Skip to content

Remove mock _check_prior_probability #1463

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
copybara-service merged 1 commit into from
Feb 21, 2026
Merged

Remove mock _check_prior_probability #1463

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
43 changes: 0 additions & 43 deletions meridian/model/eda/eda_engine.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -2477,46 +2477,3 @@ def check_population_corr_raw_media(
explanation=eda_constants.POPULATION_CORRELATION_RAW_MEDIA_INFO,
check_name='check_population_corr_raw_media',
)

def _check_prior_probability(
self,
) -> eda_outcome.EDAOutcome[eda_outcome.PriorProbabilityArtifact]:
"""Checks the prior probability of a negative baseline.
Returns:
An EDAOutcome object containing a PriorProbabilityArtifact. The artifact
includes a mock prior negative baseline probability and a DataArray of
mock mean prior contributions per channel.
"""
# TODO: b/476128592 - currently, this check is blocked. for the meantime,
# we will return mock data for the report.
channel_names = self._model_context.input_data.get_all_channels()
mean_prior_contribution = np.random.uniform(
size=len(channel_names), low=0.0, high=0.05
)
mean_prior_contribution_da = xr.DataArray(
mean_prior_contribution,
coords={constants.CHANNEL: channel_names},
dims=[constants.CHANNEL],
)

artifact = eda_outcome.PriorProbabilityArtifact(
level=eda_outcome.AnalysisLevel.OVERALL,
prior_negative_baseline_prob=0.123,
mean_prior_contribution_da=mean_prior_contribution_da,
)

findings = [
eda_outcome.EDAFinding(
severity=eda_outcome.EDASeverity.INFO,
explanation=eda_constants.PRIOR_PROBABILITY_REPORT_INFO,
finding_cause=eda_outcome.FindingCause.NONE,
associated_artifact=artifact,
)
]

return eda_outcome.EDAOutcome(
check_type=eda_outcome.EDACheckType.PRIOR_PROBABILITY,
findings=findings,
analysis_artifacts=[artifact],
)
Loading