LinearTrendEffect#

class pymc_marketing.mmm.additive_effect.LinearTrendEffect(**data)[source]#

Wrapper for LinearTrend to use with MMM’s MuEffect protocol.

This class adapts the LinearTrend component to be used as an additive effect in the MMM model.

Parameters:

trendLinearTrend: The LinearTrend instance to wrap.
prefixstr: The prefix to use for variables in the model.
date_dim_namestr: The name of the date dimension in the model.

Examples

Out of sample predictions:

Note

No new changepoints are used for the out of sample predictions. The trend effect is linearly extrapolated from the last changepoint.

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt

import pymc as pm

from pymc_marketing.mmm.linear_trend import LinearTrend
from pymc_marketing.mmm.additive_effect import LinearTrendEffect

seed = sum(map(ord, "LinearTrend out of sample"))
rng = np.random.default_rng(seed)

class MockMMM:
    pass

dates = pd.date_range("2025-01-01", periods=52, freq="W")
coords = {"date": dates}
model = pm.Model(coords=coords)

mock_mmm = MockMMM()
mock_mmm.dims = ()
mock_mmm.model = model

effect = LinearTrendEffect(
    trend=LinearTrend(n_changepoints=8),
    prefix="trend",
)

with mock_mmm.model:
    effect.create_data(mock_mmm)
    pm.Deterministic(
        "effect",
        effect.create_effect(mock_mmm),
        dims="date",
    )

    idata = pm.sample_prior_predictive(random_seed=rng)

idata["posterior"] = idata.prior

n_new = 10 + 1
new_dates = pd.date_range(
    dates.max(),
    periods=n_new,
    freq="W",
)

with mock_mmm.model:
    mock_mmm.model.set_dim("date", n_new, new_dates)

    effect.set_data(mock_mmm, mock_mmm.model, None)

    pm.sample_posterior_predictive(
        idata,
        var_names=["effect"],
        random_seed=rng,
        extend_inferencedata=True,
    )

draw = rng.choice(range(idata.posterior.sizes["draw"]))
sel = dict(chain=0, draw=draw)

before = idata.posterior.effect.sel(sel).to_series()
after = idata.posterior_predictive.effect.sel(sel).to_series()

ax = before.plot(color="C0")
after.plot(color="C0", linestyle="dashed", ax=ax)
plt.show()

(Source code, png, hires.png, pdf)

../../_images/pymc_marketing-mmm-additive_effect-LinearTrendEffect-1.png

Methods

`LinearTrendEffect.__init__`(**data)	Create a new model by parsing and validating input data from keyword arguments.
`LinearTrendEffect.construct`([_fields_set])
`LinearTrendEffect.copy`(*[, include, ...])	Returns a copy of the model.
`LinearTrendEffect.create_data`(mmm)	Create the required data in the model.
`LinearTrendEffect.create_effect`(mmm)	Create the trend effect in the model.
`LinearTrendEffect.dict`(*[, include, ...])
`LinearTrendEffect.from_orm`(obj)
`LinearTrendEffect.json`(*[, include, ...])
`LinearTrendEffect.model_construct`([_fields_set])	Creates a new instance of the `Model` class with validated data.
`LinearTrendEffect.model_copy`(*[, update, deep])	!!! abstract "Usage Documentation"
`LinearTrendEffect.model_dump`(*[, mode, ...])	!!! abstract "Usage Documentation"
`LinearTrendEffect.model_dump_json`(*[, ...])	!!! abstract "Usage Documentation"
`LinearTrendEffect.model_json_schema`([...])	Generates a JSON schema for a model class.
`LinearTrendEffect.model_parametrized_name`(params)	Compute the class name for parametrizations of generic classes.
`LinearTrendEffect.model_post_init`(context, /)	Override this method to perform additional initialization after `__init__` and `model_construct`.
`LinearTrendEffect.model_rebuild`(*[, force, ...])	Try to rebuild the pydantic-core schema for the model.
`LinearTrendEffect.model_validate`(obj, *[, ...])	Validate a pydantic model instance.
`LinearTrendEffect.model_validate_json`(...[, ...])	!!! abstract "Usage Documentation"
`LinearTrendEffect.model_validate_strings`(obj, *)	Validate the given object with string data against the Pydantic model.
`LinearTrendEffect.parse_file`(path, *[, ...])
`LinearTrendEffect.parse_obj`(obj)
`LinearTrendEffect.parse_raw`(b, *[, ...])
`LinearTrendEffect.schema`([by_alias, ...])
`LinearTrendEffect.schema_json`(*[, by_alias, ...])
`LinearTrendEffect.set_data`(mmm, model, X)	Set the data for new predictions.
`LinearTrendEffect.update_forward_refs`(**localns)
`LinearTrendEffect.validate`(value)

Attributes

`model_computed_fields`
`model_config`	Configuration for the model, should be a dictionary conforming to [`ConfigDict`][pydantic.config.ConfigDict].
`model_extra`	Get extra fields set during validation.
`model_fields`
`model_fields_set`	Returns the set of fields that have been explicitly set on this model instance.
`trend`
`prefix`
`date_dim_name`
`linear_trend_first_date`