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Adds a `prediction` plot family to `giant analyze`, alongside the existing rollout-vs-reference comparison, and extends `giant predict` to make it possible: - `giant predict --coord global` gains schema v3 (`--truth/--no-truth`, default on): writes true_* physical columns and true secondary lists alongside the predictions, so the output is fully paired. - New `giant/analysis/prediction.py` builds one canonical true/pred frame (`paired_frame`) from either predict coord mode. - `catalog.py` gains 35 `pred_*` specs: marginals, 2D truth-vs-pred scatter (new `heatmap2d` kind), residuals/relative-residuals/calibration profiles, KS/bias/RMSE scorecards, n_sec + secondary-species confusion matrices, direction-alignment and constraint-violation checks, and a correlation delta. Two new Reduced kinds (`paired_hist`, `heatmap2d`) get renderers. Every spec degrades to kind="unavailable" with no --prediction given. - `condor.py`/`cli.py`: `--prediction`/`--prediction-label` on `analyze prep`/`submit`, threaded through RunMeta and every compute job. Full test suite (1162 tests), ruff, and ty all pass. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01PpxE9nij3ujg9XcuzvQ26q
87 lines
3.4 KiB
Python
87 lines
3.4 KiB
Python
X_DIM = 9
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# Original Phase-2 continuous conditioning: pre_pos(3), log(pre_E)(1),
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# pre_dir(3), layer_id(1). This is the slice ConditionEncoder's "embedding"
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# mode reads from cond_cont (see giant/model/network.py); n_sec and
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# log(e_sec) are not part of it — they are *outputs* predicted by Stage 1,
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# not conditioning inputs.
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COND_DIM_BASE = 8
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# Particle physical-property conditioning: log(mass)(1), charge(1). See
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# giant/particles.py.
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PARTICLE_PHYS_DIM = 2
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# Material physical-property conditioning: Z_eff(1), A_eff(1),
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# log(density)(1), log(X0)(1), log(lambda_int)(1). See giant/materials.py.
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MATERIAL_PHYS_DIM = 5
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# Conditioning continuous-feature width. cond_cont is unconditionally this
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# wide regardless of model_config["conditioning"]: "physical" mode computes
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# the trailing PARTICLE_PHYS_DIM + MATERIAL_PHYS_DIM columns for real,
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# "embedding" mode zero-fills them (and never reads them) — see
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# giant.data.transforms.build_features/build_cond_features. Bumped 8->15 for
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# physical-property conditioning, the same kind of breaking bump as Phase 1
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# (10) -> Phase 2 (8).
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COND_DIM = COND_DIM_BASE + PARTICLE_PHYS_DIM + MATERIAL_PHYS_DIM # 15
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# Maximum number of secondary slots. From data: max(n_sec)=14 in PbWO4 dataset;
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# K_MAX=15 covers it with one spare slot.
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K_MAX = 15
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# Per-slot continuous width: stick-breaking logit(1) + local dir(3).
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CONT_SLOT_DIM = 4
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# Per-slot secondary target dimension: CONT_SLOT_DIM (stick-breaking logit +
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# local dir) + PARTICLE_PHYS_DIM (log(mass), charge — the secondary's
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# predicted physical identity, regressed directly against real physics
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# targets rather than a learned/snapped embedding).
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SEC_SLOT_DIM = CONT_SLOT_DIM + PARTICLE_PHYS_DIM # 6
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# ConditionEncoder's physical-property sub-MLP output width (see
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# giant/model/network.py) and "embedding" mode's pdg_emb/mat_emb width.
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# Independent of SEC_SLOT_DIM — unlike Phase 2, Stage 2's per-slot physical
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# output width is fixed by PARTICLE_PHYS_DIM, not by this.
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EMB_DIM = 16
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# Flattened Stage-2 target dimension
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SEC_DIM = K_MAX * SEC_SLOT_DIM # 15 * 6 = 90
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# Stage-1 9D target names (unchanged from energy-conservation PoC)
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LOCAL_TARGET_NAMES = [
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"log_step_length",
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"edep_logit",
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"sec_logit",
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"post_dx",
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"post_dy",
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"post_dz",
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"travel_dx",
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"travel_dy",
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"travel_dz",
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]
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# Parquet schema metadata written by `giant predict` and checked by
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# giant.analysis loaders, so a file's format can be verified without
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# guessing from its column names.
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PREDICT_COORD_METADATA_KEY = "giant.predict.coord"
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PREDICT_SCHEMA_VERSION_KEY = "giant.predict.schema_version"
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PREDICT_SCHEMA_VERSION = "3"
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# Whether a --coord global predict parquet also carries true_* / true_sec_*
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# columns (v3+; "1"/"0"). Lets analysis code tell a paired prediction file
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# apart from a --no-truth one without sniffing for column presence.
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PREDICT_TRUTH_METADATA_KEY = "giant.predict.has_truth"
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# Coord-metadata value tagging a `giant rollout` steps parquet (world frame,
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# autoregressive shower output). Distinct from predict's "global"/"local".
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ROLLOUT_COORD_VALUE = "rollout"
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# Per-track termination reasons recorded by the rollout driver. "escaped"
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# energy is treated as leakage (not deposited); every other stop deposits the
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# track's remaining energy locally so total energy is conserved.
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TERM_ESCAPED = "escaped"
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TERM_ENERGY_CUTOFF = "energy_cutoff"
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TERM_MAX_STEPS = "max_steps"
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TERM_NATURAL_END = "natural_end"
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TERM_UNKNOWN_PDG = "unknown_pdg"
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TERM_MAX_TRACKS = "max_tracks"
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