fix: suppress buggy output from pytensor.function

src/hssm/utils.py

@@ -9,8 +9,10 @@
 _parse_bambi().
 """
 
+import contextlib
 import itertools
 import logging
+import os
 from copy import deepcopy
 from typing import Any, Literal, cast
 
@@ -548,3 +550,43 @@ def _rearrange_data(data: pd.DataFrame | np.ndarray) -> pd.DataFrame | np.ndarra
 def _split_array(data: np.ndarray | list[int], divisor: int) -> list[np.ndarray]:
     num_splits = len(data) // divisor + (1 if len(data) % divisor != 0 else 0)
     return [tmp.astype(int) for tmp in np.array_split(data, num_splits)]
+
+
+class SuppressOutput:
+    """Context manager for suppressing output.
+
+    This context manager redirects both stdout and stderr to `os.devnull`,
+    effectively silencing all output during the execution of the block.
+    It also disables logging by setting the logging level to `CRITICAL`.
+
+    Examples
+    --------
+    >>> with SuppressOutput():
+    ...     grad_func = pytensor.function(
+    ...         [v, a, z, t],
+    ...         grad,
+    ...         mode=nan_guard_mode,
+    ...     )
+
+    Methods
+    -------
+    __enter__()
+        Redirects stdout and stderr, and disables logging.
+
+    __exit__(exc_type, exc_value, traceback)
+        Restores stdout, stderr, and logging upon exit.
+    """
+
+    def __enter__(self):  # noqa: D105
+        self._null_file = open(os.devnull, "w")
+        self._stdout_context = contextlib.redirect_stdout(self._null_file)
+        self._stderr_context = contextlib.redirect_stderr(self._null_file)
+        self._stdout_context.__enter__()
+        self._stderr_context.__enter__()
+        logging.disable(logging.CRITICAL)  # Disable logging
+
+    def __exit__(self, exc_type, exc_value, traceback):  # noqa: D105
+        self._stdout_context.__exit__(exc_type, exc_value, traceback)
+        self._stderr_context.__exit__(exc_type, exc_value, traceback)
+        self._null_file.close()
+        logging.disable(logging.NOTSET)  # Re-enable logging

tests/test_likelihoods.py

@@ -6,6 +6,7 @@
 
 from pathlib import Path
 from itertools import product
+from hssm.utils import SuppressOutput
 
 import numpy as np
 import pandas as pd
@@ -109,11 +110,15 @@ def test_analytical_gradient():
     size = cav_data_numpy.shape[0]
     logp = logp_ddm(cav_data_numpy, v, a, z, t).sum()
     grad = pt.grad(logp, wrt=[v, a, z, t])
-    grad_func = pytensor.function(
-        [v, a, z, t],
-        grad,
-        mode=nan_guard_mode,
-    )
+
+    # Temporary measure to suppress output from pytensor.function
+    # See issues #594 in hssm and #1037 in pymc-devs/pytensor repos
+    with SuppressOutput():
+        grad_func = pytensor.function(
+            [v, a, z, t],
+            grad,
+            mode=nan_guard_mode,
+        )
     v_test = np.random.normal(size=size)
     a_test = np.random.uniform(0.0001, 2, size=size)
     z_test = np.random.uniform(0.1, 1.0, size=size)
@@ -123,13 +128,15 @@ def test_analytical_gradient():
 
     assert np.all(np.isfinite(grad), axis=None), "Gradient contains non-finite values."
 
-    grad_func_sdv = pytensor.function(
-        [v, a, z, t, sv],
-        pt.grad(
-            logp_ddm_sdv(cav_data_numpy, v, a, z, t, sv).sum(), wrt=[v, a, z, t, sv]
-        ),
-        mode=nan_guard_mode,
-    )
+    # Also temporary
+    with SuppressOutput():
+        grad_func_sdv = pytensor.function(
+            [v, a, z, t, sv],
+            pt.grad(
+                logp_ddm_sdv(cav_data_numpy, v, a, z, t, sv).sum(), wrt=[v, a, z, t, sv]
+            ),
+            mode=nan_guard_mode,
+        )
 
     grad_sdv = np.array(grad_func_sdv(v_test, a_test, z_test, t_test, sv_test))