Add WeeklyFourier

pymc_marketing/constants.py

@@ -15,3 +15,4 @@
 
 DAYS_IN_YEAR: float = 365.25
 DAYS_IN_MONTH: float = DAYS_IN_YEAR / 12
+DAYS_IN_WEEK: int = 7

pymc_marketing/mmm/__init__.py

@@ -35,7 +35,7 @@
     TanhSaturationBaselined,
     saturation_from_dict,
 )
-from pymc_marketing.mmm.fourier import MonthlyFourier, YearlyFourier
+from pymc_marketing.mmm.fourier import MonthlyFourier, WeeklyFourier, YearlyFourier
 from pymc_marketing.mmm.hsgp import (
     HSGP,
     CovFunc,
@@ -85,6 +85,7 @@
     "SaturationTransformation",
     "TanhSaturation",
     "TanhSaturationBaselined",
+    "WeeklyFourier",
     "WeibullCDFAdstock",
     "WeibullPDFAdstock",
     "YearlyFourier",

pymc_marketing/mmm/fourier.py

@@ -21,6 +21,7 @@
 
 - Yearly Fourier: A yearly seasonality with a period of 365.25 days
 - Monthly Fourier: A monthly seasonality with a period of 365.25 / 12 days
+- Weekly Fourier: A weekly seasonality with a period of 7 days
 
 .. plot::
     :context: close-figs
@@ -221,7 +222,7 @@
 from pydantic import BaseModel, Field, InstanceOf, field_serializer, model_validator
 from typing_extensions import Self
 
-from pymc_marketing.constants import DAYS_IN_MONTH, DAYS_IN_YEAR
+from pymc_marketing.constants import DAYS_IN_MONTH, DAYS_IN_WEEK, DAYS_IN_YEAR
 from pymc_marketing.deserialize import deserialize, register_deserialization
 from pymc_marketing.plot import SelToString, plot_curve, plot_hdi, plot_samples
 from pymc_marketing.prior import Prior, VariableFactory, create_dim_handler
@@ -383,9 +384,20 @@
         """
         pass  # pragma: no cover
 
+    @abstractmethod
+    def _get_days_in_period(self, dates: pd.DatetimeIndex) -> pd.Index:
+        """Return the relevant day within the characteristic periodicity.
+
+        Returns
+        -------
+        int or float
+            The relevant period within the characteristic periodicity
+        """
+        pass
+
     def apply(
         self,
-        dayofyear: pt.TensorLike,
+        dayofperiod: pt.TensorLike,
         result_callback: Callable[[pt.TensorVariable], None] | None = None,
     ) -> pt.TensorVariable:
         """Apply fourier seasonality to day of year.
@@ -394,8 +406,8 @@
 
         Parameters
         ----------
-        dayofyear : pt.TensorLike
-            Day of year.
+        dayofperiod : pt.TensorLike
+            Day of year or weekday
         result_callback : Callable[[pt.TensorVariable], None], optional
             Callback function to apply to the result, by default None
 
@@ -431,7 +443,7 @@
                 fourier.apply(dayofyear, result_callback=callback)
 
         """
-        periods = dayofyear / self.days_in_period
+        periods = dayofperiod / self.days_in_period
 
         model = pm.modelcontext(None)
         model.add_coord(self.prefix, self.nodes)
@@ -506,15 +518,15 @@
             start_date = self.get_default_start_date(start_date=start_date)
             date_range = pd.date_range(
                 start=start_date,
-                periods=int(self.days_in_period) + 1,
+                periods=np.ceil(self.days_in_period) + 1,
                 freq="D",
             )
             coords["date"] = date_range.to_numpy()
-            dayofyear = date_range.dayofyear.to_numpy()
+            dayofperiod = self._get_days_in_period(date_range).to_numpy()
 
         else:
             coords["day"] = full_period
-            dayofyear = full_period
+            dayofperiod = full_period
 
         for key, values in parameters[self.variable_name].coords.items():
             if key in {"chain", "draw", self.prefix}:
@@ -525,7 +537,7 @@
             name = f"{self.prefix}_trend"
             pm.Deterministic(
                 name,
-                self.apply(dayofyear=dayofyear),
+                self.apply(dayofperiod=dayofperiod),
                 dims=tuple(coords.keys()),
             )
 
@@ -777,6 +789,16 @@
         current_year = datetime.datetime.now().year
         return datetime.datetime(year=current_year, month=1, day=1)
 
+    def _get_days_in_period(self, dates: pd.DatetimeIndex) -> pd.Index:
+        """Return the dayofyear within the yearly periodicity.
+
+        Returns
+        -------
+        int or float
+            The relevant period within the characteristic periodicity
+        """
+        return dates.dayofyear
+
 
 class MonthlyFourier(FourierBase):
     """Monthly fourier seasonality.
@@ -799,11 +821,11 @@
         mu = np.array([0, 0, 0.5, 0])
         b = 0.075
         dist = Prior("Laplace", mu=mu, b=b, dims="fourier")
-        yearly = MonthlyFourier(n_order=2, prior=dist)
-        prior = yearly.sample_prior(samples=100)
-        curve = yearly.sample_curve(prior)
+        monthly = MonthlyFourier(n_order=2, prior=dist)
+        prior = monthly.sample_prior(samples=100)
+        curve = monthly.sample_curve(prior)
 
-        _, axes = yearly.plot_curve(curve)
+        _, axes = monthly.plot_curve(curve)
         axes[0].set(title="Monthly Fourier Seasonality")
         plt.show()
 
@@ -832,6 +854,83 @@
         now = datetime.datetime.now()
         return datetime.datetime(year=now.year, month=now.month, day=1)
 
+    def _get_days_in_period(self, dates: pd.DatetimeIndex) -> pd.Index:
+        """Return the dayofyear within the yearly periodicity.
+
+        Returns
+        -------
+        int or float
+            The relevant period within the characteristic periodicity
+        """
+        return dates.dayofyear
+
+
+class WeeklyFourier(FourierBase):
+    """Weekly fourier seasonality.
+
+    .. plot::
+        :context: close-figs
+
+        import arviz as az
+        import matplotlib.pyplot as plt
+        import numpy as np
+
+        from pymc_marketing.mmm import WeeklyFourier
+        from pymc_marketing.prior import Prior
+
+        az.style.use("arviz-white")
+
+        seed = sum(map(ord, "Weekly"))
+        rng = np.random.default_rng(seed)
+
+        mu = np.array([0, 0, 0.5, 0])
+        b = 0.075
+        dist = Prior("Laplace", mu=mu, b=b, dims="fourier")
+        weekly = WeeklyFourier(n_order=2, prior=dist)
+        prior = weekly.sample_prior(samples=100)
+        curve = weekly.sample_curve(prior)
+
+        _, axes = weekly.plot_curve(curve)
+        axes[0].set(title="Weekly Fourier Seasonality")
+        plt.show()
+
+    n_order : int
+        Number of fourier modes to use.
+    prefix : str, optional
+        Alternative prefix for the fourier seasonality, by default None or
+        "fourier"
+    prior : Prior | VariableFactory, optional
+        Prior distribution or VariableFactory for the fourier seasonality beta parameters, by
+        default `Prior("Laplace", mu=0, b=1)`
+    name : str, optional
+        Name of the variable that multiplies the fourier modes, by default None
+    variable_name : str, optional
+        Name of the variable that multiplies the fourier modes, by default None
+
+    """
+
+    days_in_period: float = DAYS_IN_WEEK
+
+    def _get_default_start_date(self) -> datetime.datetime:
+        """Get the default start date for weekly seasonality.
+
+        Returns the first day of the current month.
+        """
+        now = datetime.datetime.now()
+        return datetime.datetime.fromisocalendar(
+            year=now.year, week=now.isocalendar().week, day=1
+        )
+
+    def _get_days_in_period(self, dates: pd.DatetimeIndex) -> pd.Index:
+        """Return the weekday within the weekly periodicity.
+
+        Returns
+        -------
+        int or float
+            The relevant period within the characteristic periodicity
+        """
+        return dates.weekday
+
 
 def _is_yearly_fourier(data: Any) -> bool:
     return data.get("class") == "YearlyFourier"
@@ -841,6 +940,10 @@
     return data.get("class") == "MonthlyFourier"
 
 
+def _is_weekly_fourier(data: Any) -> bool:
+    return data.get("class") == "WeeklyFourier"
+
+
 register_deserialization(
     is_type=_is_yearly_fourier,
     deserialize=lambda data: YearlyFourier.from_dict(data),
@@ -850,3 +953,7 @@
     is_type=_is_monthly_fourier,
     deserialize=lambda data: MonthlyFourier.from_dict(data),
 )
+
+register_deserialization(
+    is_type=_is_weekly_fourier, deserialize=lambda data: WeeklyFourier.from_dict(data)
+)