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Mozda bi bilo prakticnije ukoliko bi se timestamp kada je job kreiran nalazio van details-a obzirom da se details odnosi na stvari potrebne za job a timestamp mi pamtimo dodatno.

Trenutno:

{
  "automl_job_id": "b1af8962-e294-478d-b9dd-a1b08c9393a8-20241009105004",
  "details": {
    "job_name": "Boston Housing Regression 2",
    "data_source": "https://raw.githubusercontent.com/Drashko73/datasets/master/boston_housing/housing.csv",
    "target_variable": "medv",
    "problem_type": "regression",
    "generations": 100,
    "population_size": 100,
    "offspring_size": 100,
    "mutation_rate": 0.9,
    "crossover_rate": 0.1,
    "scoring": "neg_mean_squared_error",
    "cv": 5,
    "subsample": 1,
    "max_time_mins": 5,
    "max_eval_time_mins": 5,
    "random_state": 42,
    "config_dict": null,
    "template": null,
    "early_stop": 5,
    "automl_library": "tpot",
    "timestamp_job_created": "2024-10-09T10:50:04.002830"
  }
}

Predlog kako bi izgledalo:

{
  "automl_job_id": "b1af8962-e294-478d-b9dd-a1b08c9393a8-20241009105004",
  "timestamp_job_created": "2024-10-09T10:50:04.002830",
  "details": {
    "job_name": "Boston Housing Regression 2",
    "data_source": "https://raw.githubusercontent.com/Drashko73/datasets/master/boston_housing/housing.csv",
    "target_variable": "medv",
    "problem_type": "regression",
    "generations": 100,
    "population_size": 100,
    "offspring_size": 100,
    "mutation_rate": 0.9,
    "crossover_rate": 0.1,
    "scoring": "neg_mean_squared_error",
    "cv": 5,
    "subsample": 1,
    "max_time_mins": 5,
    "max_eval_time_mins": 5,
    "random_state": 42,
    "config_dict": null,
    "template": null,
    "early_stop": 5,
    "automl_library": "tpot"
  }
}