To get training request for a model:

We trained 'sales_model' previously, to view the training request that we sent, we can all this API. As you will see, ML engine adds some more data points to the request for help.

Method : GET

URI : /ml/<schema_name>/<model_name>/request

Example

curl -X GET http://192.168.1.105:18080/ml/website/sales_model/request

Response

{
   "schema-name":"website",
   "training_details":{
      "training_source":"visitor_upload.txt",
      "file_size_mb":1,
      "target_idx":5,
      "input_format":"JSON",
      "is_src_global":0,
      "train_speed":3,
      "bucket_name":"ml_bucket_info",
      "training_source_type":1,
      "expected_format":"SVM"
   },
   "algo_type":"SVM",
   "tune_params":1,
   "attr_list":[
      {
         "position":0,
         "name":"vid"
      },
      {
         "position":1,
         "name":"prod"
      },
      {
         "position":2,
         "name":"pgid"
      },
      {
         "position":3,
         "name":"catid"
      },
      {
         "name":"items",
         "position":4
      },
      {
         "name":"price",
         "position":5
      }
   ],
   "scale":1,
   "attr_type":3,
   "algo_param":{
      "probability":0,
      "svm_type":3,
      "eps_svr":0.1,
      "kernel_type":0,
      "shrinking":0,
      "cost":100,
      "termination_criteria":0.001
   },
   "model_name":"sales_model",
   "train_start_ts":1648550728425224,
   "train_end_ts":1648550728434816,
   "train_req_state":25,
   "tuned_algo_params":{
      "C":0.0625,
      "g":4,
      "cache_size":100,
      "coef0":0,
      "degree":3,
      "eps":0.001,
      "kernel_type":0,
      "nr_weight":0,
      "nu":0.5,
      "p":0.1,
      "prob":0,
      "shrinking":0,
      "svm_type":3,
      "train_perf":704
   },
   "train_log":{
      "log":[
         "1648550728424954 : received train request",
         "verification done",
         "retrieved the training file [ sales_model__website__visitor_upload.txt ] from BRS",
         "file reformat done",
         "scaling and tuning the model params, by training many different models",
         "scaling and tuning done, selected params = 0.062500, 4.000000, 704.514490",
         "starting training for model [ sales_model__website ]",
         "1648550728434808 : training successful!"
      ],
      "schema-name":"website",
      "model_name":"sales_model",
      "algo_type":"SVM",
      "train_start_ts":1648550728425224,
      "train_end_ts":1648550728434809,
      "errorcode":0
   }
}