Change logger names from all "pymc" to module name (__name__)

Author: pdb5627

pymc/__init__.py

@@ -16,7 +16,7 @@
 
 import logging
 
-_log = logging.getLogger("pymc")
+_log = logging.getLogger(__name__)
 
 if not logging.root.handlers:
     _log.setLevel(logging.INFO)

pymc/backends/arviz.py

@@ -46,7 +46,7 @@
 
 ___all__ = [""]
 
-_log = logging.getLogger("pymc")
+_log = logging.getLogger(__name__)
 
 # random variable object ...
 Var = Any  # pylint: disable=invalid-name

pymc/backends/base.py

@@ -42,7 +42,7 @@
 from pymc.model import modelcontext
 from pymc.util import get_var_name
 
-logger = logging.getLogger("pymc")
+logger = logging.getLogger(__name__)
 
 
 class BackendError(Exception):

pymc/backends/mcbackend.py

@@ -37,7 +37,7 @@
     flatten_steps,
 )
 
-_log = logging.getLogger("pymc")
+_log = logging.getLogger(__name__)
 
 
 def find_data(pmodel: Model) -> List[mcb.DataVariable]:

pymc/backends/report.py

@@ -19,7 +19,7 @@
 
 from pymc.stats.convergence import _LEVELS, SamplerWarning
 
-logger = logging.getLogger("pymc")
+logger = logging.getLogger(__name__)
 
 
 class SamplerReport:

pymc/distributions/simulator.py

@@ -29,7 +29,7 @@
 
 __all__ = ["Simulator"]
 
-_log = logging.getLogger("pymc")
+_log = logging.getLogger(__name__)
 
 
 class SimulatorRV(RandomVariable):

pymc/ode/ode.py

@@ -26,7 +26,7 @@
 from pymc.exceptions import DtypeError, ShapeError
 from pymc.ode import utils
 
-_log = logging.getLogger("pymc")
+_log = logging.getLogger(__name__)
 floatX = pytensor.config.floatX
 
 

pymc/sampling/forward.py

@@ -80,7 +80,7 @@
 ArrayLike: TypeAlias = Union[np.ndarray, List[float]]
 PointList: TypeAlias = List[PointType]
 
-_log = logging.getLogger("pymc")
+_log = logging.getLogger(__name__)
 
 
 def get_vars_in_point_list(trace, model):

pymc/sampling/mcmc.py

@@ -86,7 +86,7 @@ def __call__(self, trace: IBaseTrace, draw: Draw):
         pass
 
 
-_log = logging.getLogger("pymc")
+_log = logging.getLogger(__name__)
 
 
 def instantiate_steppers(

pymc/sampling/parallel.py

@@ -32,7 +32,7 @@
 from pymc.exceptions import SamplingError
 from pymc.util import RandomSeed
 
-logger = logging.getLogger("pymc")
+logger = logging.getLogger(__name__)
 
 
 class ParallelSamplingError(Exception):

pymc/sampling/population.py

@@ -45,7 +45,7 @@
 Step: TypeAlias = Union[BlockedStep, CompoundStep]
 
 
-_log = logging.getLogger("pymc")
+_log = logging.getLogger(__name__)
 
 
 def _sample_population(

pymc/smc/sampling.py

@@ -193,7 +193,7 @@ def sample_smc(
 
     model = modelcontext(model)
 
-    _log = logging.getLogger("pymc")
+    _log = logging.getLogger(__name__)
     _log.info("Initializing SMC sampler...")
     _log.info(
         f"Sampling {chains} chain{'s' if chains > 1 else ''} "

pymc/stats/convergence.py

@@ -29,7 +29,7 @@
     "critical": logging.CRITICAL,
 }
 
-logger = logging.getLogger("pymc")
+logger = logging.getLogger(__name__)
 
 
 @enum.unique

pymc/step_methods/hmc/base_hmc.py

@@ -35,7 +35,7 @@
 from pymc.tuning import guess_scaling
 from pymc.util import get_value_vars_from_user_vars
 
-logger = logging.getLogger("pymc")
+logger = logging.getLogger(__name__)
 
 
 class DivergenceInfo(NamedTuple):

tests/sampling/test_forward.py

@@ -805,12 +805,12 @@ def test_logging_sampled_basic_rvs_prior(self, caplog):
 
         with m:
             pm.sample_prior_predictive(samples=1)
-        assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [x, z]")]
+        assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [x, z]")]
         caplog.clear()
 
         with m:
             pm.sample_prior_predictive(samples=1, var_names=["x"])
-        assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [x]")]
+        assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [x]")]
         caplog.clear()
 
     def test_logging_sampled_basic_rvs_posterior(self, caplog):
@@ -823,40 +823,44 @@ def test_logging_sampled_basic_rvs_posterior(self, caplog):
         idata = az_from_dict(posterior={"x": np.zeros(5), "x_det": np.ones(5), "y": np.ones(5)})
         with m:
             pm.sample_posterior_predictive(idata)
-        assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [z]")]
+        assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [z]")]
         caplog.clear()
 
         with m:
             pm.sample_posterior_predictive(idata, var_names=["y", "z"])
-        assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [y, z]")]
+        assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [y, z]")]
         caplog.clear()
 
         # Resampling `x` will force resampling of `y`, even if it is in trace
         with m:
             pm.sample_posterior_predictive(idata, var_names=["x", "z"])
-        assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [x, y, z]")]
+        assert caplog.record_tuples == [
+            ("pymc.sampling.forward", logging.INFO, "Sampling: [x, y, z]")
+        ]
         caplog.clear()
 
         # Missing deterministic `x_det` does not show in the log, even if it is being
         # recomputed, only `y` RV shows
         idata = az_from_dict(posterior={"x": np.zeros(5)})
         with m:
             pm.sample_posterior_predictive(idata)
-        assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [y, z]")]
+        assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [y, z]")]
         caplog.clear()
 
         # Missing deterministic `x_det` does not cause recomputation of downstream `y` RV
         idata = az_from_dict(posterior={"x": np.zeros(5), "y": np.ones(5)})
         with m:
             pm.sample_posterior_predictive(idata)
-        assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [z]")]
+        assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [z]")]
         caplog.clear()
 
         # Missing `x` causes sampling of downstream `y` RV, even if it is present in trace
         idata = az_from_dict(posterior={"y": np.ones(5)})
         with m:
             pm.sample_posterior_predictive(idata)
-        assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [x, y, z]")]
+        assert caplog.record_tuples == [
+            ("pymc.sampling.forward", logging.INFO, "Sampling: [x, y, z]")
+        ]
         caplog.clear()
 
     def test_logging_sampled_basic_rvs_posterior_deterministic(self, caplog):
@@ -871,7 +875,7 @@ def test_logging_sampled_basic_rvs_posterior_deterministic(self, caplog):
         idata = az_from_dict(posterior={"x": np.zeros(5), "x_det": np.ones(5), "y": np.ones(5)})
         with m:
             pm.sample_posterior_predictive(idata, var_names=["x_det", "z"])
-        assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [y, z]")]
+        assert caplog.record_tuples == [("pymc.sampling.forward", logging.INFO, "Sampling: [y, z]")]
         caplog.clear()
 
     @staticmethod
@@ -938,19 +942,25 @@ def test_logging_sampled_basic_rvs_posterior_mutable(self, mock_sample_results,
             # MultiTrace will only have the actual MCMC posterior samples but no information on
             # the MutableData and mutable coordinate values, so it will always assume they are volatile
             # and resample their descendants
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [a, b, sigma, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [a, b, sigma, y]")
+            ]
             caplog.clear()
         elif kind == "InferenceData":
             # InferenceData has all MCMC posterior samples and the values for both coordinates and
             # data containers. This enables it to see that no data has changed and it should only
             # resample the observed variable
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [y]")
+            ]
             caplog.clear()
         elif kind == "Dataset":
             # Dataset has all MCMC posterior samples and the values of the coordinates. This
             # enables it to see that the coordinates have not changed, but the MutableData is
             # assumed volatile by default
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [b, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [b, y]")
+            ]
             caplog.clear()
 
         original_offsets = model["offsets"].get_value()
@@ -959,13 +969,19 @@ def test_logging_sampled_basic_rvs_posterior_mutable(self, mock_sample_results,
             pm.set_data({"offsets": original_offsets + 1})
             pm.sample_posterior_predictive(samples)
         if kind == "MultiTrace":
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [a, b, sigma, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [a, b, sigma, y]")
+            ]
             caplog.clear()
         elif kind == "InferenceData":
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [b, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [b, y]")
+            ]
             caplog.clear()
         elif kind == "Dataset":
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [b, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [b, y]")
+            ]
             caplog.clear()
 
         with model:
@@ -974,13 +990,19 @@ def test_logging_sampled_basic_rvs_posterior_mutable(self, mock_sample_results,
             pm.set_data({"offsets": original_offsets, "y_obs": np.zeros((10, 4))})
             pm.sample_posterior_predictive(samples)
         if kind == "MultiTrace":
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [a, b, sigma, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [a, b, sigma, y]")
+            ]
             caplog.clear()
         elif kind == "InferenceData":
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [a, sigma, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [a, sigma, y]")
+            ]
             caplog.clear()
         elif kind == "Dataset":
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [a, b, sigma, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [a, b, sigma, y]")
+            ]
             caplog.clear()
 
         with model:
@@ -990,13 +1012,19 @@ def test_logging_sampled_basic_rvs_posterior_mutable(self, mock_sample_results,
             pm.set_data({"offsets": original_offsets + 1, "y_obs": np.zeros((10, 3))})
             pm.sample_posterior_predictive(samples)
         if kind == "MultiTrace":
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [a, b, sigma, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [a, b, sigma, y]")
+            ]
             caplog.clear()
         elif kind == "InferenceData":
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [a, b, sigma, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [a, b, sigma, y]")
+            ]
             caplog.clear()
         elif kind == "Dataset":
-            assert caplog.record_tuples == [("pymc", logging.INFO, "Sampling: [a, b, sigma, y]")]
+            assert caplog.record_tuples == [
+                ("pymc.sampling.forward", logging.INFO, "Sampling: [a, b, sigma, y]")
+            ]
             caplog.clear()