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Revert "fix: replace all /home/termius/ → /home/lahargnedebartoli/ (VM username fix)"
ClaudeThis reverts commit 64c86437f1d27b920790b2c054fc0287114390d9.1dc0b7f -
fix: replace all /home/termius/ → /home/lahargnedebartoli/ (VM username fix)
Claudehttps://claude.ai/code/session_0127NuynGmn5fsJBnjDbTpjg0eabc42
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Brain cycle 82: fix calibration mutation bias — venn_abers boosted across all 3 GA impls
ClaudePROBLEM: venn_abers is the confirmed best calibration (S13 pareto_best 0.21773, delta -0.00543), but mutation weights were PENALIZING it: - hf-space/app.py: init=22 → mutation=14 (dropped by 36%) - genetic_loop_v3.py: venn_abers MISSING ENTIRELY (only isotonic/sigmoid/none) FIXES: 1. hf-space/app.py mutation: venn_abers 14→25, none 22→16 - Now mutation preserves venn_abers instead of reverting to uncalibrated 2. genetic_loop_v3.py: add venn_abers to Kaggle GPU loop - Init + mutation both now use weighted choices matching HF island config - GPU Karpathy sessions will now explore venn_abers for first time Expected: GPU Kaggle loop discovers venn_abers advantage within first session. HF islands maintain calibration gains instead of mutating them away. https://claude.ai/code/session_01KtBfaHJZ3YzfFqzerNp23fd1af5f1
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d1: DART dropout trees on LightGBM in GA loop — expected Brier delta -0.0025
P012: Add boosting_type=dart, drop_rate=0.1, skip_drop=0.5, uniform_drop=True to LGBMClassifier in _build_model (genetic_loop_v3.py). DART prevents first-tree dominance, improves probability spread across [0.3,0.7], reduces reliability component of Brier. Consistent with Rashmi & Gilad-Bachrach 2015 (AISTATS). Already verified working in app.py stacking path — now applied to main GA loop.8a90d3f
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evolution: 4 critical fixes — elitism + CatBoost cap + feature penalty + n_splits=3
1. Brier-based elitism: protect top-2 by raw Brier in addition to composite (prevents fossil loss like S15's 0.22159 config disappearing) 2. CatBoost CPU cap: n_estimators=60 + early_stopping=15 on CPU (3-5x speedup on S10/S13 where catboost dominates) 3. Feature penalty: -0.05 per 200 features above 80 threshold (breaks the n_feat=200 trap that causes convergence) 4. Default n_splits: 5→3 for 1.6x faster walk-forward CV Expected: fleet 219→350 gen/hr, prevents config loss, better Brier pressure Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>9d4933d
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feat: NBA Quant Full Blast — 4 workstreams
WS1: Real box score data pipeline - New ops/backfill-boxscores.py — fetches FGM/FGA/FG3M/FG3A/FTM/FTA/OREB/DREB/PF from NBA API - engine.py _parse_stats() detects real data, computes real possessions/eFG%/pace/ORtg/DRtg - app.py /api/command now supports backfill_boxscores (background thread) WS2: Feature engine cleanup - skip_placeholder flag skips categories 11-15 (all zeros without real data) - Fitness weights: Brier 70%, Cal 20%, ROI 5%, Sharpe 5% (was 60/20/10/10) - Soft penalty 0.001 per feature above 80 (replaces hard cliff at 150) WS3: Karpathy autoresearch loop (S10 side) - POST /api/checkpoint — saves best individual + Brier to disk - POST /api/rollback — reverts to specific or best checkpoint - GET /api/checkpoint/best — returns lowest Brier checkpoint - Rule 7 LIVE_REGRESSION in run_logger — auto-rollback if live Brier drifts WS4: Prediction bridge - fetch_evolved_predictions() calls S10 /api/predict - predict_game() blends 60% evolved + 40% ensemble when available Co-A…34dbefa -
feat: S10 /api/predict endpoint + fix auto-cut rules
Added /api/predict POST endpoint — uses best evolved individual to generate live NBA game predictions. Trains on full historical data with evolved feature selection + hyperparams. Accepts team pairs or date (fetches ESPN schedule). Fixed run_logger.py auto-cut rules: - BRIER_FLOOR: raised threshold 30→50 gens, moderate params instead of full reset, clears brier_history after trigger to prevent repeated firing - STAGNATION: moderate mutation boost (2x capped at 0.15) instead of destructive pop_size 200 + mutation 0.20 Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>308a1d2
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fix: Supabase logging to public schema + 47 secrets on S10
- All SQL now uses explicit public.nba_evolution_* table names (DB user defaults to n8n_engine_1 schema, was silently writing there) - Direct psycopg2 INSERT for gen/cycle logging (bypasses pool issues) - Deep numpy-safe JSON serializer for /api/status endpoint - deploy.py expanded from 5 to 47 secrets (all LLM keys, DBs, Telegram, search, GitHub, HF tokens) - Data confirmed flowing: 24 gens, 240 evals, 9 runs in Supabase public schema Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>4450892 -
fix: RunLogger DB connection diagnostics + robust error handling
- Log when DATABASE_URL is not set - Test connection on first use (SELECT 1) - Return True (not empty list) for successful INSERT/UPDATE - Flush print statements for HF Space logs visibility Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>1938fd0 -
feat: Supabase run logger + auto-cut + 2058 features (25 categories)
- RunLogger: logs every gen/cycle/eval to Supabase PostgreSQL (4 tables) - 6 auto-cut rules: regression, stagnation, ROI, diversity, features, brier floor - Integrated into app.py (S10) and genetic_loop_v3.py - Feature engine expanded: 580 → 2058 features across 25 categories - New categories: interactions, advanced rolling, season trajectory, lineup, game theory, environmental, cross-window momentum, market II, power ratings, fatigue - S10 API endpoints: /api/run-stats, /api/cuts, /api/brier-trend, /api/recent-runs - Deploy script for S10 HF Space Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>4a39063