Quick Start

Install

Install the dependencies listed in Installation first.

Initialize

Generate starter configs with the ADS CLI:

ads operator init -t regression --overwrite --output ~/regression/

The generated files always include:

  • regression.yaml

  • regression_operator_local_python_backend.yaml

  • regression_operator_local_container_backend.yaml

If your ADS CLI defaults are configured for OCI Data Science Jobs, init also generates:

  • regression_job_container_backend.yaml

  • regression_job_python_backend.yaml

Prepare the YAML

Open ~/regression/regression.yaml and fill in the training data, optional test data, target column, and output directory.

Example:

kind: operator
type: regression
version: v1
spec:
  training_data:
    url: /path/to/train.csv
  test_data:
    url: /path/to/test.csv
  output_directory:
    url: /path/to/results
  target_column: target
  model: linear_regression
  model_kwargs:
    tuning_n_trials: 0
  generate_report: true
  generate_explanations: false

Why tuning_n_trials: 0 in the example?

The current explicit-model implementations use Optuna-backed tuning by default. Setting tuning_n_trials: 0 makes the first run faster and easier to validate.

Verify

Validate the configuration before running:

ads operator verify -f ~/regression/regression.yaml

Run Locally

Run the operator in the local python backend:

ads operator run -f ~/regression/regression.yaml -b local

Artifacts

For a run with both training and test data, you should expect:

  • training_predictions.csv

  • test_predictions.csv

  • training_metrics.csv

  • test_metrics.csv

  • report.html when generate_report: true

  • model.pkl

If you also set generate_explanations: true, the run can additionally produce global_explanations.csv. For example, the checked-in regression test asset produces prediction and metric outputs like:

input_value,predicted_value,residual
13.0,12.94857982370225,0.051420176297749975
14.6,14.525857002938292,0.07414299706170802

And training metrics like:

metric,value
rmse,0.2652270970202646
mae,0.1846327130264453
mse,0.07034541299379685
r2,0.9853933943119193
mape,1.0921881463703744

Open the HTML report after the run:

open /path/to/results/report.html