meridian.model.media.RfTensors

Container for Reach and Frequency (RF) media tensors.

meridian.model.media.RfTensors(
    reach: (meridian.backend.Tensor | None) = None,
    frequency: (meridian.backend.Tensor | None) = None,
    rf_impressions: (meridian.backend.Tensor | None) = None,
    rf_spend: (meridian.backend.Tensor | None) = None,
    reach_transformer: (meridian.model.transformers.MediaTransformer | None) = None,
    reach_scaled: (meridian.backend.Tensor | None) = None,
    prior_reach_scaled_counterfactual: (meridian.backend.Tensor | None) = None,
    prior_denominator: (meridian.backend.Tensor | None) = None
)

reach A tensor constructed from InputData.reach.
frequency A tensor constructed from InputData.frequency.
rf_impressions A tensor constructed from InputData.reach * InputData.frequency.
rf_spend A tensor constructed from InputData.rf_spend.
reach_transformer A MediaTransformer to scale RF tensors using the model's RF data.
reach_scaled A reach tensor after pre-modeling transformations including population scaling and scaling by the median non-zero value.
prior_reach_scaled_counterfactual A tensor containing reach_scaled values corresponding to the counterfactual scenario required for the prior calculation. For ROI priors, the counterfactual scenario is where reach is set to zero during the calibration period. For mROI priors, the counterfactual scenario is where reach is increased by a small factor for all n_rf_times. For contribution priors, the counterfactual scenario is where reach is set to zero for all n_rf_times. This attribute is set to None when it would otherwise be a tensor of zeros, i.e., when contribution contribution priors are used, or when ROI priors are used and rf_roi_calibration_period is None.
prior_denominator If ROI, mROI, or contribution priors are used, this represents the denominator. It is a tensor with dimension equal to n_rf_channels. For ROI priors, it is the spend during the overlapping time periods between the calibration period and the modeling time window. For mROI priors, it is the ROI prior denominator multiplied by a small factor. For contribution priors, it is the total observed outcome (repeated for each channel).

Methods

__eq__

__eq__(
    other
)

Return self==value.

frequency None
prior_denominator None
prior_reach_scaled_counterfactual None
reach None
reach_scaled None
reach_transformer None
rf_impressions None
rf_spend None