2_main_stanford.py
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import numpy
import time
import sys
import subprocess
import os
import random
from modules.data import load
from modules.rnn.nnet_for_dependency_trees import model2
#from modules.metrics.accuracy import conlleval
from modules.utils.tools import shuffle, words_in_from_down_to_top_order, load_conll_data, load_stanford_data2, filter_embeddings
import theano.tensor as T
import theano
import itertools
import os.path
import pickle
if __name__ == '__main__':
theano.config.floatX = 'float64'
file_with_filtered_embeddings = "embeddings/embedding_and_words2ids.pkl"
if not os.path.exists(file_with_filtered_embeddings):
print("Cannot find file with only needed embeddings. We use 'filter_embeddings' in order to create it.")
filter_embeddings(["data/sst/train/dlabels.txt", "data/sst/train/dparents.txt","data/sst/train/sents.toks", "data/sst/dev/dlabels.txt", "data/sst/dev/dparents.txt","data/sst/dev/sents.toks", "data/sst/test/dlabels.txt", "data/sst/test/dparents.txt","data/sst/test/sents.toks"],
"/home/norbert/Doktorat/clarin2sent/treelstm/data/glove/glove.840B.300d.txt",
file_with_filtered_embeddings)
s = {'lr':0.01, #0.03 (przy r=0.05) dobilo do dopasowania 0.9 na 500 obserwacjach po 15 epokach
#bo 40 epoki, bylo ok 0.9, a potem spadlo do stalej predykcji rownej 0,
#chociaz w zbiorze treningowym nie bylo ani jednaj obserwacji z etykieta 0 !!!
#0.03 (r=0.05) przy 5000 obs do 15 epoki stalo na ok 75% a potem spadlo dor predykcji stalej rownej 0
#0.05 - reszta j.w. nic sie nie nauczyl, a przy 10 iteracji predykcja spadla do stalej - 0
'verbose':1,
'decay':False, # decay on the learning rate if improvement stops
'nepochs':200,
'seed':345,
'nh':300, # dimension of hidden state
'nc':3 , # number of y classes
'ds':30} # dimension of sentiment state
# instanciate the model
numpy.random.seed(s['seed'])
random.seed(s['seed'])
rnn = model2( nh = s['nh'],
nc = s['nc'],
ds = s['ds'],
w2v_model_path = file_with_filtered_embeddings, #sciezka do pliku z embeddingami
max_phrase_length = 60 # przydaloby sie to uzaleznic od danych, ale nie jest to konieczne.
# Wazne, ze to jest wartosc nie mniejsza niz dlugosc najdluzszego zdania w danych
)
train_data = load_stanford_data2("data/sst/train/dlabels.txt", "data/sst/train/dparents.txt","data/sst/train/sents.toks",rnn.words2ids,False)[::8][0:1000]
#tmp = load_stanford_data2("data/sst/train/dlabels.txt", "data/sst/train/dparents.txt","data/sst/train/sents.toks",rnn.words2ids,False)[::8][0:1000]
#print(train_data[0][2])
#print([x[2] for x in tmp[0:3]])
#2/0
test_data = load_stanford_data2("data/sst/test/dlabels.txt", "data/sst/test/dparents.txt","data/sst/test/sents.toks",rnn.words2ids,False)[::2][0:300]
n_train = len(train_data)
n_test = len(test_data)
#to do: training with early stopping on validation set
#train_data = theano.shared(train_data)
#test_data = theano.shared(test_data)
print("Number of training phrases: ", n_train)
s['clr'] = s['lr']
for e in xrange(s['nepochs']):
if e < 2:
continue
if e>2:
time.sleep(900)
print("epoch: ", e)
# shuffle
#shuffle([train_data], s['seed'])
tic = time.time()
for i in range(n_train):
if i > 0 and i % 100 == 0:
print(i)
print(time.time()-tic)
counts = numpy.zeros((3,3),dtype='int')
for z in range(n_test):
pred = rnn.classify(test_data[z][0],test_data[z][1], test_data[z][2])
for j in range(len(pred)):
counts[pred[j], test_data[z][3][j]] += 1
print("On test set:")
print counts
print numpy.diag(counts).sum()/float(counts.sum())
#if i % 3 == 0:
#print rnn.print_ep(train_data[i][0],train_data[i][1], train_data[i][2], train_data[i][3])
rnn.train(train_data[i][0],train_data[i][1], train_data[i][2], train_data[i][3], s['clr'])
#rnn.normalize()
#if s['verbose']:
# print ('[learning] epoch %i >> %2.2f%%'%(e,(i+1)*100./n_train),'completed in %.2f (sec) <<\r'%(time.time()-tic))
# sys.stdout.flush()
print(time.time()-tic)
rnn.save("saved_models3", e)
tic = time.time()
# Train
counts = numpy.zeros((3,3),dtype='int')
for i in range(n_train):
if i % 4 == 0: #sprawdzamy dopasowanie na 1/4 zbioru zeby oszczedzic czas
pred = rnn.classify(train_data[i][0],train_data[i][1], train_data[i][2])
for j in range(len(pred)):
counts[pred[j], train_data[i][3][j]] += 1
print("On train set:")
print counts
print numpy.diag(counts).sum()/float(counts.sum())
# Test:
counts = numpy.zeros((3,3),dtype='int')
for i in range(n_test):
#if i % 5 == 0:
pred = rnn.classify(test_data[i][0],test_data[i][1], test_data[i][2])
for j in range(len(pred)):
counts[pred[j], test_data[i][3][j]] += 1
print("On test set:")
print counts
print numpy.diag(counts).sum()/float(counts.sum())
if e < 2:
print(time.time()-tic)