Split posterior precision update into two branches (#263)

* split posterior update of precision into two branches

* add tests

* fix api docs

docs/source/api.rst

@@ -89,7 +89,7 @@ Prediction error steps
 Compute the value and volatility prediction errors of a given node. The prediction error can only be computed after the posterior update (or observation) of a given node.
 
 Binary state nodes
-^^^^^^^^^^^^^^^^^^
+------------------
 
 .. currentmodule:: pyhgf.updates.prediction_error.binary
 
@@ -100,7 +100,7 @@ Binary state nodes
     binary_finite_state_node_prediction_error
 
 Categorical state nodes
-^^^^^^^^^^^^^^^^^^^^^^^
+-----------------------
 
 .. currentmodule:: pyhgf.updates.prediction_error.categorical
 
@@ -110,7 +110,7 @@ Categorical state nodes
     categorical_state_prediction_error
 
 Continuous state nodes
-^^^^^^^^^^^^^^^^^^^^^^
+----------------------
 
 .. currentmodule:: pyhgf.updates.prediction_error.continuous
 
@@ -122,7 +122,7 @@ Continuous state nodes
     continuous_node_prediction_error
 
 Dirichlet state nodes
-^^^^^^^^^^^^^^^^^^^^^
+---------------------
 
 .. currentmodule:: pyhgf.updates.prediction_error.dirichlet
 
@@ -137,7 +137,7 @@ Dirichlet state nodes
     clusters_likelihood
 
 Exponential family
-^^^^^^^^^^^^^^^^^^
+------------------
 
 .. currentmodule:: pyhgf.updates.prediction_error.exponential
 

pyhgf/updates/posterior/continuous/__init__.py

@@ -3,6 +3,5 @@
 
 __all__ = [
     "continuous_node_posterior_update_ehgf",
-    "continuous_node_posterior_update_unbounded",
     "continuous_node_posterior_update",
 ]

pyhgf/updates/posterior/continuous/posterior_update_precision_continuous_node.py

@@ -5,6 +5,8 @@
 
 import jax.numpy as jnp
 from jax import grad, jit
+from jax.lax import cond
+from jax.tree_util import Partial
 
 from pyhgf.typing import Edges
 
@@ -31,7 +33,7 @@ def posterior_update_precision_continuous_node(
 
     Where :math:`\kappa_j` is the volatility coupling strength between the child node
     and the state node and :math:`\delta_j^{(k)}` is the value prediction error that
-    was computed before hand by
+    was computed beforehand by
     :py:func:`pyhgf.updates.prediction_errors.continuous.continuous_node_value_prediction_error`.
 
     For non-linear value coupling:
@@ -80,8 +82,9 @@ def posterior_update_precision_continuous_node(
         The attributes of the probabilistic nodes.
     edges :
         The edges of the probabilistic nodes as a tuple of
-        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as node number.
-        For each node, the index list value and volatility parents and children.
+        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as the number
+        of nodes. For each node, the index lists the value and volatility parents and
+        children.
     node_idx :
         Pointer to the value parent node that will be updated.
     time_step :
@@ -108,6 +111,60 @@ def posterior_update_precision_continuous_node(
        Mathys, C. (2023). The generalized Hierarchical Gaussian Filter (Version 1).
        arXiv. https://doi.org/10.48550/ARXIV.2305.10937
 
+    """
+    # ----------------------------------------------------------------------------------
+    # Decide which update to use depending on the presence of observed value in the
+    # children nodes. If no values were observed, the precision should increase
+    # as a function of time using the function precision_missing_values(). Otherwise,
+    # we use regular HGF updates for value and volatility couplings.
+    # ----------------------------------------------------------------------------------
+
+    # For all children, get the `observed` flag - if all these values are 0.0, the node
+    # has not received any observations and we should call precision_missing_values()
+    observations = []
+    if edges[node_idx].value_children is not None:
+        for children_idx in edges[node_idx].value_children:  # type: ignore
+            observations.append(attributes[children_idx]["observed"])
+    if edges[node_idx].volatility_children is not None:
+        for children_idx in edges[node_idx].volatility_children:  # type: ignore
+            observations.append(attributes[children_idx]["observed"])
+    observations = jnp.any(jnp.array(observations))
+
+    posterior_precision = cond(
+        observations,
+        Partial(precision_update, edges=edges, node_idx=node_idx),
+        Partial(precision_update_missing_values, edges=edges, node_idx=node_idx),
+        attributes,
+    )
+
+    return posterior_precision
+
+
+@partial(jit, static_argnames=("edges", "node_idx"))
+def precision_update(attributes: Dict, edges: Edges, node_idx: int) -> float:
+    """Compute new precision in the case of observed values.
+
+    Parameters
+    ----------
+    attributes :
+        The attributes of the probabilistic nodes.
+    edges :
+        The edges of the probabilistic nodes as a tuple of
+        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as the number
+        of nodes. For each node, the index lists the value and volatility parents and
+        children.
+    node_idx :
+        Pointer to the value parent node that will be updated.
+    time_step :
+        The time elapsed between this observation and the previous one.
+
+    Returns
+    -------
+    posterior_precision :
+        The new posterior precision when at least one of the children has
+        observed a new value. We then use the regular HGF update for volatility
+        coupling.
+
     """
     # sum the prediction errors from both value and volatility coupling
     precision_weigthed_prediction_error = 0.0
@@ -177,13 +234,41 @@ def posterior_update_precision_continuous_node(
     )
 
     # ensure the new precision is greater than 0
-    observed_posterior_precision = jnp.where(
+    posterior_precision = jnp.where(
         posterior_precision > 1e-128, posterior_precision, jnp.nan
     )
 
-    # additionnal steps for unobserved values
-    # ---------------------------------------
+    return posterior_precision
 
+
+@partial(jit, static_argnames=("edges", "node_idx"))
+def precision_update_missing_values(
+    attributes: Dict, edges: Edges, node_idx: int
+) -> float:
+    """Compute new precision in the case of missing observations.
+
+    Parameters
+    ----------
+    attributes :
+        The attributes of the probabilistic nodes.
+    edges :
+        The edges of the probabilistic nodes as a tuple of
+        :py:class:`pyhgf.typing.Indexes`. The tuple has the same length as the number
+        of nodes. For each node, the index lists the value and volatility parents and
+        children.
+    node_idx :
+        Pointer to the value parent node that will be updated.
+    time_step :
+        The time elapsed between this observation and the previous one.
+
+    Returns
+    -------
+    posterior_precision_missing_values :
+        The new posterior precision in the case of missing values in all child nodes.
+        The new precision decreases proportionally to the time elapsed, accounting for
+        the influence of volatility parents.
+
+    """
     # List the node's volatility parents
     volatility_parents_idxs = edges[node_idx].volatility_parents
 
@@ -201,29 +286,13 @@ def posterior_update_precision_continuous_node(
                 volatility_coupling * attributes[volatility_parents_idx]["mean"]
             )
 
-    # compute the predicted_volatility from the total volatility
+    # compute the new predicted_volatility from the total volatility
     time_step = attributes[-1]["time_step"]
     predicted_volatility = time_step * jnp.exp(total_volatility)
 
     # Estimate the new precision for the continuous state node
-    unobserved_posterior_precision = 1 / (
+    posterior_precision_missing_values = 1 / (
         (1 / attributes[node_idx]["precision"]) + predicted_volatility
     )
 
-    # for all children, look at the values of VAPE
-    # if all these values are NaNs, the node has not received observations
-    observations = []
-    if edges[node_idx].value_children is not None:
-        for children_idx in edges[node_idx].value_children:  # type: ignore
-            observations.append(attributes[children_idx]["observed"])
-    if edges[node_idx].volatility_children is not None:
-        for children_idx in edges[node_idx].volatility_children:  # type: ignore
-            observations.append(attributes[children_idx]["observed"])
-    observations = jnp.any(jnp.array(observations))
-
-    posterior_precision = (
-        unobserved_posterior_precision * (1 - observations)  # type: ignore
-        + observed_posterior_precision * observations
-    )
-
-    return posterior_precision
+    return posterior_precision_missing_values

tests/test_updates/posterior/continuous.py

@@ -1,10 +1,11 @@
 # Author: Nicolas Legrand <nicolas.legrand@cas.au.dk>
 
+import jax.numpy as jnp
+
 from pyhgf.model import Network
 from pyhgf.updates.posterior.continuous import (
     continuous_node_posterior_update,
     continuous_node_posterior_update_ehgf,
-    continuous_node_posterior_update_unbounded,
 )
 
 
@@ -20,17 +21,56 @@ def test_continuous_posterior_updates():
 
     # Standard HGF updates -------------------------------------------------------------
     # ----------------------------------------------------------------------------------
+
+    # value update
+    attributes, edges, _ = network.get_network()
+    attributes[0]["temp"]["value_prediction_error"] = 1.0357
+    attributes[0]["mean"] = 1.0357
+
+    new_attributes = continuous_node_posterior_update(
+        attributes=attributes, node_idx=1, edges=edges
+    )
+    assert jnp.isclose(new_attributes[1]["mean"], 0.51785)
+
+    # volatility update
     attributes, edges, _ = network.get_network()
-    _ = continuous_node_posterior_update(attributes=attributes, node_idx=2, edges=edges)
+    attributes[1]["temp"]["effective_precision"] = 0.01798621006309986
+    attributes[1]["temp"]["value_prediction_error"] = 0.5225493907928467
+    attributes[1]["temp"]["volatility_prediction_error"] = -0.23639076948165894
+    attributes[1]["expected_precision"] = 0.9820137619972229
+    attributes[1]["mean"] = 0.5225493907928467
+    attributes[1]["precision"] = 1.9820137023925781
+
+    new_attributes = continuous_node_posterior_update(
+        attributes=attributes, node_idx=2, edges=edges
+    )
+    assert jnp.isclose(new_attributes[1]["mean"], -0.0021212)
+    assert jnp.isclose(new_attributes[1]["precision"], 1.0022112)
 
     # eHGF updates ---------------------------------------------------------------------
     # ----------------------------------------------------------------------------------
-    _ = continuous_node_posterior_update_ehgf(
+
+    # value update
+    attributes, edges, _ = network.get_network()
+    attributes[0]["temp"]["value_prediction_error"] = 1.0357
+    attributes[0]["mean"] = 1.0357
+
+    new_attributes = continuous_node_posterior_update_ehgf(
         attributes=attributes, node_idx=2, edges=edges
     )
+    assert jnp.isclose(new_attributes[1]["mean"], 0.51785)
 
-    # unbounded updates ----------------------------------------------------------------
-    # ----------------------------------------------------------------------------------
-    _ = continuous_node_posterior_update_unbounded(
+    # volatility update
+    attributes, edges, _ = network.get_network()
+    attributes[1]["temp"]["effective_precision"] = 0.01798621006309986
+    attributes[1]["temp"]["value_prediction_error"] = 0.5225493907928467
+    attributes[1]["temp"]["volatility_prediction_error"] = -0.23639076948165894
+    attributes[1]["expected_precision"] = 0.9820137619972229
+    attributes[1]["mean"] = 0.5225493907928467
+    attributes[1]["precision"] = 1.9820137023925781
+
+    new_attributes = continuous_node_posterior_update_ehgf(
         attributes=attributes, node_idx=2, edges=edges
     )
+    assert jnp.isclose(new_attributes[1]["mean"], -0.00212589)
+    assert jnp.isclose(new_attributes[1]["precision"], 1.0022112)