NNUE search integration: FH% tables, NODE_TYPE refinement, TREEDUMP rewrite

- data.c/chess.h: widen the joint FH% table's late axis 16->17, dedicating
  the new bucket 0 to a whole-node ("will something here fail high at
  all") sample rather than sharing it with the first-leftover-move curve.
  Add an NNUE-eval counterpart table (g_uJointFHThousandths_NNUE) and an
  NNUE counterpart of the beta-side table, selected at runtime based on
  whether NNUE eval is active.
- search.c/searchsup.c: add _RefineNodeTypeGuess, which uses the FH%
  tables (via g_NodeTypeGuessTable) to refine a node's NODE_TYPE guess
  instead of the old heuristic; thread uParentMoveFHEstimate through
  PLY_INFO so a node can see its parent's prediction for the move that
  led to it.
- generate.c: initialize sKillers[2]/[3].uBonus unconditionally (an
  uninitialized-stack read caught by valgrind under LTO; harmless today
  but a real bug).
- dumptree.c/chess.h/GNUmakefile: replace the long-dead DUMP_TREE XML
  tree dump (written for a GUI this project no longer has) with TREEDUMP,
  a flat, path-keyed move/result trace meant to diff against an
  identically-instrumented Stockfish build -- see docs/TREEDUMP_FORMAT.md.
- docs/DEEPER_PRUNING.md: notes on closing the search-efficiency gap vs
  Stockfish.
- crafty_egtb/stockfish_xboard/typhoon_nnue: wrapper scripts so
  match_play.py's Engine class can drive Crafty, Stockfish (via
  PolyGlot), and an NNUE-enabled typhoon the same way it drives HCE
  typhoon.
- tests/lop: rename lop.txt -> lop.ep_; update compare_lop.cpp.
- annotate_pgns: add aggregate_marginsafety.py, dedup_pgn.py,
  fit_lazy_probe.py; update annotate.py/run_nnue_pipeline.py for the NNUE
  training pipeline.
- .gitignore: ignore compile_commands.json (clangd build DB) and
  eval.nnue (a symlink into the untracked head_reference/ comparison
  baseline), neither of which belongs in the repo.

Co-Authored-By: Claude Sonnet 5 <[email protected]>

1 files changed, 0 insertions, 254 deletions

diff --git a/src/eval_tune/king_safety_data/HANDOFF.md b/src/eval_tune/king_safety_data/HANDOFF.md
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--- a/src/eval_tune/king_safety_data/HANDOFF.md
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-# King-safety defect regression: handoff
-
-## Goal
-
-`_EvalKing` (eval.c) sums a pile of hand-picked "defect" constants into one
-scalar (`uCounter`), then runs that through a single lookup table
-(`KING_SAFETY_BY_COUNTER`). The hand-picked weights assume every defect
-counts equally, which is suspect -- e.g. a queen touching an undefended
-ring square and a knight touching one add the exact same `+1`. This
-project mines real games to see which defects actually predict getting
-mated, so `eval.c`'s weights can be informed by data instead of guesswork.
-
-**Method**: find real middlegame checkmates in human games, look at the
-losing king's position N plies earlier, and compare its raw defect
-pattern against a matched "sound" control position (drawn games) with the
-same attacker material and development level. Fit a logistic regression;
-features with a real, stable coefficient are candidates for reweighting
-in `eval.c`. See `CLAUDE.md`'s "King-safety eval improvement project"
-section (search for it) for the full narrative this file summarizes.
-
-## Where everything is
-
-- **Final training data**: `remine_v4/king_features_matched_v4.csv` --
-  86,362 rows (43,181 matched positive/control pairs). **Use this one.**
-- `remine_v4/merged.csv` -- raw mined FENs before feature extraction
-  (172,735 rows: 43,181 positives + 129,554 control candidates).
-- `remine_v4/king_features_v4.csv` -- all 172,735 rows with full
-  `_EvalKing` defect breakdown, before matching trims it down to the
-  86,362 confound-controlled rows.
-- Pipeline scripts live in `eval_tune/` (sibling of this dir), not here:
-  `mine_king_mates.py`, `shard_mine.py`, `extract_king_features.py`,
-  `match_controls.py`, `train_king_safety_model.py`.
-- Engine side: `chess.h`'s `KING_DEFECT_DUMP_INFO` struct,
-  `_EvalKing`'s `#ifdef KING_DEFECT_DUMP` instrumentation in eval.c, and
-  command.c's `evalkingdump` command -- all gated behind the
-  `KING_DEFECT_DUMP=1` gmake build flag (NOT part of a normal build).
-  `../typhoon_kingdump` is a saved binary built with that flag; rebuild
-  it (`gmake clean` first -- flag changes don't trigger recompilation on
-  their own, see "Gotchas" below) if `eval.c`/`chess.h`/`command.c`
-  change.
-
-## Final result (from `king_features_matched_v4.csv`)
-
-Logistic regression, 80/20 train/test split, standardized coefficients.
-Test AUC 0.91 (plausible for "engineered danger features vs. proven-sound
-draws", not suspiciously perfect). Ranked by |coefficient|, most-to-least
-trustworthy tier first (stability = full-population vs. top-tercile-army-
-strength fits agreeing):
-
-**Tier 1 -- dominant, very stable**: `filestormopenfile` (+0.57 to +0.80),
-`filestormpawn` (+0.51 to +0.54). These are eval.c's `KingFileDefects[3]`
-and `KingStormingPawnDefects[8]` tables (open/half-open file near the
-king; how far an enemy pawn has advanced up that file), already
-independent tables -- lowest-risk place to start a real retune.
-
-**Tier 2 -- strong, stable**: `front_offboard`, `front_def`, `sq4_offboard`,
-`front_undef`, `front_atk_minor`, `front_atk_rook`, `misscastleshort`,
-`front_flight` (+0.33 to +0.45). "front" = the diagonal/vertical ring
-squares one rank away, in the direction each side attacks toward (see
-"Feature schema" below for the exact slot mapping -- do NOT assume this
-means "in front of the king" the way a human would first guess; see the
-correction note there). `sq3`/`sq4` are the two same-rank side squares,
-kept individually resolved.
-
-**Tier 3 -- moderate, less stable**: `back_offboard` (drifts the most of
-any surviving feature, treat with suspicion), `sq3_undef`,
-`attackerarmyscaler`, `queenproximity`, `back_atk_minor`, `front_atk_queen`.
-
-**Confirmed noise -- do not retune based on this data**: `minorsathome`
-(coefficient ~0.001, was previously the #1 "signal" before two sampling
-bugs got fixed -- see "Bugs found" below), `initloc`, `castled` (exactly
-0.0000, fully collinear with `misscastleshort`/`misscastlelong`).
-
-## Translating this into eval.c -- confidence and plan
-
-This is NOT a "throw away eval.c and use the model" situation. The plan
-is targeted retuning of existing constants/tables, informed by relative
-coefficient *ratios*, not literal log-odds-to-centipawn conversion:
-
-1. **`KingFileDefects`/`KingStormingPawnDefects` deserve a real weight
-   increase relative to everything else** -- they're the strongest
-   signal by a clear margin and already live in their own small,
-   isolated tables (`_ComputeFileStormDefects` in eval.c). Lowest risk,
-   highest expected payoff, start here.
-2. **`EmptyAttackedSquare`/`EmptyUnattackedSquare`'s front/back asymmetry
-   is directionally right but probably too weak a spread** -- front-
-   bucket features consistently outrank back-bucket ones here by a good
-   margin.
-3. **`misscastleshort`'s constant likely deserves a bump.**
-4. **Don't touch `KING_INITIAL_COUNTER_BY_LOCATION` or the undeveloped-
-   minors term** based on this data -- both came back as noise.
-5. Neither `KingFileDefects`/`KingStormingPawnDefects` nor the
-   ring-scan tables are currently `DNA_ARRAY`-registered (checked: only
-   `KING_SAFETY_BY_COUNTER` and `KING_QUEEN_PROXIMITY_DANGER` are under
-   king-safety). Retuning them means a manual edit (optionally promoting
-   them to file scope + registering as DNA arrays for future tuning),
-   not "rerun the tuner."
-6. **Absolute magnitude has to be calibrated the normal way** -- run any
-   change through `sd10` ECM suite comparison against `head_reference/`
-   and `match_play.py`'s gated head-to-head match, same as any other
-   hand-tuned eval constant. The regression tells you *shape*
-   (which defects matter more relative to each other), not the right
-   centipawn magnitude for a chess engine's scoring scale.
-7. `CountKingSafetyDefects` (the cheap lazy-eval proxy) stays untouched
-   until a real change lands and is validated -- agreed early in this
-   project, still the right call.
-
-## Feature schema (read this before extending the pipeline)
-
-`KingSafetyDeltas` in eval.c (`-17,-16,-15,-1,+1,+15,+16,+17`) defines 8
-ring slots, indices 0-7 in that order. **Correction made mid-project**:
-slots 3 and 4 (deltas -1/+1) are the two SAME-RANK side squares, not
-"front of the king" -- an earlier description in conversation got this
-wrong. The diagonal/vertical slots (0,1,2 and 5,6,7) are the ones with a
-real front/back asymmetry, matching `EmptyAttackedSquare`'s own existing
-color-dependent weighting (WHITE's front is slots 0/1/2, BLACK's is
-5/6/7 -- see `_EvalKing`'s comments and `KING_RING_FRONT`-style logic).
-
-`extract_king_features.py` dumps full per-slot resolution (all 8 slots x
-4 piece types x several state flags) since the engine-side cost of doing
-so is free once you're calling `evalkingdump` anyway. `train_king_safety_model.py`
-is where slots 0/1/2/5/6/7 get bucketed into "front"/"back" sums
-(color-dependent per row's `mated_color_or_sampled_color`), while slots 3/4
-stay individually resolved -- this bucketing was added in this session
-after the diagonal per-slot coefficients turned out to be the noisiest,
-most sign-flipping entries in every full-vs-subset drift comparison.
-`filestorm` is deliberately NOT used as a training feature -- its two
-independent sub-components (`filestormopenfile`, `filestormpawn`) are
-used instead, since they map onto two already-separate eval.c tables and
-there's no reason to make the regression re-derive their relative
-importance from one pre-summed number.
-
-## Bugs found and fixed this session (read before trusting old data)
-
-If you find any `.csv` file in this project OTHER than
-`remine_v4/*_v4.csv` and its merged.csv, IT PREDATES ONE OR MORE OF THESE
-FIXES AND SHOULD NOT BE USED. (Older files were deleted during cleanup,
-but if this pipeline gets rerun and produces intermediate files again,
-this is the checklist for whether they're trustworthy.)
-
-1. **Bare-king endgame mates leaking into "middlegame" positives.** Only
-   checking the attacker's material (`>= 800cp`, roughly rook+bishop) let
-   trivial K+Q-vs-lone-K technique mates through. Fixed by also requiring
-   `total_non_pawn_piece_count(board) >= 8` (mirrors `_EvalKing`'s own
-   `pos->uNonPawnCount[WHITE][0] + pos->uNonPawnCount[BLACK][0] < 8`
-   endgame boundary exactly) -- see `is_middlegame()` in
-   `mine_king_mates.py`.
-2. **`min_ply` too low (10, i.e. move 5) for control sampling.** A large
-   fraction of the pool's draws are short draws-by-agreement close to
-   `filter_pgn.py`'s own 40-ply floor. Combined with a large end-guard
-   (staying clear of the game's end), the only valid sampling window on a
-   short draw was the early opening -- where minors are legitimately
-   still undeveloped for completely ordinary reasons. This alone caused
-   `minorsathome` to look like a massive, spurious "signal" (controls
-   showed `minorsathome>=2` in ~85% of samples). Verified the engine's
-   own computation was correct on a real developed-middlegame FEN --
-   100% a sampling-window bug, not an eval bug. Fixed: `min_ply` raised
-   to 20 (move 10), and a game too short to satisfy both `min_ply` and
-   the end-guard is now skipped entirely rather than force-sampled early.
-3. **Control-sampling ply selection secretly always picked the earliest
-   plies in a game's valid window.** `rng.shuffle(candidates)` followed
-   by `for idx in sorted(candidates)` completely undid the shuffle.
-   Fixed in two steps (the first fix wasn't sufficient on its own --
-   see the code comment in `mine_controls()` for why): randomly
-   subsample which plies to try, replay ascending through all of them
-   ONCE (required -- boards can only be walked forward), collect every
-   result that passes filters into a list, THEN randomly choose which
-   results to actually keep via `rng.sample` on that list. Verified:
-   sampled-ply-as-fraction-of-game-length now has a median around 0.46
-   with real tail into 60%+, instead of clustering at 20-30%.
-4. **Matching-radius bug treated two very differently-scaled confound
-   columns with one shared distance metric.** `attackerarmyscaler`
-   spans 0-31 (`ASSERT`-confirmed hard clamp ceiling), `minorsathome`
-   spans 0-4. A single Chebyshev radius meant a "tight" radius-2 match
-   still let `minorsathome` drift by up to 2 -- literally half its
-   entire range. This is why `minorsathome` was STILL the #1 "signal"
-   even after an initial matching attempt. Fixed: `match_controls.py`
-   now takes `--exact-cols` (must match exactly, no radius search --
-   `minorsathome` defaults here) and `--radius-cols` (allowed to expand
-   outward, `attackerarmyscaler` defaults here) as separate concepts.
-5. **KDTree-based nearest-neighbor matching matched almost nothing** (308
-   of 43,181 positives) because the match columns are low-cardinality
-   integers -- huge exact-value clusters mean a KDTree's "k nearest" are
-   massively tied, so the first few positives processed in a cluster
-   exhaust the shared candidate set and everyone else in that cluster
-   fails to match. Replaced with exact-bucket + expanding-integer-radius
-   matching (see `match_controls.py`), which has no such collision
-   problem.
-6. **`_EvalKing`'s per-square ring scan implicitly assumed all 8 slots
-   were geometrically equivalent for feature purposes**; slots 3/4
-   (side squares) vs. the diagonal slots turned out to behave
-   differently enough that per-slot resolution on the diagonals was
-   pure noise at the sample sizes available -- see "Feature schema"
-   above.
-7. **Board-diagram spam.** `setboard` prints a full ASCII board dump on
-   every call by default (`g_Options.fShouldPost`, see `DumpPosition` in
-   gamelist.c), which dominated per-row I/O cost during feature
-   extraction. Fixed by prepending `nopost` to every extraction script.
-8. **Two separate concurrent-process races during interactive mining**,
-   both self-inflicted mid-session: (a) `$!` after a `cd ... && nohup
-   ...` pipeline captured the wrong PID, leaving a stale duplicate
-   process racing on the same output file as a supposedly-fresh rerun;
-   (b) killing that process didn't fully stop it before a new one
-   started. No lasting damage (caught via `ps aux` before trusting any
-   output), but: **always verify a background job is actually singular
-   via `ps aux | grep <script>` before trusting its output**, especially
-   after a kill+relaunch sequence.
-9. **`sizeof(KING_DEFECT_DUMP)` name collision with the
-   `-DKING_DEFECT_DUMP` build flag.** The build flag is defined as
-   literal `1` by the compiler; naming the struct type the same thing
-   caused the preprocessor to mangle the struct declaration wherever the
-   type name appeared. Renamed the struct to `KING_DEFECT_DUMP_INFO`.
-   If you ever add a new `#ifdef SOME_NAME` build flag AND a same-named
-   type/variable in the same session, expect this failure mode.
-10. **`gmake` doesn't recompile on a flag-only change.** After the struct
-    rename (#9) and other `chess.h` edits, a `gmake KING_DEFECT_DUMP=1`
-    without a preceding `gmake clean` silently reused stale `.o` files
-    from an earlier flag-less build, producing a binary that didn't
-    actually have `evalkingdump` registered, with no compile error at
-    all. Always `gmake clean` first when a build-flag combination
-    changes, not just when headers change (this is already called out
-    in `CLAUDE.md` for header changes, but the flag-only case is easy to
-    forget too).
-
-## Performance notes for rerunning
-
-- `pgn/twic_filtered.pgn` (642,268 games) mines in a few minutes when
-  sharded 12-24 ways via `shard_mine.py` (byte-range sharding on real
-  game boundaries, found via a fast raw-text `[Event ` scan). Don't run
-  it unsharded -- a single-process full scan took ~40 minutes.
-- Feature extraction (`extract_king_features.py`) is the slow stage:
-  ~54 minutes for 172,735 rows sharded 18 ways on a contended 24-core
-  box. `nopost` (fix #7 above) is essential -- without it, board-diagram
-  printing dominates I/O cost.
-- `match_controls.py` is pure in-memory bucket lookups, seconds even for
-  the full dataset -- never worth sharding.
-- If `twic_filtered.pgn` (642k games, >=2400 Elo / >40 ply filtered) ever
-  proves insufficient for match density, `pgn/twic.pgn` is 7-8x larger
-  (4,099,094 games, unfiltered) and available as a fallback source --
-  not needed this run (100% match rate achieved on `twic_filtered.pgn`
-  once the above bugs were fixed).
-
-## Suggested next steps
-
-1. Pick 2-3 concrete constant changes from Tier 1/2 above (start with
-   `KingFileDefects`/`KingStormingPawnDefects` -- lowest risk).
-2. Land them as a normal `eval.c` diff, run `precommit_check.sh`.
-3. `sd10` comparison across the three curated suites against
-   `head_reference/`.
-4. `match_play.py` gated head-to-head match against the pre-change DNA.
-5. Only keep the change if `LOWER95 >= 0.5`.
-6. Once a change lands, consider whether `CountKingSafetyDefects` (lazy
-   eval) needs corresponding rework -- deferred by design until a real
-   change is validated (see project goal section above).