efecto_aditivo

Efectos aditivos para el modelo de mezcla de marketing multidimensional.

Ejemplo de un efecto aditivo personalizado

1. Componente de efecto negativo personalizado (agregado como un MuEffect)

import numpy as np
import pandas as pd
import pymc as pm
import pymc.dims as pmd
from pymc_extras.prior import create_dim_handler

# A simple custom effect that penalizes certain dates/segments with a
# negative-only coefficient. This is not a "control" in the MMM sense, so
# give it a different name/prefix to avoid clashing with built-in controls.
class PenaltyEffect:
    '''Example MuEffect that applies a negative coefficient to a user-specified pattern.
    '''

    def __init__(self, name: str, penalty_provider):
        self.name = name
        self.penalty_provider = penalty_provider

    def create_data(self, mmm):
        # Produce penalty values aligned with model dates (and optional extra dims)
        dates = safe_to_datetime(mmm.model.coords["date"], "date")
        penalty = self.penalty_provider(dates)
        pmd.Data(f"{self.name}_penalty", penalty, dims=("date", *mmm.dims))

    def create_effect(self, mmm):
        model = mmm.model
        penalty = model[f"{self.name}_penalty"]  # dims: (date, *mmm.dims)

        # Negative-only coefficient per extra dims, broadcast over date
        coef = pmd.TruncatedNormal(f"{self.name}_coef", mu=-0.5, sigma=-0.05, lower=-1.0, upper=0.0, dims=mmm.dims)

        dim_handler = create_dim_handler(("date", *mmm.dims))
        effect = pmd.Deterministic(
            f"{self.name}_effect_contribution",
            dim_handler(coef, mmm.dims) * penalty,
            dims=("date", *mmm.dims),
        )
        return effect  # Must have dims ("date", *mmm.dims)

    def set_data(self, mmm, model, X):
        # Update to future dates during posterior predictive
        dates = safe_to_datetime(model.coords["date"], "date")
        penalty = self.penalty_provider(dates)
        pm.set_data({f"{self.name}_penalty": penalty}, model=model)

Usage
-----
# Example weekend penalty (Sat/Sun = 1, else 0), applied per geo if present
weekend_penalty = PenaltyEffect(
    name="brand_penalty",
    penalty_provider=lambda dates: pd.Series(dates)
    .dt.dayofweek.isin([5, 6])
    .astype(float)
    .to_numpy()[:, None]  # if mmm.dims == ("geo",), broadcast over geo
)

# Build your MMM as usual (with channels, etc.), then add the effect before build/fit:
# mmm = MMM(...)
# mmm.mu_effects.append(weekend_penalty)
# mmm.build_model(X, y)
# mmm.fit(X, y, ...)
# At prediction time, the effect updates itself via set_data.

Cómo funciona

• Los efectos Mu siguen un protocolo simple: create_data(mmm), create_effect(mmm), y set_data(mmm, model, X).

• During MMM.build_model(...), each effect’s create_data is called first to introduce any needed pmd.Data. Then create_effect must return a tensor with dims («date», *mmm.dims) that is added additively to the model mean.

• During posterior predictive, set_data is called with the cloned PyMC model and the new coordinates; update any pmd.Data you created using pm.set_data.

Consejos para componentes personalizados

• Utilice prefijos de variable únicos para evitar conflictos de nombres con elementos integrados como los controles. No llame a su componente «control»; elija un nombre/prefijo distinto.

• Siga los patrones utilizados por los efectos proporcionados en este módulo (por ejemplo, FourierEffect, LinearTrendEffect, EventAdditiveEffect): - En create_data, derive y registre cualquier entrada requerida en el modelo. - En create_effect, construya expresiones de PyTensor y devuelva una contribución.

  con dims («date», *mmm.dims). Si necesita broadcasting, utilice pymc_extras.prior.create_dim_handler como se mostró arriba.

  • En set_data, actualice las variables de datos cuando cambien las fechas/dimensiones.

Functions

safe_to_datetime(coords_values[, ...])

Safely convert coordinates to datetime, with validation.

Clases

EventAdditiveEffect(**data)	Clase de efecto de evento para el MMM.
FourierEffect(**data)	Efecto aditivo de estacionalidad de Fourier para MMM.
LinearTrendEffect(*, trend, prefix[, ...])	Envoltura para LinearTrend para usar con el protocolo MuEffect de MMM.
Modelo(*args, **kwargs)	Protocolo MMM.
MuEffect(**data)	Abstract base class for arbitrary additive mu effects.