MMM.add_lift_test_measurements#

MMM.add_lift_test_measurements(df_lift_test, dist=<class 'pymc.distributions.continuous.Gamma'>, name='lift_measurements')[source]#

Add lift tests to the model.

The model for the difference of a channel’s saturation curve is created from x and x + delta_x for each channel. This random variable is then conditioned using the empirical lift, delta_y, and sigma of the lift test with the specified distribution dist.

The pseudo-code for the lift test is as follows:

model_estimated_lift = saturation_curve(x + delta_x) - saturation_curve(x)
empirical_lift = delta_y
dist(abs(model_estimated_lift), sigma=sigma, observed=abs(empirical_lift))

The model has to be built before adding the lift tests.

Parameters:

df_lift_testpd.DataFrame

DataFrame with lift test results with at least the following columns:

DIM_NAME: dimension name. One column per dimension in mmm.dims.
channel: channel name. Must be present in channel_columns.
x: x axis value of the lift test.
delta_x: change in x axis value of the lift test.
delta_y: change in y axis value of the lift test.
sigma: standard deviation of the lift test.

distpm.Distribution, optional

The distribution to use for the likelihood, by default pm.Gamma

namestr, optional

The name of the likelihood of the lift test contribution(s), by default “lift_measurements”. Name change required if calling this method multiple times.

Raises:

RuntimeError: If the model has not been built yet.
KeyError: If the ‘channel’ column or any of the model dimensions is not present in df_lift_test.

Examples

Build the model first then add lift test measurements.

import pandas as pd
import numpy as np

from pymc_marketing.mmm import GeometricAdstock, LogisticSaturation

from pymc_marketing.mmm.multidimensional import MMM

model = MMM(
    date_column="date",
    channel_columns=["x1", "x2"],
    target_column="target",
    adstock=GeometricAdstock(l_max=8),
    saturation=LogisticSaturation(),
    yearly_seasonality=2,
    dims=("geo",),
)

X = pd.DataFrame(
    {
        "date": np.tile(
            pd.date_range(start="2025-01-01", end="2025-05-01", freq="W"), 2
        ),
        "x1": np.random.rand(34),
        "x2": np.random.rand(34),
        "target": np.random.rand(34),
        "geo": 17 * ["FIN"] + 17 * ["SWE"],
    }
)
y = X["target"]

model.build_model(X.drop(columns=["target"]), y)

df_lift_test = pd.DataFrame(
    {
        "channel": ["x1", "x1"],
        "geo": ["FIN", "SWE"],
        "x": [1, 1],
        "delta_x": [0.1, 0.2],
        "delta_y": [0.1, 0.1],
        "sigma": [0.1, 0.1],
    }
)

model.add_lift_test_measurements(df_lift_test)