Add red outline visual indicators for infeasible arms in ScatterPlot and ArmEffectsPlot#5026
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Add red outline visual indicators for infeasible arms in ScatterPlot and ArmEffectsPlot#5026sdaulton wants to merge 1 commit into
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…and ArmEffectsPlot Summary: The ScatterPlot and ArmEffectsPlot analyses compute `p_feasible_mean` for each arm but only displayed it in the hover tooltip. Users had to hover over each point to discover constraint violations. This change adds a red outline around scatter points for arms that are likely infeasible (`p_feasible_mean < 0.1`), making constraint violations immediately visible. A "Likely Infeasible" legend entry is also added when any infeasible arms are present. In addition, `prepare_arm_data()` previously only included columns for the requested (plotted) metrics in its DataFrame. When a constraint metric differed from the plotted metrics, `_prepare_p_feasible()` couldn't find the constraint column, filled it with NaN, and all arms incorrectly appeared feasible. This fix ensures `_prepare_modeled_arm_data()` extracts all predicted metrics and `_prepare_raw_arm_data()` extracts all available data metrics, so constraint feasibility is computed correctly regardless of which metrics are plotted. Changes: - `plotly/utils.py`: Add `MINIMUM_P_FEASIBLE` threshold (renamed from `MINIMUM_CONTRAINT_VIOLATION_THRESHOLD`), `get_infeasibility_marker_line()` helper, and infeasibility constants. - `plotly/scatter.py`, `plotly/arm_effects.py`: Apply marker line to each trial's scatter points and add a "Likely Infeasible" legend entry. - `analysis/utils.py`: Fix `_prepare_modeled_arm_data` to extract all predicted metrics (not just plotted ones), `_prepare_raw_arm_data` to extract all available data metrics, `_get_status_quo_df` to handle extra columns in NaN checks and status quo column selection. Extra columns are stripped before returning from `prepare_arm_data()` to preserve the API contract. - Tests: Add `TestScatterPlotInfeasibility` and `TestArmEffectsPlotInfeasibility` classes covering red outlines on infeasible arms, legend presence/absence, and the case where the constraint metric differs from the plotted metrics. Reviewed By: mpolson64 Differential Revision: D94783502
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Summary:
The ScatterPlot and ArmEffectsPlot analyses compute
p_feasible_meanfor each arm but only displayed it in the hover tooltip. Users had to hover over each point to discover constraint violations.This change adds a red outline around scatter points for arms that are likely infeasible (
p_feasible_mean < 0.1), making constraint violations immediately visible. A "Likely Infeasible" legend entry is also added when any infeasible arms are present.In addition,
prepare_arm_data()previously only included columns for the requested (plotted) metrics in its DataFrame. When a constraint metric differed from the plotted metrics,_prepare_p_feasible()couldn't find the constraint column, filled it with NaN, and all arms incorrectly appeared feasible. This fix ensures_prepare_modeled_arm_data()extracts all predicted metrics and_prepare_raw_arm_data()extracts all available data metrics, so constraint feasibility is computed correctly regardless of which metrics are plotted.Changes:
plotly/utils.py: AddMINIMUM_P_FEASIBLEthreshold (renamed fromMINIMUM_CONTRAINT_VIOLATION_THRESHOLD),get_infeasibility_marker_line()helper, and infeasibility constants.plotly/scatter.py,plotly/arm_effects.py: Apply marker line to each trial's scatter points and add a "Likely Infeasible" legend entry.analysis/utils.py: Fix_prepare_modeled_arm_datato extract all predicted metrics (not just plotted ones),_prepare_raw_arm_datato extract all available data metrics,_get_status_quo_dfto handle extra columns in NaN checks and status quo column selection. Extra columns are stripped before returning fromprepare_arm_data()to preserve the API contract.TestScatterPlotInfeasibilityandTestArmEffectsPlotInfeasibilityclasses covering red outlines on infeasible arms, legend presence/absence, and the case where the constraint metric differs from the plotted metrics.Reviewed By: mpolson64
Differential Revision: D94783502