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
A reach tensor after pre-modeling transformations including
population scaling and scaling by the median non-zero value.
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
.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.
