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import nni
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn import linear_model
import logging
import numpy as np
from sklearn.metrics import r2_score
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lars
from sklearn.linear_model import ARDRegression

LOG = logging.getLogger('sklearn_regression')

def load_data():
    '''Load dataset, use boston dataset'''
    boston = load_boston()
    X_train, X_test, y_train, y_test = train_test_split(
        boston.data, boston.target, random_state=99, test_size=0.25)
    #normalize data
    ss_X = StandardScaler()
    ss_y = StandardScaler()

    X_train = ss_X.fit_transform(X_train)
    X_test = ss_X.transform(X_test)
    y_train = ss_y.fit_transform(y_train[:, None])[:, 0]
    y_test = ss_y.transform(y_test[:, None])[:, 0]

    return X_train, X_test, y_train, y_test

def get_default_parameters():
    '''get default parameters'''
    params = {'model_name': 'LinearRegression'}
    return params

def get_model(PARAMS):
    '''Get model according to parameters'''
    model_dict = {
        'LinearRegression': LinearRegression(),
        'Ridge': Ridge(),
        'Lars': Lars(),
        'ARDRegression': ARDRegression()

    }
    if not model_dict.get(PARAMS['model_name']):
        LOG.exception('Not supported model!')
        exit(1)

    model = model_dict[PARAMS['model_name']]
    model.normalize = bool(PARAMS['normalize'])

    return model

def run(X_train, X_test, y_train, y_test, model):
    '''Train model and predict result'''
    model.fit(X_train, y_train)
    predict_y = model.predict(X_test)
    score = r2_score(y_test, predict_y)
    LOG.debug('r2 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
