How to use the nni.get_next_parameter function in nni

# Copyright (c) Microsoft Corporation.
# Licensed under the MIT license.

import nni

params = nni.get_next_parameter()
print('params:', params)
x = params['x']

nni.report_final_result(x)

return model

def run(X_train, X_test, y_train, y_test, model):
    '''Train model and predict result'''
    model.fit(X_train, y_train)
    score = model.score(X_test, y_test)
    LOG.debug('score: %s' % score)
    nni.report_final_result(score)

if __name__ == '__main__':
    X_train, X_test, y_train, y_test = load_data()

    try:
        # get parameters from tuner
        RECEIVED_PARAMS = nni.get_next_parameter()
        LOG.debug(RECEIVED_PARAMS)
        PARAMS = get_default_parameters()
        PARAMS.update(RECEIVED_PARAMS)
        LOG.debug(PARAMS)
        model = get_model(PARAMS)
        run(X_train, X_test, y_train, y_test, model)
    except Exception as exception:
        LOG.exception(exception)
        raise

SendMetrics(),
            EarlyStopping(min_delta=0.001, patience=10),
            TensorBoard(log_dir=TENSORBOARD_DIR),
        ],
    )

    # trial report final acc to tuner
    _, acc = net.evaluate(x_test, y_test)
    logger.debug("Final result is: %.3f", acc)
    nni.report_final_result(acc)


if __name__ == "__main__":
    try:
        # trial get next parameter from network morphism tuner
        RCV_CONFIG = nni.get_next_parameter()
        logger.debug(RCV_CONFIG)
        parse_rev_args(RCV_CONFIG)
        train_eval()
    except Exception as exception:
        logger.exception(exception)
        raise

from sklearn.preprocessing import LabelEncoder

sys.path.append('../../')

from fe_util import *
from model import *

logger = logging.getLogger('auto-fe-examples')

if __name__ == '__main__':
    file_name = '~/Downloads/haberman.data'
    target_name = 'Label'
    id_index = 'Id'

    # get parameters from tuner
    RECEIVED_PARAMS = nni.get_next_parameter()
    logger.info("Received params:\n", RECEIVED_PARAMS)
    
    # list is a column_name generate from tuner
    df = pd.read_csv(file_name, sep = ',')
    df.columns = [
        'c1', 'c2', 'n1', 'Label'
    ]
    df['Label'] = df['Label'] -1 #LabelEncoder().fit_transform(df['Label'])
    
    if 'sample_feature' in RECEIVED_PARAMS.keys():
        sample_col = RECEIVED_PARAMS['sample_feature']
    else:
        sample_col = []
    
    # raw feaure + sample_feature
    df = name2feature(df, sample_col, target_name)

if layer_name == 'Empty':
            # Empty Layer
            params[key] = ['Empty']
        elif layer_name == 'Conv':
            # Conv layer
            params[key] = [layer_name, value['kernel_size'], value['kernel_size']]
        else:
            # Pooling Layer
            params[key] = [layer_name, value['pooling_size'], value['pooling_size']]
    return params


if __name__ == '__main__':
    try:
        # get parameters form tuner
        data = nni.get_next_parameter()
        logger.debug(data)

        RCV_PARAMS = parse_init_json(data)
        logger.debug(RCV_PARAMS)
        params = vars(get_params())
        params.update(RCV_PARAMS)
        print(RCV_PARAMS)

        main(params)
    except Exception as exception:
        logger.exception(exception)
        raise

# predict
    y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)

    # eval
    rmse = mean_squared_error(y_test, y_pred) ** 0.5
    print('The rmse of prediction is:', rmse)

    nni.report_final_result(rmse)

if __name__ == '__main__':
    lgb_train, lgb_eval, X_test, y_test = load_data()

    try:
        # get parameters from tuner
        RECEIVED_PARAMS = nni.get_next_parameter()
        LOG.debug(RECEIVED_PARAMS)
        PARAMS = get_default_parameters()
        PARAMS.update(RECEIVED_PARAMS)
        LOG.debug(PARAMS)

        # train
        run(lgb_train, lgb_eval, PARAMS, X_test, y_test)
    except Exception as exception:
        LOG.exception(exception)
        raise

from sklearn.preprocessing import LabelEncoder

sys.path.append('../../')

from fe_util import *
from model import *

logger = logging.getLogger('auto-fe-examples')

if __name__ == '__main__':
    file_name = ' ~/Downloads/breast-cancer.data'
    target_name = 'Class'
    id_index = 'Id'

    # get parameters from tuner
    RECEIVED_PARAMS = nni.get_next_parameter()
    logger.info("Received params:\n", RECEIVED_PARAMS)
    
    # list is a column_name generate from tuner
    df = pd.read_csv(file_name, sep = ',')
    df.columns = [
        'Class', 'age', 'menopause', 'tumor-size', 'inv-nodes',
        'node-caps', 'deg-malig', 'breast', 'breast-quad', 'irradiat'
    ]
    df['Class'] = LabelEncoder().fit_transform(df['Class'])
    
    if 'sample_feature' in RECEIVED_PARAMS.keys():
        sample_col = RECEIVED_PARAMS['sample_feature']
    else:
        sample_col = []
    
    # raw feaure + sample_feature

SendMetrics(),
            EarlyStopping(min_delta=0.001, patience=10),
            TensorBoard(log_dir=TENSORBOARD_DIR),
        ],
    )

    # trial report final acc to tuner
    _, acc = net.evaluate(x_test, y_test)
    logger.debug("Final result is: %.3f", acc)
    nni.report_final_result(acc)


if __name__ == "__main__":
    try:
        # trial get next parameter from network morphism tuner
        RCV_CONFIG = nni.get_next_parameter()
        logger.debug(RCV_CONFIG)
        parse_rev_args(RCV_CONFIG)
        train_eval()
    except Exception as exception:
        logger.exception(exception)
        raise

'learning_rate': 0.001
    }

if __name__ == '__main__':
    PARSER = argparse.ArgumentParser()
    PARSER.add_argument("--batch_size", type=int, default=200, help="batch size", required=False)
    PARSER.add_argument("--epochs", type=int, default=10, help="Train epochs", required=False)
    PARSER.add_argument("--num_train", type=int, default=60000, help="Number of train samples to be used, maximum 60000", required=False)
    PARSER.add_argument("--num_test", type=int, default=10000, help="Number of test samples to be used, maximum 10000", required=False)

    ARGS, UNKNOWN = PARSER.parse_known_args()

    try:
        # get parameters from tuner
        # RECEIVED_PARAMS = {"optimizer": "Adam", "learning_rate": 0.00001}
        RECEIVED_PARAMS = nni.get_next_parameter()
        LOG.debug(RECEIVED_PARAMS)
        PARAMS = generate_default_params()
        PARAMS.update(RECEIVED_PARAMS)
        # train
        train(ARGS, PARAMS)
    except Exception as e:
        LOG.exception(e)
        raise