BuildMergedModel#

class pymc_marketing.mmm.budget_optimizer.BuildMergedModel(models, prefixes=None, merge_on='channel_data', use_every_n_draw=1)[source]#

Merge multiple optimizer-compatible models into a single model.

This wrapper combines several optimizer-compatible MMM wrappers by: - Merging their posterior InferenceData with per-model prefixes - Optionally thinning posterior draws via use_every_n_draw - Exposing a persistent merged PyMC Model for optimization through

_set_predictors_for_optimization and a dynamic model property for inspection when needed

Parameters:

modelslist[OptimizerCompatibleModelWrapper]: A list of wrappers that each expose idata and _set_predictors_for_optimization(num_periods: int) -> Model.
prefixeslist[str] | None, optional: Per-model prefixes used when merging. If None, defaults to ["model1", "model2", ...] with one prefix per model.
merge_onstr | None, optional, default “channel_data”: Name of a variable expected to be present in all models and that should remain unprefixed and be used for aligning/merging dims (e.g., "channel_data"). If None, no variable is treated as shared and all variables/dims are prefixed.
use_every_n_drawint, optional, default 1: Thinning factor applied when merging idatas. Keeps every n-th draw.

Attributes:

prefixeslist[str]: The final list of prefixes used for each model.
modelslist[OptimizerCompatibleModelWrapper]: The provided list of wrappers.
num_modelsint: Number of models being merged.
num_periodsint | None: Number of forecast periods inferred from the primary model (if available).
idataarviz.InferenceData: The merged and prefixed posterior (and data) container.
adstockAny: Carried over from the primary model when available.
modelpymc.Model: Return the merged PyMC model.

Examples

Merge three multidimensional MMMs into a single optimizer model:

from pymc_marketing.mmm.multidimensional import (
    MMM,
    MultiDimensionalBudgetOptimizerWrapper,
)
from pymc_marketing.mmm.budget_optimizer import (
    BuildMergedModel,
    BudgetOptimizer,
)

# Assume m1, m2, m3 are already fitted MMM instances
w1 = MultiDimensionalBudgetOptimizerWrapper(
    model=m1, start_date=start, end_date=end
)
w2 = MultiDimensionalBudgetOptimizerWrapper(
    model=m2, start_date=start, end_date=end
)
w3 = MultiDimensionalBudgetOptimizerWrapper(
    model=m3, start_date=start, end_date=end
)

merged = BuildMergedModel(
    models=[w1, w2, w3],
    prefixes=["north", "south", "west"],
    merge_on="channel_data",
    use_every_n_draw=2,
)

optimizer = BudgetOptimizer(
    model=merged,
    num_periods=merged.num_periods,
    response_variable="north_total_media_contribution_original_scale",
)

Single model: auto-prefix and thin draws:

merged_single = BuildMergedModel(
    models=[w1],
    prefixes=None,  # auto -> ["model1"]
    merge_on="channel_data",
    use_every_n_draw=5,
)
m_opt = merged_single._set_predictors_for_optimization(
    num_periods=merged_single.num_periods
)

Merge everything with prefixes (no shared variable retained):

merged_all_prefixed = BuildMergedModel(
    models=[w1, w2],
    prefixes=["a", "b"],
    merge_on=None,  # do not keep any unprefixed variable
)

Methods

BuildMergedModel.__init__(models[, ...])

Attributes

`model`	Return the merged PyMC model.
`adstock`
`idata`