go-train-delay-ml-pipeline-main-flow

go-train-delay-ml-pipeline-main-flow.rs

// convert to feature and target dataset
let (features, target) = pg_source_app.pipeline.feature_and_target(&df).await;

//convert feature to smartcore data matrix
let x_matrix = pg_source_app
                .pipeline
                .feature_to_matrix(&features.unwrap())
                .await;

// convert target to class data vec<f64>
let y = pg_source_app
                .pipeline
                .target_to_ndarray(&target.unwrap())
                .await?;
// // cross validate the model
// let result = pg_source_app.pipeline.k_fold_cross_validate(&x_matrix.unwrap(), &y).await?;
// info!("{}", format!("cross validate result: {:?}", result));

// train the model and save the model
let model_path = pg_source_app
                .pipeline
                .linear_regression_model_build(&x_matrix.unwrap(), &y)
                .await?;
                
info!("{}", format!("build success linear regression model saved path: {:?}", model_path));