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Include tvp scaling in contribution grid #1253

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merged 2 commits into from
Dec 3, 2024
Merged

Include tvp scaling in contribution grid #1253

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cf666ad
multiply by the media variable if present
wd60622 Dec 3, 2024
ded485d
test with expected result
wd60622 Dec 3, 2024
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13 changes: 11 additions & 2 deletions pymc_marketing/mmm/mmm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -622,7 +622,15 @@ def channel_contributions_forward_pass(
progressbar=False,
)

return idata.posterior_predictive.channel_contributions.to_numpy()
channel_contributions = idata.posterior_predictive.channel_contributions
if self.time_varying_media:
# This is coupled with the name of the
# latent process Deterministic
name = "media_temporal_latent_multiplier"
mutliplier = self.fit_result[name]
channel_contributions = channel_contributions * mutliplier

return channel_contributions.to_numpy()

@property
def _serializable_model_config(self) -> dict[str, Any]:
Expand Down Expand Up @@ -996,7 +1004,8 @@ def get_channel_contributions_forward_pass_grid(
delta * self.preprocessed_data["X"][self.channel_columns].to_numpy()
)
channel_contribution_forward_pass = self.channel_contributions_forward_pass(
channel_data=channel_data, disable_logger_stdout=True
channel_data=channel_data,
disable_logger_stdout=True,
)
channel_contributions.append(channel_contribution_forward_pass)
return DataArray(
Expand Down
26 changes: 26 additions & 0 deletions tests/mmm/test_mmm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1264,3 +1264,29 @@ def test_missing_attrs_to_defaults(toy_X, toy_y) -> None:

# clean up
os.remove(file)


def test_channel_contributions_forward_pass_time_varying_media(toy_X, toy_y) -> None:
mmm = MMM(
date_column="date",
channel_columns=["channel_1", "channel_2"],
control_columns=["control_1", "control_2"],
adstock=GeometricAdstock(l_max=2),
saturation=LogisticSaturation(),
time_varying_media=True,
)
mmm = mock_fit(mmm, toy_X, toy_y)

posterior = mmm.fit_result

baseline_contributions = posterior["baseline_channel_contributions"]
multiplier = posterior["media_temporal_latent_multiplier"]
target_scale = mmm.y.max()
recovered_contributions = baseline_contributions * multiplier * target_scale
media_contributions = mmm.channel_contributions_forward_pass(
mmm.preprocessed_data["X"][mmm.channel_columns].to_numpy()
)
np.testing.assert_allclose(
recovered_contributions.to_numpy(),
media_contributions,
)
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