fixes

pymc_marketing/clv/utils.py

@@ -47,7 +47,10 @@ def customer_lifetime_value(
     discount_rate: float = 0.00,
     time_unit: str = "D",
 ) -> xarray.DataArray:
-    """Compute the average lifetime value for a group of one or more customers,
+    """
+    Compute customer lifetime value.
+
+    Compute the average lifetime value for a group of one or more customers
     and apply a discount rate for net present value estimations.
     Note `future_t` is measured in months regardless of `time_unit` specified.
 
@@ -86,7 +89,21 @@ def customer_lifetime_value(
         raise ValueError("Required column future_spend missing")
 
     def _squeeze_dims(x: xarray.DataArray):
-        """This utility is required for MAP-fitted model predictions to broadcast properly"""
+        """
+        Squeeze dimensions for MAP-fitted model predictions.
+
+        This utility is required for MAP-fitted model predictions to broadcast properly.
+
+        Parameters
+        ----------
+        x : xarray.DataArray
+            DataArray to squeeze dimensions for.
+
+        Returns
+        -------
+        xarray.DataArray
+            DataArray with squeezed dimensions.
+        """
         dims_to_squeeze: tuple[str, ...] = ()
         if "chain" in x.dims and len(x.chain) == 1:
             dims_to_squeeze += ("chain",)
@@ -421,6 +438,7 @@ def rfm_train_test_split(
     sort_transactions: bool | None = True,
 ) -> pandas.DataFrame:
     """Summarize transaction data and split into training and tests datasets for CLV modeling.
+
     This can also be used to evaluate the impact of a time-based intervention like a marketing campaign.
 
     This transforms a DataFrame of transaction data of the form:
@@ -713,7 +731,23 @@ def rfm_segments(
 
 
 def _rfm_quartile_labels(column_name, max_label_range):
-    """Called internally by rfm_segments to label quartiles for each variable"""
+    """
+    Label quartiles for each variable.
+
+    Called internally by rfm_segments to label quartiles for each variable.
+
+    Parameters
+    ----------
+    column_name : str
+        The name of the column to label.
+    max_label_range : int
+        The maximum range of labels to create.
+
+    Returns
+    -------
+    list[int]
+        A list of labels for the column.
+    """
     # recency labels must be reversed because lower values are more desirable
     if column_name == "r_quartile":
         return list(range(max_label_range - 1, 0, -1))

pymc_marketing/utils.py

@@ -18,6 +18,18 @@
 
 
 def from_netcdf(filepath: str | Path) -> az.InferenceData:
+    """Load inference data from a netcdf file.
+
+    Parameters
+    ----------
+    filepath : str or Path
+        The path to the netcdf file.
+
+    Returns
+    -------
+    az.InferenceData
+        The inference data.
+    """
     with warnings.catch_warnings():
         warnings.filterwarnings(
             "ignore",