An Example Data Science Model

The simple data science example below, performs stratified random sampling on the hald-small-binary dataset. Beginning in datasources, the contents of hald-small-binary.txt are imported into the mySrc data source. Next, under datasets, the mySrc data source is bound to the myData dataset while also selecting the Y variable as the stratum variable. Moving on to transformer, the mySampler transformer is constructed using the stratified random sampling algorithm with a sample size equal to 10. Lastly, under actions, the stratified random sampler, mySampler, "transforms", or samples from, the myData dataset.

{
   modelName: "ExampleDM",
   modelType: "datamining",
   datasources: {
    mySrc: {
      type: 'csv',
      connection: 'hald-small-binary.txt',
      direction: 'import'
    }
  },
  datasets: {
    myData: {
      binding: 'mySrc',
      strataCol: 'Y'
    }
  },
  transformer: {
    mySampler: {
      type: 'transformation',
      algorithm: 'stratifiedSampling',
      parameters: {
        sampleSize: 10
      }
    }
  },
  actions: {
    sampleData: {
      data: 'myData',
      action: 'transform',
      evaluations: [
        'transformation'
      ]
    }
  }
}

As in both the optimization and simulation examples above, the results are given in JSON which contain the ten sampled records: 1, 2, 3, 5, 6, 7, 8, 9, 11 and 13.

{ 
  "status": {
    "id": "2590+ExampleDM+2020-02-26-17-22-28-146373",
    "code": 0,
    "codeText": "Success"
  },
  "results": ["sampleData.transformation"],
  "sampleData": {
    "transformation": { 
      "objectType": "dataFrame", 
      "name": "Sample:myData", 
      "order": "col",
      "rowNames": [
        "Record 11", "Record 8", "Record 2", "Record 9", "Record 1",    
        "Record 7", "Record 13", "Record 6", "Record 3", "Record 5"
      ],    
      "colNames": ["X1", "X2", "X3", "X4", "Weights"], 
      "colTypes": ["double", "double", "double", "double", "double"],
      "indexCols": null, 
      "data": [
         [1, 1, 1, 2, 7, 3, 10, 11, 11, 7],
         [40, 31, 29, 54, 26, 71, 68, 55, 56, 52],
         [23, 22, 15, 18, 6, 17, 8, 9, 8, 6],
         [34, 44, 52, 22, 60, 6, 12, 22, 20, 33],
         [3, 2, 3, 1, 1, 1, 1, 1, 2, 1]    
      ] 
    } 
  } 
}