Date Validation and MMM Model Hamonization (Pydantic) (#824)

* validate base mmm init class

* validate dateformat

* add comment about date

* remove ()

pymc_marketing/mmm/base.py

@@ -46,19 +46,23 @@
 from pymc_marketing.model_builder import ModelBuilder
 
 __all__ = ["MMMModelBuilder", "BaseValidateMMM"]
+from pydantic import Field, validate_call
 
 
 class MMMModelBuilder(ModelBuilder):
     model: pm.Model
     _model_type = "BaseMMM"
     version = "0.0.2"
 
+    @validate_call
     def __init__(
         self,
-        date_column: str,
-        channel_columns: list[str] | tuple[str],
-        model_config: dict | None = None,
-        sampler_config: dict | None = None,
+        date_column: str = Field(..., description="Column name of the date variable."),
+        channel_columns: list[str] = Field(
+            min_length=1, description="Column names of the media channel variables."
+        ),
+        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

pymc_marketing/mmm/delayed_saturated_mmm.py

@@ -125,7 +125,7 @@ def __init__(
         Parameter
         ---------
         date_column : str
-            Column name of the date variable.
+            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
@@ -236,7 +236,13 @@ def _generate_and_preprocess_model_data(  # type: ignore
         _time_resolution: int
             The time resolution of the date index. Used by TVP.
         """
-        date_data = X[self.date_column]
+        try:
+            date_data = pd.to_datetime(X[self.date_column])
+        except Exception as e:
+            raise ValueError(
+                f"Could not convert {self.date_column} to datetime. Please check the date format."
+            ) from e
+
         channel_data = X[self.channel_columns]
 
         coords: dict[str, Any] = {

tests/mmm/test_delayed_saturated_mmm.py

@@ -82,6 +82,23 @@ def toy_X(generate_data) -> pd.DataFrame:
     return generate_data(date_data)
 
 
+@pytest.fixture(scope="module")
+def toy_X_with_bad_dates() -> pd.DataFrame:
+    bad_date_data = ["a", "b", "c", "d", "e"]
+    n: int = len(bad_date_data)
+    return pd.DataFrame(
+        data={
+            "date": bad_date_data,
+            "channel_1": rng.integers(low=0, high=400, size=n),
+            "channel_2": rng.integers(low=0, high=50, size=n),
+            "control_1": rng.gamma(shape=1000, scale=500, size=n),
+            "control_2": rng.gamma(shape=100, scale=5, size=n),
+            "other_column_1": rng.integers(low=0, high=100, size=n),
+            "other_column_2": rng.normal(loc=0, scale=1, size=n),
+        }
+    )
+
+
 @pytest.fixture(scope="class")
 def model_config_requiring_serialization() -> dict:
     model_config = {
@@ -206,6 +223,22 @@ def deep_equal(dict1, dict2):
         assert model.sampler_config == model2.sampler_config
         os.remove("test_save_load")
 
+    def test_bad_date_column(self, toy_X_with_bad_dates) -> None:
+        with pytest.raises(
+            ValueError,
+            match="Could not convert bad_date_column to datetime. Please check the date format.",
+        ):
+            my_mmm = MMM(
+                date_column="bad_date_column",
+                channel_columns=["channel_1", "channel_2"],
+                adstock_max_lag=4,
+                control_columns=["control_1", "control_2"],
+                adstock="geometric",
+                saturation="logistic",
+            )
+            y = np.ones(toy_X_with_bad_dates.shape[0])
+            my_mmm.build_model(X=toy_X_with_bad_dates, y=y)
+
     @pytest.mark.parametrize(
         argnames="adstock_max_lag",
         argvalues=[1, 4],