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Refactor response curves to use native counterfactual scaling. #1649

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Refactor response curves to use native counterfactual scaling. #1649

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23 changes: 10 additions & 13 deletions meridian/analysis/analyzer.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3721,28 +3721,25 @@ def response_curves(
len(self.model_context.input_data.get_all_paid_channels()),
3,
))
adjusted_data = tensors.DataTensors(
media=filled_data.media,
reach=reach,
frequency=frequency,
revenue_per_kpi=filled_data.revenue_per_kpi,
)
for i, multiplier in enumerate(spend_multipliers):
if multiplier == 0:
incremental_outcome[i, :, :] = backend.zeros(
(len(self.model_context.input_data.get_all_paid_channels()), 3),
dtype=backend.float_dtype,
) # Last dimension = 3 for the mean, ci_lo and ci_hi.
continue
# pylint: disable=protected-access # TODO: Move to DataTensorsBuilder.
scaled_data = tensors._scale_tensors_by_multiplier(
data=DataTensors(
media=filled_data.media,
reach=reach,
frequency=frequency,
revenue_per_kpi=filled_data.revenue_per_kpi,
),
multiplier=multiplier,
by_reach=by_reach,
)
# pylint: enable=protected-access
inc_outcome_temp = self.incremental_outcome(
use_posterior=use_posterior,
new_data=scaled_data.filter_fields(constants.PAID_DATA),
new_data=adjusted_data.filter_fields(constants.PAID_DATA),
scaling_factor0=0.0,
scaling_factor1=multiplier,
by_reach=by_reach,
inverse_transform_outcome=True,
batch_size=batch_size,
use_kpi=use_kpi,
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