adaptive_sampling
- pydantic model dakota.spec.method.adaptive_sampling.AdaptiveSamplingSelection
Generated model for AdaptiveSamplingSelection
Show JSON schema
{ "title": "AdaptiveSamplingSelection", "description": "Generated model for AdaptiveSamplingSelection", "type": "object", "properties": { "adaptive_sampling": { "$ref": "#/$defs/AdaptiveSamplingConfig", "x-aliases": [ "nond_adaptive_sampling" ], "x-materialization": [ { "ir_key": "method.algorithm", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "ADAPTIVE_SAMPLING" } ] } }, "$defs": { "AdaptiveSamplingConfig": { "additionalProperties": false, "description": "(Experimental) Adaptively refine a Gaussian process surrogate", "properties": { "model_pointer": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Identifier for model block to be used by a method", "title": "Model Pointer", "x-block-pointer": "model", "x-materialization": [ { "ir_key": "method.model_pointer", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "rng": { "anyOf": [ { "$ref": "#/$defs/RngOptionsContext2Mt19937" }, { "$ref": "#/$defs/RngOptionsContext2Rnum2" } ], "description": "Selection of a random number generator", "title": "Rng", "x-model-default": "RngOptionsContext2Mt19937", "x-union-pattern": 1 }, "response_levels": { "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1ResponseLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Values at which to estimate desired statistics for each response", "x-materialization": [ { "ir_key": "method.nond.response_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "probability_levels": { "anyOf": [ { "$ref": "#/$defs/ProbabilityLevelsContext2ProbabilityLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Specify probability levels at which to estimate the corresponding response value", "x-materialization": [ { "ir_key": "method.nond.probability_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "gen_reliability_levels": { "anyOf": [ { "$ref": "#/$defs/GenReliabilityLevelsGenReliabilityLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Specify generalized relability levels at which to estimate the corresponding response value", "x-materialization": [ { "ir_key": "method.nond.gen_reliability_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "distribution": { "anyOf": [ { "$ref": "#/$defs/DistributionCumulComplContext1Cumulative" }, { "$ref": "#/$defs/DistributionCumulComplContext1Complementary" } ], "description": "Selection of cumulative or complementary cumulative functions", "title": "Distribution", "x-model-default": "DistributionCumulComplContext1Cumulative", "x-union-pattern": 1 }, "max_iterations": { "default": 9223372036854775807, "description": "Number of iterations allowed for optimizers and adaptive UQ methods", "minimum": 0, "title": "Max Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.max_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "import_build_points_file": { "anyOf": [ { "$ref": "#/$defs/ImportBuildImportBuildPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "File containing points you wish to use to build a surrogate", "x-aliases": [ "import_points_file" ] }, "id_method": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Name the method block; helpful when there are multiple", "title": "Id Method", "x-materialization": [ { "ir_key": "method.id", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "output": { "anyOf": [ { "$ref": "#/$defs/Debug" }, { "$ref": "#/$defs/Verbose" }, { "$ref": "#/$defs/Normal" }, { "$ref": "#/$defs/Quiet" }, { "$ref": "#/$defs/Silent" } ], "description": "Control how much method information is written to the screen and output file", "title": "Output", "x-model-default": "Normal", "x-union-pattern": 1 }, "final_solutions": { "default": 0, "description": "Number of designs returned as the best solutions", "minimum": 0, "title": "Final Solutions", "type": "integer", "x-materialization": [ { "ir_key": "method.final_solutions", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "initial_samples": { "default": 0, "description": "Initial number of samples for sampling-based methods", "title": "Initial Samples", "type": "integer", "x-aliases": [ "samples" ], "x-materialization": [ { "ir_key": "method.samples", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "seed": { "anyOf": [ { "exclusiveMinimum": 0, "type": "integer" }, { "type": "null" } ], "default": null, "description": "Seed of the random number generator", "title": "Seed", "x-materialization": [ { "ir_key": "method.random_seed", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "samples_on_emulator": { "default": 0, "description": "Number of samples at which to evaluate an emulator (surrogate)", "title": "Samples On Emulator", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.samples_on_emulator", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "fitness_metric": { "anyOf": [ { "$ref": "#/$defs/PredictedVariance" }, { "$ref": "#/$defs/FitnessMetricDistance" }, { "$ref": "#/$defs/Gradient" } ], "description": "(Experimental) Specify the ``fitness_metric`` used to select the next point", "title": "Fitness Metric", "x-model-default": "PredictedVariance", "x-union-pattern": 1 }, "batch_selection": { "anyOf": [ { "$ref": "#/$defs/Naive" }, { "$ref": "#/$defs/DistancePenalty" }, { "$ref": "#/$defs/Topology" }, { "$ref": "#/$defs/ConstantLiar" } ], "description": "(Experimental) How to select new points", "title": "Batch Selection", "x-model-default": "Naive", "x-union-pattern": 1 }, "refinement_samples": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Number of samples used to refine a probability estimate or sampling design.", "title": "Refinement Samples", "x-materialization": [ { "ir_key": "method.nond.refinement_samples", "ir_value_type": "IntVector", "storage_type": "DIRECT_VALUE" } ] }, "export_approx_points_file": { "anyOf": [ { "$ref": "#/$defs/AdaptiveSamplingExportApproxPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "Output file for surrogate model value evaluations", "x-aliases": [ "export_points_file" ] }, "misc_options": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "(Experimental) Hook for algorithm-specific adaptive sampling options", "title": "Misc Options", "x-materialization": [ { "ir_key": "method.coliny.misc_options", "ir_value_type": "StringArray", "storage_type": "DIRECT_VALUE" } ] } }, "title": "AdaptiveSamplingConfig", "type": "object" }, "AdaptiveSamplingExportApproxPointsFile": { "additionalProperties": false, "description": "Output file for surrogate model value evaluations", "properties": { "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MethodExportApproxFormatCustomAnnotated" }, { "$ref": "#/$defs/MethodExportApproxFormatAnnotated" }, { "$ref": "#/$defs/MethodExportApproxFormatFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "MethodExportApproxFormatAnnotated", "x-union-pattern": 1 }, "filename": { "description": "Output file for surrogate model value evaluations", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.export_approx_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "filename" ], "title": "AdaptiveSamplingExportApproxPointsFile", "type": "object" }, "ConstantLiar": { "additionalProperties": false, "description": "Use information from the existing surrogate model to predict what the surrogate upgrade will be with new points.", "properties": { "constant_liar": { "const": true, "default": true, "description": "Use information from the existing surrogate model to predict what the surrogate upgrade will be with new points.", "title": "Constant Liar", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "cl" } ] } }, "title": "ConstantLiar", "type": "object" }, "Debug": { "additionalProperties": false, "description": "Level 5 of 5 - maximum", "properties": { "debug": { "const": true, "default": true, "description": "Level 5 of 5 - maximum", "title": "Debug", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEBUG_OUTPUT" } ] } }, "title": "Debug", "type": "object" }, "DistancePenalty": { "additionalProperties": false, "description": "Add a penalty to spread out the points in the batch", "properties": { "distance_penalty": { "const": true, "default": true, "description": "Add a penalty to spread out the points in the batch", "title": "Distance Penalty", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "distance" } ] } }, "title": "DistancePenalty", "type": "object" }, "DistributionCumulComplContext1Complementary": { "additionalProperties": false, "description": "Computes statistics according to complementary cumulative functions", "properties": { "complementary": { "const": true, "default": true, "description": "Computes statistics according to complementary cumulative functions", "title": "Complementary", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COMPLEMENTARY" } ] } }, "title": "DistributionCumulComplContext1Complementary", "type": "object" }, "DistributionCumulComplContext1Cumulative": { "additionalProperties": false, "description": "Computes statistics according to cumulative functions", "properties": { "cumulative": { "const": true, "default": true, "description": "Computes statistics according to cumulative functions", "title": "Cumulative", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "CUMULATIVE" } ] } }, "title": "DistributionCumulComplContext1Cumulative", "type": "object" }, "FitnessMetricDistance": { "additionalProperties": false, "description": "Space filling metric", "properties": { "distance": { "const": true, "default": true, "description": "Space filling metric", "title": "Distance", "type": "boolean", "x-materialization": [ { "ir_key": "method.fitness_metric", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "distance" } ] } }, "title": "FitnessMetricDistance", "type": "object" }, "GenReliabilityLevelsGenReliabilityLevels": { "additionalProperties": false, "description": "Specify generalized relability levels at which to estimate the corresponding response value", "properties": { "values": { "description": "Specify generalized relability levels at which to estimate the corresponding response value", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_gen_reliability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``gen_reliability_levels`` correspond to which response", "title": "Num Gen Reliability Levels" } }, "required": [ "values" ], "title": "GenReliabilityLevelsGenReliabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "genreliabilitylevelsgenreliabilitylevels", "validationErrorMessage": "For genreliabilitylevelsgenreliabilitylevels, sum of num_gen_reliability_levels must equal length of values.", "validationFields": [ "num_gen_reliability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "Gradient": { "additionalProperties": false, "description": "Fill the range space of the surrogate", "properties": { "gradient": { "const": true, "default": true, "description": "Fill the range space of the surrogate", "title": "Gradient", "type": "boolean", "x-materialization": [ { "ir_key": "method.fitness_metric", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "gradient" } ] } }, "title": "Gradient", "type": "object" }, "ImportBuildImportBuildPointsFile": { "additionalProperties": false, "description": "File containing points you wish to use to build a surrogate", "properties": { "filename": { "description": "File containing points you wish to use to build a surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_build_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/ImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/ImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "ImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "ImportBuildImportBuildPointsFile", "type": "object" }, "ImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "ImportBuildPointsFileAnnotated", "type": "object" }, "ImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/ImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "ImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "ImportBuildPointsFileCustomAnnotated", "type": "object" }, "ImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "ImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "ImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "ImportBuildPointsFileFreeform", "type": "object" }, "MethodExportApproxFormatAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "MethodExportApproxFormatAnnotated", "type": "object" }, "MethodExportApproxFormatCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/MethodExportApproxFormatCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MethodExportApproxFormatCustomAnnotatedConfig" } }, "title": "MethodExportApproxFormatCustomAnnotated", "type": "object" }, "MethodExportApproxFormatCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "MethodExportApproxFormatCustomAnnotatedConfig", "type": "object" }, "MethodExportApproxFormatFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "MethodExportApproxFormatFreeform", "type": "object" }, "Naive": { "additionalProperties": false, "description": "Take the highest scoring candidates", "properties": { "naive": { "const": true, "default": true, "description": "Take the highest scoring candidates", "title": "Naive", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "naive" } ] } }, "title": "Naive", "type": "object" }, "Normal": { "additionalProperties": false, "description": "Level 3 of 5 - default", "properties": { "normal": { "const": true, "default": true, "description": "Level 3 of 5 - default", "title": "Normal", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NORMAL_OUTPUT" } ] } }, "title": "Normal", "type": "object" }, "PredictedVariance": { "additionalProperties": false, "description": "Pick points with highest variance", "properties": { "predicted_variance": { "const": true, "default": true, "description": "Pick points with highest variance", "title": "Predicted Variance", "type": "boolean", "x-materialization": [ { "ir_key": "method.fitness_metric", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "predicted_variance" } ] } }, "title": "PredictedVariance", "type": "object" }, "ProbabilityLevelsContext2ProbabilityLevels": { "additionalProperties": false, "description": "Specify probability levels at which to estimate the corresponding response value", "properties": { "values": { "description": "Specify probability levels at which to estimate the corresponding response value", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_probability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``probability_levels`` correspond to which response", "title": "Num Probability Levels" } }, "required": [ "values" ], "title": "ProbabilityLevelsContext2ProbabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "probabilitylevelscontext2probabilitylevels", "validationErrorMessage": "For probabilitylevelscontext2probabilitylevels, all elements of values must be in [0, 1].", "validationFields": [ "values" ], "validationLiterals": [], "validationRuleName": "check_probability_list" }, { "validationContext": "probabilitylevelscontext2probabilitylevels", "validationErrorMessage": "For probabilitylevelscontext2probabilitylevels, sum of num_probability_levels must equal length of values.", "validationFields": [ "num_probability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "Quiet": { "additionalProperties": false, "description": "Level 2 of 5 - less than normal", "properties": { "quiet": { "const": true, "default": true, "description": "Level 2 of 5 - less than normal", "title": "Quiet", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "QUIET_OUTPUT" } ] } }, "title": "Quiet", "type": "object" }, "ResponseLevelsComputeProbGenContext1Compute": { "additionalProperties": false, "description": "Selection of statistics to compute at each response level", "properties": { "statistic": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1Probabilities" }, { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1GenReliabilities" } ], "description": "Statistics to Compute", "title": "Statistic", "x-union-pattern": 4 }, "system": { "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1SystemSeries" }, { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1SystemParallel" }, { "type": "null" } ], "default": null, "description": "Compute system reliability (series or parallel)", "title": "System", "x-union-pattern": 2 } }, "required": [ "statistic" ], "title": "ResponseLevelsComputeProbGenContext1Compute", "type": "object" }, "ResponseLevelsComputeProbGenContext1GenReliabilities": { "additionalProperties": false, "description": "Computes generalized reliabilities associated with response levels", "properties": { "gen_reliabilities": { "const": true, "default": true, "description": "Computes generalized reliabilities associated with response levels", "title": "Gen Reliabilities", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "GEN_RELIABILITIES" } ] } }, "title": "ResponseLevelsComputeProbGenContext1GenReliabilities", "type": "object" }, "ResponseLevelsComputeProbGenContext1Probabilities": { "additionalProperties": false, "description": "Computes probabilities associated with response levels", "properties": { "probabilities": { "const": true, "default": true, "description": "Computes probabilities associated with response levels", "title": "Probabilities", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "PROBABILITIES" } ] } }, "title": "ResponseLevelsComputeProbGenContext1Probabilities", "type": "object" }, "ResponseLevelsComputeProbGenContext1ResponseLevels": { "additionalProperties": false, "description": "Values at which to estimate desired statistics for each response", "properties": { "values": { "description": "Values at which to estimate desired statistics for each response", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_response_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Number of values at which to estimate desired statistics for each response", "title": "Num Response Levels" }, "compute": { "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1Compute" }, { "type": "null" } ], "default": null, "description": "Selection of statistics to compute at each response level" } }, "required": [ "values" ], "title": "ResponseLevelsComputeProbGenContext1ResponseLevels", "type": "object", "x-model-validations": [ { "validationContext": "responselevelscomputeprobgencontext1responselevels", "validationErrorMessage": "For responselevelscomputeprobgencontext1responselevels, sum of num_response_levels must equal length of values.", "validationFields": [ "num_response_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ResponseLevelsComputeProbGenContext1SystemParallel": { "additionalProperties": false, "description": "Aggregate response statistics assuming a parallel system", "properties": { "parallel": { "const": true, "default": true, "description": "Aggregate response statistics assuming a parallel system", "title": "Parallel", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target_reduce", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SYSTEM_PARALLEL" } ] } }, "title": "ResponseLevelsComputeProbGenContext1SystemParallel", "type": "object" }, "ResponseLevelsComputeProbGenContext1SystemSeries": { "additionalProperties": false, "description": "Aggregate response statistics assuming a series system", "properties": { "series": { "const": true, "default": true, "description": "Aggregate response statistics assuming a series system", "title": "Series", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target_reduce", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SYSTEM_SERIES" } ] } }, "title": "ResponseLevelsComputeProbGenContext1SystemSeries", "type": "object" }, "RngOptionsContext2Mt19937": { "additionalProperties": false, "description": "Generates random numbers using the Mersenne twister", "properties": { "mt19937": { "const": true, "default": true, "description": "Generates random numbers using the Mersenne twister", "title": "Mt19937", "type": "boolean", "x-materialization": [ { "ir_key": "method.random_number_generator", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "mt19937" } ] } }, "title": "RngOptionsContext2Mt19937", "type": "object" }, "RngOptionsContext2Rnum2": { "additionalProperties": false, "description": "Generates pseudo-random numbers using the Pecos package", "properties": { "rnum2": { "const": true, "default": true, "description": "Generates pseudo-random numbers using the Pecos package", "title": "Rnum2", "type": "boolean", "x-materialization": [ { "ir_key": "method.random_number_generator", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "rnum2" } ] } }, "title": "RngOptionsContext2Rnum2", "type": "object" }, "Silent": { "additionalProperties": false, "description": "Level 1 of 5 - minimum", "properties": { "silent": { "const": true, "default": true, "description": "Level 1 of 5 - minimum", "title": "Silent", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SILENT_OUTPUT" } ] } }, "title": "Silent", "type": "object" }, "Topology": { "additionalProperties": false, "description": "In this selection strategy, we use information about the topology of the space from the Morse-Smale complex to identify next points to select.", "properties": { "topology": { "const": true, "default": true, "description": "In this selection strategy, we use information about the topology of the space from the Morse-Smale complex to identify next points to select.", "title": "Topology", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "topology" } ] } }, "title": "Topology", "type": "object" }, "Verbose": { "additionalProperties": false, "description": "Level 4 of 5 - more than normal", "properties": { "verbose": { "const": true, "default": true, "description": "Level 4 of 5 - more than normal", "title": "Verbose", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VERBOSE_OUTPUT" } ] } }, "title": "Verbose", "type": "object" } }, "additionalProperties": false, "required": [ "adaptive_sampling" ] }
- field adaptive_sampling: AdaptiveSamplingConfig [Required]
- classmethod get_registry() dict[str, type[MethodSelection]]
Get registry, performing deferred registration on first call
- classmethod get_union()
Generate Union from all registered selections
- pydantic model dakota.spec.method.adaptive_sampling.AdaptiveSamplingConfig
(Experimental) Adaptively refine a Gaussian process surrogate
Show JSON schema
{ "title": "AdaptiveSamplingConfig", "description": "(Experimental) Adaptively refine a Gaussian process surrogate", "type": "object", "properties": { "model_pointer": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Identifier for model block to be used by a method", "title": "Model Pointer", "x-block-pointer": "model", "x-materialization": [ { "ir_key": "method.model_pointer", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "rng": { "anyOf": [ { "$ref": "#/$defs/RngOptionsContext2Mt19937" }, { "$ref": "#/$defs/RngOptionsContext2Rnum2" } ], "description": "Selection of a random number generator", "title": "Rng", "x-model-default": "RngOptionsContext2Mt19937", "x-union-pattern": 1 }, "response_levels": { "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1ResponseLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Values at which to estimate desired statistics for each response", "x-materialization": [ { "ir_key": "method.nond.response_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "probability_levels": { "anyOf": [ { "$ref": "#/$defs/ProbabilityLevelsContext2ProbabilityLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Specify probability levels at which to estimate the corresponding response value", "x-materialization": [ { "ir_key": "method.nond.probability_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "gen_reliability_levels": { "anyOf": [ { "$ref": "#/$defs/GenReliabilityLevelsGenReliabilityLevels" }, { "type": "null" } ], "argument": "values", "default": null, "description": "Specify generalized relability levels at which to estimate the corresponding response value", "x-materialization": [ { "ir_key": "method.nond.gen_reliability_levels", "ir_value_type": "RealVectorArray", "storage_type": "RESPONSE_LEVELS_ARRAY" } ] }, "distribution": { "anyOf": [ { "$ref": "#/$defs/DistributionCumulComplContext1Cumulative" }, { "$ref": "#/$defs/DistributionCumulComplContext1Complementary" } ], "description": "Selection of cumulative or complementary cumulative functions", "title": "Distribution", "x-model-default": "DistributionCumulComplContext1Cumulative", "x-union-pattern": 1 }, "max_iterations": { "default": 9223372036854775807, "description": "Number of iterations allowed for optimizers and adaptive UQ methods", "minimum": 0, "title": "Max Iterations", "type": "integer", "x-materialization": [ { "ir_key": "method.max_iterations", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "import_build_points_file": { "anyOf": [ { "$ref": "#/$defs/ImportBuildImportBuildPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "File containing points you wish to use to build a surrogate", "x-aliases": [ "import_points_file" ] }, "id_method": { "anyOf": [ { "type": "string" }, { "type": "null" } ], "default": null, "description": "Name the method block; helpful when there are multiple", "title": "Id Method", "x-materialization": [ { "ir_key": "method.id", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "output": { "anyOf": [ { "$ref": "#/$defs/Debug" }, { "$ref": "#/$defs/Verbose" }, { "$ref": "#/$defs/Normal" }, { "$ref": "#/$defs/Quiet" }, { "$ref": "#/$defs/Silent" } ], "description": "Control how much method information is written to the screen and output file", "title": "Output", "x-model-default": "Normal", "x-union-pattern": 1 }, "final_solutions": { "default": 0, "description": "Number of designs returned as the best solutions", "minimum": 0, "title": "Final Solutions", "type": "integer", "x-materialization": [ { "ir_key": "method.final_solutions", "ir_value_type": "size_t", "storage_type": "DIRECT_VALUE" } ] }, "initial_samples": { "default": 0, "description": "Initial number of samples for sampling-based methods", "title": "Initial Samples", "type": "integer", "x-aliases": [ "samples" ], "x-materialization": [ { "ir_key": "method.samples", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "seed": { "anyOf": [ { "exclusiveMinimum": 0, "type": "integer" }, { "type": "null" } ], "default": null, "description": "Seed of the random number generator", "title": "Seed", "x-materialization": [ { "ir_key": "method.random_seed", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "samples_on_emulator": { "default": 0, "description": "Number of samples at which to evaluate an emulator (surrogate)", "title": "Samples On Emulator", "type": "integer", "x-materialization": [ { "ir_key": "method.nond.samples_on_emulator", "ir_value_type": "int", "storage_type": "DIRECT_VALUE" } ] }, "fitness_metric": { "anyOf": [ { "$ref": "#/$defs/PredictedVariance" }, { "$ref": "#/$defs/FitnessMetricDistance" }, { "$ref": "#/$defs/Gradient" } ], "description": "(Experimental) Specify the ``fitness_metric`` used to select the next point", "title": "Fitness Metric", "x-model-default": "PredictedVariance", "x-union-pattern": 1 }, "batch_selection": { "anyOf": [ { "$ref": "#/$defs/Naive" }, { "$ref": "#/$defs/DistancePenalty" }, { "$ref": "#/$defs/Topology" }, { "$ref": "#/$defs/ConstantLiar" } ], "description": "(Experimental) How to select new points", "title": "Batch Selection", "x-model-default": "Naive", "x-union-pattern": 1 }, "refinement_samples": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Number of samples used to refine a probability estimate or sampling design.", "title": "Refinement Samples", "x-materialization": [ { "ir_key": "method.nond.refinement_samples", "ir_value_type": "IntVector", "storage_type": "DIRECT_VALUE" } ] }, "export_approx_points_file": { "anyOf": [ { "$ref": "#/$defs/AdaptiveSamplingExportApproxPointsFile" }, { "type": "null" } ], "argument": "filename", "default": null, "description": "Output file for surrogate model value evaluations", "x-aliases": [ "export_points_file" ] }, "misc_options": { "anyOf": [ { "items": { "type": "string" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "(Experimental) Hook for algorithm-specific adaptive sampling options", "title": "Misc Options", "x-materialization": [ { "ir_key": "method.coliny.misc_options", "ir_value_type": "StringArray", "storage_type": "DIRECT_VALUE" } ] } }, "$defs": { "AdaptiveSamplingExportApproxPointsFile": { "additionalProperties": false, "description": "Output file for surrogate model value evaluations", "properties": { "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MethodExportApproxFormatCustomAnnotated" }, { "$ref": "#/$defs/MethodExportApproxFormatAnnotated" }, { "$ref": "#/$defs/MethodExportApproxFormatFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "MethodExportApproxFormatAnnotated", "x-union-pattern": 1 }, "filename": { "description": "Output file for surrogate model value evaluations", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.export_approx_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] } }, "required": [ "filename" ], "title": "AdaptiveSamplingExportApproxPointsFile", "type": "object" }, "ConstantLiar": { "additionalProperties": false, "description": "Use information from the existing surrogate model to predict what the surrogate upgrade will be with new points.", "properties": { "constant_liar": { "const": true, "default": true, "description": "Use information from the existing surrogate model to predict what the surrogate upgrade will be with new points.", "title": "Constant Liar", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "cl" } ] } }, "title": "ConstantLiar", "type": "object" }, "Debug": { "additionalProperties": false, "description": "Level 5 of 5 - maximum", "properties": { "debug": { "const": true, "default": true, "description": "Level 5 of 5 - maximum", "title": "Debug", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "DEBUG_OUTPUT" } ] } }, "title": "Debug", "type": "object" }, "DistancePenalty": { "additionalProperties": false, "description": "Add a penalty to spread out the points in the batch", "properties": { "distance_penalty": { "const": true, "default": true, "description": "Add a penalty to spread out the points in the batch", "title": "Distance Penalty", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "distance" } ] } }, "title": "DistancePenalty", "type": "object" }, "DistributionCumulComplContext1Complementary": { "additionalProperties": false, "description": "Computes statistics according to complementary cumulative functions", "properties": { "complementary": { "const": true, "default": true, "description": "Computes statistics according to complementary cumulative functions", "title": "Complementary", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "COMPLEMENTARY" } ] } }, "title": "DistributionCumulComplContext1Complementary", "type": "object" }, "DistributionCumulComplContext1Cumulative": { "additionalProperties": false, "description": "Computes statistics according to cumulative functions", "properties": { "cumulative": { "const": true, "default": true, "description": "Computes statistics according to cumulative functions", "title": "Cumulative", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.distribution", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "CUMULATIVE" } ] } }, "title": "DistributionCumulComplContext1Cumulative", "type": "object" }, "FitnessMetricDistance": { "additionalProperties": false, "description": "Space filling metric", "properties": { "distance": { "const": true, "default": true, "description": "Space filling metric", "title": "Distance", "type": "boolean", "x-materialization": [ { "ir_key": "method.fitness_metric", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "distance" } ] } }, "title": "FitnessMetricDistance", "type": "object" }, "GenReliabilityLevelsGenReliabilityLevels": { "additionalProperties": false, "description": "Specify generalized relability levels at which to estimate the corresponding response value", "properties": { "values": { "description": "Specify generalized relability levels at which to estimate the corresponding response value", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_gen_reliability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``gen_reliability_levels`` correspond to which response", "title": "Num Gen Reliability Levels" } }, "required": [ "values" ], "title": "GenReliabilityLevelsGenReliabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "genreliabilitylevelsgenreliabilitylevels", "validationErrorMessage": "For genreliabilitylevelsgenreliabilitylevels, sum of num_gen_reliability_levels must equal length of values.", "validationFields": [ "num_gen_reliability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "Gradient": { "additionalProperties": false, "description": "Fill the range space of the surrogate", "properties": { "gradient": { "const": true, "default": true, "description": "Fill the range space of the surrogate", "title": "Gradient", "type": "boolean", "x-materialization": [ { "ir_key": "method.fitness_metric", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "gradient" } ] } }, "title": "Gradient", "type": "object" }, "ImportBuildImportBuildPointsFile": { "additionalProperties": false, "description": "File containing points you wish to use to build a surrogate", "properties": { "filename": { "description": "File containing points you wish to use to build a surrogate", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.import_build_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] }, "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ImportBuildPointsFileCustomAnnotated" }, { "$ref": "#/$defs/ImportBuildPointsFileAnnotated" }, { "$ref": "#/$defs/ImportBuildPointsFileFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "ImportBuildPointsFileAnnotated", "x-union-pattern": 1 }, "active_only": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Import only active variables from tabular data file", "title": "Active Only", "x-materialization": [ { "ir_key": "method.import_build_active_only", "ir_value_type": "bool", "storage_type": "PRESENCE_TRUE" } ] } }, "required": [ "filename" ], "title": "ImportBuildImportBuildPointsFile", "type": "object" }, "ImportBuildPointsFileAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "ImportBuildPointsFileAnnotated", "type": "object" }, "ImportBuildPointsFileCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/ImportBuildPointsFileCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "ImportBuildPointsFileCustomAnnotatedConfig" } }, "title": "ImportBuildPointsFileCustomAnnotated", "type": "object" }, "ImportBuildPointsFileCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "ImportBuildPointsFileCustomAnnotatedConfig", "type": "object" }, "ImportBuildPointsFileFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.import_build_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "ImportBuildPointsFileFreeform", "type": "object" }, "MethodExportApproxFormatAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "MethodExportApproxFormatAnnotated", "type": "object" }, "MethodExportApproxFormatCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/MethodExportApproxFormatCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MethodExportApproxFormatCustomAnnotatedConfig" } }, "title": "MethodExportApproxFormatCustomAnnotated", "type": "object" }, "MethodExportApproxFormatCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "MethodExportApproxFormatCustomAnnotatedConfig", "type": "object" }, "MethodExportApproxFormatFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "MethodExportApproxFormatFreeform", "type": "object" }, "Naive": { "additionalProperties": false, "description": "Take the highest scoring candidates", "properties": { "naive": { "const": true, "default": true, "description": "Take the highest scoring candidates", "title": "Naive", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "naive" } ] } }, "title": "Naive", "type": "object" }, "Normal": { "additionalProperties": false, "description": "Level 3 of 5 - default", "properties": { "normal": { "const": true, "default": true, "description": "Level 3 of 5 - default", "title": "Normal", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "NORMAL_OUTPUT" } ] } }, "title": "Normal", "type": "object" }, "PredictedVariance": { "additionalProperties": false, "description": "Pick points with highest variance", "properties": { "predicted_variance": { "const": true, "default": true, "description": "Pick points with highest variance", "title": "Predicted Variance", "type": "boolean", "x-materialization": [ { "ir_key": "method.fitness_metric", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "predicted_variance" } ] } }, "title": "PredictedVariance", "type": "object" }, "ProbabilityLevelsContext2ProbabilityLevels": { "additionalProperties": false, "description": "Specify probability levels at which to estimate the corresponding response value", "properties": { "values": { "description": "Specify probability levels at which to estimate the corresponding response value", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_probability_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Specify which ``probability_levels`` correspond to which response", "title": "Num Probability Levels" } }, "required": [ "values" ], "title": "ProbabilityLevelsContext2ProbabilityLevels", "type": "object", "x-model-validations": [ { "validationContext": "probabilitylevelscontext2probabilitylevels", "validationErrorMessage": "For probabilitylevelscontext2probabilitylevels, all elements of values must be in [0, 1].", "validationFields": [ "values" ], "validationLiterals": [], "validationRuleName": "check_probability_list" }, { "validationContext": "probabilitylevelscontext2probabilitylevels", "validationErrorMessage": "For probabilitylevelscontext2probabilitylevels, sum of num_probability_levels must equal length of values.", "validationFields": [ "num_probability_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "Quiet": { "additionalProperties": false, "description": "Level 2 of 5 - less than normal", "properties": { "quiet": { "const": true, "default": true, "description": "Level 2 of 5 - less than normal", "title": "Quiet", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "QUIET_OUTPUT" } ] } }, "title": "Quiet", "type": "object" }, "ResponseLevelsComputeProbGenContext1Compute": { "additionalProperties": false, "description": "Selection of statistics to compute at each response level", "properties": { "statistic": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1Probabilities" }, { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1GenReliabilities" } ], "description": "Statistics to Compute", "title": "Statistic", "x-union-pattern": 4 }, "system": { "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1SystemSeries" }, { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1SystemParallel" }, { "type": "null" } ], "default": null, "description": "Compute system reliability (series or parallel)", "title": "System", "x-union-pattern": 2 } }, "required": [ "statistic" ], "title": "ResponseLevelsComputeProbGenContext1Compute", "type": "object" }, "ResponseLevelsComputeProbGenContext1GenReliabilities": { "additionalProperties": false, "description": "Computes generalized reliabilities associated with response levels", "properties": { "gen_reliabilities": { "const": true, "default": true, "description": "Computes generalized reliabilities associated with response levels", "title": "Gen Reliabilities", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "GEN_RELIABILITIES" } ] } }, "title": "ResponseLevelsComputeProbGenContext1GenReliabilities", "type": "object" }, "ResponseLevelsComputeProbGenContext1Probabilities": { "additionalProperties": false, "description": "Computes probabilities associated with response levels", "properties": { "probabilities": { "const": true, "default": true, "description": "Computes probabilities associated with response levels", "title": "Probabilities", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "PROBABILITIES" } ] } }, "title": "ResponseLevelsComputeProbGenContext1Probabilities", "type": "object" }, "ResponseLevelsComputeProbGenContext1ResponseLevels": { "additionalProperties": false, "description": "Values at which to estimate desired statistics for each response", "properties": { "values": { "description": "Values at which to estimate desired statistics for each response", "items": { "type": "number" }, "title": "Values", "type": "array" }, "num_response_levels": { "anyOf": [ { "items": { "type": "integer" }, "type": "array" }, { "type": "null" } ], "default": null, "description": "Number of values at which to estimate desired statistics for each response", "title": "Num Response Levels" }, "compute": { "anyOf": [ { "$ref": "#/$defs/ResponseLevelsComputeProbGenContext1Compute" }, { "type": "null" } ], "default": null, "description": "Selection of statistics to compute at each response level" } }, "required": [ "values" ], "title": "ResponseLevelsComputeProbGenContext1ResponseLevels", "type": "object", "x-model-validations": [ { "validationContext": "responselevelscomputeprobgencontext1responselevels", "validationErrorMessage": "For responselevelscomputeprobgencontext1responselevels, sum of num_response_levels must equal length of values.", "validationFields": [ "num_response_levels", "values" ], "validationLiterals": [], "validationRuleName": "check_sum_equals_length" } ] }, "ResponseLevelsComputeProbGenContext1SystemParallel": { "additionalProperties": false, "description": "Aggregate response statistics assuming a parallel system", "properties": { "parallel": { "const": true, "default": true, "description": "Aggregate response statistics assuming a parallel system", "title": "Parallel", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target_reduce", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SYSTEM_PARALLEL" } ] } }, "title": "ResponseLevelsComputeProbGenContext1SystemParallel", "type": "object" }, "ResponseLevelsComputeProbGenContext1SystemSeries": { "additionalProperties": false, "description": "Aggregate response statistics assuming a series system", "properties": { "series": { "const": true, "default": true, "description": "Aggregate response statistics assuming a series system", "title": "Series", "type": "boolean", "x-materialization": [ { "ir_key": "method.nond.response_level_target_reduce", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SYSTEM_SERIES" } ] } }, "title": "ResponseLevelsComputeProbGenContext1SystemSeries", "type": "object" }, "RngOptionsContext2Mt19937": { "additionalProperties": false, "description": "Generates random numbers using the Mersenne twister", "properties": { "mt19937": { "const": true, "default": true, "description": "Generates random numbers using the Mersenne twister", "title": "Mt19937", "type": "boolean", "x-materialization": [ { "ir_key": "method.random_number_generator", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "mt19937" } ] } }, "title": "RngOptionsContext2Mt19937", "type": "object" }, "RngOptionsContext2Rnum2": { "additionalProperties": false, "description": "Generates pseudo-random numbers using the Pecos package", "properties": { "rnum2": { "const": true, "default": true, "description": "Generates pseudo-random numbers using the Pecos package", "title": "Rnum2", "type": "boolean", "x-materialization": [ { "ir_key": "method.random_number_generator", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "rnum2" } ] } }, "title": "RngOptionsContext2Rnum2", "type": "object" }, "Silent": { "additionalProperties": false, "description": "Level 1 of 5 - minimum", "properties": { "silent": { "const": true, "default": true, "description": "Level 1 of 5 - minimum", "title": "Silent", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "SILENT_OUTPUT" } ] } }, "title": "Silent", "type": "object" }, "Topology": { "additionalProperties": false, "description": "In this selection strategy, we use information about the topology of the space from the Morse-Smale complex to identify next points to select.", "properties": { "topology": { "const": true, "default": true, "description": "In this selection strategy, we use information about the topology of the space from the Morse-Smale complex to identify next points to select.", "title": "Topology", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "topology" } ] } }, "title": "Topology", "type": "object" }, "Verbose": { "additionalProperties": false, "description": "Level 4 of 5 - more than normal", "properties": { "verbose": { "const": true, "default": true, "description": "Level 4 of 5 - more than normal", "title": "Verbose", "type": "boolean", "x-materialization": [ { "ir_key": "method.output", "ir_value_type": "short", "storage_type": "PRESENCE_ENUM", "stored_value": "VERBOSE_OUTPUT" } ] } }, "title": "Verbose", "type": "object" } }, "additionalProperties": false }
- Fields:
- field batch_selection: Naive | DistancePenalty | Topology | ConstantLiar [Optional]
(Experimental) How to select new points
- field distribution: DistributionCumulComplContext1Cumulative | DistributionCumulComplContext1Complementary [Optional]
Selection of cumulative or complementary cumulative functions
- field export_approx_points_file: AdaptiveSamplingExportApproxPointsFile | None = None
Output file for surrogate model value evaluations
- field final_solutions: int = 0
Number of designs returned as the best solutions
- Constraints:
ge = 0
- field fitness_metric: PredictedVariance | FitnessMetricDistance | Gradient [Optional]
(Experimental) Specify the
fitness_metricused to select the next point
- field gen_reliability_levels: GenReliabilityLevelsGenReliabilityLevels | None = None
Specify generalized relability levels at which to estimate the corresponding response value
- field id_method: str | None = None
Name the method block; helpful when there are multiple
- field import_build_points_file: ImportBuildImportBuildPointsFile | None = None
File containing points you wish to use to build a surrogate
- field initial_samples: int = 0
Initial number of samples for sampling-based methods
- field max_iterations: int = 9223372036854775807
Number of iterations allowed for optimizers and adaptive UQ methods
- Constraints:
ge = 0
- field misc_options: list[str] | None = None
(Experimental) Hook for algorithm-specific adaptive sampling options
- field model_pointer: str | None = None
Identifier for model block to be used by a method
- field output: Debug | Verbose | Normal | Quiet | Silent [Optional]
Control how much method information is written to the screen and output file
- field probability_levels: ProbabilityLevelsContext2ProbabilityLevels | None = None
Specify probability levels at which to estimate the corresponding response value
- field refinement_samples: list[int] | None = None
Number of samples used to refine a probability estimate or sampling design.
- field response_levels: ResponseLevelsComputeProbGenContext1ResponseLevels | None = None
Values at which to estimate desired statistics for each response
- field rng: RngOptionsContext2Mt19937 | RngOptionsContext2Rnum2 [Optional]
Selection of a random number generator
- field samples_on_emulator: int = 0
Number of samples at which to evaluate an emulator (surrogate)
- field seed: int | None = None
Seed of the random number generator
- Constraints:
gt = 0
Generated Pydantic models for method.adaptive_sampling
- pydantic model dakota.spec.method.adaptive_sampling.AdaptiveSamplingExportApproxPointsFile
Output file for surrogate model value evaluations
Show JSON schema
{ "title": "AdaptiveSamplingExportApproxPointsFile", "description": "Output file for surrogate model value evaluations", "type": "object", "properties": { "format": { "anchor": true, "anyOf": [ { "$ref": "#/$defs/MethodExportApproxFormatCustomAnnotated" }, { "$ref": "#/$defs/MethodExportApproxFormatAnnotated" }, { "$ref": "#/$defs/MethodExportApproxFormatFreeform" } ], "description": "Tabular Format", "title": "Format", "x-model-default": "MethodExportApproxFormatAnnotated", "x-union-pattern": 1 }, "filename": { "description": "Output file for surrogate model value evaluations", "title": "Filename", "type": "string", "x-materialization": [ { "ir_key": "method.export_approx_points_file", "ir_value_type": "String", "storage_type": "DIRECT_VALUE" } ] } }, "$defs": { "MethodExportApproxFormatAnnotated": { "additionalProperties": false, "description": "Selects annotated tabular file format", "properties": { "annotated": { "const": true, "default": true, "description": "Selects annotated tabular file format", "title": "Annotated", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_ANNOTATED" } ] } }, "title": "MethodExportApproxFormatAnnotated", "type": "object" }, "MethodExportApproxFormatCustomAnnotated": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "custom_annotated": { "$ref": "#/$defs/MethodExportApproxFormatCustomAnnotatedConfig", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ], "x-model-default": "MethodExportApproxFormatCustomAnnotatedConfig" } }, "title": "MethodExportApproxFormatCustomAnnotated", "type": "object" }, "MethodExportApproxFormatCustomAnnotatedConfig": { "additionalProperties": false, "description": "Selects custom-annotated tabular file format", "properties": { "header": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable header row in custom-annotated tabular file", "title": "Header", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_HEADER" } ] }, "eval_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable evaluation ID column in custom-annotated tabular file", "title": "Eval Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_EVAL_ID" } ] }, "interface_id": { "anyOf": [ { "const": true, "type": "boolean" }, { "type": "null" } ], "default": null, "description": "Enable interface ID column in custom-annotated tabular file", "title": "Interface Id", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "AUGMENT_ENUM", "stored_value": "TABULAR_IFACE_ID" } ] } }, "title": "MethodExportApproxFormatCustomAnnotatedConfig", "type": "object" }, "MethodExportApproxFormatFreeform": { "additionalProperties": false, "description": "Selects freeform file format", "properties": { "freeform": { "const": true, "default": true, "description": "Selects freeform file format", "title": "Freeform", "type": "boolean", "x-materialization": [ { "ir_key": "method.export_approx_format", "ir_value_type": "unsigned short", "storage_type": "PRESENCE_ENUM", "stored_value": "TABULAR_NONE" } ] } }, "title": "MethodExportApproxFormatFreeform", "type": "object" } }, "additionalProperties": false, "required": [ "filename" ] }
- Fields:
- field filename: str [Required]
Output file for surrogate model value evaluations
- field format: MethodExportApproxFormatCustomAnnotated | MethodExportApproxFormatAnnotated | MethodExportApproxFormatFreeform [Optional]
Tabular Format
- pydantic model dakota.spec.method.adaptive_sampling.ConstantLiar
Use information from the existing surrogate model to predict what the surrogate upgrade will be with new points.
Show JSON schema
{ "title": "ConstantLiar", "description": "Use information from the existing surrogate model to predict what the surrogate upgrade will be with new points.", "type": "object", "properties": { "constant_liar": { "const": true, "default": true, "description": "Use information from the existing surrogate model to predict what the surrogate upgrade will be with new points.", "title": "Constant Liar", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "cl" } ] } }, "additionalProperties": false }
- Fields:
- field constant_liar: Literal[True] = True
Use information from the existing surrogate model to predict what the surrogate upgrade will be with new points.
- pydantic model dakota.spec.method.adaptive_sampling.DistancePenalty
Add a penalty to spread out the points in the batch
Show JSON schema
{ "title": "DistancePenalty", "description": "Add a penalty to spread out the points in the batch", "type": "object", "properties": { "distance_penalty": { "const": true, "default": true, "description": "Add a penalty to spread out the points in the batch", "title": "Distance Penalty", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "distance" } ] } }, "additionalProperties": false }
- Fields:
- field distance_penalty: Literal[True] = True
Add a penalty to spread out the points in the batch
- pydantic model dakota.spec.method.adaptive_sampling.FitnessMetricDistance
Space filling metric
Show JSON schema
{ "title": "FitnessMetricDistance", "description": "Space filling metric", "type": "object", "properties": { "distance": { "const": true, "default": true, "description": "Space filling metric", "title": "Distance", "type": "boolean", "x-materialization": [ { "ir_key": "method.fitness_metric", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "distance" } ] } }, "additionalProperties": false }
- Fields:
- field distance: Literal[True] = True
Space filling metric
- pydantic model dakota.spec.method.adaptive_sampling.Gradient
Fill the range space of the surrogate
Show JSON schema
{ "title": "Gradient", "description": "Fill the range space of the surrogate", "type": "object", "properties": { "gradient": { "const": true, "default": true, "description": "Fill the range space of the surrogate", "title": "Gradient", "type": "boolean", "x-materialization": [ { "ir_key": "method.fitness_metric", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "gradient" } ] } }, "additionalProperties": false }
- Fields:
- field gradient: Literal[True] = True
Fill the range space of the surrogate
- pydantic model dakota.spec.method.adaptive_sampling.Naive
Take the highest scoring candidates
Show JSON schema
{ "title": "Naive", "description": "Take the highest scoring candidates", "type": "object", "properties": { "naive": { "const": true, "default": true, "description": "Take the highest scoring candidates", "title": "Naive", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "naive" } ] } }, "additionalProperties": false }
- Fields:
- field naive: Literal[True] = True
Take the highest scoring candidates
- pydantic model dakota.spec.method.adaptive_sampling.PredictedVariance
Pick points with highest variance
Show JSON schema
{ "title": "PredictedVariance", "description": "Pick points with highest variance", "type": "object", "properties": { "predicted_variance": { "const": true, "default": true, "description": "Pick points with highest variance", "title": "Predicted Variance", "type": "boolean", "x-materialization": [ { "ir_key": "method.fitness_metric", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "predicted_variance" } ] } }, "additionalProperties": false }
- field predicted_variance: Literal[True] = True
Pick points with highest variance
- pydantic model dakota.spec.method.adaptive_sampling.Topology
In this selection strategy, we use information about the topology of the space from the Morse-Smale complex to identify next points to select.
Show JSON schema
{ "title": "Topology", "description": "In this selection strategy, we use information about the topology of the space from the Morse-Smale complex to identify next points to select.", "type": "object", "properties": { "topology": { "const": true, "default": true, "description": "In this selection strategy, we use information about the topology of the space from the Morse-Smale complex to identify next points to select.", "title": "Topology", "type": "boolean", "x-materialization": [ { "ir_key": "method.batch_selection", "ir_value_type": "String", "storage_type": "PRESENCE_LITERAL", "stored_value": "topology" } ] } }, "additionalProperties": false }
- Fields:
- field topology: Literal[True] = True
In this selection strategy, we use information about the topology of the space from the Morse-Smale complex to identify next points to select.
