoregressive.py new file mode 100644 index 000000000..0eca94295 --- /dev/null +++ b/pymc_extras/statespace/models/structural/components/autoregressive.py @@ -0,0 +1,122 @@ +import numpy as np + +from pymc_extras.statespace.models.structural.core import Component +from pymc_extras.statespace.models.structural.utils import order_to_mask +from pymc_extras.statespace.utils.constants import AR_PARAM_DIM + + +class AutoregressiveComponent(Component): + r""" + Autoregressive timeseries component + + Parameters + ---------- + order: int or sequence of int + + If int, the number of lags to include in the model. + If a sequence, an array-like of zeros and ones indicating which lags to include in the model. + + Notes + ----- + An autoregressive component can be thought of as a way o introducing serially correlated errors into the model. + The process is modeled: + + .. math:: + x_t = \sum_{i=1}^p \rho_i x_{t-i} + + Where ``p``, the number of autoregressive terms to model, is the order of the process. By default, all lags up to + ``p`` are included in the model. To disable lags, pass a list of zeros and ones to the ``order`` argumnet. For + example, ``order=[1, 1, 0, 1]`` would become: + + .. math:: + x_t = \rho_1 x_{t-1} + \rho_2 x_{t-1} + \rho_4 x_{t-1} + + The coefficient :math:`\rho_3` has been constrained to zero. + + .. warning:: This class is meant to be used as a component in a structural time series model. For modeling of + stationary processes with ARIMA, use ``statespace.BayesianSARIMA``. + + Examples + -------- + Model a timeseries as an AR(2) process with non-zero mean: + + .. code:: python + + from pymc_extras.statespace import structural as st + import pymc as pm + import pytensor.tensor as pt + + trend = st.LevelTrendComponent(order=1, innovations_order=0) + ar = st.AutoregressiveComponent(2) + ss_mod = (trend + ar).build() + + with pm.Model(coords=ss_mod.coords) as model: + P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0']) + intitial_trend = pm.Normal('initial_trend', sigma=10, dims=ss_mod.param_dims['initial_trend']) + ar_params = pm.Normal('ar_params', dims=ss_mod.param_dims['ar_params']) + sigma_ar = pm.Exponential('sigma_ar', 1, dims=ss_mod.param_dims['sigma_ar']) + + ss_mod.build_statespace_graph(data) + idata = pm.sample(nuts_sampler='numpyro') + + """ + + def __init__( + self, + order: int = 1, + name: str = "AutoRegressive", + observed_state_names: list[str] | None = None, + ): + if observed_state_names is None: + observed_state_names = ["data"] + + order = order_to_mask(order) + ar_lags = np.flatnonzero(order).ravel().astype(int) + 1 + k_states = len(order) + k_posdef = k_endog = len(observed_state_names) + + self.order = order + self.ar_lags = ar_lags + + super().__init__( + name=name, + k_endog=k_endog, + k_states=k_states, + k_posdef=k_posdef, + measurement_error=True, + combine_hidden_states=True, + observed_state_names=observed_state_names, + obs_state_idxs=np.r_[[1.0], np.zeros(k_states - 1)], + ) + + def populate_component_properties(self): + self.state_names = [f"L{i + 1}.data" for i in range(self.k_states)] + self.shock_names = [f"{self.name}_innovation"] + self.param_names = ["ar_params", "sigma_ar"] + self.param_dims = {"ar_params": (AR_PARAM_DIM,)} + self.coords = {AR_PARAM_DIM: self.ar_lags.tolist()} + + self.param_info = { + "ar_params": { + "shape": (self.k_states,), + "constraints": None, + "dims": (AR_PARAM_DIM,), + }, + "sigma_ar": {"shape": (), "constraints": "Positive", "dims": None}, + } + + def make_symbolic_graph(self) -> None: + k_nonzero = int(sum(self.order)) + ar_params = self.make_and_register_variable("ar_params", shape=(k_nonzero,)) + sigma_ar = self.make_and_register_variable("sigma_ar", shape=()) + + T = np.eye(self.k_states, k=-1) + self.ssm["transition", :, :] = T + self.ssm["selection", 0, 0] = 1 + self.ssm["design", 0, 0] = 1 + + ar_idx = ("transition", np.zeros(k_nonzero, dtype="int"), np.nonzero(self.order)[0]) + self.ssm[ar_idx] = ar_params + + cov_idx = ("state_cov", *np.diag_indices(1)) + self.ssm[cov_idx] = sigma_ar**2 diff --git a/pymc_extras/statespace/models/structural/components/cycle.py b/pymc_extras/statespace/models/structural/components/cycle.py new file mode 100644 index 000000000..4c0f4603f --- /dev/null +++ b/pymc_extras/statespace/models/structural/components/cycle.py @@ -0,0 +1,201 @@ +import numpy as np + +from pytensor import tensor as pt + +from pymc_extras.statespace.models.structural.core import Component +from pymc_extras.statespace.models.structural.utils import _frequency_transition_block + + +class CycleComponent(Component): + r""" + A component for modeling longer-term cyclical effects + + Parameters + ---------- + name: str + Name of the component. Used in generated coordinates and state names. If None, a descriptive name will be + used. + + cycle_length: int, optional + The length of the cycle, in the calendar units of your data. For example, if your data is monthly, and you + want to model a 12-month cycle, use ``cycle_length=12``. You cannot specify both ``cycle_length`` and + ``estimate_cycle_length``. + + estimate_cycle_length: bool, default False + Whether to estimate the cycle length. If True, an additional parameter, ``cycle_length`` will be added to the + model. You cannot specify both ``cycle_length`` and ``estimate_cycle_length``. + + dampen: bool, default False + Whether to dampen the cycle by multiplying by a dampening factor :math:`\rho` at every timestep. If true, + an additional parameter, ``dampening_factor`` will be added to the model. + + innovations: bool, default True + Whether to include stochastic innovations in the strength of the seasonal effect. If True, an additional + parameter, ``sigma_{name}`` will be added to the model. + + Notes + ----- + The cycle component is very similar in implementation to the frequency domain seasonal component, expect that it + is restricted to n=1. The cycle component can be expressed: + + .. math:: + \begin{align} + \gamma_t &= \rho \gamma_{t-1} \cos \lambda + \rho \gamma_{t-1}^\star \sin \lambda + \omega_{t} \\ + \gamma_{t}^\star &= -\rho \gamma_{t-1} \sin \lambda + \rho \gamma_{t-1}^\star \cos \lambda + \omega_{t}^\star \\ + \lambda &= \frac{2\pi}{s} + \end{align} + + Where :math:`s` is the ``cycle_length``. [1] recommend that this component be used for longer term cyclical + effects, such as business cycles, and that the seasonal component be used for shorter term effects, such as + weekly or monthly seasonality. + + Unlike a FrequencySeasonality component, the length of a CycleComponent can be estimated. + + Examples + -------- + Estimate a business cycle with length between 6 and 12 years: + + .. code:: python + + from pymc_extras.statespace import structural as st + import pymc as pm + import pytensor.tensor as pt + import pandas as pd + import numpy as np + + data = np.random.normal(size=(100, 1)) + + # Build the structural model + grw = st.LevelTrendComponent(order=1, innovations_order=1) + cycle = st.CycleComponent('business_cycle', estimate_cycle_length=True, dampen=False) + ss_mod = (grw + cycle).build() + + # Estimate with PyMC + with pm.Model(coords=ss_mod.coords) as model: + P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states), dims=ss_mod.param_dims['P0']) + intitial_trend = pm.Normal('initial_trend', dims=ss_mod.param_dims['initial_trend']) + sigma_trend = pm.HalfNormal('sigma_trend', dims=ss_mod.param_dims['sigma_trend']) + + cycle_strength = pm.Normal('business_cycle') + cycle_length = pm.Uniform('business_cycle_length', lower=6, upper=12) + + sigma_cycle = pm.HalfNormal('sigma_business_cycle', sigma=1) + ss_mod.build_statespace_graph(data) + + idata = pm.sample(nuts_sampler='numpyro') + + References + ---------- + .. [1] Durbin, James, and Siem Jan Koopman. 2012. + Time Series Analysis by State Space Methods: Second Edition. + Oxford University Press. + """ + + def __init__( + self, + name: str | None = None, + cycle_length: int | None = None, + estimate_cycle_length: bool = False, + dampen: bool = False, + innovations: bool = True, + observed_state_names: list[str] | None = None, + ): + if observed_state_names is None: + observed_state_names = ["data"] + + if cycle_length is None and not estimate_cycle_length: + raise ValueError("Must specify cycle_length if estimate_cycle_length is False") + if cycle_length is not None and estimate_cycle_length: + raise ValueError("Cannot specify cycle_length if estimate_cycle_length is True") + if name is None: + cycle = int(cycle_length) if cycle_length is not None else "Estimate" + name = f"Cycle[s={cycle}, dampen={dampen}, innovations={innovations}]" + + self.estimate_cycle_length = estimate_cycle_length + self.cycle_length = cycle_length + self.innovations = innovations + self.dampen = dampen + self.n_coefs = 1 + + k_endog = len(observed_state_names) + + k_states = 2 * k_endog + k_posdef = 2 * k_endog + + obs_state_idx = np.zeros(k_states) + obs_state_idx[slice(0, k_states, 2)] = 1 + + super().__init__( + name=name, + k_endog=k_endog, + k_states=k_states, + k_posdef=k_posdef, + measurement_error=False, + combine_hidden_states=True, + obs_state_idxs=obs_state_idx, + observed_state_names=observed_state_names, + ) + + def make_symbolic_graph(self) -> None: + self.ssm["design", 0, slice(0, self.k_states, 2)] = 1 + self.ssm["selection", :, :] = np.eye(self.k_states) + self.param_dims = {self.name: (f"{self.name}_state",)} + self.coords = {f"{self.name}_state": self.state_names} + + init_state = self.make_and_register_variable(f"{self.name}", shape=(self.k_states,)) + + self.ssm["initial_state", :] = init_state + + if self.estimate_cycle_length: + lamb = self.make_and_register_variable(f"{self.name}_length", shape=()) + else: + lamb = self.cycle_length + + if self.dampen: + rho = self.make_and_register_variable(f"{self.name}_dampening_factor", shape=()) + else: + rho = 1 + + T = rho * _frequency_transition_block(lamb, j=1) + self.ssm["transition", :, :] = T + + if self.innovations: + sigma_cycle = self.make_and_register_variable(f"sigma_{self.name}", shape=()) + self.ssm["state_cov", :, :] = pt.eye(self.k_posdef) * sigma_cycle**2 + + def populate_component_properties(self): + self.state_names = [f"{self.name}_{f}" for f in ["Cos", "Sin"]] + self.param_names = [f"{self.name}"] + + self.param_info = { + f"{self.name}": { + "shape": (2,), + "constraints": None, + "dims": (f"{self.name}_state",), + } + } + + if self.estimate_cycle_length: + self.param_names += [f"{self.name}_length"] + self.param_info[f"{self.name}_length"] = { + "shape": (), + "constraints": "Positive, non-zero", + "dims": None, + } + + if self.dampen: + self.param_names += [f"{self.name}_dampening_factor"] + self.param_info[f"{self.name}_dampening_factor"] = { + "shape": (), + "constraints": "0 < x ≤ 1", + "dims": None, + } + + if self.innovations: + self.param_names += [f"sigma_{self.name}"] + self.param_info[f"sigma_{self.name}"] = { + "shape": (), + "constraints": "Positive", + "dims": None, + } + self.shock_names = self.state_names.copy() diff --git a/pymc_extras/statespace/models/structural/components/level_trend.py b/pymc_extras/statespace/models/structural/components/level_trend.py new file mode 100644 index 000000000..b3372f822 --- /dev/null +++ b/pymc_extras/statespace/models/structural/components/level_trend.py @@ -0,0 +1,196 @@ +import numpy as np + +from pymc_extras.statespace.models.structural.core import Component +from pymc_extras.statespace.models.structural.utils import order_to_mask +from pymc_extras.statespace.utils.constants import POSITION_DERIVATIVE_NAMES + + +class LevelTrendComponent(Component): + r""" + Level and trend component of a structural time series model + + Parameters + ---------- + __________ + order : int + + Number of time derivatives of the trend to include in the model. For example, when order=3, the trend will + be of the form ``y = a + b * t + c * t ** 2``, where the coefficients ``a, b, c`` come from the initial + state values. + + innovations_order : int or sequence of int, optional + + The number of stochastic innovations to include in the model. By default, ``innovations_order = order`` + + Notes + ----- + This class implements the level and trend components of the general structural time series model. In the most + general form, the level and trend is described by a system of two time-varying equations. + + .. math:: + \begin{align} + \mu_{t+1} &= \mu_t + \nu_t + \zeta_t \\ + \nu_{t+1} &= \nu_t + \xi_t + \zeta_t &\sim N(0, \sigma_\zeta) \\ + \xi_t &\sim N(0, \sigma_\xi) + \end{align} + + Where :math:`\mu_{t+1}` is the mean of the timeseries at time t, and :math:`\nu_t` is the drift or the slope of + the process. When both innovations :math:`\zeta_t` and :math:`\xi_t` are included in the model, it is known as a + *local linear trend* model. This system of two equations, corresponding to ``order=2``, can be expanded or + contracted by adding or removing equations. ``order=3`` would add an acceleration term to the sytsem: + + .. math:: + \begin{align} + \mu_{t+1} &= \mu_t + \nu_t + \zeta_t \\ + \nu_{t+1} &= \nu_t + \eta_t + \xi_t \\ + \eta_{t+1} &= \eta_{t-1} + \omega_t \\ + \zeta_t &\sim N(0, \sigma_\zeta) \\ + \xi_t &\sim N(0, \sigma_\xi) \\ + \omega_t &\sim N(0, \sigma_\omega) + \end{align} + + After setting all innovation terms to zero and defining initial states :math:`\mu_0, \nu_0, \eta_0`, these equations + can be collapsed to: + + .. math:: + \mu_t = \mu_0 + \nu_0 \cdot t + \eta_0 \cdot t^2 + + Which clarifies how the order and initial states influence the model. In particular, the initial states are the + coefficients on the intercept, slope, acceleration, and so on. + + In this light, allowing for innovations can be understood as allowing these coefficients to vary over time. Each + component can be individually selected for time variation by passing a list to the ``innovations_order`` argument. + For example, a constant intercept with time varying trend and acceleration is specified as ``order=3, + innovations_order=[0, 1, 1]``. + + By choosing the ``order`` and ``innovations_order``, a large variety of models can be obtained. Notable + models include: + + * Constant intercept, ``order=1, innovations_order=0`` + + .. math:: + \mu_t = \mu + + * Constant linear slope, ``order=2, innovations_order=0`` + + .. math:: + \mu_t = \mu_{t-1} + \nu + + * Gaussian Random Walk, ``order=1, innovations_order=1`` + + .. math:: + \mu_t = \mu_{t-1} + \zeta_t + + * Gaussian Random Walk with Drift, ``order=2, innovations_order=1`` + + .. math:: + \mu_t = \mu_{t-1} + \nu + \zeta_t + + * Smooth Trend, ``order=2, innovations_order=[0, 1]`` + + .. math:: + \begin{align} + \mu_t &= \mu_{t-1} + \nu_{t-1} \\ + \nu_t &= \nu_{t-1} + \xi_t + \end{align} + + * Local Level, ``order=2, innovations_order=2`` + + [1] notes that the smooth trend model produces more gradually changing slopes than the full local linear trend + model, and is equivalent to an "integrated trend model". + + References + ---------- + .. [1] Durbin, James, and Siem Jan Koopman. 2012. + Time Series Analysis by State Space Methods: Second Edition. + Oxford University Press. + + """ + + def __init__( + self, + order: int | list[int] = 2, + innovations_order: int | list[int] | None = None, + name: str = "LevelTrend", + observed_state_names: list[str] | None = None, + ): + if innovations_order is None: + innovations_order = order + + if observed_state_names is None: + observed_state_names = ["data"] + + self._order_mask = order_to_mask(order) + max_state = np.flatnonzero(self._order_mask)[-1].item() + 1 + + # If the user passes excess zeros, raise an error. The alternative is to prune them, but this would cause + # the shape of the state to be different to what the user expects. + if len(self._order_mask) > max_state: + raise ValueError( + f"order={order} is invalid. The highest derivative should not be set to zero. If you want a " + f"lower order model, explicitly omit the zeros." + ) + k_states = max_state + + if isinstance(innovations_order, int): + n = innovations_order + innovations_order = order_to_mask(k_states) + if n > 0: + innovations_order[n:] = False + else: + innovations_order[:] = False + else: + innovations_order = order_to_mask(innovations_order) + + self.innovations_order = innovations_order[:max_state] + k_posdef = int(sum(innovations_order)) + + super().__init__( + name, + k_endog=len(observed_state_names), + k_states=k_states, + k_posdef=k_posdef, + observed_state_names=observed_state_names, + measurement_error=False, + combine_hidden_states=False, + obs_state_idxs=np.array([1.0] + [0.0] * (k_states - 1)), + ) + + def populate_component_properties(self): + name_slice = POSITION_DERIVATIVE_NAMES[: self.k_states] + self.param_names = ["initial_trend"] + self.state_names = [name for name, mask in zip(name_slice, self._order_mask) if mask] + self.param_dims = {"initial_trend": ("trend_state",)} + self.coords = {"trend_state": self.state_names} + self.param_info = {"initial_trend": {"shape": (self.k_states,), "constraints": None}} + + if self.k_posdef > 0: + self.param_names += ["sigma_trend"] + self.shock_names = [ + name for name, mask in zip(name_slice, self.innovations_order) if mask + ] + self.param_dims["sigma_trend"] = ("trend_shock",) + self.coords["trend_shock"] = self.shock_names + self.param_info["sigma_trend"] = {"shape": (self.k_posdef,), "constraints": "Positive"} + + for name in self.param_names: + self.param_info[name]["dims"] = self.param_dims[name] + + def make_symbolic_graph(self) -> None: + initial_trend = self.make_and_register_variable("initial_trend", shape=(self.k_states,)) + self.ssm["initial_state", :] = initial_trend + triu_idx = np.triu_indices(self.k_states) + self.ssm[np.s_["transition", triu_idx[0], triu_idx[1]]] = 1 + + R = np.eye(self.k_states) + R = R[:, self.innovations_order] + self.ssm["selection", :, :] = R + + self.ssm["design", 0, :] = np.array([1.0] + [0.0] * (self.k_states - 1)) + + if self.k_posdef > 0: + sigma_trend = self.make_and_register_variable("sigma_trend", shape=(self.k_posdef,)) + diag_idx = np.diag_indices(self.k_posdef) + idx = np.s_["state_cov", diag_idx[0], diag_idx[1]] + self.ssm[idx] = sigma_trend**2 diff --git a/pymc_extras/statespace/models/structural/components/measurement_error.py b/pymc_extras/statespace/models/structural/components/measurement_error.py new file mode 100644 index 000000000..4017f0551 --- /dev/null +++ b/pymc_extras/statespace/models/structural/components/measurement_error.py @@ -0,0 +1,80 @@ +import numpy as np + +from pymc_extras.statespace.models.structural.core import Component + + +class MeasurementError(Component): + r""" + Measurement error term for a structural timeseries model + + Parameters + ---------- + name: str, optional + + Name of the observed data. Default is "obs". + + Notes + ----- + This component should only be used in combination with other components, because it has no states. It's only use + is to add a variance parameter to the model, associated with the observation noise matrix H. + + Examples + -------- + Create and estimate a deterministic linear trend with measurement error + + .. code:: python + + from pymc_extras.statespace import structural as st + import pymc as pm + import pytensor.tensor as pt + + trend = st.LevelTrendComponent(order=2, innovations_order=0) + error = st.MeasurementError() + ss_mod = (trend + error).build() + + with pm.Model(coords=ss_mod.coords) as model: + P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0']) + intitial_trend = pm.Normal('initial_trend', sigma=10, dims=ss_mod.param_dims['initial_trend']) + sigma_obs = pm.Exponential('sigma_obs', 1, dims=ss_mod.param_dims['sigma_obs']) + + ss_mod.build_statespace_graph(data) + idata = pm.sample(nuts_sampler='numpyro') + """ + + def __init__( + self, name: str = "MeasurementError", observed_state_names: list[str] | None = None + ): + if observed_state_names is None: + observed_state_names = ["data"] + + k_endog = len(observed_state_names) + k_states = 0 + k_posdef = 0 + + super().__init__( + name, + k_endog, + k_states, + k_posdef, + measurement_error=True, + combine_hidden_states=False, + observed_state_names=observed_state_names, + ) + + def populate_component_properties(self): + self.param_names = [f"sigma_{self.name}"] + self.param_dims = {} + self.param_info = { + f"sigma_{self.name}": { + "shape": (), + "constraints": "Positive", + "dims": None, + } + } + + def make_symbolic_graph(self) -> None: + sigma_shape = () + error_sigma = self.make_and_register_variable(f"sigma_{self.name}", shape=sigma_shape) + diag_idx = np.diag_indices(self.k_endog) + idx = np.s_["obs_cov", diag_idx[0], diag_idx[1]] + self.ssm[idx] = error_sigma**2 diff --git a/pymc_extras/statespace/models/structural/components/regression.py b/pymc_extras/statespace/models/structural/components/regression.py new file mode 100644 index 000000000..c290812c2 --- /dev/null +++ b/pymc_extras/statespace/models/structural/components/regression.py @@ -0,0 +1,111 @@ +import numpy as np + +from pytensor import tensor as pt + +from pymc_extras.statespace.models.structural.core import Component +from pymc_extras.statespace.utils.constants import TIME_DIM + + +class RegressionComponent(Component): + def __init__( + self, + k_exog: int | None = None, + name: str | None = "Exogenous", + state_names: list[str] | None = None, + observed_state_names: list[str] | None = None, + innovations=False, + ): + if observed_state_names is None: + observed_state_names = ["data"] + + self.innovations = innovations + k_exog = self._handle_input_data(k_exog, state_names, name) + + k_states = k_exog + k_endog = len(observed_state_names) + k_posdef = k_exog + + super().__init__( + name=name, + k_endog=k_endog, + k_states=k_states, + k_posdef=k_posdef, + state_names=self.state_names, + observed_state_names=observed_state_names, + measurement_error=False, + combine_hidden_states=False, + exog_names=[f"data_{name}"], + obs_state_idxs=np.ones(k_states), + ) + + @staticmethod + def _get_state_names(k_exog: int | None, state_names: list[str] | None, name: str): + if k_exog is None and state_names is None: + raise ValueError("Must specify at least one of k_exog or state_names") + if state_names is not None and k_exog is not None: + if len(state_names) != k_exog: + raise ValueError(f"Expected {k_exog} state names, found {len(state_names)}") + elif k_exog is None: + k_exog = len(state_names) + else: + state_names = [f"{name}_{i + 1}" for i in range(k_exog)] + + return k_exog, state_names + + def _handle_input_data(self, k_exog: int, state_names: list[str] | None, name) -> int: + k_exog, state_names = self._get_state_names(k_exog, state_names, name) + self.state_names = state_names + + return k_exog + + def make_symbolic_graph(self) -> None: + betas = self.make_and_register_variable(f"beta_{self.name}", shape=(self.k_states,)) + regression_data = self.make_and_register_data( + f"data_{self.name}", shape=(None, self.k_states) + ) + + self.ssm["initial_state", :] = betas + self.ssm["transition", :, :] = np.eye(self.k_states) + self.ssm["selection", :, :] = np.eye(self.k_states) + self.ssm["design"] = pt.expand_dims(regression_data, 1) + + if self.innovations: + sigma_beta = self.make_and_register_variable( + f"sigma_beta_{self.name}", (self.k_states,) + ) + row_idx, col_idx = np.diag_indices(self.k_states) + self.ssm["state_cov", row_idx, col_idx] = sigma_beta**2 + + def populate_component_properties(self) -> None: + self.shock_names = self.state_names + + self.param_names = [f"beta_{self.name}"] + self.data_names = [f"data_{self.name}"] + self.param_dims = { + f"beta_{self.name}": ("exog_state",), + } + + self.param_info = { + f"beta_{self.name}": { + "shape": (self.k_states,), + "constraints": None, + "dims": ("exog_state",), + }, + } + + self.data_info = { + f"data_{self.name}": { + "shape": (None, self.k_states), + "dims": (TIME_DIM, "exog_state"), + }, + } + self.coords = {"exog_state": self.state_names} + + if self.innovations: + self.param_names += [f"sigma_beta_{self.name}"] + self.param_dims[f"sigma_beta_{self.name}"] = "exog_state" + self.param_info[f"sigma_beta_{self.name}"] = { + "shape": (), + "constraints": "Positive", + "dims": ("exog_state",), + } diff --git a/pymc_extras/statespace/models/structural/components/seasonality.py b/pymc_extras/statespace/models/structural/components/seasonality.py new file mode 100644 index 000000000..20f47636f --- /dev/null +++ b/pymc_extras/statespace/models/structural/components/seasonality.py @@ -0,0 +1,353 @@ +import numpy as np + +from pytensor import tensor as pt + +from pymc_extras.statespace.models.structural.core import Component +from pymc_extras.statespace.models.structural.utils import _frequency_transition_block + + +class TimeSeasonality(Component): + r""" + Seasonal component, modeled in the time domain + + Parameters + ---------- + season_length: int + The number of periods in a single seasonal cycle, e.g. 12 for monthly data with annual seasonal pattern, 7 for + daily data with weekly seasonal pattern, etc. + + innovations: bool, default True + Whether to include stochastic innovations in the strength of the seasonal effect + + name: str, default None + A name for this seasonal component. Used to label dimensions and coordinates. Useful when multiple seasonal + components are included in the same model. Default is ``f"Seasonal[s={season_length}]"`` + + state_names: list of str, default None + List of strings for seasonal effect labels. If provided, it must be of length ``season_length``. An example + would be ``state_names = ['Mon', 'Tue', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun']`` when data is daily with a weekly + seasonal pattern (``season_length = 7``). + + If None, states will be numbered ``[State_0, ..., State_s]`` + + remove_first_state: bool, default True + If True, the first state will be removed from the model. This is done because there are only n-1 degrees of + freedom in the seasonal component, and one state is not identified. If False, the first state will be + included in the model, but it will not be identified -- you will need to handle this in the priors (e.g. with + ZeroSumNormal). + + Notes + ----- + A seasonal effect is any pattern that repeats every fixed interval. Although there are many possible ways to + model seasonal effects, the implementation used here is the one described by [1] as the "canonical" time domain + representation. The seasonal component can be expressed: + + .. math:: + \gamma_t = -\sum_{i=1}^{s-1} \gamma_{t-i} + \omega_t, \quad \omega_t \sim N(0, \sigma_\gamma) + + Where :math:`s` is the ``seasonal_length`` parameter and :math:`\omega_t` is the (optional) stochastic innovation. + To give interpretation to the :math:`\gamma` terms, it is helpful to work through the algebra for a simple + example. Let :math:`s=4`, and omit the shock term. Define initial conditions :math:`\gamma_0, \gamma_{-1}, + \gamma_{-2}`. The value of the seasonal component for the first 5 timesteps will be: + + .. math:: + \begin{align} + \gamma_1 &= -\gamma_0 - \gamma_{-1} - \gamma_{-2} \\ + \gamma_2 &= -\gamma_1 - \gamma_0 - \gamma_{-1} \\ + &= -(-\gamma_0 - \gamma_{-1} - \gamma_{-2}) - \gamma_0 - \gamma_{-1} \\ + &= (\gamma_0 - \gamma_0 )+ (\gamma_{-1} - \gamma_{-1}) + \gamma_{-2} \\ + &= \gamma_{-2} \\ + \gamma_3 &= -\gamma_2 - \gamma_1 - \gamma_0 \\ + &= -\gamma_{-2} - (-\gamma_0 - \gamma_{-1} - \gamma_{-2}) - \gamma_0 \\ + &= (\gamma_{-2} - \gamma_{-2}) + \gamma_{-1} + (\gamma_0 - \gamma_0) \\ + &= \gamma_{-1} \\ + \gamma_4 &= -\gamma_3 - \gamma_2 - \gamma_1 \\ + &= -\gamma_{-1} - \gamma_{-2} -(-\gamma_0 - \gamma_{-1} - \gamma_{-2}) \\ + &= (\gamma_{-2} - \gamma_{-2}) + (\gamma_{-1} - \gamma_{-1}) + \gamma_0 \\ + &= \gamma_0 \\ + \gamma_5 &= -\gamma_4 - \gamma_3 - \gamma_2 \\ + &= -\gamma_0 - \gamma_{-1} - \gamma_{-2} \\ + &= \gamma_1 + \end{align} + + This exercise shows that, given a list ``initial_conditions`` of length ``s-1``, the effects of this model will be: + + - Period 1: ``-sum(initial_conditions)`` + - Period 2: ``initial_conditions[-1]`` + - Period 3: ``initial_conditions[-2]`` + - ... + - Period s: ``initial_conditions[0]`` + - Period s+1: ``-sum(initial_condition)`` + + And so on. So for interpretation, the ``season_length - 1`` initial states are, when reversed, the coefficients + associated with ``state_names[1:]``. + + .. warning:: + Although the ``state_names`` argument expects a list of length ``season_length``, only ``state_names[1:]`` + will be saved as model dimensions, since the 1st coefficient is not identified (it is defined as + :math:`-\sum_{i=1}^{s} \gamma_{t-i}`). + + Examples + -------- + Estimate monthly with a model with a gaussian random walk trend and monthly seasonality: + + .. code:: python + + from pymc_extras.statespace import structural as st + import pymc as pm + import pytensor.tensor as pt + import pandas as pd + + # Get month names + state_names = pd.date_range('1900-01-01', '1900-12-31', freq='MS').month_name().tolist() + + # Build the structural model + grw = st.LevelTrendComponent(order=1, innovations_order=1) + annual_season = st.TimeSeasonality(season_length=12, name='annual', state_names=state_names, innovations=False) + ss_mod = (grw + annual_season).build() + + # Estimate with PyMC + with pm.Model(coords=ss_mod.coords) as model: + P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0']) + intitial_trend = pm.Deterministic('initial_trend', pt.zeros(1), dims=ss_mod.param_dims['initial_trend']) + annual_coefs = pm.Normal('annual_coefs', sigma=1e-2, dims=ss_mod.param_dims['annual_coefs']) + trend_sigmas = pm.HalfNormal('trend_sigmas', sigma=1e-6, dims=ss_mod.param_dims['trend_sigmas']) + ss_mod.build_statespace_graph(data) + idata = pm.sample(nuts_sampler='numpyro') + + References + ---------- + .. [1] Durbin, James, and Siem Jan Koopman. 2012. + Time Series Analysis by State Space Methods: Second Edition. + Oxford University Press. + """ + + def __init__( + self, + season_length: int, + innovations: bool = True, + name: str | None = None, + state_names: list | None = None, + remove_first_state: bool = True, + observed_state_names: list[str] | None = None, + ): + if observed_state_names is None: + observed_state_names = ["data"] + + if name is None: + name = f"Seasonal[s={season_length}]" + if state_names is None: + state_names = [f"{name}_{i}" for i in range(season_length)] + else: + if len(state_names) != season_length: + raise ValueError( + f"state_names must be a list of length season_length, got {len(state_names)}" + ) + state_names = state_names.copy() + + self.innovations = innovations + self.remove_first_state = remove_first_state + + if self.remove_first_state: + # In traditional models, the first state isn't identified, so we can help out the user by automatically + # discarding it. + # TODO: Can this be stashed and reconstructed automatically somehow? + state_names.pop(0) + + k_states = season_length - int(self.remove_first_state) + + super().__init__( + name=name, + k_endog=len(observed_state_names), + k_states=k_states, + k_posdef=int(innovations), + state_names=state_names, + observed_state_names=observed_state_names, + measurement_error=False, + combine_hidden_states=True, + obs_state_idxs=np.r_[[1.0], np.zeros(k_states - 1)], + ) + + def populate_component_properties(self): + self.param_names = [f"{self.name}_coefs"] + self.param_info = { + f"{self.name}_coefs": { + "shape": (self.k_states,), + "constraints": None, + "dims": (f"{self.name}_state",), + } + } + self.param_dims = {f"{self.name}_coefs": (f"{self.name}_state",)} + self.coords = {f"{self.name}_state": self.state_names} + + if self.innovations: + self.param_names += [f"sigma_{self.name}"] + self.param_info[f"sigma_{self.name}"] = { + "shape": (), + "constraints": "Positive", + "dims": None, + } + self.shock_names = [f"{self.name}"] + + def make_symbolic_graph(self) -> None: + if self.remove_first_state: + # In this case, parameters are normalized to sum to zero, so the current state is the negative sum of + # all previous states. + T = np.eye(self.k_states, k=-1) + T[0, :] = -1 + else: + # In this case we assume the user to be responsible for ensuring the states sum to zero, so T is just a + # circulant matrix that cycles between the states. + T = np.eye(self.k_states, k=1) + T[-1, 0] = 1 + + self.ssm["transition", :, :] = T + self.ssm["design", 0, 0] = 1 + + initial_states = self.make_and_register_variable( + f"{self.name}_coefs", shape=(self.k_states,) + ) + self.ssm["initial_state", np.arange(self.k_states, dtype=int)] = initial_states + + if self.innovations: + self.ssm["selection", 0, 0] = 1 + season_sigma = self.make_and_register_variable(f"sigma_{self.name}", shape=()) + cov_idx = ("state_cov", *np.diag_indices(1)) + self.ssm[cov_idx] = season_sigma**2 + + +class FrequencySeasonality(Component): + r""" + Seasonal component, modeled in the frequency domain + + Parameters + ---------- + season_length: float + The number of periods in a single seasonal cycle, e.g. 12 for monthly data with annual seasonal pattern, 7 for + daily data with weekly seasonal pattern, etc. Non-integer seasonal_length is also permitted, for example + 365.2422 days in a (solar) year. + + n: int + Number of fourier features to include in the seasonal component. Default is ``season_length // 2``, which + is the maximum possible. A smaller number can be used for a more wave-like seasonal pattern. + + name: str, default None + A name for this seasonal component. Used to label dimensions and coordinates. Useful when multiple seasonal + components are included in the same model. Default is ``f"Seasonal[s={season_length}, n={n}]"`` + + innovations: bool, default True + Whether to include stochastic innovations in the strength of the seasonal effect + + Notes + ----- + A seasonal effect is any pattern that repeats every fixed interval. Although there are many possible ways to + model seasonal effects, the implementation used here is the one described by [1] as the "canonical" frequency domain + representation. The seasonal component can be expressed: + + .. math:: + \begin{align} + \gamma_t &= \sum_{j=1}^{2n} \gamma_{j,t} \\ + \gamma_{j, t+1} &= \gamma_{j,t} \cos \lambda_j + \gamma_{j,t}^\star \sin \lambda_j + \omega_{j, t} \\ + \gamma_{j, t}^\star &= -\gamma_{j,t} \sin \lambda_j + \gamma_{j,t}^\star \cos \lambda_j + \omega_{j,t}^\star + \lambda_j &= \frac{2\pi j}{s} + \end{align} + + Where :math:`s` is the ``seasonal_length``. + + Unlike a ``TimeSeasonality`` component, a ``FrequencySeasonality`` component does not require integer season + length. In addition, for long seasonal periods, it is possible to obtain a more compact state space representation + by choosing ``n << s // 2``. Using ``TimeSeasonality``, an annual seasonal pattern in daily data requires 364 + states, whereas ``FrequencySeasonality`` always requires ``2 * n`` states, regardless of the ``seasonal_length``. + The price of this compactness is less representational power. At ``n = 1``, the seasonal pattern will be a pure + sine wave. At ``n = s // 2``, any arbitrary pattern can be represented. + + One cost of the added flexibility of ``FrequencySeasonality`` is reduced interpretability. States of this model are + coefficients :math:`\gamma_1, \gamma^\star_1, \gamma_2, \gamma_2^\star ..., \gamma_n, \gamma^\star_n` associated + with different frequencies in the fourier representation of the seasonal pattern. As a result, it is not possible + to isolate and identify a "Monday" effect, for instance. + """ + + def __init__( + self, + season_length, + n=None, + name=None, + innovations=True, + observed_state_names: list[str] | None = None, + ): + if observed_state_names is None: + observed_state_names = ["data"] + + if n is None: + n = int(season_length // 2) + if name is None: + name = f"Frequency[s={season_length}, n={n}]" + + k_states = n * 2 + self.n = n + self.season_length = season_length + self.innovations = innovations + + # If the model is completely saturated (n = s // 2), the last state will not be identified, so it shouldn't + # get a parameter assigned to it and should just be fixed to zero. + # Test this way (rather than n == s // 2) to catch cases when n is non-integer. + self.last_state_not_identified = self.season_length / self.n == 2.0 + self.n_coefs = k_states - int(self.last_state_not_identified) + + obs_state_idx = np.zeros(k_states) + obs_state_idx[slice(0, k_states, 2)] = 1 + + super().__init__( + name=name, + k_endog=1, + k_states=k_states, + k_posdef=k_states * int(self.innovations), + observed_state_names=observed_state_names, + measurement_error=False, + combine_hidden_states=True, + obs_state_idxs=obs_state_idx, + ) + + def make_symbolic_graph(self) -> None: + self.ssm["design", 0, slice(0, self.k_states, 2)] = 1 + + init_state = self.make_and_register_variable(f"{self.name}", shape=(self.n_coefs,)) + + init_state_idx = np.arange(self.n_coefs, dtype=int) + self.ssm["initial_state", init_state_idx] = init_state + + T_mats = [_frequency_transition_block(self.season_length, j + 1) for j in range(self.n)] + T = pt.linalg.block_diag(*T_mats) + self.ssm["transition", :, :] = T + + if self.innovations: + sigma_season = self.make_and_register_variable(f"sigma_{self.name}", shape=()) + self.ssm["state_cov", :, :] = pt.eye(self.k_posdef) * sigma_season**2 + self.ssm["selection", :, :] = np.eye(self.k_states) + + def populate_component_properties(self): + self.state_names = [f"{self.name}_{f}_{i}" for i in range(self.n) for f in ["Cos", "Sin"]] + self.param_names = [f"{self.name}"] + + self.param_dims = {self.name: (f"{self.name}_state",)} + self.param_info = { + f"{self.name}": { + "shape": (self.k_states - int(self.last_state_not_identified),), + "constraints": None, + "dims": (f"{self.name}_state",), + } + } + + init_state_idx = np.arange(self.k_states, dtype=int) + if self.last_state_not_identified: + init_state_idx = init_state_idx[:-1] + self.coords = {f"{self.name}_state": [self.state_names[i] for i in init_state_idx]} + + if self.innovations: + self.shock_names = self.state_names.copy() + self.param_names += [f"sigma_{self.name}"] + self.param_info[f"sigma_{self.name}"] = { + "shape": (), + "constraints": "Positive", + "dims": None, + } diff --git a/pymc_extras/statespace/models/structural/core.py b/pymc_extras/statespace/models/structural/core.py new file mode 100644 index 000000000..1a273a6e2 --- /dev/null +++ b/pymc_extras/statespace/models/structural/core.py @@ -0,0 +1,697 @@ +import functools as ft +import logging + +from collections.abc import Sequence +from itertools import pairwise +from typing import Any + +import numpy as np +import xarray as xr + +from pytensor import Mode, Variable, config +from pytensor import tensor as pt + +from pymc_extras.statespace.core import PyMCStateSpace, PytensorRepresentation +from pymc_extras.statespace.models.utilities import ( + conform_time_varying_and_time_invariant_matrices, + make_default_coords, +) +from pymc_extras.statespace.utils.constants import ( + ALL_STATE_AUX_DIM, + ALL_STATE_DIM, + LONG_MATRIX_NAMES, +) + +_log = logging.getLogger(__name__) +floatX = config.floatX + + +class StructuralTimeSeries(PyMCStateSpace): + r""" + Structural Time Series Model + + The structural time series model, named by [1] and presented in statespace form in [2], is a framework for + decomposing a univariate time series into level, trend, seasonal, and cycle components. It also admits the + possibility of exogenous regressors. Unlike the SARIMAX framework, the time series is not assumed to be stationary. + + Notes + ----- + + .. math:: + + y_t = \mu_t + \gamma_t + c_t + \varepsilon_t + + """ + + def __init__( + self, + ssm: PytensorRepresentation, + name: str, + state_names: list[str], + observed_state_names: list[str], + data_names: list[str], + shock_names: list[str], + param_names: list[str], + exog_names: list[str], + param_dims: dict[str, tuple[int]], + coords: dict[str, Sequence], + param_info: dict[str, dict[str, Any]], + data_info: dict[str, dict[str, Any]], + component_info: dict[str, dict[str, Any]], + measurement_error: bool, + name_to_variable: dict[str, Variable], + name_to_data: dict[str, Variable] | None = None, + verbose: bool = True, + filter_type: str = "standard", + mode: str | Mode | None = None, + ): + name = "StructuralTimeSeries" if name is None else name + + self._name = name + self._observed_state_names = observed_state_names + + k_states, k_posdef, k_endog = ssm.k_states, ssm.k_posdef, ssm.k_endog + param_names, param_dims, param_info = self._add_inital_state_cov_to_properties( + param_names, param_dims, param_info, k_states + ) + self._state_names = state_names.copy() + self._data_names = data_names.copy() + self._shock_names = shock_names.copy() + self._param_names = param_names.copy() + self._param_dims = param_dims.copy() + + default_coords = make_default_coords(self) + coords.update(default_coords) + + self._coords = coords + self._param_info = param_info.copy() + self._data_info = data_info.copy() + self.measurement_error = measurement_error + + super().__init__( + k_endog, + k_states, + max(1, k_posdef), + filter_type=filter_type, + verbose=verbose, + measurement_error=measurement_error, + mode=mode, + ) + self.ssm = ssm.copy() + + if k_posdef == 0: + # If there is no randomness in the model, add dummy matrices to the representation to avoid errors + # when we go to construct random variables from the matrices + self.ssm.k_posdef = self.k_posdef + self.ssm.shapes["state_cov"] = (1, 1, 1) + self.ssm["state_cov"] = pt.zeros((1, 1, 1)) + + self.ssm.shapes["selection"] = (1, self.k_states, 1) + self.ssm["selection"] = pt.zeros((1, self.k_states, 1)) + + self._component_info = component_info.copy() + + self._name_to_variable = name_to_variable.copy() + self._name_to_data = name_to_data.copy() + + self._exog_names = exog_names.copy() + self._needs_exog_data = len(exog_names) > 0 + + P0 = self.make_and_register_variable("P0", shape=(self.k_states, self.k_states)) + self.ssm["initial_state_cov"] = P0 + + @staticmethod + def _add_inital_state_cov_to_properties(param_names, param_dims, param_info, k_states): + param_names += ["P0"] + param_dims["P0"] = (ALL_STATE_DIM, ALL_STATE_AUX_DIM) + param_info["P0"] = { + "shape": (k_states, k_states), + "constraints": "Positive semi-definite", + "dims": param_dims["P0"], + } + + return param_names, param_dims, param_info + + @property + def param_names(self): + return self._param_names + + @property + def data_names(self) -> list[str]: + return self._data_names + + @property + def state_names(self): + return self._state_names + + @property + def observed_states(self): + return self._observed_state_names + + @property + def shock_names(self): + return self._shock_names + + @property + def param_dims(self): + return self._param_dims + + @property + def coords(self) -> dict[str, Sequence]: + return self._coords + + @property + def param_info(self) -> dict[str, dict[str, Any]]: + return self._param_info + + @property + def data_info(self) -> dict[str, dict[str, Any]]: + return self._data_info + + def make_symbolic_graph(self) -> None: + """ + Assign placeholder pytensor variables among statespace matrices in positions where PyMC variables will go. + + Notes + ----- + This assignment is handled by the components, so this function is implemented only to avoid the + NotImplementedError raised by the base class. + """ + + pass + + def _state_slices_from_info(self): + info = self._component_info.copy() + comp_states = np.cumsum([0] + [info["k_states"] for info in info.values()]) + state_slices = [slice(i, j) for i, j in pairwise(comp_states)] + + return state_slices + + def _hidden_states_from_data(self, data): + state_slices = self._state_slices_from_info() + info = self._component_info + names = info.keys() + result = [] + + for i, (name, s) in enumerate(zip(names, state_slices)): + obs_idx = info[name]["obs_state_idx"] + if obs_idx is None: + continue + + X = data[..., s] + if info[name]["combine_hidden_states"]: + sum_idx = np.flatnonzero(obs_idx) + result.append(X[..., sum_idx].sum(axis=-1)[..., None]) + else: + comp_names = self.state_names[s] + for j, state_name in enumerate(comp_names): + result.append(X[..., j, None]) + + return np.concatenate(result, axis=-1) + + def _get_subcomponent_names(self): + state_slices = self._state_slices_from_info() + info = self._component_info + names = info.keys() + result = [] + + for i, (name, s) in enumerate(zip(names, state_slices)): + if info[name]["combine_hidden_states"]: + result.append(name) + else: + comp_names = self.state_names[s] + result.extend([f"{name}[{comp_name}]" for comp_name in comp_names]) + return result + + def extract_components_from_idata(self, idata: xr.Dataset) -> xr.Dataset: + r""" + Extract interpretable hidden states from an InferenceData returned by a PyMCStateSpace sampling method + + Parameters + ---------- + idata: Dataset + A Dataset object, returned by a PyMCStateSpace sampling method + + Returns + ------- + idata: Dataset + An Dataset object with hidden states transformed to represent only the "interpretable" subcomponents + of the structural model. + + Notes + ----- + In general, a structural statespace model can be represented as: + + .. math:: + y_t = \mu_t + \nu_t + \cdots + \gamma_t + c_t + \xi_t + \epsilon_t \tag{1} + + Where: + + - :math:`\mu_t` is the level of the data at time t + - :math:`\nu_t` is the slope of the data at time t + - :math:`\cdots` are higher time derivatives of the position (acceleration, jerk, etc) at time t + - :math:`\gamma_t` is the seasonal component at time t + - :math:`c_t` is the cycle component at time t + - :math:`\xi_t` is the autoregressive error at time t + - :math:`\varepsilon_t` is the measurement error at time t + + In state space form, some or all of these components are represented as linear combinations of other + subcomponents, making interpretation of the outputs of the outputs difficult. The purpose of this function is + to take the expended statespace representation and return a "reduced form" of only the components shown in + equation (1). + """ + + def _extract_and_transform_variable(idata, new_state_names): + *_, time_dim, state_dim = idata.dims + state_func = ft.partial(self._hidden_states_from_data) + new_idata = xr.apply_ufunc( + state_func, + idata, + input_core_dims=[[time_dim, state_dim]], + output_core_dims=[[time_dim, state_dim]], + exclude_dims={state_dim}, + ) + new_idata.coords.update({state_dim: new_state_names}) + return new_idata + + var_names = list(idata.data_vars.keys()) + is_latent = [idata[name].shape[-1] == self.k_states for name in var_names] + new_state_names = self._get_subcomponent_names() + + latent_names = [name for latent, name in zip(is_latent, var_names) if latent] + dropped_vars = set(var_names) - set(latent_names) + if len(dropped_vars) > 0: + _log.warning( + f'Variables {", ".join(dropped_vars)} do not contain all hidden states (their last dimension ' + f"is not {self.k_states}). They will not be present in the modified idata." + ) + if len(dropped_vars) == len(var_names): + raise ValueError( + "Provided idata had no variables with all hidden states; cannot extract components." + ) + + idata_new = xr.Dataset( + { + name: _extract_and_transform_variable(idata[name], new_state_names) + for name in latent_names + } + ) + return idata_new + + +class Component: + r""" + Base class for a component of a structural timeseries model. + + This base class contains a subset of the class attributes of the PyMCStateSpace class, and none of the class + methods. The purpose of a component is to allow the partial definition of a structural model. Components are + assembled into a full model by the StructuralTimeSeries class. + + Parameters + ---------- + name: str + The name of the component + k_endog: int + Number of endogenous variables being modeled. + k_states: int + Number of hidden states in the component model + k_posdef: int + Rank of the state covariance matrix, or the number of sources of innovations in the component model + observed_state_names: str or list or str, optional + Names of the observed states associated with this component. Must have the same length as k_endog. If not + provided, generic names are generated: ``observed_state_1, observed_state_2, ..., observed_state_k_endog``. + measurement_error: bool + Whether the observation associated with the component has measurement error. Default is False. + combine_hidden_states: bool + Flag for the ``extract_hidden_states_from_data`` method. When ``True``, hidden states from the component model + are extracted as ``hidden_states[:, np.flatnonzero(Z)]``. Should be True in models where hidden states + individually have no interpretation, such as seasonal or autoregressive components. + """ + + def __init__( + self, + name, + k_endog, + k_states, + k_posdef, + state_names=None, + observed_state_names=None, + data_names=None, + shock_names=None, + param_names=None, + exog_names=None, + representation: PytensorRepresentation | None = None, + measurement_error=False, + combine_hidden_states=True, + component_from_sum=False, + obs_state_idxs=None, + ): + self.name = name + self.k_endog = k_endog + self.k_states = k_states + self.k_posdef = k_posdef + self.measurement_error = measurement_error + + self.state_names = state_names if state_names is not None else [] + self.observed_state_names = observed_state_names if observed_state_names is not None else [] + self.data_names = data_names if data_names is not None else [] + self.shock_names = shock_names if shock_names is not None else [] + self.param_names = param_names if param_names is not None else [] + self.exog_names = exog_names if exog_names is not None else [] + + self.needs_exog_data = len(self.exog_names) > 0 + self.coords = {} + self.param_dims = {} + + self.param_info = {} + self.data_info = {} + + self.param_counts = {} + + if representation is None: + self.ssm = PytensorRepresentation(k_endog=k_endog, k_states=k_states, k_posdef=k_posdef) + else: + self.ssm = representation + + self._name_to_variable = {} + self._name_to_data = {} + + if not component_from_sum: + self.populate_component_properties() + self.make_symbolic_graph() + + self._component_info = { + self.name: { + "k_states": self.k_states, + "k_enodg": self.k_endog, + "k_posdef": self.k_posdef, + "observed_state_names": self.observed_state_names, + "combine_hidden_states": combine_hidden_states, + "obs_state_idx": obs_state_idxs, + } + } + + def make_and_register_variable(self, name, shape, dtype=floatX) -> Variable: + r""" + Helper function to create a pytensor symbolic variable and register it in the _name_to_variable dictionary + + Parameters + ---------- + name : str + The name of the placeholder variable. Must be the name of a model parameter. + shape : int or tuple of int + Shape of the parameter + dtype : str, default pytensor.config.floatX + dtype of the parameter + + Notes + ----- + Symbolic pytensor variables are used in the ``make_symbolic_graph`` method as placeholders for PyMC random + variables. The change is made in the ``_insert_random_variables`` method via ``pytensor.graph_replace``. To + make the change, a dictionary mapping pytensor variables to PyMC random variables needs to be constructed. + + The purpose of this method is to: + 1. Create the placeholder symbolic variables + 2. Register the placeholder variable in the ``_name_to_variable`` dictionary + + The shape provided here will define the shape of the prior that will need to be provided by the user. + + An error is raised if the provided name has already been registered, or if the name is not present in the + ``param_names`` property. + """ + if name not in self.param_names: + raise ValueError( + f"{name} is not a model parameter. All placeholder variables should correspond to model " + f"parameters." + ) + + if name in self._name_to_variable.keys(): + raise ValueError( + f"{name} is already a registered placeholder variable with shape " + f"{self._name_to_variable[name].type.shape}" + ) + + placeholder = pt.tensor(name, shape=shape, dtype=dtype) + self._name_to_variable[name] = placeholder + return placeholder + + def make_and_register_data(self, name, shape, dtype=floatX) -> Variable: + r""" + Helper function to create a pytensor symbolic variable and register it in the _name_to_data dictionary + + Parameters + ---------- + name : str + The name of the placeholder data. Must be the name of an expected data variable. + shape : int or tuple of int + Shape of the parameter + dtype : str, default pytensor.config.floatX + dtype of the parameter + + Notes + ----- + See docstring for make_and_register_variable for more details. This function is similar, but handles data + inputs instead of model parameters. + + An error is raised if the provided name has already been registered, or if the name is not present in the + ``data_names`` property. + """ + if name not in self.data_names: + raise ValueError( + f"{name} is not a model parameter. All placeholder variables should correspond to model " + f"parameters." + ) + + if name in self._name_to_data.keys(): + raise ValueError( + f"{name} is already a registered placeholder variable with shape " + f"{self._name_to_data[name].type.shape}" + ) + + placeholder = pt.tensor(name, shape=shape, dtype=dtype) + self._name_to_data[name] = placeholder + return placeholder + + def make_symbolic_graph(self) -> None: + raise NotImplementedError + + def populate_component_properties(self): + raise NotImplementedError + + def _get_combined_shapes(self, other): + k_states = self.k_states + other.k_states + k_posdef = self.k_posdef + other.k_posdef + if self.k_endog != other.k_endog: + raise NotImplementedError( + "Merging elements with different numbers of observed states is not supported." + ) + k_endog = self.k_endog + + return k_states, k_posdef, k_endog + + def _combine_statespace_representations(self, other): + def make_slice(name, x, o_x): + ndim = max(x.ndim, o_x.ndim) + return (name,) + (slice(None, None, None),) * ndim + + k_states, k_posdef, k_endog = self._get_combined_shapes(other) + + self_matrices = [self.ssm[name] for name in LONG_MATRIX_NAMES] + other_matrices = [other.ssm[name] for name in LONG_MATRIX_NAMES] + + x0, P0, c, d, T, Z, R, H, Q = ( + self.ssm[make_slice(name, x, o_x)] + for name, x, o_x in zip(LONG_MATRIX_NAMES, self_matrices, other_matrices) + ) + o_x0, o_P0, o_c, o_d, o_T, o_Z, o_R, o_H, o_Q = ( + other.ssm[make_slice(name, x, o_x)] + for name, x, o_x in zip(LONG_MATRIX_NAMES, self_matrices, other_matrices) + ) + + initial_state = pt.concatenate(conform_time_varying_and_time_invariant_matrices(x0, o_x0)) + initial_state.name = x0.name + + initial_state_cov = pt.linalg.block_diag(P0, o_P0) + initial_state_cov.name = P0.name + + state_intercept = pt.concatenate(conform_time_varying_and_time_invariant_matrices(c, o_c)) + state_intercept.name = c.name + + obs_intercept = d + o_d + obs_intercept.name = d.name + + transition = pt.linalg.block_diag(T, o_T) + transition.name = T.name + + design = pt.concatenate(conform_time_varying_and_time_invariant_matrices(Z, o_Z), axis=-1) + design.name = Z.name + + selection = pt.linalg.block_diag(R, o_R) + selection.name = R.name + + obs_cov = H + o_H + obs_cov.name = H.name + + state_cov = pt.linalg.block_diag(Q, o_Q) + state_cov.name = Q.name + + new_ssm = PytensorRepresentation( + k_endog=k_endog, + k_states=k_states, + k_posdef=k_posdef, + initial_state=initial_state, + initial_state_cov=initial_state_cov, + state_intercept=state_intercept, + obs_intercept=obs_intercept, + transition=transition, + design=design, + selection=selection, + obs_cov=obs_cov, + state_cov=state_cov, + ) + + return new_ssm + + def _combine_property(self, other, name, allow_duplicates=True): + self_prop = getattr(self, name) + if isinstance(self_prop, list) and allow_duplicates: + return self_prop + getattr(other, name) + elif isinstance(self_prop, list) and not allow_duplicates: + return self_prop + [x for x in getattr(other, name) if x not in self_prop] + elif isinstance(self_prop, dict): + new_prop = self_prop.copy() + new_prop.update(getattr(other, name)) + return new_prop + + def _combine_component_info(self, other): + combined_info = {} + for key, value in self._component_info.items(): + if not key.startswith("StateSpace"): + if key in combined_info.keys(): + raise ValueError(f"Found duplicate component named {key}") + combined_info[key] = value + + for key, value in other._component_info.items(): + if not key.startswith("StateSpace"): + if key in combined_info.keys(): + raise ValueError(f"Found duplicate component named {key}") + combined_info[key] = value + + return combined_info + + def _make_combined_name(self): + components = self._component_info.keys() + name = f'StateSpace[{", ".join(components)}]' + return name + + def __add__(self, other): + state_names = self._combine_property(other, "state_names") + data_names = self._combine_property(other, "data_names") + observed_state_names = self._combine_property( + other, "observed_state_names", allow_duplicates=False + ) + + param_names = self._combine_property(other, "param_names") + shock_names = self._combine_property(other, "shock_names") + param_info = self._combine_property(other, "param_info") + data_info = self._combine_property(other, "data_info") + param_dims = self._combine_property(other, "param_dims") + coords = self._combine_property(other, "coords") + exog_names = self._combine_property(other, "exog_names") + + _name_to_variable = self._combine_property(other, "_name_to_variable") + _name_to_data = self._combine_property(other, "_name_to_data") + + measurement_error = any([self.measurement_error, other.measurement_error]) + + k_states, k_posdef, k_endog = self._get_combined_shapes(other) + + ssm = self._combine_statespace_representations(other) + + new_comp = Component( + name="", + k_endog=k_endog, + k_states=k_states, + k_posdef=k_posdef, + observed_state_names=observed_state_names, + measurement_error=measurement_error, + representation=ssm, + component_from_sum=True, + ) + new_comp._component_info = self._combine_component_info(other) + new_comp.name = new_comp._make_combined_name() + + names_and_props = [ + ("state_names", state_names), + ("observed_state_names", observed_state_names), + ("data_names", data_names), + ("param_names", param_names), + ("shock_names", shock_names), + ("param_dims", param_dims), + ("coords", coords), + ("param_dims", param_dims), + ("param_info", param_info), + ("data_info", data_info), + ("exog_names", exog_names), + ("_name_to_variable", _name_to_variable), + ("_name_to_data", _name_to_data), + ] + + for prop, value in names_and_props: + setattr(new_comp, prop, value) + + return new_comp + + def build( + self, name=None, filter_type="standard", verbose=True, mode: str | Mode | None = None + ): + """ + Build a StructuralTimeSeries statespace model from the current component(s) + + Parameters + ---------- + name: str, optional + Name of the exogenous data being modeled. Default is "data" + + filter_type : str, optional + The type of Kalman filter to use. Valid options are "standard", "univariate", "single", "cholesky", and + "steady_state". For more information, see the docs for each filter. Default is "standard". + + verbose : bool, optional + If True, displays information about the initialized model. Defaults to True. + + mode: str or Mode, optional + Pytensor compile mode, used in auxiliary sampling methods such as ``sample_conditional_posterior`` and + ``forecast``. The mode does **not** effect calls to ``pm.sample``. + + Regardless of whether a mode is specified, it can always be overwritten via the ``compile_kwargs`` argument + to all sampling methods. + + Returns + ------- + PyMCStateSpace + An initialized instance of a PyMCStateSpace, constructed using the system matrices contained in the + components. + """ + + return StructuralTimeSeries( + self.ssm, + name=name, + state_names=self.state_names, + observed_state_names=self.observed_state_names, + data_names=self.data_names, + shock_names=self.shock_names, + param_names=self.param_names, + param_dims=self.param_dims, + coords=self.coords, + param_info=self.param_info, + data_info=self.data_info, + component_info=self._component_info, + measurement_error=self.measurement_error, + exog_names=self.exog_names, + name_to_variable=self._name_to_variable, + name_to_data=self._name_to_data, + filter_type=filter_type, + verbose=verbose, + mode=mode, + ) diff --git a/pymc_extras/statespace/models/structural/utils.py b/pymc_extras/statespace/models/structural/utils.py new file mode 100644 index 000000000..d75252225 --- /dev/null +++ b/pymc_extras/statespace/models/structural/utils.py @@ -0,0 +1,16 @@ +import numpy as np + +from pytensor import tensor as pt + + +def order_to_mask(order): + if isinstance(order, int): + return np.ones(order).astype(bool) + else: + return np.array(order).astype(bool) + + +def _frequency_transition_block(s, j): + lam = 2 * np.pi * j / s + + return pt.stack([[pt.cos(lam), pt.sin(lam)], [-pt.sin(lam), pt.cos(lam)]]) diff --git a/tests/statespace/models/structural/__init__.py b/tests/statespace/models/structural/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/statespace/models/structural/components/__init__.py b/tests/statespace/models/structural/components/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/tests/statespace/models/structural/components/test_autoregressive.py b/tests/statespace/models/structural/components/test_autoregressive.py new file mode 100644 index 000000000..f68a34de6 --- /dev/null +++ b/tests/statespace/models/structural/components/test_autoregressive.py @@ -0,0 +1,28 @@ +import numpy as np +import pytest + +from numpy.testing import assert_allclose +from pytensor import config + +from pymc_extras.statespace.models import structural as st +from tests.statespace.models.structural.conftest import _assert_basic_coords_correct +from tests.statespace.test_utilities import simulate_from_numpy_model + + +@pytest.mark.parametrize("order", [1, 2, [1, 0, 1]], ids=["AR1", "AR2", "AR(1,0,1)"]) +def test_autoregressive_model(order, rng): + ar = st.AutoregressiveComponent(order=order) + params = { + "ar_params": np.full((sum(ar.order),), 0.5, dtype=config.floatX), + "sigma_ar": 0.0, + } + + x, y = simulate_from_numpy_model(ar, rng, params, steps=100) + + # Check coords + ar.build(verbose=False) + _assert_basic_coords_correct(ar) + lags = np.arange(len(order) if isinstance(order, list) else order, dtype="int") + 1 + if isinstance(order, list): + lags = lags[np.flatnonzero(order)] + assert_allclose(ar.coords["ar_lag"], lags) diff --git a/tests/statespace/models/structural/components/test_cycle.py b/tests/statespace/models/structural/components/test_cycle.py new file mode 100644 index 000000000..b24eae290 --- /dev/null +++ b/tests/statespace/models/structural/components/test_cycle.py @@ -0,0 +1,52 @@ +import numpy as np + +from numpy.testing import assert_allclose +from pytensor import config + +from pymc_extras.statespace.models import structural as st +from tests.statespace.models.structural.conftest import _assert_basic_coords_correct +from tests.statespace.test_utilities import assert_pattern_repeats, simulate_from_numpy_model + +ATOL = 1e-8 if config.floatX.endswith("64") else 1e-4 +RTOL = 0 if config.floatX.endswith("64") else 1e-6 + + +cycle_test_vals = zip([None, None, 3, 5, 10], [False, True, True, False, False]) + + +def test_cycle_component_deterministic(rng): + cycle = st.CycleComponent( + name="cycle", cycle_length=12, estimate_cycle_length=False, innovations=False + ) + params = {"cycle": np.array([1.0, 1.0], dtype=config.floatX)} + x, y = simulate_from_numpy_model(cycle, rng, params, steps=12 * 12) + + assert_pattern_repeats(y, 12, atol=ATOL, rtol=RTOL) + + +def test_cycle_component_with_dampening(rng): + cycle = st.CycleComponent( + name="cycle", cycle_length=12, estimate_cycle_length=False, innovations=False, dampen=True + ) + params = {"cycle": np.array([10.0, 10.0], dtype=config.floatX), "cycle_dampening_factor": 0.75} + x, y = simulate_from_numpy_model(cycle, rng, params, steps=100) + + # Check that the cycle dampens to zero over time + assert_allclose(y[-1], 0.0, atol=ATOL, rtol=RTOL) + + +def test_cycle_component_with_innovations_and_cycle_length(rng): + cycle = st.CycleComponent( + name="cycle", estimate_cycle_length=True, innovations=True, dampen=True + ) + params = { + "cycle": np.array([1.0, 1.0], dtype=config.floatX), + "cycle_length": 12.0, + "cycle_dampening_factor": 0.95, + "sigma_cycle": 1.0, + } + + x, y = simulate_from_numpy_model(cycle, rng, params) + + cycle.build(verbose=False) + _assert_basic_coords_correct(cycle) diff --git a/tests/statespace/models/structural/components/test_level_trend.py b/tests/statespace/models/structural/components/test_level_trend.py new file mode 100644 index 000000000..9b48ba5b9 --- /dev/null +++ b/tests/statespace/models/structural/components/test_level_trend.py @@ -0,0 +1,24 @@ +import numpy as np + +from numpy.testing import assert_allclose +from pytensor import config + +from pymc_extras.statespace.models import structural as st +from tests.statespace.models.structural.conftest import _assert_basic_coords_correct +from tests.statespace.test_utilities import simulate_from_numpy_model + +ATOL = 1e-8 if config.floatX.endswith("64") else 1e-4 +RTOL = 0 if config.floatX.endswith("64") else 1e-6 + + +def test_level_trend_model(rng): + mod = st.LevelTrendComponent(order=2, innovations_order=0) + params = {"initial_trend": [0.0, 1.0]} + x, y = simulate_from_numpy_model(mod, rng, params) + + assert_allclose(np.diff(y), 1, atol=ATOL, rtol=RTOL) + + # Check coords + mod = mod.build(verbose=False) + _assert_basic_coords_correct(mod) + assert mod.coords["trend_state"] == ["level", "trend"] diff --git a/tests/statespace/models/structural/components/test_measurement_error.py b/tests/statespace/models/structural/components/test_measurement_error.py new file mode 100644 index 000000000..752e8513c --- /dev/null +++ b/tests/statespace/models/structural/components/test_measurement_error.py @@ -0,0 +1,10 @@ +from pymc_extras.statespace.models import structural as st +from tests.statespace.models.structural.conftest import _assert_basic_coords_correct + + +def test_measurement_error(rng): + mod = st.MeasurementError("obs") + st.LevelTrendComponent(order=2) + mod = mod.build(verbose=False) + + _assert_basic_coords_correct(mod) + assert "sigma_obs" in mod.param_names diff --git a/tests/statespace/models/structural/components/test_regression.py b/tests/statespace/models/structural/components/test_regression.py new file mode 100644 index 000000000..504ee8eb2 --- /dev/null +++ b/tests/statespace/models/structural/components/test_regression.py @@ -0,0 +1,69 @@ +import numpy as np +import pandas as pd +import pymc as pm + +from numpy.testing import assert_allclose +from pytensor import config +from pytensor import tensor as pt + +from pymc_extras.statespace.models import structural as st +from tests.statespace.models.structural.conftest import _assert_basic_coords_correct +from tests.statespace.test_utilities import simulate_from_numpy_model + +ATOL = 1e-8 if config.floatX.endswith("64") else 1e-4 +RTOL = 0 if config.floatX.endswith("64") else 1e-6 + + +def test_exogenous_component(rng): + data = rng.normal(size=(100, 2)).astype(config.floatX) + mod = st.RegressionComponent(state_names=["feature_1", "feature_2"], name="exog") + + params = {"beta_exog": np.array([1.0, 2.0], dtype=config.floatX)} + exog_data = {"data_exog": data} + x, y = simulate_from_numpy_model(mod, rng, params, exog_data) + + # Check that the generated data is just a linear regression + assert_allclose(y, data @ params["beta_exog"], atol=ATOL, rtol=RTOL) + + mod.build(verbose=False) + _assert_basic_coords_correct(mod) + assert mod.coords["exog_state"] == ["feature_1", "feature_2"] + + +def test_adding_exogenous_component(rng): + data = rng.normal(size=(100, 2)).astype(config.floatX) + reg = st.RegressionComponent(state_names=["a", "b"], name="exog") + ll = st.LevelTrendComponent(name="level") + + seasonal = st.FrequencySeasonality(name="annual", season_length=12, n=4) + mod = reg + ll + seasonal + + assert mod.ssm["design"].eval({"data_exog": data}).shape == (100, 1, 2 + 2 + 8) + assert_allclose(mod.ssm["design", 5, 0, :2].eval({"data_exog": data}), data[5]) + + +def test_filter_scans_time_varying_design_matrix(rng): + time_idx = pd.date_range(start="2000-01-01", freq="D", periods=100) + data = pd.DataFrame(rng.normal(size=(100, 2)), columns=["a", "b"], index=time_idx) + + y = pd.DataFrame(rng.normal(size=(100, 1)), columns=["data"], index=time_idx) + + reg = st.RegressionComponent(state_names=["a", "b"], name="exog") + mod = reg.build(verbose=False) + + with pm.Model(coords=mod.coords) as m: + data_exog = pm.Data("data_exog", data.values) + + x0 = pm.Normal("x0", dims=["state"]) + P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"]) + beta_exog = pm.Normal("beta_exog", dims=["exog_state"]) + + mod.build_statespace_graph(y) + x0, P0, c, d, T, Z, R, H, Q = mod.unpack_statespace() + pm.Deterministic("Z", Z) + + prior = pm.sample_prior_predictive(draws=10) + + prior_Z = prior.prior.Z.values + assert prior_Z.shape == (1, 10, 100, 1, 2) + assert_allclose(prior_Z[0, :, :, 0, :], data.values[None].repeat(10, axis=0)) diff --git a/tests/statespace/models/structural/components/test_seasonality.py b/tests/statespace/models/structural/components/test_seasonality.py new file mode 100644 index 000000000..61ad4b198 --- /dev/null +++ b/tests/statespace/models/structural/components/test_seasonality.py @@ -0,0 +1,83 @@ +import numpy as np +import pytest + +from pytensor import config + +from pymc_extras.statespace.models import structural as st +from tests.statespace.models.structural.conftest import _assert_basic_coords_correct +from tests.statespace.test_utilities import assert_pattern_repeats, simulate_from_numpy_model + +ATOL = 1e-8 if config.floatX.endswith("64") else 1e-4 +RTOL = 0 if config.floatX.endswith("64") else 1e-6 + + +@pytest.mark.parametrize("s", [10, 25, 50]) +@pytest.mark.parametrize("innovations", [True, False]) +@pytest.mark.parametrize("remove_first_state", [True, False]) +@pytest.mark.filterwarnings( + "ignore:divide by zero encountered in matmul:RuntimeWarning", + "ignore:overflow encountered in matmul:RuntimeWarning", + "ignore:invalid value encountered in matmul:RuntimeWarning", +) +def test_time_seasonality(s, innovations, remove_first_state, rng): + def random_word(rng): + return "".join(rng.choice(list("abcdefghijklmnopqrstuvwxyz")) for _ in range(5)) + + state_names = [random_word(rng) for _ in range(s)] + mod = st.TimeSeasonality( + season_length=s, + innovations=innovations, + name="season", + state_names=state_names, + remove_first_state=remove_first_state, + ) + x0 = np.zeros(mod.k_states, dtype=config.floatX) + x0[0] = 1 + + params = {"season_coefs": x0} + if mod.innovations: + params["sigma_season"] = 0.0 + + x, y = simulate_from_numpy_model(mod, rng, params) + y = y.ravel() + if not innovations: + assert_pattern_repeats(y, s, atol=ATOL, rtol=RTOL) + + # Check coords + mod.build(verbose=False) + _assert_basic_coords_correct(mod) + test_slice = slice(1, None) if remove_first_state else slice(None) + assert mod.coords["season_state"] == state_names[test_slice] + + +def get_shift_factor(s): + s_str = str(s) + if "." not in s_str: + return 1 + _, decimal = s_str.split(".") + return 10 ** len(decimal) + + +@pytest.mark.parametrize("n", [*np.arange(1, 6, dtype="int").tolist(), None]) +@pytest.mark.parametrize("s", [5, 10, 25, 25.2]) +def test_frequency_seasonality(n, s, rng): + mod = st.FrequencySeasonality(season_length=s, n=n, name="season") + x0 = rng.normal(size=mod.n_coefs).astype(config.floatX) + params = {"season": x0, "sigma_season": 0.0} + k = get_shift_factor(s) + T = int(s * k) + + x, y = simulate_from_numpy_model(mod, rng, params, steps=2 * T) + assert_pattern_repeats(y, T, atol=ATOL, rtol=RTOL) + + # Check coords + mod.build(verbose=False) + _assert_basic_coords_correct(mod) + if n is None: + n = int(s // 2) + states = [f"season_{f}_{i}" for i in range(n) for f in ["Cos", "Sin"]] + + # Remove the last state when the model is completely saturated + if s / n == 2.0: + states.pop() + assert mod.coords["season_state"] == states diff --git a/tests/statespace/models/structural/conftest.py b/tests/statespace/models/structural/conftest.py new file mode 100644 index 000000000..63ce45c1b --- /dev/null +++ b/tests/statespace/models/structural/conftest.py @@ -0,0 +1,27 @@ +import numpy as np +import pytest + +from pymc_extras.statespace.utils.constants import ( + ALL_STATE_AUX_DIM, + ALL_STATE_DIM, + OBS_STATE_AUX_DIM, + OBS_STATE_DIM, + SHOCK_AUX_DIM, + SHOCK_DIM, +) + +TEST_SEED = sum(map(ord, "Structural Statespace")) + + +@pytest.fixture(scope="session") +def rng(): + return np.random.default_rng(TEST_SEED) + + +def _assert_basic_coords_correct(mod): + assert mod.coords[ALL_STATE_DIM] == mod.state_names + assert mod.coords[ALL_STATE_AUX_DIM] == mod.state_names + assert mod.coords[SHOCK_DIM] == mod.shock_names + assert mod.coords[SHOCK_AUX_DIM] == mod.shock_names + assert mod.coords[OBS_STATE_DIM] == ["data"] + assert mod.coords[OBS_STATE_AUX_DIM] == ["data"] diff --git a/tests/statespace/models/test_structural.py b/tests/statespace/models/structural/test_against_statsmodels.py similarity index 62% rename from tests/statespace/models/test_structural.py rename to tests/statespace/models/structural/test_against_statsmodels.py index 1662e164a..94da8afe1 100644 --- a/tests/statespace/models/test_structural.py +++ b/tests/statespace/models/structural/test_against_statsmodels.py @@ -4,15 +4,11 @@ from collections import defaultdict import numpy as np -import pandas as pd -import pymc as pm import pytensor -import pytensor.tensor as pt import pytest import statsmodels.api as sm from numpy.testing import assert_allclose -from scipy import linalg from pymc_extras.statespace import structural as st from pymc_extras.statespace.utils.constants import ( @@ -29,8 +25,6 @@ rng, ) from tests.statespace.test_utilities import ( - assert_pattern_repeats, - simulate_from_numpy_model, unpack_symbolic_matrices_with_params, ) @@ -106,15 +100,6 @@ def _assert_coord_shapes_match_matrices(mod, params): ), f"Q expected to have shape (n_shocks, n_shocks), found {Q.shape[-2:]}" -def _assert_basic_coords_correct(mod): - assert mod.coords[ALL_STATE_DIM] == mod.state_names - assert mod.coords[ALL_STATE_AUX_DIM] == mod.state_names - assert mod.coords[SHOCK_DIM] == mod.shock_names - assert mod.coords[SHOCK_AUX_DIM] == mod.shock_names - assert mod.coords[OBS_STATE_DIM] == ["data"] - assert mod.coords[OBS_STATE_AUX_DIM] == ["data"] - - def _assert_keys_match(test_dict, expected_dict): expected_keys = list(expected_dict.keys()) param_keys = list(test_dict.keys()) @@ -548,293 +533,3 @@ def test_structural_model_against_statsmodels( _assert_param_dims_correct(built_model.param_dims, expected_dims) _assert_coords_correct(built_model.coords, expected_coords) _assert_params_info_correct(built_model.param_info, built_model.coords, built_model.param_dims) - - -def test_level_trend_model(rng): - mod = st.LevelTrendComponent(order=2, innovations_order=0) - params = {"initial_trend": [0.0, 1.0]} - x, y = simulate_from_numpy_model(mod, rng, params) - - assert_allclose(np.diff(y), 1, atol=ATOL, rtol=RTOL) - - # Check coords - mod = mod.build(verbose=False) - _assert_basic_coords_correct(mod) - assert mod.coords["trend_state"] == ["level", "trend"] - - -def test_measurement_error(rng): - mod = st.MeasurementError("obs") + st.LevelTrendComponent(order=2) - mod = mod.build(verbose=False) - - _assert_basic_coords_correct(mod) - assert "sigma_obs" in mod.param_names - - -@pytest.mark.parametrize("order", [1, 2, [1, 0, 1]], ids=["AR1", "AR2", "AR(1,0,1)"]) -def test_autoregressive_model(order, rng): - ar = st.AutoregressiveComponent(order=order) - params = { - "ar_params": np.full((sum(ar.order),), 0.5, dtype=floatX), - "sigma_ar": 0.0, - } - - x, y = simulate_from_numpy_model(ar, rng, params, steps=100) - - # Check coords - ar.build(verbose=False) - _assert_basic_coords_correct(ar) - lags = np.arange(len(order) if isinstance(order, list) else order, dtype="int") + 1 - if isinstance(order, list): - lags = lags[np.flatnonzero(order)] - assert_allclose(ar.coords["ar_lag"], lags) - - -@pytest.mark.parametrize("s", [10, 25, 50]) -@pytest.mark.parametrize("innovations", [True, False]) -@pytest.mark.parametrize("remove_first_state", [True, False]) -@pytest.mark.filterwarnings( - "ignore:divide by zero encountered in matmul:RuntimeWarning", - "ignore:overflow encountered in matmul:RuntimeWarning", - "ignore:invalid value encountered in matmul:RuntimeWarning", -) -def test_time_seasonality(s, innovations, remove_first_state, rng): - def random_word(rng): - return "".join(rng.choice(list("abcdefghijklmnopqrstuvwxyz")) for _ in range(5)) - - state_names = [random_word(rng) for _ in range(s)] - mod = st.TimeSeasonality( - season_length=s, - innovations=innovations, - name="season", - state_names=state_names, - remove_first_state=remove_first_state, - ) - x0 = np.zeros(mod.k_states, dtype=floatX) - x0[0] = 1 - - params = {"season_coefs": x0} - if mod.innovations: - params["sigma_season"] = 0.0 - - x, y = simulate_from_numpy_model(mod, rng, params) - y = y.ravel() - if not innovations: - assert_pattern_repeats(y, s, atol=ATOL, rtol=RTOL) - - # Check coords - mod.build(verbose=False) - _assert_basic_coords_correct(mod) - test_slice = slice(1, None) if remove_first_state else slice(None) - assert mod.coords["season_state"] == state_names[test_slice] - - -def get_shift_factor(s): - s_str = str(s) - if "." not in s_str: - return 1 - _, decimal = s_str.split(".") - return 10 ** len(decimal) - - -@pytest.mark.parametrize("n", [*np.arange(1, 6, dtype="int").tolist(), None]) -@pytest.mark.parametrize("s", [5, 10, 25, 25.2]) -def test_frequency_seasonality(n, s, rng): - mod = st.FrequencySeasonality(season_length=s, n=n, name="season") - x0 = rng.normal(size=mod.n_coefs).astype(floatX) - params = {"season": x0, "sigma_season": 0.0} - k = get_shift_factor(s) - T = int(s * k) - - x, y = simulate_from_numpy_model(mod, rng, params, steps=2 * T) - assert_pattern_repeats(y, T, atol=ATOL, rtol=RTOL) - - # Check coords - mod.build(verbose=False) - _assert_basic_coords_correct(mod) - if n is None: - n = int(s // 2) - states = [f"season_{f}_{i}" for i in range(n) for f in ["Cos", "Sin"]] - - # Remove the last state when the model is completely saturated - if s / n == 2.0: - states.pop() - assert mod.coords["season_state"] == states - - -cycle_test_vals = zip([None, None, 3, 5, 10], [False, True, True, False, False]) - - -def test_cycle_component_deterministic(rng): - cycle = st.CycleComponent( - name="cycle", cycle_length=12, estimate_cycle_length=False, innovations=False - ) - params = {"cycle": np.array([1.0, 1.0], dtype=floatX)} - x, y = simulate_from_numpy_model(cycle, rng, params, steps=12 * 12) - - assert_pattern_repeats(y, 12, atol=ATOL, rtol=RTOL) - - -def test_cycle_component_with_dampening(rng): - cycle = st.CycleComponent( - name="cycle", cycle_length=12, estimate_cycle_length=False, innovations=False, dampen=True - ) - params = {"cycle": np.array([10.0, 10.0], dtype=floatX), "cycle_dampening_factor": 0.75} - x, y = simulate_from_numpy_model(cycle, rng, params, steps=100) - - # Check that the cycle dampens to zero over time - assert_allclose(y[-1], 0.0, atol=ATOL, rtol=RTOL) - - -def test_cycle_component_with_innovations_and_cycle_length(rng): - cycle = st.CycleComponent( - name="cycle", estimate_cycle_length=True, innovations=True, dampen=True - ) - params = { - "cycle": np.array([1.0, 1.0], dtype=floatX), - "cycle_length": 12.0, - "cycle_dampening_factor": 0.95, - "sigma_cycle": 1.0, - } - - x, y = simulate_from_numpy_model(cycle, rng, params) - - cycle.build(verbose=False) - _assert_basic_coords_correct(cycle) - - -def test_exogenous_component(rng): - data = rng.normal(size=(100, 2)).astype(floatX) - mod = st.RegressionComponent(state_names=["feature_1", "feature_2"], name="exog") - - params = {"beta_exog": np.array([1.0, 2.0], dtype=floatX)} - exog_data = {"data_exog": data} - x, y = simulate_from_numpy_model(mod, rng, params, exog_data) - - # Check that the generated data is just a linear regression - assert_allclose(y, data @ params["beta_exog"], atol=ATOL, rtol=RTOL) - - mod.build(verbose=False) - _assert_basic_coords_correct(mod) - assert mod.coords["exog_state"] == ["feature_1", "feature_2"] - - -def test_adding_exogenous_component(rng): - data = rng.normal(size=(100, 2)).astype(floatX) - reg = st.RegressionComponent(state_names=["a", "b"], name="exog") - ll = st.LevelTrendComponent(name="level") - - seasonal = st.FrequencySeasonality(name="annual", season_length=12, n=4) - mod = reg + ll + seasonal - - assert mod.ssm["design"].eval({"data_exog": data}).shape == (100, 1, 2 + 2 + 8) - assert_allclose(mod.ssm["design", 5, 0, :2].eval({"data_exog": data}), data[5]) - - -def test_add_components(): - ll = st.LevelTrendComponent(order=2) - se = st.TimeSeasonality(name="seasonal", season_length=12) - mod = ll + se - - ll_params = { - "initial_trend": np.zeros(2, dtype=floatX), - "sigma_trend": np.ones(2, dtype=floatX), - } - se_params = { - "seasonal_coefs": np.ones(11, dtype=floatX), - "sigma_seasonal": 1.0, - } - all_params = ll_params.copy() - all_params.update(se_params) - - (ll_x0, ll_P0, ll_c, ll_d, ll_T, ll_Z, ll_R, ll_H, ll_Q) = unpack_symbolic_matrices_with_params( - ll, ll_params - ) - (se_x0, se_P0, se_c, se_d, se_T, se_Z, se_R, se_H, se_Q) = unpack_symbolic_matrices_with_params( - se, se_params - ) - x0, P0, c, d, T, Z, R, H, Q = unpack_symbolic_matrices_with_params(mod, all_params) - - for property in ["param_names", "shock_names", "param_info", "coords", "param_dims"]: - assert [x in getattr(mod, property) for x in getattr(ll, property)] - assert [x in getattr(mod, property) for x in getattr(se, property)] - - ll_mats = [ll_T, ll_R, ll_Q] - se_mats = [se_T, se_R, se_Q] - all_mats = [T, R, Q] - - for ll_mat, se_mat, all_mat in zip(ll_mats, se_mats, all_mats): - assert_allclose(all_mat, linalg.block_diag(ll_mat, se_mat), atol=ATOL, rtol=RTOL) - - ll_mats = [ll_x0, ll_c, ll_Z] - se_mats = [se_x0, se_c, se_Z] - all_mats = [x0, c, Z] - axes = [0, 0, 1] - - for ll_mat, se_mat, all_mat, axis in zip(ll_mats, se_mats, all_mats, axes): - assert_allclose(all_mat, np.concatenate([ll_mat, se_mat], axis=axis), atol=ATOL, rtol=RTOL) - - -def test_filter_scans_time_varying_design_matrix(rng): - time_idx = pd.date_range(start="2000-01-01", freq="D", periods=100) - data = pd.DataFrame(rng.normal(size=(100, 2)), columns=["a", "b"], index=time_idx) - - y = pd.DataFrame(rng.normal(size=(100, 1)), columns=["data"], index=time_idx) - - reg = st.RegressionComponent(state_names=["a", "b"], name="exog") - mod = reg.build(verbose=False) - - with pm.Model(coords=mod.coords) as m: - data_exog = pm.Data("data_exog", data.values) - - x0 = pm.Normal("x0", dims=["state"]) - P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"]) - beta_exog = pm.Normal("beta_exog", dims=["exog_state"]) - - mod.build_statespace_graph(y) - x0, P0, c, d, T, Z, R, H, Q = mod.unpack_statespace() - pm.Deterministic("Z", Z) - - prior = pm.sample_prior_predictive(draws=10) - - prior_Z = prior.prior.Z.values - assert prior_Z.shape == (1, 10, 100, 1, 2) - assert_allclose(prior_Z[0, :, :, 0, :], data.values[None].repeat(10, axis=0)) - - -@pytest.mark.skipif(floatX.endswith("32"), reason="Prior covariance not PSD at half-precision") -def test_extract_components_from_idata(rng): - time_idx = pd.date_range(start="2000-01-01", freq="D", periods=100) - data = pd.DataFrame(rng.normal(size=(100, 2)), columns=["a", "b"], index=time_idx) - - y = pd.DataFrame(rng.normal(size=(100, 1)), columns=["data"], index=time_idx) - - ll = st.LevelTrendComponent() - season = st.FrequencySeasonality(name="seasonal", season_length=12, n=2, innovations=False) - reg = st.RegressionComponent(state_names=["a", "b"], name="exog") - me = st.MeasurementError("obs") - mod = (ll + season + reg + me).build(verbose=False) - - with pm.Model(coords=mod.coords) as m: - data_exog = pm.Data("data_exog", data.values) - - x0 = pm.Normal("x0", dims=["state"]) - P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"]) - beta_exog = pm.Normal("beta_exog", dims=["exog_state"]) - initial_trend = pm.Normal("initial_trend", dims=["trend_state"]) - sigma_trend = pm.Exponential("sigma_trend", 1, dims=["trend_shock"]) - seasonal_coefs = pm.Normal("seasonal", dims=["seasonal_state"]) - sigma_obs = pm.Exponential("sigma_obs", 1) - - mod.build_statespace_graph(y) - - x0, P0, c, d, T, Z, R, H, Q = mod.unpack_statespace() - prior = pm.sample_prior_predictive(draws=10) - - filter_prior = mod.sample_conditional_prior(prior) - comp_prior = mod.extract_components_from_idata(filter_prior) - comp_states = comp_prior.filtered_prior.coords["state"].values - expected_states = ["LevelTrend[level]", "LevelTrend[trend]", "seasonal", "exog[a]", "exog[b]"] - missing = set(comp_states) - set(expected_states) - - assert len(missing) == 0, missing diff --git a/tests/statespace/models/structural/test_core.py b/tests/statespace/models/structural/test_core.py new file mode 100644 index 000000000..500e8b1a2 --- /dev/null +++ b/tests/statespace/models/structural/test_core.py @@ -0,0 +1,102 @@ +import numpy as np +import pandas as pd +import pymc as pm +import pytest + +from numpy.testing import assert_allclose +from pytensor import config +from pytensor import tensor as pt +from scipy import linalg + +from pymc_extras.statespace.models import structural as st +from tests.statespace.test_utilities import unpack_symbolic_matrices_with_params + +floatX = config.floatX +ATOL = 1e-8 if floatX.endswith("64") else 1e-4 +RTOL = 0 if floatX.endswith("64") else 1e-6 + + +def test_add_components(): + ll = st.LevelTrendComponent(order=2) + se = st.TimeSeasonality(name="seasonal", season_length=12) + mod = ll + se + + ll_params = { + "initial_trend": np.zeros(2, dtype=floatX), + "sigma_trend": np.ones(2, dtype=floatX), + } + se_params = { + "seasonal_coefs": np.ones(11, dtype=floatX), + "sigma_seasonal": 1.0, + } + all_params = ll_params.copy() + all_params.update(se_params) + + (ll_x0, ll_P0, ll_c, ll_d, ll_T, ll_Z, ll_R, ll_H, ll_Q) = unpack_symbolic_matrices_with_params( + ll, ll_params + ) + (se_x0, se_P0, se_c, se_d, se_T, se_Z, se_R, se_H, se_Q) = unpack_symbolic_matrices_with_params( + se, se_params + ) + x0, P0, c, d, T, Z, R, H, Q = unpack_symbolic_matrices_with_params(mod, all_params) + + for property in ["param_names", "shock_names", "param_info", "coords", "param_dims"]: + assert [x in getattr(mod, property) for x in getattr(ll, property)] + assert [x in getattr(mod, property) for x in getattr(se, property)] + + assert (mod.observed_state_names == ll.observed_state_names) and ( + ll.observed_state_names == se.observed_state_names + ) + + ll_mats = [ll_T, ll_R, ll_Q] + se_mats = [se_T, se_R, se_Q] + all_mats = [T, R, Q] + + for ll_mat, se_mat, all_mat in zip(ll_mats, se_mats, all_mats): + assert_allclose(all_mat, linalg.block_diag(ll_mat, se_mat), atol=ATOL, rtol=RTOL) + + ll_mats = [ll_x0, ll_c, ll_Z] + se_mats = [se_x0, se_c, se_Z] + all_mats = [x0, c, Z] + axes = [0, 0, 1] + + for ll_mat, se_mat, all_mat, axis in zip(ll_mats, se_mats, all_mats, axes): + assert_allclose(all_mat, np.concatenate([ll_mat, se_mat], axis=axis), atol=ATOL, rtol=RTOL) + + +@pytest.mark.skipif(floatX.endswith("32"), reason="Prior covariance not PSD at half-precision") +def test_extract_components_from_idata(rng): + time_idx = pd.date_range(start="2000-01-01", freq="D", periods=100) + data = pd.DataFrame(rng.normal(size=(100, 2)), columns=["a", "b"], index=time_idx) + + y = pd.DataFrame(rng.normal(size=(100, 1)), columns=["data"], index=time_idx) + + ll = st.LevelTrendComponent() + season = st.FrequencySeasonality(name="seasonal", season_length=12, n=2, innovations=False) + reg = st.RegressionComponent(state_names=["a", "b"], name="exog") + me = st.MeasurementError("obs") + mod = (ll + season + reg + me).build(verbose=False) + + with pm.Model(coords=mod.coords) as m: + data_exog = pm.Data("data_exog", data.values) + + x0 = pm.Normal("x0", dims=["state"]) + P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"]) + beta_exog = pm.Normal("beta_exog", dims=["exog_state"]) + initial_trend = pm.Normal("initial_trend", dims=["trend_state"]) + sigma_trend = pm.Exponential("sigma_trend", 1, dims=["trend_shock"]) + seasonal_coefs = pm.Normal("seasonal", dims=["seasonal_state"]) + sigma_obs = pm.Exponential("sigma_obs", 1) + + mod.build_statespace_graph(y) + + x0, P0, c, d, T, Z, R, H, Q = mod.unpack_statespace() + prior = pm.sample_prior_predictive(draws=10) + + filter_prior = mod.sample_conditional_prior(prior) + comp_prior = mod.extract_components_from_idata(filter_prior) + comp_states = comp_prior.filtered_prior.coords["state"].values + expected_states = ["LevelTrend[level]", "LevelTrend[trend]", "seasonal", "exog[a]", "exog[b]"] + missing = set(comp_states) - set(expected_states) + + assert len(missing) == 0, missing From b970a6c53d4335b208217d076e265314b39a664f Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Tue, 24 Jun 2025 22:17:31 +0200 Subject: [PATCH 02/21] Allow combination of components with different numbers of observed states --- .../statespace/models/structural/core.py | 35 +- pymc_extras/statespace/models/utilities.py | 256 +++++++++++++++ tests/statespace/models/test_utilities.py | 298 ++++++++++++++++++ tests/statespace/test_utilities.py | 2 +- 4 files changed, 583 insertions(+), 8 deletions(-) create mode 100644 tests/statespace/models/test_utilities.py diff --git a/pymc_extras/statespace/models/structural/core.py b/pymc_extras/statespace/models/structural/core.py index 1a273a6e2..913c58e17 100644 --- a/pymc_extras/statespace/models/structural/core.py +++ b/pymc_extras/statespace/models/structural/core.py @@ -13,7 +13,9 @@ from pymc_extras.statespace.core import PyMCStateSpace, PytensorRepresentation from pymc_extras.statespace.models.utilities import ( + add_tensors_by_dim_labels, conform_time_varying_and_time_invariant_matrices, + join_tensors_by_dim_labels, make_default_coords, ) from pymc_extras.statespace.utils.constants import ( @@ -481,11 +483,13 @@ def populate_component_properties(self): def _get_combined_shapes(self, other): k_states = self.k_states + other.k_states k_posdef = self.k_posdef + other.k_posdef - if self.k_endog != other.k_endog: - raise NotImplementedError( - "Merging elements with different numbers of observed states is not supported." + if self.k_endog == other.k_endog: + k_endog = self.k_endog + else: + combined_states = self._combine_property( + other, "observed_state_names", allow_duplicates=False ) - k_endog = self.k_endog + k_endog = len(combined_states) return k_states, k_posdef, k_endog @@ -499,6 +503,9 @@ def make_slice(name, x, o_x): self_matrices = [self.ssm[name] for name in LONG_MATRIX_NAMES] other_matrices = [other.ssm[name] for name in LONG_MATRIX_NAMES] + self_observed_states = self.observed_state_names + other_observed_states = other.observed_state_names + x0, P0, c, d, T, Z, R, H, Q = ( self.ssm[make_slice(name, x, o_x)] for name, x, o_x in zip(LONG_MATRIX_NAMES, self_matrices, other_matrices) @@ -517,19 +524,33 @@ def make_slice(name, x, o_x): state_intercept = pt.concatenate(conform_time_varying_and_time_invariant_matrices(c, o_c)) state_intercept.name = c.name - obs_intercept = d + o_d + obs_intercept = add_tensors_by_dim_labels( + d, o_d, labels=self_observed_states, other_labels=other_observed_states, labeled_axis=-1 + ) obs_intercept.name = d.name transition = pt.linalg.block_diag(T, o_T) transition.name = T.name - design = pt.concatenate(conform_time_varying_and_time_invariant_matrices(Z, o_Z), axis=-1) + design = join_tensors_by_dim_labels( + *conform_time_varying_and_time_invariant_matrices(Z, o_Z), + labels=self_observed_states, + other_labels=other_observed_states, + labeled_axis=-2, + join_axis=-1, + ) design.name = Z.name selection = pt.linalg.block_diag(R, o_R) selection.name = R.name - obs_cov = H + o_H + obs_cov = add_tensors_by_dim_labels( + H, + o_H, + labels=self_observed_states, + other_labels=other_observed_states, + labeled_axis=(-1, -2), + ) obs_cov.name = H.name state_cov = pt.linalg.block_diag(Q, o_Q) diff --git a/pymc_extras/statespace/models/utilities.py b/pymc_extras/statespace/models/utilities.py index 6bc22370b..ad2ba33b0 100644 --- a/pymc_extras/statespace/models/utilities.py +++ b/pymc_extras/statespace/models/utilities.py @@ -1,6 +1,10 @@ +from typing import cast as type_cast + import numpy as np import pytensor.tensor as pt +from pytensor.tensor import TensorVariable + from pymc_extras.statespace.utils.constants import ( ALL_STATE_AUX_DIM, ALL_STATE_DIM, @@ -374,6 +378,258 @@ def conform_time_varying_and_time_invariant_matrices(A, B): return A, B +def normalize_axis(x, axis): + """ + Convert negative axis values to positive axis values + """ + if isinstance(axis, tuple): + return tuple([normalize_axis(x, i) for i in axis]) + if axis < 0: + axis = x.ndim + axis + return axis + + +def reorder_from_labels( + x: TensorVariable, + labels: list[str], + ordered_labels: list[str], + labeled_axis: int | tuple[int, int], +) -> TensorVariable: + """ + Reorder an input tensor along request axis/axes based on lists of string labels + + Parameters + ---------- + x: TensorVariable + Input tensor + labels: list of str + Labels associated with values of the input tensor ``x``, along the ``labeled_axis``. At runtime, should have + ``x.shape[labeled_axis] == len(labels)`` + ordered_labels: list of str + Target ordering according to which ``x`` will be reordered. + labeled_axis: int or tuple of int + Axis along which ``x`` will be labeled. If a tuple, each axis will be assumed to have identical labels, and + and reorganization will be done on all requested axes together (NOT fancy indexing!) + + Returns + ------- + x_sorted: TensorVariable + Output tensor sorted along ``labeled_axis`` according to ``ordered_labels`` + """ + n_out = len(ordered_labels) + label_to_index = {label: index for index, label in enumerate(ordered_labels)} + + missing_labels = [label for label in ordered_labels if label not in labels] + indices = np.argsort([label_to_index[label] for label in [*labels, *missing_labels]]) + + if isinstance(labeled_axis, int): + labeled_axis = (labeled_axis,) + + if indices.tolist() != list(range(n_out)): + for axis in labeled_axis: + idx = np.s_[tuple([slice(None, None) if i != axis else indices for i in range(x.ndim)])] + x = x[idx] + + return x + + +def pad_and_reorder( + x: TensorVariable, labels: list[str], ordered_labels: list[str], labeled_axis: int +) -> TensorVariable: + """ + Pad input tensor ``x`` along the `labeled_axis` to match the length of ``ordered_labels``, then reorder the + padded dimension to match the ordering in ``ordered_labels``. + + Parameters + ---------- + x: TensorVariable + Input tensor + labels: list of str + String labels associated with the `x` tensor at the ``labeled_axis`` dimension. At runtime, should have + ``x.shape[labeled_axis] == len(labels)``. ``labels`` should be a subset of ``ordered_labels``. + ordered_labels: list of str + Target ordering according to which ``x`` will be reordered. + labeled_axis: int + Axis along which ``x`` will be labeled. + + Returns + ------- + x_padded: TensorVariable + Output tensor padded along ``labeled_axis`` according to ``ordered_labels``, then reordered. + + """ + n_out = len(ordered_labels) + n_missing = n_out - len(labels) + + if n_missing > 0: + zeros = pt.zeros( + tuple([x.shape[i] if i != labeled_axis else n_missing for i in range(x.ndim)]) + ) + x_padded = pt.concatenate([x, zeros], axis=labeled_axis) + else: + x_padded = x + + return reorder_from_labels(x_padded, labels, ordered_labels, labeled_axis) + + +def ndim_pad_and_reorder( + x: TensorVariable, + labels: list[str], + ordered_labels: list[str], + labeled_axis: int | tuple[int, int], +) -> TensorVariable: + """ + Pad input tensor ``x`` along the `labeled_axis` to match the length of ``ordered_labels``, then reorder the + padded dimension to match the ordering in ``ordered_labels``. + + Unlike ``pad_and_reorder``, this function allows padding and reordering to be done simultaneously on multiple + axes. In this case, reordering is done jointly on all axes -- it does *not* use fancy indexing. + + Parameters + ---------- + x: TensorVariable + Input tensor + labels: list of str + Labels associated with values of the input tensor ``x``, along the ``labeled_axis``. At runtime, should have + ``x.shape[labeled_axis] == len(labels)``. If ``labeled_axis`` is a tuple, all axes are assumed to have the + same labels. + ordered_labels: list of str + Target ordering according to which ``x`` will be reordered. ``labels`` should be a subset of ``ordered_labels``. + labeled_axis: int or tuple of int + Axis along which ``x`` will be labeled. If a tuple, each axis will be assumed to have identical labels, and + and reorganization will be done on all requested axes together (NOT fancy indexing!) + + Returns + ------- + x_sorted: TensorVariable + Output tensor. Each ``labeled_axis`` is padded to the length of ``ordered_labels``, then reordered. + """ + n_missing = len(ordered_labels) - len(labels) + + if isinstance(labeled_axis, int): + labeled_axis = (labeled_axis,) + + if n_missing > 0: + pad_size = [(0, 0) if i not in labeled_axis else (0, n_missing) for i in range(x.ndim)] + x = pt.pad(x, pad_size, mode="constant", constant_values=0) + + return reorder_from_labels(x, labels, ordered_labels, labeled_axis) + + +def add_tensors_by_dim_labels( + tensor: TensorVariable, + other_tensor: TensorVariable, + labels: list[str], + other_labels: list[str], + labeled_axis: int | tuple[int, int] = -1, +) -> TensorVariable: + """ + Add two tensors based on labels associated with one dimension. + + When combining statespace matrices associated with structural components with potentially different states, it is + important to make sure that duplicated states are handled correctly. For bias vectors and covariance matrices, + duplicated states should be summed. + + When a state appears in one component but not another, that state should be treated as an implicit zero in the + components where the state does not appear. This amounts to padding the relevant matrices with zeros before + performing the addition. + + When labeled_axis is a tuple, each provided label is assumed to be identically labeled in each input tensor. This + is the case, for example, when working with a covariance matrix. In this case, padding and alignment will be + done on each indicated index. + + Parameters + ---------- + tensor: TensorVariable + A statespace matrix to be summed with ``other_matrix``. + other_tensor: TensorVariable + A statespace matrix to be summed with ``matrix``. + labels: list of str + Dimension labels associated with ``matrix``, on the ``labeled_axis`` dimension. + other_labels: list of str + Dimension labels associated with ``other_matrix``, on the ``labeled_axis`` dimension. + labeled_axis: int or tuple of int + Dimension that is labeled by ``labels`` and ``other_labels``. ``matrix.shape[labeled_axis]`` must have the + shape of ``len(labels)`` at runtime. + + Returns + ------- + result: TensorVariable + Result of addition of ``matrix`` and ``other_matrix``, along the ``labeled_axis`` dimension. The ordering of + the output will be ``labels + [label for label in other_labels if label not in labels]``. That is, ``labels`` + come first, followed by any new labels introduced by ``other_labels``. + + """ + labeled_axis = normalize_axis(tensor, labeled_axis) + new_labels = [label for label in other_labels if label not in labels] + combined_labels = type_cast(list[str], [*labels, *new_labels]) + + # If there is no overlap at all, directly concatenate the two matrices -- there's no need to worry about the order + # of things, or padding. This is equivalent to padding both out with zeros then adding them. + if combined_labels == [*labels, *other_labels]: + if isinstance(labeled_axis, int): + return pt.concatenate([tensor, other_tensor], axis=labeled_axis) + else: + # In the case where we want to align multiple dimensions, use block_diag to accomplish padding on the last + # two dimensions + dims = [*[i for i in range(tensor.ndim) if i not in labeled_axis], *labeled_axis] + return pt.linalg.block_diag( + type_cast(TensorVariable, tensor.transpose(*dims)), + type_cast(TensorVariable, other_tensor.transpose(*dims)), + ) + # Otherwise, there are two possibilities. If all labels are the same, we might need to re-order one or both to get + # them to agree. If *some* labels are the same, we will need to pad first, then potentially re-order. In any case, + # the final step is just to add the padded and re-ordered tensors. + fn = pad_and_reorder if isinstance(labeled_axis, int) else ndim_pad_and_reorder + + padded_tensor = fn( + tensor, + labels=type_cast(list[str], labels), + ordered_labels=combined_labels, + labeled_axis=labeled_axis, + ) + padded_tensor.name = tensor.name + + padded_other_tensor = fn( + other_tensor, + labels=type_cast(list[str], other_labels), + ordered_labels=combined_labels, + labeled_axis=labeled_axis, + ) + + padded_other_tensor.name = other_tensor.name + + return padded_tensor + padded_other_tensor + + +def join_tensors_by_dim_labels( + tensor: TensorVariable, + other_tensor: TensorVariable, + labels: list[str], + other_labels: list[str], + labeled_axis: int = -1, + join_axis: int = -1, + block_diag_join: bool = False, +) -> TensorVariable: + labeled_axis = normalize_axis(tensor, labeled_axis) + new_labels = [label for label in other_labels if label not in labels] + combined_labels = [*labels, *new_labels] + + # Check for no overlap first. In this case, do a block_diagonal join, which implicitly results in padding zeros + # everywhere they are needed -- no other sorting or padding necessary + if combined_labels == [*labels, *other_labels]: + return pt.linalg.block_diag(tensor, other_tensor) + + # Otherwise there is either total overlap or partial overlap. Let the padding and reordering function figure it out. + tensor = ndim_pad_and_reorder(tensor, labels, combined_labels, labeled_axis) + other_tensor = ndim_pad_and_reorder(other_tensor, other_labels, combined_labels, labeled_axis) + + if block_diag_join: + return pt.linalg.block_diag(tensor, other_tensor) + else: + return pt.concatenate([tensor, other_tensor], axis=join_axis) + + def get_exog_dims_from_idata(exog_name, idata): if exog_name in idata.posterior.data_vars: exog_dims = idata.posterior[exog_name].dims[2:] diff --git a/tests/statespace/models/test_utilities.py b/tests/statespace/models/test_utilities.py new file mode 100644 index 000000000..b667658e4 --- /dev/null +++ b/tests/statespace/models/test_utilities.py @@ -0,0 +1,298 @@ +import numpy as np +import pytest + +from pytensor import function +from pytensor import tensor as pt + +from pymc_extras.statespace.models.utilities import ( + add_tensors_by_dim_labels, + join_tensors_by_dim_labels, + reorder_from_labels, +) + + +def test_reorder_from_labels(): + x = pt.tensor("x", shape=(None, None)) + labels = ["A", "B", "D"] + combined_labels = ["A", "D", "B"] + + x_sorted = reorder_from_labels(x, labels, combined_labels, labeled_axis=0) + fn = function([x], x_sorted) + + test_val = np.eye(3) * np.arange(1, 4) + idx = np.array([0, 2, 1]) + out = fn(test_val) + np.testing.assert_allclose(out, test_val[idx, :]) + + x_sorted = reorder_from_labels(x, labels, combined_labels, labeled_axis=1) + fn = function([x], x_sorted) + + out = fn(test_val) + np.testing.assert_allclose(out, test_val[:, idx]) + + x_sorted = reorder_from_labels(x, labels, combined_labels, labeled_axis=(0, 1)) + fn = function([x], x_sorted) + + out = fn(test_val) + np.testing.assert_allclose(out, test_val[np.ix_(idx, idx)]) + + +def make_zeros(x): + if x.ndim == 1: + zeros = np.zeros( + 1, + ) + else: + zeros = np.zeros((x.shape[0], 1)) + return zeros + + +def add(left, right): + return left + right + + +def same_but_mixed(left, right): + return left + right[..., np.array([1, 2, 0])] + + +def concat(left, right): + return np.concatenate([left, right], axis=-1) + + +def pad_and_add_left(left, right): + left = np.concatenate([left, make_zeros(left)], axis=-1) + return left + right + + +def pad_and_add_right(left, right): + right = np.concatenate([right, make_zeros(right)], axis=-1) + return left + right + + +def mixed_and_padded(left, right): + left = np.concatenate([left, make_zeros(left)], axis=-1) + right = right[..., np.array([2, 1, 0])] + return left + right + + +@pytest.mark.parametrize( + "left_names, right_names, expected_computation", + [ + (["data"], ["data"], add), + (["A", "C", "B"], ["B", "A", "C"], same_but_mixed), + (["data"], ["different_data"], concat), + (["data"], ["data", "different_data"], pad_and_add_left), + (["data", "more_data"], ["data"], pad_and_add_right), + (["A", "B"], ["D", "B", "A"], mixed_and_padded), + ], + ids=[ + "same_names", + "same_but_mixed", + "different_names", + "overlap_right", + "overlap_left", + "pad_and_mix", + ], +) +@pytest.mark.parametrize("ndim", [1, 2], ids=["vector", "matrix"]) +def test_add_matrices_by_observed_state_names(left_names, right_names, expected_computation, ndim): + rng = np.random.default_rng() + n_left = len(left_names) + n_right = len(right_names) + + left = pt.tensor("left", shape=(None,) * ndim) + right = pt.tensor("right", shape=(None,) * ndim) + + result = add_tensors_by_dim_labels(left, right, left_names, right_names) + fn = function([left, right], result) + + left_value = rng.normal(size=(n_left,) if ndim == 1 else (10, n_left)) + right_value = rng.normal(size=(n_right,) if ndim == 1 else (10, n_right)) + + np.testing.assert_allclose( + fn(left_value, right_value), expected_computation(left_value, right_value) + ) + + +class TestAddCovarianceMatrices: + def _setup_H(self, states_1, states_2): + n_1 = len(states_1) + n_2 = len(states_2) + + H_1 = pt.tensor("H_1", shape=(n_1, n_1)) + H_2 = pt.tensor("H_2", shape=(n_2, n_2)) + + return H_1, H_2 + + @pytest.mark.parametrize("n_states", [1, 3], ids=["1x1", "3x3"]) + def test_add_fully_overlapping_covariance_matrices(self, n_states): + rng = np.random.default_rng() + states = list("ABCD") + + observed_states_1 = states[:n_states] + observed_states_2 = states[:n_states] + + H_1, H_2 = self._setup_H(observed_states_1, observed_states_2) + res = add_tensors_by_dim_labels( + H_1, H_2, observed_states_1, observed_states_2, labeled_axis=(0, 1) + ) + + fn = function([H_1, H_2], res) + + H_1_val = rng.normal(size=(n_states, n_states)) + H_2_val = rng.normal(size=(n_states, n_states)) + + np.testing.assert_allclose(fn(H_1_val, H_2_val), H_1_val + H_2_val) + + def test_add_fully_overlapping_mixed_covariance_matrices(self): + rng = np.random.default_rng() + + observed_states_1 = ["A", "B", "C", "D"] + observed_states_2 = ["A", "B", "C", "D"] + rng.shuffle(observed_states_2) + + H_1, H_2 = self._setup_H(observed_states_1, observed_states_2) + + res = add_tensors_by_dim_labels( + H_1, H_2, observed_states_1, observed_states_2, labeled_axis=(0, 1) + ) + + H_1_val = rng.normal(size=(4, 4)) + H_2_val = rng.normal(size=(4, 4)) + + fn = function([H_1, H_2], res) + + state_to_idx = {name: idx for idx, name in enumerate(observed_states_1)} + idx = np.argsort([state_to_idx[state] for state in observed_states_2]) + + np.testing.assert_allclose(fn(H_1_val, H_2_val), H_1_val + H_2_val[np.ix_(idx, idx)]) + + def test_add_non_overlapping_covaraince_matrices(self): + rng = np.random.default_rng() + + observed_states_1 = ["A", "B"] + observed_states_2 = ["C", "D"] + + H_1, H_2 = self._setup_H(observed_states_1, observed_states_2) + + res = add_tensors_by_dim_labels( + H_1, H_2, observed_states_1, observed_states_2, labeled_axis=(0, 1) + ) + + H_1_val = rng.normal(size=(2, 2)) + H_2_val = rng.normal(size=(2, 2)) + zeros = np.zeros_like(H_1_val) + + fn = function([H_1, H_2], res) + + np.testing.assert_allclose( + fn(H_1_val, H_2_val), np.block([[H_1_val, zeros], [zeros, H_2_val]]) + ) + + def test_add_partially_overlapping_covaraince_matrices(self): + rng = np.random.default_rng() + observed_states_1 = ["A", "B"] + observed_states_2 = ["B", "C", "D", "A"] + H_1, H_2 = self._setup_H(observed_states_1, observed_states_2) + + res = add_tensors_by_dim_labels( + H_1, H_2, observed_states_1, observed_states_2, labeled_axis=(-2, -1) + ) + + fn = function([H_1, H_2], res) + H_1_val = rng.normal(size=(2, 2)) + H_2_val = rng.normal(size=(4, 4)) + + upper = np.zeros((4, 4)) + upper_idx = np.ix_([0, 1], [0, 1]) + upper[upper_idx] = H_1_val + expected_value = upper + H_2_val[np.ix_([3, 0, 1, 2], [3, 0, 1, 2])] + + np.testing.assert_allclose(fn(H_1_val, H_2_val), expected_value) + + +class TestJoinDesignMatrices: + def _setup_Z(self, states_1, states_2, k_endog=2): + Z_1 = pt.tensor("Z_1", shape=(len(states_1), k_endog)) + Z_2 = pt.tensor("Z_2", shape=(len(states_2), k_endog)) + + return Z_1, Z_2 + + def test_join_fully_overlapping_design_matrices(self): + observed_states_1 = ["A"] + observed_states_2 = ["A"] + + Z_1, Z_2 = self._setup_Z(observed_states_1, observed_states_2) + res = join_tensors_by_dim_labels( + Z_1, Z_2, observed_states_1, observed_states_2, labeled_axis=0, join_axis=1 + ) + + fn = function([Z_1, Z_2], res) + + Z_1_val = np.array([[1.0, 0.0]]) + Z_2_val = np.array([[0.0, 1.0]]) + + np.testing.assert_allclose(fn(Z_1_val, Z_2_val), np.array([[1.0, 0.0, 0.0, 1.0]])) + + def test_join_fully_overlapping_mixed_design_matrices(self): + observed_states_1 = ["A", "B", "C"] + observed_states_2 = ["C", "B", "A"] + + Z_1, Z_2 = self._setup_Z(observed_states_1, observed_states_2, k_endog=3) + res = join_tensors_by_dim_labels( + Z_1, Z_2, observed_states_1, observed_states_2, labeled_axis=0, join_axis=1 + ) + + fn = function([Z_1, Z_2], res) + + Z_1_val = np.array([[1.0, 0.0, 1.0], [0.0, 1.0, 0.0], [1.0, 0.0, 1.0]]) + Z_2_val = np.array([[0.0, 1.0, 1.0], [1.0, 0.0, 1.0], [1.0, 0.0, 0.0]]) + + # Rows 0 and 2 should be swapped in the output, because the ordering A, B, C becomes canonical as it was passed + # in first, and because we said the labeled dim was axis=0. After reordering, the matrices should be + # concatenated on axis = 1 (again, as requested). + np.testing.assert_allclose( + fn(Z_1_val, Z_2_val), + np.array( + [ + [1.0, 0.0, 1.0, 1.0, 0.0, 0.0], + [0.0, 1.0, 0.0, 1.0, 0.0, 1.0], + [1.0, 0.0, 1.0, 0.0, 1.0, 1.0], + ] + ), + ) + + def test_join_non_overlapping_design_matrices(self): + observed_states_1 = ["A"] + observed_states_2 = ["B"] + + Z_1, Z_2 = self._setup_Z(observed_states_1, observed_states_2) + fn = function( + [Z_1, Z_2], join_tensors_by_dim_labels(Z_1, Z_2, observed_states_1, observed_states_2) + ) + + Z_1_val = np.array([[1.0, 0.0]]) + Z_2_val = np.array([[1.0, 0.0]]) + out = fn(Z_1_val, Z_2_val) + + np.testing.assert_allclose(out, [[1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0]]) + + def test_join_partially_overlapping_design_matrices(self): + observed_states_1 = ["A"] + observed_states_2 = ["A", "B", "C"] + + Z_1, Z_2 = self._setup_Z(observed_states_1, observed_states_2) + res = join_tensors_by_dim_labels( + Z_1, Z_2, observed_states_1, observed_states_2, labeled_axis=0, join_axis=1 + ) + fn = function([Z_1, Z_2], res) + + Z_1_val = np.array([[1.0, 0.0]]) + Z_2_val = np.array([[0.0, 1.0], [1.0, 0.0], [1.0, 0.0]]) + + # Z_1 should be zero padded with the missing observed states, then concatenated along axis = -1 + expected_output = np.array( + [[1.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 0.0], [0.0, 0.0, 1.0, 0.0]] + ) + + np.testing.assert_allclose(fn(Z_1_val, Z_2_val), expected_output) diff --git a/tests/statespace/test_utilities.py b/tests/statespace/test_utilities.py index 91c8ed513..6837d4fe7 100644 --- a/tests/statespace/test_utilities.py +++ b/tests/statespace/test_utilities.py @@ -272,7 +272,7 @@ def simulate_from_numpy_model(mod, rng, param_dict, data_dict=None, steps=100): else: y[t] = (d + Z[t] @ x[t] + error).squeeze() - return x, y + return x, y.squeeze() def assert_pattern_repeats(y, T, atol, rtol): From 7cae4875b848f4ffbd74e83f7af3fe1ec54c9e7b Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Wed, 25 Jun 2025 05:27:56 +0200 Subject: [PATCH 03/21] Allow multiple observed in LevelTrend component --- .../structural/components/level_trend.py | 75 ++++++++++++++----- .../structural/components/test_level_trend.py | 67 +++++++++++++++++ tests/statespace/test_utilities.py | 3 +- 3 files changed, 125 insertions(+), 20 deletions(-) diff --git a/pymc_extras/statespace/models/structural/components/level_trend.py b/pymc_extras/statespace/models/structural/components/level_trend.py index b3372f822..b6735007f 100644 --- a/pymc_extras/statespace/models/structural/components/level_trend.py +++ b/pymc_extras/statespace/models/structural/components/level_trend.py @@ -1,5 +1,7 @@ import numpy as np +from scipy import linalg + from pymc_extras.statespace.models.structural.core import Component from pymc_extras.statespace.models.structural.utils import order_to_mask from pymc_extras.statespace.utils.constants import POSITION_DERIVATIVE_NAMES @@ -120,6 +122,7 @@ def __init__( if observed_state_names is None: observed_state_names = ["data"] + k_endog = len(observed_state_names) self._order_mask = order_to_mask(order) max_state = np.flatnonzero(self._order_mask)[-1].item() + 1 @@ -148,49 +151,83 @@ def __init__( super().__init__( name, - k_endog=len(observed_state_names), - k_states=k_states, - k_posdef=k_posdef, + k_endog=k_endog, + k_states=k_states * k_endog, + k_posdef=k_posdef * k_endog, observed_state_names=observed_state_names, measurement_error=False, combine_hidden_states=False, - obs_state_idxs=np.array([1.0] + [0.0] * (k_states - 1)), + obs_state_idxs=np.tile(np.array([1.0] + [0.0] * (k_states - 1)), k_endog), ) def populate_component_properties(self): - name_slice = POSITION_DERIVATIVE_NAMES[: self.k_states] + k_endog = self.k_endog + k_states = self.k_states // k_endog + k_posdef = self.k_posdef // k_endog + + name_slice = POSITION_DERIVATIVE_NAMES[:k_states] self.param_names = ["initial_trend"] self.state_names = [name for name, mask in zip(name_slice, self._order_mask) if mask] self.param_dims = {"initial_trend": ("trend_state",)} self.coords = {"trend_state": self.state_names} - self.param_info = {"initial_trend": {"shape": (self.k_states,), "constraints": None}} + + if k_endog > 1: + self.param_dims["trend_state"] = ( + "trend_endog", + "trend_state", + ) + self.coords["trend_endog"] = self.observed_state_names + + shape = (k_endog, k_states) if k_endog > 1 else (k_states,) + self.param_info = {"initial_trend": {"shape": shape, "constraints": None}} if self.k_posdef > 0: self.param_names += ["sigma_trend"] self.shock_names = [ name for name, mask in zip(name_slice, self.innovations_order) if mask ] - self.param_dims["sigma_trend"] = ("trend_shock",) + self.param_dims["sigma_trend"] = ( + ("trend_shock",) if k_endog == 1 else ("trend_endog", "trend_shock") + ) self.coords["trend_shock"] = self.shock_names - self.param_info["sigma_trend"] = {"shape": (self.k_posdef,), "constraints": "Positive"} + self.param_info["sigma_trend"] = { + "shape": (k_posdef,) if k_endog == 1 else (k_endog, k_posdef), + "constraints": "Positive", + } for name in self.param_names: self.param_info[name]["dims"] = self.param_dims[name] def make_symbolic_graph(self) -> None: - initial_trend = self.make_and_register_variable("initial_trend", shape=(self.k_states,)) - self.ssm["initial_state", :] = initial_trend - triu_idx = np.triu_indices(self.k_states) - self.ssm[np.s_["transition", triu_idx[0], triu_idx[1]]] = 1 + k_endog = self.k_endog + k_states = self.k_states // k_endog + k_posdef = self.k_posdef // k_endog - R = np.eye(self.k_states) + initial_trend = self.make_and_register_variable( + "initial_trend", + shape=(k_states,) if k_endog == 1 else (k_endog, k_states), + ) + self.ssm["initial_state", :] = initial_trend.ravel() + + triu_idx = np.triu_indices(k_states) + T = np.zeros((k_states, k_states)) + T[triu_idx[0], triu_idx[1]] = 1 + + self.ssm["transition"] = linalg.block_diag(*[T for _ in range(k_endog)]) + + R = np.eye(k_states) R = R[:, self.innovations_order] - self.ssm["selection", :, :] = R - self.ssm["design", 0, :] = np.array([1.0] + [0.0] * (self.k_states - 1)) + self.ssm["selection", :, :] = linalg.block_diag(*[R for _ in range(k_endog)]) - if self.k_posdef > 0: - sigma_trend = self.make_and_register_variable("sigma_trend", shape=(self.k_posdef,)) - diag_idx = np.diag_indices(self.k_posdef) + Z = np.array([1.0] + [0.0] * (k_states - 1)).reshape((1, -1)) + self.ssm["design"] = linalg.block_diag(*[Z for _ in range(k_endog)]) + + if k_posdef > 0: + sigma_trend = self.make_and_register_variable( + "sigma_trend", + shape=(k_posdef,) if k_endog == 1 else (k_endog, k_posdef), + ) + diag_idx = np.diag_indices(k_posdef * k_endog) idx = np.s_["state_cov", diag_idx[0], diag_idx[1]] - self.ssm[idx] = sigma_trend**2 + self.ssm[idx] = (sigma_trend**2).ravel() diff --git a/tests/statespace/models/structural/components/test_level_trend.py b/tests/statespace/models/structural/components/test_level_trend.py index 9b48ba5b9..64f04b403 100644 --- a/tests/statespace/models/structural/components/test_level_trend.py +++ b/tests/statespace/models/structural/components/test_level_trend.py @@ -22,3 +22,70 @@ def test_level_trend_model(rng): mod = mod.build(verbose=False) _assert_basic_coords_correct(mod) assert mod.coords["trend_state"] == ["level", "trend"] + + +def test_level_trend_multiple_observed_construction(): + mod = st.LevelTrendComponent( + order=2, innovations_order=1, observed_state_names=["data_1", "data_2", "data_3"] + ) + mod = mod.build(verbose=False) + assert mod.k_endog == 3 + assert mod.k_states == 6 + assert mod.k_posdef == 3 + + assert mod.coords["trend_state"] == ["level", "trend"] + assert mod.coords["trend_endog"] == ["data_1", "data_2", "data_3"] + + Z = mod.ssm["design"].eval() + T = mod.ssm["transition"].eval() + R = mod.ssm["selection"].eval() + + np.testing.assert_allclose( + Z, + np.array( + [ + [1.0, 0.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 1.0, 0.0], + ] + ), + ) + + np.testing.assert_allclose( + T, + np.array( + [ + [1.0, 1.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 1.0, 0.0, 0.0, 0.0, 0.0], + [0.0, 0.0, 1.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 1.0, 0.0, 0.0], + [0.0, 0.0, 0.0, 0.0, 1.0, 1.0], + [0.0, 0.0, 0.0, 0.0, 0.0, 1.0], + ] + ), + ) + + np.testing.assert_allclose( + R, + np.array( + [ + [1.0, 0.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 0.0], + [0.0, 0.0, 1.0], + [0.0, 0.0, 0.0], + ] + ), + ) + + +def test_level_trend_multiple_observed(rng): + mod = st.LevelTrendComponent( + order=2, innovations_order=0, observed_state_names=["data_1", "data_2", "data_3"] + ) + params = {"initial_trend": np.array([[0.0, 1.0], [0.0, 2.0], [0.0, 3.0]])} + + x, y = simulate_from_numpy_model(mod, rng, params) + assert (np.diff(y, axis=0) == np.array([[1.0, 2.0, 3.0]])).all().all() + assert (np.diff(x, axis=0) == np.array([[1.0, 0.0, 2.0, 0.0, 3.0, 0.0]])).all().all() diff --git a/tests/statespace/test_utilities.py b/tests/statespace/test_utilities.py index 6837d4fe7..ab054ba19 100644 --- a/tests/statespace/test_utilities.py +++ b/tests/statespace/test_utilities.py @@ -242,11 +242,12 @@ def simulate_from_numpy_model(mod, rng, param_dict, data_dict=None, steps=100): Helper function to visualize the components outside of a PyMC model context """ x0, P0, c, d, T, Z, R, H, Q = unpack_symbolic_matrices_with_params(mod, param_dict, data_dict) + k_endog = mod.k_endog k_states = mod.k_states k_posdef = mod.k_posdef x = np.zeros((steps, k_states)) - y = np.zeros(steps) + y = np.zeros((steps, k_endog)) x[0] = x0 y[0] = (Z @ x0).squeeze() if Z.ndim == 2 else (Z[0] @ x0).squeeze() From bba84317501dfbc52d69187205cd8e69447df4e6 Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Wed, 25 Jun 2025 06:01:32 +0200 Subject: [PATCH 04/21] Allow multiple observed states in measurement error component --- .../components/measurement_error.py | 12 +++++++--- .../components/test_measurement_error.py | 22 +++++++++++++++++++ .../structural/test_against_statsmodels.py | 4 ++-- .../statespace/models/structural/test_core.py | 10 +++++++++ 4 files changed, 43 insertions(+), 5 deletions(-) diff --git a/pymc_extras/statespace/models/structural/components/measurement_error.py b/pymc_extras/statespace/models/structural/components/measurement_error.py index 4017f0551..b62c8fce2 100644 --- a/pymc_extras/statespace/models/structural/components/measurement_error.py +++ b/pymc_extras/statespace/models/structural/components/measurement_error.py @@ -64,16 +64,22 @@ def __init__( def populate_component_properties(self): self.param_names = [f"sigma_{self.name}"] self.param_dims = {} + self.coords = {} + + if self.k_endog > 1: + self.param_dims[f"sigma_{self.name}"] = (f"endog_{self.name}",) + self.coords[f"endog_{self.name}"] = self.observed_state_names + self.param_info = { f"sigma_{self.name}": { - "shape": (), + "shape": (self.k_endog,) if self.k_endog > 1 else (), "constraints": "Positive", - "dims": None, + "dims": (f"endog_{self.name}",) if self.k_endog > 1 else None, } } def make_symbolic_graph(self) -> None: - sigma_shape = () + sigma_shape = () if self.k_endog == 1 else (self.k_endog,) error_sigma = self.make_and_register_variable(f"sigma_{self.name}", shape=sigma_shape) diag_idx = np.diag_indices(self.k_endog) idx = np.s_["obs_cov", diag_idx[0], diag_idx[1]] diff --git a/tests/statespace/models/structural/components/test_measurement_error.py b/tests/statespace/models/structural/components/test_measurement_error.py index 752e8513c..ba6a654f9 100644 --- a/tests/statespace/models/structural/components/test_measurement_error.py +++ b/tests/statespace/models/structural/components/test_measurement_error.py @@ -1,3 +1,5 @@ +import numpy as np + from pymc_extras.statespace.models import structural as st from tests.statespace.models.structural.conftest import _assert_basic_coords_correct @@ -8,3 +10,23 @@ def test_measurement_error(rng): _assert_basic_coords_correct(mod) assert "sigma_obs" in mod.param_names + + +def test_measurement_error_multiple_observed(): + mod = st.MeasurementError("obs", observed_state_names=["data_1", "data_2"]) + assert mod.k_endog == 2 + assert mod.coords["endog_obs"] == ["data_1", "data_2"] + assert mod.param_dims["sigma_obs"] == ("endog_obs",) + + +def test_build_with_measurement_error_subset(): + ll = st.LevelTrendComponent(order=2, observed_state_names=["data_1", "data_2", "data_3"]) + me = st.MeasurementError("obs", observed_state_names=["data_1", "data_3"]) + mod = (ll + me).build() + + H = mod.ssm["obs_cov"] + assert H.type.shape == (3, 3) + np.testing.assert_allclose( + H.eval({"sigma_obs": [1.0, 3.0]}), + np.array([[1.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 9.0]]), + ) diff --git a/tests/statespace/models/structural/test_against_statsmodels.py b/tests/statespace/models/structural/test_against_statsmodels.py index 94da8afe1..3495ecc14 100644 --- a/tests/statespace/models/structural/test_against_statsmodels.py +++ b/tests/statespace/models/structural/test_against_statsmodels.py @@ -416,8 +416,8 @@ def create_structural_model_and_equivalent_statsmodel( expected_coords[AR_PARAM_DIM] += tuple(list(range(1, autoregressive + 1))) expected_coords[ALL_STATE_DIM] += ar_names expected_coords[ALL_STATE_AUX_DIM] += ar_names - expected_coords[SHOCK_DIM] += ["ar_innovation"] - expected_coords[SHOCK_AUX_DIM] += ["ar_innovation"] + expected_coords[SHOCK_DIM] += ["data_ar_innovation"] + expected_coords[SHOCK_AUX_DIM] += ["data_ar_innovation"] sm_params["sigma2.ar"] = sigma2 for i, rho in enumerate(ar_params): diff --git a/tests/statespace/models/structural/test_core.py b/tests/statespace/models/structural/test_core.py index 500e8b1a2..46115b659 100644 --- a/tests/statespace/models/structural/test_core.py +++ b/tests/statespace/models/structural/test_core.py @@ -64,6 +64,16 @@ def test_add_components(): assert_allclose(all_mat, np.concatenate([ll_mat, se_mat], axis=axis), atol=ATOL, rtol=RTOL) +def test_add_components_multiple_observed(): + ll = st.LevelTrendComponent(order=2, observed_state_names=["data_1", "data_2"]) + me = st.MeasurementError(name="obs", observed_state_names=["data_1", "data_2"]) + + mod = (ll + me).build() + + for property in ["param_names", "shock_names", "param_info", "coords", "param_dims"]: + assert [x in getattr(mod, property) for x in getattr(ll, property)] + + @pytest.mark.skipif(floatX.endswith("32"), reason="Prior covariance not PSD at half-precision") def test_extract_components_from_idata(rng): time_idx = pd.date_range(start="2000-01-01", freq="D", periods=100) From 0a84576f039809866b3727500cbbe4548dd99ebd Mon Sep 17 00:00:00 2001 From: jessegrabowski Date: Wed, 25 Jun 2025 22:07:38 +0800 Subject: [PATCH 05/21] Allow multiple observed in AutoRegressive component --- .../structural/components/autoregressive.py | 89 +++++++++++++++---- .../components/test_autoregressive.py | 19 ++++ 2 files changed, 92 insertions(+), 16 deletions(-) diff --git a/pymc_extras/statespace/models/structural/components/autoregressive.py b/pymc_extras/statespace/models/structural/components/autoregressive.py index 0eca94295..441913fe9 100644 --- a/pymc_extras/statespace/models/structural/components/autoregressive.py +++ b/pymc_extras/statespace/models/structural/components/autoregressive.py @@ -1,4 +1,5 @@ import numpy as np +import pytensor.tensor as pt from pymc_extras.statespace.models.structural.core import Component from pymc_extras.statespace.models.structural.utils import order_to_mask @@ -70,10 +71,11 @@ def __init__( if observed_state_names is None: observed_state_names = ["data"] + k_posdef = k_endog = len(observed_state_names) + order = order_to_mask(order) ar_lags = np.flatnonzero(order).ravel().astype(int) + 1 k_states = len(order) - k_posdef = k_endog = len(observed_state_names) self.order = order self.ar_lags = ar_lags @@ -81,42 +83,97 @@ def __init__( super().__init__( name=name, k_endog=k_endog, - k_states=k_states, + k_states=k_states * k_endog, k_posdef=k_posdef, measurement_error=True, combine_hidden_states=True, observed_state_names=observed_state_names, - obs_state_idxs=np.r_[[1.0], np.zeros(k_states - 1)], + obs_state_idxs=np.tile(np.r_[[1.0], np.zeros(k_states - 1)], k_endog), ) def populate_component_properties(self): - self.state_names = [f"L{i + 1}.data" for i in range(self.k_states)] - self.shock_names = [f"{self.name}_innovation"] + self.state_names = [ + f"L{i + 1}.{state_name}" + for i in range(self.k_states) + for state_name in self.observed_state_names + ] + self.shock_names = [f"{name}_{self.name}_innovation" for name in self.observed_state_names] self.param_names = ["ar_params", "sigma_ar"] self.param_dims = {"ar_params": (AR_PARAM_DIM,)} self.coords = {AR_PARAM_DIM: self.ar_lags.tolist()} + if self.k_endog > 1: + self.param_dims["ar_params"] = ( + f"{self.name}_endog", + AR_PARAM_DIM, + ) + self.param_dims["sigma_ar"] = (f"{self.name}_endog",) + + self.coords[f"{self.name}_endog"] = self.observed_state_names + self.param_info = { "ar_params": { - "shape": (self.k_states,), + "shape": (self.k_states,) if self.k_endog == 1 else (self.k_endog, self.k_states), "constraints": None, - "dims": (AR_PARAM_DIM,), + "dims": (AR_PARAM_DIM,) + if self.k_endog == 1 + else ( + f"{self.name}_endog", + AR_PARAM_DIM, + ), + }, + "sigma_ar": { + "shape": () if self.k_endog == 1 else (self.k_endog,), + "constraints": "Positive", + "dims": None if self.k_endog == 1 else (f"{self.name}_endog",), }, - "sigma_ar": {"shape": (), "constraints": "Positive", "dims": None}, } def make_symbolic_graph(self) -> None: + k_endog = self.k_endog + k_states = self.k_states // k_endog + k_posdef = self.k_posdef + k_nonzero = int(sum(self.order)) - ar_params = self.make_and_register_variable("ar_params", shape=(k_nonzero,)) - sigma_ar = self.make_and_register_variable("sigma_ar", shape=()) + ar_params = self.make_and_register_variable( + "ar_params", shape=(k_nonzero,) if k_endog == 1 else (k_endog, k_nonzero) + ) + sigma_ar = self.make_and_register_variable( + "sigma_ar", shape=() if k_endog == 1 else (k_endog,) + ) + + if k_endog == 1: + T = pt.eye(k_states, k=-1) + ar_idx = (np.zeros(k_nonzero, dtype="int"), np.nonzero(self.order)[0]) + T = T[ar_idx].set(ar_params) + + else: + transition_matrices = [] + + for i in range(k_endog): + T = pt.eye(k_states, k=-1) + ar_idx = (np.zeros(k_nonzero, dtype="int"), np.nonzero(self.order)[0]) + T = T[ar_idx].set(ar_params[i]) + transition_matrices.append(T) + T = pt.specify_shape( + pt.linalg.block_diag(*transition_matrices), (self.k_states, self.k_states) + ) - T = np.eye(self.k_states, k=-1) self.ssm["transition", :, :] = T - self.ssm["selection", 0, 0] = 1 - self.ssm["design", 0, 0] = 1 - ar_idx = ("transition", np.zeros(k_nonzero, dtype="int"), np.nonzero(self.order)[0]) - self.ssm[ar_idx] = ar_params + R = np.eye(k_states) + R_mask = np.full((k_states), False) + R_mask[0] = True + R = R[:, R_mask] + + self.ssm["selection", :, :] = pt.specify_shape( + pt.linalg.block_diag(*[R for _ in range(k_endog)]), (self.k_states, self.k_posdef) + ) + + Z = pt.zeros((1, k_states))[0, 0].set(1.0) + self.ssm["design", :, :] = pt.specify_shape( + pt.linalg.block_diag(*[Z for _ in range(k_endog)]), (self.k_endog, self.k_states) + ) - cov_idx = ("state_cov", *np.diag_indices(1)) + cov_idx = ("state_cov", *np.diag_indices(k_posdef)) self.ssm[cov_idx] = sigma_ar**2 diff --git a/tests/statespace/models/structural/components/test_autoregressive.py b/tests/statespace/models/structural/components/test_autoregressive.py index f68a34de6..21234aa2a 100644 --- a/tests/statespace/models/structural/components/test_autoregressive.py +++ b/tests/statespace/models/structural/components/test_autoregressive.py @@ -26,3 +26,22 @@ def test_autoregressive_model(order, rng): if isinstance(order, list): lags = lags[np.flatnonzero(order)] assert_allclose(ar.coords["ar_lag"], lags) + + +def test_autoregressive_multiple_observed(rng): + ar = st.AutoregressiveComponent(order=3, observed_state_names=["data_1", "data_2"]) + mod = ar.build(verbose=False) + + params = { + "ar_params": np.full( + ( + 2, + sum(ar.order), + ), + 0.5, + dtype=config.floatX, + ), + "sigma_ar": np.ones((2,)) * 1e-3, + } + + x, y = simulate_from_numpy_model(ar, rng, params, steps=100) From 480f4fb7a9aedcdd4767b5095a8690590e2ad06f Mon Sep 17 00:00:00 2001 From: Alexandre Andorra Date: Tue, 1 Jul 2025 09:37:14 -0400 Subject: [PATCH 06/21] Fix typo in docstrings --- pymc_extras/statespace/models/utilities.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/pymc_extras/statespace/models/utilities.py b/pymc_extras/statespace/models/utilities.py index ad2ba33b0..31de98f85 100644 --- a/pymc_extras/statespace/models/utilities.py +++ b/pymc_extras/statespace/models/utilities.py @@ -541,21 +541,21 @@ def add_tensors_by_dim_labels( Parameters ---------- tensor: TensorVariable - A statespace matrix to be summed with ``other_matrix``. + A statespace matrix to be summed with ``other_tensor``. other_tensor: TensorVariable - A statespace matrix to be summed with ``matrix``. + A statespace matrix to be summed with ``tensor``. labels: list of str - Dimension labels associated with ``matrix``, on the ``labeled_axis`` dimension. + Dimension labels associated with ``tensor``, on the ``labeled_axis`` dimension. other_labels: list of str - Dimension labels associated with ``other_matrix``, on the ``labeled_axis`` dimension. + Dimension labels associated with ``other_tensor``, on the ``labeled_axis`` dimension. labeled_axis: int or tuple of int - Dimension that is labeled by ``labels`` and ``other_labels``. ``matrix.shape[labeled_axis]`` must have the + Dimension that is labeled by ``labels`` and ``other_labels``. ``tensor.shape[labeled_axis]`` must have the shape of ``len(labels)`` at runtime. Returns ------- result: TensorVariable - Result of addition of ``matrix`` and ``other_matrix``, along the ``labeled_axis`` dimension. The ordering of + Result of addition of ``tensor`` and ``other_tensor``, along the ``labeled_axis`` dimension. The ordering of the output will be ``labels + [label for label in other_labels if label not in labels]``. That is, ``labels`` come first, followed by any new labels introduced by ``other_labels``. From a898eb69c156a894fce4d6d7175dc8aa797ec801 Mon Sep 17 00:00:00 2001 From: Alex Andorra Date: Tue, 1 Jul 2025 14:02:09 -0400 Subject: [PATCH 07/21] Allow multiple observed in Cycle component --- .../models/structural/components/cycle.py | 140 +++++++++++++++--- .../structural/components/test_cycle.py | 88 +++++++++++ .../statespace/models/structural/conftest.py | 5 +- 3 files changed, 211 insertions(+), 22 deletions(-) diff --git a/pymc_extras/statespace/models/structural/components/cycle.py b/pymc_extras/statespace/models/structural/components/cycle.py index 4c0f4603f..b47febf5d 100644 --- a/pymc_extras/statespace/models/structural/components/cycle.py +++ b/pymc_extras/statespace/models/structural/components/cycle.py @@ -1,6 +1,7 @@ import numpy as np from pytensor import tensor as pt +from scipy import linalg from pymc_extras.statespace.models.structural.core import Component from pymc_extras.statespace.models.structural.utils import _frequency_transition_block @@ -10,6 +11,10 @@ class CycleComponent(Component): r""" A component for modeling longer-term cyclical effects + Supports both univariate and multivariate time series. For multivariate time series, + each endogenous variable gets its own independent cycle component with separate + cosine/sine states and optional variable-specific innovation variances. + Parameters ---------- name: str @@ -32,6 +37,11 @@ class CycleComponent(Component): innovations: bool, default True Whether to include stochastic innovations in the strength of the seasonal effect. If True, an additional parameter, ``sigma_{name}`` will be added to the model. + For multivariate time series, this is a vector (variable-specific innovation variances). + + observed_state_names: list[str], optional + Names of the observed state variables. For univariate time series, defaults to ``["data"]``. + For multivariate time series, specify a list of names for each endogenous variable. Notes ----- @@ -51,8 +61,16 @@ class CycleComponent(Component): Unlike a FrequencySeasonality component, the length of a CycleComponent can be estimated. + **Multivariate Support:** + For multivariate time series with k endogenous variables, the component creates: + - 2k states (cosine and sine components for each variable) + - Block diagonal transition and selection matrices + - Variable-specific innovation variances (optional) + - Proper parameter shapes: (k, 2) for initial states, (k,) for innovation variances + Examples -------- + **Univariate Example:** Estimate a business cycle with length between 6 and 12 years: .. code:: python @@ -84,6 +102,35 @@ class CycleComponent(Component): idata = pm.sample(nuts_sampler='numpyro') + **Multivariate Example:** + Model cycles for multiple economic indicators with variable-specific innovation variances: + + .. code:: python + + # Multivariate cycle component + cycle = st.CycleComponent( + name='business_cycle', + cycle_length=12, + estimate_cycle_length=False, + innovations=True, + dampen=True, + observed_state_names=['gdp', 'unemployment', 'inflation'] + ) + + # Build the model + ss_mod = cycle.build() + + # In PyMC model: + with pm.Model(coords=ss_mod.coords) as model: + # Initial states: shape (3, 2) for 3 variables, 2 states each + cycle_init = pm.Normal('business_cycle', dims=('business_cycle_endog', 'business_cycle_state')) + + # Dampening factor: scalar (shared across variables) + dampening = pm.Uniform('business_cycle_dampening_factor', lower=0.8, upper=1.0) + + # Innovation variances: shape (3,) for variable-specific variances + sigma_cycle = pm.HalfNormal('sigma_business_cycle', dims=('business_cycle_endog',)) + References ---------- .. [1] Durbin, James, and Siem Jan Koopman. 2012. @@ -137,14 +184,23 @@ def __init__( ) def make_symbolic_graph(self) -> None: - self.ssm["design", 0, slice(0, self.k_states, 2)] = 1 - self.ssm["selection", :, :] = np.eye(self.k_states) - self.param_dims = {self.name: (f"{self.name}_state",)} - self.coords = {f"{self.name}_state": self.state_names} + if self.k_endog == 1: + self.ssm["design", 0, slice(0, self.k_states, 2)] = 1 + self.ssm["selection", :, :] = np.eye(self.k_states) + init_state = self.make_and_register_variable(f"{self.name}", shape=(self.k_states,)) + + else: + Z = np.array([1.0, 0.0]).reshape((1, -1)) + design_matrix = linalg.block_diag(*[Z for _ in range(self.k_endog)]) + self.ssm["design", :, :] = pt.as_tensor_variable(design_matrix) - init_state = self.make_and_register_variable(f"{self.name}", shape=(self.k_states,)) + R = np.eye(2) # 2x2 identity for each cycle component + selection_matrix = linalg.block_diag(*[R for _ in range(self.k_endog)]) + self.ssm["selection", :, :] = pt.as_tensor_variable(selection_matrix) - self.ssm["initial_state", :] = init_state + init_state = self.make_and_register_variable(f"{self.name}", shape=(self.k_endog, 2)) + + self.ssm["initial_state", :] = init_state.ravel() if self.estimate_cycle_length: lamb = self.make_and_register_variable(f"{self.name}_length", shape=()) @@ -157,23 +213,59 @@ def make_symbolic_graph(self) -> None: rho = 1 T = rho * _frequency_transition_block(lamb, j=1) - self.ssm["transition", :, :] = T + if self.k_endog == 1: + self.ssm["transition", :, :] = T + else: + # can't make the linalg.block_diag logic work here + # doing it manually for now + for i in range(self.k_endog): + start_idx = i * 2 + end_idx = (i + 1) * 2 + self.ssm["transition", start_idx:end_idx, start_idx:end_idx] = T if self.innovations: - sigma_cycle = self.make_and_register_variable(f"sigma_{self.name}", shape=()) - self.ssm["state_cov", :, :] = pt.eye(self.k_posdef) * sigma_cycle**2 + if self.k_endog == 1: + sigma_cycle = self.make_and_register_variable(f"sigma_{self.name}", shape=()) + self.ssm["state_cov", :, :] = pt.eye(self.k_posdef) * sigma_cycle**2 + else: + sigma_cycle = self.make_and_register_variable( + f"sigma_{self.name}", shape=(self.k_endog,) + ) + # can't make the linalg.block_diag logic work here + # doing it manually for now + for i in range(self.k_endog): + start_idx = i * 2 + end_idx = (i + 1) * 2 + Q_block = pt.eye(2) * sigma_cycle[i] ** 2 + self.ssm["state_cov", start_idx:end_idx, start_idx:end_idx] = Q_block def populate_component_properties(self): self.state_names = [f"{self.name}_{f}" for f in ["Cos", "Sin"]] self.param_names = [f"{self.name}"] - self.param_info = { - f"{self.name}": { - "shape": (2,), - "constraints": None, - "dims": (f"{self.name}_state",), + if self.k_endog == 1: + self.param_dims = {self.name: (f"{self.name}_state",)} + self.coords = {f"{self.name}_state": self.state_names} + self.param_info = { + f"{self.name}": { + "shape": (2,), + "constraints": None, + "dims": (f"{self.name}_state",), + } + } + else: + self.param_dims = {self.name: (f"{self.name}_endog", f"{self.name}_state")} + self.coords = { + f"{self.name}_state": self.state_names, + f"{self.name}_endog": self.observed_state_names, + } + self.param_info = { + f"{self.name}": { + "shape": (self.k_endog, 2), + "constraints": None, + "dims": (f"{self.name}_endog", f"{self.name}_state"), + } } - } if self.estimate_cycle_length: self.param_names += [f"{self.name}_length"] @@ -193,9 +285,17 @@ def populate_component_properties(self): if self.innovations: self.param_names += [f"sigma_{self.name}"] - self.param_info[f"sigma_{self.name}"] = { - "shape": (), - "constraints": "Positive", - "dims": None, - } + if self.k_endog == 1: + self.param_info[f"sigma_{self.name}"] = { + "shape": (), + "constraints": "Positive", + "dims": None, + } + else: + self.param_dims[f"sigma_{self.name}"] = (f"{self.name}_endog",) + self.param_info[f"sigma_{self.name}"] = { + "shape": (self.k_endog,), + "constraints": "Positive", + "dims": (f"{self.name}_endog",), + } self.shock_names = self.state_names.copy() diff --git a/tests/statespace/models/structural/components/test_cycle.py b/tests/statespace/models/structural/components/test_cycle.py index b24eae290..987cbf914 100644 --- a/tests/statespace/models/structural/components/test_cycle.py +++ b/tests/statespace/models/structural/components/test_cycle.py @@ -45,8 +45,96 @@ def test_cycle_component_with_innovations_and_cycle_length(rng): "cycle_dampening_factor": 0.95, "sigma_cycle": 1.0, } + x, y = simulate_from_numpy_model(cycle, rng, params) + + cycle.build(verbose=False) + _assert_basic_coords_correct(cycle) + + +def test_cycle_multivariate_deterministic(rng): + """Test multivariate cycle component with deterministic cycles.""" + cycle = st.CycleComponent( + name="cycle", + cycle_length=12, + estimate_cycle_length=False, + innovations=False, + observed_state_names=["data_1", "data_2", "data_3"], + ) + params = {"cycle": np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]], dtype=config.floatX)} + x, y = simulate_from_numpy_model(cycle, rng, params, steps=12 * 12) + + # Check that each variable has a cyclical pattern with the expected period + for i in range(3): + assert_pattern_repeats(y[:, i], 12, atol=ATOL, rtol=RTOL) + + # Check that the cycles have different amplitudes (different initial states) + assert np.std(y[:, 0]) > 0 + assert np.std(y[:, 1]) > 0 + assert np.std(y[:, 2]) > 0 + # The second and third variables should have larger amplitudes due to larger initial states + assert np.std(y[:, 1]) > np.std(y[:, 0]) + assert np.std(y[:, 2]) > np.std(y[:, 0]) + +def test_cycle_multivariate_with_dampening(rng): + """Test multivariate cycle component with dampening.""" + cycle = st.CycleComponent( + name="cycle", + cycle_length=12, + estimate_cycle_length=False, + innovations=False, + dampen=True, + observed_state_names=["data_1", "data_2", "data_3"], + ) + params = { + "cycle": np.array([[10.0, 10.0], [20.0, 20.0], [30.0, 30.0]], dtype=config.floatX), + "cycle_dampening_factor": 0.75, + } + x, y = simulate_from_numpy_model(cycle, rng, params, steps=100) + + # Check that all cycles dampen to zero over time + for i in range(3): + assert_allclose(y[-1, i], 0.0, atol=ATOL, rtol=RTOL) + + # Check that the dampening pattern is consistent across variables + # The variables should dampen at the same rate but with different initial amplitudes + for i in range(1, 3): + # The ratio of final to initial values should be similar across variables + ratio_0 = abs(y[-1, 0] / y[0, 0]) if y[0, 0] != 0 else 0 + ratio_i = abs(y[-1, i] / y[0, i]) if y[0, i] != 0 else 0 + assert_allclose(ratio_0, ratio_i, atol=1e-2, rtol=1e-2) + + +def test_cycle_multivariate_with_innovations_and_cycle_length(rng): + """Test multivariate cycle component with innovations and estimated cycle length.""" + cycle = st.CycleComponent( + name="cycle", + estimate_cycle_length=True, + innovations=True, + dampen=True, + observed_state_names=["data_1", "data_2", "data_3"], + ) + params = { + "cycle": np.array([[1.0, 1.0], [2.0, 2.0], [3.0, 3.0]], dtype=config.floatX), + "cycle_length": 12.0, + "cycle_dampening_factor": 0.95, + "sigma_cycle": np.array([0.5, 1.0, 1.5]), # Different innovation variances per variable + } x, y = simulate_from_numpy_model(cycle, rng, params) cycle.build(verbose=False) _assert_basic_coords_correct(cycle) + + assert cycle.coords["cycle_state"] == ["cycle_Cos", "cycle_Sin"] + assert cycle.coords["cycle_endog"] == ["data_1", "data_2", "data_3"] + + assert cycle.k_endog == 3 + assert cycle.k_states == 6 # 2 states per variable + assert cycle.k_posdef == 6 # 2 innovations per variable + + # Check that the data has the expected shape + assert y.shape[1] == 3 # 3 variables + + # Check that each variable shows some variation (due to innovations) + for i in range(3): + assert np.std(y[:, i]) > 0 diff --git a/tests/statespace/models/structural/conftest.py b/tests/statespace/models/structural/conftest.py index 63ce45c1b..c5f2396bc 100644 --- a/tests/statespace/models/structural/conftest.py +++ b/tests/statespace/models/structural/conftest.py @@ -23,5 +23,6 @@ def _assert_basic_coords_correct(mod): assert mod.coords[ALL_STATE_AUX_DIM] == mod.state_names assert mod.coords[SHOCK_DIM] == mod.shock_names assert mod.coords[SHOCK_AUX_DIM] == mod.shock_names - assert mod.coords[OBS_STATE_DIM] == ["data"] - assert mod.coords[OBS_STATE_AUX_DIM] == ["data"] + expected_obs = mod.observed_state_names if hasattr(mod, "observed_state_names") else ["data"] + assert mod.coords[OBS_STATE_DIM] == expected_obs + assert mod.coords[OBS_STATE_AUX_DIM] == expected_obs From 62d07507b6c357639daa88c870e5658697bcf29a Mon Sep 17 00:00:00 2001 From: Alex Andorra Date: Tue, 1 Jul 2025 17:05:58 -0400 Subject: [PATCH 08/21] Fix Cycle docstring examples --- .../statespace/models/structural/components/cycle.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/pymc_extras/statespace/models/structural/components/cycle.py b/pymc_extras/statespace/models/structural/components/cycle.py index b47febf5d..bcbfed2a1 100644 --- a/pymc_extras/statespace/models/structural/components/cycle.py +++ b/pymc_extras/statespace/models/structural/components/cycle.py @@ -94,13 +94,13 @@ class CycleComponent(Component): intitial_trend = pm.Normal('initial_trend', dims=ss_mod.param_dims['initial_trend']) sigma_trend = pm.HalfNormal('sigma_trend', dims=ss_mod.param_dims['sigma_trend']) - cycle_strength = pm.Normal('business_cycle') + cycle_strength = pm.Normal("business_cycle", dims=ss_mod.param_dims["business_cycle"]) cycle_length = pm.Uniform('business_cycle_length', lower=6, upper=12) sigma_cycle = pm.HalfNormal('sigma_business_cycle', sigma=1) - ss_mod.build_statespace_graph(data) - idata = pm.sample(nuts_sampler='numpyro') + ss_mod.build_statespace_graph(data) + idata = pm.sample() **Multivariate Example:** Model cycles for multiple economic indicators with variable-specific innovation variances: @@ -122,6 +122,7 @@ class CycleComponent(Component): # In PyMC model: with pm.Model(coords=ss_mod.coords) as model: + P0 = pm.Deterministic("P0", pt.eye(ss_mod.k_states), dims=ss_mod.param_dims["P0"]) # Initial states: shape (3, 2) for 3 variables, 2 states each cycle_init = pm.Normal('business_cycle', dims=('business_cycle_endog', 'business_cycle_state')) @@ -131,6 +132,9 @@ class CycleComponent(Component): # Innovation variances: shape (3,) for variable-specific variances sigma_cycle = pm.HalfNormal('sigma_business_cycle', dims=('business_cycle_endog',)) + ss_mod.build_statespace_graph(data) + idata = pm.sample() + References ---------- .. [1] Durbin, James, and Siem Jan Koopman. 2012. From 152e96276aea111c8d9bb5004b7dc8869c870029 Mon Sep 17 00:00:00 2001 From: Alex Andorra Date: Wed, 2 Jul 2025 18:10:05 -0400 Subject: [PATCH 09/21] Use pytensor block_diag for Cycle --- .../models/structural/components/cycle.py | 40 ++++++++++--------- 1 file changed, 21 insertions(+), 19 deletions(-) diff --git a/pymc_extras/statespace/models/structural/components/cycle.py b/pymc_extras/statespace/models/structural/components/cycle.py index bcbfed2a1..1636c51b1 100644 --- a/pymc_extras/statespace/models/structural/components/cycle.py +++ b/pymc_extras/statespace/models/structural/components/cycle.py @@ -1,6 +1,7 @@ import numpy as np from pytensor import tensor as pt +from pytensor.tensor.slinalg import block_diag from scipy import linalg from pymc_extras.statespace.models.structural.core import Component @@ -96,7 +97,6 @@ class CycleComponent(Component): cycle_strength = pm.Normal("business_cycle", dims=ss_mod.param_dims["business_cycle"]) cycle_length = pm.Uniform('business_cycle_length', lower=6, upper=12) - sigma_cycle = pm.HalfNormal('sigma_business_cycle', sigma=1) ss_mod.build_statespace_graph(data) @@ -124,13 +124,15 @@ class CycleComponent(Component): with pm.Model(coords=ss_mod.coords) as model: P0 = pm.Deterministic("P0", pt.eye(ss_mod.k_states), dims=ss_mod.param_dims["P0"]) # Initial states: shape (3, 2) for 3 variables, 2 states each - cycle_init = pm.Normal('business_cycle', dims=('business_cycle_endog', 'business_cycle_state')) + cycle_init = pm.Normal('business_cycle', dims=ss_mod.param_dims["business_cycle"]) # Dampening factor: scalar (shared across variables) - dampening = pm.Uniform('business_cycle_dampening_factor', lower=0.8, upper=1.0) + dampening = pm.Beta("business_cycle_dampening_factor", 2, 2) # Innovation variances: shape (3,) for variable-specific variances - sigma_cycle = pm.HalfNormal('sigma_business_cycle', dims=('business_cycle_endog',)) + sigma_cycle = pm.HalfNormal( + "sigma_business_cycle", dims=ss_mod.param_dims["sigma_business_cycle"] + ) ss_mod.build_statespace_graph(data) idata = pm.sample() @@ -220,12 +222,8 @@ def make_symbolic_graph(self) -> None: if self.k_endog == 1: self.ssm["transition", :, :] = T else: - # can't make the linalg.block_diag logic work here - # doing it manually for now - for i in range(self.k_endog): - start_idx = i * 2 - end_idx = (i + 1) * 2 - self.ssm["transition", start_idx:end_idx, start_idx:end_idx] = T + transition = block_diag(*[T for _ in range(self.k_endog)]) + self.ssm["transition"] = pt.specify_shape(transition, (self.k_states, self.k_states)) if self.innovations: if self.k_endog == 1: @@ -235,16 +233,20 @@ def make_symbolic_graph(self) -> None: sigma_cycle = self.make_and_register_variable( f"sigma_{self.name}", shape=(self.k_endog,) ) - # can't make the linalg.block_diag logic work here - # doing it manually for now - for i in range(self.k_endog): - start_idx = i * 2 - end_idx = (i + 1) * 2 - Q_block = pt.eye(2) * sigma_cycle[i] ** 2 - self.ssm["state_cov", start_idx:end_idx, start_idx:end_idx] = Q_block + state_cov = block_diag( + *[pt.eye(2) * sigma_cycle[i] ** 2 for i in range(self.k_endog)] + ) + self.ssm["state_cov"] = pt.specify_shape(state_cov, (self.k_states, self.k_states)) def populate_component_properties(self): - self.state_names = [f"{self.name}_{f}" for f in ["Cos", "Sin"]] + if self.k_endog == 1: + self.state_names = [f"{self.name}_{f}" for f in ["Cos", "Sin"]] + else: + # For multivariate cycles, create state names for each observed state + self.state_names = [] + for var_name in self.observed_state_names: + self.state_names.extend([f"{self.name}_{var_name}_{f}" for f in ["Cos", "Sin"]]) + self.param_names = [f"{self.name}"] if self.k_endog == 1: @@ -260,7 +262,7 @@ def populate_component_properties(self): else: self.param_dims = {self.name: (f"{self.name}_endog", f"{self.name}_state")} self.coords = { - f"{self.name}_state": self.state_names, + f"{self.name}_state": [f"{self.name}_Cos", f"{self.name}_Sin"], f"{self.name}_endog": self.observed_state_names, } self.param_info = { From 7e9bb071425e8659291a816b963c795b96c1043c Mon Sep 17 00:00:00 2001 From: Jonathan Dekermanjian Date: Sat, 5 Jul 2025 08:23:29 -0600 Subject: [PATCH 10/21] 1. updated level_trend component coord/param labels 2. Adjusted the regression component to allow multivariate regression component specification 3. Added a notebook for quick evaluation of the adjustments and additions made --- notebooks/multivariate_ssm.ipynb | 729 +++++++ pymc_extras/statespace/models/structural.py | 1679 +++++++++++++++++ .../structural/components/level_trend.py | 8 +- .../structural/components/regression.py | 44 +- tests/statespace/models/test_structural.py | 840 +++++++++ 5 files changed, 3284 insertions(+), 16 deletions(-) create mode 100644 notebooks/multivariate_ssm.ipynb create mode 100644 pymc_extras/statespace/models/structural.py create mode 100644 tests/statespace/models/test_structural.py diff --git a/notebooks/multivariate_ssm.ipynb b/notebooks/multivariate_ssm.ipynb new file mode 100644 index 000000000..83cc74e2e --- /dev/null +++ b/notebooks/multivariate_ssm.ipynb @@ -0,0 +1,729 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 16, + "id": "a5b7dcb3", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd\n", + "from pymc_extras.statespace.models import structural as st\n", + "\n", + "import pymc as pm\n", + "import arviz as az\n", + "import pytensor.tensor as pt\n", + "\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f8bfe995", + "metadata": {}, + "outputs": [], + "source": [ + "rng = np.random" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "a96a731b", + "metadata": {}, + "outputs": [], + "source": [ + "def simulate_local_level_with_exog(\n", + " n_endog: int = 1,\n", + " time_steps: int = 100,\n", + " mu0: float = 0.0,\n", + " sigma_eta: float = 1.0,\n", + " sigma_eps: float = 0.5,\n", + " beta1: float = 2.0,\n", + " beta2: float = -1.5,\n", + " seed: int | None = None,\n", + "):\n", + " \"\"\"\n", + " Simulates a local level model with exogenous variables.\n", + "\n", + " Parameters\n", + " ----------\n", + " n_endog: int\n", + " The number of series to simulate\n", + " time_steps: int\n", + " The length of the time-series to simulate\n", + " mu0: float\n", + " The initial state\n", + " sigma_eta: float\n", + " The level innovations standard deviation\n", + " sigma_eps: float\n", + " The observations standard deviation\n", + " beta1: float\n", + " The weight of the binary exogenous variable\n", + " beta2: float\n", + " The weight of the continuous exogenous variable\n", + " seed: int\n", + " Random generator seed for reproducibility\n", + "\n", + " Returns\n", + " -------\n", + " ys: dict[str, float]\n", + " n_endog number of observations\n", + " mu: float\n", + " latent state\n", + " x1: int\n", + " binary exogenous observations\n", + " x2: float\n", + " continuous exogenous observations\n", + " \"\"\"\n", + " if seed is not None:\n", + " np.random.seed(seed)\n", + "\n", + " # init state and observation vectors\n", + " mu = np.zeros(time_steps)\n", + " y = np.zeros(time_steps)\n", + "\n", + " # initial state\n", + " mu[0] = mu0\n", + "\n", + " # simulate exogenous variables\n", + " # binary variable\n", + " x1 = np.random.binomial(1, 0.2, size=time_steps)\n", + "\n", + " # continous variable\n", + " x2 = np.random.normal(0, 1, size=time_steps)\n", + "\n", + " # simulate latent state (local level)\n", + " for t in range(1, time_steps):\n", + " mu[t] = mu[t - 1] + np.random.normal(0, sigma_eta)\n", + "\n", + " # simulate observations\n", + " ys = {\n", + " f\"y{i+1}\": mu + beta1 * x1 + beta2 * x2 + np.random.normal(0, sigma_eps, size=time_steps)\n", + " for i in range(n_endog)\n", + " }\n", + "\n", + " return ys, mu, x1, x2" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a4130131", + "metadata": {}, + "outputs": [], + "source": [ + "# Simulate\n", + "T = 100\n", + "true_sigma_eta = 0.3\n", + "true_sigma_eps = 0.6\n", + "true_beta1 = 3.0\n", + "true_beta2 = -1.0\n", + "ys, mu, x1, x2 = simulate_local_level_with_exog(\n", + " n_endog=3,\n", + " time_steps=T,\n", + " mu0=0,\n", + " sigma_eta=true_sigma_eta,\n", + " sigma_eps=true_sigma_eps,\n", + " beta1=true_beta1,\n", + " beta2=true_beta2,\n", + " seed=42,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5e9acbb8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y = ys[\"y1\"]\n", + "\n", + "# Plot\n", + "plt.figure(figsize=(12, 6))\n", + "for k, vy in ys.items():\n", + " plt.plot(vy, label=f\"Observed ${k}_t$\", alpha=0.6)\n", + "plt.plot(mu, label=\"Latent level $\\\\mu_t$\", linewidth=2)\n", + "plt.plot(x1 * 5, label=\"Binary Exog $x^{(1)}_t$\", linestyle=\"--\") # need to blow up to see it\n", + "plt.plot(x2, label=\"Continuous Exog $x^{(2)}_t$\", linestyle=\":\")\n", + "plt.legend()\n", + "plt.title(\"Local Level Model with Exogenous Variables\")\n", + "plt.xlabel(\"Time\")\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "f23bb2ed", + "metadata": {}, + "source": [ + "# Quick and dirty test" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "d51ff06e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
                                  Model Requirements                                   \n",
+       "                                                                                       \n",
+       "  Variable        Shape       Constraints                                  Dimensions  \n",
+       " ───────────────────────────────────────────────────────────────────────────────────── \n",
+       "  initial_trend   (3, 1)                               ('trend_endog', 'trend_state')  \n",
+       "  sigma_trend     (3, 1)      Positive                 ('trend_endog', 'trend_shock')  \n",
+       "  beta_exog       (3, 2)                                 ('exog_endog', 'exog_state')  \n",
+       "  P0              (9, 9)      Positive semi-definite           ('state', 'state_aux')  \n",
+       "                                                                                       \n",
+       "  data_exog       (None, 2)   pm.Data                          ('time', 'exog_state')  \n",
+       "                                                                                       \n",
+       "These parameters should be assigned priors inside a PyMC model block before calling the\n",
+       "                            build_statespace_graph method.                             \n",
+       "
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meansdhdi_3%hdi_97%mcse_meanmcse_sdess_bulkess_tailr_hat
beta_exog[y1, x1]2.9470.7291.4334.2780.0160.0142232.02077.01.00
beta_exog[y1, x2]-0.9910.681-2.2700.3140.0120.0143102.02334.01.00
beta_exog[y2, x1]3.0070.7131.6374.3560.0150.0152152.02107.01.00
beta_exog[y2, x2]-1.0470.714-2.4710.4080.0140.0193007.01644.01.00
beta_exog[y3, x1]2.8000.7121.4384.1650.0130.0193473.02458.01.00
beta_exog[y3, x2]-0.9340.675-2.1840.4260.0130.0143017.02326.01.00
sigma_trend[y1, level]0.7570.0570.6570.8710.0020.002878.0367.01.01
sigma_trend[y2, level]0.9270.0680.8101.0620.0020.0011846.01633.01.00
sigma_trend[y3, level]0.8470.0620.7330.9610.0010.0012009.02855.01.00
\n", + "
" + ], + "text/plain": [ + " mean sd hdi_3% hdi_97% mcse_mean mcse_sd \\\n", + "beta_exog[y1, x1] 2.947 0.729 1.433 4.278 0.016 0.014 \n", + "beta_exog[y1, x2] -0.991 0.681 -2.270 0.314 0.012 0.014 \n", + "beta_exog[y2, x1] 3.007 0.713 1.637 4.356 0.015 0.015 \n", + "beta_exog[y2, x2] -1.047 0.714 -2.471 0.408 0.014 0.019 \n", + "beta_exog[y3, x1] 2.800 0.712 1.438 4.165 0.013 0.019 \n", + "beta_exog[y3, x2] -0.934 0.675 -2.184 0.426 0.013 0.014 \n", + "sigma_trend[y1, level] 0.757 0.057 0.657 0.871 0.002 0.002 \n", + "sigma_trend[y2, level] 0.927 0.068 0.810 1.062 0.002 0.001 \n", + "sigma_trend[y3, level] 0.847 0.062 0.733 0.961 0.001 0.001 \n", + "\n", + " ess_bulk ess_tail r_hat \n", + "beta_exog[y1, x1] 2232.0 2077.0 1.00 \n", + "beta_exog[y1, x2] 3102.0 2334.0 1.00 \n", + "beta_exog[y2, x1] 2152.0 2107.0 1.00 \n", + "beta_exog[y2, x2] 3007.0 1644.0 1.00 \n", + "beta_exog[y3, x1] 3473.0 2458.0 1.00 \n", + "beta_exog[y3, x2] 3017.0 2326.0 1.00 \n", + "sigma_trend[y1, level] 878.0 367.0 1.01 \n", + "sigma_trend[y2, level] 1846.0 1633.0 1.00 \n", + "sigma_trend[y3, level] 2009.0 2855.0 1.00 " + ] + }, + "execution_count": 107, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "az.summary(idata, var_names=[\"beta_exog\", \"sigma_trend\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "id": "3684616b", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "az.plot_posterior(\n", + " idata,\n", + " var_names=\"beta_exog\",\n", + " ref_val={\n", + " \"beta_exog\": [\n", + " {\"exog_endog\": \"y1\", \"exog_state\": \"x1\", \"ref_val\": true_beta1},\n", + " {\"exog_endog\": \"y1\", \"exog_state\": \"x2\", \"ref_val\": true_beta2},\n", + " ]\n", + " },\n", + ");" + ] + }, + { + "cell_type": "markdown", + "id": "1b85190c", + "metadata": {}, + "source": [ + "# Need to test with missing data" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "pymc-extras-test", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.11" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pymc_extras/statespace/models/structural.py b/pymc_extras/statespace/models/structural.py new file mode 100644 index 000000000..a982366c3 --- /dev/null +++ b/pymc_extras/statespace/models/structural.py @@ -0,0 +1,1679 @@ +import functools as ft +import logging + +from abc import ABC +from collections.abc import Sequence +from itertools import pairwise +from typing import Any + +import numpy as np +import pytensor +import pytensor.tensor as pt +import xarray as xr + +from pytensor import Variable +from pytensor.compile.mode import Mode + +from pymc_extras.statespace.core import PytensorRepresentation +from pymc_extras.statespace.core.statespace import PyMCStateSpace +from pymc_extras.statespace.models.utilities import ( + conform_time_varying_and_time_invariant_matrices, + make_default_coords, +) +from pymc_extras.statespace.utils.constants import ( + ALL_STATE_AUX_DIM, + ALL_STATE_DIM, + AR_PARAM_DIM, + LONG_MATRIX_NAMES, + POSITION_DERIVATIVE_NAMES, + TIME_DIM, +) + +_log = logging.getLogger("pymc.experimental.statespace") + +floatX = pytensor.config.floatX + + +def order_to_mask(order): + if isinstance(order, int): + return np.ones(order).astype(bool) + else: + return np.array(order).astype(bool) + + +def _frequency_transition_block(s, j): + lam = 2 * np.pi * j / s + + return pt.stack([[pt.cos(lam), pt.sin(lam)], [-pt.sin(lam), pt.cos(lam)]]) + + +class StructuralTimeSeries(PyMCStateSpace): + r""" + Structural Time Series Model + + The structural time series model, named by [1] and presented in statespace form in [2], is a framework for + decomposing a univariate time series into level, trend, seasonal, and cycle components. It also admits the + possibility of exogenous regressors. Unlike the SARIMAX framework, the time series is not assumed to be stationary. + + Notes + ----- + + .. math:: + y_t = \mu_t + \gamma_t + c_t + \varepsilon_t + + """ + + def __init__( + self, + ssm: PytensorRepresentation, + state_names: list[str], + data_names: list[str], + shock_names: list[str], + param_names: list[str], + exog_names: list[str], + param_dims: dict[str, tuple[int]], + coords: dict[str, Sequence], + param_info: dict[str, dict[str, Any]], + data_info: dict[str, dict[str, Any]], + component_info: dict[str, dict[str, Any]], + measurement_error: bool, + name_to_variable: dict[str, Variable], + name_to_data: dict[str, Variable] | None = None, + name: str | None = None, + verbose: bool = True, + filter_type: str = "standard", + mode: str | Mode | None = None, + ): + # Add the initial state covariance to the parameters + if name is None: + name = "data" + self._name = name + + k_states, k_posdef, k_endog = ssm.k_states, ssm.k_posdef, ssm.k_endog + param_names, param_dims, param_info = self._add_inital_state_cov_to_properties( + param_names, param_dims, param_info, k_states + ) + self._state_names = state_names.copy() + self._data_names = data_names.copy() + self._shock_names = shock_names.copy() + self._param_names = param_names.copy() + self._param_dims = param_dims.copy() + + default_coords = make_default_coords(self) + coords.update(default_coords) + + self._coords = coords + self._param_info = param_info.copy() + self._data_info = data_info.copy() + self.measurement_error = measurement_error + + super().__init__( + k_endog, + k_states, + max(1, k_posdef), + filter_type=filter_type, + verbose=verbose, + measurement_error=measurement_error, + mode=mode, + ) + self.ssm = ssm.copy() + + if k_posdef == 0: + # If there is no randomness in the model, add dummy matrices to the representation to avoid errors + # when we go to construct random variables from the matrices + self.ssm.k_posdef = self.k_posdef + self.ssm.shapes["state_cov"] = (1, 1, 1) + self.ssm["state_cov"] = pt.zeros((1, 1, 1)) + + self.ssm.shapes["selection"] = (1, self.k_states, 1) + self.ssm["selection"] = pt.zeros((1, self.k_states, 1)) + + self._component_info = component_info.copy() + + self._name_to_variable = name_to_variable.copy() + self._name_to_data = name_to_data.copy() + + self._exog_names = exog_names.copy() + self._needs_exog_data = len(exog_names) > 0 + + P0 = self.make_and_register_variable("P0", shape=(self.k_states, self.k_states)) + self.ssm["initial_state_cov"] = P0 + + @staticmethod + def _add_inital_state_cov_to_properties(param_names, param_dims, param_info, k_states): + param_names += ["P0"] + param_dims["P0"] = (ALL_STATE_DIM, ALL_STATE_AUX_DIM) + param_info["P0"] = { + "shape": (k_states, k_states), + "constraints": "Positive semi-definite", + "dims": param_dims["P0"], + } + + return param_names, param_dims, param_info + + @property + def param_names(self): + return self._param_names + + @property + def data_names(self) -> list[str]: + return self._data_names + + @property + def state_names(self): + return self._state_names + + @property + def observed_states(self): + return [self._name] + + @property + def shock_names(self): + return self._shock_names + + @property + def param_dims(self): + return self._param_dims + + @property + def coords(self) -> dict[str, Sequence]: + return self._coords + + @property + def param_info(self) -> dict[str, dict[str, Any]]: + return self._param_info + + @property + def data_info(self) -> dict[str, dict[str, Any]]: + return self._data_info + + def make_symbolic_graph(self) -> None: + """ + Assign placeholder pytensor variables among statespace matrices in positions where PyMC variables will go. + + Notes + ----- + This assignment is handled by the components, so this function is implemented only to avoid the + NotImplementedError raised by the base class. + """ + + pass + + def _state_slices_from_info(self): + info = self._component_info.copy() + comp_states = np.cumsum([0] + [info["k_states"] for info in info.values()]) + state_slices = [slice(i, j) for i, j in pairwise(comp_states)] + + return state_slices + + def _hidden_states_from_data(self, data): + state_slices = self._state_slices_from_info() + info = self._component_info + names = info.keys() + result = [] + + for i, (name, s) in enumerate(zip(names, state_slices)): + obs_idx = info[name]["obs_state_idx"] + if obs_idx is None: + continue + + X = data[..., s] + if info[name]["combine_hidden_states"]: + sum_idx = np.flatnonzero(obs_idx) + result.append(X[..., sum_idx].sum(axis=-1)[..., None]) + else: + comp_names = self.state_names[s] + for j, state_name in enumerate(comp_names): + result.append(X[..., j, None]) + + return np.concatenate(result, axis=-1) + + def _get_subcomponent_names(self): + state_slices = self._state_slices_from_info() + info = self._component_info + names = info.keys() + result = [] + + for i, (name, s) in enumerate(zip(names, state_slices)): + if info[name]["combine_hidden_states"]: + result.append(name) + else: + comp_names = self.state_names[s] + result.extend([f"{name}[{comp_name}]" for comp_name in comp_names]) + return result + + def extract_components_from_idata(self, idata: xr.Dataset) -> xr.Dataset: + r""" + Extract interpretable hidden states from an InferenceData returned by a PyMCStateSpace sampling method + + Parameters + ---------- + idata: Dataset + A Dataset object, returned by a PyMCStateSpace sampling method + + Returns + ------- + idata: Dataset + An Dataset object with hidden states transformed to represent only the "interpretable" subcomponents + of the structural model. + + Notes + ----- + In general, a structural statespace model can be represented as: + + .. math:: + y_t = \mu_t + \nu_t + \cdots + \gamma_t + c_t + \xi_t + \epsilon_t \tag{1} + + Where: + + - :math:`\mu_t` is the level of the data at time t + - :math:`\nu_t` is the slope of the data at time t + - :math:`\cdots` are higher time derivatives of the position (acceleration, jerk, etc) at time t + - :math:`\gamma_t` is the seasonal component at time t + - :math:`c_t` is the cycle component at time t + - :math:`\xi_t` is the autoregressive error at time t + - :math:`\varepsilon_t` is the measurement error at time t + + In state space form, some or all of these components are represented as linear combinations of other + subcomponents, making interpretation of the outputs of the outputs difficult. The purpose of this function is + to take the expended statespace representation and return a "reduced form" of only the components shown in + equation (1). + """ + + def _extract_and_transform_variable(idata, new_state_names): + *_, time_dim, state_dim = idata.dims + state_func = ft.partial(self._hidden_states_from_data) + new_idata = xr.apply_ufunc( + state_func, + idata, + input_core_dims=[[time_dim, state_dim]], + output_core_dims=[[time_dim, state_dim]], + exclude_dims={state_dim}, + ) + new_idata.coords.update({state_dim: new_state_names}) + return new_idata + + var_names = list(idata.data_vars.keys()) + is_latent = [idata[name].shape[-1] == self.k_states for name in var_names] + new_state_names = self._get_subcomponent_names() + + latent_names = [name for latent, name in zip(is_latent, var_names) if latent] + dropped_vars = set(var_names) - set(latent_names) + if len(dropped_vars) > 0: + _log.warning( + f'Variables {", ".join(dropped_vars)} do not contain all hidden states (their last dimension ' + f"is not {self.k_states}). They will not be present in the modified idata." + ) + if len(dropped_vars) == len(var_names): + raise ValueError( + "Provided idata had no variables with all hidden states; cannot extract components." + ) + + idata_new = xr.Dataset( + { + name: _extract_and_transform_variable(idata[name], new_state_names) + for name in latent_names + } + ) + return idata_new + + +class Component(ABC): + r""" + Base class for a component of a structural timeseries model. + + This base class contains a subset of the class attributes of the PyMCStateSpace class, and none of the class + methods. The purpose of a component is to allow the partial definition of a structural model. Components are + assembled into a full model by the StructuralTimeSeries class. + + Parameters + ---------- + name: str + The name of the component + k_endog: int + Number of endogenous variables being modeled. Currently, must be one because structural models only support + univariate data. + k_states: int + Number of hidden states in the component model + k_posdef: int + Rank of the state covariance matrix, or the number of sources of innovations in the component model + measurement_error: bool + Whether the observation associated with the component has measurement error. Default is False. + combine_hidden_states: bool + Flag for the ``extract_hidden_states_from_data`` method. When ``True``, hidden states from the component model + are extracted as ``hidden_states[:, np.flatnonzero(Z)]``. Should be True in models where hidden states + individually have no interpretation, such as seasonal or autoregressive components. + """ + + def __init__( + self, + name, + k_endog, + k_states, + k_posdef, + state_names=None, + data_names=None, + shock_names=None, + param_names=None, + exog_names=None, + representation: PytensorRepresentation | None = None, + measurement_error=False, + combine_hidden_states=True, + component_from_sum=False, + obs_state_idxs=None, + ): + self.name = name + self.k_endog = k_endog + self.k_states = k_states + self.k_posdef = k_posdef + self.measurement_error = measurement_error + + self.state_names = state_names if state_names is not None else [] + self.data_names = data_names if data_names is not None else [] + self.shock_names = shock_names if shock_names is not None else [] + self.param_names = param_names if param_names is not None else [] + self.exog_names = exog_names if exog_names is not None else [] + + self.needs_exog_data = len(self.exog_names) > 0 + self.coords = {} + self.param_dims = {} + + self.param_info = {} + self.data_info = {} + + self.param_counts = {} + + if representation is None: + self.ssm = PytensorRepresentation(k_endog=k_endog, k_states=k_states, k_posdef=k_posdef) + else: + self.ssm = representation + + self._name_to_variable = {} + self._name_to_data = {} + + if not component_from_sum: + self.populate_component_properties() + self.make_symbolic_graph() + + self._component_info = { + self.name: { + "k_states": self.k_states, + "k_enodg": self.k_endog, + "k_posdef": self.k_posdef, + "combine_hidden_states": combine_hidden_states, + "obs_state_idx": obs_state_idxs, + } + } + + def make_and_register_variable(self, name, shape, dtype=floatX) -> Variable: + r""" + Helper function to create a pytensor symbolic variable and register it in the _name_to_variable dictionary + + Parameters + ---------- + name : str + The name of the placeholder variable. Must be the name of a model parameter. + shape : int or tuple of int + Shape of the parameter + dtype : str, default pytensor.config.floatX + dtype of the parameter + + Notes + ----- + Symbolic pytensor variables are used in the ``make_symbolic_graph`` method as placeholders for PyMC random + variables. The change is made in the ``_insert_random_variables`` method via ``pytensor.graph_replace``. To + make the change, a dictionary mapping pytensor variables to PyMC random variables needs to be constructed. + + The purpose of this method is to: + 1. Create the placeholder symbolic variables + 2. Register the placeholder variable in the ``_name_to_variable`` dictionary + + The shape provided here will define the shape of the prior that will need to be provided by the user. + + An error is raised if the provided name has already been registered, or if the name is not present in the + ``param_names`` property. + """ + if name not in self.param_names: + raise ValueError( + f"{name} is not a model parameter. All placeholder variables should correspond to model " + f"parameters." + ) + + if name in self._name_to_variable.keys(): + raise ValueError( + f"{name} is already a registered placeholder variable with shape " + f"{self._name_to_variable[name].type.shape}" + ) + + placeholder = pt.tensor(name, shape=shape, dtype=dtype) + self._name_to_variable[name] = placeholder + return placeholder + + def make_and_register_data(self, name, shape, dtype=floatX) -> Variable: + r""" + Helper function to create a pytensor symbolic variable and register it in the _name_to_data dictionary + + Parameters + ---------- + name : str + The name of the placeholder data. Must be the name of an expected data variable. + shape : int or tuple of int + Shape of the parameter + dtype : str, default pytensor.config.floatX + dtype of the parameter + + Notes + ----- + See docstring for make_and_register_variable for more details. This function is similar, but handles data + inputs instead of model parameters. + + An error is raised if the provided name has already been registered, or if the name is not present in the + ``data_names`` property. + """ + if name not in self.data_names: + raise ValueError( + f"{name} is not a model parameter. All placeholder variables should correspond to model " + f"parameters." + ) + + if name in self._name_to_data.keys(): + raise ValueError( + f"{name} is already a registered placeholder variable with shape " + f"{self._name_to_data[name].type.shape}" + ) + + placeholder = pt.tensor(name, shape=shape, dtype=dtype) + self._name_to_data[name] = placeholder + return placeholder + + def make_symbolic_graph(self) -> None: + raise NotImplementedError + + def populate_component_properties(self): + raise NotImplementedError + + def _get_combined_shapes(self, other): + k_states = self.k_states + other.k_states + k_posdef = self.k_posdef + other.k_posdef + if self.k_endog != other.k_endog: + raise NotImplementedError( + "Merging elements with different numbers of observed states is not supported.>" + ) + k_endog = self.k_endog + + return k_states, k_posdef, k_endog + + def _combine_statespace_representations(self, other): + def make_slice(name, x, o_x): + ndim = max(x.ndim, o_x.ndim) + return (name,) + (slice(None, None, None),) * ndim + + k_states, k_posdef, k_endog = self._get_combined_shapes(other) + + self_matrices = [self.ssm[name] for name in LONG_MATRIX_NAMES] + other_matrices = [other.ssm[name] for name in LONG_MATRIX_NAMES] + + x0, P0, c, d, T, Z, R, H, Q = ( + self.ssm[make_slice(name, x, o_x)] + for name, x, o_x in zip(LONG_MATRIX_NAMES, self_matrices, other_matrices) + ) + o_x0, o_P0, o_c, o_d, o_T, o_Z, o_R, o_H, o_Q = ( + other.ssm[make_slice(name, x, o_x)] + for name, x, o_x in zip(LONG_MATRIX_NAMES, self_matrices, other_matrices) + ) + + initial_state = pt.concatenate(conform_time_varying_and_time_invariant_matrices(x0, o_x0)) + initial_state.name = x0.name + + initial_state_cov = pt.linalg.block_diag(P0, o_P0) + initial_state_cov.name = P0.name + + state_intercept = pt.concatenate(conform_time_varying_and_time_invariant_matrices(c, o_c)) + state_intercept.name = c.name + + obs_intercept = d + o_d + obs_intercept.name = d.name + + transition = pt.linalg.block_diag(T, o_T) + transition.name = T.name + + design = pt.concatenate(conform_time_varying_and_time_invariant_matrices(Z, o_Z), axis=-1) + design.name = Z.name + + selection = pt.linalg.block_diag(R, o_R) + selection.name = R.name + + obs_cov = H + o_H + obs_cov.name = H.name + + state_cov = pt.linalg.block_diag(Q, o_Q) + state_cov.name = Q.name + + new_ssm = PytensorRepresentation( + k_endog=k_endog, + k_states=k_states, + k_posdef=k_posdef, + initial_state=initial_state, + initial_state_cov=initial_state_cov, + state_intercept=state_intercept, + obs_intercept=obs_intercept, + transition=transition, + design=design, + selection=selection, + obs_cov=obs_cov, + state_cov=state_cov, + ) + + return new_ssm + + def _combine_property(self, other, name): + self_prop = getattr(self, name) + if isinstance(self_prop, list): + return self_prop + getattr(other, name) + elif isinstance(self_prop, dict): + new_prop = self_prop.copy() + new_prop.update(getattr(other, name)) + return new_prop + + def _combine_component_info(self, other): + combined_info = {} + for key, value in self._component_info.items(): + if not key.startswith("StateSpace"): + if key in combined_info.keys(): + raise ValueError(f"Found duplicate component named {key}") + combined_info[key] = value + + for key, value in other._component_info.items(): + if not key.startswith("StateSpace"): + if key in combined_info.keys(): + raise ValueError(f"Found duplicate component named {key}") + combined_info[key] = value + + return combined_info + + def _make_combined_name(self): + components = self._component_info.keys() + name = f'StateSpace[{", ".join(components)}]' + return name + + def __add__(self, other): + state_names = self._combine_property(other, "state_names") + data_names = self._combine_property(other, "data_names") + param_names = self._combine_property(other, "param_names") + shock_names = self._combine_property(other, "shock_names") + param_info = self._combine_property(other, "param_info") + data_info = self._combine_property(other, "data_info") + param_dims = self._combine_property(other, "param_dims") + coords = self._combine_property(other, "coords") + exog_names = self._combine_property(other, "exog_names") + + _name_to_variable = self._combine_property(other, "_name_to_variable") + _name_to_data = self._combine_property(other, "_name_to_data") + + measurement_error = any([self.measurement_error, other.measurement_error]) + + k_states, k_posdef, k_endog = self._get_combined_shapes(other) + ssm = self._combine_statespace_representations(other) + + new_comp = Component( + name="", + k_endog=1, + k_states=k_states, + k_posdef=k_posdef, + measurement_error=measurement_error, + representation=ssm, + component_from_sum=True, + ) + new_comp._component_info = self._combine_component_info(other) + new_comp.name = new_comp._make_combined_name() + + names_and_props = [ + ("state_names", state_names), + ("data_names", data_names), + ("param_names", param_names), + ("shock_names", shock_names), + ("param_dims", param_dims), + ("coords", coords), + ("param_dims", param_dims), + ("param_info", param_info), + ("data_info", data_info), + ("exog_names", exog_names), + ("_name_to_variable", _name_to_variable), + ("_name_to_data", _name_to_data), + ] + + for prop, value in names_and_props: + setattr(new_comp, prop, value) + + return new_comp + + def build( + self, name=None, filter_type="standard", verbose=True, mode: str | Mode | None = None + ): + """ + Build a StructuralTimeSeries statespace model from the current component(s) + + Parameters + ---------- + name: str, optional + Name of the exogenous data being modeled. Default is "data" + + filter_type : str, optional + The type of Kalman filter to use. Valid options are "standard", "univariate", "single", "cholesky", and + "steady_state". For more information, see the docs for each filter. Default is "standard". + + verbose : bool, optional + If True, displays information about the initialized model. Defaults to True. + + mode: str or Mode, optional + Pytensor compile mode, used in auxiliary sampling methods such as ``sample_conditional_posterior`` and + ``forecast``. The mode does **not** effect calls to ``pm.sample``. + + Regardless of whether a mode is specified, it can always be overwritten via the ``compile_kwargs`` argument + to all sampling methods. + + Returns + ------- + PyMCStateSpace + An initialized instance of a PyMCStateSpace, constructed using the system matrices contained in the + components. + """ + + return StructuralTimeSeries( + self.ssm, + name=name, + state_names=self.state_names, + data_names=self.data_names, + shock_names=self.shock_names, + param_names=self.param_names, + param_dims=self.param_dims, + coords=self.coords, + param_info=self.param_info, + data_info=self.data_info, + component_info=self._component_info, + measurement_error=self.measurement_error, + exog_names=self.exog_names, + name_to_variable=self._name_to_variable, + name_to_data=self._name_to_data, + filter_type=filter_type, + verbose=verbose, + mode=mode, + ) + + +class LevelTrendComponent(Component): + r""" + Level and trend component of a structural time series model + + Parameters + ---------- + __________ + order : int + + Number of time derivatives of the trend to include in the model. For example, when order=3, the trend will + be of the form ``y = a + b * t + c * t ** 2``, where the coefficients ``a, b, c`` come from the initial + state values. + + innovations_order : int or sequence of int, optional + + The number of stochastic innovations to include in the model. By default, ``innovations_order = order`` + + Notes + ----- + This class implements the level and trend components of the general structural time series model. In the most + general form, the level and trend is described by a system of two time-varying equations. + + .. math:: + \begin{align} + \mu_{t+1} &= \mu_t + \nu_t + \zeta_t \\ + \nu_{t+1} &= \nu_t + \xi_t + \zeta_t &\sim N(0, \sigma_\zeta) \\ + \xi_t &\sim N(0, \sigma_\xi) + \end{align} + + Where :math:`\mu_{t+1}` is the mean of the timeseries at time t, and :math:`\nu_t` is the drift or the slope of + the process. When both innovations :math:`\zeta_t` and :math:`\xi_t` are included in the model, it is known as a + *local linear trend* model. This system of two equations, corresponding to ``order=2``, can be expanded or + contracted by adding or removing equations. ``order=3`` would add an acceleration term to the sytsem: + + .. math:: + \begin{align} + \mu_{t+1} &= \mu_t + \nu_t + \zeta_t \\ + \nu_{t+1} &= \nu_t + \eta_t + \xi_t \\ + \eta_{t+1} &= \eta_{t-1} + \omega_t \\ + \zeta_t &\sim N(0, \sigma_\zeta) \\ + \xi_t &\sim N(0, \sigma_\xi) \\ + \omega_t &\sim N(0, \sigma_\omega) + \end{align} + + After setting all innovation terms to zero and defining initial states :math:`\mu_0, \nu_0, \eta_0`, these equations + can be collapsed to: + + .. math:: + \mu_t = \mu_0 + \nu_0 \cdot t + \eta_0 \cdot t^2 + + Which clarifies how the order and initial states influence the model. In particular, the initial states are the + coefficients on the intercept, slope, acceleration, and so on. + + In this light, allowing for innovations can be understood as allowing these coefficients to vary over time. Each + component can be individually selected for time variation by passing a list to the ``innovations_order`` argument. + For example, a constant intercept with time varying trend and acceleration is specified as ``order=3, + innovations_order=[0, 1, 1]``. + + By choosing the ``order`` and ``innovations_order``, a large variety of models can be obtained. Notable + models include: + + * Constant intercept, ``order=1, innovations_order=0`` + + .. math:: + \mu_t = \mu + + * Constant linear slope, ``order=2, innovations_order=0`` + + .. math:: + \mu_t = \mu_{t-1} + \nu + + * Gaussian Random Walk, ``order=1, innovations_order=1`` + + .. math:: + \mu_t = \mu_{t-1} + \zeta_t + + * Gaussian Random Walk with Drift, ``order=2, innovations_order=1`` + + .. math:: + \mu_t = \mu_{t-1} + \nu + \zeta_t + + * Smooth Trend, ``order=2, innovations_order=[0, 1]`` + + .. math:: + \begin{align} + \mu_t &= \mu_{t-1} + \nu_{t-1} \\ + \nu_t &= \nu_{t-1} + \xi_t + \end{align} + + * Local Level, ``order=2, innovations_order=2`` + + [1] notes that the smooth trend model produces more gradually changing slopes than the full local linear trend + model, and is equivalent to an "integrated trend model". + + References + ---------- + .. [1] Durbin, James, and Siem Jan Koopman. 2012. + Time Series Analysis by State Space Methods: Second Edition. + Oxford University Press. + + """ + + def __init__( + self, + order: int | list[int] = 2, + innovations_order: int | list[int] | None = None, + name: str = "LevelTrend", + ): + if innovations_order is None: + innovations_order = order + + self._order_mask = order_to_mask(order) + max_state = np.flatnonzero(self._order_mask)[-1].item() + 1 + + # If the user passes excess zeros, raise an error. The alternative is to prune them, but this would cause + # the shape of the state to be different to what the user expects. + if len(self._order_mask) > max_state: + raise ValueError( + f"order={order} is invalid. The highest derivative should not be set to zero. If you want a " + f"lower order model, explicitly omit the zeros." + ) + k_states = max_state + + if isinstance(innovations_order, int): + n = innovations_order + innovations_order = order_to_mask(k_states) + if n > 0: + innovations_order[n:] = False + else: + innovations_order[:] = False + else: + innovations_order = order_to_mask(innovations_order) + + self.innovations_order = innovations_order[:max_state] + k_posdef = int(sum(innovations_order)) + + super().__init__( + name, + k_endog=1, + k_states=k_states, + k_posdef=k_posdef, + measurement_error=False, + combine_hidden_states=False, + obs_state_idxs=np.array([1.0] + [0.0] * (k_states - 1)), + ) + + def populate_component_properties(self): + name_slice = POSITION_DERIVATIVE_NAMES[: self.k_states] + self.param_names = ["initial_trend"] + self.state_names = [name for name, mask in zip(name_slice, self._order_mask) if mask] + self.param_dims = {"initial_trend": ("trend_state",)} + self.coords = {"trend_state": self.state_names} + self.param_info = {"initial_trend": {"shape": (self.k_states,), "constraints": None}} + + if self.k_posdef > 0: + self.param_names += ["sigma_trend"] + self.shock_names = [ + name for name, mask in zip(name_slice, self.innovations_order) if mask + ] + self.param_dims["sigma_trend"] = ("trend_shock",) + self.coords["trend_shock"] = self.shock_names + self.param_info["sigma_trend"] = {"shape": (self.k_posdef,), "constraints": "Positive"} + + for name in self.param_names: + self.param_info[name]["dims"] = self.param_dims[name] + + def make_symbolic_graph(self) -> None: + initial_trend = self.make_and_register_variable("initial_trend", shape=(self.k_states,)) + self.ssm["initial_state", :] = initial_trend + triu_idx = np.triu_indices(self.k_states) + self.ssm[np.s_["transition", triu_idx[0], triu_idx[1]]] = 1 + + R = np.eye(self.k_states) + R = R[:, self.innovations_order] + self.ssm["selection", :, :] = R + + self.ssm["design", 0, :] = np.array([1.0] + [0.0] * (self.k_states - 1)) + + if self.k_posdef > 0: + sigma_trend = self.make_and_register_variable("sigma_trend", shape=(self.k_posdef,)) + diag_idx = np.diag_indices(self.k_posdef) + idx = np.s_["state_cov", diag_idx[0], diag_idx[1]] + self.ssm[idx] = sigma_trend**2 + + +class MeasurementError(Component): + r""" + Measurement error term for a structural timeseries model + + Parameters + ---------- + name: str, optional + + Name of the observed data. Default is "obs". + + Notes + ----- + This component should only be used in combination with other components, because it has no states. It's only use + is to add a variance parameter to the model, associated with the observation noise matrix H. + + Examples + -------- + Create and estimate a deterministic linear trend with measurement error + + .. code:: python + + from pymc_extras.statespace import structural as st + import pymc as pm + import pytensor.tensor as pt + + trend = st.LevelTrendComponent(order=2, innovations_order=0) + error = st.MeasurementError() + ss_mod = (trend + error).build() + + with pm.Model(coords=ss_mod.coords) as model: + P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0']) + intitial_trend = pm.Normal('initial_trend', sigma=10, dims=ss_mod.param_dims['initial_trend']) + sigma_obs = pm.Exponential('sigma_obs', 1, dims=ss_mod.param_dims['sigma_obs']) + + ss_mod.build_statespace_graph(data) + idata = pm.sample(nuts_sampler='numpyro') + """ + + def __init__(self, name: str = "MeasurementError"): + k_endog = 1 + k_states = 0 + k_posdef = 0 + + super().__init__( + name, k_endog, k_states, k_posdef, measurement_error=True, combine_hidden_states=False + ) + + def populate_component_properties(self): + self.param_names = [f"sigma_{self.name}"] + self.param_dims = {} + self.param_info = { + f"sigma_{self.name}": { + "shape": (), + "constraints": "Positive", + "dims": None, + } + } + + def make_symbolic_graph(self) -> None: + sigma_shape = () + error_sigma = self.make_and_register_variable(f"sigma_{self.name}", shape=sigma_shape) + diag_idx = np.diag_indices(self.k_endog) + idx = np.s_["obs_cov", diag_idx[0], diag_idx[1]] + self.ssm[idx] = error_sigma**2 + + +class AutoregressiveComponent(Component): + r""" + Autoregressive timeseries component + + Parameters + ---------- + order: int or sequence of int + + If int, the number of lags to include in the model. + If a sequence, an array-like of zeros and ones indicating which lags to include in the model. + + Notes + ----- + An autoregressive component can be thought of as a way o introducing serially correlated errors into the model. + The process is modeled: + + .. math:: + x_t = \sum_{i=1}^p \rho_i x_{t-i} + + Where ``p``, the number of autoregressive terms to model, is the order of the process. By default, all lags up to + ``p`` are included in the model. To disable lags, pass a list of zeros and ones to the ``order`` argumnet. For + example, ``order=[1, 1, 0, 1]`` would become: + + .. math:: + x_t = \rho_1 x_{t-1} + \rho_2 x_{t-1} + \rho_4 x_{t-1} + + The coefficient :math:`\rho_3` has been constrained to zero. + + .. warning:: This class is meant to be used as a component in a structural time series model. For modeling of + stationary processes with ARIMA, use ``statespace.BayesianSARIMA``. + + Examples + -------- + Model a timeseries as an AR(2) process with non-zero mean: + + .. code:: python + + from pymc_extras.statespace import structural as st + import pymc as pm + import pytensor.tensor as pt + + trend = st.LevelTrendComponent(order=1, innovations_order=0) + ar = st.AutoregressiveComponent(2) + ss_mod = (trend + ar).build() + + with pm.Model(coords=ss_mod.coords) as model: + P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0']) + intitial_trend = pm.Normal('initial_trend', sigma=10, dims=ss_mod.param_dims['initial_trend']) + ar_params = pm.Normal('ar_params', dims=ss_mod.param_dims['ar_params']) + sigma_ar = pm.Exponential('sigma_ar', 1, dims=ss_mod.param_dims['sigma_ar']) + + ss_mod.build_statespace_graph(data) + idata = pm.sample(nuts_sampler='numpyro') + + """ + + def __init__(self, order: int = 1, name: str = "AutoRegressive"): + order = order_to_mask(order) + ar_lags = np.flatnonzero(order).ravel().astype(int) + 1 + k_states = len(order) + + self.order = order + self.ar_lags = ar_lags + + super().__init__( + name=name, + k_endog=1, + k_states=k_states, + k_posdef=1, + measurement_error=True, + combine_hidden_states=True, + obs_state_idxs=np.r_[[1.0], np.zeros(k_states - 1)], + ) + + def populate_component_properties(self): + self.state_names = [f"L{i + 1}.data" for i in range(self.k_states)] + self.shock_names = [f"{self.name}_innovation"] + self.param_names = ["ar_params", "sigma_ar"] + self.param_dims = {"ar_params": (AR_PARAM_DIM,)} + self.coords = {AR_PARAM_DIM: self.ar_lags.tolist()} + + self.param_info = { + "ar_params": { + "shape": (self.k_states,), + "constraints": None, + "dims": (AR_PARAM_DIM,), + }, + "sigma_ar": {"shape": (), "constraints": "Positive", "dims": None}, + } + + def make_symbolic_graph(self) -> None: + k_nonzero = int(sum(self.order)) + ar_params = self.make_and_register_variable("ar_params", shape=(k_nonzero,)) + sigma_ar = self.make_and_register_variable("sigma_ar", shape=()) + + T = np.eye(self.k_states, k=-1) + self.ssm["transition", :, :] = T + self.ssm["selection", 0, 0] = 1 + self.ssm["design", 0, 0] = 1 + + ar_idx = ("transition", np.zeros(k_nonzero, dtype="int"), np.nonzero(self.order)[0]) + self.ssm[ar_idx] = ar_params + + cov_idx = ("state_cov", *np.diag_indices(1)) + self.ssm[cov_idx] = sigma_ar**2 + + +class TimeSeasonality(Component): + r""" + Seasonal component, modeled in the time domain + + Parameters + ---------- + season_length: int + The number of periods in a single seasonal cycle, e.g. 12 for monthly data with annual seasonal pattern, 7 for + daily data with weekly seasonal pattern, etc. + + innovations: bool, default True + Whether to include stochastic innovations in the strength of the seasonal effect + + name: str, default None + A name for this seasonal component. Used to label dimensions and coordinates. Useful when multiple seasonal + components are included in the same model. Default is ``f"Seasonal[s={season_length}]"`` + + state_names: list of str, default None + List of strings for seasonal effect labels. If provided, it must be of length ``season_length``. An example + would be ``state_names = ['Mon', 'Tue', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun']`` when data is daily with a weekly + seasonal pattern (``season_length = 7``). + + If None, states will be numbered ``[State_0, ..., State_s]`` + + remove_first_state: bool, default True + If True, the first state will be removed from the model. This is done because there are only n-1 degrees of + freedom in the seasonal component, and one state is not identified. If False, the first state will be + included in the model, but it will not be identified -- you will need to handle this in the priors (e.g. with + ZeroSumNormal). + + Notes + ----- + A seasonal effect is any pattern that repeats every fixed interval. Although there are many possible ways to + model seasonal effects, the implementation used here is the one described by [1] as the "canonical" time domain + representation. The seasonal component can be expressed: + + .. math:: + \gamma_t = -\sum_{i=1}^{s-1} \gamma_{t-i} + \omega_t, \quad \omega_t \sim N(0, \sigma_\gamma) + + Where :math:`s` is the ``seasonal_length`` parameter and :math:`\omega_t` is the (optional) stochastic innovation. + To give interpretation to the :math:`\gamma` terms, it is helpful to work through the algebra for a simple + example. Let :math:`s=4`, and omit the shock term. Define initial conditions :math:`\gamma_0, \gamma_{-1}, + \gamma_{-2}`. The value of the seasonal component for the first 5 timesteps will be: + + .. math:: + \begin{align} + \gamma_1 &= -\gamma_0 - \gamma_{-1} - \gamma_{-2} \\ + \gamma_2 &= -\gamma_1 - \gamma_0 - \gamma_{-1} \\ + &= -(-\gamma_0 - \gamma_{-1} - \gamma_{-2}) - \gamma_0 - \gamma_{-1} \\ + &= (\gamma_0 - \gamma_0 )+ (\gamma_{-1} - \gamma_{-1}) + \gamma_{-2} \\ + &= \gamma_{-2} \\ + \gamma_3 &= -\gamma_2 - \gamma_1 - \gamma_0 \\ + &= -\gamma_{-2} - (-\gamma_0 - \gamma_{-1} - \gamma_{-2}) - \gamma_0 \\ + &= (\gamma_{-2} - \gamma_{-2}) + \gamma_{-1} + (\gamma_0 - \gamma_0) \\ + &= \gamma_{-1} \\ + \gamma_4 &= -\gamma_3 - \gamma_2 - \gamma_1 \\ + &= -\gamma_{-1} - \gamma_{-2} -(-\gamma_0 - \gamma_{-1} - \gamma_{-2}) \\ + &= (\gamma_{-2} - \gamma_{-2}) + (\gamma_{-1} - \gamma_{-1}) + \gamma_0 \\ + &= \gamma_0 \\ + \gamma_5 &= -\gamma_4 - \gamma_3 - \gamma_2 \\ + &= -\gamma_0 - \gamma_{-1} - \gamma_{-2} \\ + &= \gamma_1 + \end{align} + + This exercise shows that, given a list ``initial_conditions`` of length ``s-1``, the effects of this model will be: + + - Period 1: ``-sum(initial_conditions)`` + - Period 2: ``initial_conditions[-1]`` + - Period 3: ``initial_conditions[-2]`` + - ... + - Period s: ``initial_conditions[0]`` + - Period s+1: ``-sum(initial_condition)`` + + And so on. So for interpretation, the ``season_length - 1`` initial states are, when reversed, the coefficients + associated with ``state_names[1:]``. + + .. warning:: + Although the ``state_names`` argument expects a list of length ``season_length``, only ``state_names[1:]`` + will be saved as model dimensions, since the 1st coefficient is not identified (it is defined as + :math:`-\sum_{i=1}^{s} \gamma_{t-i}`). + + Examples + -------- + Estimate monthly with a model with a gaussian random walk trend and monthly seasonality: + + .. code:: python + + from pymc_extras.statespace import structural as st + import pymc as pm + import pytensor.tensor as pt + import pandas as pd + + # Get month names + state_names = pd.date_range('1900-01-01', '1900-12-31', freq='MS').month_name().tolist() + + # Build the structural model + grw = st.LevelTrendComponent(order=1, innovations_order=1) + annual_season = st.TimeSeasonality(season_length=12, name='annual', state_names=state_names, innovations=False) + ss_mod = (grw + annual_season).build() + + # Estimate with PyMC + with pm.Model(coords=ss_mod.coords) as model: + P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0']) + intitial_trend = pm.Deterministic('initial_trend', pt.zeros(1), dims=ss_mod.param_dims['initial_trend']) + annual_coefs = pm.Normal('annual_coefs', sigma=1e-2, dims=ss_mod.param_dims['annual_coefs']) + trend_sigmas = pm.HalfNormal('trend_sigmas', sigma=1e-6, dims=ss_mod.param_dims['trend_sigmas']) + ss_mod.build_statespace_graph(data) + idata = pm.sample(nuts_sampler='numpyro') + + References + ---------- + .. [1] Durbin, James, and Siem Jan Koopman. 2012. + Time Series Analysis by State Space Methods: Second Edition. + Oxford University Press. + """ + + def __init__( + self, + season_length: int, + innovations: bool = True, + name: str | None = None, + state_names: list | None = None, + remove_first_state: bool = True, + ): + if name is None: + name = f"Seasonal[s={season_length}]" + if state_names is None: + state_names = [f"{name}_{i}" for i in range(season_length)] + else: + if len(state_names) != season_length: + raise ValueError( + f"state_names must be a list of length season_length, got {len(state_names)}" + ) + state_names = state_names.copy() + self.innovations = innovations + self.remove_first_state = remove_first_state + + if self.remove_first_state: + # In traditional models, the first state isn't identified, so we can help out the user by automatically + # discarding it. + # TODO: Can this be stashed and reconstructed automatically somehow? + state_names.pop(0) + + k_states = season_length - int(self.remove_first_state) + + super().__init__( + name=name, + k_endog=1, + k_states=k_states, + k_posdef=int(innovations), + state_names=state_names, + measurement_error=False, + combine_hidden_states=True, + obs_state_idxs=np.r_[[1.0], np.zeros(k_states - 1)], + ) + + def populate_component_properties(self): + self.param_names = [f"{self.name}_coefs"] + self.param_info = { + f"{self.name}_coefs": { + "shape": (self.k_states,), + "constraints": None, + "dims": (f"{self.name}_state",), + } + } + self.param_dims = {f"{self.name}_coefs": (f"{self.name}_state",)} + self.coords = {f"{self.name}_state": self.state_names} + + if self.innovations: + self.param_names += [f"sigma_{self.name}"] + self.param_info[f"sigma_{self.name}"] = { + "shape": (), + "constraints": "Positive", + "dims": None, + } + self.shock_names = [f"{self.name}"] + + def make_symbolic_graph(self) -> None: + if self.remove_first_state: + # In this case, parameters are normalized to sum to zero, so the current state is the negative sum of + # all previous states. + T = np.eye(self.k_states, k=-1) + T[0, :] = -1 + else: + # In this case we assume the user to be responsible for ensuring the states sum to zero, so T is just a + # circulant matrix that cycles between the states. + T = np.eye(self.k_states, k=1) + T[-1, 0] = 1 + + self.ssm["transition", :, :] = T + self.ssm["design", 0, 0] = 1 + + initial_states = self.make_and_register_variable( + f"{self.name}_coefs", shape=(self.k_states,) + ) + self.ssm["initial_state", np.arange(self.k_states, dtype=int)] = initial_states + + if self.innovations: + self.ssm["selection", 0, 0] = 1 + season_sigma = self.make_and_register_variable(f"sigma_{self.name}", shape=()) + cov_idx = ("state_cov", *np.diag_indices(1)) + self.ssm[cov_idx] = season_sigma**2 + + +class FrequencySeasonality(Component): + r""" + Seasonal component, modeled in the frequency domain + + Parameters + ---------- + season_length: float + The number of periods in a single seasonal cycle, e.g. 12 for monthly data with annual seasonal pattern, 7 for + daily data with weekly seasonal pattern, etc. Non-integer seasonal_length is also permitted, for example + 365.2422 days in a (solar) year. + + n: int + Number of fourier features to include in the seasonal component. Default is ``season_length // 2``, which + is the maximum possible. A smaller number can be used for a more wave-like seasonal pattern. + + name: str, default None + A name for this seasonal component. Used to label dimensions and coordinates. Useful when multiple seasonal + components are included in the same model. Default is ``f"Seasonal[s={season_length}, n={n}]"`` + + innovations: bool, default True + Whether to include stochastic innovations in the strength of the seasonal effect + + Notes + ----- + A seasonal effect is any pattern that repeats every fixed interval. Although there are many possible ways to + model seasonal effects, the implementation used here is the one described by [1] as the "canonical" frequency domain + representation. The seasonal component can be expressed: + + .. math:: + \begin{align} + \gamma_t &= \sum_{j=1}^{2n} \gamma_{j,t} \\ + \gamma_{j, t+1} &= \gamma_{j,t} \cos \lambda_j + \gamma_{j,t}^\star \sin \lambda_j + \omega_{j, t} \\ + \gamma_{j, t}^\star &= -\gamma_{j,t} \sin \lambda_j + \gamma_{j,t}^\star \cos \lambda_j + \omega_{j,t}^\star + \lambda_j &= \frac{2\pi j}{s} + \end{align} + + Where :math:`s` is the ``seasonal_length``. + + Unlike a ``TimeSeasonality`` component, a ``FrequencySeasonality`` component does not require integer season + length. In addition, for long seasonal periods, it is possible to obtain a more compact state space representation + by choosing ``n << s // 2``. Using ``TimeSeasonality``, an annual seasonal pattern in daily data requires 364 + states, whereas ``FrequencySeasonality`` always requires ``2 * n`` states, regardless of the ``seasonal_length``. + The price of this compactness is less representational power. At ``n = 1``, the seasonal pattern will be a pure + sine wave. At ``n = s // 2``, any arbitrary pattern can be represented. + + One cost of the added flexibility of ``FrequencySeasonality`` is reduced interpretability. States of this model are + coefficients :math:`\gamma_1, \gamma^\star_1, \gamma_2, \gamma_2^\star ..., \gamma_n, \gamma^\star_n` associated + with different frequencies in the fourier representation of the seasonal pattern. As a result, it is not possible + to isolate and identify a "Monday" effect, for instance. + """ + + def __init__(self, season_length, n=None, name=None, innovations=True): + if n is None: + n = int(season_length // 2) + if name is None: + name = f"Frequency[s={season_length}, n={n}]" + + k_states = n * 2 + self.n = n + self.season_length = season_length + self.innovations = innovations + + # If the model is completely saturated (n = s // 2), the last state will not be identified, so it shouldn't + # get a parameter assigned to it and should just be fixed to zero. + # Test this way (rather than n == s // 2) to catch cases when n is non-integer. + self.last_state_not_identified = self.season_length / self.n == 2.0 + self.n_coefs = k_states - int(self.last_state_not_identified) + + obs_state_idx = np.zeros(k_states) + obs_state_idx[slice(0, k_states, 2)] = 1 + + super().__init__( + name=name, + k_endog=1, + k_states=k_states, + k_posdef=k_states * int(self.innovations), + measurement_error=False, + combine_hidden_states=True, + obs_state_idxs=obs_state_idx, + ) + + def make_symbolic_graph(self) -> None: + self.ssm["design", 0, slice(0, self.k_states, 2)] = 1 + + init_state = self.make_and_register_variable(f"{self.name}", shape=(self.n_coefs,)) + + init_state_idx = np.arange(self.n_coefs, dtype=int) + self.ssm["initial_state", init_state_idx] = init_state + + T_mats = [_frequency_transition_block(self.season_length, j + 1) for j in range(self.n)] + T = pt.linalg.block_diag(*T_mats) + self.ssm["transition", :, :] = T + + if self.innovations: + sigma_season = self.make_and_register_variable(f"sigma_{self.name}", shape=()) + self.ssm["state_cov", :, :] = pt.eye(self.k_posdef) * sigma_season**2 + self.ssm["selection", :, :] = np.eye(self.k_states) + + def populate_component_properties(self): + self.state_names = [f"{self.name}_{f}_{i}" for i in range(self.n) for f in ["Cos", "Sin"]] + self.param_names = [f"{self.name}"] + + self.param_dims = {self.name: (f"{self.name}_state",)} + self.param_info = { + f"{self.name}": { + "shape": (self.k_states - int(self.last_state_not_identified),), + "constraints": None, + "dims": (f"{self.name}_state",), + } + } + + init_state_idx = np.arange(self.k_states, dtype=int) + if self.last_state_not_identified: + init_state_idx = init_state_idx[:-1] + self.coords = {f"{self.name}_state": [self.state_names[i] for i in init_state_idx]} + + if self.innovations: + self.shock_names = self.state_names.copy() + self.param_names += [f"sigma_{self.name}"] + self.param_info[f"sigma_{self.name}"] = { + "shape": (), + "constraints": "Positive", + "dims": None, + } + + +class CycleComponent(Component): + r""" + A component for modeling longer-term cyclical effects + + Parameters + ---------- + name: str + Name of the component. Used in generated coordinates and state names. If None, a descriptive name will be + used. + + cycle_length: int, optional + The length of the cycle, in the calendar units of your data. For example, if your data is monthly, and you + want to model a 12-month cycle, use ``cycle_length=12``. You cannot specify both ``cycle_length`` and + ``estimate_cycle_length``. + + estimate_cycle_length: bool, default False + Whether to estimate the cycle length. If True, an additional parameter, ``cycle_length`` will be added to the + model. You cannot specify both ``cycle_length`` and ``estimate_cycle_length``. + + dampen: bool, default False + Whether to dampen the cycle by multiplying by a dampening factor :math:`\rho` at every timestep. If true, + an additional parameter, ``dampening_factor`` will be added to the model. + + innovations: bool, default True + Whether to include stochastic innovations in the strength of the seasonal effect. If True, an additional + parameter, ``sigma_{name}`` will be added to the model. + + Notes + ----- + The cycle component is very similar in implementation to the frequency domain seasonal component, expect that it + is restricted to n=1. The cycle component can be expressed: + + .. math:: + \begin{align} + \gamma_t &= \rho \gamma_{t-1} \cos \lambda + \rho \gamma_{t-1}^\star \sin \lambda + \omega_{t} \\ + \gamma_{t}^\star &= -\rho \gamma_{t-1} \sin \lambda + \rho \gamma_{t-1}^\star \cos \lambda + \omega_{t}^\star \\ + \lambda &= \frac{2\pi}{s} + \end{align} + + Where :math:`s` is the ``cycle_length``. [1] recommend that this component be used for longer term cyclical + effects, such as business cycles, and that the seasonal component be used for shorter term effects, such as + weekly or monthly seasonality. + + Unlike a FrequencySeasonality component, the length of a CycleComponent can be estimated. + + Examples + -------- + Estimate a business cycle with length between 6 and 12 years: + + .. code:: python + + from pymc_extras.statespace import structural as st + import pymc as pm + import pytensor.tensor as pt + import pandas as pd + import numpy as np + + data = np.random.normal(size=(100, 1)) + + # Build the structural model + grw = st.LevelTrendComponent(order=1, innovations_order=1) + cycle = st.CycleComponent('business_cycle', estimate_cycle_length=True, dampen=False) + ss_mod = (grw + cycle).build() + + # Estimate with PyMC + with pm.Model(coords=ss_mod.coords) as model: + P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states), dims=ss_mod.param_dims['P0']) + intitial_trend = pm.Normal('initial_trend', dims=ss_mod.param_dims['initial_trend']) + sigma_trend = pm.HalfNormal('sigma_trend', dims=ss_mod.param_dims['sigma_trend']) + + cycle_strength = pm.Normal('business_cycle') + cycle_length = pm.Uniform('business_cycle_length', lower=6, upper=12) + + sigma_cycle = pm.HalfNormal('sigma_business_cycle', sigma=1) + ss_mod.build_statespace_graph(data) + + idata = pm.sample(nuts_sampler='numpyro') + + References + ---------- + .. [1] Durbin, James, and Siem Jan Koopman. 2012. + Time Series Analysis by State Space Methods: Second Edition. + Oxford University Press. + """ + + def __init__( + self, + name: str | None = None, + cycle_length: int | None = None, + estimate_cycle_length: bool = False, + dampen: bool = False, + innovations: bool = True, + ): + if cycle_length is None and not estimate_cycle_length: + raise ValueError("Must specify cycle_length if estimate_cycle_length is False") + if cycle_length is not None and estimate_cycle_length: + raise ValueError("Cannot specify cycle_length if estimate_cycle_length is True") + if name is None: + cycle = int(cycle_length) if cycle_length is not None else "Estimate" + name = f"Cycle[s={cycle}, dampen={dampen}, innovations={innovations}]" + + self.estimate_cycle_length = estimate_cycle_length + self.cycle_length = cycle_length + self.innovations = innovations + self.dampen = dampen + self.n_coefs = 1 + + k_states = 2 + k_endog = 1 + k_posdef = 2 + + obs_state_idx = np.zeros(k_states) + obs_state_idx[slice(0, k_states, 2)] = 1 + + super().__init__( + name=name, + k_endog=k_endog, + k_states=k_states, + k_posdef=k_posdef, + measurement_error=False, + combine_hidden_states=True, + obs_state_idxs=obs_state_idx, + ) + + def make_symbolic_graph(self) -> None: + self.ssm["design", 0, slice(0, self.k_states, 2)] = 1 + self.ssm["selection", :, :] = np.eye(self.k_states) + self.param_dims = {self.name: (f"{self.name}_state",)} + self.coords = {f"{self.name}_state": self.state_names} + + init_state = self.make_and_register_variable(f"{self.name}", shape=(self.k_states,)) + + self.ssm["initial_state", :] = init_state + + if self.estimate_cycle_length: + lamb = self.make_and_register_variable(f"{self.name}_length", shape=()) + else: + lamb = self.cycle_length + + if self.dampen: + rho = self.make_and_register_variable(f"{self.name}_dampening_factor", shape=()) + else: + rho = 1 + + T = rho * _frequency_transition_block(lamb, j=1) + self.ssm["transition", :, :] = T + + if self.innovations: + sigma_cycle = self.make_and_register_variable(f"sigma_{self.name}", shape=()) + self.ssm["state_cov", :, :] = pt.eye(self.k_posdef) * sigma_cycle**2 + + def populate_component_properties(self): + self.state_names = [f"{self.name}_{f}" for f in ["Cos", "Sin"]] + self.param_names = [f"{self.name}"] + + self.param_info = { + f"{self.name}": { + "shape": (2,), + "constraints": None, + "dims": (f"{self.name}_state",), + } + } + + if self.estimate_cycle_length: + self.param_names += [f"{self.name}_length"] + self.param_info[f"{self.name}_length"] = { + "shape": (), + "constraints": "Positive, non-zero", + "dims": None, + } + + if self.dampen: + self.param_names += [f"{self.name}_dampening_factor"] + self.param_info[f"{self.name}_dampening_factor"] = { + "shape": (), + "constraints": "0 < x ≤ 1", + "dims": None, + } + + if self.innovations: + self.param_names += [f"sigma_{self.name}"] + self.param_info[f"sigma_{self.name}"] = { + "shape": (), + "constraints": "Positive", + "dims": None, + } + self.shock_names = self.state_names.copy() + + +class RegressionComponent(Component): + def __init__( + self, + k_exog: int | None = None, + name: str | None = "Exogenous", + state_names: list[str] | None = None, + innovations=False, + ): + self.innovations = innovations + k_exog = self._handle_input_data(k_exog, state_names, name) + + k_states = k_exog + k_endog = 1 + k_posdef = k_exog + + super().__init__( + name=name, + k_endog=k_endog, + k_states=k_states, + k_posdef=k_posdef, + state_names=self.state_names, + measurement_error=False, + combine_hidden_states=False, + exog_names=[f"data_{name}"], + obs_state_idxs=np.ones(k_states), + ) + + @staticmethod + def _get_state_names(k_exog: int | None, state_names: list[str] | None, name: str): + if k_exog is None and state_names is None: + raise ValueError("Must specify at least one of k_exog or state_names") + if state_names is not None and k_exog is not None: + if len(state_names) != k_exog: + raise ValueError(f"Expected {k_exog} state names, found {len(state_names)}") + elif k_exog is None: + k_exog = len(state_names) + else: + state_names = [f"{name}_{i + 1}" for i in range(k_exog)] + + return k_exog, state_names + + def _handle_input_data(self, k_exog: int, state_names: list[str] | None, name) -> int: + k_exog, state_names = self._get_state_names(k_exog, state_names, name) + self.state_names = state_names + + return k_exog + + def make_symbolic_graph(self) -> None: + betas = self.make_and_register_variable(f"beta_{self.name}", shape=(self.k_states,)) + regression_data = self.make_and_register_data( + f"data_{self.name}", shape=(None, self.k_states) + ) + + self.ssm["initial_state", :] = betas + self.ssm["transition", :, :] = np.eye(self.k_states) + self.ssm["selection", :, :] = np.eye(self.k_states) + self.ssm["design"] = pt.expand_dims(regression_data, 1) + + if self.innovations: + sigma_beta = self.make_and_register_variable( + f"sigma_beta_{self.name}", (self.k_states,) + ) + row_idx, col_idx = np.diag_indices(self.k_states) + self.ssm["state_cov", row_idx, col_idx] = sigma_beta**2 + + def populate_component_properties(self) -> None: + self.shock_names = self.state_names + + self.param_names = [f"beta_{self.name}"] + self.data_names = [f"data_{self.name}"] + self.param_dims = { + f"beta_{self.name}": ("exog_state",), + } + + self.param_info = { + f"beta_{self.name}": { + "shape": (self.k_states,), + "constraints": None, + "dims": ("exog_state",), + }, + } + + self.data_info = { + f"data_{self.name}": { + "shape": (None, self.k_states), + "dims": (TIME_DIM, "exog_state"), + }, + } + self.coords = {"exog_state": self.state_names} + + if self.innovations: + self.param_names += [f"sigma_beta_{self.name}"] + self.param_dims[f"sigma_beta_{self.name}"] = "exog_state" + self.param_info[f"sigma_beta_{self.name}"] = { + "shape": (), + "constraints": "Positive", + "dims": ("exog_state",), + } diff --git a/pymc_extras/statespace/models/structural/components/level_trend.py b/pymc_extras/statespace/models/structural/components/level_trend.py index b6735007f..1563dde72 100644 --- a/pymc_extras/statespace/models/structural/components/level_trend.py +++ b/pymc_extras/statespace/models/structural/components/level_trend.py @@ -167,15 +167,19 @@ def populate_component_properties(self): name_slice = POSITION_DERIVATIVE_NAMES[:k_states] self.param_names = ["initial_trend"] - self.state_names = [name for name, mask in zip(name_slice, self._order_mask) if mask] + base_names = [name for name, mask in zip(name_slice, self._order_mask) if mask] + self.state_names = [ + f"{name}[{obs_name}]" for obs_name in self.observed_state_names for name in base_names + ] self.param_dims = {"initial_trend": ("trend_state",)} - self.coords = {"trend_state": self.state_names} + self.coords = {"trend_state": base_names} if k_endog > 1: self.param_dims["trend_state"] = ( "trend_endog", "trend_state", ) + self.param_dims = {"initial_trend": ("trend_endog", "trend_state")} self.coords["trend_endog"] = self.observed_state_names shape = (k_endog, k_states) if k_endog > 1 else (k_states,) diff --git a/pymc_extras/statespace/models/structural/components/regression.py b/pymc_extras/statespace/models/structural/components/regression.py index c290812c2..435db50d1 100644 --- a/pymc_extras/statespace/models/structural/components/regression.py +++ b/pymc_extras/statespace/models/structural/components/regression.py @@ -1,6 +1,7 @@ import numpy as np from pytensor import tensor as pt +from scipy import linalg from pymc_extras.statespace.models.structural.core import Component from pymc_extras.statespace.utils.constants import TIME_DIM @@ -28,8 +29,8 @@ def __init__( super().__init__( name=name, k_endog=k_endog, - k_states=k_states, - k_posdef=k_posdef, + k_states=k_states * k_endog, + k_posdef=k_posdef * k_endog, state_names=self.state_names, observed_state_names=observed_state_names, measurement_error=False, @@ -59,15 +60,21 @@ def _handle_input_data(self, k_exog: int, state_names: list[str] | None, name) - return k_exog def make_symbolic_graph(self) -> None: - betas = self.make_and_register_variable(f"beta_{self.name}", shape=(self.k_states,)) - regression_data = self.make_and_register_data( - f"data_{self.name}", shape=(None, self.k_states) - ) + k_endog = self.k_endog + k_states = self.k_states // k_endog + self.k_posdef // k_endog + + betas = self.make_and_register_variable(f"beta_{self.name}", shape=(k_endog, k_states)) + regression_data = self.make_and_register_data(f"data_{self.name}", shape=(None, k_states)) - self.ssm["initial_state", :] = betas - self.ssm["transition", :, :] = np.eye(self.k_states) + self.ssm["initial_state", :] = betas.reshape((1, -1)).squeeze() + T = np.eye(k_states) + self.ssm["transition", :, :] = linalg.block_diag(*[T for _ in range(k_endog)]) self.ssm["selection", :, :] = np.eye(self.k_states) - self.ssm["design"] = pt.expand_dims(regression_data, 1) + Z = pt.linalg.block_diag(*[pt.expand_dims(regression_data, 1) for _ in range(k_endog)]) + self.ssm["design"] = pt.specify_shape( + Z, (None, k_endog, regression_data.type.shape[1] * k_endog) + ) if self.innovations: sigma_beta = self.make_and_register_variable( @@ -77,29 +84,38 @@ def make_symbolic_graph(self) -> None: self.ssm["state_cov", row_idx, col_idx] = sigma_beta**2 def populate_component_properties(self) -> None: + k_endog = self.k_endog + k_states = self.k_states // k_endog + self.k_posdef // k_endog + self.shock_names = self.state_names self.param_names = [f"beta_{self.name}"] self.data_names = [f"data_{self.name}"] self.param_dims = { - f"beta_{self.name}": ("exog_state",), + f"beta_{self.name}": ("exog_endog", "exog_state"), } + base_names = self.state_names + self.state_names = [ + f"{name}[{obs_name}]" for obs_name in self.observed_state_names for name in base_names + ] + self.param_info = { f"beta_{self.name}": { - "shape": (self.k_states,), + "shape": (k_endog, k_states), "constraints": None, - "dims": ("exog_state",), + "dims": ("exog_endog", "exog_state"), }, } self.data_info = { f"data_{self.name}": { - "shape": (None, self.k_states), + "shape": (None, k_states), "dims": (TIME_DIM, "exog_state"), }, } - self.coords = {"exog_state": self.state_names} + self.coords = {"exog_state": base_names, "exog_endog": self.observed_state_names} if self.innovations: self.param_names += [f"sigma_beta_{self.name}"] diff --git a/tests/statespace/models/test_structural.py b/tests/statespace/models/test_structural.py new file mode 100644 index 000000000..1662e164a --- /dev/null +++ b/tests/statespace/models/test_structural.py @@ -0,0 +1,840 @@ +import functools as ft +import warnings + +from collections import defaultdict + +import numpy as np +import pandas as pd +import pymc as pm +import pytensor +import pytensor.tensor as pt +import pytest +import statsmodels.api as sm + +from numpy.testing import assert_allclose +from scipy import linalg + +from pymc_extras.statespace import structural as st +from pymc_extras.statespace.utils.constants import ( + ALL_STATE_AUX_DIM, + ALL_STATE_DIM, + AR_PARAM_DIM, + OBS_STATE_AUX_DIM, + OBS_STATE_DIM, + SHOCK_AUX_DIM, + SHOCK_DIM, + SHORT_NAME_TO_LONG, +) +from tests.statespace.shared_fixtures import ( # pylint: disable=unused-import + rng, +) +from tests.statespace.test_utilities import ( + assert_pattern_repeats, + simulate_from_numpy_model, + unpack_symbolic_matrices_with_params, +) + +floatX = pytensor.config.floatX +ATOL = 1e-8 if floatX.endswith("64") else 1e-4 +RTOL = 0 if floatX.endswith("64") else 1e-6 + + +def _assert_all_statespace_matrices_match(mod, params, sm_mod): + x0, P0, c, d, T, Z, R, H, Q = unpack_symbolic_matrices_with_params(mod, params) + + sm_x0, sm_H0, sm_P0 = sm_mod.initialization() + + if len(x0) > 0: + assert_allclose(x0, sm_x0) + + for name, matrix in zip(["T", "R", "Z", "Q"], [T, R, Z, Q]): + long_name = SHORT_NAME_TO_LONG[name] + if np.any([x == 0 for x in matrix.shape]): + continue + assert_allclose( + sm_mod.ssm[long_name], + matrix, + err_msg=f"matrix {name} does not match statsmodels", + atol=ATOL, + rtol=RTOL, + ) + + +def _assert_coord_shapes_match_matrices(mod, params): + if "initial_state_cov" not in params: + params["initial_state_cov"] = np.eye(mod.k_states) + + x0, P0, c, d, T, Z, R, H, Q = unpack_symbolic_matrices_with_params(mod, params) + + n_states = len(mod.coords[ALL_STATE_DIM]) + + # There will always be one shock dimension -- dummies are inserted into fully deterministic models to avoid errors + # in the state space representation. + n_shocks = max(1, len(mod.coords[SHOCK_DIM])) + n_obs = len(mod.coords[OBS_STATE_DIM]) + + assert x0.shape[-1:] == ( + n_states, + ), f"x0 expected to have shape (n_states, ), found {x0.shape[-1:]}" + assert P0.shape[-2:] == ( + n_states, + n_states, + ), f"P0 expected to have shape (n_states, n_states), found {P0.shape[-2:]}" + assert c.shape[-1:] == ( + n_states, + ), f"c expected to have shape (n_states, ), found {c.shape[-1:]}" + assert d.shape[-1:] == (n_obs,), f"d expected to have shape (n_obs, ), found {d.shape[-1:]}" + assert T.shape[-2:] == ( + n_states, + n_states, + ), f"T expected to have shape (n_states, n_states), found {T.shape[-2:]}" + assert Z.shape[-2:] == ( + n_obs, + n_states, + ), f"Z expected to have shape (n_obs, n_states), found {Z.shape[-2:]}" + assert R.shape[-2:] == ( + n_states, + n_shocks, + ), f"R expected to have shape (n_states, n_shocks), found {R.shape[-2:]}" + assert H.shape[-2:] == ( + n_obs, + n_obs, + ), f"H expected to have shape (n_obs, n_obs), found {H.shape[-2:]}" + assert Q.shape[-2:] == ( + n_shocks, + n_shocks, + ), f"Q expected to have shape (n_shocks, n_shocks), found {Q.shape[-2:]}" + + +def _assert_basic_coords_correct(mod): + assert mod.coords[ALL_STATE_DIM] == mod.state_names + assert mod.coords[ALL_STATE_AUX_DIM] == mod.state_names + assert mod.coords[SHOCK_DIM] == mod.shock_names + assert mod.coords[SHOCK_AUX_DIM] == mod.shock_names + assert mod.coords[OBS_STATE_DIM] == ["data"] + assert mod.coords[OBS_STATE_AUX_DIM] == ["data"] + + +def _assert_keys_match(test_dict, expected_dict): + expected_keys = list(expected_dict.keys()) + param_keys = list(test_dict.keys()) + key_diff = set(expected_keys) - set(param_keys) + assert len(key_diff) == 0, f'{", ".join(key_diff)} were not found in the test_dict keys.' + + key_diff = set(param_keys) - set(expected_keys) + assert ( + len(key_diff) == 0 + ), f'{", ".join(key_diff)} were keys of the tests_dict not in expected_dict.' + + +def _assert_param_dims_correct(param_dims, expected_dims): + if len(expected_dims) == 0 and len(param_dims) == 0: + return + + _assert_keys_match(param_dims, expected_dims) + for param, dims in expected_dims.items(): + assert dims == param_dims[param], f"dims for parameter {param} do not match" + + +def _assert_coords_correct(coords, expected_coords): + if len(coords) == 0 and len(expected_coords) == 0: + return + + _assert_keys_match(coords, expected_coords) + for dim, labels in expected_coords.items(): + assert labels == coords[dim], f"labels on dimension {dim} do not match" + + +def _assert_params_info_correct(param_info, coords, param_dims): + for param in param_info.keys(): + info = param_info[param] + + dims = info["dims"] + labels = [coords[dim] for dim in dims] if dims is not None else None + if labels is not None: + assert param in param_dims.keys() + inferred_dims = param_dims[param] + else: + inferred_dims = None + + shape = tuple(len(label) for label in labels) if labels is not None else () + + assert info["shape"] == shape + assert dims == inferred_dims + + +def create_structural_model_and_equivalent_statsmodel( + rng, + level: bool | None = False, + trend: bool | None = False, + seasonal: int | None = None, + freq_seasonal: list[dict] | None = None, + cycle: bool = False, + autoregressive: int | None = None, + exog: np.ndarray | None = None, + irregular: bool | None = False, + stochastic_level: bool | None = True, + stochastic_trend: bool | None = False, + stochastic_seasonal: bool | None = True, + stochastic_freq_seasonal: list[bool] | None = None, + stochastic_cycle: bool | None = False, + damped_cycle: bool | None = False, +): + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + mod = ft.partial( + sm.tsa.UnobservedComponents, + level=level, + trend=trend, + seasonal=seasonal, + freq_seasonal=freq_seasonal, + cycle=cycle, + autoregressive=autoregressive, + exog=exog, + irregular=irregular, + stochastic_level=stochastic_level, + stochastic_trend=stochastic_trend, + stochastic_seasonal=stochastic_seasonal, + stochastic_freq_seasonal=stochastic_freq_seasonal, + stochastic_cycle=stochastic_cycle, + damped_cycle=damped_cycle, + mle_regression=False, + ) + + params = {} + sm_params = {} + sm_init = {} + expected_param_dims = defaultdict(tuple) + expected_coords = defaultdict(list) + expected_param_dims["P0"] += ("state", "state_aux") + + default_states = [ + ALL_STATE_DIM, + ALL_STATE_AUX_DIM, + OBS_STATE_DIM, + OBS_STATE_AUX_DIM, + SHOCK_DIM, + SHOCK_AUX_DIM, + ] + default_values = [[], [], ["data"], ["data"], [], []] + for dim, value in zip(default_states, default_values): + expected_coords[dim] += value + + components = [] + + if irregular: + sigma2 = np.abs(rng.normal()).astype(floatX).item() + params["sigma_irregular"] = np.sqrt(sigma2) + sm_params["sigma2.irregular"] = sigma2 + + comp = st.MeasurementError("irregular") + components.append(comp) + + level_trend_order = [0, 0] + level_trend_innov_order = [0, 0] + + if level: + level_trend_order[0] = 1 + expected_coords["trend_state"] += [ + "level", + ] + expected_coords[ALL_STATE_DIM] += [ + "level", + ] + expected_coords[ALL_STATE_AUX_DIM] += [ + "level", + ] + if stochastic_level: + level_trend_innov_order[0] = 1 + expected_coords["trend_shock"] += ["level"] + expected_coords[SHOCK_DIM] += [ + "level", + ] + expected_coords[SHOCK_AUX_DIM] += [ + "level", + ] + + if trend: + level_trend_order[1] = 1 + expected_coords["trend_state"] += [ + "trend", + ] + expected_coords[ALL_STATE_DIM] += [ + "trend", + ] + expected_coords[ALL_STATE_AUX_DIM] += [ + "trend", + ] + + if stochastic_trend: + level_trend_innov_order[1] = 1 + expected_coords["trend_shock"] += ["trend"] + expected_coords[SHOCK_DIM] += ["trend"] + expected_coords[SHOCK_AUX_DIM] += ["trend"] + + if level or trend: + expected_param_dims["initial_trend"] += ("trend_state",) + level_value = np.where( + level_trend_order, + rng.normal( + size=2, + ).astype(floatX), + np.zeros(2, dtype=floatX), + ) + sigma_level_value2 = np.abs(rng.normal(size=(2,)))[ + np.array(level_trend_innov_order, dtype="bool") + ] + max_order = np.flatnonzero(level_value)[-1].item() + 1 + level_trend_order = level_trend_order[:max_order] + + params["initial_trend"] = level_value[:max_order] + sm_init["level"] = level_value[0] + sm_init["trend"] = level_value[1] + + if sum(level_trend_innov_order) > 0: + expected_param_dims["sigma_trend"] += ("trend_shock",) + params["sigma_trend"] = np.sqrt(sigma_level_value2) + + sigma_level_value = sigma_level_value2.tolist() + if stochastic_level: + sigma = sigma_level_value.pop(0) + sm_params["sigma2.level"] = sigma + if stochastic_trend: + sigma = sigma_level_value.pop(0) + sm_params["sigma2.trend"] = sigma + + comp = st.LevelTrendComponent( + name="level", order=level_trend_order, innovations_order=level_trend_innov_order + ) + components.append(comp) + + if seasonal is not None: + state_names = [f"seasonal_{i}" for i in range(seasonal)][1:] + seasonal_coefs = rng.normal(size=(seasonal - 1,)).astype(floatX) + params["seasonal_coefs"] = seasonal_coefs + expected_param_dims["seasonal_coefs"] += ("seasonal_state",) + + expected_coords["seasonal_state"] += tuple(state_names) + expected_coords[ALL_STATE_DIM] += state_names + expected_coords[ALL_STATE_AUX_DIM] += state_names + + seasonal_dict = { + "seasonal" if i == 0 else f"seasonal.L{i}": c for i, c in enumerate(seasonal_coefs) + } + sm_init.update(seasonal_dict) + + if stochastic_seasonal: + sigma2 = np.abs(rng.normal()).astype(floatX) + params["sigma_seasonal"] = np.sqrt(sigma2) + sm_params["sigma2.seasonal"] = sigma2 + expected_coords[SHOCK_DIM] += [ + "seasonal", + ] + expected_coords[SHOCK_AUX_DIM] += [ + "seasonal", + ] + + comp = st.TimeSeasonality( + name="seasonal", season_length=seasonal, innovations=stochastic_seasonal + ) + components.append(comp) + + if freq_seasonal is not None: + state_count = 0 + for d, has_innov in zip(freq_seasonal, stochastic_freq_seasonal): + n = d["harmonics"] + s = d["period"] + last_state_not_identified = (s / n) == 2.0 + n_states = 2 * n - int(last_state_not_identified) + state_names = [f"seasonal_{s}_{f}_{i}" for i in range(n) for f in ["Cos", "Sin"]] + + seasonal_params = rng.normal(size=n_states).astype(floatX) + + params[f"seasonal_{s}"] = seasonal_params + expected_param_dims[f"seasonal_{s}"] += (f"seasonal_{s}_state",) + expected_coords[ALL_STATE_DIM] += state_names + expected_coords[ALL_STATE_AUX_DIM] += state_names + expected_coords[f"seasonal_{s}_state"] += ( + tuple(state_names[:-1]) if last_state_not_identified else tuple(state_names) + ) + + for param in seasonal_params: + sm_init[f"freq_seasonal.{state_count}"] = param + state_count += 1 + if last_state_not_identified: + sm_init[f"freq_seasonal.{state_count}"] = 0.0 + state_count += 1 + + if has_innov: + sigma2 = np.abs(rng.normal()).astype(floatX) + params[f"sigma_seasonal_{s}"] = np.sqrt(sigma2) + sm_params[f"sigma2.freq_seasonal_{s}({n})"] = sigma2 + expected_coords[SHOCK_DIM] += state_names + expected_coords[SHOCK_AUX_DIM] += state_names + + comp = st.FrequencySeasonality( + name=f"seasonal_{s}", season_length=s, n=n, innovations=has_innov + ) + components.append(comp) + + if cycle: + cycle_length = np.random.choice(np.arange(2, 12)).astype(floatX) + + # Statsmodels takes the frequency not the cycle length, so convert it. + sm_params["frequency.cycle"] = 2.0 * np.pi / cycle_length + params["cycle_length"] = cycle_length + + init_cycle = rng.normal(size=(2,)).astype(floatX) + params["cycle"] = init_cycle + expected_param_dims["cycle"] += ("cycle_state",) + + state_names = ["cycle_Cos", "cycle_Sin"] + expected_coords["cycle_state"] += state_names + expected_coords[ALL_STATE_DIM] += state_names + expected_coords[ALL_STATE_AUX_DIM] += state_names + + sm_init["cycle"] = init_cycle[0] + sm_init["cycle.auxilliary"] = init_cycle[1] + + if stochastic_cycle: + sigma2 = np.abs(rng.normal()).astype(floatX) + params["sigma_cycle"] = np.sqrt(sigma2) + expected_coords[SHOCK_DIM] += state_names + expected_coords[SHOCK_AUX_DIM] += state_names + + sm_params["sigma2.cycle"] = sigma2 + + if damped_cycle: + rho = rng.beta(1, 1) + params["cycle_dampening_factor"] = rho + sm_params["damping.cycle"] = rho + + comp = st.CycleComponent( + name="cycle", + dampen=damped_cycle, + innovations=stochastic_cycle, + estimate_cycle_length=True, + ) + + components.append(comp) + + if autoregressive is not None: + ar_names = [f"L{i+1}.data" for i in range(autoregressive)] + ar_params = rng.normal(size=(autoregressive,)).astype(floatX) + if autoregressive == 1: + ar_params = ar_params.item() + sigma2 = np.abs(rng.normal()).astype(floatX) + + params["ar_params"] = ar_params + params["sigma_ar"] = np.sqrt(sigma2) + expected_param_dims["ar_params"] += (AR_PARAM_DIM,) + expected_coords[AR_PARAM_DIM] += tuple(list(range(1, autoregressive + 1))) + expected_coords[ALL_STATE_DIM] += ar_names + expected_coords[ALL_STATE_AUX_DIM] += ar_names + expected_coords[SHOCK_DIM] += ["ar_innovation"] + expected_coords[SHOCK_AUX_DIM] += ["ar_innovation"] + + sm_params["sigma2.ar"] = sigma2 + for i, rho in enumerate(ar_params): + sm_init[f"ar.L{i+1}"] = 0 + sm_params[f"ar.L{i+1}"] = rho + + comp = st.AutoregressiveComponent(name="ar", order=autoregressive) + components.append(comp) + + if exog is not None: + names = [f"x{i + 1}" for i in range(exog.shape[1])] + betas = rng.normal(size=(exog.shape[1],)).astype(floatX) + params["beta_exog"] = betas + params["data_exog"] = exog + expected_param_dims["beta_exog"] += ("exog_state",) + expected_param_dims["data_exog"] += ("time", "exog_data") + + expected_coords["exog_state"] += tuple(names) + + for i, beta in enumerate(betas): + sm_params[f"beta.x{i + 1}"] = beta + sm_init[f"beta.x{i+1}"] = beta + comp = st.RegressionComponent(name="exog", state_names=names) + components.append(comp) + + st_mod = components.pop(0) + for comp in components: + st_mod += comp + return mod, st_mod, params, sm_params, sm_init, expected_param_dims, expected_coords + + +@pytest.mark.parametrize( + "level, trend, stochastic_level, stochastic_trend, irregular", + [ + (False, False, False, False, True), + (True, True, True, True, True), + (True, True, False, True, False), + ], +) +@pytest.mark.parametrize("autoregressive", [None, 3]) +@pytest.mark.parametrize("seasonal, stochastic_seasonal", [(None, False), (12, False), (12, True)]) +@pytest.mark.parametrize( + "freq_seasonal, stochastic_freq_seasonal", + [ + (None, None), + ([{"period": 12, "harmonics": 2}], [False]), + ([{"period": 12, "harmonics": 6}], [True]), + ], +) +@pytest.mark.parametrize( + "cycle, damped_cycle, stochastic_cycle", + [(False, False, False), (True, False, True), (True, True, True)], +) +@pytest.mark.filterwarnings("ignore::statsmodels.tools.sm_exceptions.ConvergenceWarning") +@pytest.mark.filterwarnings("ignore::statsmodels.tools.sm_exceptions.SpecificationWarning") +def test_structural_model_against_statsmodels( + level, + trend, + stochastic_level, + stochastic_trend, + irregular, + autoregressive, + seasonal, + stochastic_seasonal, + freq_seasonal, + stochastic_freq_seasonal, + cycle, + damped_cycle, + stochastic_cycle, + rng, +): + retvals = create_structural_model_and_equivalent_statsmodel( + rng, + level=level, + trend=trend, + seasonal=seasonal, + freq_seasonal=freq_seasonal, + cycle=cycle, + damped_cycle=damped_cycle, + autoregressive=autoregressive, + irregular=irregular, + stochastic_level=stochastic_level, + stochastic_trend=stochastic_trend, + stochastic_seasonal=stochastic_seasonal, + stochastic_freq_seasonal=stochastic_freq_seasonal, + stochastic_cycle=stochastic_cycle, + ) + f_sm_mod, mod, params, sm_params, sm_init, expected_dims, expected_coords = retvals + + data = rng.normal(size=(100,)).astype(floatX) + sm_mod = f_sm_mod(data) + + if len(sm_init) > 0: + init_array = np.concatenate( + [np.atleast_1d(sm_init[k]).ravel() for k in sm_mod.state_names if k != "dummy"] + ) + sm_mod.initialize_known(init_array, np.eye(sm_mod.k_states)) + else: + sm_mod.initialize_default() + + if len(sm_params) > 0: + param_array = np.concatenate( + [np.atleast_1d(sm_params[k]).ravel() for k in sm_mod.param_names] + ) + sm_mod.update(param_array, transformed=True) + + _assert_all_statespace_matrices_match(mod, params, sm_mod) + + built_model = mod.build(verbose=False, mode="FAST_RUN") + assert built_model.mode == "FAST_RUN" + + _assert_coord_shapes_match_matrices(built_model, params) + _assert_param_dims_correct(built_model.param_dims, expected_dims) + _assert_coords_correct(built_model.coords, expected_coords) + _assert_params_info_correct(built_model.param_info, built_model.coords, built_model.param_dims) + + +def test_level_trend_model(rng): + mod = st.LevelTrendComponent(order=2, innovations_order=0) + params = {"initial_trend": [0.0, 1.0]} + x, y = simulate_from_numpy_model(mod, rng, params) + + assert_allclose(np.diff(y), 1, atol=ATOL, rtol=RTOL) + + # Check coords + mod = mod.build(verbose=False) + _assert_basic_coords_correct(mod) + assert mod.coords["trend_state"] == ["level", "trend"] + + +def test_measurement_error(rng): + mod = st.MeasurementError("obs") + st.LevelTrendComponent(order=2) + mod = mod.build(verbose=False) + + _assert_basic_coords_correct(mod) + assert "sigma_obs" in mod.param_names + + +@pytest.mark.parametrize("order", [1, 2, [1, 0, 1]], ids=["AR1", "AR2", "AR(1,0,1)"]) +def test_autoregressive_model(order, rng): + ar = st.AutoregressiveComponent(order=order) + params = { + "ar_params": np.full((sum(ar.order),), 0.5, dtype=floatX), + "sigma_ar": 0.0, + } + + x, y = simulate_from_numpy_model(ar, rng, params, steps=100) + + # Check coords + ar.build(verbose=False) + _assert_basic_coords_correct(ar) + lags = np.arange(len(order) if isinstance(order, list) else order, dtype="int") + 1 + if isinstance(order, list): + lags = lags[np.flatnonzero(order)] + assert_allclose(ar.coords["ar_lag"], lags) + + +@pytest.mark.parametrize("s", [10, 25, 50]) +@pytest.mark.parametrize("innovations", [True, False]) +@pytest.mark.parametrize("remove_first_state", [True, False]) +@pytest.mark.filterwarnings( + "ignore:divide by zero encountered in matmul:RuntimeWarning", + "ignore:overflow encountered in matmul:RuntimeWarning", + "ignore:invalid value encountered in matmul:RuntimeWarning", +) +def test_time_seasonality(s, innovations, remove_first_state, rng): + def random_word(rng): + return "".join(rng.choice(list("abcdefghijklmnopqrstuvwxyz")) for _ in range(5)) + + state_names = [random_word(rng) for _ in range(s)] + mod = st.TimeSeasonality( + season_length=s, + innovations=innovations, + name="season", + state_names=state_names, + remove_first_state=remove_first_state, + ) + x0 = np.zeros(mod.k_states, dtype=floatX) + x0[0] = 1 + + params = {"season_coefs": x0} + if mod.innovations: + params["sigma_season"] = 0.0 + + x, y = simulate_from_numpy_model(mod, rng, params) + y = y.ravel() + if not innovations: + assert_pattern_repeats(y, s, atol=ATOL, rtol=RTOL) + + # Check coords + mod.build(verbose=False) + _assert_basic_coords_correct(mod) + test_slice = slice(1, None) if remove_first_state else slice(None) + assert mod.coords["season_state"] == state_names[test_slice] + + +def get_shift_factor(s): + s_str = str(s) + if "." not in s_str: + return 1 + _, decimal = s_str.split(".") + return 10 ** len(decimal) + + +@pytest.mark.parametrize("n", [*np.arange(1, 6, dtype="int").tolist(), None]) +@pytest.mark.parametrize("s", [5, 10, 25, 25.2]) +def test_frequency_seasonality(n, s, rng): + mod = st.FrequencySeasonality(season_length=s, n=n, name="season") + x0 = rng.normal(size=mod.n_coefs).astype(floatX) + params = {"season": x0, "sigma_season": 0.0} + k = get_shift_factor(s) + T = int(s * k) + + x, y = simulate_from_numpy_model(mod, rng, params, steps=2 * T) + assert_pattern_repeats(y, T, atol=ATOL, rtol=RTOL) + + # Check coords + mod.build(verbose=False) + _assert_basic_coords_correct(mod) + if n is None: + n = int(s // 2) + states = [f"season_{f}_{i}" for i in range(n) for f in ["Cos", "Sin"]] + + # Remove the last state when the model is completely saturated + if s / n == 2.0: + states.pop() + assert mod.coords["season_state"] == states + + +cycle_test_vals = zip([None, None, 3, 5, 10], [False, True, True, False, False]) + + +def test_cycle_component_deterministic(rng): + cycle = st.CycleComponent( + name="cycle", cycle_length=12, estimate_cycle_length=False, innovations=False + ) + params = {"cycle": np.array([1.0, 1.0], dtype=floatX)} + x, y = simulate_from_numpy_model(cycle, rng, params, steps=12 * 12) + + assert_pattern_repeats(y, 12, atol=ATOL, rtol=RTOL) + + +def test_cycle_component_with_dampening(rng): + cycle = st.CycleComponent( + name="cycle", cycle_length=12, estimate_cycle_length=False, innovations=False, dampen=True + ) + params = {"cycle": np.array([10.0, 10.0], dtype=floatX), "cycle_dampening_factor": 0.75} + x, y = simulate_from_numpy_model(cycle, rng, params, steps=100) + + # Check that the cycle dampens to zero over time + assert_allclose(y[-1], 0.0, atol=ATOL, rtol=RTOL) + + +def test_cycle_component_with_innovations_and_cycle_length(rng): + cycle = st.CycleComponent( + name="cycle", estimate_cycle_length=True, innovations=True, dampen=True + ) + params = { + "cycle": np.array([1.0, 1.0], dtype=floatX), + "cycle_length": 12.0, + "cycle_dampening_factor": 0.95, + "sigma_cycle": 1.0, + } + + x, y = simulate_from_numpy_model(cycle, rng, params) + + cycle.build(verbose=False) + _assert_basic_coords_correct(cycle) + + +def test_exogenous_component(rng): + data = rng.normal(size=(100, 2)).astype(floatX) + mod = st.RegressionComponent(state_names=["feature_1", "feature_2"], name="exog") + + params = {"beta_exog": np.array([1.0, 2.0], dtype=floatX)} + exog_data = {"data_exog": data} + x, y = simulate_from_numpy_model(mod, rng, params, exog_data) + + # Check that the generated data is just a linear regression + assert_allclose(y, data @ params["beta_exog"], atol=ATOL, rtol=RTOL) + + mod.build(verbose=False) + _assert_basic_coords_correct(mod) + assert mod.coords["exog_state"] == ["feature_1", "feature_2"] + + +def test_adding_exogenous_component(rng): + data = rng.normal(size=(100, 2)).astype(floatX) + reg = st.RegressionComponent(state_names=["a", "b"], name="exog") + ll = st.LevelTrendComponent(name="level") + + seasonal = st.FrequencySeasonality(name="annual", season_length=12, n=4) + mod = reg + ll + seasonal + + assert mod.ssm["design"].eval({"data_exog": data}).shape == (100, 1, 2 + 2 + 8) + assert_allclose(mod.ssm["design", 5, 0, :2].eval({"data_exog": data}), data[5]) + + +def test_add_components(): + ll = st.LevelTrendComponent(order=2) + se = st.TimeSeasonality(name="seasonal", season_length=12) + mod = ll + se + + ll_params = { + "initial_trend": np.zeros(2, dtype=floatX), + "sigma_trend": np.ones(2, dtype=floatX), + } + se_params = { + "seasonal_coefs": np.ones(11, dtype=floatX), + "sigma_seasonal": 1.0, + } + all_params = ll_params.copy() + all_params.update(se_params) + + (ll_x0, ll_P0, ll_c, ll_d, ll_T, ll_Z, ll_R, ll_H, ll_Q) = unpack_symbolic_matrices_with_params( + ll, ll_params + ) + (se_x0, se_P0, se_c, se_d, se_T, se_Z, se_R, se_H, se_Q) = unpack_symbolic_matrices_with_params( + se, se_params + ) + x0, P0, c, d, T, Z, R, H, Q = unpack_symbolic_matrices_with_params(mod, all_params) + + for property in ["param_names", "shock_names", "param_info", "coords", "param_dims"]: + assert [x in getattr(mod, property) for x in getattr(ll, property)] + assert [x in getattr(mod, property) for x in getattr(se, property)] + + ll_mats = [ll_T, ll_R, ll_Q] + se_mats = [se_T, se_R, se_Q] + all_mats = [T, R, Q] + + for ll_mat, se_mat, all_mat in zip(ll_mats, se_mats, all_mats): + assert_allclose(all_mat, linalg.block_diag(ll_mat, se_mat), atol=ATOL, rtol=RTOL) + + ll_mats = [ll_x0, ll_c, ll_Z] + se_mats = [se_x0, se_c, se_Z] + all_mats = [x0, c, Z] + axes = [0, 0, 1] + + for ll_mat, se_mat, all_mat, axis in zip(ll_mats, se_mats, all_mats, axes): + assert_allclose(all_mat, np.concatenate([ll_mat, se_mat], axis=axis), atol=ATOL, rtol=RTOL) + + +def test_filter_scans_time_varying_design_matrix(rng): + time_idx = pd.date_range(start="2000-01-01", freq="D", periods=100) + data = pd.DataFrame(rng.normal(size=(100, 2)), columns=["a", "b"], index=time_idx) + + y = pd.DataFrame(rng.normal(size=(100, 1)), columns=["data"], index=time_idx) + + reg = st.RegressionComponent(state_names=["a", "b"], name="exog") + mod = reg.build(verbose=False) + + with pm.Model(coords=mod.coords) as m: + data_exog = pm.Data("data_exog", data.values) + + x0 = pm.Normal("x0", dims=["state"]) + P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"]) + beta_exog = pm.Normal("beta_exog", dims=["exog_state"]) + + mod.build_statespace_graph(y) + x0, P0, c, d, T, Z, R, H, Q = mod.unpack_statespace() + pm.Deterministic("Z", Z) + + prior = pm.sample_prior_predictive(draws=10) + + prior_Z = prior.prior.Z.values + assert prior_Z.shape == (1, 10, 100, 1, 2) + assert_allclose(prior_Z[0, :, :, 0, :], data.values[None].repeat(10, axis=0)) + + +@pytest.mark.skipif(floatX.endswith("32"), reason="Prior covariance not PSD at half-precision") +def test_extract_components_from_idata(rng): + time_idx = pd.date_range(start="2000-01-01", freq="D", periods=100) + data = pd.DataFrame(rng.normal(size=(100, 2)), columns=["a", "b"], index=time_idx) + + y = pd.DataFrame(rng.normal(size=(100, 1)), columns=["data"], index=time_idx) + + ll = st.LevelTrendComponent() + season = st.FrequencySeasonality(name="seasonal", season_length=12, n=2, innovations=False) + reg = st.RegressionComponent(state_names=["a", "b"], name="exog") + me = st.MeasurementError("obs") + mod = (ll + season + reg + me).build(verbose=False) + + with pm.Model(coords=mod.coords) as m: + data_exog = pm.Data("data_exog", data.values) + + x0 = pm.Normal("x0", dims=["state"]) + P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"]) + beta_exog = pm.Normal("beta_exog", dims=["exog_state"]) + initial_trend = pm.Normal("initial_trend", dims=["trend_state"]) + sigma_trend = pm.Exponential("sigma_trend", 1, dims=["trend_shock"]) + seasonal_coefs = pm.Normal("seasonal", dims=["seasonal_state"]) + sigma_obs = pm.Exponential("sigma_obs", 1) + + mod.build_statespace_graph(y) + + x0, P0, c, d, T, Z, R, H, Q = mod.unpack_statespace() + prior = pm.sample_prior_predictive(draws=10) + + filter_prior = mod.sample_conditional_prior(prior) + comp_prior = mod.extract_components_from_idata(filter_prior) + comp_states = comp_prior.filtered_prior.coords["state"].values + expected_states = ["LevelTrend[level]", "LevelTrend[trend]", "seasonal", "exog[a]", "exog[b]"] + missing = set(comp_states) - set(expected_states) + + assert len(missing) == 0, missing From c0a4a47effa235598deca04966a24c17155389c9 Mon Sep 17 00:00:00 2001 From: Jonathan Dekermanjian Date: Sat, 5 Jul 2025 08:43:56 -0600 Subject: [PATCH 11/21] 1. removed incorrectly comitted file test_structural.py 2. replaced scipy block diag with pytensor block diag 3. Added forecast to test model in multivariate ssm notebook --- notebooks/multivariate_ssm.ipynb | 847 +++++++++++++++--- .../structural/components/regression.py | 3 +- tests/statespace/models/test_structural.py | 840 ----------------- 3 files changed, 735 insertions(+), 955 deletions(-) delete mode 100644 tests/statespace/models/test_structural.py diff --git a/notebooks/multivariate_ssm.ipynb b/notebooks/multivariate_ssm.ipynb index 83cc74e2e..dc86c5879 100644 --- a/notebooks/multivariate_ssm.ipynb +++ b/notebooks/multivariate_ssm.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 16, + "execution_count": 1, "id": "a5b7dcb3", "metadata": {}, "outputs": [], @@ -30,7 +30,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 3, "id": "a96a731b", "metadata": {}, "outputs": [], @@ -110,7 +110,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 4, "id": "a4130131", "metadata": {}, "outputs": [], @@ -135,13 +135,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "5e9acbb8", "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -177,7 +177,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 6, "id": "d51ff06e", "metadata": {}, "outputs": [ @@ -237,7 +237,7 @@ }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 7, "id": "eec30de3", "metadata": {}, "outputs": [ @@ -273,7 +273,7 @@ }, { "cell_type": "code", - "execution_count": 106, + "execution_count": 8, "id": "05830b2b", "metadata": {}, "outputs": [ @@ -387,7 +387,7 @@ " Finished Chains:\n", " 4\n", "

\n", - "

Sampling for 42 seconds

\n", + "

Sampling for 43 seconds

\n", "

\n", " Estimated Time to Completion:\n", " now\n", @@ -418,7 +418,7 @@ " \n", " \n", " 2000\n", - " 10\n", + " 4\n", " 0.52\n", " 7\n", " \n", @@ -431,8 +431,8 @@ " \n", " \n", " 2000\n", - " 10\n", - " 0.52\n", + " 5\n", + " 0.51\n", " 7\n", " \n", " \n", @@ -444,8 +444,8 @@ " \n", " \n", " 2000\n", - " 87\n", - " 0.53\n", + " 6\n", + " 0.55\n", " 7\n", " \n", " \n", @@ -457,8 +457,8 @@ " \n", " \n", " 2000\n", - " 4\n", - " 0.52\n", + " 13\n", + " 0.53\n", " 7\n", " \n", " \n", @@ -468,7 +468,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -484,7 +484,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 9, "id": "466fb92a", "metadata": {}, "outputs": [ @@ -524,110 +524,110 @@ " \n", " beta_exog[y1, x1]\n", " 2.947\n", - " 0.729\n", - " 1.433\n", - " 4.278\n", - " 0.016\n", - " 0.014\n", - " 2232.0\n", - " 2077.0\n", - " 1.00\n", + " 0.708\n", + " 1.603\n", + " 4.324\n", + " 0.012\n", + " 0.015\n", + " 3842.0\n", + " 2349.0\n", + " 1.0\n", " \n", " \n", " beta_exog[y1, x2]\n", - " -0.991\n", - " 0.681\n", - " -2.270\n", - " 0.314\n", + " -0.984\n", + " 0.711\n", + " -2.443\n", + " 0.335\n", " 0.012\n", - " 0.014\n", - " 3102.0\n", - " 2334.0\n", - " 1.00\n", + " 0.015\n", + " 3976.0\n", + " 2520.0\n", + " 1.0\n", " \n", " \n", " beta_exog[y2, x1]\n", - " 3.007\n", - " 0.713\n", - " 1.637\n", - " 4.356\n", - " 0.015\n", - " 0.015\n", - " 2152.0\n", - " 2107.0\n", - " 1.00\n", + " 2.984\n", + " 0.732\n", + " 1.580\n", + " 4.441\n", + " 0.014\n", + " 0.016\n", + " 2873.0\n", + " 1903.0\n", + " 1.0\n", " \n", " \n", " beta_exog[y2, x2]\n", - " -1.047\n", - " 0.714\n", - " -2.471\n", - " 0.408\n", - " 0.014\n", - " 0.019\n", - " 3007.0\n", - " 1644.0\n", - " 1.00\n", + " -1.040\n", + " 0.719\n", + " -2.241\n", + " 0.519\n", + " 0.013\n", + " 0.016\n", + " 3148.0\n", + " 2390.0\n", + " 1.0\n", " \n", " \n", " beta_exog[y3, x1]\n", - " 2.800\n", - " 0.712\n", - " 1.438\n", - " 4.165\n", + " 2.776\n", + " 0.711\n", + " 1.423\n", + " 4.204\n", " 0.013\n", - " 0.019\n", - " 3473.0\n", - " 2458.0\n", - " 1.00\n", + " 0.015\n", + " 3406.0\n", + " 2475.0\n", + " 1.0\n", " \n", " \n", " beta_exog[y3, x2]\n", - " -0.934\n", - " 0.675\n", - " -2.184\n", - " 0.426\n", + " -0.946\n", + " 0.691\n", + " -2.254\n", + " 0.386\n", " 0.013\n", - " 0.014\n", - " 3017.0\n", - " 2326.0\n", - " 1.00\n", + " 0.017\n", + " 3252.0\n", + " 2249.0\n", + " 1.0\n", " \n", " \n", " sigma_trend[y1, level]\n", - " 0.757\n", - " 0.057\n", - " 0.657\n", - " 0.871\n", - " 0.002\n", - " 0.002\n", - " 878.0\n", - " 367.0\n", - " 1.01\n", + " 0.755\n", + " 0.055\n", + " 0.654\n", + " 0.858\n", + " 0.001\n", + " 0.001\n", + " 6764.0\n", + " 3005.0\n", + " 1.0\n", " \n", " \n", " sigma_trend[y2, level]\n", - " 0.927\n", - " 0.068\n", - " 0.810\n", - " 1.062\n", - " 0.002\n", + " 0.926\n", + " 0.067\n", + " 0.799\n", + " 1.051\n", + " 0.001\n", " 0.001\n", - " 1846.0\n", - " 1633.0\n", - " 1.00\n", + " 6923.0\n", + " 2801.0\n", + " 1.0\n", " \n", " \n", " sigma_trend[y3, level]\n", - " 0.847\n", - " 0.062\n", - " 0.733\n", - " 0.961\n", + " 0.848\n", + " 0.063\n", + " 0.738\n", + " 0.975\n", " 0.001\n", " 0.001\n", - " 2009.0\n", - " 2855.0\n", - " 1.00\n", + " 6697.0\n", + " 2873.0\n", + " 1.0\n", " \n", " \n", "\n", @@ -635,29 +635,29 @@ ], "text/plain": [ " mean sd hdi_3% hdi_97% mcse_mean mcse_sd \\\n", - "beta_exog[y1, x1] 2.947 0.729 1.433 4.278 0.016 0.014 \n", - "beta_exog[y1, x2] -0.991 0.681 -2.270 0.314 0.012 0.014 \n", - "beta_exog[y2, x1] 3.007 0.713 1.637 4.356 0.015 0.015 \n", - "beta_exog[y2, x2] -1.047 0.714 -2.471 0.408 0.014 0.019 \n", - "beta_exog[y3, x1] 2.800 0.712 1.438 4.165 0.013 0.019 \n", - "beta_exog[y3, x2] -0.934 0.675 -2.184 0.426 0.013 0.014 \n", - "sigma_trend[y1, level] 0.757 0.057 0.657 0.871 0.002 0.002 \n", - "sigma_trend[y2, level] 0.927 0.068 0.810 1.062 0.002 0.001 \n", - "sigma_trend[y3, level] 0.847 0.062 0.733 0.961 0.001 0.001 \n", + "beta_exog[y1, x1] 2.947 0.708 1.603 4.324 0.012 0.015 \n", + "beta_exog[y1, x2] -0.984 0.711 -2.443 0.335 0.012 0.015 \n", + "beta_exog[y2, x1] 2.984 0.732 1.580 4.441 0.014 0.016 \n", + "beta_exog[y2, x2] -1.040 0.719 -2.241 0.519 0.013 0.016 \n", + "beta_exog[y3, x1] 2.776 0.711 1.423 4.204 0.013 0.015 \n", + "beta_exog[y3, x2] -0.946 0.691 -2.254 0.386 0.013 0.017 \n", + "sigma_trend[y1, level] 0.755 0.055 0.654 0.858 0.001 0.001 \n", + "sigma_trend[y2, level] 0.926 0.067 0.799 1.051 0.001 0.001 \n", + "sigma_trend[y3, level] 0.848 0.063 0.738 0.975 0.001 0.001 \n", "\n", " ess_bulk ess_tail r_hat \n", - "beta_exog[y1, x1] 2232.0 2077.0 1.00 \n", - "beta_exog[y1, x2] 3102.0 2334.0 1.00 \n", - "beta_exog[y2, x1] 2152.0 2107.0 1.00 \n", - "beta_exog[y2, x2] 3007.0 1644.0 1.00 \n", - "beta_exog[y3, x1] 3473.0 2458.0 1.00 \n", - "beta_exog[y3, x2] 3017.0 2326.0 1.00 \n", - "sigma_trend[y1, level] 878.0 367.0 1.01 \n", - "sigma_trend[y2, level] 1846.0 1633.0 1.00 \n", - "sigma_trend[y3, level] 2009.0 2855.0 1.00 " + "beta_exog[y1, x1] 3842.0 2349.0 1.0 \n", + "beta_exog[y1, x2] 3976.0 2520.0 1.0 \n", + "beta_exog[y2, x1] 2873.0 1903.0 1.0 \n", + "beta_exog[y2, x2] 3148.0 2390.0 1.0 \n", + "beta_exog[y3, x1] 3406.0 2475.0 1.0 \n", + "beta_exog[y3, x2] 3252.0 2249.0 1.0 \n", + "sigma_trend[y1, level] 6764.0 3005.0 1.0 \n", + "sigma_trend[y2, level] 6923.0 2801.0 1.0 \n", + "sigma_trend[y3, level] 6697.0 2873.0 1.0 " ] }, - "execution_count": 107, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -668,13 +668,13 @@ }, { "cell_type": "code", - "execution_count": 108, + "execution_count": 10, "id": "3684616b", "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "

" ] @@ -696,6 +696,627 @@ ");" ] }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7dc5d11b", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No start date provided. Using the last date in the data index. To silence this warning, explicitly pass a start date or set verbose = False\n", + "/Users/dekermanjian/Desktop/Open_Source_Contributions/pymc-extras/pymc_extras/statespace/utils/data_tools.py:74: UserWarning: No time index found on the supplied data. A simple range index will be automatically generated.\n", + " warnings.warn(NO_TIME_INDEX_WARNING)\n", + "/opt/miniconda3/envs/pymc-extras-test/lib/python3.12/site-packages/pytensor/link/jax/linker.py:32: UserWarning: The RandomType SharedVariables [RNG()] will not be used in the compiled JAX graph. Instead a copy will be used.\n", + " warnings.warn(\n", + "Sampling: [forecast_combined]\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fe28f382d9e04444ac804b184abdd524", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
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+      ],
+      "text/plain": []
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "
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+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "\n",
+       "
<xarray.Dataset> Size: 4MB\n",
+       "Dimensions:            (chain: 4, draw: 1000, time: 10, state: 9,\n",
+       "                        observed_state: 3)\n",
+       "Coordinates:\n",
+       "  * chain              (chain) int64 32B 0 1 2 3\n",
+       "  * draw               (draw) int64 8kB 0 1 2 3 4 5 ... 994 995 996 997 998 999\n",
+       "  * time               (time) int64 80B 100 101 102 103 104 105 106 107 108 109\n",
+       "  * state              (state) <U9 324B 'level[y1]' 'level[y2]' ... 'x2[y3]'\n",
+       "  * observed_state     (observed_state) <U2 24B 'y1' 'y2' 'y3'\n",
+       "Data variables:\n",
+       "    forecast_latent    (chain, draw, time, state) float64 3MB 2.679 ... -1.022\n",
+       "    forecast_observed  (chain, draw, time, observed_state) float64 960kB 1.81...\n",
+       "Attributes:\n",
+       "    created_at:                 2025-07-05T14:42:22.808692+00:00\n",
+       "    arviz_version:              0.21.0\n",
+       "    inference_library:          pymc\n",
+       "    inference_library_version:  5.23.0
"
+      ],
+      "text/plain": [
+       " Size: 4MB\n",
+       "Dimensions:            (chain: 4, draw: 1000, time: 10, state: 9,\n",
+       "                        observed_state: 3)\n",
+       "Coordinates:\n",
+       "  * chain              (chain) int64 32B 0 1 2 3\n",
+       "  * draw               (draw) int64 8kB 0 1 2 3 4 5 ... 994 995 996 997 998 999\n",
+       "  * time               (time) int64 80B 100 101 102 103 104 105 106 107 108 109\n",
+       "  * state              (state)  None:
 
         self.ssm["initial_state", :] = betas.reshape((1, -1)).squeeze()
         T = np.eye(k_states)
-        self.ssm["transition", :, :] = linalg.block_diag(*[T for _ in range(k_endog)])
+        self.ssm["transition", :, :] = pt.linalg.block_diag(*[T for _ in range(k_endog)])
         self.ssm["selection", :, :] = np.eye(self.k_states)
         Z = pt.linalg.block_diag(*[pt.expand_dims(regression_data, 1) for _ in range(k_endog)])
         self.ssm["design"] = pt.specify_shape(
diff --git a/tests/statespace/models/test_structural.py b/tests/statespace/models/test_structural.py
deleted file mode 100644
index 1662e164a..000000000
--- a/tests/statespace/models/test_structural.py
+++ /dev/null
@@ -1,840 +0,0 @@
-import functools as ft
-import warnings
-
-from collections import defaultdict
-
-import numpy as np
-import pandas as pd
-import pymc as pm
-import pytensor
-import pytensor.tensor as pt
-import pytest
-import statsmodels.api as sm
-
-from numpy.testing import assert_allclose
-from scipy import linalg
-
-from pymc_extras.statespace import structural as st
-from pymc_extras.statespace.utils.constants import (
-    ALL_STATE_AUX_DIM,
-    ALL_STATE_DIM,
-    AR_PARAM_DIM,
-    OBS_STATE_AUX_DIM,
-    OBS_STATE_DIM,
-    SHOCK_AUX_DIM,
-    SHOCK_DIM,
-    SHORT_NAME_TO_LONG,
-)
-from tests.statespace.shared_fixtures import (  # pylint: disable=unused-import
-    rng,
-)
-from tests.statespace.test_utilities import (
-    assert_pattern_repeats,
-    simulate_from_numpy_model,
-    unpack_symbolic_matrices_with_params,
-)
-
-floatX = pytensor.config.floatX
-ATOL = 1e-8 if floatX.endswith("64") else 1e-4
-RTOL = 0 if floatX.endswith("64") else 1e-6
-
-
-def _assert_all_statespace_matrices_match(mod, params, sm_mod):
-    x0, P0, c, d, T, Z, R, H, Q = unpack_symbolic_matrices_with_params(mod, params)
-
-    sm_x0, sm_H0, sm_P0 = sm_mod.initialization()
-
-    if len(x0) > 0:
-        assert_allclose(x0, sm_x0)
-
-    for name, matrix in zip(["T", "R", "Z", "Q"], [T, R, Z, Q]):
-        long_name = SHORT_NAME_TO_LONG[name]
-        if np.any([x == 0 for x in matrix.shape]):
-            continue
-        assert_allclose(
-            sm_mod.ssm[long_name],
-            matrix,
-            err_msg=f"matrix {name} does not match statsmodels",
-            atol=ATOL,
-            rtol=RTOL,
-        )
-
-
-def _assert_coord_shapes_match_matrices(mod, params):
-    if "initial_state_cov" not in params:
-        params["initial_state_cov"] = np.eye(mod.k_states)
-
-    x0, P0, c, d, T, Z, R, H, Q = unpack_symbolic_matrices_with_params(mod, params)
-
-    n_states = len(mod.coords[ALL_STATE_DIM])
-
-    # There will always be one shock dimension -- dummies are inserted into fully deterministic models to avoid errors
-    # in the state space representation.
-    n_shocks = max(1, len(mod.coords[SHOCK_DIM]))
-    n_obs = len(mod.coords[OBS_STATE_DIM])
-
-    assert x0.shape[-1:] == (
-        n_states,
-    ), f"x0 expected to have shape (n_states, ), found {x0.shape[-1:]}"
-    assert P0.shape[-2:] == (
-        n_states,
-        n_states,
-    ), f"P0 expected to have shape (n_states, n_states), found {P0.shape[-2:]}"
-    assert c.shape[-1:] == (
-        n_states,
-    ), f"c expected to have shape (n_states, ), found {c.shape[-1:]}"
-    assert d.shape[-1:] == (n_obs,), f"d expected to have shape (n_obs, ), found {d.shape[-1:]}"
-    assert T.shape[-2:] == (
-        n_states,
-        n_states,
-    ), f"T expected to have shape (n_states, n_states), found {T.shape[-2:]}"
-    assert Z.shape[-2:] == (
-        n_obs,
-        n_states,
-    ), f"Z expected to have shape (n_obs, n_states), found {Z.shape[-2:]}"
-    assert R.shape[-2:] == (
-        n_states,
-        n_shocks,
-    ), f"R expected to have shape (n_states, n_shocks), found {R.shape[-2:]}"
-    assert H.shape[-2:] == (
-        n_obs,
-        n_obs,
-    ), f"H expected to have shape (n_obs, n_obs), found {H.shape[-2:]}"
-    assert Q.shape[-2:] == (
-        n_shocks,
-        n_shocks,
-    ), f"Q expected to have shape (n_shocks, n_shocks), found {Q.shape[-2:]}"
-
-
-def _assert_basic_coords_correct(mod):
-    assert mod.coords[ALL_STATE_DIM] == mod.state_names
-    assert mod.coords[ALL_STATE_AUX_DIM] == mod.state_names
-    assert mod.coords[SHOCK_DIM] == mod.shock_names
-    assert mod.coords[SHOCK_AUX_DIM] == mod.shock_names
-    assert mod.coords[OBS_STATE_DIM] == ["data"]
-    assert mod.coords[OBS_STATE_AUX_DIM] == ["data"]
-
-
-def _assert_keys_match(test_dict, expected_dict):
-    expected_keys = list(expected_dict.keys())
-    param_keys = list(test_dict.keys())
-    key_diff = set(expected_keys) - set(param_keys)
-    assert len(key_diff) == 0, f'{", ".join(key_diff)} were not found in the test_dict keys.'
-
-    key_diff = set(param_keys) - set(expected_keys)
-    assert (
-        len(key_diff) == 0
-    ), f'{", ".join(key_diff)} were keys of the tests_dict not in expected_dict.'
-
-
-def _assert_param_dims_correct(param_dims, expected_dims):
-    if len(expected_dims) == 0 and len(param_dims) == 0:
-        return
-
-    _assert_keys_match(param_dims, expected_dims)
-    for param, dims in expected_dims.items():
-        assert dims == param_dims[param], f"dims for parameter {param} do not match"
-
-
-def _assert_coords_correct(coords, expected_coords):
-    if len(coords) == 0 and len(expected_coords) == 0:
-        return
-
-    _assert_keys_match(coords, expected_coords)
-    for dim, labels in expected_coords.items():
-        assert labels == coords[dim], f"labels on dimension {dim} do not match"
-
-
-def _assert_params_info_correct(param_info, coords, param_dims):
-    for param in param_info.keys():
-        info = param_info[param]
-
-        dims = info["dims"]
-        labels = [coords[dim] for dim in dims] if dims is not None else None
-        if labels is not None:
-            assert param in param_dims.keys()
-            inferred_dims = param_dims[param]
-        else:
-            inferred_dims = None
-
-        shape = tuple(len(label) for label in labels) if labels is not None else ()
-
-        assert info["shape"] == shape
-        assert dims == inferred_dims
-
-
-def create_structural_model_and_equivalent_statsmodel(
-    rng,
-    level: bool | None = False,
-    trend: bool | None = False,
-    seasonal: int | None = None,
-    freq_seasonal: list[dict] | None = None,
-    cycle: bool = False,
-    autoregressive: int | None = None,
-    exog: np.ndarray | None = None,
-    irregular: bool | None = False,
-    stochastic_level: bool | None = True,
-    stochastic_trend: bool | None = False,
-    stochastic_seasonal: bool | None = True,
-    stochastic_freq_seasonal: list[bool] | None = None,
-    stochastic_cycle: bool | None = False,
-    damped_cycle: bool | None = False,
-):
-    with warnings.catch_warnings():
-        warnings.simplefilter("ignore")
-        mod = ft.partial(
-            sm.tsa.UnobservedComponents,
-            level=level,
-            trend=trend,
-            seasonal=seasonal,
-            freq_seasonal=freq_seasonal,
-            cycle=cycle,
-            autoregressive=autoregressive,
-            exog=exog,
-            irregular=irregular,
-            stochastic_level=stochastic_level,
-            stochastic_trend=stochastic_trend,
-            stochastic_seasonal=stochastic_seasonal,
-            stochastic_freq_seasonal=stochastic_freq_seasonal,
-            stochastic_cycle=stochastic_cycle,
-            damped_cycle=damped_cycle,
-            mle_regression=False,
-        )
-
-    params = {}
-    sm_params = {}
-    sm_init = {}
-    expected_param_dims = defaultdict(tuple)
-    expected_coords = defaultdict(list)
-    expected_param_dims["P0"] += ("state", "state_aux")
-
-    default_states = [
-        ALL_STATE_DIM,
-        ALL_STATE_AUX_DIM,
-        OBS_STATE_DIM,
-        OBS_STATE_AUX_DIM,
-        SHOCK_DIM,
-        SHOCK_AUX_DIM,
-    ]
-    default_values = [[], [], ["data"], ["data"], [], []]
-    for dim, value in zip(default_states, default_values):
-        expected_coords[dim] += value
-
-    components = []
-
-    if irregular:
-        sigma2 = np.abs(rng.normal()).astype(floatX).item()
-        params["sigma_irregular"] = np.sqrt(sigma2)
-        sm_params["sigma2.irregular"] = sigma2
-
-        comp = st.MeasurementError("irregular")
-        components.append(comp)
-
-    level_trend_order = [0, 0]
-    level_trend_innov_order = [0, 0]
-
-    if level:
-        level_trend_order[0] = 1
-        expected_coords["trend_state"] += [
-            "level",
-        ]
-        expected_coords[ALL_STATE_DIM] += [
-            "level",
-        ]
-        expected_coords[ALL_STATE_AUX_DIM] += [
-            "level",
-        ]
-        if stochastic_level:
-            level_trend_innov_order[0] = 1
-            expected_coords["trend_shock"] += ["level"]
-            expected_coords[SHOCK_DIM] += [
-                "level",
-            ]
-            expected_coords[SHOCK_AUX_DIM] += [
-                "level",
-            ]
-
-    if trend:
-        level_trend_order[1] = 1
-        expected_coords["trend_state"] += [
-            "trend",
-        ]
-        expected_coords[ALL_STATE_DIM] += [
-            "trend",
-        ]
-        expected_coords[ALL_STATE_AUX_DIM] += [
-            "trend",
-        ]
-
-        if stochastic_trend:
-            level_trend_innov_order[1] = 1
-            expected_coords["trend_shock"] += ["trend"]
-            expected_coords[SHOCK_DIM] += ["trend"]
-            expected_coords[SHOCK_AUX_DIM] += ["trend"]
-
-    if level or trend:
-        expected_param_dims["initial_trend"] += ("trend_state",)
-        level_value = np.where(
-            level_trend_order,
-            rng.normal(
-                size=2,
-            ).astype(floatX),
-            np.zeros(2, dtype=floatX),
-        )
-        sigma_level_value2 = np.abs(rng.normal(size=(2,)))[
-            np.array(level_trend_innov_order, dtype="bool")
-        ]
-        max_order = np.flatnonzero(level_value)[-1].item() + 1
-        level_trend_order = level_trend_order[:max_order]
-
-        params["initial_trend"] = level_value[:max_order]
-        sm_init["level"] = level_value[0]
-        sm_init["trend"] = level_value[1]
-
-        if sum(level_trend_innov_order) > 0:
-            expected_param_dims["sigma_trend"] += ("trend_shock",)
-            params["sigma_trend"] = np.sqrt(sigma_level_value2)
-
-        sigma_level_value = sigma_level_value2.tolist()
-        if stochastic_level:
-            sigma = sigma_level_value.pop(0)
-            sm_params["sigma2.level"] = sigma
-        if stochastic_trend:
-            sigma = sigma_level_value.pop(0)
-            sm_params["sigma2.trend"] = sigma
-
-        comp = st.LevelTrendComponent(
-            name="level", order=level_trend_order, innovations_order=level_trend_innov_order
-        )
-        components.append(comp)
-
-    if seasonal is not None:
-        state_names = [f"seasonal_{i}" for i in range(seasonal)][1:]
-        seasonal_coefs = rng.normal(size=(seasonal - 1,)).astype(floatX)
-        params["seasonal_coefs"] = seasonal_coefs
-        expected_param_dims["seasonal_coefs"] += ("seasonal_state",)
-
-        expected_coords["seasonal_state"] += tuple(state_names)
-        expected_coords[ALL_STATE_DIM] += state_names
-        expected_coords[ALL_STATE_AUX_DIM] += state_names
-
-        seasonal_dict = {
-            "seasonal" if i == 0 else f"seasonal.L{i}": c for i, c in enumerate(seasonal_coefs)
-        }
-        sm_init.update(seasonal_dict)
-
-        if stochastic_seasonal:
-            sigma2 = np.abs(rng.normal()).astype(floatX)
-            params["sigma_seasonal"] = np.sqrt(sigma2)
-            sm_params["sigma2.seasonal"] = sigma2
-            expected_coords[SHOCK_DIM] += [
-                "seasonal",
-            ]
-            expected_coords[SHOCK_AUX_DIM] += [
-                "seasonal",
-            ]
-
-        comp = st.TimeSeasonality(
-            name="seasonal", season_length=seasonal, innovations=stochastic_seasonal
-        )
-        components.append(comp)
-
-    if freq_seasonal is not None:
-        state_count = 0
-        for d, has_innov in zip(freq_seasonal, stochastic_freq_seasonal):
-            n = d["harmonics"]
-            s = d["period"]
-            last_state_not_identified = (s / n) == 2.0
-            n_states = 2 * n - int(last_state_not_identified)
-            state_names = [f"seasonal_{s}_{f}_{i}" for i in range(n) for f in ["Cos", "Sin"]]
-
-            seasonal_params = rng.normal(size=n_states).astype(floatX)
-
-            params[f"seasonal_{s}"] = seasonal_params
-            expected_param_dims[f"seasonal_{s}"] += (f"seasonal_{s}_state",)
-            expected_coords[ALL_STATE_DIM] += state_names
-            expected_coords[ALL_STATE_AUX_DIM] += state_names
-            expected_coords[f"seasonal_{s}_state"] += (
-                tuple(state_names[:-1]) if last_state_not_identified else tuple(state_names)
-            )
-
-            for param in seasonal_params:
-                sm_init[f"freq_seasonal.{state_count}"] = param
-                state_count += 1
-            if last_state_not_identified:
-                sm_init[f"freq_seasonal.{state_count}"] = 0.0
-                state_count += 1
-
-            if has_innov:
-                sigma2 = np.abs(rng.normal()).astype(floatX)
-                params[f"sigma_seasonal_{s}"] = np.sqrt(sigma2)
-                sm_params[f"sigma2.freq_seasonal_{s}({n})"] = sigma2
-                expected_coords[SHOCK_DIM] += state_names
-                expected_coords[SHOCK_AUX_DIM] += state_names
-
-            comp = st.FrequencySeasonality(
-                name=f"seasonal_{s}", season_length=s, n=n, innovations=has_innov
-            )
-            components.append(comp)
-
-    if cycle:
-        cycle_length = np.random.choice(np.arange(2, 12)).astype(floatX)
-
-        # Statsmodels takes the frequency not the cycle length, so convert it.
-        sm_params["frequency.cycle"] = 2.0 * np.pi / cycle_length
-        params["cycle_length"] = cycle_length
-
-        init_cycle = rng.normal(size=(2,)).astype(floatX)
-        params["cycle"] = init_cycle
-        expected_param_dims["cycle"] += ("cycle_state",)
-
-        state_names = ["cycle_Cos", "cycle_Sin"]
-        expected_coords["cycle_state"] += state_names
-        expected_coords[ALL_STATE_DIM] += state_names
-        expected_coords[ALL_STATE_AUX_DIM] += state_names
-
-        sm_init["cycle"] = init_cycle[0]
-        sm_init["cycle.auxilliary"] = init_cycle[1]
-
-        if stochastic_cycle:
-            sigma2 = np.abs(rng.normal()).astype(floatX)
-            params["sigma_cycle"] = np.sqrt(sigma2)
-            expected_coords[SHOCK_DIM] += state_names
-            expected_coords[SHOCK_AUX_DIM] += state_names
-
-            sm_params["sigma2.cycle"] = sigma2
-
-        if damped_cycle:
-            rho = rng.beta(1, 1)
-            params["cycle_dampening_factor"] = rho
-            sm_params["damping.cycle"] = rho
-
-        comp = st.CycleComponent(
-            name="cycle",
-            dampen=damped_cycle,
-            innovations=stochastic_cycle,
-            estimate_cycle_length=True,
-        )
-
-        components.append(comp)
-
-    if autoregressive is not None:
-        ar_names = [f"L{i+1}.data" for i in range(autoregressive)]
-        ar_params = rng.normal(size=(autoregressive,)).astype(floatX)
-        if autoregressive == 1:
-            ar_params = ar_params.item()
-        sigma2 = np.abs(rng.normal()).astype(floatX)
-
-        params["ar_params"] = ar_params
-        params["sigma_ar"] = np.sqrt(sigma2)
-        expected_param_dims["ar_params"] += (AR_PARAM_DIM,)
-        expected_coords[AR_PARAM_DIM] += tuple(list(range(1, autoregressive + 1)))
-        expected_coords[ALL_STATE_DIM] += ar_names
-        expected_coords[ALL_STATE_AUX_DIM] += ar_names
-        expected_coords[SHOCK_DIM] += ["ar_innovation"]
-        expected_coords[SHOCK_AUX_DIM] += ["ar_innovation"]
-
-        sm_params["sigma2.ar"] = sigma2
-        for i, rho in enumerate(ar_params):
-            sm_init[f"ar.L{i+1}"] = 0
-            sm_params[f"ar.L{i+1}"] = rho
-
-        comp = st.AutoregressiveComponent(name="ar", order=autoregressive)
-        components.append(comp)
-
-    if exog is not None:
-        names = [f"x{i + 1}" for i in range(exog.shape[1])]
-        betas = rng.normal(size=(exog.shape[1],)).astype(floatX)
-        params["beta_exog"] = betas
-        params["data_exog"] = exog
-        expected_param_dims["beta_exog"] += ("exog_state",)
-        expected_param_dims["data_exog"] += ("time", "exog_data")
-
-        expected_coords["exog_state"] += tuple(names)
-
-        for i, beta in enumerate(betas):
-            sm_params[f"beta.x{i + 1}"] = beta
-            sm_init[f"beta.x{i+1}"] = beta
-        comp = st.RegressionComponent(name="exog", state_names=names)
-        components.append(comp)
-
-    st_mod = components.pop(0)
-    for comp in components:
-        st_mod += comp
-    return mod, st_mod, params, sm_params, sm_init, expected_param_dims, expected_coords
-
-
-@pytest.mark.parametrize(
-    "level, trend, stochastic_level, stochastic_trend, irregular",
-    [
-        (False, False, False, False, True),
-        (True, True, True, True, True),
-        (True, True, False, True, False),
-    ],
-)
-@pytest.mark.parametrize("autoregressive", [None, 3])
-@pytest.mark.parametrize("seasonal, stochastic_seasonal", [(None, False), (12, False), (12, True)])
-@pytest.mark.parametrize(
-    "freq_seasonal, stochastic_freq_seasonal",
-    [
-        (None, None),
-        ([{"period": 12, "harmonics": 2}], [False]),
-        ([{"period": 12, "harmonics": 6}], [True]),
-    ],
-)
-@pytest.mark.parametrize(
-    "cycle, damped_cycle, stochastic_cycle",
-    [(False, False, False), (True, False, True), (True, True, True)],
-)
-@pytest.mark.filterwarnings("ignore::statsmodels.tools.sm_exceptions.ConvergenceWarning")
-@pytest.mark.filterwarnings("ignore::statsmodels.tools.sm_exceptions.SpecificationWarning")
-def test_structural_model_against_statsmodels(
-    level,
-    trend,
-    stochastic_level,
-    stochastic_trend,
-    irregular,
-    autoregressive,
-    seasonal,
-    stochastic_seasonal,
-    freq_seasonal,
-    stochastic_freq_seasonal,
-    cycle,
-    damped_cycle,
-    stochastic_cycle,
-    rng,
-):
-    retvals = create_structural_model_and_equivalent_statsmodel(
-        rng,
-        level=level,
-        trend=trend,
-        seasonal=seasonal,
-        freq_seasonal=freq_seasonal,
-        cycle=cycle,
-        damped_cycle=damped_cycle,
-        autoregressive=autoregressive,
-        irregular=irregular,
-        stochastic_level=stochastic_level,
-        stochastic_trend=stochastic_trend,
-        stochastic_seasonal=stochastic_seasonal,
-        stochastic_freq_seasonal=stochastic_freq_seasonal,
-        stochastic_cycle=stochastic_cycle,
-    )
-    f_sm_mod, mod, params, sm_params, sm_init, expected_dims, expected_coords = retvals
-
-    data = rng.normal(size=(100,)).astype(floatX)
-    sm_mod = f_sm_mod(data)
-
-    if len(sm_init) > 0:
-        init_array = np.concatenate(
-            [np.atleast_1d(sm_init[k]).ravel() for k in sm_mod.state_names if k != "dummy"]
-        )
-        sm_mod.initialize_known(init_array, np.eye(sm_mod.k_states))
-    else:
-        sm_mod.initialize_default()
-
-    if len(sm_params) > 0:
-        param_array = np.concatenate(
-            [np.atleast_1d(sm_params[k]).ravel() for k in sm_mod.param_names]
-        )
-        sm_mod.update(param_array, transformed=True)
-
-    _assert_all_statespace_matrices_match(mod, params, sm_mod)
-
-    built_model = mod.build(verbose=False, mode="FAST_RUN")
-    assert built_model.mode == "FAST_RUN"
-
-    _assert_coord_shapes_match_matrices(built_model, params)
-    _assert_param_dims_correct(built_model.param_dims, expected_dims)
-    _assert_coords_correct(built_model.coords, expected_coords)
-    _assert_params_info_correct(built_model.param_info, built_model.coords, built_model.param_dims)
-
-
-def test_level_trend_model(rng):
-    mod = st.LevelTrendComponent(order=2, innovations_order=0)
-    params = {"initial_trend": [0.0, 1.0]}
-    x, y = simulate_from_numpy_model(mod, rng, params)
-
-    assert_allclose(np.diff(y), 1, atol=ATOL, rtol=RTOL)
-
-    # Check coords
-    mod = mod.build(verbose=False)
-    _assert_basic_coords_correct(mod)
-    assert mod.coords["trend_state"] == ["level", "trend"]
-
-
-def test_measurement_error(rng):
-    mod = st.MeasurementError("obs") + st.LevelTrendComponent(order=2)
-    mod = mod.build(verbose=False)
-
-    _assert_basic_coords_correct(mod)
-    assert "sigma_obs" in mod.param_names
-
-
-@pytest.mark.parametrize("order", [1, 2, [1, 0, 1]], ids=["AR1", "AR2", "AR(1,0,1)"])
-def test_autoregressive_model(order, rng):
-    ar = st.AutoregressiveComponent(order=order)
-    params = {
-        "ar_params": np.full((sum(ar.order),), 0.5, dtype=floatX),
-        "sigma_ar": 0.0,
-    }
-
-    x, y = simulate_from_numpy_model(ar, rng, params, steps=100)
-
-    # Check coords
-    ar.build(verbose=False)
-    _assert_basic_coords_correct(ar)
-    lags = np.arange(len(order) if isinstance(order, list) else order, dtype="int") + 1
-    if isinstance(order, list):
-        lags = lags[np.flatnonzero(order)]
-    assert_allclose(ar.coords["ar_lag"], lags)
-
-
-@pytest.mark.parametrize("s", [10, 25, 50])
-@pytest.mark.parametrize("innovations", [True, False])
-@pytest.mark.parametrize("remove_first_state", [True, False])
-@pytest.mark.filterwarnings(
-    "ignore:divide by zero encountered in matmul:RuntimeWarning",
-    "ignore:overflow encountered in matmul:RuntimeWarning",
-    "ignore:invalid value encountered in matmul:RuntimeWarning",
-)
-def test_time_seasonality(s, innovations, remove_first_state, rng):
-    def random_word(rng):
-        return "".join(rng.choice(list("abcdefghijklmnopqrstuvwxyz")) for _ in range(5))
-
-    state_names = [random_word(rng) for _ in range(s)]
-    mod = st.TimeSeasonality(
-        season_length=s,
-        innovations=innovations,
-        name="season",
-        state_names=state_names,
-        remove_first_state=remove_first_state,
-    )
-    x0 = np.zeros(mod.k_states, dtype=floatX)
-    x0[0] = 1
-
-    params = {"season_coefs": x0}
-    if mod.innovations:
-        params["sigma_season"] = 0.0
-
-    x, y = simulate_from_numpy_model(mod, rng, params)
-    y = y.ravel()
-    if not innovations:
-        assert_pattern_repeats(y, s, atol=ATOL, rtol=RTOL)
-
-    # Check coords
-    mod.build(verbose=False)
-    _assert_basic_coords_correct(mod)
-    test_slice = slice(1, None) if remove_first_state else slice(None)
-    assert mod.coords["season_state"] == state_names[test_slice]
-
-
-def get_shift_factor(s):
-    s_str = str(s)
-    if "." not in s_str:
-        return 1
-    _, decimal = s_str.split(".")
-    return 10 ** len(decimal)
-
-
-@pytest.mark.parametrize("n", [*np.arange(1, 6, dtype="int").tolist(), None])
-@pytest.mark.parametrize("s", [5, 10, 25, 25.2])
-def test_frequency_seasonality(n, s, rng):
-    mod = st.FrequencySeasonality(season_length=s, n=n, name="season")
-    x0 = rng.normal(size=mod.n_coefs).astype(floatX)
-    params = {"season": x0, "sigma_season": 0.0}
-    k = get_shift_factor(s)
-    T = int(s * k)
-
-    x, y = simulate_from_numpy_model(mod, rng, params, steps=2 * T)
-    assert_pattern_repeats(y, T, atol=ATOL, rtol=RTOL)
-
-    # Check coords
-    mod.build(verbose=False)
-    _assert_basic_coords_correct(mod)
-    if n is None:
-        n = int(s // 2)
-    states = [f"season_{f}_{i}" for i in range(n) for f in ["Cos", "Sin"]]
-
-    # Remove the last state when the model is completely saturated
-    if s / n == 2.0:
-        states.pop()
-    assert mod.coords["season_state"] == states
-
-
-cycle_test_vals = zip([None, None, 3, 5, 10], [False, True, True, False, False])
-
-
-def test_cycle_component_deterministic(rng):
-    cycle = st.CycleComponent(
-        name="cycle", cycle_length=12, estimate_cycle_length=False, innovations=False
-    )
-    params = {"cycle": np.array([1.0, 1.0], dtype=floatX)}
-    x, y = simulate_from_numpy_model(cycle, rng, params, steps=12 * 12)
-
-    assert_pattern_repeats(y, 12, atol=ATOL, rtol=RTOL)
-
-
-def test_cycle_component_with_dampening(rng):
-    cycle = st.CycleComponent(
-        name="cycle", cycle_length=12, estimate_cycle_length=False, innovations=False, dampen=True
-    )
-    params = {"cycle": np.array([10.0, 10.0], dtype=floatX), "cycle_dampening_factor": 0.75}
-    x, y = simulate_from_numpy_model(cycle, rng, params, steps=100)
-
-    # Check that the cycle dampens to zero over time
-    assert_allclose(y[-1], 0.0, atol=ATOL, rtol=RTOL)
-
-
-def test_cycle_component_with_innovations_and_cycle_length(rng):
-    cycle = st.CycleComponent(
-        name="cycle", estimate_cycle_length=True, innovations=True, dampen=True
-    )
-    params = {
-        "cycle": np.array([1.0, 1.0], dtype=floatX),
-        "cycle_length": 12.0,
-        "cycle_dampening_factor": 0.95,
-        "sigma_cycle": 1.0,
-    }
-
-    x, y = simulate_from_numpy_model(cycle, rng, params)
-
-    cycle.build(verbose=False)
-    _assert_basic_coords_correct(cycle)
-
-
-def test_exogenous_component(rng):
-    data = rng.normal(size=(100, 2)).astype(floatX)
-    mod = st.RegressionComponent(state_names=["feature_1", "feature_2"], name="exog")
-
-    params = {"beta_exog": np.array([1.0, 2.0], dtype=floatX)}
-    exog_data = {"data_exog": data}
-    x, y = simulate_from_numpy_model(mod, rng, params, exog_data)
-
-    # Check that the generated data is just a linear regression
-    assert_allclose(y, data @ params["beta_exog"], atol=ATOL, rtol=RTOL)
-
-    mod.build(verbose=False)
-    _assert_basic_coords_correct(mod)
-    assert mod.coords["exog_state"] == ["feature_1", "feature_2"]
-
-
-def test_adding_exogenous_component(rng):
-    data = rng.normal(size=(100, 2)).astype(floatX)
-    reg = st.RegressionComponent(state_names=["a", "b"], name="exog")
-    ll = st.LevelTrendComponent(name="level")
-
-    seasonal = st.FrequencySeasonality(name="annual", season_length=12, n=4)
-    mod = reg + ll + seasonal
-
-    assert mod.ssm["design"].eval({"data_exog": data}).shape == (100, 1, 2 + 2 + 8)
-    assert_allclose(mod.ssm["design", 5, 0, :2].eval({"data_exog": data}), data[5])
-
-
-def test_add_components():
-    ll = st.LevelTrendComponent(order=2)
-    se = st.TimeSeasonality(name="seasonal", season_length=12)
-    mod = ll + se
-
-    ll_params = {
-        "initial_trend": np.zeros(2, dtype=floatX),
-        "sigma_trend": np.ones(2, dtype=floatX),
-    }
-    se_params = {
-        "seasonal_coefs": np.ones(11, dtype=floatX),
-        "sigma_seasonal": 1.0,
-    }
-    all_params = ll_params.copy()
-    all_params.update(se_params)
-
-    (ll_x0, ll_P0, ll_c, ll_d, ll_T, ll_Z, ll_R, ll_H, ll_Q) = unpack_symbolic_matrices_with_params(
-        ll, ll_params
-    )
-    (se_x0, se_P0, se_c, se_d, se_T, se_Z, se_R, se_H, se_Q) = unpack_symbolic_matrices_with_params(
-        se, se_params
-    )
-    x0, P0, c, d, T, Z, R, H, Q = unpack_symbolic_matrices_with_params(mod, all_params)
-
-    for property in ["param_names", "shock_names", "param_info", "coords", "param_dims"]:
-        assert [x in getattr(mod, property) for x in getattr(ll, property)]
-        assert [x in getattr(mod, property) for x in getattr(se, property)]
-
-    ll_mats = [ll_T, ll_R, ll_Q]
-    se_mats = [se_T, se_R, se_Q]
-    all_mats = [T, R, Q]
-
-    for ll_mat, se_mat, all_mat in zip(ll_mats, se_mats, all_mats):
-        assert_allclose(all_mat, linalg.block_diag(ll_mat, se_mat), atol=ATOL, rtol=RTOL)
-
-    ll_mats = [ll_x0, ll_c, ll_Z]
-    se_mats = [se_x0, se_c, se_Z]
-    all_mats = [x0, c, Z]
-    axes = [0, 0, 1]
-
-    for ll_mat, se_mat, all_mat, axis in zip(ll_mats, se_mats, all_mats, axes):
-        assert_allclose(all_mat, np.concatenate([ll_mat, se_mat], axis=axis), atol=ATOL, rtol=RTOL)
-
-
-def test_filter_scans_time_varying_design_matrix(rng):
-    time_idx = pd.date_range(start="2000-01-01", freq="D", periods=100)
-    data = pd.DataFrame(rng.normal(size=(100, 2)), columns=["a", "b"], index=time_idx)
-
-    y = pd.DataFrame(rng.normal(size=(100, 1)), columns=["data"], index=time_idx)
-
-    reg = st.RegressionComponent(state_names=["a", "b"], name="exog")
-    mod = reg.build(verbose=False)
-
-    with pm.Model(coords=mod.coords) as m:
-        data_exog = pm.Data("data_exog", data.values)
-
-        x0 = pm.Normal("x0", dims=["state"])
-        P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"])
-        beta_exog = pm.Normal("beta_exog", dims=["exog_state"])
-
-        mod.build_statespace_graph(y)
-        x0, P0, c, d, T, Z, R, H, Q = mod.unpack_statespace()
-        pm.Deterministic("Z", Z)
-
-        prior = pm.sample_prior_predictive(draws=10)
-
-    prior_Z = prior.prior.Z.values
-    assert prior_Z.shape == (1, 10, 100, 1, 2)
-    assert_allclose(prior_Z[0, :, :, 0, :], data.values[None].repeat(10, axis=0))
-
-
-@pytest.mark.skipif(floatX.endswith("32"), reason="Prior covariance not PSD at half-precision")
-def test_extract_components_from_idata(rng):
-    time_idx = pd.date_range(start="2000-01-01", freq="D", periods=100)
-    data = pd.DataFrame(rng.normal(size=(100, 2)), columns=["a", "b"], index=time_idx)
-
-    y = pd.DataFrame(rng.normal(size=(100, 1)), columns=["data"], index=time_idx)
-
-    ll = st.LevelTrendComponent()
-    season = st.FrequencySeasonality(name="seasonal", season_length=12, n=2, innovations=False)
-    reg = st.RegressionComponent(state_names=["a", "b"], name="exog")
-    me = st.MeasurementError("obs")
-    mod = (ll + season + reg + me).build(verbose=False)
-
-    with pm.Model(coords=mod.coords) as m:
-        data_exog = pm.Data("data_exog", data.values)
-
-        x0 = pm.Normal("x0", dims=["state"])
-        P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"])
-        beta_exog = pm.Normal("beta_exog", dims=["exog_state"])
-        initial_trend = pm.Normal("initial_trend", dims=["trend_state"])
-        sigma_trend = pm.Exponential("sigma_trend", 1, dims=["trend_shock"])
-        seasonal_coefs = pm.Normal("seasonal", dims=["seasonal_state"])
-        sigma_obs = pm.Exponential("sigma_obs", 1)
-
-        mod.build_statespace_graph(y)
-
-        x0, P0, c, d, T, Z, R, H, Q = mod.unpack_statespace()
-        prior = pm.sample_prior_predictive(draws=10)
-
-    filter_prior = mod.sample_conditional_prior(prior)
-    comp_prior = mod.extract_components_from_idata(filter_prior)
-    comp_states = comp_prior.filtered_prior.coords["state"].values
-    expected_states = ["LevelTrend[level]", "LevelTrend[trend]", "seasonal", "exog[a]", "exog[b]"]
-    missing = set(comp_states) - set(expected_states)
-
-    assert len(missing) == 0, missing

From 1f3dc3a87cc81f24b2beb464249f76f51287b05a Mon Sep 17 00:00:00 2001
From: Jonathan Dekermanjian 
Date: Sat, 5 Jul 2025 08:48:04 -0600
Subject: [PATCH 12/21] removed incorrectly committed file structural.py

---
 pymc_extras/statespace/models/structural.py | 1679 -------------------
 1 file changed, 1679 deletions(-)
 delete mode 100644 pymc_extras/statespace/models/structural.py

diff --git a/pymc_extras/statespace/models/structural.py b/pymc_extras/statespace/models/structural.py
deleted file mode 100644
index a982366c3..000000000
--- a/pymc_extras/statespace/models/structural.py
+++ /dev/null
@@ -1,1679 +0,0 @@
-import functools as ft
-import logging
-
-from abc import ABC
-from collections.abc import Sequence
-from itertools import pairwise
-from typing import Any
-
-import numpy as np
-import pytensor
-import pytensor.tensor as pt
-import xarray as xr
-
-from pytensor import Variable
-from pytensor.compile.mode import Mode
-
-from pymc_extras.statespace.core import PytensorRepresentation
-from pymc_extras.statespace.core.statespace import PyMCStateSpace
-from pymc_extras.statespace.models.utilities import (
-    conform_time_varying_and_time_invariant_matrices,
-    make_default_coords,
-)
-from pymc_extras.statespace.utils.constants import (
-    ALL_STATE_AUX_DIM,
-    ALL_STATE_DIM,
-    AR_PARAM_DIM,
-    LONG_MATRIX_NAMES,
-    POSITION_DERIVATIVE_NAMES,
-    TIME_DIM,
-)
-
-_log = logging.getLogger("pymc.experimental.statespace")
-
-floatX = pytensor.config.floatX
-
-
-def order_to_mask(order):
-    if isinstance(order, int):
-        return np.ones(order).astype(bool)
-    else:
-        return np.array(order).astype(bool)
-
-
-def _frequency_transition_block(s, j):
-    lam = 2 * np.pi * j / s
-
-    return pt.stack([[pt.cos(lam), pt.sin(lam)], [-pt.sin(lam), pt.cos(lam)]])
-
-
-class StructuralTimeSeries(PyMCStateSpace):
-    r"""
-    Structural Time Series Model
-
-    The structural time series model, named by [1] and presented in statespace form in [2], is a framework for
-    decomposing a univariate time series into level, trend, seasonal, and cycle components. It also admits the
-    possibility of exogenous regressors. Unlike the SARIMAX framework, the time series is not assumed to be stationary.
-
-    Notes
-    -----
-
-    .. math::
-         y_t = \mu_t + \gamma_t + c_t + \varepsilon_t
-
-    """
-
-    def __init__(
-        self,
-        ssm: PytensorRepresentation,
-        state_names: list[str],
-        data_names: list[str],
-        shock_names: list[str],
-        param_names: list[str],
-        exog_names: list[str],
-        param_dims: dict[str, tuple[int]],
-        coords: dict[str, Sequence],
-        param_info: dict[str, dict[str, Any]],
-        data_info: dict[str, dict[str, Any]],
-        component_info: dict[str, dict[str, Any]],
-        measurement_error: bool,
-        name_to_variable: dict[str, Variable],
-        name_to_data: dict[str, Variable] | None = None,
-        name: str | None = None,
-        verbose: bool = True,
-        filter_type: str = "standard",
-        mode: str | Mode | None = None,
-    ):
-        # Add the initial state covariance to the parameters
-        if name is None:
-            name = "data"
-        self._name = name
-
-        k_states, k_posdef, k_endog = ssm.k_states, ssm.k_posdef, ssm.k_endog
-        param_names, param_dims, param_info = self._add_inital_state_cov_to_properties(
-            param_names, param_dims, param_info, k_states
-        )
-        self._state_names = state_names.copy()
-        self._data_names = data_names.copy()
-        self._shock_names = shock_names.copy()
-        self._param_names = param_names.copy()
-        self._param_dims = param_dims.copy()
-
-        default_coords = make_default_coords(self)
-        coords.update(default_coords)
-
-        self._coords = coords
-        self._param_info = param_info.copy()
-        self._data_info = data_info.copy()
-        self.measurement_error = measurement_error
-
-        super().__init__(
-            k_endog,
-            k_states,
-            max(1, k_posdef),
-            filter_type=filter_type,
-            verbose=verbose,
-            measurement_error=measurement_error,
-            mode=mode,
-        )
-        self.ssm = ssm.copy()
-
-        if k_posdef == 0:
-            # If there is no randomness in the model, add dummy matrices to the representation to avoid errors
-            # when we go to construct random variables from the matrices
-            self.ssm.k_posdef = self.k_posdef
-            self.ssm.shapes["state_cov"] = (1, 1, 1)
-            self.ssm["state_cov"] = pt.zeros((1, 1, 1))
-
-            self.ssm.shapes["selection"] = (1, self.k_states, 1)
-            self.ssm["selection"] = pt.zeros((1, self.k_states, 1))
-
-        self._component_info = component_info.copy()
-
-        self._name_to_variable = name_to_variable.copy()
-        self._name_to_data = name_to_data.copy()
-
-        self._exog_names = exog_names.copy()
-        self._needs_exog_data = len(exog_names) > 0
-
-        P0 = self.make_and_register_variable("P0", shape=(self.k_states, self.k_states))
-        self.ssm["initial_state_cov"] = P0
-
-    @staticmethod
-    def _add_inital_state_cov_to_properties(param_names, param_dims, param_info, k_states):
-        param_names += ["P0"]
-        param_dims["P0"] = (ALL_STATE_DIM, ALL_STATE_AUX_DIM)
-        param_info["P0"] = {
-            "shape": (k_states, k_states),
-            "constraints": "Positive semi-definite",
-            "dims": param_dims["P0"],
-        }
-
-        return param_names, param_dims, param_info
-
-    @property
-    def param_names(self):
-        return self._param_names
-
-    @property
-    def data_names(self) -> list[str]:
-        return self._data_names
-
-    @property
-    def state_names(self):
-        return self._state_names
-
-    @property
-    def observed_states(self):
-        return [self._name]
-
-    @property
-    def shock_names(self):
-        return self._shock_names
-
-    @property
-    def param_dims(self):
-        return self._param_dims
-
-    @property
-    def coords(self) -> dict[str, Sequence]:
-        return self._coords
-
-    @property
-    def param_info(self) -> dict[str, dict[str, Any]]:
-        return self._param_info
-
-    @property
-    def data_info(self) -> dict[str, dict[str, Any]]:
-        return self._data_info
-
-    def make_symbolic_graph(self) -> None:
-        """
-        Assign placeholder pytensor variables among statespace matrices in positions where PyMC variables will go.
-
-        Notes
-        -----
-        This assignment is handled by the components, so this function is implemented only to avoid the
-        NotImplementedError raised by the base class.
-        """
-
-        pass
-
-    def _state_slices_from_info(self):
-        info = self._component_info.copy()
-        comp_states = np.cumsum([0] + [info["k_states"] for info in info.values()])
-        state_slices = [slice(i, j) for i, j in pairwise(comp_states)]
-
-        return state_slices
-
-    def _hidden_states_from_data(self, data):
-        state_slices = self._state_slices_from_info()
-        info = self._component_info
-        names = info.keys()
-        result = []
-
-        for i, (name, s) in enumerate(zip(names, state_slices)):
-            obs_idx = info[name]["obs_state_idx"]
-            if obs_idx is None:
-                continue
-
-            X = data[..., s]
-            if info[name]["combine_hidden_states"]:
-                sum_idx = np.flatnonzero(obs_idx)
-                result.append(X[..., sum_idx].sum(axis=-1)[..., None])
-            else:
-                comp_names = self.state_names[s]
-                for j, state_name in enumerate(comp_names):
-                    result.append(X[..., j, None])
-
-        return np.concatenate(result, axis=-1)
-
-    def _get_subcomponent_names(self):
-        state_slices = self._state_slices_from_info()
-        info = self._component_info
-        names = info.keys()
-        result = []
-
-        for i, (name, s) in enumerate(zip(names, state_slices)):
-            if info[name]["combine_hidden_states"]:
-                result.append(name)
-            else:
-                comp_names = self.state_names[s]
-                result.extend([f"{name}[{comp_name}]" for comp_name in comp_names])
-        return result
-
-    def extract_components_from_idata(self, idata: xr.Dataset) -> xr.Dataset:
-        r"""
-        Extract interpretable hidden states from an InferenceData returned by a PyMCStateSpace sampling method
-
-        Parameters
-        ----------
-        idata: Dataset
-            A Dataset object, returned by a PyMCStateSpace sampling method
-
-        Returns
-        -------
-        idata: Dataset
-            An Dataset object with hidden states transformed to represent only the "interpretable" subcomponents
-            of the structural model.
-
-        Notes
-        -----
-        In general, a structural statespace model can be represented as:
-
-        .. math::
-            y_t = \mu_t + \nu_t + \cdots + \gamma_t + c_t + \xi_t + \epsilon_t \tag{1}
-
-        Where:
-
-            - :math:`\mu_t` is the level of the data at time t
-            - :math:`\nu_t` is the slope of the data at time t
-            - :math:`\cdots` are higher time derivatives of the position (acceleration, jerk, etc) at time t
-            - :math:`\gamma_t` is the seasonal component at time t
-            - :math:`c_t` is the cycle component at time t
-            - :math:`\xi_t` is the autoregressive error at time t
-            - :math:`\varepsilon_t` is the measurement error at time t
-
-        In state space form, some or all of these components are represented as linear combinations of other
-        subcomponents, making interpretation of the outputs of the outputs difficult. The purpose of this function is
-        to take the expended statespace representation and return a "reduced form" of only the components shown in
-        equation (1).
-        """
-
-        def _extract_and_transform_variable(idata, new_state_names):
-            *_, time_dim, state_dim = idata.dims
-            state_func = ft.partial(self._hidden_states_from_data)
-            new_idata = xr.apply_ufunc(
-                state_func,
-                idata,
-                input_core_dims=[[time_dim, state_dim]],
-                output_core_dims=[[time_dim, state_dim]],
-                exclude_dims={state_dim},
-            )
-            new_idata.coords.update({state_dim: new_state_names})
-            return new_idata
-
-        var_names = list(idata.data_vars.keys())
-        is_latent = [idata[name].shape[-1] == self.k_states for name in var_names]
-        new_state_names = self._get_subcomponent_names()
-
-        latent_names = [name for latent, name in zip(is_latent, var_names) if latent]
-        dropped_vars = set(var_names) - set(latent_names)
-        if len(dropped_vars) > 0:
-            _log.warning(
-                f'Variables {", ".join(dropped_vars)} do not contain all hidden states (their last dimension '
-                f"is not {self.k_states}). They will not be present in the modified idata."
-            )
-        if len(dropped_vars) == len(var_names):
-            raise ValueError(
-                "Provided idata had no variables with all hidden states; cannot extract components."
-            )
-
-        idata_new = xr.Dataset(
-            {
-                name: _extract_and_transform_variable(idata[name], new_state_names)
-                for name in latent_names
-            }
-        )
-        return idata_new
-
-
-class Component(ABC):
-    r"""
-    Base class for a component of a structural timeseries model.
-
-    This base class contains a subset of the class attributes of the PyMCStateSpace class, and none of the class
-    methods. The purpose of a component is to allow the partial definition of a structural model. Components are
-    assembled into a full model by the StructuralTimeSeries class.
-
-    Parameters
-    ----------
-    name: str
-        The name of the component
-    k_endog: int
-        Number of endogenous variables being modeled. Currently, must be one because structural models only support
-        univariate data.
-    k_states: int
-        Number of hidden states in the component model
-    k_posdef: int
-        Rank of the state covariance matrix, or the number of sources of innovations in the component model
-    measurement_error: bool
-        Whether the observation associated with the component has measurement error. Default is False.
-    combine_hidden_states: bool
-        Flag for the ``extract_hidden_states_from_data`` method. When ``True``, hidden states from the component model
-        are extracted as ``hidden_states[:, np.flatnonzero(Z)]``. Should be True in models where hidden states
-        individually have no interpretation, such as seasonal or autoregressive components.
-    """
-
-    def __init__(
-        self,
-        name,
-        k_endog,
-        k_states,
-        k_posdef,
-        state_names=None,
-        data_names=None,
-        shock_names=None,
-        param_names=None,
-        exog_names=None,
-        representation: PytensorRepresentation | None = None,
-        measurement_error=False,
-        combine_hidden_states=True,
-        component_from_sum=False,
-        obs_state_idxs=None,
-    ):
-        self.name = name
-        self.k_endog = k_endog
-        self.k_states = k_states
-        self.k_posdef = k_posdef
-        self.measurement_error = measurement_error
-
-        self.state_names = state_names if state_names is not None else []
-        self.data_names = data_names if data_names is not None else []
-        self.shock_names = shock_names if shock_names is not None else []
-        self.param_names = param_names if param_names is not None else []
-        self.exog_names = exog_names if exog_names is not None else []
-
-        self.needs_exog_data = len(self.exog_names) > 0
-        self.coords = {}
-        self.param_dims = {}
-
-        self.param_info = {}
-        self.data_info = {}
-
-        self.param_counts = {}
-
-        if representation is None:
-            self.ssm = PytensorRepresentation(k_endog=k_endog, k_states=k_states, k_posdef=k_posdef)
-        else:
-            self.ssm = representation
-
-        self._name_to_variable = {}
-        self._name_to_data = {}
-
-        if not component_from_sum:
-            self.populate_component_properties()
-            self.make_symbolic_graph()
-
-        self._component_info = {
-            self.name: {
-                "k_states": self.k_states,
-                "k_enodg": self.k_endog,
-                "k_posdef": self.k_posdef,
-                "combine_hidden_states": combine_hidden_states,
-                "obs_state_idx": obs_state_idxs,
-            }
-        }
-
-    def make_and_register_variable(self, name, shape, dtype=floatX) -> Variable:
-        r"""
-        Helper function to create a pytensor symbolic variable and register it in the _name_to_variable dictionary
-
-        Parameters
-        ----------
-        name : str
-            The name of the placeholder variable. Must be the name of a model parameter.
-        shape : int or tuple of int
-            Shape of the parameter
-        dtype : str, default pytensor.config.floatX
-            dtype of the parameter
-
-        Notes
-        -----
-        Symbolic pytensor variables are used in the ``make_symbolic_graph`` method as placeholders for PyMC random
-        variables. The change is made in the ``_insert_random_variables`` method via ``pytensor.graph_replace``. To
-        make the change, a dictionary mapping pytensor variables to PyMC random variables needs to be constructed.
-
-        The purpose of this method is to:
-            1.  Create the placeholder symbolic variables
-            2.  Register the placeholder variable in the ``_name_to_variable`` dictionary
-
-        The shape provided here will define the shape of the prior that will need to be provided by the user.
-
-        An error is raised if the provided name has already been registered, or if the name is not present in the
-        ``param_names`` property.
-        """
-        if name not in self.param_names:
-            raise ValueError(
-                f"{name} is not a model parameter. All placeholder variables should correspond to model "
-                f"parameters."
-            )
-
-        if name in self._name_to_variable.keys():
-            raise ValueError(
-                f"{name} is already a registered placeholder variable with shape "
-                f"{self._name_to_variable[name].type.shape}"
-            )
-
-        placeholder = pt.tensor(name, shape=shape, dtype=dtype)
-        self._name_to_variable[name] = placeholder
-        return placeholder
-
-    def make_and_register_data(self, name, shape, dtype=floatX) -> Variable:
-        r"""
-        Helper function to create a pytensor symbolic variable and register it in the _name_to_data dictionary
-
-        Parameters
-        ----------
-        name : str
-            The name of the placeholder data. Must be the name of an expected data variable.
-        shape : int or tuple of int
-            Shape of the parameter
-        dtype : str, default pytensor.config.floatX
-            dtype of the parameter
-
-        Notes
-        -----
-        See docstring for make_and_register_variable for more details. This function is similar, but handles data
-        inputs instead of model parameters.
-
-        An error is raised if the provided name has already been registered, or if the name is not present in the
-        ``data_names`` property.
-        """
-        if name not in self.data_names:
-            raise ValueError(
-                f"{name} is not a model parameter. All placeholder variables should correspond to model "
-                f"parameters."
-            )
-
-        if name in self._name_to_data.keys():
-            raise ValueError(
-                f"{name} is already a registered placeholder variable with shape "
-                f"{self._name_to_data[name].type.shape}"
-            )
-
-        placeholder = pt.tensor(name, shape=shape, dtype=dtype)
-        self._name_to_data[name] = placeholder
-        return placeholder
-
-    def make_symbolic_graph(self) -> None:
-        raise NotImplementedError
-
-    def populate_component_properties(self):
-        raise NotImplementedError
-
-    def _get_combined_shapes(self, other):
-        k_states = self.k_states + other.k_states
-        k_posdef = self.k_posdef + other.k_posdef
-        if self.k_endog != other.k_endog:
-            raise NotImplementedError(
-                "Merging elements with different numbers of observed states is not supported.>"
-            )
-        k_endog = self.k_endog
-
-        return k_states, k_posdef, k_endog
-
-    def _combine_statespace_representations(self, other):
-        def make_slice(name, x, o_x):
-            ndim = max(x.ndim, o_x.ndim)
-            return (name,) + (slice(None, None, None),) * ndim
-
-        k_states, k_posdef, k_endog = self._get_combined_shapes(other)
-
-        self_matrices = [self.ssm[name] for name in LONG_MATRIX_NAMES]
-        other_matrices = [other.ssm[name] for name in LONG_MATRIX_NAMES]
-
-        x0, P0, c, d, T, Z, R, H, Q = (
-            self.ssm[make_slice(name, x, o_x)]
-            for name, x, o_x in zip(LONG_MATRIX_NAMES, self_matrices, other_matrices)
-        )
-        o_x0, o_P0, o_c, o_d, o_T, o_Z, o_R, o_H, o_Q = (
-            other.ssm[make_slice(name, x, o_x)]
-            for name, x, o_x in zip(LONG_MATRIX_NAMES, self_matrices, other_matrices)
-        )
-
-        initial_state = pt.concatenate(conform_time_varying_and_time_invariant_matrices(x0, o_x0))
-        initial_state.name = x0.name
-
-        initial_state_cov = pt.linalg.block_diag(P0, o_P0)
-        initial_state_cov.name = P0.name
-
-        state_intercept = pt.concatenate(conform_time_varying_and_time_invariant_matrices(c, o_c))
-        state_intercept.name = c.name
-
-        obs_intercept = d + o_d
-        obs_intercept.name = d.name
-
-        transition = pt.linalg.block_diag(T, o_T)
-        transition.name = T.name
-
-        design = pt.concatenate(conform_time_varying_and_time_invariant_matrices(Z, o_Z), axis=-1)
-        design.name = Z.name
-
-        selection = pt.linalg.block_diag(R, o_R)
-        selection.name = R.name
-
-        obs_cov = H + o_H
-        obs_cov.name = H.name
-
-        state_cov = pt.linalg.block_diag(Q, o_Q)
-        state_cov.name = Q.name
-
-        new_ssm = PytensorRepresentation(
-            k_endog=k_endog,
-            k_states=k_states,
-            k_posdef=k_posdef,
-            initial_state=initial_state,
-            initial_state_cov=initial_state_cov,
-            state_intercept=state_intercept,
-            obs_intercept=obs_intercept,
-            transition=transition,
-            design=design,
-            selection=selection,
-            obs_cov=obs_cov,
-            state_cov=state_cov,
-        )
-
-        return new_ssm
-
-    def _combine_property(self, other, name):
-        self_prop = getattr(self, name)
-        if isinstance(self_prop, list):
-            return self_prop + getattr(other, name)
-        elif isinstance(self_prop, dict):
-            new_prop = self_prop.copy()
-            new_prop.update(getattr(other, name))
-            return new_prop
-
-    def _combine_component_info(self, other):
-        combined_info = {}
-        for key, value in self._component_info.items():
-            if not key.startswith("StateSpace"):
-                if key in combined_info.keys():
-                    raise ValueError(f"Found duplicate component named {key}")
-                combined_info[key] = value
-
-        for key, value in other._component_info.items():
-            if not key.startswith("StateSpace"):
-                if key in combined_info.keys():
-                    raise ValueError(f"Found duplicate component named {key}")
-                combined_info[key] = value
-
-        return combined_info
-
-    def _make_combined_name(self):
-        components = self._component_info.keys()
-        name = f'StateSpace[{", ".join(components)}]'
-        return name
-
-    def __add__(self, other):
-        state_names = self._combine_property(other, "state_names")
-        data_names = self._combine_property(other, "data_names")
-        param_names = self._combine_property(other, "param_names")
-        shock_names = self._combine_property(other, "shock_names")
-        param_info = self._combine_property(other, "param_info")
-        data_info = self._combine_property(other, "data_info")
-        param_dims = self._combine_property(other, "param_dims")
-        coords = self._combine_property(other, "coords")
-        exog_names = self._combine_property(other, "exog_names")
-
-        _name_to_variable = self._combine_property(other, "_name_to_variable")
-        _name_to_data = self._combine_property(other, "_name_to_data")
-
-        measurement_error = any([self.measurement_error, other.measurement_error])
-
-        k_states, k_posdef, k_endog = self._get_combined_shapes(other)
-        ssm = self._combine_statespace_representations(other)
-
-        new_comp = Component(
-            name="",
-            k_endog=1,
-            k_states=k_states,
-            k_posdef=k_posdef,
-            measurement_error=measurement_error,
-            representation=ssm,
-            component_from_sum=True,
-        )
-        new_comp._component_info = self._combine_component_info(other)
-        new_comp.name = new_comp._make_combined_name()
-
-        names_and_props = [
-            ("state_names", state_names),
-            ("data_names", data_names),
-            ("param_names", param_names),
-            ("shock_names", shock_names),
-            ("param_dims", param_dims),
-            ("coords", coords),
-            ("param_dims", param_dims),
-            ("param_info", param_info),
-            ("data_info", data_info),
-            ("exog_names", exog_names),
-            ("_name_to_variable", _name_to_variable),
-            ("_name_to_data", _name_to_data),
-        ]
-
-        for prop, value in names_and_props:
-            setattr(new_comp, prop, value)
-
-        return new_comp
-
-    def build(
-        self, name=None, filter_type="standard", verbose=True, mode: str | Mode | None = None
-    ):
-        """
-        Build a StructuralTimeSeries statespace model from the current component(s)
-
-        Parameters
-        ----------
-        name: str, optional
-            Name of the exogenous data being modeled. Default is "data"
-
-        filter_type : str, optional
-            The type of Kalman filter to use. Valid options are "standard", "univariate", "single", "cholesky", and
-            "steady_state". For more information, see the docs for each filter. Default is "standard".
-
-        verbose : bool, optional
-            If True, displays information about the initialized model. Defaults to True.
-
-        mode: str or Mode, optional
-            Pytensor compile mode, used in auxiliary sampling methods such as ``sample_conditional_posterior`` and
-            ``forecast``. The mode does **not** effect calls to ``pm.sample``.
-
-            Regardless of whether a mode is specified, it can always be overwritten via the ``compile_kwargs`` argument
-            to all sampling methods.
-
-        Returns
-        -------
-        PyMCStateSpace
-            An initialized instance of a PyMCStateSpace, constructed using the system matrices contained in the
-            components.
-        """
-
-        return StructuralTimeSeries(
-            self.ssm,
-            name=name,
-            state_names=self.state_names,
-            data_names=self.data_names,
-            shock_names=self.shock_names,
-            param_names=self.param_names,
-            param_dims=self.param_dims,
-            coords=self.coords,
-            param_info=self.param_info,
-            data_info=self.data_info,
-            component_info=self._component_info,
-            measurement_error=self.measurement_error,
-            exog_names=self.exog_names,
-            name_to_variable=self._name_to_variable,
-            name_to_data=self._name_to_data,
-            filter_type=filter_type,
-            verbose=verbose,
-            mode=mode,
-        )
-
-
-class LevelTrendComponent(Component):
-    r"""
-    Level and trend component of a structural time series model
-
-    Parameters
-    ----------
-    __________
-    order : int
-
-        Number of time derivatives of the trend to include in the model. For example, when order=3, the trend will
-        be of the form ``y = a + b * t + c * t ** 2``, where the coefficients ``a, b, c`` come from the initial
-        state values.
-
-    innovations_order : int or sequence of int, optional
-
-        The number of stochastic innovations to include in the model. By default, ``innovations_order = order``
-
-    Notes
-    -----
-    This class implements the level and trend components of the general structural time series model. In the most
-    general form, the level and trend is described by a system of two time-varying equations.
-
-    .. math::
-        \begin{align}
-            \mu_{t+1} &= \mu_t + \nu_t + \zeta_t \\
-            \nu_{t+1} &= \nu_t + \xi_t
-            \zeta_t &\sim N(0, \sigma_\zeta) \\
-            \xi_t &\sim N(0, \sigma_\xi)
-        \end{align}
-
-    Where :math:`\mu_{t+1}` is the mean of the timeseries at time t, and :math:`\nu_t` is the drift or the slope of
-    the process. When both innovations :math:`\zeta_t` and :math:`\xi_t` are included in the model, it is known as a
-    *local linear trend* model. This system of two equations, corresponding to ``order=2``, can be expanded or
-    contracted by adding or removing equations. ``order=3`` would add an acceleration term to the sytsem:
-
-    .. math::
-        \begin{align}
-            \mu_{t+1} &= \mu_t + \nu_t + \zeta_t \\
-            \nu_{t+1} &= \nu_t + \eta_t + \xi_t \\
-            \eta_{t+1} &= \eta_{t-1} + \omega_t \\
-            \zeta_t &\sim N(0, \sigma_\zeta) \\
-            \xi_t &\sim N(0, \sigma_\xi) \\
-            \omega_t &\sim N(0, \sigma_\omega)
-        \end{align}
-
-    After setting all innovation terms to zero and defining initial states :math:`\mu_0, \nu_0, \eta_0`, these equations
-    can be collapsed to:
-
-    .. math::
-        \mu_t = \mu_0 + \nu_0 \cdot t + \eta_0 \cdot t^2
-
-    Which clarifies how the order and initial states influence the model. In particular, the initial states are the
-    coefficients on the intercept, slope, acceleration, and so on.
-
-    In this light, allowing for innovations can be understood as allowing these coefficients to vary over time. Each
-    component can be individually selected for time variation by passing a list to the ``innovations_order`` argument.
-    For example, a constant intercept with time varying trend and acceleration is specified as ``order=3,
-    innovations_order=[0, 1, 1]``.
-
-    By choosing the ``order`` and ``innovations_order``, a large variety of models can be obtained. Notable
-    models include:
-
-    * Constant intercept, ``order=1, innovations_order=0``
-
-    .. math::
-        \mu_t = \mu
-
-    * Constant linear slope, ``order=2, innovations_order=0``
-
-    .. math::
-        \mu_t = \mu_{t-1} + \nu
-
-    * Gaussian Random Walk, ``order=1, innovations_order=1``
-
-    .. math::
-        \mu_t = \mu_{t-1} + \zeta_t
-
-    * Gaussian Random Walk with Drift, ``order=2, innovations_order=1``
-
-    .. math::
-        \mu_t = \mu_{t-1} + \nu + \zeta_t
-
-    * Smooth Trend, ``order=2, innovations_order=[0, 1]``
-
-    .. math::
-        \begin{align}
-            \mu_t &= \mu_{t-1} + \nu_{t-1} \\
-            \nu_t &= \nu_{t-1} + \xi_t
-        \end{align}
-
-    * Local Level, ``order=2, innovations_order=2``
-
-    [1] notes that the smooth trend model produces more gradually changing slopes than the full local linear trend
-    model, and is equivalent to an "integrated trend model".
-
-    References
-    ----------
-    .. [1] Durbin, James, and Siem Jan Koopman. 2012.
-        Time Series Analysis by State Space Methods: Second Edition.
-        Oxford University Press.
-
-    """
-
-    def __init__(
-        self,
-        order: int | list[int] = 2,
-        innovations_order: int | list[int] | None = None,
-        name: str = "LevelTrend",
-    ):
-        if innovations_order is None:
-            innovations_order = order
-
-        self._order_mask = order_to_mask(order)
-        max_state = np.flatnonzero(self._order_mask)[-1].item() + 1
-
-        # If the user passes excess zeros, raise an error. The alternative is to prune them, but this would cause
-        # the shape of the state to be different to what the user expects.
-        if len(self._order_mask) > max_state:
-            raise ValueError(
-                f"order={order} is invalid. The highest derivative should not be set to zero. If you want a "
-                f"lower order model, explicitly omit the zeros."
-            )
-        k_states = max_state
-
-        if isinstance(innovations_order, int):
-            n = innovations_order
-            innovations_order = order_to_mask(k_states)
-            if n > 0:
-                innovations_order[n:] = False
-            else:
-                innovations_order[:] = False
-        else:
-            innovations_order = order_to_mask(innovations_order)
-
-        self.innovations_order = innovations_order[:max_state]
-        k_posdef = int(sum(innovations_order))
-
-        super().__init__(
-            name,
-            k_endog=1,
-            k_states=k_states,
-            k_posdef=k_posdef,
-            measurement_error=False,
-            combine_hidden_states=False,
-            obs_state_idxs=np.array([1.0] + [0.0] * (k_states - 1)),
-        )
-
-    def populate_component_properties(self):
-        name_slice = POSITION_DERIVATIVE_NAMES[: self.k_states]
-        self.param_names = ["initial_trend"]
-        self.state_names = [name for name, mask in zip(name_slice, self._order_mask) if mask]
-        self.param_dims = {"initial_trend": ("trend_state",)}
-        self.coords = {"trend_state": self.state_names}
-        self.param_info = {"initial_trend": {"shape": (self.k_states,), "constraints": None}}
-
-        if self.k_posdef > 0:
-            self.param_names += ["sigma_trend"]
-            self.shock_names = [
-                name for name, mask in zip(name_slice, self.innovations_order) if mask
-            ]
-            self.param_dims["sigma_trend"] = ("trend_shock",)
-            self.coords["trend_shock"] = self.shock_names
-            self.param_info["sigma_trend"] = {"shape": (self.k_posdef,), "constraints": "Positive"}
-
-        for name in self.param_names:
-            self.param_info[name]["dims"] = self.param_dims[name]
-
-    def make_symbolic_graph(self) -> None:
-        initial_trend = self.make_and_register_variable("initial_trend", shape=(self.k_states,))
-        self.ssm["initial_state", :] = initial_trend
-        triu_idx = np.triu_indices(self.k_states)
-        self.ssm[np.s_["transition", triu_idx[0], triu_idx[1]]] = 1
-
-        R = np.eye(self.k_states)
-        R = R[:, self.innovations_order]
-        self.ssm["selection", :, :] = R
-
-        self.ssm["design", 0, :] = np.array([1.0] + [0.0] * (self.k_states - 1))
-
-        if self.k_posdef > 0:
-            sigma_trend = self.make_and_register_variable("sigma_trend", shape=(self.k_posdef,))
-            diag_idx = np.diag_indices(self.k_posdef)
-            idx = np.s_["state_cov", diag_idx[0], diag_idx[1]]
-            self.ssm[idx] = sigma_trend**2
-
-
-class MeasurementError(Component):
-    r"""
-    Measurement error term for a structural timeseries model
-
-    Parameters
-    ----------
-    name: str, optional
-
-        Name of the observed data. Default is "obs".
-
-    Notes
-    -----
-    This component should only be used in combination with other components, because it has no states. It's only use
-    is to add a variance parameter to the model, associated with the observation noise matrix H.
-
-    Examples
-    --------
-    Create and estimate a deterministic linear trend with measurement error
-
-    .. code:: python
-
-        from pymc_extras.statespace import structural as st
-        import pymc as pm
-        import pytensor.tensor as pt
-
-        trend = st.LevelTrendComponent(order=2, innovations_order=0)
-        error = st.MeasurementError()
-        ss_mod = (trend + error).build()
-
-        with pm.Model(coords=ss_mod.coords) as model:
-            P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0'])
-            intitial_trend = pm.Normal('initial_trend', sigma=10, dims=ss_mod.param_dims['initial_trend'])
-            sigma_obs = pm.Exponential('sigma_obs', 1, dims=ss_mod.param_dims['sigma_obs'])
-
-            ss_mod.build_statespace_graph(data)
-            idata = pm.sample(nuts_sampler='numpyro')
-    """
-
-    def __init__(self, name: str = "MeasurementError"):
-        k_endog = 1
-        k_states = 0
-        k_posdef = 0
-
-        super().__init__(
-            name, k_endog, k_states, k_posdef, measurement_error=True, combine_hidden_states=False
-        )
-
-    def populate_component_properties(self):
-        self.param_names = [f"sigma_{self.name}"]
-        self.param_dims = {}
-        self.param_info = {
-            f"sigma_{self.name}": {
-                "shape": (),
-                "constraints": "Positive",
-                "dims": None,
-            }
-        }
-
-    def make_symbolic_graph(self) -> None:
-        sigma_shape = ()
-        error_sigma = self.make_and_register_variable(f"sigma_{self.name}", shape=sigma_shape)
-        diag_idx = np.diag_indices(self.k_endog)
-        idx = np.s_["obs_cov", diag_idx[0], diag_idx[1]]
-        self.ssm[idx] = error_sigma**2
-
-
-class AutoregressiveComponent(Component):
-    r"""
-    Autoregressive timeseries component
-
-    Parameters
-    ----------
-    order: int or sequence of int
-
-        If int, the number of lags to include in the model.
-        If a sequence, an array-like of zeros and ones indicating which lags to include in the model.
-
-    Notes
-    -----
-    An autoregressive component can be thought of as a way o introducing serially correlated errors into the model.
-    The process is modeled:
-
-    .. math::
-        x_t = \sum_{i=1}^p \rho_i x_{t-i}
-
-    Where ``p``, the number of autoregressive terms to model, is the order of the process. By default, all lags up to
-    ``p`` are included in the model. To disable lags, pass a list of zeros and ones to the ``order`` argumnet. For
-    example, ``order=[1, 1, 0, 1]`` would become:
-
-    .. math::
-        x_t = \rho_1 x_{t-1} + \rho_2 x_{t-1} + \rho_4 x_{t-1}
-
-    The coefficient :math:`\rho_3` has been constrained to zero.
-
-    .. warning:: This class is meant to be used as a component in a structural time series model. For modeling of
-              stationary processes with ARIMA, use ``statespace.BayesianSARIMA``.
-
-    Examples
-    --------
-    Model a timeseries as an AR(2) process with non-zero mean:
-
-    .. code:: python
-
-        from pymc_extras.statespace import structural as st
-        import pymc as pm
-        import pytensor.tensor as pt
-
-        trend = st.LevelTrendComponent(order=1, innovations_order=0)
-        ar = st.AutoregressiveComponent(2)
-        ss_mod = (trend + ar).build()
-
-        with pm.Model(coords=ss_mod.coords) as model:
-            P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0'])
-            intitial_trend = pm.Normal('initial_trend', sigma=10, dims=ss_mod.param_dims['initial_trend'])
-            ar_params = pm.Normal('ar_params', dims=ss_mod.param_dims['ar_params'])
-            sigma_ar = pm.Exponential('sigma_ar', 1, dims=ss_mod.param_dims['sigma_ar'])
-
-            ss_mod.build_statespace_graph(data)
-            idata = pm.sample(nuts_sampler='numpyro')
-
-    """
-
-    def __init__(self, order: int = 1, name: str = "AutoRegressive"):
-        order = order_to_mask(order)
-        ar_lags = np.flatnonzero(order).ravel().astype(int) + 1
-        k_states = len(order)
-
-        self.order = order
-        self.ar_lags = ar_lags
-
-        super().__init__(
-            name=name,
-            k_endog=1,
-            k_states=k_states,
-            k_posdef=1,
-            measurement_error=True,
-            combine_hidden_states=True,
-            obs_state_idxs=np.r_[[1.0], np.zeros(k_states - 1)],
-        )
-
-    def populate_component_properties(self):
-        self.state_names = [f"L{i + 1}.data" for i in range(self.k_states)]
-        self.shock_names = [f"{self.name}_innovation"]
-        self.param_names = ["ar_params", "sigma_ar"]
-        self.param_dims = {"ar_params": (AR_PARAM_DIM,)}
-        self.coords = {AR_PARAM_DIM: self.ar_lags.tolist()}
-
-        self.param_info = {
-            "ar_params": {
-                "shape": (self.k_states,),
-                "constraints": None,
-                "dims": (AR_PARAM_DIM,),
-            },
-            "sigma_ar": {"shape": (), "constraints": "Positive", "dims": None},
-        }
-
-    def make_symbolic_graph(self) -> None:
-        k_nonzero = int(sum(self.order))
-        ar_params = self.make_and_register_variable("ar_params", shape=(k_nonzero,))
-        sigma_ar = self.make_and_register_variable("sigma_ar", shape=())
-
-        T = np.eye(self.k_states, k=-1)
-        self.ssm["transition", :, :] = T
-        self.ssm["selection", 0, 0] = 1
-        self.ssm["design", 0, 0] = 1
-
-        ar_idx = ("transition", np.zeros(k_nonzero, dtype="int"), np.nonzero(self.order)[0])
-        self.ssm[ar_idx] = ar_params
-
-        cov_idx = ("state_cov", *np.diag_indices(1))
-        self.ssm[cov_idx] = sigma_ar**2
-
-
-class TimeSeasonality(Component):
-    r"""
-    Seasonal component, modeled in the time domain
-
-    Parameters
-    ----------
-    season_length: int
-        The number of periods in a single seasonal cycle, e.g. 12 for monthly data with annual seasonal pattern, 7 for
-        daily data with weekly seasonal pattern, etc.
-
-    innovations: bool, default True
-        Whether to include stochastic innovations in the strength of the seasonal effect
-
-    name: str, default None
-        A name for this seasonal component. Used to label dimensions and coordinates. Useful when multiple seasonal
-        components are included in the same model. Default is ``f"Seasonal[s={season_length}]"``
-
-    state_names: list of str, default None
-        List of strings for seasonal effect labels. If provided, it must be of length ``season_length``. An example
-        would be ``state_names = ['Mon', 'Tue', 'Wed', 'Thur', 'Fri', 'Sat', 'Sun']`` when data is daily with a weekly
-        seasonal pattern (``season_length = 7``).
-
-        If None, states will be numbered ``[State_0, ..., State_s]``
-
-    remove_first_state: bool, default True
-        If True, the first state will be removed from the model. This is done because there are only n-1 degrees of
-        freedom in the seasonal component, and one state is not identified. If False, the first state will be
-        included in the model, but it will not be identified -- you will need to handle this in the priors (e.g. with
-        ZeroSumNormal).
-
-    Notes
-    -----
-    A seasonal effect is any pattern that repeats every fixed interval. Although there are many possible ways to
-    model seasonal effects, the implementation used here is the one described by [1] as the "canonical" time domain
-    representation. The seasonal component can be expressed:
-
-    .. math::
-        \gamma_t = -\sum_{i=1}^{s-1} \gamma_{t-i} + \omega_t, \quad \omega_t \sim N(0, \sigma_\gamma)
-
-    Where :math:`s` is the ``seasonal_length`` parameter and :math:`\omega_t` is the (optional) stochastic innovation.
-    To give interpretation to the :math:`\gamma` terms, it is helpful to work  through the algebra for a simple
-    example. Let :math:`s=4`, and omit the shock term. Define initial conditions :math:`\gamma_0, \gamma_{-1},
-    \gamma_{-2}`. The value of the seasonal component for the first 5 timesteps will be:
-
-    .. math::
-        \begin{align}
-            \gamma_1 &= -\gamma_0 - \gamma_{-1} - \gamma_{-2} \\
-             \gamma_2 &= -\gamma_1 - \gamma_0 - \gamma_{-1} \\
-                       &= -(-\gamma_0 - \gamma_{-1} - \gamma_{-2}) - \gamma_0 - \gamma_{-1}  \\
-                       &= (\gamma_0 - \gamma_0 )+ (\gamma_{-1} - \gamma_{-1}) + \gamma_{-2} \\
-                       &= \gamma_{-2} \\
-              \gamma_3 &= -\gamma_2 - \gamma_1 - \gamma_0  \\
-                       &= -\gamma_{-2} - (-\gamma_0 - \gamma_{-1} - \gamma_{-2}) - \gamma_0 \\
-                       &=  (\gamma_{-2} - \gamma_{-2}) + \gamma_{-1} + (\gamma_0 - \gamma_0) \\
-                       &= \gamma_{-1} \\
-              \gamma_4 &= -\gamma_3 - \gamma_2 - \gamma_1 \\
-                       &= -\gamma_{-1} - \gamma_{-2} -(-\gamma_0 - \gamma_{-1} - \gamma_{-2}) \\
-                       &= (\gamma_{-2} - \gamma_{-2}) + (\gamma_{-1} - \gamma_{-1}) + \gamma_0 \\
-                       &= \gamma_0 \\
-              \gamma_5 &= -\gamma_4 - \gamma_3 - \gamma_2 \\
-                       &= -\gamma_0 - \gamma_{-1} - \gamma_{-2} \\
-                       &= \gamma_1
-        \end{align}
-
-    This exercise shows that, given a list ``initial_conditions`` of length ``s-1``, the effects of this model will be:
-
-        - Period 1: ``-sum(initial_conditions)``
-        - Period 2: ``initial_conditions[-1]``
-        - Period 3: ``initial_conditions[-2]``
-        - ...
-        - Period s: ``initial_conditions[0]``
-        - Period s+1: ``-sum(initial_condition)``
-
-    And so on. So for interpretation, the ``season_length - 1`` initial states are, when reversed, the coefficients
-    associated with ``state_names[1:]``.
-
-    .. warning::
-        Although the ``state_names`` argument expects a list of length ``season_length``, only ``state_names[1:]``
-        will be saved as model dimensions, since the 1st coefficient is not identified (it is defined as
-        :math:`-\sum_{i=1}^{s} \gamma_{t-i}`).
-
-    Examples
-    --------
-    Estimate monthly with a model with a gaussian random walk trend and monthly seasonality:
-
-    .. code:: python
-
-        from pymc_extras.statespace import structural as st
-        import pymc as pm
-        import pytensor.tensor as pt
-        import pandas as pd
-
-        # Get month names
-        state_names = pd.date_range('1900-01-01', '1900-12-31', freq='MS').month_name().tolist()
-
-        # Build the structural model
-        grw = st.LevelTrendComponent(order=1, innovations_order=1)
-        annual_season = st.TimeSeasonality(season_length=12, name='annual', state_names=state_names, innovations=False)
-        ss_mod = (grw + annual_season).build()
-
-        # Estimate with PyMC
-        with pm.Model(coords=ss_mod.coords) as model:
-            P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states) * 10, dims=ss_mod.param_dims['P0'])
-            intitial_trend = pm.Deterministic('initial_trend', pt.zeros(1), dims=ss_mod.param_dims['initial_trend'])
-            annual_coefs = pm.Normal('annual_coefs', sigma=1e-2, dims=ss_mod.param_dims['annual_coefs'])
-            trend_sigmas = pm.HalfNormal('trend_sigmas', sigma=1e-6, dims=ss_mod.param_dims['trend_sigmas'])
-            ss_mod.build_statespace_graph(data)
-            idata = pm.sample(nuts_sampler='numpyro')
-
-    References
-    ----------
-    .. [1] Durbin, James, and Siem Jan Koopman. 2012.
-        Time Series Analysis by State Space Methods: Second Edition.
-        Oxford University Press.
-    """
-
-    def __init__(
-        self,
-        season_length: int,
-        innovations: bool = True,
-        name: str | None = None,
-        state_names: list | None = None,
-        remove_first_state: bool = True,
-    ):
-        if name is None:
-            name = f"Seasonal[s={season_length}]"
-        if state_names is None:
-            state_names = [f"{name}_{i}" for i in range(season_length)]
-        else:
-            if len(state_names) != season_length:
-                raise ValueError(
-                    f"state_names must be a list of length season_length, got {len(state_names)}"
-                )
-            state_names = state_names.copy()
-        self.innovations = innovations
-        self.remove_first_state = remove_first_state
-
-        if self.remove_first_state:
-            # In traditional models, the first state isn't identified, so we can help out the user by automatically
-            # discarding it.
-            # TODO: Can this be stashed and reconstructed automatically somehow?
-            state_names.pop(0)
-
-        k_states = season_length - int(self.remove_first_state)
-
-        super().__init__(
-            name=name,
-            k_endog=1,
-            k_states=k_states,
-            k_posdef=int(innovations),
-            state_names=state_names,
-            measurement_error=False,
-            combine_hidden_states=True,
-            obs_state_idxs=np.r_[[1.0], np.zeros(k_states - 1)],
-        )
-
-    def populate_component_properties(self):
-        self.param_names = [f"{self.name}_coefs"]
-        self.param_info = {
-            f"{self.name}_coefs": {
-                "shape": (self.k_states,),
-                "constraints": None,
-                "dims": (f"{self.name}_state",),
-            }
-        }
-        self.param_dims = {f"{self.name}_coefs": (f"{self.name}_state",)}
-        self.coords = {f"{self.name}_state": self.state_names}
-
-        if self.innovations:
-            self.param_names += [f"sigma_{self.name}"]
-            self.param_info[f"sigma_{self.name}"] = {
-                "shape": (),
-                "constraints": "Positive",
-                "dims": None,
-            }
-            self.shock_names = [f"{self.name}"]
-
-    def make_symbolic_graph(self) -> None:
-        if self.remove_first_state:
-            # In this case, parameters are normalized to sum to zero, so the current state is the negative sum of
-            # all previous states.
-            T = np.eye(self.k_states, k=-1)
-            T[0, :] = -1
-        else:
-            # In this case we assume the user to be responsible for ensuring the states sum to zero, so T is just a
-            # circulant matrix that cycles between the states.
-            T = np.eye(self.k_states, k=1)
-            T[-1, 0] = 1
-
-        self.ssm["transition", :, :] = T
-        self.ssm["design", 0, 0] = 1
-
-        initial_states = self.make_and_register_variable(
-            f"{self.name}_coefs", shape=(self.k_states,)
-        )
-        self.ssm["initial_state", np.arange(self.k_states, dtype=int)] = initial_states
-
-        if self.innovations:
-            self.ssm["selection", 0, 0] = 1
-            season_sigma = self.make_and_register_variable(f"sigma_{self.name}", shape=())
-            cov_idx = ("state_cov", *np.diag_indices(1))
-            self.ssm[cov_idx] = season_sigma**2
-
-
-class FrequencySeasonality(Component):
-    r"""
-    Seasonal component, modeled in the frequency domain
-
-    Parameters
-    ----------
-    season_length: float
-        The number of periods in a single seasonal cycle, e.g. 12 for monthly data with annual seasonal pattern, 7 for
-        daily data with weekly seasonal pattern, etc. Non-integer seasonal_length is also permitted, for example
-        365.2422 days in a (solar) year.
-
-    n: int
-        Number of fourier features to include in the seasonal component. Default is ``season_length // 2``, which
-        is the maximum possible. A smaller number can be used for a more wave-like seasonal pattern.
-
-    name: str, default None
-        A name for this seasonal component. Used to label dimensions and coordinates. Useful when multiple seasonal
-        components are included in the same model. Default is ``f"Seasonal[s={season_length}, n={n}]"``
-
-    innovations: bool, default True
-        Whether to include stochastic innovations in the strength of the seasonal effect
-
-    Notes
-    -----
-    A seasonal effect is any pattern that repeats every fixed interval. Although there are many possible ways to
-    model seasonal effects, the implementation used here is the one described by [1] as the "canonical" frequency domain
-    representation. The seasonal component can be expressed:
-
-    .. math::
-        \begin{align}
-            \gamma_t &= \sum_{j=1}^{2n} \gamma_{j,t} \\
-            \gamma_{j, t+1} &= \gamma_{j,t} \cos \lambda_j + \gamma_{j,t}^\star \sin \lambda_j + \omega_{j, t} \\
-            \gamma_{j, t}^\star &= -\gamma_{j,t} \sin \lambda_j + \gamma_{j,t}^\star \cos \lambda_j + \omega_{j,t}^\star
-            \lambda_j &= \frac{2\pi j}{s}
-        \end{align}
-
-    Where :math:`s` is the ``seasonal_length``.
-
-    Unlike a ``TimeSeasonality`` component, a ``FrequencySeasonality`` component does not require integer season
-    length. In addition, for long seasonal periods, it is possible to obtain a more compact state space representation
-    by choosing ``n << s // 2``. Using ``TimeSeasonality``, an annual seasonal pattern in daily data requires 364
-    states, whereas ``FrequencySeasonality`` always requires ``2 * n`` states, regardless of the ``seasonal_length``.
-    The price of this compactness is less representational power. At ``n = 1``, the seasonal pattern will be a pure
-    sine wave. At ``n = s // 2``, any arbitrary pattern can be represented.
-
-    One cost of the added flexibility of ``FrequencySeasonality`` is reduced interpretability. States of this model are
-    coefficients :math:`\gamma_1, \gamma^\star_1, \gamma_2, \gamma_2^\star ..., \gamma_n, \gamma^\star_n` associated
-    with different frequencies in the fourier representation of the seasonal pattern. As a result, it is not possible
-    to isolate and identify a "Monday" effect, for instance.
-    """
-
-    def __init__(self, season_length, n=None, name=None, innovations=True):
-        if n is None:
-            n = int(season_length // 2)
-        if name is None:
-            name = f"Frequency[s={season_length}, n={n}]"
-
-        k_states = n * 2
-        self.n = n
-        self.season_length = season_length
-        self.innovations = innovations
-
-        # If the model is completely saturated (n = s // 2), the last state will not be identified, so it shouldn't
-        # get a parameter assigned to it and should just be fixed to zero.
-        # Test this way (rather than n == s // 2) to catch cases when n is non-integer.
-        self.last_state_not_identified = self.season_length / self.n == 2.0
-        self.n_coefs = k_states - int(self.last_state_not_identified)
-
-        obs_state_idx = np.zeros(k_states)
-        obs_state_idx[slice(0, k_states, 2)] = 1
-
-        super().__init__(
-            name=name,
-            k_endog=1,
-            k_states=k_states,
-            k_posdef=k_states * int(self.innovations),
-            measurement_error=False,
-            combine_hidden_states=True,
-            obs_state_idxs=obs_state_idx,
-        )
-
-    def make_symbolic_graph(self) -> None:
-        self.ssm["design", 0, slice(0, self.k_states, 2)] = 1
-
-        init_state = self.make_and_register_variable(f"{self.name}", shape=(self.n_coefs,))
-
-        init_state_idx = np.arange(self.n_coefs, dtype=int)
-        self.ssm["initial_state", init_state_idx] = init_state
-
-        T_mats = [_frequency_transition_block(self.season_length, j + 1) for j in range(self.n)]
-        T = pt.linalg.block_diag(*T_mats)
-        self.ssm["transition", :, :] = T
-
-        if self.innovations:
-            sigma_season = self.make_and_register_variable(f"sigma_{self.name}", shape=())
-            self.ssm["state_cov", :, :] = pt.eye(self.k_posdef) * sigma_season**2
-            self.ssm["selection", :, :] = np.eye(self.k_states)
-
-    def populate_component_properties(self):
-        self.state_names = [f"{self.name}_{f}_{i}" for i in range(self.n) for f in ["Cos", "Sin"]]
-        self.param_names = [f"{self.name}"]
-
-        self.param_dims = {self.name: (f"{self.name}_state",)}
-        self.param_info = {
-            f"{self.name}": {
-                "shape": (self.k_states - int(self.last_state_not_identified),),
-                "constraints": None,
-                "dims": (f"{self.name}_state",),
-            }
-        }
-
-        init_state_idx = np.arange(self.k_states, dtype=int)
-        if self.last_state_not_identified:
-            init_state_idx = init_state_idx[:-1]
-        self.coords = {f"{self.name}_state": [self.state_names[i] for i in init_state_idx]}
-
-        if self.innovations:
-            self.shock_names = self.state_names.copy()
-            self.param_names += [f"sigma_{self.name}"]
-            self.param_info[f"sigma_{self.name}"] = {
-                "shape": (),
-                "constraints": "Positive",
-                "dims": None,
-            }
-
-
-class CycleComponent(Component):
-    r"""
-    A component for modeling longer-term cyclical effects
-
-    Parameters
-    ----------
-    name: str
-        Name of the component. Used in generated coordinates and state names. If None, a descriptive name will be
-        used.
-
-    cycle_length: int, optional
-        The length of the cycle, in the calendar units of your data. For example, if your data is monthly, and you
-        want to model a 12-month cycle, use ``cycle_length=12``. You cannot specify both ``cycle_length`` and
-        ``estimate_cycle_length``.
-
-    estimate_cycle_length: bool, default False
-        Whether to estimate the cycle length. If True, an additional parameter, ``cycle_length`` will be added to the
-        model. You cannot specify both ``cycle_length`` and ``estimate_cycle_length``.
-
-    dampen: bool, default False
-        Whether to dampen the cycle by multiplying by a dampening factor :math:`\rho` at every timestep. If true,
-        an additional parameter, ``dampening_factor`` will be added to the model.
-
-    innovations: bool, default True
-        Whether to include stochastic innovations in the strength of the seasonal effect. If True, an additional
-        parameter, ``sigma_{name}`` will be added to the model.
-
-    Notes
-    -----
-    The cycle component is very similar in implementation to the frequency domain seasonal component, expect that it
-    is restricted to n=1. The cycle component can be expressed:
-
-    .. math::
-        \begin{align}
-            \gamma_t &= \rho \gamma_{t-1} \cos \lambda + \rho \gamma_{t-1}^\star \sin \lambda + \omega_{t} \\
-            \gamma_{t}^\star &= -\rho \gamma_{t-1} \sin \lambda + \rho \gamma_{t-1}^\star \cos \lambda + \omega_{t}^\star \\
-            \lambda &= \frac{2\pi}{s}
-        \end{align}
-
-    Where :math:`s` is the ``cycle_length``. [1] recommend that this component be used for longer term cyclical
-    effects, such as business cycles, and that the seasonal component be used for shorter term effects, such as
-    weekly or monthly seasonality.
-
-    Unlike a FrequencySeasonality component, the length of a CycleComponent can be estimated.
-
-    Examples
-    --------
-    Estimate a business cycle with length between 6 and 12 years:
-
-    .. code:: python
-
-        from pymc_extras.statespace import structural as st
-        import pymc as pm
-        import pytensor.tensor as pt
-        import pandas as pd
-        import numpy as np
-
-        data = np.random.normal(size=(100, 1))
-
-        # Build the structural model
-        grw = st.LevelTrendComponent(order=1, innovations_order=1)
-        cycle = st.CycleComponent('business_cycle', estimate_cycle_length=True, dampen=False)
-        ss_mod = (grw + cycle).build()
-
-        # Estimate with PyMC
-        with pm.Model(coords=ss_mod.coords) as model:
-            P0 = pm.Deterministic('P0', pt.eye(ss_mod.k_states), dims=ss_mod.param_dims['P0'])
-            intitial_trend = pm.Normal('initial_trend', dims=ss_mod.param_dims['initial_trend'])
-            sigma_trend = pm.HalfNormal('sigma_trend', dims=ss_mod.param_dims['sigma_trend'])
-
-            cycle_strength = pm.Normal('business_cycle')
-            cycle_length = pm.Uniform('business_cycle_length', lower=6, upper=12)
-
-            sigma_cycle = pm.HalfNormal('sigma_business_cycle', sigma=1)
-            ss_mod.build_statespace_graph(data)
-
-            idata = pm.sample(nuts_sampler='numpyro')
-
-    References
-    ----------
-    .. [1] Durbin, James, and Siem Jan Koopman. 2012.
-        Time Series Analysis by State Space Methods: Second Edition.
-        Oxford University Press.
-    """
-
-    def __init__(
-        self,
-        name: str | None = None,
-        cycle_length: int | None = None,
-        estimate_cycle_length: bool = False,
-        dampen: bool = False,
-        innovations: bool = True,
-    ):
-        if cycle_length is None and not estimate_cycle_length:
-            raise ValueError("Must specify cycle_length if estimate_cycle_length is False")
-        if cycle_length is not None and estimate_cycle_length:
-            raise ValueError("Cannot specify cycle_length if estimate_cycle_length is True")
-        if name is None:
-            cycle = int(cycle_length) if cycle_length is not None else "Estimate"
-            name = f"Cycle[s={cycle}, dampen={dampen}, innovations={innovations}]"
-
-        self.estimate_cycle_length = estimate_cycle_length
-        self.cycle_length = cycle_length
-        self.innovations = innovations
-        self.dampen = dampen
-        self.n_coefs = 1
-
-        k_states = 2
-        k_endog = 1
-        k_posdef = 2
-
-        obs_state_idx = np.zeros(k_states)
-        obs_state_idx[slice(0, k_states, 2)] = 1
-
-        super().__init__(
-            name=name,
-            k_endog=k_endog,
-            k_states=k_states,
-            k_posdef=k_posdef,
-            measurement_error=False,
-            combine_hidden_states=True,
-            obs_state_idxs=obs_state_idx,
-        )
-
-    def make_symbolic_graph(self) -> None:
-        self.ssm["design", 0, slice(0, self.k_states, 2)] = 1
-        self.ssm["selection", :, :] = np.eye(self.k_states)
-        self.param_dims = {self.name: (f"{self.name}_state",)}
-        self.coords = {f"{self.name}_state": self.state_names}
-
-        init_state = self.make_and_register_variable(f"{self.name}", shape=(self.k_states,))
-
-        self.ssm["initial_state", :] = init_state
-
-        if self.estimate_cycle_length:
-            lamb = self.make_and_register_variable(f"{self.name}_length", shape=())
-        else:
-            lamb = self.cycle_length
-
-        if self.dampen:
-            rho = self.make_and_register_variable(f"{self.name}_dampening_factor", shape=())
-        else:
-            rho = 1
-
-        T = rho * _frequency_transition_block(lamb, j=1)
-        self.ssm["transition", :, :] = T
-
-        if self.innovations:
-            sigma_cycle = self.make_and_register_variable(f"sigma_{self.name}", shape=())
-            self.ssm["state_cov", :, :] = pt.eye(self.k_posdef) * sigma_cycle**2
-
-    def populate_component_properties(self):
-        self.state_names = [f"{self.name}_{f}" for f in ["Cos", "Sin"]]
-        self.param_names = [f"{self.name}"]
-
-        self.param_info = {
-            f"{self.name}": {
-                "shape": (2,),
-                "constraints": None,
-                "dims": (f"{self.name}_state",),
-            }
-        }
-
-        if self.estimate_cycle_length:
-            self.param_names += [f"{self.name}_length"]
-            self.param_info[f"{self.name}_length"] = {
-                "shape": (),
-                "constraints": "Positive, non-zero",
-                "dims": None,
-            }
-
-        if self.dampen:
-            self.param_names += [f"{self.name}_dampening_factor"]
-            self.param_info[f"{self.name}_dampening_factor"] = {
-                "shape": (),
-                "constraints": "0 < x ≤ 1",
-                "dims": None,
-            }
-
-        if self.innovations:
-            self.param_names += [f"sigma_{self.name}"]
-            self.param_info[f"sigma_{self.name}"] = {
-                "shape": (),
-                "constraints": "Positive",
-                "dims": None,
-            }
-            self.shock_names = self.state_names.copy()
-
-
-class RegressionComponent(Component):
-    def __init__(
-        self,
-        k_exog: int | None = None,
-        name: str | None = "Exogenous",
-        state_names: list[str] | None = None,
-        innovations=False,
-    ):
-        self.innovations = innovations
-        k_exog = self._handle_input_data(k_exog, state_names, name)
-
-        k_states = k_exog
-        k_endog = 1
-        k_posdef = k_exog
-
-        super().__init__(
-            name=name,
-            k_endog=k_endog,
-            k_states=k_states,
-            k_posdef=k_posdef,
-            state_names=self.state_names,
-            measurement_error=False,
-            combine_hidden_states=False,
-            exog_names=[f"data_{name}"],
-            obs_state_idxs=np.ones(k_states),
-        )
-
-    @staticmethod
-    def _get_state_names(k_exog: int | None, state_names: list[str] | None, name: str):
-        if k_exog is None and state_names is None:
-            raise ValueError("Must specify at least one of k_exog or state_names")
-        if state_names is not None and k_exog is not None:
-            if len(state_names) != k_exog:
-                raise ValueError(f"Expected {k_exog} state names, found {len(state_names)}")
-        elif k_exog is None:
-            k_exog = len(state_names)
-        else:
-            state_names = [f"{name}_{i + 1}" for i in range(k_exog)]
-
-        return k_exog, state_names
-
-    def _handle_input_data(self, k_exog: int, state_names: list[str] | None, name) -> int:
-        k_exog, state_names = self._get_state_names(k_exog, state_names, name)
-        self.state_names = state_names
-
-        return k_exog
-
-    def make_symbolic_graph(self) -> None:
-        betas = self.make_and_register_variable(f"beta_{self.name}", shape=(self.k_states,))
-        regression_data = self.make_and_register_data(
-            f"data_{self.name}", shape=(None, self.k_states)
-        )
-
-        self.ssm["initial_state", :] = betas
-        self.ssm["transition", :, :] = np.eye(self.k_states)
-        self.ssm["selection", :, :] = np.eye(self.k_states)
-        self.ssm["design"] = pt.expand_dims(regression_data, 1)
-
-        if self.innovations:
-            sigma_beta = self.make_and_register_variable(
-                f"sigma_beta_{self.name}", (self.k_states,)
-            )
-            row_idx, col_idx = np.diag_indices(self.k_states)
-            self.ssm["state_cov", row_idx, col_idx] = sigma_beta**2
-
-    def populate_component_properties(self) -> None:
-        self.shock_names = self.state_names
-
-        self.param_names = [f"beta_{self.name}"]
-        self.data_names = [f"data_{self.name}"]
-        self.param_dims = {
-            f"beta_{self.name}": ("exog_state",),
-        }
-
-        self.param_info = {
-            f"beta_{self.name}": {
-                "shape": (self.k_states,),
-                "constraints": None,
-                "dims": ("exog_state",),
-            },
-        }
-
-        self.data_info = {
-            f"data_{self.name}": {
-                "shape": (None, self.k_states),
-                "dims": (TIME_DIM, "exog_state"),
-            },
-        }
-        self.coords = {"exog_state": self.state_names}
-
-        if self.innovations:
-            self.param_names += [f"sigma_beta_{self.name}"]
-            self.param_dims[f"sigma_beta_{self.name}"] = "exog_state"
-            self.param_info[f"sigma_beta_{self.name}"] = {
-                "shape": (),
-                "constraints": "Positive",
-                "dims": ("exog_state",),
-            }

From 0c4590eb88f30ede71f57be08ffb42d0182ffaab Mon Sep 17 00:00:00 2001
From: jessegrabowski 
Date: Sun, 6 Jul 2025 11:59:59 +0800
Subject: [PATCH 13/21] Always count names to determine k_endog

---
 pymc_extras/statespace/models/structural/core.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/pymc_extras/statespace/models/structural/core.py b/pymc_extras/statespace/models/structural/core.py
index 913c58e17..d5fbe4e6e 100644
--- a/pymc_extras/statespace/models/structural/core.py
+++ b/pymc_extras/statespace/models/structural/core.py
@@ -483,13 +483,12 @@ def populate_component_properties(self):
     def _get_combined_shapes(self, other):
         k_states = self.k_states + other.k_states
         k_posdef = self.k_posdef + other.k_posdef
-        if self.k_endog == other.k_endog:
-            k_endog = self.k_endog
-        else:
-            combined_states = self._combine_property(
-                other, "observed_state_names", allow_duplicates=False
-            )
-            k_endog = len(combined_states)
+
+        # To count endog states, we have to count unique names between the two components.
+        combined_states = self._combine_property(
+            other, "observed_state_names", allow_duplicates=False
+        )
+        k_endog = len(combined_states)
 
         return k_states, k_posdef, k_endog
 

From 3c5124d6ce6c662fa8462ef0595cb042a2bb14ef Mon Sep 17 00:00:00 2001
From: jessegrabowski 
Date: Sun, 6 Jul 2025 12:11:52 +0800
Subject: [PATCH 14/21] LevelTrend state/shock names depend on component name

---
 .../structural/components/level_trend.py      | 70 ++++++++++------
 .../structural/components/test_level_trend.py | 83 +++++++++++++++++--
 .../structural/test_against_statsmodels.py    | 16 ++--
 3 files changed, 126 insertions(+), 43 deletions(-)

diff --git a/pymc_extras/statespace/models/structural/components/level_trend.py b/pymc_extras/statespace/models/structural/components/level_trend.py
index 1563dde72..7e301e8d8 100644
--- a/pymc_extras/statespace/models/structural/components/level_trend.py
+++ b/pymc_extras/statespace/models/structural/components/level_trend.py
@@ -1,6 +1,5 @@
 import numpy as np
-
-from scipy import linalg
+import pytensor.tensor as pt
 
 from pymc_extras.statespace.models.structural.core import Component
 from pymc_extras.statespace.models.structural.utils import order_to_mask
@@ -114,7 +113,7 @@ def __init__(
         self,
         order: int | list[int] = 2,
         innovations_order: int | list[int] | None = None,
-        name: str = "LevelTrend",
+        name: str = "level_trend",
         observed_state_names: list[str] | None = None,
     ):
         if innovations_order is None:
@@ -166,35 +165,46 @@ def populate_component_properties(self):
         k_posdef = self.k_posdef // k_endog
 
         name_slice = POSITION_DERIVATIVE_NAMES[:k_states]
-        self.param_names = ["initial_trend"]
+        self.param_names = [f"{self.name}_initial"]
         base_names = [name for name, mask in zip(name_slice, self._order_mask) if mask]
         self.state_names = [
-            f"{name}[{obs_name}]" for obs_name in self.observed_state_names for name in base_names
+            f"{name}[{obs_name}]" if k_endog > 1 else name
+            for obs_name in self.observed_state_names
+            for name in base_names
         ]
-        self.param_dims = {"initial_trend": ("trend_state",)}
-        self.coords = {"trend_state": base_names}
+        self.param_dims = {f"{self.name}_initial": (f"{self.name}_state",)}
+        self.coords = {f"{self.name}_state": base_names}
 
         if k_endog > 1:
-            self.param_dims["trend_state"] = (
-                "trend_endog",
-                "trend_state",
+            self.param_dims[f"{self.name}_state"] = (
+                f"{self.name}_endog",
+                f"{self.name}_state",
             )
-            self.param_dims = {"initial_trend": ("trend_endog", "trend_state")}
-            self.coords["trend_endog"] = self.observed_state_names
+            self.param_dims = {f"{self.name}_initial": (f"{self.name}_endog", f"{self.name}_state")}
+            self.coords[f"{self.name}_endog"] = self.observed_state_names
 
         shape = (k_endog, k_states) if k_endog > 1 else (k_states,)
-        self.param_info = {"initial_trend": {"shape": shape, "constraints": None}}
+        self.param_info = {f"{self.name}_initial": {"shape": shape, "constraints": None}}
 
         if self.k_posdef > 0:
-            self.param_names += ["sigma_trend"]
-            self.shock_names = [
+            self.param_names += [f"{self.name}_sigma"]
+
+            shock_base_names = [
                 name for name, mask in zip(name_slice, self.innovations_order) if mask
             ]
-            self.param_dims["sigma_trend"] = (
-                ("trend_shock",) if k_endog == 1 else ("trend_endog", "trend_shock")
+            self.shock_names = [
+                f"{name}[{obs_name}]" if k_endog > 1 else name
+                for obs_name in self.observed_state_names
+                for name in shock_base_names
+            ]
+
+            self.param_dims[f"{self.name}_sigma"] = (
+                (f"{self.name}_shock",)
+                if k_endog == 1
+                else (f"{self.name}_endog", f"{self.name}_shock")
             )
-            self.coords["trend_shock"] = self.shock_names
-            self.param_info["sigma_trend"] = {
+            self.coords[f"{self.name}_shock"] = self.shock_names
+            self.param_info[f"{self.name}_sigma"] = {
                 "shape": (k_posdef,) if k_endog == 1 else (k_endog, k_posdef),
                 "constraints": "Positive",
             }
@@ -208,28 +218,34 @@ def make_symbolic_graph(self) -> None:
         k_posdef = self.k_posdef // k_endog
 
         initial_trend = self.make_and_register_variable(
-            "initial_trend",
+            f"{self.name}_initial",
             shape=(k_states,) if k_endog == 1 else (k_endog, k_states),
         )
         self.ssm["initial_state", :] = initial_trend.ravel()
 
-        triu_idx = np.triu_indices(k_states)
-        T = np.zeros((k_states, k_states))
-        T[triu_idx[0], triu_idx[1]] = 1
+        triu_idx = pt.triu_indices(k_states)
+        T = pt.zeros((k_states, k_states))[triu_idx[0], triu_idx[1]].set(1)
 
-        self.ssm["transition"] = linalg.block_diag(*[T for _ in range(k_endog)])
+        self.ssm["transition", :, :] = pt.specify_shape(
+            pt.linalg.block_diag(*[T for _ in range(k_endog)]), (self.k_states, self.k_states)
+        )
 
         R = np.eye(k_states)
         R = R[:, self.innovations_order]
 
-        self.ssm["selection", :, :] = linalg.block_diag(*[R for _ in range(k_endog)])
+        self.ssm["selection", :, :] = pt.specify_shape(
+            pt.linalg.block_diag(*[R for _ in range(k_endog)]), (self.k_states, self.k_posdef)
+        )
 
         Z = np.array([1.0] + [0.0] * (k_states - 1)).reshape((1, -1))
-        self.ssm["design"] = linalg.block_diag(*[Z for _ in range(k_endog)])
+
+        self.ssm["design", :, :] = pt.specify_shape(
+            pt.linalg.block_diag(*[Z for _ in range(k_endog)]), (self.k_endog, self.k_states)
+        )
 
         if k_posdef > 0:
             sigma_trend = self.make_and_register_variable(
-                "sigma_trend",
+                f"{self.name}_sigma",
                 shape=(k_posdef,) if k_endog == 1 else (k_endog, k_posdef),
             )
             diag_idx = np.diag_indices(k_posdef * k_endog)
diff --git a/tests/statespace/models/structural/components/test_level_trend.py b/tests/statespace/models/structural/components/test_level_trend.py
index 64f04b403..45503912a 100644
--- a/tests/statespace/models/structural/components/test_level_trend.py
+++ b/tests/statespace/models/structural/components/test_level_trend.py
@@ -1,4 +1,5 @@
 import numpy as np
+import pytensor
 
 from numpy.testing import assert_allclose
 from pytensor import config
@@ -13,7 +14,7 @@
 
 def test_level_trend_model(rng):
     mod = st.LevelTrendComponent(order=2, innovations_order=0)
-    params = {"initial_trend": [0.0, 1.0]}
+    params = {"level_trend_initial": [0.0, 1.0]}
     x, y = simulate_from_numpy_model(mod, rng, params)
 
     assert_allclose(np.diff(y), 1, atol=ATOL, rtol=RTOL)
@@ -21,7 +22,7 @@ def test_level_trend_model(rng):
     # Check coords
     mod = mod.build(verbose=False)
     _assert_basic_coords_correct(mod)
-    assert mod.coords["trend_state"] == ["level", "trend"]
+    assert mod.coords["level_trend_state"] == ["level", "trend"]
 
 
 def test_level_trend_multiple_observed_construction():
@@ -33,12 +34,22 @@ def test_level_trend_multiple_observed_construction():
     assert mod.k_states == 6
     assert mod.k_posdef == 3
 
-    assert mod.coords["trend_state"] == ["level", "trend"]
-    assert mod.coords["trend_endog"] == ["data_1", "data_2", "data_3"]
+    assert mod.coords["level_trend_state"] == ["level", "trend"]
+    assert mod.coords["level_trend_endog"] == ["data_1", "data_2", "data_3"]
 
-    Z = mod.ssm["design"].eval()
-    T = mod.ssm["transition"].eval()
-    R = mod.ssm["selection"].eval()
+    assert mod.state_names == [
+        "level[data_1]",
+        "trend[data_1]",
+        "level[data_2]",
+        "trend[data_2]",
+        "level[data_3]",
+        "trend[data_3]",
+    ]
+    assert mod.shock_names == ["level_shock[data_1]", "level_shock[data_2]", "level_shock[data_3]"]
+
+    Z, T, R = pytensor.function(
+        [], [mod.ssm["design"], mod.ssm["transition"], mod.ssm["selection"]], mode="FAST_COMPILE"
+    )()
 
     np.testing.assert_allclose(
         Z,
@@ -84,8 +95,64 @@ def test_level_trend_multiple_observed(rng):
     mod = st.LevelTrendComponent(
         order=2, innovations_order=0, observed_state_names=["data_1", "data_2", "data_3"]
     )
-    params = {"initial_trend": np.array([[0.0, 1.0], [0.0, 2.0], [0.0, 3.0]])}
+    params = {"level_trend_initial": np.array([[0.0, 1.0], [0.0, 2.0], [0.0, 3.0]])}
 
     x, y = simulate_from_numpy_model(mod, rng, params)
     assert (np.diff(y, axis=0) == np.array([[1.0, 2.0, 3.0]])).all().all()
     assert (np.diff(x, axis=0) == np.array([[1.0, 0.0, 2.0, 0.0, 3.0, 0.0]])).all().all()
+
+
+def test_add_level_trend_with_different_observed():
+    mod_1 = st.LevelTrendComponent(
+        name="ll", order=2, innovations_order=[0, 1], observed_state_names=["data_1"]
+    )
+    mod_2 = st.LevelTrendComponent(
+        name="grw", order=1, innovations_order=[1], observed_state_names=["data_2"]
+    )
+
+    mod = (mod_1 + mod_2).build(verbose=False)
+    assert mod.k_endog == 2
+    assert mod.k_states == 3
+    assert mod.k_posdef == 2
+
+    assert mod.coords["ll_state"] == ["level", "trend"]
+    assert mod.coords["grw_state"] == ["level"]
+
+    assert mod.state_names == ["level[data_1]", "trend[data_1]", "level[data_2]"]
+    assert mod.shock_names == ["trend_shock[data_1]", "level_shock[data_2]"]
+
+    Z, T, R = pytensor.function(
+        [], [mod.ssm["design"], mod.ssm["transition"], mod.ssm["selection"]], mode="FAST_COMPILE"
+    )()
+
+    np.testing.assert_allclose(
+        Z,
+        np.array(
+            [
+                [1.0, 0.0, 0.0],
+                [0.0, 0.0, 1.0],
+            ]
+        ),
+    )
+
+    np.testing.assert_allclose(
+        T,
+        np.array(
+            [
+                [1.0, 1.0, 0.0],
+                [0.0, 1.0, 0.0],
+                [0.0, 0.0, 1.0],
+            ]
+        ),
+    )
+
+    np.testing.assert_allclose(
+        R,
+        np.array(
+            [
+                [0.0, 0.0],
+                [1.0, 0.0],
+                [0.0, 1.0],
+            ]
+        ),
+    )
diff --git a/tests/statespace/models/structural/test_against_statsmodels.py b/tests/statespace/models/structural/test_against_statsmodels.py
index 3495ecc14..98318711f 100644
--- a/tests/statespace/models/structural/test_against_statsmodels.py
+++ b/tests/statespace/models/structural/test_against_statsmodels.py
@@ -220,7 +220,7 @@ def create_structural_model_and_equivalent_statsmodel(
 
     if level:
         level_trend_order[0] = 1
-        expected_coords["trend_state"] += [
+        expected_coords["level_state"] += [
             "level",
         ]
         expected_coords[ALL_STATE_DIM] += [
@@ -231,7 +231,7 @@ def create_structural_model_and_equivalent_statsmodel(
         ]
         if stochastic_level:
             level_trend_innov_order[0] = 1
-            expected_coords["trend_shock"] += ["level"]
+            expected_coords["level_shock"] += ["level"]
             expected_coords[SHOCK_DIM] += [
                 "level",
             ]
@@ -241,7 +241,7 @@ def create_structural_model_and_equivalent_statsmodel(
 
     if trend:
         level_trend_order[1] = 1
-        expected_coords["trend_state"] += [
+        expected_coords["level_state"] += [
             "trend",
         ]
         expected_coords[ALL_STATE_DIM] += [
@@ -253,12 +253,12 @@ def create_structural_model_and_equivalent_statsmodel(
 
         if stochastic_trend:
             level_trend_innov_order[1] = 1
-            expected_coords["trend_shock"] += ["trend"]
+            expected_coords["level_shock"] += ["trend"]
             expected_coords[SHOCK_DIM] += ["trend"]
             expected_coords[SHOCK_AUX_DIM] += ["trend"]
 
     if level or trend:
-        expected_param_dims["initial_trend"] += ("trend_state",)
+        expected_param_dims["level_initial"] += ("level_state",)
         level_value = np.where(
             level_trend_order,
             rng.normal(
@@ -272,13 +272,13 @@ def create_structural_model_and_equivalent_statsmodel(
         max_order = np.flatnonzero(level_value)[-1].item() + 1
         level_trend_order = level_trend_order[:max_order]
 
-        params["initial_trend"] = level_value[:max_order]
+        params["level_initial"] = level_value[:max_order]
         sm_init["level"] = level_value[0]
         sm_init["trend"] = level_value[1]
 
         if sum(level_trend_innov_order) > 0:
-            expected_param_dims["sigma_trend"] += ("trend_shock",)
-            params["sigma_trend"] = np.sqrt(sigma_level_value2)
+            expected_param_dims["level_sigma"] += ("level_shock",)
+            params["level_sigma"] = np.sqrt(sigma_level_value2)
 
         sigma_level_value = sigma_level_value2.tolist()
         if stochastic_level:

From b9322559ab061fefab5e8f19d8b5dbff5b82f42e Mon Sep 17 00:00:00 2001
From: jessegrabowski 
Date: Sun, 6 Jul 2025 12:52:46 +0800
Subject: [PATCH 15/21] Update tests to new names

---
 tests/statespace/core/test_statespace.py        | 16 +++++++++-------
 tests/statespace/utils/test_coord_assignment.py | 12 ++++++------
 2 files changed, 15 insertions(+), 13 deletions(-)

diff --git a/tests/statespace/core/test_statespace.py b/tests/statespace/core/test_statespace.py
index bfcd114ae..947069d86 100644
--- a/tests/statespace/core/test_statespace.py
+++ b/tests/statespace/core/test_statespace.py
@@ -167,7 +167,9 @@ def exog_pymc_mod(exog_ss_mod, exog_data):
         P0_diag = pm.Gamma("P0_diag", alpha=2, beta=4, dims=["state"])
         P0 = pm.Deterministic("P0", pt.diag(P0_diag), dims=["state", "state_aux"])
 
-        initial_trend = pm.Normal("initial_trend", mu=[0], sigma=[0.005], dims=["trend_state"])
+        initial_trend = pm.Normal(
+            "level_trend_initial", mu=[0], sigma=[0.005], dims=["level_trend_state"]
+        )
 
         data_exog = pm.Data(
             "data_exog", exog_data["x1"].values[:, None], dims=["time", "exog_state"]
@@ -184,12 +186,12 @@ def pymc_mod_no_exog(ss_mod_no_exog, rng):
     y = pd.DataFrame(rng.normal(size=(100, 1)).astype(floatX), columns=["y"])
 
     with pm.Model(coords=ss_mod_no_exog.coords) as m:
-        initial_trend = pm.Normal("initial_trend", dims=["trend_state"])
+        initial_trend = pm.Normal("level_trend_initial", dims=["level_trend_state"])
         P0_sigma = pm.Exponential("P0_sigma", 1)
         P0 = pm.Deterministic(
             "P0", pt.eye(ss_mod_no_exog.k_states) * P0_sigma, dims=["state", "state_aux"]
         )
-        sigma_trend = pm.Exponential("sigma_trend", 1, dims=["trend_shock"])
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, dims=["level_trend_shock"])
         ss_mod_no_exog.build_statespace_graph(y)
 
     return m
@@ -204,12 +206,12 @@ def pymc_mod_no_exog_dt(ss_mod_no_exog_dt, rng):
     )
 
     with pm.Model(coords=ss_mod_no_exog_dt.coords) as m:
-        initial_trend = pm.Normal("initial_trend", dims=["trend_state"])
+        initial_trend = pm.Normal("level_trend_initial", dims=["level_trend_state"])
         P0_sigma = pm.Exponential("P0_sigma", 1)
         P0 = pm.Deterministic(
             "P0", pt.eye(ss_mod_no_exog_dt.k_states) * P0_sigma, dims=["state", "state_aux"]
         )
-        sigma_trend = pm.Exponential("sigma_trend", 1, dims=["trend_shock"])
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, dims=["level_trend_shock"])
         ss_mod_no_exog_dt.build_statespace_graph(y)
 
     return m
@@ -313,7 +315,7 @@ def test_build_statespace_graph_warns_if_data_has_nans():
     ss_mod = st.LevelTrendComponent(order=1, innovations_order=0).build(verbose=False)
 
     with pm.Model() as pymc_mod:
-        initial_trend = pm.Normal("initial_trend", shape=(1,))
+        initial_trend = pm.Normal("level_trend_initial", shape=(1,))
         P0 = pm.Deterministic("P0", pt.eye(1, dtype=floatX))
         with pytest.warns(pm.ImputationWarning):
             ss_mod.build_statespace_graph(
@@ -326,7 +328,7 @@ def test_build_statespace_graph_raises_if_data_has_missing_fill():
     ss_mod = st.LevelTrendComponent(order=1, innovations_order=0).build(verbose=False)
 
     with pm.Model() as pymc_mod:
-        initial_trend = pm.Normal("initial_trend", shape=(1,))
+        initial_trend = pm.Normal("level_trend_initial", shape=(1,))
         P0 = pm.Deterministic("P0", pt.eye(1, dtype=floatX))
         with pytest.raises(ValueError, match="Provided data contains the value 1.0"):
             data = np.ones((10, 1), dtype=floatX)
diff --git a/tests/statespace/utils/test_coord_assignment.py b/tests/statespace/utils/test_coord_assignment.py
index a3b419914..fe846c4fe 100644
--- a/tests/statespace/utils/test_coord_assignment.py
+++ b/tests/statespace/utils/test_coord_assignment.py
@@ -80,8 +80,8 @@ def _create_model(f):
                 dims="state",
             )
             P0 = pm.Deterministic("P0", pt.diag(P0_diag), dims=("state", "state_aux"))
-            initial_trend = pm.Normal("initial_trend", dims="trend_state")
-            sigma_trend = pm.Exponential("sigma_trend", 1, dims="trend_shock")
+            initial_trend = pm.Normal("level_trend_initial", dims="level_trend_state")
+            sigma_trend = pm.Exponential("level_trend_sigma", 1, dims="level_trend_shock")
             ss_mod.build_statespace_graph(data, save_kalman_filter_outputs_in_idata=True)
         return mod
 
@@ -103,8 +103,8 @@ def test_model_build_without_coords(load_dataset):
     with pm.Model() as mod:
         P0_diag = pm.Exponential("P0_diag", 1, shape=(2,))
         P0 = pm.Deterministic("P0", pt.diag(P0_diag))
-        initial_trend = pm.Normal("initial_trend", shape=(2,))
-        sigma_trend = pm.Exponential("sigma_trend", 1, shape=(2,))
+        initial_trend = pm.Normal("level_trend_initial", shape=(2,))
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, shape=(2,))
         ss_mod.build_statespace_graph(data, register_data=False)
 
     assert mod.coords == {}
@@ -131,8 +131,8 @@ def make_model(index):
         P0_diag = pm.Gamma("P0_diag", alpha=5, beta=5)
         P0 = pm.Deterministic("P0", pt.eye(ss_mod.k_states) * P0_diag, dims=P0_dims)
 
-        initial_trend = pm.Normal("initial_trend", dims=initial_trend_dims)
-        sigma_trend = pm.Gamma("sigma_trend", alpha=2, beta=50, dims=sigma_trend_dims)
+        initial_trend = pm.Normal("level_trend_initial", dims=initial_trend_dims)
+        sigma_trend = pm.Gamma("level_trend_sigma", alpha=2, beta=50, dims=sigma_trend_dims)
 
         with pytest.warns(UserWarning, match="No time index found on the supplied data"):
             ss_mod.build_statespace_graph(

From 6debd2375db833c511b5ddefc2069d4853ae1c46 Mon Sep 17 00:00:00 2001
From: jessegrabowski 
Date: Sun, 6 Jul 2025 13:08:33 +0800
Subject: [PATCH 16/21] More test updates

---
 tests/statespace/filters/test_distributions.py       | 12 ++++++------
 .../models/structural/components/test_level_trend.py |  2 +-
 tests/statespace/models/structural/test_core.py      |  8 ++++----
 3 files changed, 11 insertions(+), 11 deletions(-)

diff --git a/tests/statespace/filters/test_distributions.py b/tests/statespace/filters/test_distributions.py
index 1958d0bf0..383257196 100644
--- a/tests/statespace/filters/test_distributions.py
+++ b/tests/statespace/filters/test_distributions.py
@@ -52,8 +52,8 @@ def pymc_model(data):
         data = pm.Data("data", data.values)
         P0_diag = pm.Exponential("P0_diag", 1, shape=(2,))
         P0 = pm.Deterministic("P0", pt.diag(P0_diag))
-        initial_trend = pm.Normal("initial_trend", shape=(2,))
-        sigma_trend = pm.Exponential("sigma_trend", 1, shape=(2,))
+        initial_trend = pm.Normal("level_trend_initial", shape=(2,))
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, shape=(2,))
 
     return mod
 
@@ -69,8 +69,8 @@ def pymc_model_2(data):
     with pm.Model(coords=coords) as mod:
         P0_diag = pm.Exponential("P0_diag", 1, shape=(2,))
         P0 = pm.Deterministic("P0", pt.diag(P0_diag))
-        initial_trend = pm.Normal("initial_trend", shape=(2,))
-        sigma_trend = pm.Exponential("sigma_trend", 1, shape=(2,))
+        initial_trend = pm.Normal("level_trend_initial", shape=(2,))
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, shape=(2,))
         sigma_me = pm.Exponential("sigma_error", 1)
 
     return mod
@@ -207,8 +207,8 @@ def test_lgss_with_time_varying_inputs(output_name, rng):
         exog_data = pm.Data("data_exog", X)
         P0_diag = pm.Exponential("P0_diag", 1, shape=(mod.k_states,))
         P0 = pm.Deterministic("P0", pt.diag(P0_diag))
-        initial_trend = pm.Normal("initial_trend", shape=(2,))
-        sigma_trend = pm.Exponential("sigma_trend", 1, shape=(2,))
+        initial_trend = pm.Normal("level_trend_initial", shape=(2,))
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, shape=(2,))
         beta_exog = pm.Normal("beta_exog", shape=(3,))
 
         mod._insert_random_variables()
diff --git a/tests/statespace/models/structural/components/test_level_trend.py b/tests/statespace/models/structural/components/test_level_trend.py
index 45503912a..37f8c14a2 100644
--- a/tests/statespace/models/structural/components/test_level_trend.py
+++ b/tests/statespace/models/structural/components/test_level_trend.py
@@ -45,7 +45,7 @@ def test_level_trend_multiple_observed_construction():
         "level[data_3]",
         "trend[data_3]",
     ]
-    assert mod.shock_names == ["level_shock[data_1]", "level_shock[data_2]", "level_shock[data_3]"]
+    assert mod.shock_names == ["level[data_1]", "level[data_2]", "level[data_3]"]
 
     Z, T, R = pytensor.function(
         [], [mod.ssm["design"], mod.ssm["transition"], mod.ssm["selection"]], mode="FAST_COMPILE"
diff --git a/tests/statespace/models/structural/test_core.py b/tests/statespace/models/structural/test_core.py
index 46115b659..50059bd43 100644
--- a/tests/statespace/models/structural/test_core.py
+++ b/tests/statespace/models/structural/test_core.py
@@ -22,8 +22,8 @@ def test_add_components():
     mod = ll + se
 
     ll_params = {
-        "initial_trend": np.zeros(2, dtype=floatX),
-        "sigma_trend": np.ones(2, dtype=floatX),
+        "level_trend_initial": np.zeros(2, dtype=floatX),
+        "level_trend_sigma": np.ones(2, dtype=floatX),
     }
     se_params = {
         "seasonal_coefs": np.ones(11, dtype=floatX),
@@ -93,8 +93,8 @@ def test_extract_components_from_idata(rng):
         x0 = pm.Normal("x0", dims=["state"])
         P0 = pm.Deterministic("P0", pt.eye(mod.k_states), dims=["state", "state_aux"])
         beta_exog = pm.Normal("beta_exog", dims=["exog_state"])
-        initial_trend = pm.Normal("initial_trend", dims=["trend_state"])
-        sigma_trend = pm.Exponential("sigma_trend", 1, dims=["trend_shock"])
+        initial_trend = pm.Normal("level_trend_initial", dims=["level_trend_state"])
+        sigma_trend = pm.Exponential("level_trend_sigma", 1, dims=["level_trend_shock"])
         seasonal_coefs = pm.Normal("seasonal", dims=["seasonal_state"])
         sigma_obs = pm.Exponential("sigma_obs", 1)
 

From fbc61a14bf2adf47d056a4bad587abfeea991dc8 Mon Sep 17 00:00:00 2001
From: jessegrabowski 
Date: Sun, 6 Jul 2025 13:25:37 +0800
Subject: [PATCH 17/21] Delay dropping data names from states/coords until
 `.build`

---
 .../structural/components/level_trend.py      |  6 ++--
 .../statespace/models/structural/core.py      | 34 +++++++++++++++----
 .../structural/components/test_level_trend.py |  2 +-
 3 files changed, 31 insertions(+), 11 deletions(-)

diff --git a/pymc_extras/statespace/models/structural/components/level_trend.py b/pymc_extras/statespace/models/structural/components/level_trend.py
index 7e301e8d8..c9077f7b0 100644
--- a/pymc_extras/statespace/models/structural/components/level_trend.py
+++ b/pymc_extras/statespace/models/structural/components/level_trend.py
@@ -168,9 +168,7 @@ def populate_component_properties(self):
         self.param_names = [f"{self.name}_initial"]
         base_names = [name for name, mask in zip(name_slice, self._order_mask) if mask]
         self.state_names = [
-            f"{name}[{obs_name}]" if k_endog > 1 else name
-            for obs_name in self.observed_state_names
-            for name in base_names
+            f"{name}[{obs_name}]" for obs_name in self.observed_state_names for name in base_names
         ]
         self.param_dims = {f"{self.name}_initial": (f"{self.name}_state",)}
         self.coords = {f"{self.name}_state": base_names}
@@ -193,7 +191,7 @@ def populate_component_properties(self):
                 name for name, mask in zip(name_slice, self.innovations_order) if mask
             ]
             self.shock_names = [
-                f"{name}[{obs_name}]" if k_endog > 1 else name
+                f"{name}[{obs_name}]"
                 for obs_name in self.observed_state_names
                 for name in shock_base_names
             ]
diff --git a/pymc_extras/statespace/models/structural/core.py b/pymc_extras/statespace/models/structural/core.py
index d5fbe4e6e..418f57123 100644
--- a/pymc_extras/statespace/models/structural/core.py
+++ b/pymc_extras/statespace/models/structural/core.py
@@ -76,16 +76,19 @@ def __init__(
         param_names, param_dims, param_info = self._add_inital_state_cov_to_properties(
             param_names, param_dims, param_info, k_states
         )
-        self._state_names = state_names.copy()
-        self._data_names = data_names.copy()
-        self._shock_names = shock_names.copy()
-        self._param_names = param_names.copy()
-        self._param_dims = param_dims.copy()
+
+        self._state_names = self._strip_data_names_if_unambiguous(state_names, k_endog)
+        self._data_names = self._strip_data_names_if_unambiguous(data_names, k_endog)
+        self._shock_names = self._strip_data_names_if_unambiguous(shock_names, k_endog)
+        self._param_names = self._strip_data_names_if_unambiguous(param_names, k_endog)
+        self._param_dims = param_dims
 
         default_coords = make_default_coords(self)
         coords.update(default_coords)
 
-        self._coords = coords
+        self._coords = {
+            k: self._strip_data_names_if_unambiguous(v, k_endog) for k, v in coords.items()
+        }
         self._param_info = param_info.copy()
         self._data_info = data_info.copy()
         self.measurement_error = measurement_error
@@ -122,6 +125,25 @@ def __init__(
         P0 = self.make_and_register_variable("P0", shape=(self.k_states, self.k_states))
         self.ssm["initial_state_cov"] = P0
 
+    def _strip_data_names_if_unambiguous(self, names: list[str], k_endog: int):
+        """
+        State names from components should always be of the form name[data_name], in the case that the component is
+        associated with multiple observed states. Not doing so leads to ambiguity -- we might have two level states,
+        but which goes to which observed component? So we set `level[data_1]` and `level[data_2]`.
+
+        In cases where there is only one observed state (when k_endog == 1), we can strip the data part and just use
+        the state name. This is a bit cleaner.
+        """
+        if k_endog == 1:
+            [data_name] = self.observed_states
+            return [
+                name.replace(f"[{data_name}]", "") if isinstance(name, str) else name
+                for name in names
+            ]
+
+        else:
+            return names
+
     @staticmethod
     def _add_inital_state_cov_to_properties(param_names, param_dims, param_info, k_states):
         param_names += ["P0"]
diff --git a/tests/statespace/models/structural/components/test_level_trend.py b/tests/statespace/models/structural/components/test_level_trend.py
index 37f8c14a2..c8a9c419a 100644
--- a/tests/statespace/models/structural/components/test_level_trend.py
+++ b/tests/statespace/models/structural/components/test_level_trend.py
@@ -119,7 +119,7 @@ def test_add_level_trend_with_different_observed():
     assert mod.coords["grw_state"] == ["level"]
 
     assert mod.state_names == ["level[data_1]", "trend[data_1]", "level[data_2]"]
-    assert mod.shock_names == ["trend_shock[data_1]", "level_shock[data_2]"]
+    assert mod.shock_names == ["trend[data_1]", "level[data_2]"]
 
     Z, T, R = pytensor.function(
         [], [mod.ssm["design"], mod.ssm["transition"], mod.ssm["selection"]], mode="FAST_COMPILE"

From 85b78fe375373cec604f0c324e99a74ceac105d3 Mon Sep 17 00:00:00 2001
From: jessegrabowski 
Date: Sun, 6 Jul 2025 13:38:51 +0800
Subject: [PATCH 18/21] Remove docstring typo

---
 .../statespace/models/structural/components/level_trend.py       | 1 -
 1 file changed, 1 deletion(-)

diff --git a/pymc_extras/statespace/models/structural/components/level_trend.py b/pymc_extras/statespace/models/structural/components/level_trend.py
index c9077f7b0..4a8418543 100644
--- a/pymc_extras/statespace/models/structural/components/level_trend.py
+++ b/pymc_extras/statespace/models/structural/components/level_trend.py
@@ -12,7 +12,6 @@ class LevelTrendComponent(Component):
 
     Parameters
     ----------
-    __________
     order : int
 
         Number of time derivatives of the trend to include in the model. For example, when order=3, the trend will

From a6327b77b78d18b0ac335a8c221f60dcadba9855 Mon Sep 17 00:00:00 2001
From: jessegrabowski 
Date: Sun, 6 Jul 2025 18:16:30 +0800
Subject: [PATCH 19/21] Update autoregressive component and tests

---
 .../structural/components/autoregressive.py   |  29 +++--
 .../components/test_autoregressive.py         | 113 ++++++++++++++++--
 .../statespace/models/structural/conftest.py  |   1 +
 .../structural/test_against_statsmodels.py    |   8 +-
 4 files changed, 123 insertions(+), 28 deletions(-)

diff --git a/pymc_extras/statespace/models/structural/components/autoregressive.py b/pymc_extras/statespace/models/structural/components/autoregressive.py
index 441913fe9..a0dfbdff2 100644
--- a/pymc_extras/statespace/models/structural/components/autoregressive.py
+++ b/pymc_extras/statespace/models/structural/components/autoregressive.py
@@ -65,7 +65,7 @@ class AutoregressiveComponent(Component):
     def __init__(
         self,
         order: int = 1,
-        name: str = "AutoRegressive",
+        name: str = "auto_regressive",
         observed_state_names: list[str] | None = None,
     ):
         if observed_state_names is None:
@@ -92,27 +92,30 @@ def __init__(
         )
 
     def populate_component_properties(self):
+        k_states = self.k_states // self.k_endog
+
         self.state_names = [
-            f"L{i + 1}.{state_name}"
-            for i in range(self.k_states)
+            f"L{i + 1}[{state_name}]"
             for state_name in self.observed_state_names
+            for i in range(k_states)
         ]
-        self.shock_names = [f"{name}_{self.name}_innovation" for name in self.observed_state_names]
-        self.param_names = ["ar_params", "sigma_ar"]
-        self.param_dims = {"ar_params": (AR_PARAM_DIM,)}
-        self.coords = {AR_PARAM_DIM: self.ar_lags.tolist()}
+
+        self.shock_names = self.observed_state_names.copy()
+        self.param_names = [f"{self.name}_params", f"{self.name}_sigma"]
+        self.param_dims = {f"{self.name}_params": (f"{self.name}_lag",)}
+        self.coords = {f"{self.name}_lag": self.ar_lags.tolist()}
 
         if self.k_endog > 1:
-            self.param_dims["ar_params"] = (
+            self.param_dims[f"{self.name}_params"] = (
                 f"{self.name}_endog",
                 AR_PARAM_DIM,
             )
-            self.param_dims["sigma_ar"] = (f"{self.name}_endog",)
+            self.param_dims[f"{self.name}_sigma"] = (f"{self.name}_endog",)
 
             self.coords[f"{self.name}_endog"] = self.observed_state_names
 
         self.param_info = {
-            "ar_params": {
+            f"{self.name}_params": {
                 "shape": (self.k_states,) if self.k_endog == 1 else (self.k_endog, self.k_states),
                 "constraints": None,
                 "dims": (AR_PARAM_DIM,)
@@ -122,7 +125,7 @@ def populate_component_properties(self):
                     AR_PARAM_DIM,
                 ),
             },
-            "sigma_ar": {
+            f"{self.name}_sigma": {
                 "shape": () if self.k_endog == 1 else (self.k_endog,),
                 "constraints": "Positive",
                 "dims": None if self.k_endog == 1 else (f"{self.name}_endog",),
@@ -136,10 +139,10 @@ def make_symbolic_graph(self) -> None:
 
         k_nonzero = int(sum(self.order))
         ar_params = self.make_and_register_variable(
-            "ar_params", shape=(k_nonzero,) if k_endog == 1 else (k_endog, k_nonzero)
+            f"{self.name}_params", shape=(k_nonzero,) if k_endog == 1 else (k_endog, k_nonzero)
         )
         sigma_ar = self.make_and_register_variable(
-            "sigma_ar", shape=() if k_endog == 1 else (k_endog,)
+            f"{self.name}_sigma", shape=() if k_endog == 1 else (k_endog,)
         )
 
         if k_endog == 1:
diff --git a/tests/statespace/models/structural/components/test_autoregressive.py b/tests/statespace/models/structural/components/test_autoregressive.py
index 21234aa2a..34458905a 100644
--- a/tests/statespace/models/structural/components/test_autoregressive.py
+++ b/tests/statespace/models/structural/components/test_autoregressive.py
@@ -1,8 +1,10 @@
 import numpy as np
+import pytensor
 import pytest
 
 from numpy.testing import assert_allclose
 from pytensor import config
+from pytensor.graph.basic import explicit_graph_inputs
 
 from pymc_extras.statespace.models import structural as st
 from tests.statespace.models.structural.conftest import _assert_basic_coords_correct
@@ -11,29 +13,44 @@
 
 @pytest.mark.parametrize("order", [1, 2, [1, 0, 1]], ids=["AR1", "AR2", "AR(1,0,1)"])
 def test_autoregressive_model(order, rng):
-    ar = st.AutoregressiveComponent(order=order)
+    k = sum(order) if isinstance(order, list) else order
+    ar = st.AutoregressiveComponent(order=order).build(verbose=False)
     params = {
-        "ar_params": np.full((sum(ar.order),), 0.5, dtype=config.floatX),
-        "sigma_ar": 0.0,
+        "auto_regressive_params": np.full((k,), 0.5, dtype=config.floatX),
+        "auto_regressive_sigma": 0.1,
+        "initial_state_cov": np.eye(k),
     }
 
-    x, y = simulate_from_numpy_model(ar, rng, params, steps=100)
-
     # Check coords
-    ar.build(verbose=False)
     _assert_basic_coords_correct(ar)
+
     lags = np.arange(len(order) if isinstance(order, list) else order, dtype="int") + 1
     if isinstance(order, list):
         lags = lags[np.flatnonzero(order)]
-    assert_allclose(ar.coords["ar_lag"], lags)
+    assert_allclose(ar.coords["auto_regressive_lag"], lags)
 
 
-def test_autoregressive_multiple_observed(rng):
+def test_autoregressive_multiple_observed_build(rng):
     ar = st.AutoregressiveComponent(order=3, observed_state_names=["data_1", "data_2"])
     mod = ar.build(verbose=False)
 
+    assert mod.k_endog == 2
+    assert mod.k_states == 6
+    assert mod.k_posdef == 2
+
+    assert mod.state_names == [
+        "L1[data_1]",
+        "L2[data_1]",
+        "L3[data_1]",
+        "L1[data_2]",
+        "L2[data_2]",
+        "L3[data_2]",
+    ]
+
+    assert mod.shock_names == ["data_1", "data_2"]
+
     params = {
-        "ar_params": np.full(
+        "auto_regressive_params": np.full(
             (
                 2,
                 sum(ar.order),
@@ -41,7 +58,81 @@ def test_autoregressive_multiple_observed(rng):
             0.5,
             dtype=config.floatX,
         ),
-        "sigma_ar": np.ones((2,)) * 1e-3,
+        "auto_regressive_sigma": np.array([0.05, 0.12]),
     }
+    _, _, _, _, T, Z, R, _, Q = mod._unpack_statespace_with_placeholders()
+    input_vars = explicit_graph_inputs([T, Z, R, Q])
+    fn = pytensor.function(
+        inputs=list(input_vars),
+        outputs=[T, Z, R, Q],
+        mode="FAST_COMPILE",
+    )
+
+    T, Z, R, Q = fn(**params)
+
+    np.testing.assert_allclose(
+        T,
+        np.array(
+            [
+                [0.5, 0.5, 0.5, 0.0, 0.0, 0.0],
+                [1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.5, 0.5, 0.5],
+                [0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
+            ]
+        ),
+    )
+
+    np.testing.assert_allclose(
+        Z, np.array([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0]])
+    )
+
+    np.testing.assert_allclose(
+        R, np.array([[1.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 1.0], [0.0, 0.0], [0.0, 0.0]])
+    )
+
+    np.testing.assert_allclose(Q, np.diag([0.05**2, 0.12**2]))
+
+
+def test_autoregressive_multiple_observed_data(rng):
+    ar = st.AutoregressiveComponent(order=1, observed_state_names=["data_1", "data_2", "data_3"])
+    mod = ar.build(verbose=False)
+
+    params = {
+        "auto_regressive_params": np.array([0.9, 0.8, 0.5]).reshape((3, 1)),
+        "auto_regressive_sigma": np.array([0.05, 0.12, 0.22]),
+        "initial_state_cov": np.eye(3),
+    }
+
+    # Recover the AR(1) coefficients from the simulated data via OLS
+    x, y = simulate_from_numpy_model(mod, rng, params, steps=2000)
+    for i in range(3):
+        ols_coefs = np.polyfit(y[:-1, i], y[1:, i], 1)
+        np.testing.assert_allclose(ols_coefs[0], params["auto_regressive_params"][i, 0], atol=1e-1)
+
+
+def test_add_autoregressive_different_observed():
+    mod_1 = st.AutoregressiveComponent(order=1, name="ar1", observed_state_names=["data_1"])
+    mod_2 = st.AutoregressiveComponent(name="ar6", order=6, observed_state_names=["data_2"])
+
+    mod = (mod_1 + mod_2).build(verbose=False)
+
+    print(mod.coords)
+
+    assert mod.k_endog == 2
+    assert mod.k_states == 7
+    assert mod.k_posdef == 2
+    assert mod.state_names == [
+        "L1[data_1]",
+        "L1[data_2]",
+        "L2[data_2]",
+        "L3[data_2]",
+        "L4[data_2]",
+        "L5[data_2]",
+        "L6[data_2]",
+    ]
 
-    x, y = simulate_from_numpy_model(ar, rng, params, steps=100)
+    assert mod.shock_names == ["data_1", "data_2"]
+    assert mod.coords["ar1_lag"] == [1]
+    assert mod.coords["ar6_lag"] == [1, 2, 3, 4, 5, 6]
diff --git a/tests/statespace/models/structural/conftest.py b/tests/statespace/models/structural/conftest.py
index c5f2396bc..b9e58ca68 100644
--- a/tests/statespace/models/structural/conftest.py
+++ b/tests/statespace/models/structural/conftest.py
@@ -24,5 +24,6 @@ def _assert_basic_coords_correct(mod):
     assert mod.coords[SHOCK_DIM] == mod.shock_names
     assert mod.coords[SHOCK_AUX_DIM] == mod.shock_names
     expected_obs = mod.observed_state_names if hasattr(mod, "observed_state_names") else ["data"]
+
     assert mod.coords[OBS_STATE_DIM] == expected_obs
     assert mod.coords[OBS_STATE_AUX_DIM] == expected_obs
diff --git a/tests/statespace/models/structural/test_against_statsmodels.py b/tests/statespace/models/structural/test_against_statsmodels.py
index 98318711f..6909e3caa 100644
--- a/tests/statespace/models/structural/test_against_statsmodels.py
+++ b/tests/statespace/models/structural/test_against_statsmodels.py
@@ -404,20 +404,20 @@ def create_structural_model_and_equivalent_statsmodel(
         components.append(comp)
 
     if autoregressive is not None:
-        ar_names = [f"L{i+1}.data" for i in range(autoregressive)]
+        ar_names = [f"L{i+1}" for i in range(autoregressive)]
         ar_params = rng.normal(size=(autoregressive,)).astype(floatX)
         if autoregressive == 1:
             ar_params = ar_params.item()
         sigma2 = np.abs(rng.normal()).astype(floatX)
 
         params["ar_params"] = ar_params
-        params["sigma_ar"] = np.sqrt(sigma2)
+        params["ar_sigma"] = np.sqrt(sigma2)
         expected_param_dims["ar_params"] += (AR_PARAM_DIM,)
         expected_coords[AR_PARAM_DIM] += tuple(list(range(1, autoregressive + 1)))
         expected_coords[ALL_STATE_DIM] += ar_names
         expected_coords[ALL_STATE_AUX_DIM] += ar_names
-        expected_coords[SHOCK_DIM] += ["data_ar_innovation"]
-        expected_coords[SHOCK_AUX_DIM] += ["data_ar_innovation"]
+        expected_coords[SHOCK_DIM] += ["data"]
+        expected_coords[SHOCK_AUX_DIM] += ["data"]
 
         sm_params["sigma2.ar"] = sigma2
         for i, rho in enumerate(ar_params):

From 0b20dbc332753ca863f24fd7f2e78b89a0748b16 Mon Sep 17 00:00:00 2001
From: jessegrabowski 
Date: Sun, 6 Jul 2025 22:36:57 +0800
Subject: [PATCH 20/21] Add component name to shock state names

---
 .../models/structural/components/autoregressive.py     |  2 +-
 .../structural/components/test_autoregressive.py       | 10 ++--------
 .../models/structural/test_against_statsmodels.py      |  4 ++--
 3 files changed, 5 insertions(+), 11 deletions(-)

diff --git a/pymc_extras/statespace/models/structural/components/autoregressive.py b/pymc_extras/statespace/models/structural/components/autoregressive.py
index a0dfbdff2..0a3dd0586 100644
--- a/pymc_extras/statespace/models/structural/components/autoregressive.py
+++ b/pymc_extras/statespace/models/structural/components/autoregressive.py
@@ -100,7 +100,7 @@ def populate_component_properties(self):
             for i in range(k_states)
         ]
 
-        self.shock_names = self.observed_state_names.copy()
+        self.shock_names = [f"{self.name}[{obs_name}]" for obs_name in self.observed_state_names]
         self.param_names = [f"{self.name}_params", f"{self.name}_sigma"]
         self.param_dims = {f"{self.name}_params": (f"{self.name}_lag",)}
         self.coords = {f"{self.name}_lag": self.ar_lags.tolist()}
diff --git a/tests/statespace/models/structural/components/test_autoregressive.py b/tests/statespace/models/structural/components/test_autoregressive.py
index 34458905a..71a181925 100644
--- a/tests/statespace/models/structural/components/test_autoregressive.py
+++ b/tests/statespace/models/structural/components/test_autoregressive.py
@@ -13,13 +13,7 @@
 
 @pytest.mark.parametrize("order", [1, 2, [1, 0, 1]], ids=["AR1", "AR2", "AR(1,0,1)"])
 def test_autoregressive_model(order, rng):
-    k = sum(order) if isinstance(order, list) else order
     ar = st.AutoregressiveComponent(order=order).build(verbose=False)
-    params = {
-        "auto_regressive_params": np.full((k,), 0.5, dtype=config.floatX),
-        "auto_regressive_sigma": 0.1,
-        "initial_state_cov": np.eye(k),
-    }
 
     # Check coords
     _assert_basic_coords_correct(ar)
@@ -47,7 +41,7 @@ def test_autoregressive_multiple_observed_build(rng):
         "L3[data_2]",
     ]
 
-    assert mod.shock_names == ["data_1", "data_2"]
+    assert mod.shock_names == ["auto_regressive[data_1]", "auto_regressive[data_2]"]
 
     params = {
         "auto_regressive_params": np.full(
@@ -133,6 +127,6 @@ def test_add_autoregressive_different_observed():
         "L6[data_2]",
     ]
 
-    assert mod.shock_names == ["data_1", "data_2"]
+    assert mod.shock_names == ["ar1[data_1]", "ar6[data_2]"]
     assert mod.coords["ar1_lag"] == [1]
     assert mod.coords["ar6_lag"] == [1, 2, 3, 4, 5, 6]
diff --git a/tests/statespace/models/structural/test_against_statsmodels.py b/tests/statespace/models/structural/test_against_statsmodels.py
index 6909e3caa..1db4350b5 100644
--- a/tests/statespace/models/structural/test_against_statsmodels.py
+++ b/tests/statespace/models/structural/test_against_statsmodels.py
@@ -416,8 +416,8 @@ def create_structural_model_and_equivalent_statsmodel(
         expected_coords[AR_PARAM_DIM] += tuple(list(range(1, autoregressive + 1)))
         expected_coords[ALL_STATE_DIM] += ar_names
         expected_coords[ALL_STATE_AUX_DIM] += ar_names
-        expected_coords[SHOCK_DIM] += ["data"]
-        expected_coords[SHOCK_AUX_DIM] += ["data"]
+        expected_coords[SHOCK_DIM] += ["ar"]
+        expected_coords[SHOCK_AUX_DIM] += ["ar"]
 
         sm_params["sigma2.ar"] = sigma2
         for i, rho in enumerate(ar_params):

From a8564b74fd603056c5281dd4e5a3e055b37918af Mon Sep 17 00:00:00 2001
From: jessegrabowski 
Date: Sun, 6 Jul 2025 23:13:34 +0800
Subject: [PATCH 21/21] Allow multiple observed in TimeSeasonality component

---
 .../structural/components/seasonality.py      |  57 ++++--
 .../structural/components/test_seasonality.py | 169 +++++++++++++++++-
 2 files changed, 207 insertions(+), 19 deletions(-)

diff --git a/pymc_extras/statespace/models/structural/components/seasonality.py b/pymc_extras/statespace/models/structural/components/seasonality.py
index 20f47636f..33135affd 100644
--- a/pymc_extras/statespace/models/structural/components/seasonality.py
+++ b/pymc_extras/statespace/models/structural/components/seasonality.py
@@ -154,27 +154,41 @@ def __init__(
             # TODO: Can this be stashed and reconstructed automatically somehow?
             state_names.pop(0)
 
+        self.provided_state_names = state_names
+
         k_states = season_length - int(self.remove_first_state)
+        k_endog = len(observed_state_names)
+        k_posdef = int(innovations)
 
         super().__init__(
             name=name,
-            k_endog=len(observed_state_names),
-            k_states=k_states,
-            k_posdef=int(innovations),
-            state_names=state_names,
+            k_endog=k_endog,
+            k_states=k_states * k_endog,
+            k_posdef=k_posdef * k_endog,
             observed_state_names=observed_state_names,
             measurement_error=False,
             combine_hidden_states=True,
-            obs_state_idxs=np.r_[[1.0], np.zeros(k_states - 1)],
+            obs_state_idxs=np.tile(np.array([1.0] + [0.0] * (k_states - 1)), k_endog),
         )
 
     def populate_component_properties(self):
+        k_states = self.k_states // self.k_endog
+        k_endog = self.k_endog
+
+        self.state_names = [
+            f"{state_name}[{endog_name}]"
+            for endog_name in self.observed_state_names
+            for state_name in self.provided_state_names
+        ]
         self.param_names = [f"{self.name}_coefs"]
+
         self.param_info = {
             f"{self.name}_coefs": {
-                "shape": (self.k_states,),
+                "shape": (k_states,) if k_endog == 1 else (k_endog, k_states),
                 "constraints": None,
-                "dims": (f"{self.name}_state",),
+                "dims": (f"{self.name}_state",)
+                if k_endog == 1
+                else (f"{self.name}_endog", f"{self.name}_state"),
             }
         }
         self.param_dims = {f"{self.name}_coefs": (f"{self.name}_state",)}
@@ -187,32 +201,41 @@ def populate_component_properties(self):
                 "constraints": "Positive",
                 "dims": None,
             }
-            self.shock_names = [f"{self.name}"]
+            self.shock_names = [f"{self.name}[{name}]" for name in self.observed_state_names]
 
     def make_symbolic_graph(self) -> None:
+        k_states = self.k_states // self.k_endog
+        k_posdef = self.k_posdef // self.k_endog
+        k_endog = self.k_endog
+
         if self.remove_first_state:
             # In this case, parameters are normalized to sum to zero, so the current state is the negative sum of
             # all previous states.
-            T = np.eye(self.k_states, k=-1)
+            T = np.eye(k_states, k=-1)
             T[0, :] = -1
         else:
             # In this case we assume the user to be responsible for ensuring the states sum to zero, so T is just a
             # circulant matrix that cycles between the states.
-            T = np.eye(self.k_states, k=1)
+            T = np.eye(k_states, k=1)
             T[-1, 0] = 1
 
-        self.ssm["transition", :, :] = T
-        self.ssm["design", 0, 0] = 1
+        self.ssm["transition", :, :] = pt.linalg.block_diag(*[T for _ in range(k_endog)])
+
+        Z = pt.zeros((1, k_states))[0, 0].set(1)
+        self.ssm["design", :, :] = pt.linalg.block_diag(*[Z for _ in range(k_endog)])
 
         initial_states = self.make_and_register_variable(
-            f"{self.name}_coefs", shape=(self.k_states,)
+            f"{self.name}_coefs", shape=(k_states,) if k_endog == 1 else (k_endog, k_states)
         )
-        self.ssm["initial_state", np.arange(self.k_states, dtype=int)] = initial_states
+        self.ssm["initial_state", :] = initial_states.ravel()
 
         if self.innovations:
-            self.ssm["selection", 0, 0] = 1
-            season_sigma = self.make_and_register_variable(f"sigma_{self.name}", shape=())
-            cov_idx = ("state_cov", *np.diag_indices(1))
+            R = pt.zeros((k_states, k_posdef))[0, 0].set(1.0)
+            self.ssm["selection", :, :] = pt.join(0, *[R for _ in range(k_endog)])
+            season_sigma = self.make_and_register_variable(
+                f"sigma_{self.name}", shape=() if k_endog == 1 else (k_endog,)
+            )
+            cov_idx = ("state_cov", *np.diag_indices(k_posdef * k_endog))
             self.ssm[cov_idx] = season_sigma**2
 
 
diff --git a/tests/statespace/models/structural/components/test_seasonality.py b/tests/statespace/models/structural/components/test_seasonality.py
index 61ad4b198..a62a85c0e 100644
--- a/tests/statespace/models/structural/components/test_seasonality.py
+++ b/tests/statespace/models/structural/components/test_seasonality.py
@@ -1,7 +1,9 @@
 import numpy as np
+import pytensor
 import pytest
 
 from pytensor import config
+from pytensor.graph.basic import explicit_graph_inputs
 
 from pymc_extras.statespace.models import structural as st
 from tests.statespace.models.structural.conftest import _assert_basic_coords_correct
@@ -35,7 +37,7 @@ def random_word(rng):
     x0[0] = 1
 
     params = {"season_coefs": x0}
-    if mod.innovations:
+    if innovations:
         params["sigma_season"] = 0.0
 
     x, y = simulate_from_numpy_model(mod, rng, params)
@@ -44,12 +46,175 @@ def random_word(rng):
         assert_pattern_repeats(y, s, atol=ATOL, rtol=RTOL)
 
     # Check coords
-    mod.build(verbose=False)
+    mod = mod.build(verbose=False)
     _assert_basic_coords_correct(mod)
     test_slice = slice(1, None) if remove_first_state else slice(None)
     assert mod.coords["season_state"] == state_names[test_slice]
 
 
+@pytest.mark.parametrize(
+    "remove_first_state", [True, False], ids=["remove_first_state", "keep_first_state"]
+)
+def test_time_seasonality_multiple_observed(rng, remove_first_state):
+    s = 3
+    state_names = [f"state_{i}" for i in range(s)]
+    mod = st.TimeSeasonality(
+        season_length=s,
+        innovations=True,
+        name="season",
+        state_names=state_names,
+        observed_state_names=["data_1", "data_2"],
+        remove_first_state=remove_first_state,
+    )
+    x0 = np.zeros((mod.k_endog, mod.k_states // mod.k_endog), dtype=config.floatX)
+
+    expected_states = [
+        f"state_{i}[data_{j}]" for j in range(1, 3) for i in range(int(remove_first_state), s)
+    ]
+    assert mod.state_names == expected_states
+    assert mod.shock_names == ["season[data_1]", "season[data_2]"]
+
+    x0[0, 0] = 1
+    x0[1, 0] = 2.0
+
+    params = {"season_coefs": x0, "sigma_season": np.array([0.0, 0.0], dtype=config.floatX)}
+
+    x, y = simulate_from_numpy_model(mod, rng, params, steps=123)
+    assert_pattern_repeats(y[:, 0], s, atol=ATOL, rtol=RTOL)
+    assert_pattern_repeats(y[:, 1], s, atol=ATOL, rtol=RTOL)
+
+    mod = mod.build(verbose=False)
+    x0, *_, T, Z, R, _, Q = mod._unpack_statespace_with_placeholders()
+
+    input_vars = explicit_graph_inputs([x0, T, Z, R, Q])
+
+    fn = pytensor.function(
+        inputs=list(input_vars),
+        outputs=[x0, T, Z, R, Q],
+        mode="FAST_COMPILE",
+    )
+
+    params["sigma_season"] = np.array([0.1, 0.8], dtype=config.floatX)
+    x0, T, Z, R, Q = fn(**params)
+
+    if remove_first_state:
+        expected_x0 = np.array([1.0, 0.0, 2.0, 0.0])
+
+        expected_T = np.array(
+            [
+                [-1.0, -1.0, 0.0, 0.0],
+                [1.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, -1.0, -1.0],
+                [0.0, 0.0, 1.0, 0.0],
+            ]
+        )
+        expected_R = np.array([[1.0, 1.0], [0.0, 0.0], [1.0, 1.0], [0.0, 0.0]])
+        expected_Z = np.array([[1.0, 0.0, 0.0, 0.0], [0.0, 0.0, 1.0, 0.0]])
+
+    else:
+        expected_x0 = np.array([1.0, 0.0, 0.0, 2.0, 0.0, 0.0])
+        expected_T = np.array(
+            [
+                [0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
+                [1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 1.0],
+                [0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+            ]
+        )
+        expected_R = np.array(
+            [[1.0, 1.0], [0.0, 0.0], [0.0, 0.0], [1.0, 1.0], [0.0, 0.0], [0.0, 0.0]]
+        )
+        expected_Z = np.array([[1.0, 0.0, 0.0, 0.0, 0.0, 0.0], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0]])
+
+    expected_Q = np.array([[0.1**2, 0.0], [0.0, 0.8**2]])
+
+    for matrix, expected in zip(
+        [x0, T, Z, R, Q],
+        [expected_x0, expected_T, expected_Z, expected_R, expected_Q],
+    ):
+        np.testing.assert_allclose(matrix, expected)
+
+
+def test_add_two_time_seasonality_different_observed(rng):
+    mod1 = st.TimeSeasonality(
+        season_length=3,
+        innovations=True,
+        name="season1",
+        state_names=[f"state_{i}" for i in range(3)],
+        observed_state_names=["data_1"],
+        remove_first_state=False,
+    )
+    mod2 = st.TimeSeasonality(
+        season_length=5,
+        innovations=True,
+        name="season2",
+        state_names=[f"state_{i}" for i in range(5)],
+        observed_state_names=["data_2"],
+    )
+
+    mod = (mod1 + mod2).build(verbose=False)
+
+    params = {
+        "season1_coefs": np.array([1.0, 0.0, 0.0], dtype=config.floatX),
+        "season2_coefs": np.array([3.0, 0.0, 0.0, 0.0], dtype=config.floatX),
+        "sigma_season1": np.array(0.0, dtype=config.floatX),
+        "sigma_season2": np.array(0.0, dtype=config.floatX),
+        "initial_state_cov": np.eye(mod.k_states, dtype=config.floatX),
+    }
+
+    x, y = simulate_from_numpy_model(mod, rng, params, steps=3 * 5 * 5)
+    assert_pattern_repeats(y[:, 0], 3, atol=ATOL, rtol=RTOL)
+    assert_pattern_repeats(y[:, 1], 5, atol=ATOL, rtol=RTOL)
+
+    assert mod.state_names == [
+        "state_0[data_1]",
+        "state_1[data_1]",
+        "state_2[data_1]",
+        "state_1[data_2]",
+        "state_2[data_2]",
+        "state_3[data_2]",
+        "state_4[data_2]",
+    ]
+
+    assert mod.shock_names == ["season1[data_1]", "season2[data_2]"]
+
+    x0, *_, T = mod._unpack_statespace_with_placeholders()[:5]
+    input_vars = explicit_graph_inputs([x0, T])
+    fn = pytensor.function(
+        inputs=list(input_vars),
+        outputs=[x0, T],
+        mode="FAST_COMPILE",
+    )
+
+    x0, T = fn(
+        season1_coefs=np.array([1.0, 0.0, 0.0], dtype=config.floatX),
+        season2_coefs=np.array([3.0, 0.0, 0.0, 1.2], dtype=config.floatX),
+    )
+
+    np.testing.assert_allclose(
+        np.array([1.0, 0.0, 0.0, 3.0, 0.0, 0.0, 1.2]), x0, atol=ATOL, rtol=RTOL
+    )
+
+    np.testing.assert_allclose(
+        np.array(
+            [
+                [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0],
+                [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, -1.0, -1.0, -1.0, -1.0],
+                [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0],
+                [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0],
+            ]
+        ),
+        T,
+        atol=ATOL,
+        rtol=RTOL,
+    )
+
+
 def get_shift_factor(s):
     s_str = str(s)
     if "." not in s_str: