metric_array = [ metrics.duration_sitting, metrics.duration_walking, metrics.duration_washing, metrics.duration_eating, metrics.duration_sleeping, metrics.duration_studying, metrics.number_of_unique_activities ] metric_container, date_container = metrics.run_metric_array( metric_array, csv='./output/accelerometer_metrics.csv') return metric_container, date_container if __name__ == '__main__': ts, X, y = get_raw_ts_X_y() features_figure(X[0:X.size:50], feature_names=['X', 'Y', 'Z']) X, y = preprocess_X_y(ts, X, y) (X_train, y_train), (X_test, y_test) = split_train_test(X, y) clf_grid = get_classifier_grid() clf_grid.fit(X_train, y_train) print_summary(clf_grid, X_test, y_test) metric_container_daily, date_container_daily = activity_metrics( y, ts, 'daily') figures_dict = plot_metrics(metric_container_daily, date_container_daily) for key, fig in figures_dict.items(): fig.savefig(os.path.join('./output/', key))
if __name__ == '__main__': houses = ['A'] for house_ in houses: ts, X, y = get_raw_ts_X_y(house_) X, y = preprocess_X_y(ts, X, y) (X_train, y_train), (X_test, y_test) = split_train_test(X, y) clf_grid = get_classifier_grid() clf_grid.fit(X_train, y_train) print_summary(clf_grid, X_test, y_test) metric_container_daily, date_container_daily = localisation_metrics( y, ts, 'daily') figures_dict = plot_metrics( metric_container_daily, date_container_daily, labels_=['foyer', 'bedroom', 'living_room', 'bathroom']) fig, ax = features_figure(X, feature_names=[ 'AP1', 'AP2', 'AP3', 'AP4', 'AP5', 'AP6', 'AP7', 'AP8' ]) figures_dict['features'] = fig for key, fig in figures_dict.items(): fig.savefig(os.path.join('./output/', key))