optimize-button-patterns

name	optimize-button-patterns
description	Iteratively improves cookie-popup button regex patterns in button-patterns.js against labelled-button-texts.csv. Use when labels are updated, benchmark coverage is low, or the user asks to optimize/fix button pattern regexes. Targets zero false positives and ≥90% weighted coverage per label (settings, accept, reject, acknowledge).
disable-model-invocation	true

Optimize Button Patterns

Improve regex patterns in post-processing/generate-autoconsent-rules/button-patterns.js so classifyButtonTextRegex matches the labelled dataset.

Prerequisites

1. Confirm post-processing/generate-autoconsent-rules/labelled-button-texts.csv has up-to-date labels. If the user added unlabelled rows, suggest running label-button-texts.js first.
2. For label semantics, read classifyButtonTextLLM in post-processing/generate-autoconsent-rules/detection.js — that prompt is the source of truth; do not duplicate it here.

Success criteria

Stop when all three are met:

1. Zero false positives (occurrence-weighted total across labels)
2. Per-label weighted coverage ≥ 90% for each of settings, accept, reject, and acknowledge (weightedCorrectRate in benchmark JSON; other is excluded from optimization)
3. Pattern consolidation — merge literal clusters into regexes where safe; dedupe exact duplicates

Benchmark

Run after every batch of edits:

node post-processing/benchmark-classify-button-text-regex.js \
  -o /tmp/button-pattern-benchmark.json

From stdout, read per-label weighted rates and false-positive total. From JSON (byLabel), read each label's weightedCorrectRate, weightedCorrect, weightedSupport, falsePositiveExamples, and missedExamples (sorted by occurences in the benchmark output).

A label passes when weightedCorrectRate >= 0.90 (or weightedCorrect / weightedSupport >= 0.90). Check all four labels every iteration.

Count patterns before and after with:

node -e "const p=require('./post-processing/generate-autoconsent-rules/button-patterns'); for (const k of Object.keys(p)) console.log(k, p[k].length)"

Run loop

Max ~10 iterations. Each iteration:

1. Measure

Run the benchmark. Record per-label weightedCorrectRate (and which labels are below 90%), weighted false positives, and pattern counts.

2. Fix false positives first

For each entry in falsePositiveExamples (any label where predicted === label but ground truth differs):

• Find the matching pattern in button-patterns.js (grep cleaned text; remember classification priority: reject → settings → acknowledge → accept).
• Fix by: removing the pattern, moving it to the correct list, narrowing with ^...$ anchors, or adding a NEVER_MATCH_PATTERNS guard.
• Re-run benchmark. Do not add new coverage patterns while FPs remain.

3. Improve coverage

While any label has weightedCorrectRate < 0.90, prioritize labels furthest below 90% (by gap in weighted correct count: weightedSupport - weightedCorrect).

For each iteration, take the top 15–25 missedExamples by occurences from under-target labels only.

For each miss:

• Add the smallest pattern that fixes it in the correct list (REJECT_PATTERNS, SETTINGS_PATTERNS, ACKNOWLEDGE_PATTERNS, or ACCEPT_PATTERNS).
• Prefer extending an existing regex over adding a literal string.
• Run the collision check (below) before committing.
• Re-run benchmark after each batch; FPs must not increase and no label's rate may drop below its previous value.

4. Merge patterns

Once FPs = 0 and every label has weightedCorrectRate >= 0.90:

• Remove exact duplicate literals within the same list.
• Replace clusters of 3+ similar literals with one anchored alternation regex, e.g. /^(alles accepteren|accepteer alles|alles akzeptieren)$/i.
• Re-run benchmark to confirm no regression.

5. Report

Use the output template at the end of this file.

Pattern authoring rules

• Edit button-patterns.js only. Do not change checkHeuristicPatterns in detection.js.
• Classification priority: reject → settings → acknowledge → accept → other (see classifyButtonTextRegex in detection.js).
• Essential/necessary-only phrases → reject, even when "accept" appears.
• allow / permit + selection → accept; customize / manage / show details → settings.
• Neutral dismiss (OK, close, got it) → acknowledge.
• Use anchored regexes (^...$) for short action verbs to avoid substring false positives.
• Preserve language section comments (// German, // Dutch, etc.).
• Do not sync patterns to the autoconsent codebase (out of scope).

Known pitfalls

Patterns misplaced across lists cause false positives:

Text	Correct label	Common mistake
selectie toestaan	accept	Listed in REJECT_PATTERNS_DUTCH
akzeptieren schließen	acknowledge	Listed in ACCEPT_PATTERNS

When fixing FPs, search button-patterns.js for the literal or a regex that matches the cleaned text, then move/remove/narrow.

Collision check

Before adding pattern P for label L:

1. Apply cleanButtonText when matching (same as runtime).
2. Scan labelled CSV rows: if P would match a row where label !== L, that is a collision.
3. If a high-occurrence collision exists, narrow P or skip it.
4. String patterns match exact cleaned text; regex patterns use .test(cleanedText).

Stop conditions

• All success criteria met (including per-label ≥ 90%), or
• No improvement in any under-target label's weighted rate for 2 consecutive iterations (report blockers per label), or
• User iteration budget reached

Output template

End with:

## Button pattern optimization results
- False positives: N → 0
- Per-label weighted coverage (target ≥90% each):
  - settings: X% → Y%
  - accept: X% → Y%
  - reject: X% → Y%
  - acknowledge: X% → Y%
- Pattern count: A → B
- Key changes:
  - ...
- Remaining top misses (if any, by label below target):
  - [label → predicted] "text" (xN)

Collect → label → benchmark workflow: post-processing/README.md