maximal_classification/
maximal_classification.rs

1use automl::settings::*;
2use automl::*;
3
4fn main() {
5    // Totally customize settings
6    let settings = Settings::default_classification()
7        .with_number_of_folds(3)
8        .shuffle_data(true)
9        .verbose(true)
10        .with_final_model(FinalModel::Blending {
11            algorithm: Algorithm::CategoricalNaiveBayes,
12            meta_training_fraction: 0.15,
13            meta_testing_fraction: 0.15,
14        })
15        .skip(Algorithm::RandomForestClassifier)
16        .sorted_by(Metric::Accuracy)
17        .with_preprocessing(PreProcessing::ReplaceWithPCA {
18            number_of_components: 5,
19        })
20        .with_random_forest_classifier_settings(
21            RandomForestClassifierParameters::default()
22                .with_m(100)
23                .with_max_depth(5)
24                .with_min_samples_leaf(20)
25                .with_n_trees(100)
26                .with_min_samples_split(20),
27        )
28        .with_logistic_settings(
29            LogisticRegressionParameters::default()
30                .with_alpha(1.0)
31                .with_solver(LogisticRegressionSolverName::LBFGS),
32        )
33        .with_svc_settings(
34            SVCParameters::default()
35                .with_epoch(10)
36                .with_tol(1e-10)
37                .with_c(1.0)
38                .with_kernel(Kernel::Linear),
39        )
40        .with_decision_tree_classifier_settings(
41            DecisionTreeClassifierParameters::default()
42                .with_min_samples_split(20)
43                .with_max_depth(5)
44                .with_min_samples_leaf(20),
45        )
46        .with_knn_classifier_settings(
47            KNNClassifierParameters::default()
48                .with_algorithm(KNNAlgorithmName::CoverTree)
49                .with_k(3)
50                .with_distance(Distance::Euclidean)
51                .with_weight(KNNWeightFunction::Uniform),
52        )
53        .with_gaussian_nb_settings(GaussianNBParameters::default().with_priors(vec![1.0, 1.0]))
54        .with_categorical_nb_settings(CategoricalNBParameters::default().with_alpha(1.0));
55
56    // Save the settings for later use
57    settings.save("examples/maximal_classification_settings.yaml");
58
59    // Load a dataset from smartcore and add it to the regressor
60    let mut model =
61        SupervisedModel::new(smartcore::dataset::breast_cancer::load_dataset(), settings);
62
63    // Run a model comparison with all models at default settings
64    model.train();
65
66    // Print the results
67    println!("{}", model);
68
69    // Save teh model for later
70    model.save("examples/maximal_classification_model.aml");
71}
maximal_classification/maximal_classification.rs

maximal_classification/
maximal_classification.rs
