fix: apply numerical stability hotfix for censored Normal distributions

Import censored_distribution_stability_hotfix when PyMC is available.
This fixes numerical instability in pm.Censored for Normal distributions
when values are in the extreme tails.

Addresses: brendanjmeade#341
Upstream fix: pymc-devs/pymc#7996

Author: maresb

celeri/censored_distribution_stability_hotfix.py

@@ -0,0 +1,160 @@
+"""Hotfix module providing numerically stable log-probabilities for censored Normal distributions.
+
+This is a temporary workaround until https://github.com/pymc-devs/pymc/pull/7996 is merged.
+
+The fix monkey-patches the MeasurableClip logprob to use a stable log survival function
+for Normal distributions instead of the numerically unstable log(1 - exp(logcdf)).
+
+Usage:
+    import stable_censored_hotfix  # Just import to apply the fix
+
+    with pm.Model():
+        normal_dist = pm.Normal.dist(mu=0.0, sigma=1.0)
+        y = pm.Censored("y", normal_dist, lower=None, upper=40.0, observed=data)
+"""
+
+import numpy as np
+import pytensor.tensor as pt
+from pymc.distributions.dist_math import normal_lccdf
+from pymc.logprob.abstract import _logcdf, _logprob
+from pymc.logprob.censoring import MeasurableClip
+from pymc.logprob.utils import CheckParameterValue
+from pytensor.tensor.variable import TensorConstant
+
+
+def _stable_normal_logccdf(mu, sigma, value):
+    """Numerically stable log complementary CDF (log survival function) for Normal.
+
+    Uses erfcx-based implementation that is stable even in extreme tails.
+    """
+    return normal_lccdf(mu, sigma, value)
+
+
+def _get_stable_logccdf(base_rv_op, base_rv_inputs, value, logcdf_fallback):
+    """Get numerically stable log complementary CDF if available.
+
+    For Normal distribution, uses the stable erfcx-based implementation.
+    For other distributions, falls back to log1mexp(logcdf).
+    """
+    from pytensor.tensor.random.basic import NormalRV
+
+    if isinstance(base_rv_op, NormalRV):
+        # Normal distribution: use stable implementation
+        # base_rv_inputs are: rng, size, mu, sigma
+        rng, size, mu, sigma = base_rv_inputs
+        return _stable_normal_logccdf(mu, sigma, value)
+    else:
+        # Fall back to potentially unstable computation
+        return pt.log1mexp(logcdf_fallback)
+
+
+def _stable_clip_logprob(op, values, base_rv, lower_bound, upper_bound, **kwargs):
+    r"""Stable logprob of a clipped censored distribution.
+
+    The probability is given by
+    .. math::
+        \begin{cases}
+            0 & \text{for } x < lower, \\
+            \text{CDF}(lower, dist) & \text{for } x = lower, \\
+            \text{P}(x, dist) & \text{for } lower < x < upper, \\
+            1-\text{CDF}(upper, dist) & \text {for} x = upper, \\
+            0 & \text{for } x > upper,
+        \end{cases}
+
+    """
+    (value,) = values
+
+    base_rv_op = base_rv.owner.op
+    base_rv_inputs = base_rv.owner.inputs
+
+    logprob = _logprob(base_rv_op, (value,), *base_rv_inputs, **kwargs)
+    logcdf = _logcdf(base_rv_op, value, *base_rv_inputs, **kwargs)
+
+    if base_rv_op.name:
+        logprob.name = f"{base_rv_op}_logprob"
+        logcdf.name = f"{base_rv_op}_logcdf"
+
+    is_lower_bounded, is_upper_bounded = False, False
+    if not (
+        isinstance(upper_bound, TensorConstant) and np.all(np.isinf(upper_bound.value))
+    ):
+        is_upper_bounded = True
+
+        # Use stable logccdf for Normal distributions instead of pt.log1mexp(logcdf)
+        logccdf = _get_stable_logccdf(base_rv_op, base_rv_inputs, value, logcdf)
+
+        # For right clipped discrete RVs, we need to add an extra term
+        # corresponding to the pmf at the upper bound
+        if base_rv.dtype.startswith("int"):
+            logccdf = pt.logaddexp(logccdf, logprob)
+
+        logprob = pt.switch(
+            pt.eq(value, upper_bound),
+            logccdf,
+            pt.switch(pt.gt(value, upper_bound), -np.inf, logprob),
+        )
+    if not (
+        isinstance(lower_bound, TensorConstant)
+        and np.all(np.isneginf(lower_bound.value))
+    ):
+        is_lower_bounded = True
+        logprob = pt.switch(
+            pt.eq(value, lower_bound),
+            logcdf,
+            pt.switch(pt.lt(value, lower_bound), -np.inf, logprob),
+        )
+
+    if is_lower_bounded and is_upper_bounded:
+        logprob = CheckParameterValue("lower_bound <= upper_bound")(
+            logprob, pt.all(pt.le(lower_bound, upper_bound))
+        )
+
+    return logprob
+
+
+def _apply_fix():
+    """Apply the fix by overriding the singledispatch registry."""
+    # Use the register decorator to replace the existing function
+    _logprob.register(MeasurableClip, _stable_clip_logprob)
+
+
+# Apply the fix on import
+_apply_fix()
+
+
+def verify_fix():
+    """Verify that the stable implementation works correctly."""
+    import pymc as pm
+    import scipy.stats as st
+
+    with pm.Model() as model:
+        normal_dist = pm.Normal.dist(mu=0.0, sigma=1.0)
+        pm.Censored("y", normal_dist, lower=None, upper=40.0)
+
+    logp_fn = model.compile_logp()
+    result = logp_fn({"y": 40.0})
+    expected = st.norm(0, 1).logsf(40.0)
+
+    if not np.isfinite(result):
+        raise RuntimeError(
+            f"Stable censored fix not working: got {result}, expected {expected}"
+        )
+
+    if not np.isclose(result, expected, rtol=1e-6):
+        raise RuntimeError(
+            f"Stable censored result mismatch: got {result}, expected {expected}"
+        )
+
+    return True
+
+
+if __name__ == "__main__":
+    print("Verifying stable censored fix...")
+    verify_fix()
+    print("✓ Stable censored fix is working correctly!")
+    print("\nUsage:")
+    print("  import stable_censored_hotfix  # Just import to apply the fix")
+    print("  ")
+    print("  with pm.Model():")
+    print("      normal_dist = pm.Normal.dist(mu=0.0, sigma=1.0)")
+    print("      y = pm.Censored('y', normal_dist, lower=None, upper=40.0)")

celeri/solve_mcmc.py

@@ -19,6 +19,11 @@ class PymcModel:
 else:
     from pymc import Model as PymcModel
 
+    # Apply numerical stability fix for censored Normal distributions.
+    # This is a workaround for https://github.com/pymc-devs/pymc/pull/7996
+    # Fixes issue https://github.com/brendanjmeade/celeri/issues/341
+    import celeri.censored_distribution_stability_hotfix  # noqa: F401
+
 
 DIRECTION_IDX = {
     "strike_slip": slice(None, None, 2),