{ "entries": [ { "aliases": [], "canonical_term": "execution_backend", "changes": [ "execution_backend" ], "docs_anchor": "execution-engine-versus-optimizer-engine", "engine_support": { "dag-ml": "partial", "dual": "partial", "legacy": "supported", "native": "partial" }, "id": "run.engine", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "execution", "path": "run.engine", "reads": [], "scope": "pipeline_execution", "status": "supported", "summary": "Selects the pipeline execution backend; native is the fail-closed Archive V2 Methods subset, while dual is a bounded native-first, no-fallback oracle only for explicit array regression with KFold(shuffle=False) and PLSRegression; neither selects the HPO algorithm.", "surface": "run_argument", "token": "engine", "ui": { "control": "select", "group": "execution", "label": "Execution backend", "order": 10 }, "value_schema": { "enum": [ null, "legacy", "dag-ml", "native", "dual" ], "type": [ "string", "null" ] } }, { "aliases": [], "canonical_term": "hyperparameter_tuning", "changes": [ "candidate_fit", "selection", "final_predictor" ], "docs_anchor": "model-local-hpo-finetune-params", "engine_support": { "dag-ml": "partial", "legacy": "supported" }, "id": "pipeline.step.finetune_params", "invalidates_calibration": "always", "lifecycle_stage": "search", "path": "pipeline[].finetune_params", "reads": [ "development" ], "scope": "model_local", "status": "supported", "summary": "Runs model-local hyperparameter optimization; DAG-ML supports deterministic model_params grids/ranges and a scoped host Optuna profile, with some legacy controls still unsupported. This is never weight continuation.", "surface": "pipeline_step", "token": "finetune_params", "ui": { "control": "object", "group": "tuning", "label": "Model-local HPO", "order": 20 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, { "aliases": [ { "canonical": "dag-ml", "kind": "value", "mode": "read_only", "name": "dagml" }, { "canonical": "dag-ml", "kind": "value", "mode": "read_only", "name": "native" }, { "canonical": "n4m", "kind": "value", "mode": "read_only", "name": "methods" }, { "canonical": "n4m", "kind": "value", "mode": "read_only", "name": "libn4m" } ], "canonical_term": "hpo_or_generation_driver", "changes": [ "optimizer_algorithm", "native_generator_selection" ], "docs_anchor": "execution-engine-versus-optimizer-engine", "engine_support": { "dag-ml": "partial", "n4m": "partial", "optuna": "supported" }, "id": "pipeline.step.finetune_params.engine", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "pipeline[].finetune_params.engine", "reads": [], "scope": "model_local", "status": "partial", "summary": "Selects the model-local HPO optimizer or deterministic DAG-ML generation driver; this never selects the run() execution backend.", "surface": "nested_key", "token": "engine", "ui": { "control": "select", "group": "tuning", "label": "HPO engine", "order": 30 }, "value_schema": { "enum": [ "optuna", "n4m", "dag-ml", "grid" ], "type": "string" } }, { "aliases": [ { "canonical": "sampler", "kind": "token", "mode": "read_only", "name": "sample" } ], "canonical_term": "sampler", "changes": [ "trial_sequence" ], "docs_anchor": "canonical-aliases", "engine_support": { "dag-ml": "partial", "n4m": "partial", "optuna": "supported" }, "id": "pipeline.step.finetune_params.sampler", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "pipeline[].finetune_params.sampler", "reads": [], "scope": "model_local", "status": "partial", "summary": "Selects the HPO sampling strategy. DAG-ML's host Optuna profile supports the legacy sample alias and Optuna samplers; deterministic lowering does not use a sampler. The n4m objective adapter maps grid to a native GRID enum when available.", "surface": "nested_key", "token": "sampler", "ui": { "control": "select", "group": "tuning", "label": "Sampler", "order": 40 }, "value_schema": { "type": "string" } }, { "aliases": [], "canonical_term": "model_parameter_search_space", "changes": [ "candidate_fit", "selection", "final_predictor" ], "docs_anchor": "model-local-hpo-finetune-params", "engine_support": { "dag-ml": "partial", "n4m": "supported", "optuna": "supported" }, "id": "pipeline.step.finetune_params.model_params", "invalidates_calibration": "always", "lifecycle_stage": "search", "path": "pipeline[].finetune_params.model_params", "reads": [ "development" ], "scope": "model_local", "status": "supported", "summary": "Defines the model-local parameter search space. Optuna and n4m accept the adaptive DSL; DAG-ML native lowering currently accepts plain JSON grids plus _range_/_log_range_ generator specs.", "surface": "nested_key", "token": "model_params", "ui": { "control": "object", "group": "tuning", "label": "Model parameter space", "order": 41 }, "value_schema": { "type": "object" } }, { "aliases": [], "canonical_term": "selection_metric", "changes": [ "trial_ranking", "selection", "final_predictor" ], "docs_anchor": "model-local-hpo-finetune-params", "engine_support": { "dag-ml": "partial", "n4m": "supported", "optuna": "supported" }, "id": "pipeline.step.finetune_params.metric", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "pipeline[].finetune_params.metric", "reads": [ "development" ], "scope": "model_local", "status": "supported", "summary": "Selects the model-local optimization metric. DAG-ML native lowering currently exposes only rmse, accuracy and balanced_accuracy on the public path.", "surface": "nested_key", "token": "metric", "ui": { "control": "text", "group": "tuning", "label": "Selection metric", "order": 42 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [], "canonical_term": "selection_objective", "changes": [ "trial_ranking", "selection", "final_predictor" ], "docs_anchor": "model-local-hpo-finetune-params", "engine_support": { "dag-ml": "partial", "n4m": "supported", "optuna": "supported" }, "id": "pipeline.step.finetune_params.direction", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "pipeline[].finetune_params.direction", "reads": [ "development" ], "scope": "model_local", "status": "supported", "summary": "Selects whether the metric is minimized or maximized. DAG-ML rejects directions that contradict the selected metric's native objective.", "surface": "nested_key", "token": "direction", "ui": { "control": "select", "group": "tuning", "label": "Selection objective", "order": 43 }, "value_schema": { "enum": [ "minimize", "maximize" ], "type": "string" } }, { "aliases": [], "canonical_term": "trial_budget", "changes": [ "trial_sequence", "selection", "final_predictor" ], "docs_anchor": "model-local-hpo-finetune-params", "engine_support": { "dag-ml": "partial", "n4m": "supported", "optuna": "supported" }, "id": "pipeline.step.finetune_params.n_trials", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "pipeline[].finetune_params.n_trials", "reads": [], "scope": "model_local", "status": "supported", "summary": "Sets the adaptive HPO trial budget for Optuna and n4m. DAG-ML honors it with the explicit host Optuna profile; deterministic generation uses the grid/range candidate set instead.", "surface": "nested_key", "token": "n_trials", "ui": { "control": "number", "group": "tuning", "label": "Trial budget", "order": 44 }, "value_schema": { "minimum": 1, "type": "integer" } }, { "aliases": [], "canonical_term": "fold_search_strategy", "changes": [ "candidate_fit", "trial_ranking", "selection", "final_predictor" ], "docs_anchor": "model-local-hpo-finetune-params", "engine_support": { "dag-ml": "partial", "n4m": "partial", "optuna": "supported" }, "id": "pipeline.step.finetune_params.approach", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "pipeline[].finetune_params.approach", "reads": [ "development" ], "scope": "model_local", "status": "partial", "summary": "Controls how folds are searched. DAG-ML deterministic lowering accepts grouped; the host Optuna profile accepts single, grouped, and individual scoped searches. Individual uses a training-only inner holdout for each outer fold and a fresh refit search.", "surface": "nested_key", "token": "approach", "ui": { "control": "select", "group": "tuning", "label": "Fold search strategy", "order": 45 }, "value_schema": { "enum": [ "single", "grouped", "individual" ], "type": "string" } }, { "aliases": [], "canonical_term": "early_stopping_strategy", "changes": [ "trial_sequence", "trial_ranking", "selection", "final_predictor" ], "docs_anchor": "model-local-hpo-finetune-params", "engine_support": { "dag-ml": "unsupported", "n4m": "partial", "optuna": "supported" }, "id": "pipeline.step.finetune_params.pruner", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "pipeline[].finetune_params.pruner", "reads": [ "development" ], "scope": "model_local", "status": "partial", "summary": "Selects adaptive HPO pruning/early-stopping. Optuna and n4m expose overlapping but not identical pruner vocabularies; deterministic DAG-ML native generation rejects it.", "surface": "nested_key", "token": "pruner", "ui": { "control": "select", "group": "tuning", "label": "Pruner", "order": 46 }, "value_schema": { "enum": [ "none", "median", "successive_halving", "hyperband", "asha", "racing" ], "type": "string" } }, { "aliases": [ { "canonical": "mean", "kind": "value", "mode": "read_only", "name": "avg" } ], "canonical_term": "trial_aggregation", "changes": [ "trial_ranking" ], "docs_anchor": "canonical-aliases", "engine_support": { "dag-ml": "partial", "n4m": "partial", "optuna": "partial" }, "id": "pipeline.step.finetune_params.eval_mode", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "pipeline[].finetune_params.eval_mode", "reads": [ "development" ], "scope": "model_local", "status": "partial", "summary": "Aggregates trial scores for ranking. DAG-ML deterministic lowering accepts mean; host Optuna accepts best or mean for grouped search. avg is a read-only alias for mean.", "surface": "nested_key", "token": "eval_mode", "ui": { "control": "select", "group": "tuning", "label": "Evaluation aggregation", "order": 50 }, "value_schema": { "enum": [ "best", "mean", "robust_best" ], "type": "string" } }, { "aliases": [], "canonical_term": "trial_training_parameters", "changes": [ "trial_fit" ], "docs_anchor": "three-training-parameter-scopes", "engine_support": { "dag-ml": "partial", "n4m": "supported", "optuna": "supported" }, "id": "pipeline.step.finetune_params.train_params", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "trial_fit", "path": "pipeline[].finetune_params.train_params", "reads": [ "development" ], "scope": "hpo_trials", "status": "supported", "summary": "Configures or samples fit controls inside HPO trials. DAG-ML's host Optuna profile applies supported estimator controls to each candidate; terminal CV/refit training uses step-level train/refit_params. Deterministic lowering rejects trial-fit sampling.", "surface": "nested_key", "token": "train_params", "ui": { "control": "object", "group": "tuning", "label": "Trial training parameters", "order": 60 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, { "aliases": [], "canonical_term": "training_parameters", "changes": [ "final_predictor" ], "docs_anchor": "three-training-parameter-scopes", "engine_support": { "dag-ml": "supported", "legacy": "supported" }, "id": "pipeline.step.train_params", "invalidates_calibration": "always", "lifecycle_stage": "fit", "path": "pipeline[].train_params", "reads": [ "development" ], "scope": "normal_and_terminal_fit", "status": "supported", "summary": "Configures ordinary model training and terminal refit unless refit_params overrides a value. DAG-ML applies recognized estimator overrides and framework training-loop parameters in each fit scope; unsupported controller policies fail explicitly.", "surface": "pipeline_step", "token": "train_params", "ui": { "control": "object", "group": "training", "label": "Training parameters", "order": 70 }, "value_schema": { "type": "object" } }, { "aliases": [], "canonical_term": "refit_training_parameters", "changes": [ "final_predictor" ], "docs_anchor": "three-training-parameter-scopes", "engine_support": { "dag-ml": "partial", "legacy": "supported" }, "id": "pipeline.step.refit_params", "invalidates_calibration": "always", "lifecycle_stage": "refit", "path": "pipeline[].refit_params", "reads": [ "development" ], "scope": "winner_refit", "status": "supported", "summary": "Overrides train_params only during winner refit. DAG-ML applies recognized model overrides and framework training-loop parameters; specialized warm starts and controller policies remain unsupported. The exact {'use_all_partitions': True} PLSRegression no-op is accepted.", "surface": "pipeline_step", "token": "refit_params", "ui": { "control": "object", "group": "training", "label": "Winner refit parameters", "order": 80 }, "value_schema": { "type": "object" } }, { "aliases": [], "canonical_term": "hyperparameter_tuning", "changes": [ "candidate_fit", "selection", "final_predictor" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported", "n4m": "partial", "optuna": "supported" }, "id": "run.tuning", "invalidates_calibration": "always", "lifecycle_stage": "search", "path": "run.tuning", "reads": [ "development" ], "scope": "full_dag", "status": "partial", "summary": "Fixed-topology full-DAG HPO argument. The public subset currently executes only engine='dag-ml' single-estimator array pipelines with explicit score_data; broader DAG shapes remain fail-closed.", "surface": "run_argument", "token": "tuning", "ui": { "control": "object", "group": "tuning", "label": "Full-DAG tuning", "order": 90 }, "value_schema": { "type": "object" } }, { "aliases": [], "canonical_term": "optimizer_engine", "changes": [ "optimizer_algorithm" ], "docs_anchor": "execution-engine-versus-optimizer-engine", "engine_support": { "n4m": "partial", "optuna": "supported" }, "id": "run.tuning.engine", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "run.tuning.engine", "reads": [], "scope": "full_dag", "status": "partial", "summary": "Optimizer selector inside full-DAG tuning; it is distinct from run.engine and currently executes only the single-estimator array subset.", "surface": "nested_key", "token": "engine", "ui": { "control": "select", "group": "tuning", "label": "Full-DAG HPO engine", "order": 100 }, "value_schema": { "enum": [ "optuna", "n4m" ], "type": "string" } }, { "aliases": [], "canonical_term": "explicit_objective_scoring_cohort", "changes": [ "objective_scores", "trial_ranking", "selected_predictor" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported", "n4m": "partial", "optuna": "supported" }, "id": "run.tuning.score_data", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "run.tuning.score_data", "reads": [ "development" ], "scope": "objective_scoring", "status": "partial", "summary": "Supplies the explicit scoring cohort used by run(tuning) optimizer-driving. It is mandatory in the public subset and may be mapping, tuple/list, or explicit dataset-backed selector from SpectroDataset, DatasetConfigs, config mapping, or config/path string.", "surface": "nested_key", "token": "score_data", "ui": { "control": "dataset", "group": "tuning", "label": "Tuning score data", "order": 101 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "oneOf": [ { "additionalProperties": true, "anyOf": [ { "required": [ "X", "y" ] }, { "required": [ "X_score", "y_score" ] }, { "required": [ "dataset", "selector" ] } ], "properties": { "X": {}, "X_score": {}, "conformal_calibration": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "additionalProperties": true, "allOf": [ { "oneOf": [ { "required": [ "X" ] }, { "required": [ "X_calibration" ] }, { "required": [ "features" ] } ] }, { "oneOf": [ { "required": [ "y_true" ] }, { "required": [ "y" ] }, { "required": [ "y_calibration" ] }, { "required": [ "target" ] }, { "required": [ "targets" ] } ] } ], "properties": { "X": {}, "X_calibration": {}, "calibration_groups": { "type": "array" }, "calibration_metadata": { "$ref": "#/$defs/json_native_metadata" }, "calibration_sample_ids": { "type": "array" }, "features": {}, "groups": { "type": "array" }, "metadata": { "$ref": "#/$defs/json_native_metadata" }, "physical_sample_ids": { "type": "array" }, "sample_ids": { "type": "array" }, "target": {}, "targets": {}, "y": {}, "y_calibration": {}, "y_true": {} }, "type": "object" }, "conformal_coverage": { "exclusiveMaximum": 1, "exclusiveMinimum": 0, "type": "number" }, "conformal_score_calibration": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "additionalProperties": true, "allOf": [ { "oneOf": [ { "required": [ "X" ] }, { "required": [ "X_calibration" ] }, { "required": [ "features" ] } ] }, { "oneOf": [ { "required": [ "y_true" ] }, { "required": [ "y" ] }, { "required": [ "y_calibration" ] }, { "required": [ "target" ] }, { "required": [ "targets" ] } ] } ], "properties": { "X": {}, "X_calibration": {}, "calibration_groups": { "type": "array" }, "calibration_metadata": { "$ref": "#/$defs/json_native_metadata" }, "calibration_sample_ids": { "type": "array" }, "features": {}, "groups": { "type": "array" }, "metadata": { "$ref": "#/$defs/json_native_metadata" }, "physical_sample_ids": { "type": "array" }, "sample_ids": { "type": "array" }, "target": {}, "targets": {}, "y": {}, "y_calibration": {}, "y_true": {} }, "type": "object" }, "coverage": { "exclusiveMaximum": 1, "exclusiveMinimum": 0, "type": "number" }, "dataset": {}, "group_column": { "minLength": 1, "type": "string" }, "groups": { "type": "array" }, "include_augmented": { "type": "boolean" }, "metadata": { "$ref": "#/$defs/json_native_metadata" }, "metadata_columns": { "oneOf": [ { "minLength": 1, "type": "string" }, { "items": { "minLength": 1, "type": "string" }, "type": "array", "uniqueItems": true } ] }, "metric": { "minLength": 1, "type": "string" }, "physical_sample_ids": { "type": "array" }, "prediction_sample_ids": { "type": "array" }, "sample_id_column": { "minLength": 1, "type": "string" }, "sample_ids": { "type": "array" }, "score_groups": { "type": "array" }, "score_metadata": { "$ref": "#/$defs/json_native_metadata" }, "score_metric": { "minLength": 1, "type": "string" }, "score_sample_ids": { "type": "array" }, "selector": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "y": {}, "y_score": {} }, "type": "object" }, { "maxItems": 5, "minItems": 2, "type": "array" } ] } }, { "aliases": [], "canonical_term": "terminal_winner_projection", "changes": [ "run_result_best", "terminal_prediction_entry" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "run.tuning.winner", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "refit", "path": "run.tuning.winner", "reads": [ "external_test_or_calibration" ], "scope": "terminal_projection", "status": "partial", "summary": "Projects the refit winner to an explicit terminal prediction entry. It is required before run(tuning).calibration and can use arrays or an explicit dataset-backed selector from SpectroDataset, DatasetConfigs, config mapping, or config/path string.", "surface": "nested_key", "token": "winner", "ui": { "control": "dataset", "group": "tuning", "label": "Tuning winner projection", "order": 102 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "additionalProperties": true, "anyOf": [ { "required": [ "X", "y_true" ] }, { "required": [ "dataset", "selector" ] } ], "properties": { "X": {}, "dataset": {}, "dataset_name": { "minLength": 1, "type": "string" }, "group_column": { "minLength": 1, "type": "string" }, "include_augmented": { "type": "boolean" }, "metadata": { "$ref": "#/$defs/json_native_metadata" }, "metadata_columns": { "oneOf": [ { "minLength": 1, "type": "string" }, { "items": { "minLength": 1, "type": "string" }, "type": "array", "uniqueItems": true } ] }, "metric": { "minLength": 1, "type": "string" }, "model_name": { "minLength": 1, "type": "string" }, "physical_sample_ids": { "type": "array" }, "prediction_sample_ids": { "type": "array" }, "sample_id_column": { "minLength": 1, "type": "string" }, "sample_ids": { "type": "array" }, "score": { "type": "number" }, "selector": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "task_type": { "minLength": 1, "type": "string" }, "winner_sample_ids": { "type": "array" }, "y_true": {} }, "type": "object" } }, { "aliases": [], "canonical_term": "post_tuning_conformal_calibration", "changes": [ "calibrator", "calibrated_result" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "run.tuning.calibration", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "run.tuning.calibration", "reads": [ "terminal_prediction_entry", "prediction_outputs" ], "scope": "calibrator", "status": "partial", "summary": "Runs conformal calibration immediately after the explicit winner projection. calibration_data is derived from winner and cannot be supplied here.", "surface": "nested_key", "token": "calibration", "ui": { "control": "object", "group": "calibration", "label": "Post-tuning calibration", "order": 103 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "properties": { "as_predict_result": { "type": "boolean" }, "calibration_data": false, "coverage": { "oneOf": [ { "exclusiveMaximum": 1, "exclusiveMinimum": 0, "type": "number" }, { "items": { "exclusiveMaximum": 1, "exclusiveMinimum": 0, "type": "number" }, "minItems": 1, "type": "array", "uniqueItems": true } ] }, "method": { "const": "split_absolute_residual" }, "prediction_sample_ids": { "type": "array" }, "unit": { "const": "physical_sample" }, "workspace_conformal_id": { "minLength": 1, "type": "string" }, "workspace_metadata": { "$ref": "#/$defs/json_native_mapping" }, "y_pred": {} }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "required": [ "y_pred", "prediction_sample_ids" ], "type": "object" } }, { "aliases": [ { "canonical": "workspace_tuning_id", "kind": "token", "mode": "read_only", "name": "tuning_id" } ], "canonical_term": "tuning_workspace_identifier", "changes": [ "workspace_tuning_results" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "run.tuning.workspace_tuning_id", "invalidates_calibration": "not_applicable", "lifecycle_stage": "storage", "path": "run.tuning.workspace_tuning_id", "reads": [ "workspace_tuning_results" ], "scope": "workspace_persistence", "status": "partial", "summary": "Names the workspace TuningResult record and is required with resume=True in the public run(tuning) subset.", "surface": "nested_key", "token": "workspace_tuning_id", "ui": { "control": "select", "group": "tuning", "label": "Tuning workspace ID", "order": 104 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [], "canonical_term": "tuning_workspace_metadata", "changes": [ "workspace_tuning_results" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "run.tuning.workspace_metadata", "invalidates_calibration": "not_applicable", "lifecycle_stage": "storage", "path": "run.tuning.workspace_metadata", "reads": [], "scope": "workspace_persistence", "status": "partial", "summary": "Persists caller-provided strict JSON-native metadata alongside the workspace TuningResult without changing optimizer behavior; keys must be canonical strings and numeric values must be finite.", "surface": "nested_key", "token": "workspace_metadata", "ui": { "control": "object", "group": "tuning", "label": "Tuning workspace metadata", "order": 105 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, { "aliases": [], "canonical_term": "completed_tuning_result_replay", "changes": [ "terminal_refit", "terminal_prediction_entry" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "run.tuning.resume", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "run.tuning.resume", "reads": [ "workspace_tuning_results" ], "scope": "workspace_replay", "status": "partial", "summary": "Reuses an already completed matching workspace TuningResult in the public subset. It is not interrupted optimizer checkpoint resume.", "surface": "nested_key", "token": "resume", "ui": { "control": "select", "group": "tuning", "label": "Resume completed tuning", "order": 106 }, "value_schema": { "type": "boolean" } }, { "aliases": [], "canonical_term": "conformal_workspace_identifier", "changes": [ "workspace_conformal_results" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "run.tuning.calibration.workspace_conformal_id", "invalidates_calibration": "not_applicable", "lifecycle_stage": "storage", "path": "run.tuning.calibration.workspace_conformal_id", "reads": [], "scope": "workspace_persistence", "status": "partial", "summary": "Names the conformal workspace record produced by run(tuning).calibration; the top-level run(workspace_path=...) is inherited unless overridden in the calibration payload.", "surface": "nested_key", "token": "workspace_conformal_id", "ui": { "control": "select", "group": "calibration", "label": "Conformal workspace ID", "order": 107 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [ { "canonical": "calibration", "kind": "token", "mode": "read_only", "name": "tuning.calibration" } ], "canonical_term": "top_level_post_tuning_conformal_calibration", "changes": [ "calibrator", "calibrated_result" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "run.calibration", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "run.calibration", "reads": [ "terminal_prediction_entry", "prediction_outputs" ], "scope": "calibrator", "status": "partial", "summary": "Top-level alias for run(tuning).calibration in the public DAG-ML tuning subset. It requires run(tuning=..., engine='dag-ml') and derives calibration_data from tuning.winner.", "surface": "run_argument", "token": "calibration", "ui": { "control": "object", "group": "calibration", "label": "Post-tuning calibration", "order": 108 }, "value_schema": { "type": "object" } }, { "aliases": [ { "canonical": "conformal_calibration", "kind": "token", "mode": "read_only", "name": "conformal_score_calibration" } ], "canonical_term": "temporary_development_conformal_calibration", "changes": [ "objective_scores", "trial_ranking" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported", "n4m": "partial", "optuna": "supported" }, "id": "run.tuning.score_data.conformal_calibration", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "run.tuning.score_data.conformal_calibration", "reads": [ "development_calibration", "objective_scoring" ], "scope": "objective_scoring", "status": "partial", "summary": "Provides an explicit development calibration cohort used only for conformal-aware objective scoring. It fits a temporary calibrator per candidate and never replaces the final run(calibration=...) result.", "surface": "nested_key", "token": "conformal_calibration", "ui": { "control": "dataset", "group": "tuning", "label": "Dev conformal scoring cohort", "order": 109 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "additionalProperties": true, "allOf": [ { "oneOf": [ { "required": [ "X" ] }, { "required": [ "X_calibration" ] }, { "required": [ "features" ] } ] }, { "oneOf": [ { "required": [ "y_true" ] }, { "required": [ "y" ] }, { "required": [ "y_calibration" ] }, { "required": [ "target" ] }, { "required": [ "targets" ] } ] } ], "properties": { "X": {}, "X_calibration": {}, "calibration_groups": { "type": "array" }, "calibration_metadata": { "$ref": "#/$defs/json_native_metadata" }, "calibration_sample_ids": { "type": "array" }, "features": {}, "groups": { "type": "array" }, "metadata": { "$ref": "#/$defs/json_native_metadata" }, "physical_sample_ids": { "type": "array" }, "sample_ids": { "type": "array" }, "target": {}, "targets": {}, "y": {}, "y_calibration": {}, "y_true": {} }, "type": "object" } }, { "aliases": [ { "canonical": "conformal_coverage", "kind": "token", "mode": "read_only", "name": "coverage" } ], "canonical_term": "temporary_development_conformal_coverage", "changes": [ "objective_scores", "trial_ranking" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported", "n4m": "partial", "optuna": "supported" }, "id": "run.tuning.score_data.conformal_coverage", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "run.tuning.score_data.conformal_coverage", "reads": [ "development_calibration", "objective_scoring" ], "scope": "objective_scoring", "status": "partial", "summary": "Selects the nominal coverage for the temporary conformal scorer inside score_data. It requires score_data.conformal_calibration and affects only trial ranking, not the final run(calibration=...) result.", "surface": "nested_key", "token": "conformal_coverage", "ui": { "control": "number", "group": "tuning", "label": "Dev conformal score coverage", "order": 110 }, "value_schema": { "exclusiveMaximum": 1, "exclusiveMinimum": 0, "type": "number" } }, { "aliases": [], "canonical_term": "continuation_training", "changes": [ "predictor_weights" ], "docs_anchor": "continuation-training-retrain-finetune", "engine_support": { "dag-ml": "unsupported", "legacy": "partial" }, "id": "retrain.mode.finetune", "invalidates_calibration": "always", "lifecycle_stage": "continuation", "path": "retrain.mode=finetune", "reads": [ "existing_predictor", "new_training_data" ], "scope": "existing_predictor", "status": "partial", "summary": "Requests continuation from existing weights, never HPO. The public option exists, but weight continuation is not yet proven uniformly across model families.", "surface": "enum_value", "token": "finetune", "ui": { "control": "select", "group": "retraining", "label": "Continue model training", "order": 111 }, "value_schema": { "const": "finetune" } }, { "aliases": [], "canonical_term": "calibration_cohort", "changes": [ "calibrator" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data", "reads": [ "calibration" ], "scope": "calibrator", "status": "partial", "summary": "Exchangeable calibration cohort. The public API supports replayed-array mappings and explicit dataset-backed selectors, including DatasetConfigs/path sources, with replayed predictions, an in-memory predictor, a saved predictor bundle, a RunResult.best-like prediction entry, or a stored workspace chain plus physical sample ids.", "surface": "calibrate_argument", "token": "calibration_data", "ui": { "control": "dataset", "group": "calibration", "label": "Calibration dataset", "order": 121 }, "value_schema": { "oneOf": [ { "type": "object" }, { "type": "array" }, { "type": "string" } ] } }, { "aliases": [ { "canonical": "dataset", "kind": "token", "mode": "read_only", "name": "spectro_dataset" } ], "canonical_term": "calibration_dataset_source", "changes": [ "calibration_targets", "calibration_identity_source" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data.dataset", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.dataset", "reads": [ "calibration" ], "scope": "calibration_cohort", "status": "partial", "summary": "Selects explicit dataset-backed calibration evidence from a SpectroDataset, DatasetConfigs object, dataset config mapping, or config/path string. Replay sources include calibration_data.y_pred, predictor, predictor_bundle, predictor_result and predictor_chain_id.", "surface": "nested_key", "token": "dataset", "ui": { "control": "dataset", "group": "calibration", "label": "Calibration SpectroDataset", "order": 122 }, "value_schema": { "oneOf": [ { "type": "object" }, { "type": "string" } ] } }, { "aliases": [], "canonical_term": "calibration_selector", "changes": [ "calibration_rows" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data.selector", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.selector", "reads": [ "calibration_metadata" ], "scope": "calibration_cohort", "status": "partial", "summary": "Filters the explicit dataset-backed calibration cohort with a strict JSON-native selector mapping. It is mandatory so nirs4all never guesses a calibration partition.", "surface": "nested_key", "token": "selector", "ui": { "control": "object", "group": "calibration", "label": "Calibration selector", "order": 123 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, { "aliases": [ { "canonical": "y_pred", "kind": "token", "mode": "read_only", "name": "y_pred_calibration" }, { "canonical": "y_pred", "kind": "token", "mode": "read_only", "name": "calibration_predictions" } ], "canonical_term": "replayed_calibration_predictions", "changes": [ "nonconformity_scores", "calibrator" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data.y_pred", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.y_pred", "reads": [ "calibration_predictions" ], "scope": "calibrator", "status": "partial", "summary": "Provides already replayed point predictions for the calibration cohort. It does not trigger model replay.", "surface": "nested_key", "token": "y_pred", "ui": { "control": "array", "group": "calibration", "label": "Calibration predictions", "order": 124 }, "value_schema": { "items": { "type": "number" }, "minItems": 1, "type": "array" } }, { "aliases": [ { "canonical": "predictor", "kind": "token", "mode": "read_only", "name": "model" }, { "canonical": "predictor", "kind": "token", "mode": "read_only", "name": "estimator" } ], "canonical_term": "in_memory_calibration_predictor", "changes": [ "calibration_predictions", "nonconformity_scores", "calibrator" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data.predictor", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.predictor", "reads": [ "calibration_features", "calibration_identities" ], "scope": "calibrator", "status": "partial", "summary": "Replays an in-memory sklearn-like predictor on the selected dataset-backed calibration cohort. sample_ids/groups/metadata are forwarded when predict() accepts them; y_pred, predictor, predictor_bundle, predictor_result and predictor_chain_id are mutually exclusive.", "surface": "nested_key", "token": "predictor", "ui": { "control": "object", "group": "calibration", "label": "Calibration predictor", "order": 125 }, "value_schema": { "type": "object" } }, { "aliases": [ { "canonical": "predictor_bundle", "kind": "token", "mode": "read_only", "name": "model_bundle" }, { "canonical": "predictor_bundle", "kind": "token", "mode": "read_only", "name": "predictor_path" }, { "canonical": "predictor_bundle", "kind": "token", "mode": "read_only", "name": "model_path" } ], "canonical_term": "saved_calibration_predictor", "changes": [ "calibration_predictions", "nonconformity_scores", "calibrator" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "partial" }, "id": "calibrate.calibration_data.predictor_bundle", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.predictor_bundle", "reads": [ "calibration_features", "calibration_identities", "saved_predictor" ], "scope": "calibrator", "status": "partial", "summary": "Replays a saved predictor path through nirs4all.predict() on the selected dataset-backed calibration cohort. y_pred, predictor, predictor_bundle, predictor_result and predictor_chain_id are mutually exclusive.", "surface": "nested_key", "token": "predictor_bundle", "ui": { "control": "path", "group": "calibration", "label": "Calibration predictor bundle", "order": 126 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [ { "canonical": "predictor_result", "kind": "token", "mode": "read_only", "name": "run_result" }, { "canonical": "predictor_result", "kind": "token", "mode": "read_only", "name": "prediction_entry" }, { "canonical": "predictor_result", "kind": "token", "mode": "read_only", "name": "prediction" } ], "canonical_term": "run_result_calibration_predictor", "changes": [ "calibration_predictions", "nonconformity_scores", "calibrator" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "partial" }, "id": "calibrate.calibration_data.predictor_result", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.predictor_result", "reads": [ "calibration_features", "calibration_identities", "prediction_entry" ], "scope": "calibrator", "status": "partial", "summary": "Replays a RunResult.best-like prediction entry through nirs4all.predict() on the selected dataset-backed calibration cohort. y_pred, predictor, predictor_bundle, predictor_result and predictor_chain_id are mutually exclusive.", "surface": "nested_key", "token": "predictor_result", "ui": { "control": "object", "group": "calibration", "label": "Calibration predictor result", "order": 127 }, "value_schema": { "type": "object" } }, { "aliases": [ { "canonical": "predictor_chain_id", "kind": "token", "mode": "read_only", "name": "workspace_chain_id" } ], "canonical_term": "workspace_chain_calibration_predictor", "changes": [ "calibration_predictions", "nonconformity_scores", "calibrator" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "unsupported", "legacy": "partial" }, "id": "calibrate.calibration_data.predictor_chain_id", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.predictor_chain_id", "reads": [ "calibration_features", "calibration_identities", "workspace_chain" ], "scope": "calibrator", "status": "partial", "summary": "Replays a stored workspace chain through nirs4all.predict(chain_id=...) on the selected dataset-backed calibration cohort. Requires calibration_data.workspace_path and is mutually exclusive with other calibration replay sources.", "surface": "nested_key", "token": "predictor_chain_id", "ui": { "control": "text", "group": "calibration", "label": "Calibration workspace chain", "order": 128 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [], "canonical_term": "calibration_predictor_workspace_path", "changes": [ "calibration_predictions" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "unsupported", "legacy": "partial" }, "id": "calibrate.calibration_data.workspace_path", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.workspace_path", "reads": [ "workspace" ], "scope": "calibrator", "status": "partial", "summary": "Provides the explicit workspace root used by predictor_result or predictor_chain_id replay during dataset-backed calibration. It is not the conformal result persistence workspace.", "surface": "nested_key", "token": "workspace_path", "ui": { "control": "path", "group": "calibration", "label": "Predictor workspace path", "order": 129 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [ { "canonical": "sample_ids", "kind": "token", "mode": "read_only", "name": "calibration_sample_ids" }, { "canonical": "sample_ids", "kind": "token", "mode": "read_only", "name": "prediction_sample_ids" }, { "canonical": "sample_ids", "kind": "token", "mode": "read_only", "name": "physical_sample_ids" } ], "canonical_term": "calibration_physical_sample_ids", "changes": [ "calibration_cohort_manifest", "coverage_claim_unit" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data.sample_ids", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.sample_ids", "reads": [ "calibration_identities" ], "scope": "calibration_identity", "status": "partial", "summary": "Supplies explicit row-aligned physical sample ids for calibration. calibration_sample_ids and physical_sample_ids are accepted raw aliases; missing or duplicated ids remain fail-closed.", "surface": "nested_key", "token": "sample_ids", "ui": { "control": "array", "group": "calibration", "label": "Calibration sample IDs", "order": 130 }, "value_schema": { "items": { "minLength": 1, "type": "string" }, "minItems": 1, "type": "array" } }, { "aliases": [ { "canonical": "groups", "kind": "token", "mode": "read_only", "name": "calibration_groups" } ], "canonical_term": "calibration_groups", "changes": [ "calibration_cohort_manifest", "coverage_claim_scope" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data.groups", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.groups", "reads": [ "calibration_metadata" ], "scope": "calibration_identity", "status": "partial", "summary": "Supplies optional row-aligned group labels for raw replayed-array calibration_data mappings. group_by='group' consumes these labels for grouped conformal calibration; aliases are canonicalized exactly once and ambiguous payloads fail closed.", "surface": "nested_key", "token": "groups", "ui": { "control": "array", "group": "calibration", "label": "Calibration groups", "order": 131 }, "value_schema": { "items": { "minLength": 1, "type": "string" }, "minItems": 1, "type": "array" } }, { "aliases": [ { "canonical": "metadata", "kind": "token", "mode": "read_only", "name": "calibration_metadata" } ], "canonical_term": "calibration_metadata", "changes": [ "calibration_cohort_manifest", "coverage_claim_scope" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data.metadata", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.metadata", "reads": [ "calibration_metadata" ], "scope": "calibration_identity", "status": "partial", "summary": "Supplies optional row-aligned metadata for raw replayed-array calibration_data mappings. Metadata keys used by group_by select grouped conformal partitions; keys and JSON-native values remain fail-closed before publication.", "surface": "nested_key", "token": "metadata", "ui": { "control": "object", "group": "calibration", "label": "Calibration metadata", "order": 132 }, "value_schema": { "$ref": "#/$defs/json_native_metadata" } }, { "aliases": [ { "canonical": "sample_id_column", "kind": "token", "mode": "read_only", "name": "physical_sample_id_column" } ], "canonical_term": "calibration_physical_sample_id_column", "changes": [ "calibration_cohort_manifest", "coverage_claim_unit" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data.sample_id_column", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.sample_id_column", "reads": [ "calibration_metadata" ], "scope": "calibration_identity", "status": "partial", "summary": "Extracts physical sample ids from the selected SpectroDataset metadata column when sample_ids are not supplied directly.", "surface": "nested_key", "token": "sample_id_column", "ui": { "control": "select", "group": "calibration", "label": "Sample ID column", "order": 133 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [], "canonical_term": "calibration_group_column", "changes": [ "calibration_cohort_manifest" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data.group_column", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.group_column", "reads": [ "calibration_metadata" ], "scope": "calibration_identity", "status": "partial", "summary": "Extracts optional row-aligned group labels into the calibration cohort manifest; it does not enable grouped conformal guarantees by itself.", "surface": "nested_key", "token": "group_column", "ui": { "control": "select", "group": "calibration", "label": "Group column", "order": 134 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [], "canonical_term": "calibration_metadata_columns", "changes": [ "calibration_cohort_manifest" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.calibration_data.metadata_columns", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.calibration_data.metadata_columns", "reads": [ "calibration_metadata" ], "scope": "calibration_identity", "status": "partial", "summary": "Copies selected SpectroDataset metadata fields into the calibration cohort manifest for audit and diagnostics.", "surface": "nested_key", "token": "metadata_columns", "ui": { "control": "array", "group": "calibration", "label": "Metadata columns", "order": 135 }, "value_schema": { "oneOf": [ { "minLength": 1, "type": "string" }, { "items": { "minLength": 1, "type": "string" }, "minItems": 1, "type": "array", "uniqueItems": true } ] } }, { "aliases": [], "canonical_term": "conformal_method", "changes": [ "calibrator" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.method", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.method", "reads": [ "calibration_predictions", "calibration_targets" ], "scope": "calibrator", "status": "partial", "summary": "Conformal score and interval construction method. V1 currently supports split absolute residuals on replayed regression predictions.", "surface": "calibrate_argument", "token": "method", "ui": { "control": "select", "group": "calibration", "label": "Conformal method", "order": 136 }, "value_schema": { "enum": [ "split_absolute_residual" ], "type": "string" } }, { "aliases": [], "canonical_term": "nominal_coverage", "changes": [ "quantile", "prediction_bounds" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.coverage", "invalidates_calibration": "extends_existing", "lifecycle_stage": "calibration", "path": "calibrate.coverage", "reads": [ "calibration_scores" ], "scope": "calibrator", "status": "partial", "summary": "Nominal coverage used to select the finite-sample conformal quantile; retained scores allow deterministic interval materialization for replayed-array calibration.", "surface": "calibrate_argument", "token": "coverage", "ui": { "control": "number", "group": "calibration", "label": "Nominal coverage", "order": 140 }, "value_schema": { "oneOf": [ { "exclusiveMaximum": 1.0, "exclusiveMinimum": 0.0, "type": "number" }, { "items": { "exclusiveMaximum": 1.0, "exclusiveMinimum": 0.0, "type": "number" }, "minItems": 1, "type": "array", "uniqueItems": true } ] } }, { "aliases": [], "canonical_term": "exchangeability_unit", "changes": [ "score_aggregation", "coverage_claim" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.unit", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.unit", "reads": [ "calibration_relations", "calibration_identities" ], "scope": "calibrator", "status": "partial", "summary": "V1 exchangeability unit. The current public surface requires explicit physical sample ids for replayed-array calibration.", "surface": "calibrate_argument", "token": "unit", "ui": { "control": "select", "group": "calibration", "label": "Exchangeability unit", "order": 150 }, "value_schema": { "const": "physical_sample" } }, { "aliases": [], "canonical_term": "ordered_search_space", "changes": [ "parameter_patches", "candidate_predictors", "selection" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "n4m": "partial", "optuna": "supported" }, "id": "run.tuning.space", "invalidates_calibration": "always", "lifecycle_stage": "search", "path": "run.tuning.space", "reads": [ "development" ], "scope": "full_dag", "status": "partial", "summary": "Fixed-topology parameter search space. The public subset supports value patches on a single estimator; structural axes and full pipeline recompilation remain planned.", "surface": "nested_key", "token": "space", "ui": { "control": "object", "group": "tuning", "label": "Full-DAG search space", "order": 160 }, "value_schema": { "type": "object" } }, { "aliases": [], "canonical_term": "explicit_warm_start_trial", "changes": [ "trial_sequence", "candidate_fit", "selection" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "n4m": "partial", "optuna": "supported" }, "id": "run.tuning.force_params", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "run.tuning.force_params", "reads": [ "development", "run.tuning.space" ], "scope": "full_dag", "status": "partial", "summary": "Enqueues a caller-provided parameter assignment as the first optimizer trial. Keys must be a subset of run.tuning.space; values use the public decoded syntax, not optimizer-internal categorical labels.", "surface": "nested_key", "token": "force_params", "ui": { "control": "object", "group": "tuning", "label": "Forced first HPO trial", "order": 165 }, "value_schema": { "type": "object" } }, { "aliases": [], "canonical_term": "optimizer_seed", "changes": [ "trial_sequence", "selected_predictor" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "n4m": "partial", "optuna": "partial" }, "id": "run.tuning.seed", "invalidates_calibration": "if_predictor_changes", "lifecycle_stage": "search", "path": "run.tuning.seed", "reads": [], "scope": "full_dag", "status": "partial", "summary": "Seeds the HPO trajectory. A different seed invalidates calibration only when it changes the selected predictor.", "surface": "nested_key", "token": "seed", "ui": { "control": "number", "group": "tuning", "label": "HPO seed", "order": 170 }, "value_schema": { "maximum": 9007199254740991, "minimum": 0, "type": "integer" } }, { "aliases": [], "canonical_term": "mondrian_grouping", "changes": [ "calibration_partitions", "coverage_claim_scope" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.group_by", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.group_by", "reads": [ "calibration_metadata", "prediction_groups", "prediction_metadata" ], "scope": "calibrator", "status": "partial", "summary": "Executes grouped split-conformal calibration for the replayed-array substrate when group_by keys are present in calibration evidence and prediction rows provide matching prediction_groups or prediction_metadata. Unseen or missing prediction groups fail closed; report slices alone never create a grouped conformal guarantee.", "surface": "calibrate_argument", "token": "group_by", "ui": { "control": "array", "group": "calibration", "label": "Calibration groups", "order": 180 }, "value_schema": { "oneOf": [ { "type": "null" }, { "minLength": 1, "type": "string" }, { "items": { "minLength": 1, "type": "string" }, "minItems": 1, "type": "array", "uniqueItems": true } ] } }, { "aliases": [], "canonical_term": "prediction_group_values", "changes": [ "selected_prediction_bounds", "coverage_claim_scope" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.prediction_groups", "invalidates_calibration": "not_applicable", "lifecycle_stage": "prediction", "path": "calibrate.prediction_groups", "reads": [ "prediction_rows" ], "scope": "prediction", "status": "partial", "summary": "Row-aligned group labels for prediction rows when calibrate.group_by includes the reserved key 'group'. The labels must resolve to groups seen in calibration; missing or unseen labels fail closed without global fallback.", "surface": "calibrate_argument", "token": "prediction_groups", "ui": { "control": "array", "group": "calibration", "label": "Prediction groups", "order": 181 }, "value_schema": { "type": "array" } }, { "aliases": [], "canonical_term": "prediction_group_metadata", "changes": [ "selected_prediction_bounds", "coverage_claim_scope" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.prediction_metadata", "invalidates_calibration": "not_applicable", "lifecycle_stage": "prediction", "path": "calibrate.prediction_metadata", "reads": [ "prediction_rows" ], "scope": "prediction", "status": "partial", "summary": "Row-aligned prediction metadata used to resolve non-'group' calibrate.group_by keys. Every key named by group_by must be present, non-null and JSON-compatible for every prediction row.", "surface": "calibrate_argument", "token": "prediction_metadata", "ui": { "control": "object", "group": "calibration", "label": "Prediction metadata", "order": 182 }, "value_schema": { "oneOf": [ { "type": "object" }, { "type": "array" } ] } }, { "aliases": [], "canonical_term": "multi_target_policy", "changes": [ "nonconformity_score", "prediction_region", "coverage_claim_scope" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.multi_target", "invalidates_calibration": "replaces_existing", "lifecycle_stage": "calibration", "path": "calibrate.multi_target", "reads": [ "calibration_targets" ], "scope": "calibrator", "status": "partial", "summary": "Selects per-target marginal intervals or the replayed-array joint_max simultaneous region. joint_max requires two-dimensional y_true/y_pred arrays and uses max(abs residual) across targets per physical sample; changing it creates a different calibrator.", "surface": "calibrate_argument", "token": "multi_target", "ui": { "control": "select", "group": "calibration", "label": "Multi-target policy", "order": 190 }, "value_schema": { "enum": [ "marginal", "joint_max" ], "type": "string" } }, { "aliases": [], "canonical_term": "calibrated_result_metadata", "changes": [ "calibrated_result_metadata", "calibrated_result_fingerprint" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "calibrate.result_metadata", "invalidates_calibration": "not_applicable", "lifecycle_stage": "calibration", "path": "calibrate.result_metadata", "reads": [ "caller_metadata" ], "scope": "calibrated_result", "status": "partial", "summary": "Publishes caller-provided strict JSON-native metadata on the CalibratedRunResult before generated guarantee metadata is merged. It changes the calibrated result fingerprint but not the fitted conformal artifact or coverage guarantee.", "surface": "calibrate_argument", "token": "result_metadata", "ui": { "control": "object", "group": "calibration", "label": "Calibrated result metadata", "order": 191 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, { "aliases": [], "canonical_term": "calibrated_prediction_metadata", "changes": [ "calibrated_result_metadata", "calibrated_result_fingerprint", "source_calibrated_result_fingerprint" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "predict_calibrated.result_metadata", "invalidates_calibration": "not_applicable", "lifecycle_stage": "prediction", "path": "predict_calibrated.result_metadata", "reads": [ "caller_metadata", "stored_calibrator" ], "scope": "calibrated_prediction", "status": "partial", "summary": "Publishes caller-provided strict JSON-native metadata when applying an existing calibrated result to new replayed predictions. Generated guarantee metadata and source fingerprints are added by nirs4all after validation.", "surface": "predict_calibrated_argument", "token": "result_metadata", "ui": { "control": "object", "group": "prediction", "label": "Predict calibrated metadata", "order": 192 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, { "aliases": [], "canonical_term": "requested_coverage", "changes": [ "selected_prediction_bounds" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "predict.coverage", "invalidates_calibration": "not_applicable", "lifecycle_stage": "prediction", "path": "predict.coverage", "reads": [ "stored_calibration_quantiles" ], "scope": "prediction", "status": "partial", "summary": "Selects already materialized calibrated coverages at prediction time for calibrated replayed-array results/stores/bundles and model .n4a bundles with a conformal sidecar; requesting a valid but non-materialized coverage fails closed with the available coverages, and an invalid conformal sidecar fails validation instead of falling back to uncalibrated prediction. Full native predictor replay remains planned.", "surface": "predict_argument", "token": "coverage", "ui": { "control": "number", "group": "prediction", "label": "Prediction coverage", "order": 200 }, "value_schema": { "oneOf": [ { "exclusiveMaximum": 1.0, "exclusiveMinimum": 0.0, "type": "number" }, { "items": { "exclusiveMaximum": 1.0, "exclusiveMinimum": 0.0, "type": "number" }, "minItems": 1, "type": "array", "uniqueItems": true } ] } }, { "aliases": [], "canonical_term": "prediction_entry_selection", "changes": [ "prediction_entries" ], "docs_anchor": "planned-conformal-calibration", "engine_support": { "dag-ml": "partial", "legacy": "supported" }, "id": "predict.all_predictions", "invalidates_calibration": "not_applicable", "lifecycle_stage": "prediction", "path": "predict.all_predictions", "reads": [ "model_bundle", "stored_calibration_quantiles" ], "scope": "prediction", "status": "partial", "summary": "Requests all model prediction entries instead of the selected prediction. For model .n4a bundles with a conformal sidecar and coverage=..., all_predictions=True remains fail-closed until each entry carries calibrated identity mapping.", "surface": "predict_argument", "token": "all_predictions", "ui": { "control": "select", "group": "prediction", "label": "All prediction entries", "order": 201 }, "value_schema": { "type": "boolean" } }, { "aliases": [], "canonical_term": "workspace_prediction_publisher", "changes": [ "workspace_prediction_rows", "prediction_arrays", "result_metadata", "workspace_prediction_id" ], "docs_anchor": "workspace-prediction-bridge", "engine_support": { "dag-ml": "partial", "legacy": "supported" }, "id": "predict.save_to_workspace", "invalidates_calibration": "not_applicable", "lifecycle_stage": "prediction_storage", "path": "predict.save_to_workspace", "reads": [ "prediction_result", "prediction_input_X" ], "scope": "prediction_publishing", "status": "partial", "summary": "Publishes the returned PredictResult through the workspace prediction store, including y_pred, sample ids, model metadata, executable X/spectra when available from data, and result metadata. It does not persist conformal artifacts or renew guarantees; use save_workspace_calibrated_result for calibrated results.", "surface": "predict_argument", "token": "save_to_workspace", "ui": { "control": "select", "group": "prediction", "label": "Save prediction to workspace", "order": 202 }, "value_schema": { "type": "boolean" } }, { "aliases": [], "canonical_term": "workspace_prediction_sample_metadata", "changes": [ "prediction_sample_metadata" ], "docs_anchor": "workspace-prediction-bridge", "engine_support": { "dag-ml": "partial", "legacy": "supported" }, "id": "predict.workspace_metadata", "invalidates_calibration": "not_applicable", "lifecycle_stage": "prediction_storage", "path": "predict.workspace_metadata", "reads": [ "caller_metadata" ], "scope": "prediction_publishing", "status": "partial", "summary": "Stores caller-provided strict JSON-native sample-level metadata alongside the workspace prediction sidecar when predict.save_to_workspace is true. It affects workspace diagnostics and Studio display only, not predictions or conformal guarantees.", "surface": "predict_argument", "token": "workspace_metadata", "ui": { "control": "object", "group": "prediction", "label": "Prediction sample metadata", "order": 203 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, { "aliases": [], "canonical_term": "workspace_prediction_result_metadata", "changes": [ "result_metadata", "robustness_evidence" ], "docs_anchor": "workspace-prediction-bridge", "engine_support": { "dag-ml": "partial", "legacy": "supported" }, "id": "predict.workspace_result_metadata", "invalidates_calibration": "not_applicable", "lifecycle_stage": "prediction_storage", "path": "predict.workspace_result_metadata", "reads": [ "caller_metadata", "prediction_result_metadata" ], "scope": "prediction_publishing", "status": "partial", "summary": "Merges caller-provided strict JSON-native result-level metadata into the workspace prediction row when predict.save_to_workspace is true. This is the supported place to publish robustness_evidence such as predictor_bundle for later spectral/OOD replay; provenance-only markers are not treated as executable arrays.", "surface": "predict_argument", "token": "workspace_result_metadata", "ui": { "control": "object", "group": "prediction", "label": "Prediction result metadata", "order": 204 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, { "aliases": [], "canonical_term": "robustness_reuse_policy", "changes": [ "robustness_report" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.mode", "invalidates_calibration": "mode_dependent", "lifecycle_stage": "robustness", "path": "robustness.mode", "reads": [ "external_test_or_production" ], "scope": "robustness_campaign", "status": "partial", "summary": "Selects the robustness reuse policy. The current public API supports clean_frozen audit-only reports on already replayed predictions; matched_recalibration and structural_refit remain planned.", "surface": "robustness_argument", "token": "mode", "ui": { "control": "select", "group": "robustness", "label": "Robustness mode", "order": 210 }, "value_schema": { "enum": [ "clean_frozen", "matched_recalibration", "structural_refit" ], "type": "string", "x-executable-values": [ "clean_frozen" ] } }, { "aliases": [], "canonical_term": "robustness_scenarios", "changes": [ "robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.scenarios", "invalidates_calibration": "mode_dependent", "lifecycle_stage": "robustness", "path": "robustness.scenarios", "reads": [ "external_test_or_production" ], "scope": "robustness_campaign", "status": "partial", "summary": "Defines report cells for robustness diagnostics. The current public API accepts RobustnessScenarioSpec helpers or equivalent TCV1 JSON-native mappings for observed severity 0.0, deterministic prediction_bias, seeded prediction_noise, explicit-X spectral_noise, deterministic explicit-X spectral_offset, deterministic explicit-X spectral_scale, deterministic explicit-X spectral_slope, and deterministic explicit-X spectral_shift scenarios. Spectral scenarios require robustness.X plus either robustness.predictor or robustness.predictor_bundle; broader spectral perturbations remain planned.", "surface": "robustness_argument", "token": "scenarios", "ui": { "control": "array", "group": "robustness", "label": "Robustness scenarios", "order": 220 }, "value_schema": { "items": { "additionalProperties": true, "allOf": [ { "else": { "not": { "required": [ "distribution" ] } }, "if": { "properties": { "kind": { "enum": [ "prediction_noise", "spectral_noise" ] } }, "required": [ "kind" ] }, "then": {} } ], "properties": { "distribution": { "enum": [ "normal", "uniform" ], "type": "string" }, "kind": { "enum": [ "observed", "prediction_bias", "prediction_noise", "spectral_noise", "spectral_offset", "spectral_scale", "spectral_slope", "spectral_shift" ], "type": "string" }, "severity": { "type": "number" } }, "required": [ "kind" ], "type": "object", "x-tcv1-json-native": true }, "minItems": 1, "type": "array" } }, { "aliases": [], "canonical_term": "robustness_scenario_kind", "changes": [ "robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.scenarios.kind", "invalidates_calibration": "mode_dependent", "lifecycle_stage": "robustness", "path": "robustness.scenarios[].kind", "reads": [ "external_test_or_production" ], "scope": "robustness_campaign", "status": "partial", "summary": "Selects the audit-only scenario cell. observed leaves predictions unchanged, prediction_bias shifts already materialized predictions, prediction_noise adds seeded post-prediction noise, spectral_noise replays an explicit frozen predictor or predictor_bundle on noisy X, spectral_offset replays it on X shifted by a deterministic additive offset, spectral_scale replays it on X scaled by 1.0 + severity, spectral_slope replays it on X plus a centered linear feature ramp, and spectral_shift replays it on a feature-axis shifted X interpolation.", "surface": "nested_key", "token": "kind", "ui": { "control": "select", "group": "robustness", "label": "Robustness scenario kind", "order": 221 }, "value_schema": { "enum": [ "observed", "prediction_bias", "prediction_noise", "spectral_noise", "spectral_offset", "spectral_scale", "spectral_slope", "spectral_shift" ], "type": "string" } }, { "aliases": [], "canonical_term": "robustness_scenario_severity", "changes": [ "robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.scenarios.severity", "invalidates_calibration": "mode_dependent", "lifecycle_stage": "robustness", "path": "robustness.scenarios[].severity", "reads": [], "scope": "robustness_campaign", "status": "partial", "summary": "Controls the scenario magnitude: observed requires 0.0, prediction_bias uses it as a prediction additive offset, prediction_noise/spectral_noise use it as the normal-noise standard deviation or the uniform-noise half-width, spectral_offset uses it as an additive offset applied to every X feature, spectral_scale uses 1.0 + severity as a multiplicative factor for every X feature, spectral_slope uses it as the total centered linear ramp amplitude across the feature axis, and spectral_shift uses it as a fractional feature-axis shift.", "surface": "nested_key", "token": "severity", "ui": { "control": "number", "group": "robustness", "label": "Robustness severity", "order": 222 }, "value_schema": { "type": "number" } }, { "aliases": [], "canonical_term": "robustness_scenario_distribution", "changes": [ "robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.scenarios.distribution", "invalidates_calibration": "mode_dependent", "lifecycle_stage": "robustness", "path": "robustness.scenarios[].distribution", "reads": [], "scope": "robustness_campaign", "status": "partial", "summary": "Selects the stochastic scenario distribution. The current executable subset accepts distribution only for prediction_noise and spectral_noise, with normal noise and centered uniform noise supported fail-closed.", "surface": "nested_key", "token": "distribution", "ui": { "control": "select", "group": "robustness", "label": "Robustness distribution", "order": 223 }, "value_schema": { "enum": [ "normal", "uniform" ], "type": "string" } }, { "aliases": [], "canonical_term": "robustness_input_matrix", "changes": [ "robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.X", "invalidates_calibration": "mode_dependent", "lifecycle_stage": "robustness", "path": "robustness.X", "reads": [ "external_test_or_production" ], "scope": "robustness_campaign", "status": "partial", "summary": "Supplies the explicit input matrix used by spectral scenarios. It is never fitted on; it is perturbed for evaluation-only frozen predictor or predictor_bundle replay.", "surface": "robustness_argument", "token": "X", "ui": { "control": "array", "group": "robustness", "label": "Robustness X", "order": 224 }, "value_schema": { "type": "array" } }, { "aliases": [], "canonical_term": "robustness_frozen_predictor", "changes": [ "robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.predictor", "invalidates_calibration": "mode_dependent", "lifecycle_stage": "robustness", "path": "robustness.predictor", "reads": [ "frozen_predictor" ], "scope": "robustness_campaign", "status": "partial", "summary": "Frozen predictor replay hook for spectral scenarios. The current Python surface accepts predictor.predict(X) or a callable and does not refit or recalibrate it. It is mutually exclusive with robustness.predictor_bundle.", "surface": "robustness_argument", "token": "predictor", "ui": { "control": "object", "group": "robustness", "label": "Robustness predictor", "order": 225 }, "value_schema": { "type": "object" } }, { "aliases": [ { "canonical": "predictor_bundle", "kind": "token", "mode": "read_only", "name": "model_bundle" }, { "canonical": "predictor_bundle", "kind": "token", "mode": "read_only", "name": "predictor_path" }, { "canonical": "predictor_bundle", "kind": "token", "mode": "read_only", "name": "model_path" } ], "canonical_term": "robustness_frozen_predictor_bundle", "changes": [ "robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.predictor_bundle", "invalidates_calibration": "mode_dependent", "lifecycle_stage": "robustness", "path": "robustness.predictor_bundle", "reads": [ "frozen_predictor_bundle" ], "scope": "robustness_campaign", "status": "partial", "summary": "Saved predictor path replay hook for spectral scenarios. The current Python surface routes perturbed X through public nirs4all.predict(model=predictor_bundle, data={\"X\": X, \"sample_ids\": ...}, all_predictions=False), then recenters existing intervals by the prediction delta without refit or recalibration. It is mutually exclusive with robustness.predictor.", "surface": "robustness_argument", "token": "predictor_bundle", "ui": { "control": "path", "group": "robustness", "label": "Robustness predictor bundle", "order": 226 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [], "canonical_term": "diagnostic_slices", "changes": [ "reported_metrics" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.slice_by", "invalidates_calibration": "not_applicable", "lifecycle_stage": "reporting", "path": "robustness.slice_by", "reads": [ "external_metadata" ], "scope": "robustness_report", "status": "partial", "summary": "Adds diagnostic report slices over supplied metadata. A sliced empirical coverage is not a conditional conformal guarantee unless a separate valid grouped calibrator supports it.", "surface": "robustness_argument", "token": "slice_by", "ui": { "control": "array", "group": "robustness", "label": "Diagnostic slices", "order": 230 }, "value_schema": { "items": { "minLength": 1, "type": "string" }, "type": "array", "uniqueItems": true } }, { "aliases": [], "canonical_term": "robustness_rng_seed", "changes": [ "robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.seed", "invalidates_calibration": "mode_dependent", "lifecycle_stage": "robustness", "path": "robustness.seed", "reads": [], "scope": "robustness_campaign", "status": "partial", "summary": "Controls reproducible audit-only stochastic post-prediction scenarios such as prediction_noise. It does not change a frozen predictor or calibrator; omitted seeds use effective_seed=0 for deterministic reports.", "surface": "robustness_argument", "token": "seed", "ui": { "control": "number", "group": "robustness", "label": "Robustness seed", "order": 240 }, "value_schema": { "maximum": 9007199254740991, "minimum": 0, "type": "integer" } }, { "aliases": [], "canonical_term": "robustness_workspace_path", "changes": [ "workspace_robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.workspace_path", "invalidates_calibration": "not_applicable", "lifecycle_stage": "reporting", "path": "robustness.workspace_path", "reads": [ "workspace_store" ], "scope": "robustness_report", "status": "partial", "summary": "Persists the verified RobustnessReport in the workspace robustness_results table after the audit is computed. This does not change metrics, fingerprints, predictions, or conformal guarantees.", "surface": "robustness_argument", "token": "workspace_path", "ui": { "control": "path", "group": "robustness", "label": "Robustness workspace", "order": 250 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [ { "canonical": "workspace_robustness_id", "kind": "token", "mode": "read_only", "name": "robustness_id" } ], "canonical_term": "robustness_workspace_id", "changes": [ "workspace_robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.workspace_robustness_id", "invalidates_calibration": "not_applicable", "lifecycle_stage": "reporting", "path": "robustness.workspace_robustness_id", "reads": [], "scope": "robustness_report", "status": "partial", "summary": "Stable id for the workspace robustness_results row produced by robustness(workspace_path=...). If omitted, the store generates an id that is not injected into the report fingerprint.", "surface": "robustness_argument", "token": "workspace_robustness_id", "ui": { "control": "text", "group": "robustness", "label": "Robustness report id", "order": 251 }, "value_schema": { "minLength": 1, "type": "string" } }, { "aliases": [], "canonical_term": "robustness_workspace_name", "changes": [ "workspace_robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.workspace_name", "invalidates_calibration": "not_applicable", "lifecycle_stage": "reporting", "path": "robustness.workspace_name", "reads": [], "scope": "robustness_report", "status": "partial", "summary": "Human-readable workspace label for a persisted robustness report. It affects only workspace inventory and does not enter the report fingerprint.", "surface": "robustness_argument", "token": "workspace_name", "ui": { "control": "text", "group": "robustness", "label": "Robustness report name", "order": 252 }, "value_schema": { "type": "string" } }, { "aliases": [], "canonical_term": "robustness_workspace_metadata", "changes": [ "workspace_robustness_results" ], "docs_anchor": "planned-robustness-campaigns", "engine_support": { "dag-ml": "partial", "legacy": "unsupported" }, "id": "robustness.workspace_metadata", "invalidates_calibration": "not_applicable", "lifecycle_stage": "reporting", "path": "robustness.workspace_metadata", "reads": [], "scope": "robustness_report", "status": "partial", "summary": "Strict JSON-native workspace metadata stored alongside the robustness report row for CI/Studio indexing. Keys must be canonical strings, numeric values must be finite, and it does not enter the report fingerprint.", "surface": "robustness_argument", "token": "workspace_metadata", "ui": { "control": "object", "group": "robustness", "label": "Robustness workspace metadata", "order": 253 }, "value_schema": { "$defs": { "canonical_string_key": { "minLength": 1, "pattern": "^(?!\\s)(?!.*\\s$)(?!.*\\u0000).+$", "type": "string" }, "json_native_mapping": { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_metadata": { "oneOf": [ { "$ref": "#/$defs/json_native_mapping" }, { "items": { "$ref": "#/$defs/json_native_mapping" }, "type": "array" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true }, "json_native_value": { "oneOf": [ { "type": "null" }, { "type": "string" }, { "type": "boolean" }, { "type": "number", "x-nirs4all-finite-number": true }, { "items": { "$ref": "#/$defs/json_native_value" }, "type": "array" }, { "additionalProperties": { "$ref": "#/$defs/json_native_value" }, "propertyNames": { "$ref": "#/$defs/canonical_string_key" }, "type": "object" } ], "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, "$ref": "#/$defs/json_native_mapping", "x-nirs4all-finite-numbers": true, "x-nirs4all-json-native": true } }, { "aliases": [], "canonical_term": "optimizer_storage_uri", "changes": [ "optimizer_state" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported", "n4m": "partial", "optuna": "supported" }, "id": "run.tuning.storage", "invalidates_calibration": "not_applicable", "lifecycle_stage": "storage", "path": "run.tuning.storage", "reads": [ "optimizer_state" ], "scope": "optimizer_persistence", "status": "partial", "summary": "Optimizer-state storage URI. Optuna uses study storage such as sqlite:///study.db; n4m supports file:///absolute/checkpoint-dir for local N4MOPT checkpoint manifests in the shared PipelineObjective subset. Bare paths are rejected.", "surface": "nested_key", "token": "storage", "ui": { "control": "text", "group": "tuning", "label": "Optimizer storage URI", "order": 254 }, "value_schema": { "minLength": 1, "pattern": "^[A-Za-z][A-Za-z0-9+.-]*://", "type": "string" } }, { "aliases": [], "canonical_term": "optimizer_study_name", "changes": [ "optimizer_state" ], "docs_anchor": "planned-full-dag-tuning", "engine_support": { "dag-ml": "partial", "legacy": "unsupported", "n4m": "partial", "optuna": "supported" }, "id": "run.tuning.study_name", "invalidates_calibration": "not_applicable", "lifecycle_stage": "storage", "path": "run.tuning.study_name", "reads": [ "optimizer_state" ], "scope": "optimizer_persistence", "status": "partial", "summary": "Optuna study name or n4m local checkpoint filename stem. Whitespace is trimmed and NUL characters are rejected; n4m additionally requires a filename-safe value.", "surface": "nested_key", "token": "study_name", "ui": { "control": "text", "group": "tuning", "label": "Optimizer study name", "order": 255 }, "value_schema": { "minLength": 1, "pattern": "^[^\\u0000]+$", "type": "string" } } ], "registry_version": "1.0.3", "schema_id": "https://nirs4all.org/schemas/keyword-effects/v1", "schema_version": 1, "scope": "lifecycle-v1" }