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227 | {
"schemaId": "methods-directory/regression",
"$schema": "http://json-schema.org/draft-04/schema#",
"title": "linear methods schema",
"allOf": [
{
"schemaId": "method",
"title": "method schema (base)",
"properties": {
"type": {
"description": "general type of this method, eg. `pseudopotential`",
"type": "string"
},
"subtype": {
"description": "general subtype of this method, eg. `ultra-soft`",
"type": "string"
},
"precision": {
"description": "Object showing the actual possible precision based on theory and implementation",
"type": "object"
},
"data": {
"description": "additional data specific to method, eg. array of pseudopotentials",
"type": "object"
}
},
"required": [
"type",
"subtype"
]
}
],
"properties": {
"type": {
"enum": [
"linear",
"kernel_ridge"
]
},
"subtype": {
"enum": [
"least_squares",
"ridge"
]
},
"precision": {
"perProperty": {
"items": {
"type": "object",
"properties": {
"name": {
"description": "property name in 'flattened' format",
"type": "string"
}
},
"allOf": [
{
"schemaId": "methods-directory/regression/precision",
"title": "regression precision",
"oneOf": [
{
"schemaId": "methods-directory/regression/precision-per-property",
"title": "precision schema for regression",
"properties": {
"trainingError": {
"description": "training error of the estimator",
"type": "number"
},
"score": {
"description": "prediction score of the estimator. Eg: r2_score",
"type": "number"
}
},
"required": [
"trainingError"
]
}
]
}
]
}
}
},
"data": {
"perProperty": {
"items": {
"type": "object",
"properties": {
"name": {
"description": "property name in 'flattened' format",
"type": "string"
}
},
"allOf": [
{
"schemaId": "methods-directory/regression/data",
"title": "regression data",
"oneOf": [
{
"schemaId": "methods-directory/regression/linear/data-per-property",
"title": "linear regression parameters schema",
"properties": {
"intercept": {
"description": "intercept (shift) from the linear or non-linear fit of data points",
"type": "number"
},
"perFeature": {
"type": "array",
"description": "per-feature (property used for training the ML method/model) parameters",
"items": {
"type": "object",
"properties": {
"coefficient": {
"description": "coefficient in linear regression",
"type": "number"
}
},
"allOf": [
{
"schemaId": "methods-directory/regression/data-per-feature",
"title": "feature parameters schema",
"properties": {
"name": {
"description": "feature name",
"type": "string"
},
"importance": {
"description": "pvalue: https://en.wikipedia.org/wiki/P-value",
"type": "number"
}
},
"required": [
"name"
]
}
],
"required": [
"coefficient"
]
}
}
},
"required": [
"intercept",
"perFeature"
]
},
{
"schemaId": "methods-directory/regression/kernel-ridge/data-per-property",
"title": "linear regression parameters schema",
"properties": {
"xFit": {
"description": "training data",
"type": "array"
},
"dualCoefficients": {
"description": "dual coefficients",
"type": "array"
},
"perFeature": {
"type": "array",
"description": "per-feature (property used for training the ML method/model) parameters",
"items": {
"type": "object",
"allOf": [
{
"schemaId": "methods-directory/regression/data-per-feature",
"title": "feature parameters schema",
"properties": {
"name": {
"description": "feature name",
"type": "string"
},
"importance": {
"description": "pvalue: https://en.wikipedia.org/wiki/P-value",
"type": "number"
}
},
"required": [
"name"
]
}
],
"required": [
"name"
]
}
}
},
"required": [
"xFit",
"dualCoefficients",
"perFeature"
]
}
]
}
]
}
},
"dataSet": {
"schemaId": "methods-directory/regression/dataset",
"description": "dataset for ml",
"type": "object",
"properties": {
"exabyteIds": {
"description": "array of exabyteIds for materials in dataset",
"type": "array",
"items": {
"type": "string"
}
},
"extra": {
"description": "holder for any extra information, eg. coming from user-uploaded CSV file"
}
},
"required": [
"exabyteIds"
]
}
}
},
"required": [
"precision",
"data"
]
}
|