Log number of posterior & tuning samples

Makefile

@@ -50,6 +50,9 @@ uml: ## Install documentation dependencies and generate UML diagrams
 	pyreverse pymc_marketing/mmm -d docs/source/uml -f 'ALL' -o png -p mmm
 	pyreverse pymc_marketing/clv -d docs/source/uml -f 'ALL' -o png -p clv
 
+mlflow_server: ## Start MLflow server on port 5000
+	mlflow server --backend-store-uri sqlite:///mlruns.db --default-artifact-root ./mlruns
+
 
 #################################################################################
 # Self Documenting Commands                                                     #

pymc_marketing/mlflow.py

@@ -304,6 +304,11 @@
     - The model representation (str).
     - The model coordinates (coords.json).
 
+    Parameters
+    ----------
+    model : Model
+        The PyMC model object.
+
     """
     log_types_of_parameters(model)
 
@@ -321,6 +326,7 @@
 
 def log_sample_diagnostics(
     idata: az.InferenceData,
+    tune: int | None = None,
 ) -> None:
     """Log sample diagnostics to MLflow.
 
@@ -336,6 +342,14 @@
     - The version of the inference library
     - The version of ArviZ
 
+    Parameters
+    ----------
+    idata : az.InferenceData
+        The InferenceData object returned by the sampling method.
+    tune : int, optional
+        The number of tuning steps used in sampling. Derived from the
+        inference data if not provided.
+
     """
     if "posterior" not in idata:
         raise KeyError("InferenceData object does not contain the group posterior.")
@@ -348,19 +362,28 @@
 
     diverging = sample_stats["diverging"]
 
+    chains = posterior.sizes["chain"]
+    draws = posterior.sizes["draw"]
+    posterior_samples = chains * draws
+
+    tuning_step = sample_stats.attrs.get("tuning_steps", tune)
+    if tuning_step is not None:
+        tuning_samples = tuning_step * chains
+        mlflow.log_param("tuning_steps", tuning_step)
+        mlflow.log_param("tuning_samples", tuning_samples)
+
     total_divergences = diverging.sum().item()
     mlflow.log_metric("total_divergences", total_divergences)
     if sampling_time := sample_stats.attrs.get("sampling_time"):
         mlflow.log_metric("sampling_time", sampling_time)
         mlflow.log_metric(
             "time_per_draw",
-            sampling_time / (posterior.sizes["draw"] * posterior.sizes["chain"]),
+            sampling_time / posterior_samples,
         )
 
-    if tuning_step := sample_stats.attrs.get("tuning_steps"):
-        mlflow.log_param("tuning_steps", tuning_step)
-    mlflow.log_param("draws", posterior.sizes["draw"])
-    mlflow.log_param("chains", posterior.sizes["chain"])
+    mlflow.log_param("draws", draws)
+    mlflow.log_param("chains", chains)
+    mlflow.log_param("posterior_samples", posterior_samples)
 
     if inference_library := posterior.attrs.get("inference_library"):
         mlflow.log_param("inference_library", inference_library)
@@ -382,8 +405,7 @@
     idata : az.InferenceData
         The InferenceData object returned by the sampling method.
     save_file : str | Path
-        The path to save the InferenceData object as a net
-        CDF file.
+        The path to save the InferenceData object as a netCDF file.
 
     """
     idata.to_netcdf(str(save_file))
@@ -516,8 +538,11 @@
             mlflow.log_param("pymc_version", pm.__version__)
             mlflow.log_param("nuts_sampler", kwargs.get("nuts_sampler", "pymc"))
 
+            # Align with the default values in pymc.sample
+            tune = kwargs.get("tune", 1000)
+
             if log_sampler_info:
-                log_sample_diagnostics(idata)
+                log_sample_diagnostics(idata, tune=tune)
                 log_arviz_summary(
                     idata,
                     "summary.html",

pyproject.toml

@@ -62,6 +62,7 @@ docs = [
     "sphinx",
     "sphinxext-opengraph",
     "watermark",
+    "mlflow>=2.0.0",
 ]
 lint = ["mypy", "pandas-stubs", "pre-commit>=2.19.0", "ruff>=0.1.4"]
 test = [

tests/test_mlflow.py

@@ -231,10 +231,13 @@ def metric_checks(metrics, nuts_sampler) -> None:
 def param_checks(params, draws: int, chains: int, tune: int, nuts_sampler: str) -> None:
     assert params["draws"] == str(draws)
     assert params["chains"] == str(chains)
+    assert params["posterior_samples"] == str(draws * chains)
+
     if nuts_sampler not in ["numpyro", "blackjax"]:
         assert params["inference_library"] == nuts_sampler
-    if nuts_sampler not in ["numpyro", "nutpie", "blackjax"]:
-        assert params["tuning_steps"] == str(tune)
+
+    assert params["tuning_steps"] == str(tune)
+    assert params["tuning_samples"] == str(tune * chains)
 
     assert params["pymc_marketing_version"] == __version__