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Phani
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drafted ML json schema and examples
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‎example/job/ml_model.json

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{
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"name": "model",
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"type": "ml",
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"subtype": "supervised",
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"references": [
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"http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html"
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],
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"method": {
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"...": "include(model/ml_method.json)"
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}
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}

‎example/job/model/method/ml_workflow.json

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{
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"units": [
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{
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"...": "include(workflow/ml_unit.json)"
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}
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],
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"name": "property-prediction"
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}

‎example/job/model/method/workflow/ml_unit.json

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{
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"head": true,
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"flowchartId": "1",
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"name": "ml_predict",
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"type": "predict",
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"interface": {
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"...": "include(ml_unit/ml_predict.json)"
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}
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}

‎example/job/model/method/workflow/ml_unit/_result.json

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{
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"results": [
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{
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"name": "band_gaps",
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"args": [
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{
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"key": "model_coefficients",
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"value": [
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-3.339e-01,
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2.555e-01,
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-1.005e-02
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]
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},
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{
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"key": "intercept",
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"value": 0.45
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},
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{
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"key": "score",
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"value": 0.8
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},
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{
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"key": "model_data",
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"value": "JSON formatted string of input data table"
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}
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]
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}
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]
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}

‎example/job/model/ml_method.json

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{
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"type": "regression",
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"subtype": "linear_regression",
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"workflow": {
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"...": "include(method/ml_workflow.json)"
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}
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}

‎example/job/theory/ml/regression.json

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{
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"type": "regression",
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"subtype":"linear_regression",
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"precision":{
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"...": "include(theory/ml/regression/precision.json)"
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}
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}

‎example/job/theory/ml/regression/precision.json

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{
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"score": 0.8
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}

‎schema/job/ml_model.json

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{
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"$schema": "http://json-schema.org/draft-04/schema#",
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"title": "ML model schema",
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"properties": {
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"type": {
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"description": "general type of the model, eg. `ml`",
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"type": "string"
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},
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"subtype": {
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"description": "general subtype of the model, eg. `supervised`",
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"type": "string"
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},
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"method": {
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"$ref": "file:model/ml_method.json"
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},
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"references": {
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"type": "array",
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"items": {
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"description": "literature reference, webpage etc.",
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"type": "string"
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}
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}
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},
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"oneOf": [
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{
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"$ref": "file:theory/ml.json"
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}
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],
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"required": [
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"type",
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"subtype",
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"method"
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]
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}

‎schema/job/model/method/ml_workflow.json

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{
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"$schema": "http://json-schema.org/draft-04/schema#",
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"title": "ml workflow schema",
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"type": "object",
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"properties": {
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"name": {
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"description": "Name of the workflow. e.g. property-prediction",
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"type": "string"
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},
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"units": {
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"description": "Contains the Units of the Workflow",
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"type": "array",
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"items": {
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"$ref": "file:workflow/ml_unit.json"
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}
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}
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},
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"required": [
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"name"
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]
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}

‎schema/job/model/method/workflow/ml_unit.json

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{
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"$schema": "http://json-schema.org/draft-04/schema#",
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"title": "ml unit schema",
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"type": "object",
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"properties": {
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"type": {
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"description": "type of the unit.",
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"type": "string",
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"enum": [
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"interface",
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"modelfit",
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"predict"
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]
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},
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"name": {
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"description": "name of the unit. e.g. ml_interface",
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"type": "string"
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},
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"head": {
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"description": "Whether this unit is the first one to be executed.",
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"type": "boolean"
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},
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"flowchartId": {
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"description": "Identity of the unit in the workflow. Used to trace the execution flow of the workflow.",
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"type": "string"
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},
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"next": {
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"description": "Next unit's flowchartId. If empty, the current unit is the last.",
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"type": "string"
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},
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"results": {
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"description": "Resulting properties extracted from the unit.",
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"type": "array",
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"items": {
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"$ref": "file:ml_unit/_result.json"
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}
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},
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"interface": {
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"description": "Contains information about the ML interface unit..",
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"$ref": "file:ml_unit/ml_interface.json"
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},
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"modelfit": {
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"description": "Contains information about the assignment unit.",
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"$ref": "file:ml_unit/ml_modelfit.json"
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},
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"predict": {
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"description": "Contains information about the condition unit.",
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"$ref": "file:ml_unit/ml_predict.json"
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}
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},
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"required": [
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"type",
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"name",
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"head",
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"flowchartId"
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]
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}

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