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
)

Attributes
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 normalized by population and by the median 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.

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