by-campaign-optimizer

BY Campaign Optimizer (Active Learning)

Multi-round design campaigns generate scored designs at every iteration. This skill turns that scoring history into actionable parameter changes for the next round — training a lightweight Random Forest on the designs you already have, ranking which features actually discriminate good from bad, and proposing thresholds and design counts that target the most promising regions of feature space.

The optimizer is the iteration loop: it runs after the diagnosis step (by-failure-diagnosis) has identified which features matter, and decides what parameters to push or relax for the next round. Diagnosis answers "what went wrong?"; this skill answers "what should we try next?".

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When to Use This Skill

✅ Use this skill when:

• A design round has completed and scores are written to *_scores.json files
• The campaign has ≥10 scored designs total across all rounds
• You are deciding how to configure the next round of a multi-round campaign
• by-failure-diagnosis has identified discriminating features and you need concrete threshold values
• You want a feature-importance ranking to decide what to optimize for
• The user asks "what should I change for the next round?" or "tune my campaign"

❌ Do NOT use this skill when:

• The campaign has fewer than 10 scored designs → use rule-based defaults from by-campaign-manager instead
• This is the first round (no prior scores exist) → use by-research and by-design-workflow to set initial parameters
• Designs are failing for non-statistical reasons (e.g., compute errors, bad target structure) → run by-failure-diagnosis first to confirm the signal is real
• The campaign is a single-shot screen with no planned iterations
• You only need a "why did designs fail?" report → use by-failure-diagnosis (the diagnostic counterpart to this optimizer)

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Quick Start

from proteus_cli.campaign.active_learning import (
    has_enough_data,
    suggest_from_campaign,
)

campaign_dir = "campaigns/tnfa/campaign_20260520_001"

if has_enough_data(campaign_dir):
    result = suggest_from_campaign(campaign_dir)
    print(f"Source: {result.source}")                  # "active_learning"
    print(f"Confidence: {result.confidence}")          # "high" if >30 designs
    print(f"Top feature: {result.feature_importances[0]}")
    print(f"Recommendations: {result.recommended_parameters}")