Train and predict

Use to train and then immediately predict against the same training dataset. Can only be used with unsupervised learning models and the following algorithms:

  • BATCH_RCF
  • FIT_RCF
  • k-means

Example request: Train and predict with indexed data

  1. POST /_plugins/_ml/_train_predict/kmeans
  2. {
  3.     "parameters": {
  4.         "centroids": 2,
  5.         "iterations": 10,
  6.         "distance_type": "COSINE"
  7.     },
  8.     "input_query": {
  9.         "query": {
  10.             "bool": {
  11.                 "filter": [
  12.                     {
  13.                         "range": {
  14.                             "k1": {
  15.                                 "gte": 0
  16.                             }
  17.                         }
  18.                     }
  19.                 ]
  20.             }
  21.         },
  22.         "size": 10
  23.     },
  24.     "input_index": [
  25.         "test_data"
  26.     ]
  27. }

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Example request: Train and predict with data directly

  1. POST /_plugins/_ml/_train_predict/kmeans
  2. {
  3.     "parameters": {
  4.         "centroids": 2,
  5.         "iterations": 1,
  6.         "distance_type": "EUCLIDEAN"
  7.     },
  8.     "input_data": {
  9.         "column_metas": [
  10.             {
  11.                 "name": "k1",
  12.                 "column_type": "DOUBLE"
  13.             },
  14.             {
  15.                 "name": "k2",
  16.                 "column_type": "DOUBLE"
  17.             }
  18.         ],
  19.         "rows": [
  20.             {
  21.                 "values": [
  22.                     {
  23.                         "column_type": "DOUBLE",
  24.                         "value": 1.00
  25.                     },
  26.                     {
  27.                         "column_type": "DOUBLE",
  28.                         "value": 2.00
  29.                     }
  30.                 ]
  31.             },
  32.             {
  33.                 "values": [
  34.                     {
  35.                         "column_type": "DOUBLE",
  36.                         "value": 1.00
  37.                     },
  38.                     {
  39.                         "column_type": "DOUBLE",
  40.                         "value": 4.00
  41.                     }
  42.                 ]
  43.             },
  44.             {
  45.                 "values": [
  46.                     {
  47.                         "column_type": "DOUBLE",
  48.                         "value": 1.00
  49.                     },
  50.                     {
  51.                         "column_type": "DOUBLE",
  52.                         "value": 0.00
  53.                     }
  54.                 ]
  55.             },
  56.             {
  57.                 "values": [
  58.                     {
  59.                         "column_type": "DOUBLE",
  60.                         "value": 10.00
  61.                     },
  62.                     {
  63.                         "column_type": "DOUBLE",
  64.                         "value": 2.00
  65.                     }
  66.                 ]
  67.             },
  68.             {
  69.                 "values": [
  70.                     {
  71.                         "column_type": "DOUBLE",
  72.                         "value": 10.00
  73.                     },
  74.                     {
  75.                         "column_type": "DOUBLE",
  76.                         "value": 4.00
  77.                     }
  78.                 ]
  79.             },
  80.             {
  81.                 "values": [
  82.                     {
  83.                         "column_type": "DOUBLE",
  84.                         "value": 10.00
  85.                     },
  86.                     {
  87.                         "column_type": "DOUBLE",
  88.                         "value": 0.00
  89.                     }
  90.                 ]
  91.             }
  92.         ]
  93.     }
  94. }

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Example response

  1. {
  2.   "status" : "COMPLETED",
  3.   "prediction_result" : {
  4.     "column_metas" : [
  5.       {
  6.         "name" : "ClusterID",
  7.         "column_type" : "INTEGER"
  8.       }
  9.     ],
  10.     "rows" : [
  11.       {
  12.         "values" : [
  13.           {
  14.             "column_type" : "INTEGER",
  15.             "value" : 1
  16.           }
  17.         ]
  18.       },
  19.       {
  20.         "values" : [
  21.           {
  22.             "column_type" : "INTEGER",
  23.             "value" : 1
  24.           }
  25.         ]
  26.       },
  27.       {
  28.         "values" : [
  29.           {
  30.             "column_type" : "INTEGER",
  31.             "value" : 1
  32.           }
  33.         ]
  34.       },
  35.       {
  36.         "values" : [
  37.           {
  38.             "column_type" : "INTEGER",
  39.             "value" : 0
  40.           }
  41.         ]
  42.       },
  43.       {
  44.         "values" : [
  45.           {
  46.             "column_type" : "INTEGER",
  47.             "value" : 0
  48.           }
  49.         ]
  50.       },
  51.       {
  52.         "values" : [
  53.           {
  54.             "column_type" : "INTEGER",
  55.             "value" : 0
  56.           }
  57.         ]
  58.       }
  59.     ]
  60.   }
  61. }