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
}
}