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// SPDX-License-Identifier: Apache-2.0
/**
* @module Generators/Pipeline/AnalysisClassification
* @description Classification analysis method builders for the analysis pipeline.
*
* Contains markdown builders for the **Classification** analysis method group:
* - `significance-classification` — political significance scoring
* - `impact-matrix` — multi-dimensional impact assessment
* - `actor-mapping` — political actor classification
* - `forces-analysis` — political forces balance analysis
* - `significance-scoring` — 5-dimension EP event significance scoring
*/
import {
assessPoliticalSignificance,
buildImpactMatrix,
classifyPoliticalActors,
analyzePoliticalForces,
} from '../../utils/political-classification.js';
import {
scoreSignificance,
scoreBatch,
formatBatchMarkdown,
} from '../../utils/significance-scoring.js';
import {
sanitizeCell,
safeArr,
toClassificationInput,
buildMarkdownHeader,
impactToNum,
impactIndicator,
highestImpactDimension,
} from './analysis-helpers.js';
import type { MarkdownBuilder } from './analysis-helpers.js';
import type { AnalysisMethod } from './analysis-stage.js';
// ─── Heuristic scoring thresholds for EP event data ───────────────────────────
/** Event-count threshold above which parliamentary significance is elevated */
const EVENT_VOLUME_HIGH_THRESHOLD = 5;
/** Event-count threshold above which institutional relevance is elevated */
const EVENT_VOLUME_VERY_HIGH_THRESHOLD = 10;
/** Procedure-count threshold above which policy impact is elevated */
const PROCEDURE_VOLUME_THRESHOLD = 3;
/** Adopted-text-count threshold above which public interest is elevated */
const ADOPTED_TEXT_VOLUME_THRESHOLD = 2;
/** Base scores for EP event dimensions when data volume is high / low */
const EVENT_PARLIAMENTARY_HIGH = 6;
const EVENT_PARLIAMENTARY_LOW = 4;
const EVENT_POLICY_HIGH = 6;
const EVENT_POLICY_LOW = 3;
const EVENT_PUBLIC_HIGH = 5;
const EVENT_PUBLIC_LOW = 3;
/** Default temporal urgency for events (mid-range, adjusted at AI scoring) */
const EVENT_DEFAULT_URGENCY = 5;
const EVENT_INSTITUTIONAL_HIGH = 7;
const EVENT_INSTITUTIONAL_LOW = 4;
/** Default dimension scores for adopted texts (plenary-approved) */
const ADOPTED_PARLIAMENTARY = 7;
const ADOPTED_POLICY = 6;
const ADOPTED_PUBLIC = 5;
const ADOPTED_URGENCY = 4;
const ADOPTED_INSTITUTIONAL = 6;
/** Analysis method identifier for significance scoring */
export const METHOD_SIGNIFICANCE_SCORING_ID = 'significance-scoring' as const;
// ─── Per-method markdown builders ────────────────────────────────────────────
/**
* Build markdown for the significance classification method.
* Scores and ranks legislative items by political significance.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildSignificanceClassificationMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const input = toClassificationInput(fetchedData);
const significance = assessPoliticalSignificance(input);
const events = safeArr(fetchedData, 'events');
const docs = safeArr(fetchedData, 'documents');
const procedures = safeArr(fetchedData, 'procedures');
const adoptedTexts = safeArr(fetchedData, 'adoptedTexts');
const header = buildMarkdownHeader(
'significance-classification',
date,
significance === 'routine' ? 'medium' : 'high'
);
const sigMap: Record<string, number> = {
historic: 0.95,
critical: 0.8,
significant: 0.65,
notable: 0.45,
routine: 0.25,
};
const sigScore = sigMap[significance] ?? 0.25;
return (
header +
`# Political Significance Classification
## Overall Significance: **${significance.toUpperCase()}**
\`\`\`mermaid
quadrantChart
title Political Significance Assessment — ${date}
x-axis Low Volume --> High Volume
y-axis Low Impact --> High Impact
quadrant-1 Critical Watch
quadrant-2 Strategic Priority
quadrant-3 Monitor
quadrant-4 Routine Track
Current Assessment: [${sigScore.toFixed(2)}, ${sigScore.toFixed(2)}]
Events Signal: [${Math.min(events.length / 20, 0.95).toFixed(2)}, 0.60]
Documents Signal: [${Math.min(docs.length / 20, 0.95).toFixed(2)}, 0.55]
Procedures Signal: [${Math.min(procedures.length / 10, 0.95).toFixed(2)}, 0.75]
Adopted Texts: [${Math.min(adoptedTexts.length / 10, 0.95).toFixed(2)}, 0.85]
\`\`\`
## 5-Signal Model Scores
| Signal | Raw Data | Score |
|--------|----------|-------|
| Volume | ${events.length} events, ${docs.length} documents | ${Math.min((events.length + docs.length) / 10, 5).toFixed(1)}/5 |
| Pipeline | ${procedures.length} procedures | ${Math.min(procedures.length / 5, 5).toFixed(1)}/5 |
| Output | ${adoptedTexts.length} adopted texts | ${Math.min(adoptedTexts.length / 5, 5).toFixed(1)}/5 |
| Anomalies | Pattern deviation detection | — |
| Coalition | Group alignment analysis | — |
## Data Summary
| Metric | Value |
|--------|-------|
| Computed significance | ${significance.toUpperCase()} |
| Total data points | ${events.length + docs.length + procedures.length + adoptedTexts.length} |
| Events | ${events.length} |
| Documents | ${docs.length} |
| Procedures | ${procedures.length} |
| Adopted texts | ${adoptedTexts.length} |
| Date | ${date} |
## Date: ${date}
`
);
}
/**
* Build markdown for the impact matrix method.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildImpactMatrixMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const input = toClassificationInput(fetchedData);
const matrix = buildImpactMatrix(input);
const header = buildMarkdownHeader('impact-matrix', date, 'medium');
return (
header +
`# Political Impact Matrix
## Overall Significance: **${matrix.overallSignificance.toUpperCase()}**
\`\`\`mermaid
pie title Impact Distribution by Dimension — ${date}
"Legislative" : ${impactToNum(matrix.legislativeImpact)}
"Coalition" : ${impactToNum(matrix.coalitionImpact)}
"Public Opinion" : ${impactToNum(matrix.publicOpinionImpact)}
"Institutional" : ${impactToNum(matrix.institutionalImpact)}
"Economic" : ${impactToNum(matrix.economicImpact)}
\`\`\`
## Impact Dimensions
| Dimension | Level | Indicator | Numeric |
|-----------|-------|-----------|---------|
| Legislative | ${matrix.legislativeImpact} | ${impactIndicator(matrix.legislativeImpact)} | ${impactToNum(matrix.legislativeImpact)} |
| Coalition | ${matrix.coalitionImpact} | ${impactIndicator(matrix.coalitionImpact)} | ${impactToNum(matrix.coalitionImpact)} |
| Public Opinion | ${matrix.publicOpinionImpact} | ${impactIndicator(matrix.publicOpinionImpact)} | ${impactToNum(matrix.publicOpinionImpact)} |
| Institutional | ${matrix.institutionalImpact} | ${impactIndicator(matrix.institutionalImpact)} | ${impactToNum(matrix.institutionalImpact)} |
| Economic | ${matrix.economicImpact} | ${impactIndicator(matrix.economicImpact)} | ${impactToNum(matrix.economicImpact)} |
## Summary
| Metric | Value |
|--------|-------|
| Overall significance | ${matrix.overallSignificance.toUpperCase()} |
| Highest impact | ${highestImpactDimension(matrix)} |
| Date | ${date} |
## Date: ${date}
`
);
}
/**
* Build markdown for the actor mapping method.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildActorMappingMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const input = toClassificationInput(fetchedData);
const actors = classifyPoliticalActors(input);
const header = buildMarkdownHeader('actor-mapping', date, actors.length > 0 ? 'medium' : 'low');
const actorRows =
actors.length > 0
? actors
.map(
(a) =>
`| ${sanitizeCell(a.name)} | ${sanitizeCell(a.actorType)} | ${sanitizeCell(String(a.influence))} | ${sanitizeCell(a.position)} | ${sanitizeCell(a.role)} |`
)
.join('\n')
: '| — | — | — | — | — |';
const actorTypes = actors.length > 0 ? [...new Set(actors.map((a) => a.actorType))] : [];
const typeCounts = actorTypes.map((t) => ({
type: t,
count: actors.filter((a) => a.actorType === t).length,
}));
const mermaidPie =
typeCounts.length > 0
? typeCounts.map((tc) => ` "${tc.type}" : ${tc.count}`).join('\n')
: ' "No actors classified" : 1';
return (
header +
`# Political Actor Mapping
## Actors Identified: ${actors.length}
\`\`\`mermaid
pie title Actor Type Distribution — ${date}
${mermaidPie}
\`\`\`
## Actor Classification
| Actor | Type | Influence | Position | Role |
|-------|------|-----------|----------|------|
${actorRows}
## Type Counts
| Type | Count |
|------|-------|
${typeCounts.length > 0 ? typeCounts.map((tc) => `| ${tc.type} | ${tc.count} |`).join('\n') : '| — | 0 |'}
## Date: ${date}
`
);
}
/**
* Build markdown for the political forces analysis method.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildForcesAnalysisMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const input = toClassificationInput(fetchedData);
const forces = analyzePoliticalForces(input);
const header = buildMarkdownHeader('forces-analysis', date, 'medium');
const forceRow = (
name: string,
f: { strength: number; trend: string; keyActors: readonly string[]; confidence: string }
) =>
`| ${sanitizeCell(name)} | ${sanitizeCell(f.trend)} | ${(f.strength * 100).toFixed(0)}% | ${f.keyActors.length > 0 ? sanitizeCell(f.keyActors.join(', ')) : '—'} | ${sanitizeCell(f.confidence)} |`;
const cp = Math.max(1, Math.min(99, Math.round(forces.coalitionPower.strength * 100)));
const op = Math.max(1, Math.min(99, Math.round(forces.oppositionPower.strength * 100)));
const ib = Math.max(1, Math.min(99, Math.round(forces.institutionalBarriers.strength * 100)));
const pp = Math.max(1, Math.min(99, Math.round(forces.publicPressure.strength * 100)));
const ei = Math.max(1, Math.min(99, Math.round(forces.externalInfluences.strength * 100)));
return (
header +
`# Political Forces Analysis
\`\`\`mermaid
pie title Political Force Distribution — ${date}
"Coalition Power" : ${cp}
"Opposition Power" : ${op}
"Institutional Barriers" : ${ib}
"Public Pressure" : ${pp}
"External Influences" : ${ei}
\`\`\`
## Forces Data
| Force | Trend | Strength | Key Actors | Confidence |
|-------|-------|----------|------------|------------|
${forceRow('Coalition Power', forces.coalitionPower)}
${forceRow('Opposition Power', forces.oppositionPower)}
${forceRow('Institutional Barriers', forces.institutionalBarriers)}
${forceRow('Public Pressure', forces.publicPressure)}
${forceRow('External Influences', forces.externalInfluences)}
## Balance
| Metric | Value |
|--------|-------|
| Coalition vs Opposition | ${cp}% vs ${op}% |
| Dominant force | ${cp > op ? 'Coalition' : op > cp ? 'Opposition' : 'Balanced'} |
| Date | ${date} |
## Date: ${date}
`
);
}
/**
* Build markdown for the significance-scoring method.
* Uses the 5-dimension scoring engine to score all EP events.
*
* @param fetchedData - Raw fetched EP data
* @param date - Analysis date
* @returns Markdown content string
*/
export function buildSignificanceScoringMarkdown(
fetchedData: Record<string, unknown>,
date: string
): string {
const events = safeArr(fetchedData, 'events');
const adoptedTexts = safeArr(fetchedData, 'adoptedTexts');
const procedures = safeArr(fetchedData, 'procedures');
const header = buildMarkdownHeader(METHOD_SIGNIFICANCE_SCORING_ID, date, 'medium');
const inputs = [
...events.map((e) => {
const ev = e as Record<string, unknown>;
return {
title: String(ev['title'] ?? ev['label'] ?? 'Unknown Event'),
reference: String(ev['id'] ?? ''),
parliamentarySignificance: Math.min(
10,
events.length > EVENT_VOLUME_HIGH_THRESHOLD
? EVENT_PARLIAMENTARY_HIGH
: EVENT_PARLIAMENTARY_LOW
),
policyImpact: Math.min(
10,
procedures.length > PROCEDURE_VOLUME_THRESHOLD ? EVENT_POLICY_HIGH : EVENT_POLICY_LOW
),
publicInterest: Math.min(
10,
adoptedTexts.length > ADOPTED_TEXT_VOLUME_THRESHOLD ? EVENT_PUBLIC_HIGH : EVENT_PUBLIC_LOW
),
temporalUrgency: EVENT_DEFAULT_URGENCY,
institutionalRelevance: Math.min(
10,
events.length > EVENT_VOLUME_VERY_HIGH_THRESHOLD
? EVENT_INSTITUTIONAL_HIGH
: EVENT_INSTITUTIONAL_LOW
),
};
}),
...adoptedTexts.map((t) => {
const at = t as Record<string, unknown>;
return {
title: String(at['title'] ?? at['label'] ?? 'Adopted Text'),
reference: String(at['id'] ?? ''),
parliamentarySignificance: ADOPTED_PARLIAMENTARY,
policyImpact: ADOPTED_POLICY,
publicInterest: ADOPTED_PUBLIC,
temporalUrgency: ADOPTED_URGENCY,
institutionalRelevance: ADOPTED_INSTITUTIONAL,
};
}),
];
if (inputs.length === 0) {
return `${header}# 📈 Significance Scoring — ${date}\n\nNo events or adopted texts available for scoring.\n`;
}
const batch = scoreBatch(inputs);
const alignedScores = inputs.map((input) => scoreSignificance(input));
const batchTable = formatBatchMarkdown(inputs, alignedScores);
return `${header}# 📈 Significance Scoring — ${date}
## Summary
| Decision | Count |
|----------|:-----:|
| 📰 Publish | ${batch.summary.publish} |
| 📋 Hold | ${batch.summary.hold} |
| 🗄️ Skip | ${batch.summary.skip} |
## Batch Scoring
${batchTable}
`;
}
/** All classification method builders keyed by their AnalysisMethod identifier */
export const CLASSIFICATION_BUILDERS: Readonly<Partial<Record<AnalysisMethod, MarkdownBuilder>>> = {
'significance-classification': buildSignificanceClassificationMarkdown,
'impact-matrix': buildImpactMatrixMarkdown,
'actor-mapping': buildActorMappingMarkdown,
'forces-analysis': buildForcesAnalysisMarkdown,
[METHOD_SIGNIFICANCE_SCORING_ID]: buildSignificanceScoringMarkdown,
};
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