# -*- coding: utf-8 -*-

from __future__ import print_function

import io

import random

import sys

try:

import cPickle as pickle

except:

import pickle

from passage.preprocessing import Tokenizer

from passage.layers import Embedding, GatedRecurrent, Dense

from passage.models import RNN

from passage.utils import save, load

random.seed(0)

#textfile, labelfile, embedding_size, gru_size, num_epochs = sys.argv[1:]

textfile = 'cwi_inputs.lemmatized.txt'

labelfile = 'cwi_labels.txt'

train_text, train_labels = [], []

with io.open(textfile, 'r', encoding='utf8') as txtfin, \

io.open(labelfile, 'r') as labelfin:

for text, label in zip(txtfin, labelfin):

train_text.append(text.strip())

train_labels.append(int(label.strip()))

tokenizer = pickle.load(open('cwi_inputs.lemmatized.txt-tokenizer.pkl', 'rb'))

best_hyperparams = [(10,10,10), (10,50,10), (50,200,10), (100,10,10), (200,20,10)]

train_tokens = tokenizer.fit_transform(train_text)

'''

# Test on train.

for embedding_size, gru_size, num_epochs in best_hyperparams:

modelfile_name = 'stubborn_model.gridsearch.embedding{}.gru{}.epoch{}'.format(embedding_size, gru_size, num_epochs)

model = load(modelfile_name + '.pkl')

results = model.predict(train_tokens)

count = 0

for r, g in zip(results, train_labels):

if int(r>=0.5) == int(g):

count+=1

results = 1.0 * count /len(train_labels)

print (modelfile_name + '\t' + str(results))

'''

models = []

predictions =[]

for embedding_size, gru_size, num_epochs in best_hyperparams:

modelfile_name = 'stubborn_model.gridsearch.embedding{}.gru{}.epoch{}'.format(embedding_size, gru_size, num_epochs)

model = load(modelfile_name + '.pkl')

models.append(model)

with io.open('ensemble.train', 'w') as fout:

for instance, label in zip(train_tokens, train_labels):

line = " ".join([str(model.predict([instance])[0][0]) for model in models])

fout.write(unicode(line + " " + str(label) + '\n'))

testfile = 'cwi_test.lemmatized.txt'

test_text = []

with io.open(testfile, 'r', encoding='utf8') as fin:

for text in fin:

test_text.append(text.strip())

test_tokens = tokenizer.transform(test_text)

with io.open('ensemble.test', 'w') as fout:

results = []

for model in models:

results.append(model.predict(test_tokens))

for m1, m2, m3, m4, m5 in zip(*results):

outline = unicode(" ".join(map(str, [m1[0], m2[0], m3[0], m4[0], m5[0]])) + '\n')

#print (outline)

fout.write(outline)

"""

# Test on train held-out (Unlemmatized)

stubborn_model.embedding10.gru10.epoch10 0.831111111111

stubborn_model.embedding10.gru50.epoch10 0.804444444444

stubborn_model.embedding50.gru200.epoch10 0.813333333333

stubborn_model.embedding100.gru10.epoch10 0.817777777778

stubborn_model.embedding200.gru20.epoch10 0.831111111111

# Test on train (Unlemmatized)

stubborn_model.gridsearch.embedding10.gru10.epoch10 0.801513128616

stubborn_model.gridsearch.embedding10.gru50.epoch10 0.805073431242

stubborn_model.gridsearch.embedding50.gru200.epoch10 0.803293279929

stubborn_model.gridsearch.embedding100.gru10.epoch10 0.813974187806

stubborn_model.gridsearch.embedding200.gru20.epoch10 0.805963506898

# Test on train.lemmatized.

stubborn_model.gridsearch.embedding10.gru10.epoch10 0.807298620383

stubborn_model.gridsearch.embedding10.gru50.epoch10 0.809078771696

stubborn_model.gridsearch.embedding50.gru200.epoch10 0.804628393413

stubborn_model.gridsearch.embedding100.gru10.epoch10 0.878504672897

stubborn_model.gridsearch.embedding200.gru20.epoch10 0.877169559413

"""