Add configurable preprocessing pipelines

README.md

@@ -60,6 +60,40 @@ will perform a comparison of classifier models using cross-validation. Printing
 
 You can then perform inference using the best model with the `predict` method.
 
+## Preprocessing pipelines
+
+`automl` now supports composable preprocessing pipelines so you can build
+feature engineering recipes similar to `AutoGluon` or `caret`. Pipelines are
+defined with the [`PreprocessingStep`](https://docs.rs/automl/latest/automl/settings/enum.PreprocessingStep.html)
+enum and attached via either the `add_step` builder or by passing a full
+[`PreprocessingPipeline`](https://docs.rs/automl/latest/automl/settings/struct.PreprocessingPipeline.html).
+
+```rust
+use automl::settings::{
+    ClassificationSettings, PreprocessingPipeline, PreprocessingStep, RegressionSettings,
+    StandardizeParams,
+};
+use automl::DenseMatrix;
+
+let regression = RegressionSettings::<f64, f64, DenseMatrix<f64>, Vec<f64>>::default()
+    .add_step(PreprocessingStep::Standardize(StandardizeParams::default()))
+    .add_step(PreprocessingStep::ReplaceWithPCA {
+        number_of_components: 5,
+    });
+
+let classification = ClassificationSettings::default().with_preprocessing(
+    PreprocessingPipeline::new()
+        .add_step(PreprocessingStep::AddInteractions)
+        .add_step(PreprocessingStep::ReplaceWithSVD {
+            number_of_components: 4,
+        }),
+);
+```
+
+Pipelines preserve the order of steps. Stateful steps such as PCA, SVD, or
+standardization automatically fit during training and reuse the same fitted
+state when you call `predict`.
+
 ## Features
 
 This crate has several features that add some additional methods.

examples/maximal_regression.rs

@@ -21,9 +21,9 @@ use automl::{
     settings::{
         DecisionTreeRegressorParameters, Distance, ElasticNetParameters, FinalAlgorithm,
         KNNAlgorithmName, KNNParameters, KNNWeightFunction, Kernel, LassoParameters,
-        LinearRegressionParameters, LinearRegressionSolverName, Metric,
+        LinearRegressionParameters, LinearRegressionSolverName, Metric, PreprocessingStep,
         RandomForestRegressorParameters, RidgeRegressionParameters, RidgeRegressionSolverName,
-        SVRParameters, XGRegressorParameters,
+        SVRParameters, StandardizeParams, XGRegressorParameters,
     },
 };
 use regression_data::regression_testing_data;
@@ -41,7 +41,7 @@ fn main() -> Result<(), Failed> {
         .with_final_model(FinalAlgorithm::Best)
         .skip(RegressionAlgorithm::default_random_forest())
         .sorted_by(Metric::RSquared)
-        // .with_preprocessing(PreProcessing::AddInteractions)
+        .add_step(PreprocessingStep::Standardize(StandardizeParams::default()))
         .with_linear_settings(
             LinearRegressionParameters::default().with_solver(LinearRegressionSolverName::QR),
         )

examples/print_settings.rs

@@ -4,9 +4,10 @@ use automl::settings::{
     DecisionTreeRegressorParameters, Distance, ElasticNetParameters, ExtraTreesRegressorParameters,
     FinalAlgorithm, GaussianNBParameters, KNNAlgorithmName, KNNParameters, KNNWeightFunction,
     Kernel, LassoParameters, LinearRegressionParameters, LinearRegressionSolverName,
-    LogisticRegressionParameters, Metric, MultinomialNBParameters, Objective, PreProcessing,
-    RandomForestClassifierParameters, RandomForestRegressorParameters, RegressionSettings,
-    RidgeRegressionParameters, RidgeRegressionSolverName, SVCParameters, SVRParameters,
+    LogisticRegressionParameters, Metric, MultinomialNBParameters, Objective,
+    PreprocessingPipeline, PreprocessingStep, RandomForestClassifierParameters,
+    RandomForestRegressorParameters, RegressionSettings, RidgeRegressionParameters,
+    RidgeRegressionSolverName, SVCParameters, SVRParameters, StandardizeParams,
     XGRegressorParameters,
 };
 use serde_json::to_string_pretty;
@@ -20,7 +21,8 @@ fn build_regression_settings() -> RegressionConfig {
         .shuffle_data(true)
         .verbose(true)
         .sorted_by(Metric::RSquared)
-        .with_preprocessing(PreProcessing::AddInteractions)
+        .add_step(PreprocessingStep::Standardize(StandardizeParams::default()))
+        .add_step(PreprocessingStep::AddInteractions)
         .with_linear_settings(
             LinearRegressionParameters::default().with_solver(LinearRegressionSolverName::QR),
         )
@@ -99,12 +101,16 @@ fn build_regression_settings() -> RegressionConfig {
 }
 
 fn build_classification_settings() -> ClassificationSettings {
+    let pipeline = PreprocessingPipeline::new()
+        .add_step(PreprocessingStep::Standardize(StandardizeParams::default()))
+        .add_step(PreprocessingStep::AddInteractions);
+
     ClassificationSettings::default()
         .with_number_of_folds(6)
         .shuffle_data(true)
         .verbose(true)
         .sorted_by(Metric::Accuracy)
-        .with_preprocessing(PreProcessing::AddInteractions)
+        .with_preprocessing(pipeline)
         .with_final_model(FinalAlgorithm::Best)
         .with_knn_classifier_settings(
             KNNParameters::default()