meridian.schema.processors.marketing_processor.IncrementalOutcomeSpec

Stores parameters needed for processing a model into MarketingAnalysiss.

meridian.schema.processors.marketing_processor.IncrementalOutcomeSpec(
    *,
    aggregate_times: bool = True,
    new_data: (meridian.analysis.DataTensors | None) = None,
    media_selected_times: (Sequence[str] | None) = None,
    include_non_paid_channels: bool = False
)

aggregate_times Boolean. If True, the media summary metrics are aggregated over time. Defaults to True.
new_data Optional DataTensors container with optional tensors: media, reach, frequency, organic_media, organic_reach, organic_frequency, non_media_treatments and revenue_per_kpi. If None, the incremental outcome is calculated using the InputData provided to the Meridian object. If new_data is provided, the incremental outcome is calculated using the new tensors in new_data and the original values of the remaining tensors. For example, incremental_outcome(new_data=DataTensors(media=new_media) computes the incremental outcome using new_media and the original values of reach, frequency, organic_media, organic_reach, organic_frequency, non_media_treatments and revenue_per_kpi. If any of the tensors in new_data is provided with a different number of time periods than in InputData, then all tensors must be provided with the same number of time periods, and new_data.time must be provided.
media_selected_times Optional list of date strings specifying a subset of time periods in new_data, if provided, or from model's original media data.
include_non_paid_channels Boolean. If True, the incremental outcome includes non-paid channels. Defaults to False.

Methods

validate

View source

validate()

Checks whether each parameter in the Spec has a valid value.

Raises
ValueError If any parameter in the Spec has an invalid value.

__eq__

__eq__(
    other
)

Return self==value.

aggregate_times True
include_non_paid_channels False
media_selected_times None
new_data None