TinghuiWang / pyActLearn

    predicted_y = svm_model.predict(test_data[0])

    return predicted_y

if __name__ == '__main__':

    args_ok = False

    parser = argparse.ArgumentParser(description='Run Support Vector Machine on single resident CASAS datasets.')

    parser.add_argument('-d', '--dataset', help='Directory to original datasets')

    parser.add_argument('-o', '--output', help='Output folder')

    parser.add_argument('--h5py', help='HDF5 dataset folder')

    parser.add_argument('-k', '--kernel', help='svm kernel')

    args = parser.parse_args()

    # Default parameters

    log_filename = os.path.basename(__file__).split('.')[0] + \

                   '-%s.log' % datetime.now().strftime('%y%m%d_%H:%M:%S')

    # Setup output directory

    output_dir = args.output

    if output_dir is not None:

        output_dir = os.path.abspath(os.path.expanduser(output_dir))

        if os.path.exists(output_dir):

            # Found output_dir, check if it is a directory

            if not os.path.isdir(output_dir):

                exit('Output directory %s is found, but not a directory. Abort.' % output_dir)

        else:

            # Create directory

            os.mkdir(output_dir)

    else:

        output_dir = '.'

    log_filename = os.path.join(output_dir, log_filename)

    # Setup Logging as early as possible

    logging.basicConfig(level=logging.DEBUG,

                        format='[%(asctime)s] %(name)s:%(levelname)s:%(message)s',

                        handlers=[logging.FileHandler(log_filename),

                                  logging.StreamHandler()])

    # If dataset is specified, update h5py

    casas_data_dir = args.dataset

    if casas_data_dir is not None:

        casas_data_dir = os.path.abspath(os.path.expanduser(casas_data_dir))

        if not os.path.isdir(casas_data_dir):

            exit('CASAS dataset at %s does not exist. Abort.' % casas_data_dir)

    # Find h5py dataset first

    h5py_dir = args.h5py

    if h5py_dir is not None:

        h5py_dir = os.path.abspath(os.path.expanduser(h5py_dir))

    else:

        # Default location

        h5py_dir = os.path.join(output_dir, 'h5py')

    if os.path.exists(h5py_dir):

        if not os.path.isdir(h5py_dir):

            exit('h5py dataset location %s is not a directory. Abort.' % h5py_dir)

    # Finish check and creating all directory needed - now load datasets

    if not CASASFuel.files_exist(h5py_dir):

        if casas_data_dir is not None:

            casas_data = CASASData(path=casas_data_dir)

            casas_data.summary()

            # SVM needs to use statistical feature with per-sensor and normalization

            casas_data.populate_feature(method='stat', normalized=True, per_sensor=True)

            casas_data.export_hdf5(h5py_dir)

    casas_fuel = CASASFuel(dir_name=h5py_dir)

    # Prepare learning result

    result_pkl_file = os.path.join(output_dir, 'result.pkl')

    result = None

    if os.path.isfile(result_pkl_file):

        f = open(result_pkl_file, 'rb')

        result = pickle.load(f)

        f.close()

        if result.data != h5py_dir:

            logger.error('Result pickle file found for different dataset %s' % result.data)

            exit('Cannot save learning result at %s' % result_pkl_file)

    else:

        result = LearningResult(name='SVM', data=h5py_dir, mode='by_week')

    num_classes = casas_fuel.get_output_dims()

    # Open Fuel and get all splits

    split_list = casas_fuel.get_set_list()

    train_name = split_list[0]

    train_set = casas_fuel.get_dataset((train_name,), load_in_memory=True)

    (train_set_data) = train_set.data_sources

    # Prepare Back Annotation

    fp_back_annotated = open(os.path.join(output_dir, 'back_annotated.txt'), 'w')

    for i in range(1, len(split_list)):

        test_name = split_list[i]

        test_set = casas_fuel.get_dataset((test_name,), load_in_memory=True)

        (test_set_data) = test_set.data_sources

        # run svm

        logger.info('Training on %s, Testing on %s' % (train_name, test_name))

        if result.get_record_by_key(test_name) is None:

            prediction = training_and_test(test_name, train_set_data, test_set_data, num_classes, result)

        else:

            prediction = load_and_test(test_name, test_set_data, num_classes, result)

        casas_fuel.back_annotate(fp_back_annotated, prediction=prediction, split_id=i)

        train_name = test_name

        train_set_data = test_set_data

    f = open(result_pkl_file, 'wb')

    pickle.dump(obj=result, file=f, protocol=pickle.HIGHEST_PROTOCOL)

    f.close()

    result.export_to_xlsx(os.path.join(output_dir, 'result.xlsx'))

    predicted_y = decision_tree.classify(test_data[0])

    return predicted_y

if __name__ == '__main__':

    args_ok = False

    parser = argparse.ArgumentParser(description='Run Decision Tree on single resident CASAS datasets.')

    parser.add_argument('-d', '--dataset', help='Directory to original datasets')

    parser.add_argument('-o', '--output', help='Output folder')

    parser.add_argument('--h5py', help='HDF5 dataset folder')

    args = parser.parse_args()

    # Default parameters

    log_filename = os.path.basename(__file__).split('.')[0] + \

                   '-%s.log' % datetime.now().strftime('%y%m%d_%H:%M:%S')

    # Setup output directory

    output_dir = args.output

    if output_dir is not None:

        output_dir = os.path.abspath(os.path.expanduser(output_dir))

        if os.path.exists(output_dir):

            # Found output_dir, check if it is a directory

            if not os.path.isdir(output_dir):

                exit('Output directory %s is found, but not a directory. Abort.' % output_dir)

        else:

            # Create directory

            os.mkdir(output_dir)

    else:

        output_dir = '.'

    log_filename = os.path.join(output_dir, log_filename)

    # Setup Logging as early as possible

    logging.basicConfig(level=logging.DEBUG,

                        format='[%(asctime)s] %(name)s:%(levelname)s:%(message)s',

                        handlers=[logging.FileHandler(log_filename),

                                  logging.StreamHandler()])

    # If dataset is specified, update h5py

    casas_data_dir = args.dataset

    if casas_data_dir is not None:

        casas_data_dir = os.path.abspath(os.path.expanduser(casas_data_dir))

        if not os.path.isdir(casas_data_dir):

            exit('CASAS dataset at %s does not exist. Abort.' % casas_data_dir)

    # Find h5py dataset first

    h5py_dir = args.h5py

    if h5py_dir is not None:

        h5py_dir = os.path.abspath(os.path.expanduser(h5py_dir))

    else:

        # Default location

        h5py_dir = os.path.join(output_dir, 'h5py')

    if os.path.exists(h5py_dir):

        if not os.path.isdir(h5py_dir):

            exit('h5py dataset location %s is not a directory. Abort.' % h5py_dir)

    if not CASASFuel.files_exist(h5py_dir):

        # Finish check and creating all directory needed - now load datasets

        if casas_data_dir is not None:

            casas_data = CASASData(path=casas_data_dir)

            casas_data.summary()

            # SVM needs to use statistical feature with per-sensor and normalization

            casas_data.populate_feature(method='stat', normalized=False, per_sensor=False)

            casas_data.export_hdf5(h5py_dir)

    casas_fuel = CASASFuel(dir_name=h5py_dir)

    # Prepare learning result

    result_pkl_file = os.path.join(output_dir, 'result.pkl')

    result = None

    if os.path.isfile(result_pkl_file):

        f = open(result_pkl_file, 'rb')

        result = pickle.load(f)

        f.close()

        if result.data != h5py_dir:

            logger.error('Result pickle file found for different dataset %s' % result.data)

            exit('Cannot save learning result at %s' % result_pkl_file)

    else:

        result = LearningResult(name='DecisionTree', data=h5py_dir, mode='by_week')

    num_classes = casas_fuel.get_output_dims()

    # Open Fuel and get all splits

    split_list = casas_fuel.get_set_list()

    train_name = split_list[0]

    train_set = casas_fuel.get_dataset((train_name,), load_in_memory=True)

    (train_set_data) = train_set.data_sources

    # Prepare Back Annotation

    fp_back_annotated = open(os.path.join(output_dir, 'back_annotated.txt'), 'w')

    for i in range(1, len(split_list)):

        test_name = split_list[i]

        test_set = casas_fuel.get_dataset((test_name,), load_in_memory=True)

        (test_set_data) = test_set.data_sources

        # run svm

        logger.info('Training on %s, Testing on %s' % (train_name, test_name))

        if result.get_record_by_key(test_name) is None:

            prediction = training_and_test(test_name, train_set_data, test_set_data, num_classes, result)

        else:

            prediction = load_and_test(test_name, test_set_data, num_classes, result)

        casas_fuel.back_annotate(fp_back_annotated, prediction=prediction, split_id=i)

        train_name = test_name

        train_set_data = test_set_data

    f = open(result_pkl_file, 'wb')

    pickle.dump(obj=result, file=f, protocol=pickle.HIGHEST_PROTOCOL)

    f.close()

    result.export_to_xlsx(os.path.join(output_dir, 'result.xlsx'))