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train.py
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train.py
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# -*- coding: utf-8 -*-
#!/usr/bin/env python
from data_iterator import *
from state import *
from dialog_encdec import *
from utils import *
from evaluation import *
import time
import traceback
import os.path
import sys
import argparse
import cPickle
import logging
import search
import pprint
import numpy
import collections
import signal
import math
import matplotlib
matplotlib.use('Agg')
import pylab
class Unbuffered:
def __init__(self, stream):
self.stream = stream
def write(self, data):
self.stream.write(data)
self.stream.flush()
def __getattr__(self, attr):
return getattr(self.stream, attr)
sys.stdout = Unbuffered(sys.stdout)
logger = logging.getLogger(__name__)
### Unique RUN_ID for this execution
RUN_ID = str(time.time())
### Additional measures can be set here
measures = ["train_cost", "train_misclass",
"valid_cost", "valid_misclass",
"valid_emi", "valid_bleu",
"valid_jaccard", "valid_recall_at_1",
"valid_recall_at_5", "valid_mrr_at_5",
"tfidf_cs_at_1", "tfidf_cs_at_5"]
def init_timings():
timings = {}
for m in measures:
timings[m] = []
return timings
def save(model, timings):
print "Saving the model..."
# ignore keyboard interrupt while saving
start = time.time()
s = signal.signal(signal.SIGINT, signal.SIG_IGN)
model.save(model.state['save_dir'] + '/' + model.state['run_id'] + "_" + model.state['prefix'] + 'model.npz')
cPickle.dump(model.state, open(model.state['save_dir'] + '/' + model.state['run_id'] + "_" + model.state['prefix'] + 'state.pkl', 'w'))
numpy.savez(model.state['save_dir'] + '/' + model.state['run_id'] + "_" + model.state['prefix'] + 'timing.npz', **timings)
signal.signal(signal.SIGINT, s)
print "Model saved, took {}".format(time.time() - start)
def load(model, filename):
print "Loading the model..."
# ignore keyboard interrupt while saving
start = time.time()
s = signal.signal(signal.SIGINT, signal.SIG_IGN)
model.load(filename)
signal.signal(signal.SIGINT, s)
print "Model loaded, took {}".format(time.time() - start)
def main(args):
logging.basicConfig(level = logging.DEBUG,
format = "%(asctime)s: %(name)s: %(levelname)s: %(message)s")
state = eval(args.prototype)()
timings = init_timings()
if args.resume != "":
logger.debug("Resuming %s" % args.resume)
state_file = args.resume + '_state.pkl'
timings_file = args.resume + '_timing.npz'
if os.path.isfile(state_file) and os.path.isfile(timings_file):
logger.debug("Loading previous state")
state = cPickle.load(open(state_file, 'r'))
timings = dict(numpy.load(open(timings_file, 'r')))
for x, y in timings.items():
timings[x] = list(y)
else:
raise Exception("Cannot resume, cannot find files!")
logger.debug("State:\n{}".format(pprint.pformat(state)))
logger.debug("Timings:\n{}".format(pprint.pformat(timings)))
model = DialogEncoderDecoder(state)
rng = model.rng
if args.resume != "":
filename = args.resume + '_model.npz'
if os.path.isfile(filename):
logger.debug("Loading previous model")
load(model, filename)
else:
raise Exception("Cannot resume, cannot find model file!")
if 'run_id' not in model.state:
raise Exception('Backward compatibility not ensured! (need run_id in state)')
else:
# assign new run_id key
model.state['run_id'] = RUN_ID
logger.debug("Compile trainer")
if not state["use_nce"]:
train_batch = model.build_train_function()
else:
train_batch = model.build_nce_function()
eval_batch = model.build_eval_function()
eval_misclass_batch = model.build_eval_misclassification_function()
random_sampler = search.RandomSampler(model)
beam_sampler = search.BeamSampler(model)
logger.debug("Load data")
train_data, \
valid_data, \
test_data = get_batch_iterator(rng, state)
train_data.start()
# Build the data structures for Bleu evaluation
if 'bleu_evaluation' in state:
bleu_eval = BleuEvaluator()
jaccard_eval = JaccardEvaluator()
recall_at_1_eval = RecallEvaluator(n=1)
recall_at_5_eval = RecallEvaluator(n=5)
mrr_at_5_eval = MRREvaluator(n=5)
tfidf_cs_at_1_eval = TFIDF_CS_Evaluator(model, train_data.data_len, 1)
tfidf_cs_at_5_eval = TFIDF_CS_Evaluator(model, train_data.data_len, 5)
samples = open(state['bleu_evaluation'], 'r').readlines()
n = state['bleu_context_length']
contexts = []
targets = []
for x in samples:
sentences = x.strip().split('\t')
assert len(sentences) > n
contexts.append(sentences[:n])
targets.append(sentences[n:])
# Start looping through the dataset
step = 0
patience = state['patience']
start_time = time.time()
train_cost = 0
train_misclass = 0
train_done = 0
ex_done = 0
while (step < state['loop_iters'] and
(time.time() - start_time)/60. < state['time_stop'] and
patience >= 0):
# Sample stuff
if step % 200 == 0:
for param in model.params:
print "%s = %.4f" % (param.name, numpy.sum(param.get_value() ** 2) ** 0.5)
samples, costs = random_sampler.sample([[]], n_samples=1, n_turns=3)
print "Sampled : {}".format(samples[0])
# Training phase
batch = train_data.next()
# Train finished
if not batch:
# Restart training
logger.debug("Got None...")
break
logger.debug("[TRAIN] - Got batch %d,%d" % (batch['x'].shape[1], batch['max_length']))
x_data = batch['x']
max_length = batch['max_length']
x_cost_mask = batch['x_mask']
if state['use_nce']:
y_neg = rng.choice(size=(10, max_length, x_data.shape[1]), a=model.idim, p=model.noise_probs).astype('int32')
c = train_batch(x_data, y_neg, max_length, x_cost_mask)
else:
c = train_batch(x_data, max_length, x_cost_mask)
if numpy.isinf(c) or numpy.isnan(c):
logger.warn("Got NaN cost .. skipping")
continue
train_cost += c
# Compute word-error rate
miscl = eval_misclass_batch(x_data, max_length, x_cost_mask)
if numpy.isinf(c) or numpy.isnan(c):
logger.warn("Got NaN misclassification .. skipping")
continue
train_misclass += miscl
train_done += batch['num_preds']
this_time = time.time()
if step % state['train_freq'] == 0:
elapsed = this_time - start_time
h, m, s = ConvertTimedelta(this_time - start_time)
print ".. %.2d:%.2d:%.2d %4d mb # %d bs %d maxl %d acc_cost = %.4f acc_word_perplexity = %.4f acc_mean_word_error = %.4f " % (h, m, s,\
state['time_stop'] - (time.time() - start_time)/60.,\
step, \
batch['x'].shape[1], \
batch['max_length'], \
float(train_cost/train_done), \
math.exp(float(train_cost/train_done)), \
float(train_misclass)/float(train_done))
if valid_data is not None and\
step % state['valid_freq'] == 0 and step > 1:
valid_data.start()
valid_cost = 0
valid_misclass = 0
valid_empirical_mutual_information = 0
valid_wordpreds_done = 0
valid_triples_done = 0
# Prepare variables for plotting histogram over word-perplexities and mutual information
valid_data_len = valid_data.data_len
valid_cost_list = numpy.zeros((valid_data_len,))
valid_pmi_list = numpy.zeros((valid_data_len,))
# Prepare variables for printing the training examples the model performs best and worst on
valid_extrema_setsize = min(state['track_extrema_samples_count'], valid_data_len)
valid_extrema_samples_to_print = min(state['print_extrema_samples_count'], valid_extrema_setsize)
valid_lowest_costs = numpy.ones((valid_extrema_setsize,))*1000
valid_lowest_triples = numpy.ones((valid_extrema_setsize,state['seqlen']))*1000
valid_highest_costs = numpy.ones((valid_extrema_setsize,))*(-1000)
valid_highest_triples = numpy.ones((valid_extrema_setsize,state['seqlen']))*(-1000)
logger.debug("[VALIDATION START]")
while True:
batch = valid_data.next()
# Train finished
if not batch:
break
logger.debug("[VALID] - Got batch %d,%d" % (batch['x'].shape[1], batch['max_length']))
x_data = batch['x']
max_length = batch['max_length']
x_cost_mask = batch['x_mask']
c, c_list = eval_batch(x_data, max_length, x_cost_mask)
c_list = c_list.reshape((batch['x'].shape[1],max_length), order=(1,0))
c_list = numpy.sum(c_list, axis=1)
words_in_triples = numpy.sum(x_cost_mask, axis=0)
c_list = c_list / words_in_triples
if numpy.isinf(c) or numpy.isnan(c):
continue
valid_cost += c
nxt = min((valid_triples_done+batch['x'].shape[1]), valid_data_len)
triples_in_batch = nxt-valid_triples_done
valid_cost_list[(nxt-triples_in_batch):nxt] = numpy.exp(c_list[0:triples_in_batch])
# Store best and worst validation costs
con_costs = np.concatenate([valid_lowest_costs, c_list[0:triples_in_batch]])
con_triples = np.concatenate([valid_lowest_triples, x_data[:, 0:triples_in_batch].T], axis=0)
con_indices = con_costs.argsort()[0:valid_extrema_setsize][::1]
valid_lowest_costs = con_costs[con_indices]
valid_lowest_triples = con_triples[con_indices]
con_costs = np.concatenate([valid_highest_costs, c_list[0:triples_in_batch]])
con_triples = np.concatenate([valid_highest_triples, x_data[:, 0:triples_in_batch].T], axis=0)
con_indices = con_costs.argsort()[-valid_extrema_setsize:][::-1]
valid_highest_costs = con_costs[con_indices]
valid_highest_triples = con_triples[con_indices]
# Compute word-error rate
miscl = eval_misclass_batch(x_data, max_length, x_cost_mask)
if numpy.isinf(c) or numpy.isnan(c):
continue
valid_misclass += miscl
# Compute empirical mutual information
if state['compute_mutual_information'] == True:
# Compute marginal log-likelihood of last utterance in triple:
# We approximate it with the margina log-probabiltiy of the utterance being observed first in the triple
x_data_last_utterance = batch['x_last_utterance']
x_cost_mask_last_utterance = batch['x_mask_last_utterance']
marginal_last_utterance_loglikelihood, marginal_last_utterance_loglikelihood_list = eval_batch(x_data_last_utterance, max_length, x_cost_mask_last_utterance)
marginal_last_utterance_loglikelihood_list = marginal_last_utterance_loglikelihood_list.reshape((batch['x'].shape[1],max_length), order=(1,0))
marginal_last_utterance_loglikelihood_list = numpy.sum(marginal_last_utterance_loglikelihood_list, axis=1)
# If we wanted to normalize histogram plots by utterance length, we should enable this:
#words_in_last_utterance = numpy.sum(x_cost_mask_last_utterance, axis=0)
#marginal_last_utterance_loglikelihood_list = marginal_last_utterance_loglikelihood_list / words_in_last_utterance
# Compute marginal log-likelihood of first utterances in triple by masking the last utterance
x_cost_mask_first_utterances = x_cost_mask - x_cost_mask_last_utterance
marginal_first_utterances_loglikelihood, marginal_first_utterances_loglikelihood_list = eval_batch(x_data, max_length, x_cost_mask_first_utterances)
marginal_first_utterances_loglikelihood_list = marginal_first_utterances_loglikelihood_list.reshape((batch['x'].shape[1],max_length), order=(1,0))
marginal_first_utterances_loglikelihood_list = numpy.sum(marginal_first_utterances_loglikelihood_list, axis=1)
# If we wanted to normalize histogram plots by utterance length, we should enable this:
#words_in_first_utterances = numpy.sum(x_cost_mask_first_utterances, axis=0)
#marginal_first_utterances_loglikelihood_list = marginal_first_utterances_loglikelihood_list / words_in_first_utterances
# Compute empirical mutual information and pointwise empirical mutual information
valid_empirical_mutual_information += -c + marginal_first_utterances_loglikelihood + marginal_last_utterance_loglikelihood
valid_pmi_list[(nxt-triples_in_batch):nxt] = (-c_list*words_in_triples + marginal_first_utterances_loglikelihood_list + marginal_last_utterance_loglikelihood_list)[0:triples_in_batch]
valid_wordpreds_done += batch['num_preds']
valid_triples_done += batch['x'].shape[1]
logger.debug("[VALIDATION END]")
valid_cost /= valid_wordpreds_done
valid_misclass /= float(valid_wordpreds_done)
valid_empirical_mutual_information /= float(valid_triples_done)
if len(timings["valid_cost"]) == 0 or valid_cost < timings["valid_cost"][-1]:
patience = state['patience']
# Saving model if decrease in validation cost
save(model, timings)
elif valid_cost >= timings["valid_cost"][-1] * state['cost_threshold']:
patience -= 1
print "** valid cost = %.4f, valid word-perplexity = %.4f, valid mean word-error = %.4f, valid emp. mutual information = %.4f, patience = %d" % (float(valid_cost), float(math.exp(valid_cost)), float(valid_misclass), valid_empirical_mutual_information, patience)
timings["train_cost"].append(train_cost/train_done)
timings["train_misclass"].append(float(train_misclass)/float(train_done))
timings["valid_cost"].append(valid_cost)
timings["valid_misclass"].append(valid_misclass)
timings["valid_emi"].append(valid_empirical_mutual_information)
# Reset train cost, train misclass and train done
train_cost = 0
train_misclass = 0
train_done = 0
# Plot histogram over validation costs
try:
pylab.figure()
bins = range(0, 50, 1)
pylab.hist(valid_cost_list, normed=1, histtype='bar')
pylab.savefig(model.state['save_dir'] + '/' + model.state['run_id'] + "_" + model.state['prefix'] + 'Valid_WordPerplexities_'+ str(step) + '.png')
except:
pass
# Print 5 of 10% validation samples with highest log-likelihood
if state['track_extrema_validation_samples']==True:
print " highest word log-likelihood valid samples: "
np.random.shuffle(valid_lowest_triples)
for i in range(valid_extrema_samples_to_print):
print " Sample: {}".format(" ".join(model.indices_to_words(numpy.ravel(valid_lowest_triples[i,:]))))
print " lowest word log-likelihood valid samples: "
np.random.shuffle(valid_highest_triples)
for i in range(valid_extrema_samples_to_print):
print " Sample: {}".format(" ".join(model.indices_to_words(numpy.ravel(valid_highest_triples[i,:]))))
# Plot histogram over empirical pointwise mutual informations
if state['compute_mutual_information'] == True:
try:
pylab.figure()
bins = range(0, 100, 1)
pylab.hist(valid_pmi_list, normed=1, histtype='bar')
pylab.savefig(model.state['save_dir'] + '/' + model.state['run_id'] + "_" + model.state['prefix'] + 'Valid_PMI_'+ str(step) + '.png')
except:
pass
if 'bleu_evaluation' in state and \
step % state['valid_freq'] == 0 and step > 1:
# Compute samples with beam search
logger.debug("Executing beam search to get targets for bleu, jaccard etc.")
samples, costs = beam_sampler.sample(contexts, n_samples=5, ignore_unk=True)
logger.debug("Finished beam search.")
assert len(samples) == len(contexts)
#print 'samples', samples
# Bleu evaluation
bleu = bleu_eval.evaluate(samples, targets)
print "** bleu score = %.4f " % bleu[0]
timings["valid_bleu"].append(bleu[0])
# Jaccard evaluation
jaccard = jaccard_eval.evaluate(samples, targets)
print "** jaccard score = %.4f " % jaccard
timings["valid_jaccard"].append(jaccard)
# Recall evaluation
recall_at_1 = recall_at_1_eval.evaluate(samples, targets)
print "** recall@1 score = %.4f " % recall_at_1
timings["valid_recall_at_1"].append(recall_at_1)
recall_at_5 = recall_at_5_eval.evaluate(samples, targets)
print "** recall@5 score = %.4f " % recall_at_5
timings["valid_recall_at_5"].append(recall_at_5)
mrr_at_5 = mrr_at_5_eval.evaluate(samples, targets)
# MRR evaluation (equivalent to mean average precision)
print "** mrr@5 score = %.4f " % mrr_at_5
timings["valid_mrr_at_5"].append(mrr_at_5)
# TF-IDF cosine similarity evaluation
tfidf_cs_at_1 = tfidf_cs_at_1_eval.evaluate(samples, targets)
print "** tfidf-cs@1 score = %.4f " % tfidf_cs_at_1
timings["tfidf_cs_at_1"].append(tfidf_cs_at_1)
tfidf_cs_at_5 = tfidf_cs_at_5_eval.evaluate(samples, targets)
print "** tfidf-cs@5 score = %.4f " % tfidf_cs_at_5
timings["tfidf_cs_at_5"].append(tfidf_cs_at_5)
step += 1
logger.debug("All done, exiting...")
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument("--resume", type=str, default="", help="Resume training from that state")
parser.add_argument("--prototype", type=str, help="Use the prototype", default='prototype_state')
args = parser.parse_args()
return args
if __name__ == "__main__":
args = parse_args()
main(args)