from __future__ import print_function

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.preprocessor.normalize import Normalizer

from plasma.models.loader import Loader

#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':

from plasma.preprocessor.normalize import VarNormalizer as Normalizer #performs !much better than minmaxnormalizer

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

from plasma.preprocessor.normalize import AveragingVarNormalizer as Normalizer #performs !much better than minmaxnormalizer

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)))

from plasma.models import runner

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)