77 lines
2.5 KiB
Markdown
77 lines
2.5 KiB
Markdown
##### Insert a new experiment run
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```sql
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-- Optional: remove a previous run with the same id
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DELETE FROM public.experiment_run WHERE experiment_run_id = 1001;
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```
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```sql
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INSERT INTO public.experiment_run
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(experiment_name, run_name, username, status, error_message, created_at,
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updated_at, bucket_name, file_name, request_data, orchestrator_response_data)
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VALUES(
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'test-experiment-name',
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'test-run-name',
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'test-username',
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'ORCHESTRATOR_WAITING_PROC',
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null,
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now(),
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now(),
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'model-training',
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'training_data.csv',
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'{"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":{}}',
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null
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);
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```
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##### Upload the input dataset to MinIO
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```bash
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mc cp input_dataset.csv suse/model-training/training-sample-dataset-1001.csv
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```
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##### Temporal input payload sample
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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.
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```json
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{
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"experiment_run_id": 1001,
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"variable_columns": ["feature_a", "feature_b"],
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"target_variable": "target",
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"bucket_name": "model-training",
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"file_name": "training-sample-dataset-1001.csv",
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"line_separator": ",",
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"decimal_separator": ".",
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"date_column": "timestamp",
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"train_size": 80,
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"shuffle": true,
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"random_state": 42,
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"model_name": "test-runtime-linear-regression-model",
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"model_type": "linear_regression",
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"data_model_kwargs": {
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"lag_train": {
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"feature_a": 0,
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"feature_b": 0
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},
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"lag_val": {
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"feature_a": 0,
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"feature_b": 0
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},
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"nan_treatment": "drop",
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"rem_static_win": false,
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"static_threshold": null,
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"start_date": null,
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"end_date": null,
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"support_filters": {},
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"removed_intervals": []
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},
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"model_kwargs": {
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"degree": 1,
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"interaction_only": false,
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"scaler_name": "Standard Scaler"
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},
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"opt_params": {}
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}
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```
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