import numpy as np
import load_data
from generative_alg import *
from keras.utils.generic_utils import Progbar
from load_data import load_word_indices
from keras.preprocessing.sequence import pad_sequences
import pandas as pa
import augment
def test_points(premises, labels, noises, gtest, cmodel, hypo_len):
p = Progbar(len(premises))
hypos = []
bs = 64
for i in range(len(labels) / bs):
words, _ = generative_predict_beam(gtest, premises[i * bs: (i+1)*bs],
noises[i * bs: (i+1)*bs,None,:], labels[i * bs: (i+1)*bs], True, hypo_len)
hypos.append(words)
p.add(len(words))
hypos = np.vstack(hypos)
cpreds = cmodel.evaluate([premises[:len(hypos)], hypos], labels[:len(hypos)])
print cpreds
def print_hypos(premise, label, gen_test, beam_size, hypo_len, noise_size, wi):
words = single_generate(premise, label, gen_test, beam_size, hypo_len, noise_size)
batch_size = gen_test[0].input_layers[0].input_shape[0]
per_batch = batch_size / beam_size
premises = [premise] * per_batch
noise_input = np.random.normal(scale=0.11, size=(per_batch, 1, noise_size))
class_indices = np.ones(per_batch) * label
class_indices = load_data.convert_to_one_hot(class_indices, 3)
words, loss = generative_predict_beam(gen_test, premises, noise_input,
class_indices, True, hypo_len)
print 'Premise:', wi.print_seq(premise)
print 'Label:', load_data.LABEL_LIST[label]
print
print 'Hypotheses:'
for h in words:
print wi.print_seq(h)
def load_sentence(string, wi, len = 25):
tokens = string.split()
tokens = load_word_indices(tokens, wi.index)
return pad_sequences([tokens], maxlen = len, padding = 'pre')[0]
def find_true_examples():
models = ['8-150-2', '8-150-4', '8-150-8', '8-150-16', '8-150-32', '8-150-147', '6-150-8', '7-150-8' ,'9-226-8']
final_premises = set()
subset = {}
for model in models:
data = pa.read_csv('models/real' + model + '/dev1')
data = data[data['ctrue']]
neutr = data[data['label'] == 'neutral']
contr = data[data['label'] == 'contradiction']
entail = data[data['label'] == 'entailment']
subset[model] = [neutr, contr, entail]
premises = set(neutr['premise']) & set(contr['premise']) & set(entail['premise'])
if len(final_premises) == 0:
final_premises = premises
else:
final_premises &= premises
final_premises = list(final_premises)
with open('results/ext_examples.txt', 'w') as fi:
for i in range(len(final_premises)):
premise = final_premises[i]
fi.write(premise + '\n')
for m in models:
fi.write(m + '\n')
for l in range(3):
filtered = subset[m][l][subset[m][l]['premise'] == premise]
for f in range(len(filtered)):
hypo = filtered['hypo'].iloc[f]
label = filtered['label'].iloc[f][:4]
fi.write(label + '\t' + hypo + '\n')
fi.write('\n')
