# Copyright 2022 - 2025 The PyMC Labs Developers
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# 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.
"""Adstock transformations for MMM.
Each of these transformations is a subclass of
:class:`pymc_marketing.mmm.components.adstock.AdstockTransformation`
and defines a function that takes a time series and returns the adstocked
version of it. The parameters of the function are the parameters
of the adstock transformation.
Examples
--------
Create a new adstock transformation:
.. code-block:: python
from pymc_marketing.mmm import AdstockTransformation
from pymc_marketing.prior import Prior
class MyAdstock(AdstockTransformation):
lookup_name: str = "my_adstock"
def function(self, x, alpha):
return x * alpha
default_priors = {"alpha": Prior("HalfNormal", sigma=1)}
Plot the default priors for an adstock transformation:
.. code-block:: python
from pymc_marketing.mmm import GeometricAdstock
import matplotlib.pyplot as plt
adstock = GeometricAdstock(l_max=15)
prior = adstock.sample_prior()
curve = adstock.sample_curve(prior)
adstock.plot_curve(curve)
plt.show()
"""
from __future__ import annotations
import numpy as np
import pytensor.tensor as pt
import xarray as xr
from pydantic import Field, validate_call
from pymc_marketing.deserialize import deserialize, register_deserialization
from pymc_marketing.mmm.components.base import (
SupportedPrior,
Transformation,
create_registration_meta,
)
from pymc_marketing.mmm.transformers import (
ConvMode,
WeibullType,
delayed_adstock,
geometric_adstock,
weibull_adstock,
)
from pymc_marketing.prior import Prior
ADSTOCK_TRANSFORMATIONS: dict[str, type[AdstockTransformation]] = {}
AdstockRegistrationMeta: type[type] = create_registration_meta(ADSTOCK_TRANSFORMATIONS)
[docs]
class GeometricAdstock(AdstockTransformation):
"""Wrapper around geometric adstock function.
For more information, see :func:`pymc_marketing.mmm.transformers.geometric_adstock`.
.. plot::
:context: close-figs
import matplotlib.pyplot as plt
import numpy as np
from pymc_marketing.mmm import GeometricAdstock
rng = np.random.default_rng(0)
adstock = GeometricAdstock(l_max=10)
prior = adstock.sample_prior(random_seed=rng)
curve = adstock.sample_curve(prior)
adstock.plot_curve(curve, random_seed=rng)
plt.show()
"""
lookup_name = "geometric"
[docs]
def function(self, x, alpha):
"""Geometric adstock function."""
return geometric_adstock(
x, alpha=alpha, l_max=self.l_max, normalize=self.normalize, mode=self.mode
)
default_priors = {"alpha": Prior("Beta", alpha=1, beta=3)}
[docs]
class DelayedAdstock(AdstockTransformation):
"""Wrapper around delayed adstock function.
For more information, see :func:`pymc_marketing.mmm.transformers.delayed_adstock`.
.. plot::
:context: close-figs
import matplotlib.pyplot as plt
import numpy as np
from pymc_marketing.mmm import DelayedAdstock
rng = np.random.default_rng(0)
adstock = DelayedAdstock(l_max=10)
prior = adstock.sample_prior(random_seed=rng)
curve = adstock.sample_curve(prior)
adstock.plot_curve(curve, random_seed=rng)
plt.show()
"""
lookup_name = "delayed"
[docs]
def function(self, x, alpha, theta):
"""Delayed adstock function."""
return delayed_adstock(
x,
alpha=alpha,
theta=theta,
l_max=self.l_max,
normalize=self.normalize,
mode=self.mode,
)
default_priors = {
"alpha": Prior("Beta", alpha=1, beta=3),
"theta": Prior("HalfNormal", sigma=1),
}
[docs]
class WeibullPDFAdstock(AdstockTransformation):
"""Wrapper around weibull adstock with PDF function.
For more information, see :func:`pymc_marketing.mmm.transformers.weibull_adstock`.
.. plot::
:context: close-figs
import matplotlib.pyplot as plt
import numpy as np
from pymc_marketing.mmm import WeibullPDFAdstock
rng = np.random.default_rng(0)
adstock = WeibullPDFAdstock(l_max=10)
prior = adstock.sample_prior(random_seed=rng)
curve = adstock.sample_curve(prior)
adstock.plot_curve(curve, random_seed=rng)
plt.show()
"""
lookup_name = "weibull_pdf"
[docs]
def function(self, x, lam, k):
"""Weibull adstock function."""
return weibull_adstock(
x=x,
lam=lam,
k=k,
l_max=self.l_max,
mode=self.mode,
type=WeibullType.PDF,
normalize=self.normalize,
)
default_priors = {
"lam": Prior("Gamma", mu=2, sigma=1),
"k": Prior("Gamma", mu=3, sigma=1),
}
[docs]
class WeibullCDFAdstock(AdstockTransformation):
"""Wrapper around weibull adstock with CDF function.
For more information, see :func:`pymc_marketing.mmm.transformers.weibull_adstock`.
.. plot::
:context: close-figs
import matplotlib.pyplot as plt
import numpy as np
from pymc_marketing.mmm import WeibullCDFAdstock
rng = np.random.default_rng(0)
adstock = WeibullCDFAdstock(l_max=10)
prior = adstock.sample_prior(random_seed=rng)
curve = adstock.sample_curve(prior)
adstock.plot_curve(curve, random_seed=rng)
plt.show()
"""
lookup_name = "weibull_cdf"
[docs]
def function(self, x, lam, k):
"""Weibull adstock function."""
return weibull_adstock(
x=x,
lam=lam,
k=k,
l_max=self.l_max,
mode=self.mode,
type=WeibullType.CDF,
normalize=self.normalize,
)
default_priors = {
"lam": Prior("Gamma", mu=2, sigma=2.5),
"k": Prior("Gamma", mu=2, sigma=2.5),
}
[docs]
class NoAdstock(AdstockTransformation):
"""Wrapper around no adstock transformation."""
lookup_name: str = "no_adstock"
[docs]
def function(self, x):
"""No adstock function."""
return pt.as_tensor_variable(x)
default_priors = {}
[docs]
def update_priors(self, priors):
"""Update priors for the no adstock transformation."""
return
[docs]
def adstock_from_dict(data: dict) -> AdstockTransformation:
"""Create an adstock transformation from a dictionary."""
data = data.copy()
lookup_name = data.pop("lookup_name")
cls = ADSTOCK_TRANSFORMATIONS[lookup_name]
if "priors" in data:
data["priors"] = {k: deserialize(v) for k, v in data["priors"].items()}
return cls(**data)
def _is_adstock(data):
return "lookup_name" in data and data["lookup_name"] in ADSTOCK_TRANSFORMATIONS
register_deserialization(
is_type=_is_adstock,
deserialize=adstock_from_dict,
)
