from keras.models import Sequential, Graph

from keras.layers.embeddings import Embedding

from keras.layers.convolutional import Convolution2D, MaxPooling2D

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

import pickle

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

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]]

max_input_length = validation_data['features'].shape[1]

CNN_filters = {{choice([5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100,105,110,115,120,125,130,135,140,145,150,155,160,165,170,175,180,185,190,195,200])}}

CNN_rows = {{choice([1,2,3,4,5,6,7,8,9,10])}}

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', 'adam'])}}

is_trainable = {{choice([ True, False ])}}

D = embeddings.shape[-1]

cols = D

out_dim = train_data['labels'].shape[-1]

graph = Graph()

graph.add_input(name='txt_data', input_shape=[train_data['features'].shape[-1]], dtype='int')

graph.add_node(Embedding( input_dim = embeddings.shape[0], output_dim=D, weights=[embeddings], trainable=is_trainable, input_length = max_input_length), name='Emb', input='txt_data')

graph.add_node(Reshape((1, max_input_length, D)), name = "Reshape", input='Emb')

graph.add_node( Convolution2D(CNN_filters, CNN_rows, cols, activation='sigmoid' ) , name='Conv', input='Reshape')

sh = graph.nodes['Conv'].output_shape

graph.add_node( MaxPooling2D(pool_size=(sh[-2], sh[-1])) , name='MaxPool', input='Conv')

graph.add_node( Flatten() , name='Flat', input='MaxPool')

graph.add_node( Dense(Dense_size, activation='sigmoid',W_regularizer=l2({{uniform(0, 1)}}),activity_regularizer=activity_l2({{uniform(0, 1)}})) , name='Dtxt', input='Flat')

graph.add_node( Dropout({{uniform(0, 1)}}) , name='Dropout1', input='Dtxt')

graph.add_input(name='av_data', input_shape=[train_data['AV'].shape[-1]])

graph.add_node( Dense(Dense_size2, activation='sigmoid',W_regularizer=l2({{uniform(0, 1)}}),activity_regularizer=activity_l2({{uniform(0, 1)}})) , name='Dav', input='av_data')

graph.add_node( Dropout({{uniform(0, 1)}}) , name='Dropout2', input='Dav')

graph.add_node( Dense(Dense_size3, activation='sigmoid',W_regularizer=l2({{uniform(0, 1)}}),activity_regularizer=activity_l2({{uniform(0, 1)}})), name='Dense1', inputs=['Dropout2', 'Dropout1'], merge_mode='concat')

graph.add_node( Dropout({{uniform(0, 1)}}) , name='Dropout3', input='Dense1')

graph.add_node( Dense(out_dim, activation='linear') , name='Dense2', input='Dropout3')

graph.add_output(name='output', input = 'Dense2')

graph.compile(optimizer=opt, loss={'output':'rmse'})

graph.fit(

{

'txt_data':train_data['features'],

'av_data':train_data['AV'],

'output':train_data['labels']

},

nb_epoch=500,

batch_size=64

)

scores = graph.evaluate({'txt_data':validation_data['features'], 'av_data':validation_data['AV'], 'output':validation_data['labels']})

print(scores)