runner2.py

import sklearn.neural_network
from src.backbone import *


def run_models(use_augments):
    augmented = use_augments
    print(augmented)
    with_augments = "Augmented" if augmented else "Non_Augmented"
    main_path = Path(os.getcwd()) / "data"
    time_now = datetime.now().strftime("%d_%m_%Hh%M")
    res_path = str(os.getcwd()) + "/outputs/" + time_now + "_" + with_augments

    (
        train_features,
        val_features,
        train_labels,
        val_labels,
        test_features,
        test_labels,
    ) = load_data(main_path=main_path, subset=None)
    print_shapes(
        [train_features, val_features, train_labels, val_labels, test_features, test_labels]
    )

    if not Path.is_dir(Path(res_path)):
        os.mkdir(res_path)

    start_time = time.time()

    multi_model_run(
        train_features=train_features,
        test_features=test_features,
        train_labels=train_labels,
        test_labels=test_labels,
        val_features=val_features,
        val_labels=val_labels,
        reduce_dims="pca",
        model_list=[
            # KNeighborsClassifier,
            RandomForestClassifier,
            MLPClassifier,
            # DecisionTreeClassifier,
        ],
        model_parameters=[
            #{"n_neighbors": [3, 4, 5, 6, 7]},
            {
                "max_depth": [3, 4, 5],
                "n_estimators": [8, 10, 12],
                "max_features": [1, 2, 3],
            },
            {"hidden_layer_sizes": [28, 42, 56, 70, 84], "max_iter": [1000, 1500, 2000]},
            # {
            #    "random_state": [0, 1, 2, 3, 4],
            #    "max_depth": [10, 15, 20, 25],
            #    "max_features": [10, 15, 20, 25],
            # },
        ],
        metrics=[
            accuracy_score,
            precision_score,
            recall_score,
            f1_score,
        ],
        res_path=res_path,
        folds=5,
        preprocessing=augmented
    )

    end_time = time.time() - start_time
    with open(res_path + "/outputs.csv", "a+") as f:
        f.write(f"\nTime taken : {end_time}")


if __name__ == "__main__":
    for i in range(5):
        run_models(True)