Add CLVWrapper

pymc_marketing/mlflow.py

@@ -120,6 +120,8 @@
     msg = "This module requires mlflow. Install using `pip install mlflow`"
     raise ImportError(msg)
 
+import mlflow.artifacts
+from mlflow.pyfunc.model import PythonModel
 from mlflow.utils.autologging_utils import autologging_integration
 
 from pymc_marketing.clv.models.basic import CLVModel
@@ -471,7 +473,7 @@
             mlflow.log_metric(f"{metric}_{stat.replace('%', '')}", value)
 
 
-class MMMWrapper(mlflow.pyfunc.PythonModel):
+class MMMWrapper(PythonModel):
     """A class to prepare a PyMC Marketing Mix Model (MMM) for logging and registering in MLflow.
 
     This class extends MLflow's PythonModel to handle prediction tasks using a PyMC-based MMM.
@@ -706,8 +708,47 @@
         mlflow.register_model(model_uri, registered_model_name)
 
 
-def load_mmm(
+class CLVWrapper(PythonModel):
+    """Wrapper class for logging with MLflow."""
+
+    def __init__(self, model: CLVModel, method: str):
+        self.model = model
+        self.method = method
+
+    def predict(
+        self,
+        context: Any,
+        model_input,
+        params: dict[str, Any] | None = None,
+    ) -> Any:
+        """Perform predictions or sampling using the specified prediction method."""
+        return getattr(self.model, self.method)(model_input, **params)
+
+
+def log_clv(
+    model: CLVModel,
+    method: str,
+    artifact_path: str = "model",
+    registered_model_name: str | None = None,
+) -> None:
+    """Log a PyMC-Marketing CLV model as a native MLflow model for the current run."""
+    mlflow_clv = CLVWrapper(model=model, method=method)
+
+    mlflow.pyfunc.log_model(
+        artifact_path=artifact_path,
+        python_model=mlflow_clv,
+    )
+
+    run_id = mlflow.active_run().info.run_id
+    model_uri = f"runs:/{run_id}/{artifact_path}"
+
+    if registered_model_name:
+        mlflow.register_model(model_uri, registered_model_name)
+
+
+def _load_model(
     run_id: str,
+    cls,
     full_model: bool = False,
     keep_idata: bool = False,
     artifact_path: str = "model",
@@ -737,7 +778,6 @@
     model : mlflow.pyfunc.PyFuncModel | MMM
         The loaded MLflow PyFuncModel or MMM model.
 
-
     Examples
     --------
     .. code-block:: python
@@ -759,7 +799,7 @@
         run_id=run_id, artifact_path="idata.nc", dst_path=dst_path
     )
 
-    model = MMM.load(idata_path)
+    model = cls.load(idata_path)
 
     if not keep_idata:
         _force_load_idata_groups(model.idata)
@@ -777,6 +817,73 @@
     return model
 
 
+def load_mmm(
+    run_id: str,
+    full_model: bool = False,
+    keep_idata: bool = False,
+    artifact_path: str = "model",
+    dst_path: str | None = None,
+) -> mlflow.pyfunc.PyFuncModel | MMM:
+    """
+    Load a PyMC-Marketing MMM model from MLflow.
+
+    Can either load the full model including the InferenceData, or just the lighter PyFuncModel version.
+
+    Parameters
+    ----------
+    run_id : str
+        The MLflow run ID from which to load the model.
+    full_model : bool, default=True
+        If True, load the full MMM model including the InferenceData.
+    keep_idata : bool, default=False
+        If True, keep the downloaded InferenceData saved locally.
+    artifact_path : str, default="model"
+        The artifact path within the run where the model is stored.
+    dst_path : str | None, default=None
+        The local destination path where the InferenceData will be downloaded.
+        If None, defaults to "idata_{run_id}" to avoid conflicts when loading multiple models.
+
+    Returns
+    -------
+    model : mlflow.pyfunc.PyFuncModel | MMM
+        The loaded MLflow PyFuncModel or MMM model.
+
+
+    Examples
+    --------
+    .. code-block:: python
+
+        # Load model using run_id
+        model = load_mmm(run_id="your_run_id", full_model=True, keep_idata=True)
+    """
+    return _load_model(
+        run_id=run_id,
+        full_model=full_model,
+        keep_idata=keep_idata,
+        artifact_path=artifact_path,
+        dst_path=dst_path,
+        cls=MMM,
+    )
+
+
+def load_clv(
+    run_id: str,
+    full_model: bool = False,
+    keep_idata: bool = False,
+    artifact_path: str = "model",
+    dst_path: str | None = None,
+) -> mlflow.pyfunc.PyFuncModel | CLVModel:
+    """Load a PyMC-Marketing CLV model from MLflow."""
+    return _load_model(
+        run_id=run_id,
+        full_model=full_model,
+        keep_idata=keep_idata,
+        artifact_path=artifact_path,
+        dst_path=dst_path,
+        cls=CLVModel,
+    )
+
+
 @autologging_integration(FLAVOR_NAME)
 def autolog(
     log_sampler_info: bool = True,