from keras.models import Sequential

from keras.layers.core import Dense, Reshape, Activation, Flatten, Dropout

from keras.regularizers import l1, activity_l1, l2, activity_l2

from aiding_funcs.embeddings_handling import get_the_folds, join_folds

from aiding_funcs.label_handling import MaxMin, myRMSE, MaxMinFit

import pickle

train = pickle.load( open( "/data/dpappas/personality/train.p", "rb" ) )

no_of_folds = 10

folds = get_the_folds(train,no_of_folds)

train_data = join_folds(folds,folds.keys()[:-1])

validation_data = folds[folds.keys()[-1]]

mins, maxs = MaxMin(train_data['AV'])

T_AV = MaxMinFit(train_data['AV'], mins, maxs)

Dense_size = {{choice([50, 100, 150, 200, 250, 300, 350, 400, 450, 500])}}

Dense_size2 = {{choice([50, 100, 150, 200, 250, 300, 350, 400, 450, 500])}}

Dense_size3 = {{choice([50, 100, 150, 200, 250, 300, 350, 400, 450, 500])}}

opt = {{choice([ 'adadelta','sgd','rmsprop', 'adagrad', 'adadelta', 'adam'])}}

out_dim = 5

model = Sequential()

model.add(Dense(Dense_size, activation='sigmoid',W_regularizer=l2({{uniform(0, 1)}}),activity_regularizer=activity_l2({{uniform(0, 1)}}),input_dim = train_data['AV'].shape[-1] ))

model.add(Dense(Dense_size2, activation='sigmoid',W_regularizer=l2({{uniform(0, 1)}}),activity_regularizer=activity_l2({{uniform(0, 1)}})))

model.add(Dense(Dense_size3, activation='sigmoid',W_regularizer=l2({{uniform(0, 1)}}),activity_regularizer=activity_l2({{uniform(0, 1)}})))

model.add(Dense(out_dim, activation='linear',W_regularizer=l2({{uniform(0, 1)}}),activity_regularizer=activity_l2({{uniform(0, 1)}})))

model.compile(loss='rmse', optimizer=opt)

model.fit(T_AV, train_data['labels'], nb_epoch=500, show_accuracy=False, verbose=2)

#score = model.evaluate( validation_data['features'], validation_data['labels'])

score = model.evaluate( T_AV, train_data['labels'])

print("score : " +str(score))