Extract convergence checks function from SamplerReport

pymc/backends/report.py

@@ -13,63 +13,28 @@
 #   limitations under the License.
 
 import dataclasses
-import enum
 import logging
 
-from typing import Any, Optional
+from typing import Optional
 
 import arviz
 
-from pymc.util import get_untransformed_name, is_transformed_name
+from pymc.stats.convergence import (
+    _LEVELS,
+    SamplerWarning,
+    log_warnings,
+    run_convergence_checks,
+)
 
 logger = logging.getLogger("pymc")
 
 
-@enum.unique
-class WarningType(enum.Enum):
-    # For HMC and NUTS
-    DIVERGENCE = 1
-    TUNING_DIVERGENCE = 2
-    DIVERGENCES = 3
-    TREEDEPTH = 4
-    # Problematic sampler parameters
-    BAD_PARAMS = 5
-    # Indications that chains did not converge, eg Rhat
-    CONVERGENCE = 6
-    BAD_ACCEPTANCE = 7
-    BAD_ENERGY = 8
-
-
-@dataclasses.dataclass
-class SamplerWarning:
-    kind: WarningType
-    message: str
-    level: str
-    step: Optional[int] = None
-    exec_info: Optional[Any] = None
-    extra: Optional[Any] = None
-    divergence_point_source: Optional[dict] = None
-    divergence_point_dest: Optional[dict] = None
-    divergence_info: Optional[Any] = None
-
-
-_LEVELS = {
-    "info": logging.INFO,
-    "error": logging.ERROR,
-    "warn": logging.WARN,
-    "debug": logging.DEBUG,
-    "critical": logging.CRITICAL,
-}
-
-
 class SamplerReport:
     """Bundle warnings, convergence stats and metadata of a sampling run."""
 
     def __init__(self):
-        self._chain_warnings = {}
-        self._global_warnings = []
-        self._ess = None
-        self._rhat = None
+        self._chain_warnings: Dict[int, List[SamplerWarning]] = {}
+        self._global_warnings: List[SamplerWarning] = []
         self._n_tune = None
         self._n_draws = None
         self._t_sampling = None
@@ -109,65 +74,7 @@ def raise_ok(self, level="error"):
             raise ValueError("Serious convergence issues during sampling.")
 
     def _run_convergence_checks(self, idata: arviz.InferenceData, model):
-        if not hasattr(idata, "posterior"):
-            msg = "No posterior samples. Unable to run convergence checks"
-            warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info", None, None, None)
-            self._add_warnings([warn])
-            return
-
-        if idata["posterior"].sizes["chain"] == 1:
-            msg = (
-                "Only one chain was sampled, this makes it impossible to "
-                "run some convergence checks"
-            )
-            warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info")
-            self._add_warnings([warn])
-            return
-
-        elif idata["posterior"].sizes["chain"] < 4:
-            msg = (
-                "We recommend running at least 4 chains for robust computation of "
-                "convergence diagnostics"
-            )
-            warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info")
-            self._add_warnings([warn])
-            return
-
-        valid_name = [rv.name for rv in model.free_RVs + model.deterministics]
-        varnames = []
-        for rv in model.free_RVs:
-            rv_name = rv.name
-            if is_transformed_name(rv_name):
-                rv_name2 = get_untransformed_name(rv_name)
-                rv_name = rv_name2 if rv_name2 in valid_name else rv_name
-            if rv_name in idata["posterior"]:
-                varnames.append(rv_name)
-
-        self._ess = ess = arviz.ess(idata, var_names=varnames)
-        self._rhat = rhat = arviz.rhat(idata, var_names=varnames)
-
-        warnings = []
-        rhat_max = max(val.max() for val in rhat.values())
-        if rhat_max > 1.01:
-            msg = (
-                "The rhat statistic is larger than 1.01 for some "
-                "parameters. This indicates problems during sampling. "
-                "See https://arxiv.org/abs/1903.08008 for details"
-            )
-            warn = SamplerWarning(WarningType.CONVERGENCE, msg, "info", extra=rhat)
-            warnings.append(warn)
-
-        eff_min = min(val.min() for val in ess.values())
-        eff_per_chain = eff_min / idata["posterior"].sizes["chain"]
-        if eff_per_chain < 100:
-            msg = (
-                "The effective sample size per chain is smaller than 100 for some parameters. "
-                " A higher number is needed for reliable rhat and ess computation. "
-                "See https://arxiv.org/abs/1903.08008 for details"
-            )
-            warn = SamplerWarning(WarningType.CONVERGENCE, msg, "error", extra=ess)
-            warnings.append(warn)
-
+        warnings = run_convergence_checks(idata, model)
         self._add_warnings(warnings)
 
     def _add_warnings(self, warnings, chain=None):
@@ -178,15 +85,9 @@ def _add_warnings(self, warnings, chain=None):
         warn_list.extend(warnings)
 
     def _log_summary(self):
-        def log_warning(warn):
-            level = _LEVELS[warn.level]
-            logger.log(level, warn.message)
-
         for chain, warns in self._chain_warnings.items():
-            for warn in warns:
-                log_warning(warn)
-        for warn in self._global_warnings:
-            log_warning(warn)
+            log_warnings(warns)
+        log_warnings(self._global_warnings)
 
     def _slice(self, start, stop, step):
         report = SamplerReport()

pymc/sampling.py

@@ -70,6 +70,7 @@
 )
 from pymc.model import Model, modelcontext
 from pymc.parallel_sampling import Draw, _cpu_count
+from pymc.stats.convergence import run_convergence_checks
 from pymc.step_methods import NUTS, CompoundStep, DEMetropolis
 from pymc.step_methods.arraystep import BlockedStep, PopulationArrayStepShared
 from pymc.step_methods.hmc import quadpotential
@@ -677,7 +678,7 @@ def sample(
     _log.info(
         f'Sampling {n_chains} chain{"s" if n_chains > 1 else ""} for {n_tune:_d} tune and {n_draws:_d} draw iterations '
         f"({n_tune*n_chains:_d} + {n_draws*n_chains:_d} draws total) "
-        f"took {mtrace.report.t_sampling:.0f} seconds."
+        f"took {t_sampling:.0f} seconds."
     )
     mtrace.report._log_summary()
 
@@ -695,7 +696,8 @@ def sample(
                     stacklevel=2,
                 )
             else:
-                mtrace.report._run_convergence_checks(idata, model)
+                convergence_warnings = run_convergence_checks(idata, model)
+                mtrace.report._add_warnings(convergence_warnings)
 
         if return_inferencedata:
             return idata

pymc/stats/convergence.py

@@ -0,0 +1,117 @@
+import dataclasses
+import enum
+import logging
+
+from typing import Any, List, Optional, Sequence
+
+import arviz
+
+from pymc.util import get_untransformed_name, is_transformed_name
+
+_LEVELS = {
+    "info": logging.INFO,
+    "error": logging.ERROR,
+    "warn": logging.WARN,
+    "debug": logging.DEBUG,
+    "critical": logging.CRITICAL,
+}
+
+logger = logging.getLogger("pymc")
+
+
+@enum.unique
+class WarningType(enum.Enum):
+    # For HMC and NUTS
+    DIVERGENCE = 1
+    TUNING_DIVERGENCE = 2
+    DIVERGENCES = 3
+    TREEDEPTH = 4
+    # Problematic sampler parameters
+    BAD_PARAMS = 5
+    # Indications that chains did not converge, eg Rhat
+    CONVERGENCE = 6
+    BAD_ACCEPTANCE = 7
+    BAD_ENERGY = 8
+
+
+@dataclasses.dataclass
+class SamplerWarning:
+    kind: WarningType
+    message: str
+    level: str
+    step: Optional[int] = None
+    exec_info: Optional[Any] = None
+    extra: Optional[Any] = None
+    divergence_point_source: Optional[dict] = None
+    divergence_point_dest: Optional[dict] = None
+    divergence_info: Optional[Any] = None
+
+
+def run_convergence_checks(idata: arviz.InferenceData, model) -> List[SamplerWarning]:
+    if not hasattr(idata, "posterior"):
+        msg = "No posterior samples. Unable to run convergence checks"
+        warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info", None, None, None)
+        return [warn]
+
+    if idata["posterior"].sizes["chain"] == 1:
+        msg = (
+            "Only one chain was sampled, this makes it impossible to " "run some convergence checks"
+        )
+        warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info")
+        return [warn]
+
+    elif idata["posterior"].sizes["chain"] < 4:
+        msg = (
+            "We recommend running at least 4 chains for robust computation of "
+            "convergence diagnostics"
+        )
+        warn = SamplerWarning(WarningType.BAD_PARAMS, msg, "info")
+        return [warn]
+
+    warnings = []
+    valid_name = [rv.name for rv in model.free_RVs + model.deterministics]
+    varnames = []
+    for rv in model.free_RVs:
+        rv_name = rv.name
+        if is_transformed_name(rv_name):
+            rv_name2 = get_untransformed_name(rv_name)
+            rv_name = rv_name2 if rv_name2 in valid_name else rv_name
+        if rv_name in idata["posterior"]:
+            varnames.append(rv_name)
+
+    ess = arviz.ess(idata, var_names=varnames)
+    rhat = arviz.rhat(idata, var_names=varnames)
+
+    warnings = []
+    rhat_max = max(val.max() for val in rhat.values())
+    if rhat_max > 1.01:
+        msg = (
+            "The rhat statistic is larger than 1.01 for some "
+            "parameters. This indicates problems during sampling. "
+            "See https://arxiv.org/abs/1903.08008 for details"
+        )
+        warn = SamplerWarning(WarningType.CONVERGENCE, msg, "info", extra=rhat)
+        warnings.append(warn)
+
+    eff_min = min(val.min() for val in ess.values())
+    eff_per_chain = eff_min / idata["posterior"].sizes["chain"]
+    if eff_per_chain < 100:
+        msg = (
+            "The effective sample size per chain is smaller than 100 for some parameters. "
+            " A higher number is needed for reliable rhat and ess computation. "
+            "See https://arxiv.org/abs/1903.08008 for details"
+        )
+        warn = SamplerWarning(WarningType.CONVERGENCE, msg, "error", extra=ess)
+        warnings.append(warn)
+
+    return warnings
+
+
+def log_warning(warn: SamplerWarning):
+    level = _LEVELS[warn.level]
+    logger.log(level, warn.message)
+
+
+def log_warnings(warnings: Sequence[SamplerWarning]):
+    for warn in warnings:
+        log_warning(warn)

pymc/step_methods/hmc/base_hmc.py

@@ -21,10 +21,10 @@
 import numpy as np
 
 from pymc.aesaraf import floatX
-from pymc.backends.report import SamplerWarning, WarningType
 from pymc.blocking import DictToArrayBijection, RaveledVars
 from pymc.exceptions import SamplingError
 from pymc.model import Point, modelcontext
+from pymc.stats.convergence import SamplerWarning, WarningType
 from pymc.step_methods import step_sizes
 from pymc.step_methods.arraystep import GradientSharedStep
 from pymc.step_methods.hmc import integration

pymc/step_methods/hmc/nuts.py

@@ -17,8 +17,8 @@
 import numpy as np
 
 from pymc.aesaraf import floatX
-from pymc.backends.report import SamplerWarning, WarningType
 from pymc.math import logbern
+from pymc.stats.convergence import SamplerWarning, WarningType
 from pymc.step_methods.arraystep import Competence
 from pymc.step_methods.hmc.base_hmc import BaseHMC, DivergenceInfo, HMCStepData
 from pymc.step_methods.hmc.integration import IntegrationError

pymc/step_methods/step_sizes.py

@@ -16,7 +16,7 @@
 
 from scipy import stats
 
-from pymc.backends.report import SamplerWarning, WarningType
+from pymc.stats.convergence import SamplerWarning, WarningType
 
 
 class DualAverageAdaptation: