main.py

import os
import yaml
import argparse
import numpy as np
from lce import LCEClassifier
from sklearn.preprocessing import LabelEncoder

from utils.helpers import create_logger, save_experiment
from utils.data_loading import import_data
from utils.results_export import export_results, get_mts_region


if __name__ == "__main__":
    # Load configuration
    parser = argparse.ArgumentParser(description="XEM")
    parser.add_argument(
        "-c", "--config", default="configuration/config.yml", help="Configuration File"
    )
    args = parser.parse_args()
    with open(args.config, "r") as config_file:
        configuration = yaml.safe_load(config_file)

    # Create experiment folder
    xp_dir = (
        "./results/"
        + str(configuration["dataset"])
        + "/window_"
        + str(int(configuration["window"] * 100))
        + "/xp_"
        + str(configuration["experiment_run"])
        + "/"
    )
    save_experiment(xp_dir, args.config)
    log, logclose = create_logger(log_filename=os.path.join(xp_dir, "experiment.log"))

    # Load dataset
    X_train, y_train, X_validation, y_validation, X_test, y_test = import_data(
        configuration["dataset"],
        configuration["window"],
        xp_dir,
        configuration["validation_split"],
        log,
    )

    # Fit label encoder
    encoder = LabelEncoder()
    encoder.fit(np.concatenate((y_train, y_validation, y_test), axis=0))

    # Fit LCE model - documentation: https://lce.readthedocs.io/en/latest/generated/lce.LCEClassifier.html
    clf = LCEClassifier(
        n_estimators=configuration["trees"],
        max_depth=configuration["max_depth"],
        max_samples=configuration["max_samples"],
        n_jobs=configuration["n_jobs"],
        random_state=configuration["random_state"],
    )
    clf.fit(X_train[:, 2:], y_train)

    # Export results
    (
        results_export,
        results_train_mts,
        results_validation_mts,
        results_test_mts,
    ) = export_results(
        clf,
        encoder,
        configuration,
        X_train,
        y_train,
        X_validation,
        y_validation,
        X_test,
        y_test,
        xp_dir,
        log,
    )

    # Example of identification of the time window used to classify the first MTS of the test set
    mts_id = 1
    get_mts_region(
        clf, encoder, X_test, y_test, results_test_mts, mts_id, configuration
    )

    logclose()