feat(gfql/polars): engine followups — native multi-hop, to_fixed_point, undirected, min_hops>1, more predicates + NIE→native#1667
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… (3 files) The test-gfql-core per-file coverage audit fails on this stack because #1667 adds engine-specific (polars/cuDF-only) branches to files whose floors were generated pre-#1667 with razor-thin margins — those branches are unreachable in the pandas-only audit lane BY DESIGN (they are exercised in the polars CI lane and the dgx 4-engine conformance runs). Clean-room reproduction (fresh worktree at this tip + import-blocked polars/cudf to simulate the lane): 3312 tests pass, exactly 3 files below floor: row/frame_ops.py 60.22<65.0 (count_table polars branch + error paths), chain_let.py 70.07<70.21 and cypher/reentry/execution.py 85.33<85.66 (hair-thin engine-gate shifts). Two-part fix: - Direct unit tests for count_table's frame-op-level paths the GFQL call path can't reach (param validation intercepts first): bad-table + missing- source ValueErrors, null-in-mask counting, sibling-frame-templated and bare 0-count fallbacks. - Regenerate the 3 floors to the clean-room actuals (minus small local-vs-CI variance headroom): frame_ops 65.0->59.5, chain_let 70.21->69.5, reentry/execution 85.66->84.75. Intentional engine-specific growth, per the baseline-update-in-PR flow; all other floors untouched. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…lars' / 'polars-gpu' Two robustness gaps in engine selection: - engine='polars' without polars installed -> raw cryptic ImportError from deep in frame coercion / the lazy engine. - engine='polars-gpu' without the RAPIDS cudf_polars stack -> the missing-lib failure was caught by raise_on_fail=True and MISLABELED by _gpu_raise as "plan not GPU-executable, use engine='polars'" — pointing at the wrong fix. Add guards at the chain dispatch (compute/chain.py), pre-coercion, so the user always sees an actionable install message regardless of which query path runs: engine='polars'/'polars-gpu' both require polars (`pip install polars`); 'polars-gpu' additionally requires cudf_polars (checked via find_spec so it's consistent even for eager fast-path queries that never reach a GPU collect). lazy._engine_for keeps reporting genuine not-GPU-capable plans via _gpu_raise (unchanged; +clarifying comment). +1 regression test (polars-missing + cudf_polars-missing). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…lt | streaming)
cudf-polars has multiple executor modes; expose a switch mirroring GFQL_POLARS_CPU_STREAMING.
Default 'in-memory' (fast + stable for results that fit device memory) is unchanged; opt-in
'streaming' is the escape hatch for larger-than-device-memory results (the in-memory executor
would OOM — the F3 85M-row case). Invalid values fall back to in-memory; raise_on_fail stays
True (NO-CHEATING) either way. ('auto' size-aware switch is a planned enhancement.) +1 test
(mocks GPUEngine, no GPU needed). Full streaming-vs-in-memory crossover benchmark is dgx-gated.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…-bridge + 2 predicate bugs)
A parallel audit of the full polars NIE surface (predicates / row-pipeline / non-cypher modes)
found three real defects:
1. NO-CHEATING violation (the important one): a DAG `let({'d': call('get_degrees')})` binding
under engine='polars' SILENTLY ran the call on pandas and coerced the result back to polars
(call/executor.py ensure_engine_match), while the identical chain op `[call('get_degrees')]`
honestly raises NotImplementedError. Fix: under a polars engine, if a Plottable-method call's
result frames are not already polars, decline (NIE) instead of bridging; pass NotImplementedError
through execute_call + the DAG wrapper so it stays catchable (matching the chain surface).
pandas/cuDF engines unaffected. (158 call/DAG tests still green.)
2. Contains predicate ignored its regex flag (hardcoded literal=False) — a LITERAL pattern with
regex metacharacters (e.g. "a.b") was matched as a regex => wrong answer. Now honors regex=False
(literal substring; case-insensitive literal lowercases both sides). Differential parity verified.
3. Temporal comparison leak: _cmp_expr built `col > TemporalValue` (a non-None broken expr that
errors at df.filter / misorders) instead of declining. Now returns None for temporal-typed vals
-> honest NIE; numeric/string comparisons unaffected.
+2 regression tests. Full inventory + the tractable feature-win batch (CaseWhen, count_distinct,
str/match predicates, get_degrees-native, …) recorded in plans/gfql-engine-followups/plan.md.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
… 0 strategy)
The bug pattern (DAG silent-bridge, Contains-regex, temporal leak, multi-hop/undirected combine)
showed the prior gates only covered the CHAIN surface with hand-picked cases. This adds the core
conformance invariant as automation: for any query, on a non-pandas engine the result is EITHER
parity-equal to the pandas oracle OR an honest NotImplementedError — never silently different,
never a silent bridge, never a non-NIE crash.
Covers the cross-product the chain-only gates missed:
- predicates (GT/LT/Between/IsIn/Contains{regex,case}/Startswith/Endswith/IsNA) x {chain, let-DAG};
- traversals (single-hop parity; multi-hop / undirected-multi-edge must NIE);
- cross-SURFACE call() consistency (chain vs DAG must agree — this permanently guards the
silent-bridge class we just fixed);
- a seeded predicate fuzz asserting the invariant.
37 cases green (validates the 3 correctness fixes). Wired into bin/test-polars.sh + ci.yml's
coverage list. CPU lane here; the cudf/polars-gpu lane + carve-out sweep + coverage ledger are the
next Phase-0 prongs (plan.md).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…arve-out cases - _assert_invariant now checks EVERY available non-pandas engine (polars always; cudf + polars-gpu auto-detected) against the pandas oracle — so the dgx run covers the full cross-product. Fixed _sig to normalize cudf frames to pandas too (was polars-only — a harness completeness bug that only running on the GPU box exposed; CPU-local never exercised the cudf lane). - Added hot-path carve-out cases (node-only MATCH, unconstrained/filtered single-hop, filters on BOTH endpoints, empty results, self-loop, isolated-node seed, reverse/undirected filtered) — the fast paths bypass the general engine, so they're the highest wrong-answer risk. - 48 cases green on dgx across pandas / polars / cudf / polars-gpu. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…gregation Three feature-gap lowerings from the NIE audit (were honest NIE -> now native): - sqrt(x) -> args.sqrt(); sign(x) -> args.sign() (_lower_function). - count(DISTINCT x) -> col.drop_nulls().n_unique() (_agg_expr) — drop_nulls matches cypher/pandas nunique(dropna=True) semantics (polars n_unique() counts null as a value). Conformance-gated: the value-level matrix (cypher RETURN/aggregation cases) verifies parity across pandas/polars/cudf/polars-gpu on dgx; confirmed each runs NATIVELY (not NIE) with parity=True. 339 polars tests green on dgx. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
- CASE WHEN cond THEN a ELSE b END -> pl.when(cond.cast(Boolean)).then(a).otherwise(b) (cond cast to Boolean for Cypher 3-valued: a null WHEN takes ELSE, matching pandas). - Match -> regex anchored at START; Fullmatch -> anchored BOTH ends (case flag honored; declines on custom regex flags to avoid a flag-semantics gap). Conformance-gated: value-level matrix (63 cases) green across pandas/polars/cudf/polars-gpu on dgx; confirmed each runs NATIVELY (not NIE). 347 polars tests green on dgx. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
pandas boundary predicates accept a tuple of prefixes/suffixes (match if ANY); now OR-folds starts_with/ends_with over each element (case flag honored). Conformance-gated (67-case value-level matrix on dgx, all 4 engines); confirmed native, parity verified. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…e temporal compare
Three Phase-2d feature-gap wins, each native-or-honest and conformance-gated
(parity vs the pandas oracle OR honest NIE — no silent pandas bridge):
- get_degrees: pure group_by/count over edge endpoints, left-joined onto nodes
(Int32, isolated/src-only/dst-only -> 0, self-loop double-counted). Wired on
BOTH the chain surface (engine_polars.chain) and the let()/ref() DAG + JSON
surface (call/executor) — turns a prior NIE/silent-bridge into a real win.
- with_(extend=True): native polars with_columns, reusing the shared
lower_select_items lowering (DRY with select_polars); unlowerable item -> NIE.
- DateValue temporal comparison: chain p.gt(date(...)) on a NAIVE Datetime column
lowers to col.dt.date() <op> pl.lit(date) (the exact pandas-oracle truncation);
tz-aware DateTimeValue / TimeValue / tz columns stay honest NIE.
Conformance fixes the matrix caught (verify-not-trust):
- Cypher WHERE n.ts > date('...') lowered date() to an ISO STRING, so a Datetime
column vs that string crashed polars (InvalidOperationError, a non-NIE crash).
Now decline (honest NIE) only when the column is schema-typed temporal; a String
column holding ISO text still computes lexicographically.
- no_silent_call_bridge test now uses get_indegrees (get_degrees is native).
+20 conformance cases (get_degrees / temporal / with_extend across chain·DAG·
cypher, incl. explicit "runs natively" + "honest NIE" assertions). dgx: matrix
87 passed across pandas/polars/cudf/polars-gpu; full polars suite 530 passed.
ruff + mypy clean. (Also splits 3 pre-existing E702/F541/F841 lint nits in the
touched test files.)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…r predicate Phase-2d batch 2, each native-or-honest and conformance-gated (parity vs the pandas oracle OR honest NIE — no silent pandas bridge): - list literal `[e0, e1, ...]`: native `pl.concat_list` (order preserved, matching the pandas oracle) for a homogeneous-category list (all int / float / str / bool); mixed/nested/empty/null-dtype -> honest NIE. cudf is known to REORDER list elements (orthogonal cudf bug), so construction conformance is scoped pandas-vs-polars. - `x IN [literals]` as a row expression (distinct from the WHERE/IsIn predicate path): native 3-valued membership (null lhs masked to None to match cypher), Boolean output -> cudf-safe, full parity-or-NIE across all engines. - IsLeapYear predicate -> native `expr.dt.is_leap_year()` on a naive Datetime/Date column (Gregorian parity incl. 1900-non-leap / 2000-leap); tz-aware / non-temporal -> honest NIE. The 6 month/quarter/year boundary predicates KEEP declining — polars has no faithful boolean accessor (only rolled-datetime month_start/end), so re-deriving them would risk a subtle wrong answer (NO-CHEATING). +~15 conformance cases (list construction / IN / IsLeapYear across chain·cypher, incl. explicit native + honest-NIE assertions). Updated the row-expr unit test ([1,2,3] now lowers; [1,2.5] mixed still NIEs). dgx: matrix + full polars suite green across pandas/polars/cudf/polars-gpu; ruff + mypy clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…s on feat) get_degrees DAG dispatch (call/executor.py) and the _cmp_expr predicate unit-test import were ADDED on the followups branch after the base, so the lazy/engine/polars home-move's import rewrite didn't cover them. Repoint both to lazy.engine.polars. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ng + conformance ledger Phase-2d batch 3, each native-or-honest and conformance-gated (parity vs the pandas oracle OR honest NIE — no silent bridge); + the Phase-0 coverage-ledger automation: - get_indegrees / get_outdegrees: native single-direction group_by/count (shared helper with get_degrees), wired on chain + DAG/JSON surfaces. Matches the pandas empty-edges quirk (keep pre-existing col unchanged) exactly. Repointed the no-bridge test to `hypergraph` (architecturally pandas-only) since the degree calls are now all native. - size(x): native str.len_chars (String) / list.len (List); numeric/other -> honest NIE (pandas' row-count quirk is not replicated). substring(s,start[,len]): native str.slice for NON-NEGATIVE int-literal bounds over a String col; negative/non-literal/non-string -> NIE (negative start diverges: pandas python-slice vs polars offset/length). - test_conformance_ledger.py (Phase 0 prong #4): pure-introspection CI gate — derives the predicate universe from the live type_to_predicate registry + the exercised set from the matrix's _predicate_queries labels, and FAILS when a new predicate lands without either a conformance case or a reasoned KNOWN_UNCOVERED waiver. Dual-wired into bin/test-polars.sh AND ci.yml. CPU-only (no engine exec). dgx (--gpus all): 414 passed across pandas/polars/cudf/polars-gpu; ruff + mypy clean. All on the post-rename lazy/engine/polars/ paths. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…dtypes; rest honest-NIE) - toInteger: Int/Bool identity cast; Float truncate-toward-zero with explicit NaN/null->null mask (matches pandas astype(float).astype(int64)); String -> honest NIE (pandas raises on non-numeric, not null-on-failure — polars strict=False would fabricate nulls). - toBoolean: Boolean identity only; String/numeric token-set parsing not provable -> NIE. - toString: Bool (lowercase true/false) / Int / String; Float -> NIE (cross-engine repr diverges) and temporal/Categorical -> NIE. + conformance cases (native + honest-NIE) per dtype. toString(float) is covered by a dedicated pandas-vs-polars NIE test, not _assert_invariant (cudf formats floats differently than pandas — an orthogonal cudf repr divergence). dgx (--gpus all): 221 passed across all engines; ruff+mypy clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…-until-impl)
Multi-hop native chain prep. The repo fuzz did NOT exercise multi-hop
(_rand_edge was single-hop only), so the multi-hop NIE guard was never
tested. Amplify (TEST-ONLY; product still NIEs -> chains skip cleanly):
- _rand_edge: directed edges now emit hops in {2,3} / max_hops 1..N
(e_undirected stays single-hop; undirected-multi is a separate defect)
- _rand_graph: densify toward cycles/self-loops/parallel-edges, |E|>>|V|
- fuzz parametrize 60 -> 500; add edge-COUNT multiplicity guard
- adversarial multi-hop parity cases (cycle/self-loop/dup/empty/reverse/
both-filtered/hops>diameter/sandwiched) — NIE-skip until Stage 1
- conformance matrix: fwd-hops2/rev-hops2/maxhops3/midfilter/named/
sandwiched (parity-native after Stage 1) + tofixed/und-hops2 stay-NIE
- polars-only test_polars_chain_multihop_deferred_raises (to_fixed_point,
min_hops>1, undirected multi-hop)
Harness hardening: _frame_repr astype(object) before where(notna,None) so
cudf->pandas nullable-extension NA becomes real None (pd.NA in a signature
made res==base raise 'bool of NA ambiguous'). This surfaced a real cudf
min_hops divergence (seed node_hop None vs max_hops) — orthogonal cudf bug,
scoped out of the 4-engine matrix, polars NIE asserted directly.
dgx --gpus all: 348 passed, 352 skipped. ruff+mypy clean (test files).
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…hops, fwd+rev) THE MARQUEE FEATURE. Native multi-hop polars chain, parity-equal to pandas. Two prior shortcut attempts (flat forward∩backward intersection) shipped wrong answers and were reverted; this ports the PATH-AWARE pandas algorithm. Root cause (was NIE): _combine_edges single-hop-semijoins each edge step by prev(src)/next(dst) wavefront, dropping intermediate-hop edges (e=3 vs pandas e=10 at hops=2). The forward/reverse hops were already correct. Fix (port of pandas combine_steps has_multihop, compute/chain.py:201-209): for each fixed-length multi-hop edge step, RE-EXECUTE the forward hop over the backward-pruned edge subgraph seeded from the forward-pass entry wavefront. Backward prune = 'reaches a valid endpoint'; forward re-exec = 'reachable from seed'; their composition along the BFS frontier = exactly the valid-path edges (NOT a coarse set intersection). _combine_edges then appends all recomputed edge ids as-is (bypass the single-hop semijoin). Recomputed frames feed the EDGE combine only; node combine + name tagging keep the original backward-pruned steps (matches pandas kind=='edges' gate). Guard NARROWED (not lifted): _is_native_multihop allows only plain hops=N / max_hops, forward/reverse. Still honest-NIE for to_fixed_point, min_hops>1, output slicing, hop labels, *_query, include_zero_hop_seed, prune_to_endpoints, and undirected multi-hop (Stage 3). NO-CHEATING intact. Validation (dgx --gpus all): - amplified fuzz (500 seeds, cycles/self-loops/parallel-edges) + 8 adversarial cases (cycle/self-loop/dup-multiplicity/empty/reverse/ both-filtered/hops>diameter/sandwiched): ZERO mismatches. 497 passed, 203 skipped (deferred surfaces) — up from 348/352 (+149 now native). - conformance matrix multi-hop cases parity across all 4 engines. - 387-test polars regression suite: zero regressions. - benchmark: polars vs pandas 1.0-2.1x @10k/50k, 2.5-4.1x @100k/500k, 6.0-8.2x @500k/2M; result sets identical across pandas/polars/cudf. - ruff + mypy clean on chain.py. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ersal matrix
Extract traversal cases to _TRAVERSAL_CASES; add test_conformance_traversals_dag
running the SAME cases via let({'a': query}). Guards the silent-bridge bug class
(chain NIEs but DAG silently bridges to pandas) for multi-hop: native multi-hop
must reach the DAG/let surface too. dgx --gpus all: all 15 cases pass (parity
where native incl fwd/rev-hops2, maxhops3, midfilter, named, sandwiched; NIE
consistent for to_fixed_point / undirected-multi). ruff clean.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Extend the Phase-0 coverage ledger from predicates to cypher scalar functions. Universe = GFQL_SCALAR_FUNCTIONS (language_defs.py, importable like type_to_predicate); exercised set DERIVED by parsing function calls out of the cypher strings of a new importable _cypher_expression_queries() generator (refactored from the inline parametrize, mirrors _predicate_queries). Same 4 drift tests: registry entry must be exercised or waived; no bogus/stale/redundant waivers; reasons non-empty. 9 functions exercised (sqrt/sign/abs/coalesce/size/substring/tointeger/toboolean/ tostring), 11 waived honest one-liners (tofloat NIE; keys/labels/type/properties/range NIE; 5 internal __*__ helpers). Parser .lower()s for camelCase toInteger vs registry tointeger; intersects registry to drop aggregations/identifiers. dgx: ledger 14 passed (CPU introspection), cypher_expressions 19 passed (4-engine), ruff clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Extend the coverage ledger to the call() safelist. Universe = SAFELIST_V1 (call/validation.py, 59 entries); exercised DERIVED from a new importable _call_exercised_functions() (driven by the shared _CALL_CONSISTENCY_FNS constant the cross-surface consistency test runs on + the degree-trio dedicated tests). Same 4 drift tests. 5 exercised (get_degrees/in/out, hypergraph, limit), 54 waived honest one-liners (row-pipeline ops native-on-chain-but-NIE-via-call-executor; Plottable-method layouts/encoders/igraph/cugraph/umap pandas-cuDF-only -> no-bridge NIE; class covered by test_engine_polars_no_silent_call_bridge). The ledger immediately caught fa2_layout — a safelist entry the manual audit missed — proving the drift gate works (a new untracked safelist call now fails CI). dgx: ledger 21 passed (CPU introspection), ruff clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
… complete) Final coverage-ledger axis. Universe = ROW_PIPELINE_CALLS (row/pipeline.py, 15 ops); exercised DERIVED from importable _rowop_exercised() (labeled subjects: with_). Same 4 drift tests. 1 exercised, 14 waived honest one-liners (native frame ops not yet a labeled subject: skip/drop_cols/distinct/limit/rows; native-but-implicit-via-cypher: select/return_/where_rows/order_by/group_by/unwind; honest-NIE correlated ops: semi_apply_mark/anti_semi_apply/join_apply). Degree calls live on the call axis, not here. Coverage ledger now spans all 4 conformance axes (predicate / scalar-function / call- safelist / row-op), each deriving its universe from a live registry and failing CI when a new op lands without an assertion or an honest waiver. dgx: 28 passed, ruff clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…hains (Stage 3) Fixes the seed-18 defect: a chain of >1 undirected edge with intermediate node filters dropped a node vs pandas. Root cause = the polars backward pass: the generic undirected hop returns a ONE-SIDED wavefront (TO-side endpoints only), while pandas' fast backward branch (compute/chain.py:1090-1098) threads BOTH endpoints of the surviving edges as the step's node frame. The one-sided frame dropped an intermediate node only reachable as the frontier-side endpoint of a sibling undirected edge, which then dropped that node's incident edge in the combine. Fix (localized to the backward pass): for a single-hop undirected edge, override the reverse hop's node frame with BOTH endpoints of its surviving edges (re-joined to g._nodes for full columns). The edge set already matched pandas (the undirected hop joins both orientations); only the node frame was one-sided. _combine_edges / _combine_nodes / _apply_node_names already mirror the pandas undirected oracle. Guard NARROWED: native for plain single-hop undirected in multi-edge chains; STILL NIE for undirected mixed with fixed-length multi-hop, include_zero_hop_seed, or *_query. Undirected MULTI-hop (e_undirected(hops>=2), to_fixed_point, min_hops>1) still NIE upstream. Validation (dgx --gpus all): amplified 500-seed fuzz (now generates+runs undirected multi-edge incl. seed-18 class) ZERO mismatch; 7 new adversarial undirected cases (seed-18 midfilter, 3x undirected, alternating filters, mixed directed+undirected, named-across-boundary, self-loop) parity; deferred-raises updated; full polars suite 1036 passed / 93 skipped (was 203 skipped — undirected fuzz cases now native). ruff+mypy clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ulti-hop, Stage 4) Final fixed-length-family multi-hop surface. to_fixed_point (traverse until no new nodes) is now native for forward/reverse: _is_native_multihop no longer excludes it. The existing path-aware recompute handles it unchanged — re-run the forward to_fixed_point hop over the backward-pruned subgraph; the hop's own fixed-point detection guarantees termination (no infinite loop on cycles/self-loops). Same combine bypass as hops=N. STILL NIE: undirected to_fixed_point (direction guard), undirected multi-hop, min_hops>1. Validation (dgx --gpus all): the 500-seed amplified fuzz now also emits to_fixed_point edges over the cyclic fuzz graph (cycles/self-loops/parallel) — ZERO mismatch, proving termination + parity; 7 adversarial to_fixed_point cases (fwd/rev, self-loop, midfilter, isolated, sandwiched, named) parity; conformance fwd/rev-tofixed parity all 4 engines; deferred-raises updated (forward to_fixed_point removed, undirected to_fixed_point added). Full polars suite 1052 passed / 92 skipped. ruff+mypy clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ti-hop surface) Completes the multi-hop NIE surface. Undirected e_undirected(hops=N / max_hops) in single- and multi-edge chains is now native + parity. _is_native_multihop admits undirected (except to_fixed_point); the multi-edge guard no longer NIEs undirected+multihop. NO new combine/override code: fixed undirected multi-hop uses the generic backward hop (NOT the single-hop both-endpoint override, which stays is_simple_single_hop-gated — same as pandas, which only applies its fast both-endpoint backward branch to single-hop) plus the existing direction-agnostic path-aware recompute. A design pass proved P=Q: for fixed undirected return_as_wave_front, the pandas backward frame (matches ∪ endpoints − unreached seeds) collapses to matches == the polars visited-set BFS, so no seed-18-style node drop at hops>=2. This commit RAN the gate the proof needed. STILL NIE (principled): undirected to_fixed_point (pandas connected-components + 2-core seed retention hop.py:817-887 — no vectorized polars analogue) and min_hops>1 (cudf-divergent). Validation (dgx --gpus all): 500-seed fuzz now emits undirected multi-hop (zero skips) — ZERO mismatch; +4 adversarial undirected multi-hop cases (hops2/maxhops3/midfilter/mixed); conformance und-hops2/und-maxhops3 parity all 4 engines, und-tofixed stays NIE. Full polars suite 1150 passed / 0 skipped (every multi-hop shape now native). ruff+mypy clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…tays honest-NIE Temporal Between over a naive Datetime column with DateValue bounds now lowers natively. NO new proof obligation: the GFQL temporal Between evaluates in pandas as GE/LE (inclusive) or GT/LT (exclusive) on the same bound, and each endpoint is the exact date-truncated compare _cmp_expr already lowers with proven parity (col.dt.date() <op> pl.lit(date)). The patch composes the two proven endpoint compares, inheriting parity AND every NIE guard for free — a tz-aware DateTimeValue / TimeValue / raw-datetime / mixed / non-Datetime bound makes an endpoint decline, so the whole Between honest-NIEs (never a silent tz/instant mismatch). Temporal IsIn stays honest-NIE (design-verified keep): pandas IsIn does EXACT-instant membership (not date-truncated), and the candidate native is_in cross-precision cast is not provable from source — declining is correct, locked by a regression test. dgx --gpus all: 8 Between conformance (chain+dag, incl/excl boundary, single-day, empty, all 4 engines) parity + naive-native + tz-aware-NIE + temporal-IsIn-NIE = 11 passed; 315-test conformance+cypher regression clean. ruff+mypy clean (predicates.py). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…est-NIE forms; close ledger gap unwind is native ONLY for scalar-literal lists (unwind_polars cross-join); list-column / nested / scalar forms honestly NIE. It was untested in the matrix and waived in the row-op ledger. Add labeled conformance: 4 native literal-list cases (ints/strings/with-null/ singleton, chain+dag all engines), empty-list-drops-all-rows, scalar honest-NIE, nested-list honest-NIE, and chain-vs-cypher cross-surface consistency. Move unwind from ROW_OP_KNOWN_UNCOVERED waiver into _rowop_exercised() (the ledger redundant-waiver gate enforces the move). Omitted the list-column case (polars list-column ingestion unverified). dgx --gpus all: 8 unwind tests passed; ledger 28 passed (row-op axis rebalanced). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…NE/IsNull/NotNull Close ledger gaps (test-only; no product change). Row-op axis: add _ROW_OP_CASES labeled conformance subjects (rows/skip/limit/distinct/drop_cols frame ops + order_by/select/return_/ where_rows/group_by row_pipeline lowerings) on chain+dag, moving all 10 from ROW_OP_KNOWN_UNCOVERED -> _rowop_exercised() (axis now 12 exercised + 3 NIE-waived = ROW_PIPELINE_CALLS 15). Predicate axis: add scalar EQ/NE + str IsNull/NotNull to _predicate_queries(), removing those 4 waivers. VERIFICATION NOTE: local ruff clean + a from-source self-review confirmed param schemas (SAFELIST_V1), native dispatch (_try_native_row_op / predicate_to_expr), predicate crash-safety, and ledger set-math correct by construction; it caught + fixed a stale IsNull/NotNull waiver (CPU drift-test). NOT yet run with --gpus all (dgx-spark in a ~20min ssh outage). GPU-lane parity assertions use code paths already exercised+green elsewhere; will run matrix+ledger on dgx when back and fix-forward if any parity assertion needs re-waive. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…nal cudf bug) dgx-verified fix-forward on 73e051d. The 4-engine group_by row-op assertion failed on cudf: GFQLTypeError 'truth value of a Series is ambiguous' in the GFQL group_by execution path, triggered only when the row frame carries extra non-key/non-agg columns (the _graph fixture's f/name). polars+pandas agree; it's an orthogonal cudf-path bug (Series-truthiness; see cudf-cross-engine-findings.md #4). Move group_by out of the 4-engine _ROW_OP_CASES into a dedicated polars-vs-pandas assertion (new _assert_polars_parity_or_nie helper, reused for the min_hops work). group_by stays exercised on the ledger row-op axis. dgx --gpus all: matrix+ledger 247 passed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…uadrants Already-native (confirmed by design review + the 500-seed fuzz), but the specific undirected-single + directed-fixed-multihop quadrant wasn't a named regression. Add 3 to UND_CHAINS (und-then-fwdhops2, fwdhops2-then-und, und-mid-fwdhops2) to lock that the path-aware recompute doesn't collide with the undirected both-endpoint backward override on the shared middle wavefront. dgx --gpus all: all undirected-multiedge parity cases pass. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…/End/Quarter/Year) The 6 date-part boundary predicates now lower natively on a naive Datetime/Date column. Polars has no is_month_start/.../is_year_end BOOLEAN accessor (month_start()/month_end() ROLL to a Datetime), BUT the pandas oracle with freq=None is a pure CALENDAR-FIELD test: is_month_start = day==1; is_month_end = day==days_in_month; quarter/year add a month-set / month==N. polars dt.day()/dt.month()/dt.days_in_month() extract the SAME fields (proleptic Gregorian, leap-aware), so each compose is BIT-IDENTICAL to the oracle on non-null rows — a PROVABLE derivation, not a guess. NaT->null->fill_null(False) matches pandas False. Dtype- gated to naive Datetime/Date (tz shifts wall-clock fields -> honest NIE), exactly like IsLeapYear. This admits proven DERIVATIONS, not just single-accessor lowerings — provable parity is the NO-CHEATING bar. Replaces the *_honest_nie_polars boundary test with native-parity (4-engine — cuDF agrees, confirming the derivation is faithful) + a tz-aware NIE guard; ledger waivers updated to native. dgx --gpus all: boundary native+parity all engines; full polars suite 1201 passed. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…findings 1,2,4) Core compute fixes (pandas+cuDF row pipeline), each making cuDF match the pandas oracle without changing pandas/polars behavior. dgx --gpus all verified pandas==cudf==polars. #4 group_by 'truth value of a Series is ambiguous': the handler grouped over the WHOLE active table, carrying non-key/non-agg columns (object 'name', float 'f') into the cuDF GroupBy and tripping its Series.__bool__ path. Fix: _make_grouped now projects to key+value cols before grouping (selecting value cols before grouping cannot change group sizes/reductions -> identical on every engine). group_by promoted back into the 4-engine _ROW_OP_CASES matrix. #1 list-literal element reorder: cuDF groupby-collect (.agg(list)) gives no within-group row-order guarantee, permuting elements vs construction order (pandas groupby(sort=False) is stable). Fix: route cuDF to the order-deterministic column-wise host build (the same path list-typed elements already use); pandas melt/groupby path untouched. #2 toString(float) repr divergence: libcudf float->string != python/pandas repr (sci-notation thresholds). Fix: engine-gated host round-trip (arrow -> pandas float -> str = the exact pandas result), so cuDF is string-identical incl. 1e20 -> '1e+20'. Better than declining (which would crash, not defer). pandas/int/bool/string casts unchanged. (#3 min_hops seed hop-label stays scoped — a deeper cuDF NA-handling issue, not one-line-safe.) dgx: row-pipeline (pandas+cudf) + conformance + ledger 516 passed; group_by 4-engine + 2 new cuDF parity tests 22 passed. ruff+mypy clean (pipeline.py). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…arity Variable-length lower-bound traversals e(min_hops=N, max_hops=M) now run natively on the polars engine (CPU + polars-gpu/cudf_polars) instead of NIE. The eager polars hop ports pandas' min_hops algorithm vectorized: - NON-anti-joined revisit BFS + cumulative-closure termination (cycles bump reach depth to max_hops so the lower bound can be satisfied) - 3-case gate: max_reached<min -> empty; goal-labeled->=min -> layered backward-tree walk; cyclic-revisit-only -> unpruned ball - the exact min_hops NODE-output rule (the hard part, traced vs the pandas oracle through forward-stack/backward-pass/recompute/label-rebuild/ output-slice/seed-strip): wavefront = endpoints of the RETAINED edges, MINUS seeds never re-reached at >=min_hops, with FULL attributes only for hop-labeled nodes and NULL attributes for source-side endpoint-only nodes (so a downstream node-attribute filter rejects them, matching pandas' track_node_hops labeled path). Fixes fuzz seeds 24 (fwd n0 over-include), 404 (reverse n1 unreached-seed), 48 (reverse n5/n7 null-attr kind filter). NO-CHEATING preserved: undirected min_hops>1 still raises NotImplementedError. Validated: test_polars_chain_fuzz_parity 500/500, full polars hop+chain suites green, whole gfql/ dir 3880 passed, polars-gpu min_hops parity 8/8, mypy clean. Stale deferred/unsupported guards updated; direct-hop min_hops parity test added. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
n({col: ne(x)}) and cypher WHERE n.col <> x over a NULL cell used to KEEP the
null row on the pandas engine (NaN != x -> True), diverging from cuDF and the
polars engine (both drop it) and from pandas' own NOT n.col = x path. Per
openCypher/SQL 3VL, null <> x is null -> not a match -> row excluded, like
eq/gt/IN (which already drop nulls).
Fix: NE.__call__ masks out nulls (& s.notna()). A 4-engine audit pinned the
entire divergence to exactly 2 cells, pandas-only (filter_dict ne + cypher <>);
one predicate fix corrects both (single-entity <> routes through NE). cuDF/
polars/polars-gpu were already conformant; polars untouched (own lowering).
Behavior change for ne() on nullable columns under the default pandas engine.
Test flipped from a strict-xfail divergence marker to a 3VL parity assertion.
Validated on dgx: 4522 predicates+gfql tests pass, all 4 engines now 3VL.
(Broader openCypher null-semantics sweep + docs tracked as a follow-up issue.)
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
n({col: [.., None]}) over a NULL cell used to KEEP the null row on cuDF
(cuDF isin matched a null cell against a None list element), diverging from
pandas + polars which exclude it. Per openCypher/SQL 3VL, null IN [...] is
null -> not a member -> excluded. Fixed in filter_by_dict (& notna() on the
membership branch): no-op for pandas/polars, fix for cuDF.
Found via the #1664 conformance sweep; validated 4-engine on dgx (all give
[n0,n3] now). +test_membership_on_null_is_three_valued_logic. 287 predicate
+filter tests pass.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
The eager hop (hop_eager.py) and the lazy single-hop (hop.py) carried verbatim copies of the column-name + edge-id + node-dtype setup and the directed-pairs builder (hop.py even commented 'stay textually identical ... don't drift'). Extracted _hop_setup_columns() + _build_hop_pairs() — identical on pl.DataFrame (eager) and pl.LazyFrame (lazy), so both call sites share one implementation. Pure refactor, behavior-preserving. Validated on dgx: polars chain fuzz + hop suites 1133 pass, mypy clean. (The bigger pandas/polars node-output policy unification stays an issue — risky production-hop.py surgery + label-dtype contract from #1664/#1663 is a prerequisite.) Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
… — NIE->native Three previously-NIE cypher row surfaces now run natively on engine='polars', parity-validated vs the pandas oracle across pandas/cudf/polars/polars-gpu: - toFloat(x): int/uint/bool/float -> Float64 (NaN preserved; no fillna step, unlike toInteger — float64 has no null sentinel). Non-numeric String declines (NIE) because pandas astype(float) RAISES, not null-on-failure. - collect(x) / collect(DISTINCT x) aggregations complete the native group_by surface: drop nulls, preserve within-group first-occurrence order (collect keeps dups, DISTINCT dedups keep-first), all-null group -> []. drop_nulls() /unique(maintain_order=True), no .implode(). - where_rows / WHERE ... IN [list] membership -> is_in (null cell excluded, 3VL). Removed the stale tofloat conformance-ledger waiver; +tofloat matrix cases, tofloat-string-NIE test, collect parity test. Validated on dgx: 4242 gfql tests pass, conformance matrix+ledger+row-pipeline green, 4-engine parity, mypy clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ce without polars
Two CI failures on this layer, both diagnosed from the failing 3.8/3.14 jobs:
(A, real regression) execute_call() added an unconditional
'if isinstance(error, NotImplementedError): raise error' to propagate the polars
no-silent-bridge decline on the DAG surface — but it ALSO intercepted a
pandas/cudf NIE like fa2_layout's 'requires a GPU', so it stopped falling through
to the GFQLTypeError(E303) wrapper and test_fa2_layout_cpu_requires_gpu failed.
Gate the pass-through to engine in (POLARS, POLARS_GPU); pandas/cudf NIEs wrap to
E303 as before. Verified: fa2 test passes, full test_call_operations green (24/2).
(B, py3.14 env) test_engine_polars_conformance_matrix hardcodes the polars lane
without probing importability; on Python 3.14 (no cp314 polars wheel) polars is
absent from the lockfile, so every case reported a non-NIE ImportError as a
conformance failure (~60). Add module-level pytest.importorskip('polars') so the
matrix skips cleanly when polars is unavailable.
Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…me materialize A lone `RETURN count(*)` over a single node/edge pattern used to materialize the whole matched frame and run a constant-key group_by just to count rows (the ~770x-vs-Ladybug loss in the fair Cypher benchmark: count = 2543-8206ms while the df-path count is ~0.01ms). The Cypher lowering now emits a new `count_table` row op that reads the scanned table's height directly (or sums the boolean alias-mask column when the pattern filters) in a single reduction — no frame copy, no group_by. Guarded to the provably-equivalent shapes only: exactly one non-DISTINCT count(*), no group keys / post-agg exprs / row-level WHERE / UNWIND / paging / multi-relationship binding, and either a pure node scan (relationship_count==0) or a single relationship counted on its edge alias (== the cases _reject_unsound_relationship_multiplicity_aggregates permits). Every other shape falls through to the general aggregate path unchanged. The op replaces the rows()+with_+group_by prefix and keeps the identical trailing projection, so downstream/result is byte-identical. count_table is a native frame op (like rows/limit) routed via _POLARS_NATIVE_ROW_PIPELINE_CALLS -> op.execute -> frame_ops.count_table, so one implementation covers pandas / cuDF / polars / polars-gpu. Validation: CPU parity (pandas+polars node/edge count == oracle, fast path confirmed taken); full test_lowering suite 1394 passed; coverage ledger green (count_table accounted in _rowop_exercised + _ROW_OP_CASES + CALL_KNOWN_UNCOVERED); dgx 4-engine conformance (count_all_nodes/edges + count_table rowop, chain+dag) 55 passed on pandas/cuDF/polars/polars-gpu; ruff + mypy clean. The 5M/20M Ladybug head-to-head benchmark is the next step (needs the ladybug bench harness rebased onto this). Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…ble) The count_table row op (count(*) short-circuit) was only exercised via the polars-gated conformance matrix, so the polars-less test-gfql-core lane never executed the new frame_ops/pipeline code and the per-file coverage audit fell below baseline (CI: 'per-file coverage baseline failed', pre-existing since the count_table commit). Add always-on pandas tests: node-count + edge-count execution (incl. the dangling-edge endpoint-validation semantic), a structural lowering assert (pure count(*) -> count_table; grouped count does NOT short-circuit), the direct row-op path, and the unknown-table rejection. Validated: 11 pass locally (-k 'count_star or count_table'); ruff + mypy clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
…stale wording Review-wave findings (no behavior change except count_table engine fix): - _is_pure_count_star_shortcircuit docstring claimed post-aggregate exprs are rejected; they are in fact SUPPORTED by design (count lands in a temp column, trailing select applies the expr — verified count(*)+1 == 4, count(*)*2+1 == 7 on 3 nodes). Doc now matches code; pandas-lane tests already lock the shortcircuit + grouped-count-does-not cases. - Remove 25 dead 'if polars not in _NONPANDAS_ENGINES: skip' guards in the conformance matrix (module already importorskips polars; condition was always False). - lazy polars chain: _is_native_multihop docstring + chain NIE message listed to_fixed_point / min_hops>1 / undirected as deferred — all three are native for fwd/rev at this tip; wording now names only the genuinely deferred combos (undirected to_fixed_point / undirected min_hops>1, slicing, labels, *_query, zero-hop-seed, prune_to_endpoints). - count_table: table-is-None fallback returned a pandas frame even in a polars pipeline; now templates the 0-count row from the sibling table's engine (mirrors empty_frame discovery). - test_engine_polars_chain.py stale test-name ref; executor.py duplicate 'Engine as _Eng' import. Validated: count tests 11 pass; matrix 244 pass local-CPU (15 cudf-lane errs = local no-GPU env artifact, dgx re-validation follows); ruff+mypy clean. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
… (3 files) The test-gfql-core per-file coverage audit fails on this stack because #1667 adds engine-specific (polars/cuDF-only) branches to files whose floors were generated pre-#1667 with razor-thin margins — those branches are unreachable in the pandas-only audit lane BY DESIGN (they are exercised in the polars CI lane and the dgx 4-engine conformance runs). Clean-room reproduction (fresh worktree at this tip + import-blocked polars/cudf to simulate the lane): 3312 tests pass, exactly 3 files below floor: row/frame_ops.py 60.22<65.0 (count_table polars branch + error paths), chain_let.py 70.07<70.21 and cypher/reentry/execution.py 85.33<85.66 (hair-thin engine-gate shifts). Two-part fix: - Direct unit tests for count_table's frame-op-level paths the GFQL call path can't reach (param validation intercepts first): bad-table + missing- source ValueErrors, null-in-mask counting, sibling-frame-templated and bare 0-count fallbacks. - Regenerate the 3 floors to the clean-room actuals (minus small local-vs-CI variance headroom): frame_ops 65.0->59.5, chain_let 70.21->69.5, reentry/execution 85.66->84.75. Intentional engine-specific growth, per the baseline-update-in-PR flow; all other floors untouched. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
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Polars-engine followups stacked directly on the engine PR (#1660) — restacked 2026-07-02: an audit proved this PR has zero dependency on the index layer, so the experimental CSR-index PR (#1658) now stacks on top of this instead of underneath (each layer reviewable against what it actually uses). Extends the native polars GFQL engine to parity-or-NIE across many surfaces: native fixed multi-hop (hops=N), forward/reverse to_fixed_point, fixed undirected multi-hop, forward/reverse min_hops>1 in chain() (500/500 fuzz), more predicates (date-part boundaries, temporal Between), toInteger/toBoolean/toString/toFloat, collect/collect_distinct aggs, WHERE … IN membership, plus 3 cuDF cross-engine fixes (#1663). Test-amplification hardened the chain fuzz to compare null-aware node attributes + edge multiplicity. DRY: shared eager+lazy hop setup. All dgx-validated (parity-or-honest-NIE, NO-CHEATING). Also:
count(*)short-circuit — a newcount_tablerow op reads the scanned table's height (or alias-mask sum) in one reduction instead of materializing the full frame + constant-keygroup_by; the Cypher lowering short-circuits the pureMATCH (n)/()-[r]->() RETURN count(*)shape only (DISTINCT / WHERE / group keys / UNWIND / bindings all fall through unchanged; post-aggregate exprs likecount(*)+1compose via a temp column). Node-countMATCH (n) RETURN count(*): polars ~1 ms at 5M nodes (was ~2.5 s via materialize+group_by).openCypher-null conformance tracked in #1664; open NIE surfaces + per-engine docs in #1665.
Review notes: 38 conventional commits (merge-commit repo; adjacent fixups folded). Team-polish pass (
9c0ec314): conformance-matrix dead guards removed, stale NIE wording fixed (undirected/to_fixed_point/min_hops are native now), count_table engine-consistent empty fallback. Validation at tip: dgx 4-engine conformance matrix + ledger + lowering = 1359 pass; pandas-lane count(*) tests added (coverage-audit gate).