sientia-dataops-model-manager/input-sample.md

Insert a new experiment run

-- Optional: remove a previous run with the same id
DELETE FROM public.experiment_run WHERE experiment_run_id = 1001;

INSERT INTO public.experiment_run
(experiment_name, run_name, username, status, error_message, created_at,
updated_at, bucket_name, file_name, request_data, orchestrator_response_data)
VALUES(
	'test-experiment-name',
	'test-run-name',
	'test-username',
	'ORCHESTRATOR_WAITING_PROC',
	null,
	now(),
	now(),
	'model-training',
	'training_data.csv',
	'{"experiment_run_id":1001,"variable_columns":["feature_a","feature_b"],"target_variable":"target","bucket_name":"model-training","file_name":"training_data.csv","line_separator":",","decimal_separator":".","train_size":80,"shuffle":true,"model_name":"Linear Regression","model_type":"linear_regression","data_model_kwargs":{"lag_train":{"feature_a":0,"feature_b":0},"lag_val":{"feature_a":0,"feature_b":0},"nan_treatment":"drop"},"model_kwargs":{"degree":1,"scaler_name":"Standard Scaler"},"opt_params":{}}',
  null
);

Upload the input dataset to MinIO

mc cp input_dataset.csv suse/model-training/training-sample-dataset-1001.csv

Temporal input payload sample

Keys match TrainModelParams.from_dict in model_manager/utils/models/train_model_params.py: every field passed to _check_none must be present, including date_column; model_metadata must be non-empty for validate_business_rules(). You may omit date_format (defaults to yyyy-MM-dd HH:mm:ss). Omit optional keys (random_state, val_file_name, model_id) when defaults or None apply.

{
  "experiment_run_id": 1001,
  "variable_columns": ["feature_a", "feature_b"],
  "target_variable": "target",
  "bucket_name": "model-training",
  "file_name": "training-sample-dataset-1001.csv",
  "line_separator": ",",
  "decimal_separator": ".",
  "date_column": "timestamp",
  "train_size": 80,
  "shuffle": true,
  "random_state": 42,
  "model_name": "test-runtime-linear-regression-model",
  "model_type": "linear_regression",
  "data_model_kwargs": {
    "lag_train": {
      "feature_a": 0,
      "feature_b": 0
    },
    "lag_val": {
      "feature_a": 0,
      "feature_b": 0
    },
    "nan_treatment": "drop",
    "rem_static_win": false,
    "static_threshold": null,
    "start_date": null,
    "end_date": null,
    "support_filters": {},
    "removed_intervals": []
  },
  "model_kwargs": {
    "degree": 1,
    "interaction_only": false,
    "scaler_name": "Standard Scaler"
  },
  "opt_params": {}
}