# from plasma.models import runner

# from plasma.models.loader import Loader

# import numpy as np

# from hyperopt import Trials, tpe

# from plasma.conf import conf

# from pprint import pprint

# pprint(conf)

# #from plasma.primitives.shots import Shot, ShotList

# #from plasma.models.runner import train, make_predictions

# ,make_predictions_gpu

# if conf['data']['normalizer'] == 'minmax':

# from plasma.preprocessor.normalize import MinMaxNormalizer as Normalizer

# elif conf['data']['normalizer'] == 'meanvar':

# from plasma.preprocessor.normalize import MeanVarNormalizer as Normalizer

# elif conf['data']['normalizer'] == 'var':

# # performs !much better than minmaxnormalizer

# from plasma.preprocessor.normalize import VarNormalizer as Normalizer

# elif conf['data']['normalizer'] == 'averagevar':

# # performs !much better than minmaxnormalizer

# from plasma.preprocessor.normalize import (

# AveragingVarNormalizer as Normalizer

# )

# else:

# print('unkown normalizer. exiting')

# exit(1)

# np.random.seed(1)

# print("normalization", end='')

# nn = Normalizer(conf)

# nn.train()

# loader = Loader(conf, nn)

# shot_list_train, shot_list_validate, shot_list_test = loader.load_shotlists(

# conf)

# print("...done")

# print('Training on {} shots, testing on {} shots'.format(

# len(shot_list_train), len(shot_list_test)))

# specific_runner = runner.HyperRunner(conf, loader, shot_list_train)

# best_run, best_model = specific_runner.frnn_minimize(

# algo=tpe.suggest, max_evals=2, trials=Trials())

# print(best_run)

# print(best_model)