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* Model-vs-model comparison: candidate (selected run) against the baseline
* chosen in the header. Headline deltas, per-class deltas, top regressions,
* and the PROMOTE / DO_NOT_PROMOTE recommendation with blockers.
*/
import Alert from '@mui/material/Alert';
import AlertTitle from '@mui/material/AlertTitle';
import Box from '@mui/material/Box';
import Table from '@mui/material/Table';
import TableBody from '@mui/material/TableBody';
import TableCell from '@mui/material/TableCell';
import TableHead from '@mui/material/TableHead';
import TableRow from '@mui/material/TableRow';
import Typography from '@mui/material/Typography';
import { compareRuns, fmtCompact } from '../../services/megaeval';
import { usePoll } from '../../services/labeleval';
import { ErrorNote, LoadingBox, SectionCard, fmtPct } from '../labeleval/shared';
import { ExplainTip, InfoDot } from '../help/InfoTip';
import { DeltaText } from './shared';
/** Metrics where a positive delta is a regression rather than an improvement. */
const LOWER_IS_BETTER = new Set(['anomaly_rate', 'error_rate', 'fp', 'fn', 'loc_err', 'anomalies']);
function fmtMetricValue(v: number): string {
// Rates arrive as fractions; counts as integers > 1.
return Math.abs(v) <= 1 ? fmtPct(v) : fmtCompact(v);
}
export default function CompareTab({
runId,
baselineRunId,
refreshKey,
}: {
runId: string;
baselineRunId: string | null;
refreshKey: number;
}) {
const cmp = usePoll(
() =>
baselineRunId
? compareRuns({ candidate_run_id: runId, baseline_run_id: baselineRunId })
: Promise.resolve(null),
null,
[runId, baselineRunId, refreshKey]
);
if (!baselineRunId) {
return (
<Alert severity="info" variant="outlined">
Select a <strong>baseline run</strong> in the header to compare the current candidate against it.
</Alert>
);
}
if (cmp.error) return <ErrorNote error={cmp.error} />;
if (cmp.loading && !cmp.data) return <LoadingBox label="Comparing runs…" />;
const data = cmp.data;
if (!data) return null;
const promote = data.recommendation === 'PROMOTE';
return (
<Box sx={{ display: 'flex', flexDirection: 'column', gap: 2 }}>
<Alert severity={promote ? 'success' : 'error'} variant="filled" sx={{ fontWeight: 700 }}>
<AlertTitle sx={{ fontWeight: 800, display: 'flex', alignItems: 'center' }}>
<ExplainTip term="do_not_promote">
<Box component="span" sx={{ borderBottom: '1px dotted rgba(255,255,255,0.6)', cursor: 'help' }}>
{promote ? 'PROMOTE' : 'DO NOT PROMOTE'}
</Box>
</ExplainTip>
<Box component="span" sx={{ ml: 0.75 }}>
— {data.candidate.model_version} vs baseline {data.baseline.model_version}
</Box>
</AlertTitle>
<Typography variant="caption" sx={{ display: 'block', mb: 0.5, opacity: 0.85 }}>
{promote
? 'Every policy rule passed: no headline or safety metric dropped beyond tolerance and no cohort regressed materially.'
: 'Each line below is a violated promotion-policy rule, with the measured drop and the maximum the policy allows.'}
</Typography>
{data.blockers.length ? (
<Box component="ul" sx={{ m: 0, pl: 2.5 }}>
{data.blockers.map((b, i) => (
<li key={i}>
<Typography variant="body2">{b}</Typography>
</li>
))}
</Box>
) : (
<Typography variant="body2">No blockers under the current promotion policy.</Typography>
)}
</Alert>
<Box sx={{ display: 'flex', gap: 2, flexWrap: 'wrap', alignItems: 'flex-start' }}>
<SectionCard
title="Headline deltas"
help="Population-level metric comparison: baseline value, candidate value, and the delta (green = better, red = worse; direction-aware, e.g. lower anomaly rate is good). Hover metric names for definitions."
sx={{ flex: '1 1 380px' }}
>
<Table size="small">
<TableHead>
<TableRow>
<TableCell>Metric</TableCell>
<TableCell align="right">Baseline</TableCell>
<TableCell align="right">Candidate</TableCell>
<TableCell align="right">Δ</TableCell>
</TableRow>
</TableHead>
<TableBody>
{data.headline_deltas.map((d) => (
<TableRow key={d.metric} hover>
<TableCell sx={{ fontFamily: 'monospace' }}>{d.metric}</TableCell>
<TableCell align="right" sx={{ fontFamily: 'monospace' }}>
{fmtMetricValue(d.baseline)}
</TableCell>
<TableCell align="right" sx={{ fontFamily: 'monospace' }}>
{fmtMetricValue(d.candidate)}
</TableCell>
<TableCell align="right">
<DeltaText
delta={d.delta}
higherIsBetter={!LOWER_IS_BETTER.has(d.metric)}
asPct={Math.abs(d.baseline) <= 1 && Math.abs(d.candidate) <= 1}
/>
</TableCell>
</TableRow>
))}
</TableBody>
</Table>
</SectionCard>
<SectionCard
title="Per-class deltas"
help="The same comparison broken out per object class — a stable global average can hide a collapsed class. Vulnerable-road-user classes (pedestrian, cyclist, motorcycle) deserve the closest look."
sx={{ flex: '1 1 480px' }}
>
<Table size="small">
<TableHead>
<TableRow>
<TableCell>Class</TableCell>
<TableCell align="right">n</TableCell>
<TableCell align="right">Recall</TableCell>
<TableCell align="right">Δ recall</TableCell>
<TableCell align="right">Precision</TableCell>
<TableCell align="right">Δ precision</TableCell>
</TableRow>
</TableHead>
<TableBody>
{data.per_class.map((c) => (
<TableRow key={c.class} hover>
<TableCell sx={{ fontWeight: 600 }}>{c.class}</TableCell>
<TableCell align="right" sx={{ fontFamily: 'monospace' }}>
{fmtCompact(c.n)}
</TableCell>
<TableCell align="right" sx={{ fontFamily: 'monospace' }}>
{fmtPct(c.recall_baseline)} → {fmtPct(c.recall_candidate)}
</TableCell>
<TableCell align="right">
<DeltaText delta={c.recall_delta} />
</TableCell>
<TableCell align="right" sx={{ fontFamily: 'monospace' }}>
{fmtPct(c.precision_baseline)} → {fmtPct(c.precision_candidate)}
</TableCell>
<TableCell align="right">
<DeltaText delta={c.precision_delta} />
</TableCell>
</TableRow>
))}
</TableBody>
</Table>
</SectionCard>
</Box>
<SectionCard
title={
<Box component="span" sx={{ display: 'inline-flex', alignItems: 'center' }}>
{`Top regressions (${data.regressions.length})`}
<InfoDot term="regression_detected" size={13} />
</Box>
}
>
{data.regressions.length === 0 ? (
<Typography variant="body2" sx={{ color: '#8a949e' }}>
No cohort regressions detected against the baseline.
</Typography>
) : (
<Box sx={{ display: 'flex', flexDirection: 'column', gap: 0.75 }}>
{data.regressions.map((r) => (
<Box
key={r.cohort}
sx={{
p: 1,
border: '1px solid #5c1a1a',
bgcolor: '#2a1214',
borderRadius: 1,
display: 'flex',
alignItems: 'center',
gap: 1,
flexWrap: 'wrap',
}}
>
<ExplainTip term="regression_detected">
<Typography variant="body2" sx={{ fontWeight: 700, color: '#ef9a9a', cursor: 'help' }}>
🚨 REGRESSION:
</Typography>
</ExplainTip>
<Typography variant="body2" sx={{ fontFamily: 'monospace' }}>
{r.cohort} recall {fmtPct(r.recall_baseline)} → {fmtPct(r.recall_candidate)} (Δ{' '}
<DeltaText delta={r.recall_delta} />)
</Typography>
<Typography variant="caption" sx={{ color: '#8a949e', ml: 'auto', fontFamily: 'monospace' }}>
n={fmtCompact(r.n)}
</Typography>
</Box>
))}
</Box>
)}
</SectionCard>
{data.worst_cohorts.length ? (
<SectionCard
title="Worst cohorts (candidate)"
help="The candidate's lowest-recall cohorts with enough objects to matter, regardless of whether they regressed — chronic weak spots as opposed to new ones."
>
<Table size="small">
<TableHead>
<TableRow>
<TableCell>Cohort</TableCell>
<TableCell align="right">n</TableCell>
<TableCell align="right">Recall (baseline → candidate)</TableCell>
<TableCell align="right">Δ</TableCell>
</TableRow>
</TableHead>
<TableBody>
{data.worst_cohorts.map((c) => (
<TableRow key={c.cohort} hover>
<TableCell sx={{ fontFamily: 'monospace' }}>{c.cohort}</TableCell>
<TableCell align="right" sx={{ fontFamily: 'monospace' }}>
{fmtCompact(c.n)}
</TableCell>
<TableCell align="right" sx={{ fontFamily: 'monospace' }}>
{fmtPct(c.recall_baseline)} → {fmtPct(c.recall_candidate)}
</TableCell>
<TableCell align="right">
<DeltaText delta={c.recall_delta} />
</TableCell>
</TableRow>
))}
</TableBody>
</Table>
</SectionCard>
) : null}
</Box>
);
}
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