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)