Remove warnings during tests

pymc_marketing/clv/models/basic.py

@@ -28,6 +28,7 @@
 
 from pymc_marketing.model_builder import ModelBuilder
 from pymc_marketing.model_config import ModelConfig, parse_model_config
+from pymc_marketing.utils import from_netcdf
 
 
 class CLVModel(ModelBuilder):
@@ -186,17 +187,22 @@ def load(cls, fname: str):
         >>> imported_model = MyModel.load(name)
         """
         filepath = Path(str(fname))
-        idata = az.from_netcdf(filepath)
+        idata = from_netcdf(filepath)
         return cls._build_with_idata(idata)
 
     @classmethod
     def _build_with_idata(cls, idata: az.InferenceData):
         dataset = idata.fit_data.to_dataframe()
-        model = cls(
-            dataset,
-            model_config=json.loads(idata.attrs["model_config"]),  # type: ignore
-            sampler_config=json.loads(idata.attrs["sampler_config"]),
-        )
+        with warnings.catch_warnings():
+            warnings.filterwarnings(
+                "ignore",
+                category=DeprecationWarning,
+            )
+            model = cls(
+                dataset,
+                model_config=json.loads(idata.attrs["model_config"]),  # type: ignore
+                sampler_config=json.loads(idata.attrs["sampler_config"]),
+            )
         model.idata = idata
         model.build_model()  # type: ignore
         if model.id != idata.attrs["id"]:

pymc_marketing/clv/models/pareto_nbd.py

@@ -412,7 +412,7 @@ def _extract_predictive_variables(
             purchase_coefficient = self.fit_result["purchase_coefficient"]
             alpha = alpha_scale * np.exp(
                 -xarray.dot(
-                    purchase_coefficient, purchase_xarray, dims="purchase_covariate"
+                    purchase_coefficient, purchase_xarray, dim="purchase_covariate"
                 )
             )
             alpha.name = "alpha"
@@ -429,7 +429,7 @@ def _extract_predictive_variables(
             dropout_coefficient = self.fit_result["dropout_coefficient"]
             beta = beta_scale * np.exp(
                 -xarray.dot(
-                    dropout_coefficient, dropout_xarray, dims="dropout_covariate"
+                    dropout_coefficient, dropout_xarray, dim="dropout_covariate"
                 )
             )
             beta.name = "beta"

pymc_marketing/mmm/base.py

@@ -63,7 +63,6 @@ def __init__(
         ),
         model_config: dict | None = Field(None, description="Model configuration."),
         sampler_config: dict | None = Field(None, description="Sampler configuration."),
-        **kwargs,
     ) -> None:
         self.date_column: str = date_column
         self.channel_columns: list[str] | tuple[str] = channel_columns
@@ -392,7 +391,7 @@ def plot_posterior_predictive(
 
             if original_scale:
                 likelihood_hdi = self.get_target_transformer().inverse_transform(
-                    Xt=likelihood_hdi
+                    likelihood_hdi
                 )
 
             ax.fill_between(

pymc_marketing/mmm/budget_optimizer.py

@@ -167,21 +167,19 @@
                 "No budget bounds provided. Using default bounds (0, total_budget) for each channel.",
                 stacklevel=2,
             )
-        else:
-            if not isinstance(budget_bounds, dict):
-                raise TypeError("`budget_bounds` should be a dictionary.")
+        elif not isinstance(budget_bounds, dict):
+            raise TypeError("`budget_bounds` should be a dictionary.")
 
         if custom_constraints is None:
             constraints = {"type": "eq", "fun": lambda x: np.sum(x) - total_budget}
             warnings.warn(
                 "Using default equality constraint: The sum of all budgets should be equal to the total budget.",
                 stacklevel=2,
             )
+        elif not isinstance(custom_constraints, dict):
+            raise TypeError("`custom_constraints` should be a dictionary.")
         else:
-            if not isinstance(custom_constraints, dict):
-                raise TypeError("`custom_constraints` should be a dictionary.")
-            else:
-                constraints = custom_constraints
+            constraints = custom_constraints
 
         num_channels = len(self.parameters.keys())
         initial_guess = np.ones(num_channels) * total_budget / num_channels

pymc_marketing/mmm/delayed_saturated_mmm.py

@@ -34,9 +34,11 @@
 from pymc_marketing.mmm.budget_optimizer import BudgetOptimizer
 from pymc_marketing.mmm.components.adstock import (
     AdstockTransformation,
+    GeometricAdstock,
     _get_adstock_function,
 )
 from pymc_marketing.mmm.components.saturation import (
+    LogisticSaturation,
     SaturationTransformation,
     _get_saturation_function,
 )
@@ -54,6 +56,7 @@
 from pymc_marketing.mmm.validating import ValidateControlColumns
 from pymc_marketing.model_config import parse_model_config
 from pymc_marketing.prior import Prior
+from pymc_marketing.utils import from_netcdf
 
 __all__ = ["BaseMMM", "MMM", "DelayedSaturatedMMM"]
 
@@ -78,17 +81,20 @@ def __init__(
         channel_columns: list[str] = Field(
             min_length=1, description="Column names of the media channel variables."
         ),
-        adstock_max_lag: int = Field(
-            ...,
-            gt=0,
-            description="Number of lags to consider in the adstock transformation.",
-        ),
         adstock: str | InstanceOf[AdstockTransformation] = Field(
             ..., description="Type of adstock transformation to apply."
         ),
         saturation: str | InstanceOf[SaturationTransformation] = Field(
             ..., description="Type of saturation transformation to apply."
         ),
+        adstock_max_lag: int | None = Field(
+            None,
+            gt=0,
+            description=(
+                "Number of lags to consider in the adstock transformation. "
+                "Defaults to the max lag of the adstock transformation."
+            ),
+        ),
         time_varying_intercept: bool = Field(
             False, description="Whether to consider time-varying intercept."
         ),
@@ -118,7 +124,6 @@ def __init__(
         adstock_first: bool = Field(
             True, description="Whether to apply adstock first."
         ),
-        **kwargs,
     ) -> None:
         """Constructor method.
 
@@ -128,12 +133,13 @@ def __init__(
             Column name of the date variable. Must be parsable using ~pandas.to_datetime.
         channel_columns : List[str]
             Column names of the media channel variables.
-        adstock_max_lag : int, optional
-            Number of lags to consider in the adstock transformation.
         adstock : str | AdstockTransformation
             Type of adstock transformation to apply.
         saturation : str | SaturationTransformation
             Type of saturation transformation to apply.
+        adstock_max_lag : int, optional
+            Number of lags to consider in the adstock transformation. Defaults to the
+            max lag of the adstock transformation.
         time_varying_intercept : bool, optional
             Whether to consider time-varying intercept, by default False.
             Because the `time-varying` variable is centered around 1 and acts as a multiplier,
@@ -159,14 +165,24 @@ def __init__(
             Whether to apply adstock first, by default True.
         """
         self.control_columns = control_columns
-        self.adstock_max_lag = adstock_max_lag
         self.time_varying_intercept = time_varying_intercept
         self.time_varying_media = time_varying_media
         self.date_column = date_column
         self.validate_data = validate_data
 
         self.adstock_first = adstock_first
-        self.adstock = _get_adstock_function(function=adstock, l_max=adstock_max_lag)
+
+        if adstock_max_lag is not None:
+            warnings.warn(
+                "The `adstock_max_lag` parameter is deprecated. Use `adstock` directly",
+                DeprecationWarning,
+                stacklevel=1,
+            )
+            adstock_kwargs = {"l_max": adstock_max_lag}
+        else:
+            adstock_kwargs = {}
+
+        self.adstock = _get_adstock_function(function=adstock, **adstock_kwargs)
         self.saturation = _get_saturation_function(function=saturation)
 
         model_config = model_config or {}
@@ -184,7 +200,6 @@ def __init__(
             channel_columns=channel_columns,
             model_config=model_config,
             sampler_config=sampler_config,
-            adstock_max_lag=adstock_max_lag,
         )
 
         self.yearly_seasonality = yearly_seasonality
@@ -287,7 +302,7 @@ def _save_input_params(self, idata) -> None:
         idata.attrs["adstock_first"] = json.dumps(self.adstock_first)
         idata.attrs["control_columns"] = json.dumps(self.control_columns)
         idata.attrs["channel_columns"] = json.dumps(self.channel_columns)
-        idata.attrs["adstock_max_lag"] = json.dumps(self.adstock_max_lag)
+        idata.attrs["adstock_max_lag"] = json.dumps(self.adstock.l_max)
         idata.attrs["validate_data"] = json.dumps(self.validate_data)
         idata.attrs["yearly_seasonality"] = json.dumps(self.yearly_seasonality)
         idata.attrs["time_varying_intercept"] = json.dumps(self.time_varying_intercept)
@@ -358,7 +373,11 @@ def build_model(
 
         .. code-block:: python
 
-            from pymc_marketing.mmm import MMM
+            from pymc_marketing.mmm import (
+                GeometricAdstock,
+                LogisticSaturation
+                MMM,
+            )
             from pymc_marketing.prior import Prior
 
             custom_config = {
@@ -374,12 +393,13 @@ def build_model(
             model = MMM(
                 date_column="date_week",
                 channel_columns=["x1", "x2"],
+                adstock=GeometricAdstock(l_max=8),
+                saturation=LogisticSaturation(),
                 control_columns=[
                     "event_1",
                     "event_2",
                     "t",
                 ],
-                adstock_max_lag=8,
                 yearly_seasonality=2,
                 model_config=custom_config,
             )
@@ -634,7 +654,7 @@ def load(cls, fname: str):
         """
 
         filepath = Path(fname)
-        idata = az.from_netcdf(filepath)
+        idata = from_netcdf(filepath)
         model_config = cls._model_config_formatting(
             json.loads(idata.attrs["model_config"])
         )
@@ -844,22 +864,25 @@ class MMM(
         import numpy as np
         import pandas as pd
 
-        from pymc_marketing.mmm import MMM
+        from pymc_marketing.mmm import (
+            GeometricAdstock,
+            LogisticSaturation
+            MMM,
+        )
 
         data_url = "https://raw.githubusercontent.com/pymc-labs/pymc-marketing/main/data/mmm_example.csv"
         data = pd.read_csv(data_url, parse_dates=["date_week"])
 
         mmm = MMM(
             date_column="date_week",
-            adstock="geometric",
-            saturation="logistic",
             channel_columns=["x1", "x2"],
+            adstock=GeometricAdstock(l_max=8),
+            saturation=LogisticSaturation(),
             control_columns=[
                 "event_1",
                 "event_2",
                 "t",
             ],
-            adstock_max_lag=8,
             yearly_seasonality=2,
         )
 
@@ -880,27 +903,30 @@ class MMM(
 
         import numpy as np
 
+        from pymc_marketing.mmm import (
+            GeometricAdstock,
+            LogisticSaturation
+            MMM,
+        )
         from pymc_marketing.prior import Prior
-        from pymc_marketing.mmm import MMM
 
         my_model_config = {
             "beta_channel": Prior("LogNormal", mu=np.array([2, 1]), sigma=1),
             "likelihood": Prior("Normal", sigma=Prior("HalfNormal", sigma=2)),
         }
 
         mmm = MMM(
-            adstock="geometric",
-            saturation="logistic",
-            model_config=my_model_config,
             date_column="date_week",
             channel_columns=["x1", "x2"],
+            adstock=GeometricAdstock(l_max=8),
+            saturation=LogisticSaturation(),
             control_columns=[
                 "event_1",
                 "event_2",
                 "t",
             ],
-            adstock_max_lag=8,
             yearly_seasonality=2,
+            model_config=my_model_config,
         )
 
     As you can see, we can configure all prior and likelihood distributions via the `model_config`.
@@ -1765,18 +1791,21 @@ def add_lift_test_measurements(
             import pandas as pd
             import numpy as np
 
-            from pymc_marketing.mmm import MMM
+            from pymc_marketing.mmm import (
+                GeometricAdstock,
+                LogisticSaturation,
+                MMM,
+            )
 
             model = MMM(
-                adstock="geometric",
-                saturation="logistic",
                 date_column="date_week",
                 channel_columns=["x1", "x2"],
+                adstock=GeometricAdstock(l_max=8),
+                saturation=LogisticSaturation(),
                 control_columns=[
                     "event_1",
                     "event_2",
                 ],
-                adstock_max_lag=8,
                 yearly_seasonality=2,
             )
 
@@ -2206,7 +2235,6 @@ def __init__(
         control_columns: list[str] | None = None,
         yearly_seasonality: int | None = None,
         adstock_first: bool = True,
-        **kwargs,
     ) -> None:
         """
         Wrapper function for DelayedSaturatedMMM class initializer.
@@ -2217,22 +2245,23 @@ def __init__(
         warnings.warn(
             "The DelayedSaturatedMMM class is deprecated. Please use the MMM class instead.",
             DeprecationWarning,
-            stacklevel=2,
+            stacklevel=1,
         )
 
+        adstock = GeometricAdstock(l_max=adstock_max_lag)
+        saturation = LogisticSaturation()
+
         super().__init__(
             date_column=date_column,
             channel_columns=channel_columns,
-            adstock_max_lag=adstock_max_lag,
             time_varying_intercept=time_varying_intercept,
             time_varying_media=time_varying_media,
             model_config=model_config,
             sampler_config=sampler_config,
             validate_data=validate_data,
             control_columns=control_columns,
             yearly_seasonality=yearly_seasonality,
-            adstock="geometric",
-            saturation="logistic",
+            adstock=adstock,
+            saturation=saturation,
             adstock_first=adstock_first,
-            **kwargs,
         )