Minor improvements [MMM]

README.md

@@ -54,13 +54,15 @@ We provide a `Dockerfile` to build a Docker image for PyMC-Marketing so that is
 
 ## In-depth Bayesian Marketing Mix Modeling (MMM) in PyMC
 
-Leverage our Bayesian MMM API to tailor your marketing strategies effectively. Based on the research [Jin, Yuxue, et al. “Bayesian methods for media mix modeling with carryover and shape effects.” (2017)](https://research.google/pubs/pub46001/),  and integrating the expertise from core PyMC developers, our API provides:
+Leverage our Bayesian MMM API to tailor your marketing strategies effectively. Leveraging on top of the research article [Jin, Yuxue, et al. “Bayesian methods for media mix modeling with carryover and shape effects.” (2017)](https://research.google/pubs/pub46001/),  and extending it by integrating the expertise from core PyMC developers, our API provides:
 
 - **Custom Priors and Likelihoods**: Tailor your model to your specific business needs by including domain knowledge via prior distributions.
 - **Adstock Transformation**: Optimize the carry-over effects in your marketing channels.
 - **Saturation Effects**: Understand the diminishing returns in media investments.
+- **Customize adstock and saturation functions:** You can select from a variety of adstock and saturation functions. You can even implement your own custom functions.
 - **Time-varying Intercept:** Capture time-varying baseline contributions in your model (using modern and efficient Gaussian processes approximation methods).
 - **Visualization and Model Diagnostics**: Get a comprehensive view of your model's performance and insights.
+- **Choose among many inference algorithms**: We provide the option to choose between various NUTS samplers (e.g. BlackJax, NumPyro and Nutpie). See the [example notebook](https://www.pymc-marketing.io/en/stable/notebooks/general/other_nuts_samplers.html) for more details.
 - **Out-of-sample Predictions**: Forecast future marketing performance with credible intervals. Use this for simulations and scenario planning.
 - **Budget Optimization**: Allocate your marketing spend efficiently across various channels for maximum ROI.
 - **Experiment Calibration**: Fine-tune your model based on empirical experiments for a more unified view of marketing.
@@ -69,12 +71,14 @@ Leverage our Bayesian MMM API to tailor your marketing strategies effectively. B
 
 ```python
 import pandas as pd
-from pymc_marketing.mmm import DelayedSaturatedMMM
+from pymc_marketing.mmm import 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'])
+data = pd.read_csv(data_url, parse_dates=["date_week"])
 
-mmm = DelayedSaturatedMMM(
+mmm = MMM(
+    adstock="geometric",
+    saturation="logistic",
     date_column="date_week",
     channel_columns=["x1", "x2"],
     control_columns=[
@@ -106,6 +110,9 @@ Once the model is fitted, we can further optimize our budget allocation as we ar
 
 Explore a hands-on [simulated example](https://pymc-marketing.readthedocs.io/en/stable/notebooks/mmm/mmm_example.html) for more insights into MMM with PyMC-Marketing.
 
+${\color{red}\textbf{Warning!}}$ We will deprecate the `DelayedSaturatedMMM` class in the next releases.
+Please use the `MMM` class instead.
+
 ### Essential Reading for Marketing Mix Modeling (MMM)
 
 - [Bayesian Media Mix Modeling for Marketing Optimization](https://www.pymc-labs.com/blog-posts/bayesian-media-mix-modeling-for-marketing-optimization/)

docs/source/notebooks/mmm/mmm_budget_allocation_example.ipynb

@@ -252,9 +252,9 @@
     "### Sigmoid Function\n",
     "The sigmoid function is formulated as:\n",
     "\n",
-    "$\n",
+    "$$\n",
     "\\beta \\cdot \\frac{\\exp(-\\lambda x)}{1 + \\exp(-\\lambda x)}\n",
-    "$\n",
+    "$$\n",
     "\n",
     "Key Elements:\n",
     "* β (beta): Denotes the Asymptotic Maximum or Ceiling Value. It is the point that the function approaches as the input x becomes immense.\n",
@@ -263,9 +263,9 @@
     "### Michaelis-Menten Function\n",
     "The Michaelis-Menten function is formulated as:\n",
     "\n",
-    "$\n",
+    "$$\n",
     "\\frac{\\alpha \\times x}{\\lambda + x}\n",
-    "$\n",
+    "$$\n",
     "\n",
     "Key Elements:\n",
     "* α (Alpha or Vmax): It represents the maximum contribution (y) a channel can make, also recognized as the plateau point.\n",

pymc_marketing/constants.py

@@ -11,4 +11,4 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
-DAYS_IN_YEAR = 365.25
+DAYS_IN_YEAR: float = 365.25

pymc_marketing/mmm/base.py

@@ -251,6 +251,12 @@ def preprocess(
         return data_cp
 
     def get_target_transformer(self) -> Pipeline:
+        """Return the target transformer pipeline used for preprocessing the target variable.
+
+        Returns
+        -------
+        Pipeline
+        """
         try:
             return self.target_transformer  # type: ignore
         except AttributeError:
@@ -528,6 +534,16 @@ def _format_model_contributions(self, var_contribution: str) -> DataArray:
         return contributions.sum(contracted_dims) if contracted_dims else contributions
 
     def plot_components_contributions(self, **plt_kwargs: Any) -> plt.Figure:
+        """Plot the target variable and the posterior predictive model components in
+        the scaled space.
+
+        **plt_kwargs
+            Additional keyword arguments to pass to `plt.subplots`.
+
+        Returns
+        -------
+        plt.Figure
+        """
         channel_contributions = self._format_model_contributions(
             var_contribution="channel_contributions"
         )
@@ -610,6 +626,7 @@ def plot_components_contributions(self, **plt_kwargs: Any) -> plt.Figure:
             ax.plot(
                 np.asarray(self.X[self.date_column]),
                 np.asarray(self.preprocessed_data["y"]),  # type: ignore
+                label="scaled target",
                 color="black",
             )
             ax.legend(title="components", loc="center left", bbox_to_anchor=(1, 0.5))
@@ -621,6 +638,12 @@ def plot_components_contributions(self, **plt_kwargs: Any) -> plt.Figure:
         return fig
 
     def compute_channel_contribution_original_scale(self) -> DataArray:
+        """Compute the channel contributions in the original scale of the target variable.
+
+        Returns
+        -------
+        DataArray
+        """
         channel_contribution = az.extract(
             data=self.fit_result, var_names=["channel_contributions"], combined=False
         )
@@ -838,6 +861,19 @@ def _get_channel_contributions_share_samples(self) -> DataArray:
     def plot_channel_contribution_share_hdi(
         self, hdi_prob: float = 0.94, **plot_kwargs: Any
     ) -> plt.Figure:
+        """Plot the share of channel contributions in a forest plot.
+
+        Parameters
+        ----------
+        hdi_prob : float, optional
+            HDI value to be displayed, by default 0.94
+        **plot_kwargs
+            Additional keyword arguments to pass to `az.plot_forest`.
+
+        Returns
+        -------
+        plt.Figure
+        """
         channel_contributions_share: DataArray = (
             self._get_channel_contributions_share_samples()
         )
@@ -981,5 +1017,3 @@ class BaseValidateMMM(
     ValidateChannelColumns,
 ):
     """Base class with some validation of the inputs."""
-
-    pass

pymc_marketing/mmm/delayed_saturated_mmm.py

@@ -44,20 +44,15 @@
     add_lift_measurements_to_likelihood,
     scale_lift_measurements,
 )
-from pymc_marketing.mmm.preprocessing import (
-    MaxAbsScaleChannels,
-    MaxAbsScaleTarget,
-)
+from pymc_marketing.mmm.preprocessing import MaxAbsScaleChannels, MaxAbsScaleTarget
 from pymc_marketing.mmm.tvp import create_time_varying_intercept, infer_time_index
 from pymc_marketing.mmm.utils import (
     _get_distribution_from_dict,
     apply_sklearn_transformer_across_dim,
     create_new_spend_data,
     generate_fourier_modes,
 )
-from pymc_marketing.mmm.validating import (
-    ValidateControlColumns,
-)
+from pymc_marketing.mmm.validating import ValidateControlColumns
 
 __all__ = ["BaseMMM", "MMM", "DelayedSaturatedMMM"]
 
@@ -71,8 +66,9 @@
     .. [1] Jin, Yuxue, et al. “Bayesian methods for media mix modeling with carryover and shape effects.” (2017).
     """
 
-    _model_type = "DelayedSaturatedMMM"
-    version = "0.0.2"
+    _model_name: str = "BaseMMM"
+    _model_type: str = "BaseValidateMMM"
+    version: str = "0.0.3"
 
     def __init__(
         self,
@@ -593,6 +589,16 @@
     def _get_fourier_models_data(self, X) -> pd.DataFrame:
         """Generates fourier modes to model seasonality.
 
+        Parameters
+        ----------
+        X : Union[pd.DataFrame, pd.Series], shape (n_obs, n_features)
+            Input data for the model. To generate the Fourier modes, it must contain a date column.
+
+        Returns
+        -------
+        pd.DataFrame
+            Fourier modes (sin and cos with different frequencies) as columns in a dataframe.
+
         References
         ----------
         https://www.pymc.io/projects/examples/en/latest/time_series/Air_passengers-Prophet_with_Bayesian_workflow.html
@@ -680,7 +686,7 @@
             If the inference data that is loaded doesn't match with the model.
         """
 
-        filepath = Path(str(fname))
+        filepath = Path(fname)
         idata = az.from_netcdf(filepath)
         model_config = cls._model_config_formatting(
             json.loads(idata.attrs["model_config"])
@@ -958,7 +964,7 @@
     """  # noqa: E501
 
     _model_type = "MMM"
-    version = "0.0.2"
+    version = "0.0.1"
 
     def channel_contributions_forward_pass(
         self, channel_data: npt.NDArray[np.float_]
@@ -1095,6 +1101,8 @@
         absolute_xrange : bool, optional
             If True, the x-axis is in absolute values (input units), otherwise it is in
             relative percentage values, by default False.
+        **plt_kwargs
+            Keyword arguments to pass to `plt.subplots()`
 
         Returns
         -------
@@ -2024,14 +2032,13 @@
         inverse_scaled_channel_spend = self.channel_transformer.inverse_transform(
             np.array([list(self.optimal_allocation_dict.values())])
         )
-        original_scale_allocation_dict = {
-            k: v
-            for k, v in zip(
+        original_scale_allocation_dict = dict(
+            zip(
                 self.optimal_allocation_dict.keys(),
                 inverse_scaled_channel_spend[0],
                 strict=False,
             )
-        }
+        )
 
         synth_dataset = self._create_synth_dataset(
             df=self.X,
@@ -2209,8 +2216,9 @@
 
 
 class DelayedSaturatedMMM(MMM):
+    _model_type = "MMM"
     _model_name = "DelayedSaturatedMMM"
-    version = "0.0.2"
+    version = "0.0.3"
 
     def __init__(
         self,

tests/mmm/test_delayed_saturated_mmm.py

@@ -23,11 +23,7 @@
 
 from pymc_marketing.mmm.components.adstock import DelayedAdstock
 from pymc_marketing.mmm.components.saturation import MichaelisMentenSaturation
-from pymc_marketing.mmm.delayed_saturated_mmm import (
-    MMM,
-    BaseMMM,
-    DelayedSaturatedMMM,
-)
+from pymc_marketing.mmm.delayed_saturated_mmm import MMM, BaseMMM, DelayedSaturatedMMM
 
 seed: int = sum(map(ord, "pymc_marketing"))
 rng: np.random.Generator = np.random.default_rng(seed=seed)
@@ -340,8 +336,9 @@ def test_fit(self, toy_X: pd.DataFrame, toy_y: pd.Series) -> None:
             adstock="geometric",
             saturation="logistic",
         )
-        assert mmm.version == "0.0.2"
-        assert mmm._model_type == "DelayedSaturatedMMM"
+        assert mmm.version == "0.0.3"
+        assert mmm._model_type == "BaseValidateMMM"
+        assert mmm._model_name == "BaseMMM"
         assert mmm.model_config is not None
         n_channel: int = len(mmm.channel_columns)
         n_control: int = len(mmm.control_columns)