run_multi-task_rnn.py

# -*- coding: utf-8 -*-
"""
Created on Sun Feb 28 16:23:37 2016

@author: Bing Liu (liubing@cmu.edu)
"""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import math
import os
import sys
import time

import numpy as np
from six.moves import xrange  # pylint: disable=redefined-builtin
import tensorflow as tf

import data_utils
import multi_task_model

import subprocess
import stat


#tf.app.flags.DEFINE_float("learning_rate", 0.1, "Learning rate.")
#tf.app.flags.DEFINE_float("learning_rate_decay_factor", 0.9,
#                          "Learning rate decays by this much.")
tf.app.flags.DEFINE_float("max_gradient_norm", 5.0,
                          "Clip gradients to this norm.")
tf.app.flags.DEFINE_integer("batch_size", 16,
                            "Batch size to use during training.")
tf.app.flags.DEFINE_integer("size", 128, "Size of each model layer.")
tf.app.flags.DEFINE_integer("word_embedding_size", 128, "Size of the word embedding")
tf.app.flags.DEFINE_integer("num_layers", 1, "Number of layers in the model.")
tf.app.flags.DEFINE_integer("in_vocab_size", 10000, "max vocab Size.")
tf.app.flags.DEFINE_integer("out_vocab_size", 10000, "max tag vocab Size.")
tf.app.flags.DEFINE_string("data_dir", "/tmp", "Data directory")
tf.app.flags.DEFINE_string("train_dir", "/tmp", "Training directory.")
tf.app.flags.DEFINE_integer("max_train_data_size", 0,
                            "Limit on the size of training data (0: no limit).")
tf.app.flags.DEFINE_integer("steps_per_checkpoint", 300,
                            "How many training steps to do per checkpoint.")
tf.app.flags.DEFINE_integer("max_training_steps", 10000,
                            "Max training steps.")
tf.app.flags.DEFINE_integer("max_test_data_size", 0,
                            "Max size of test set.")
tf.app.flags.DEFINE_boolean("use_attention", True,
                            "Use attention based RNN")
tf.app.flags.DEFINE_integer("max_sequence_length", 0,
                            "Max sequence length.")
tf.app.flags.DEFINE_float("dropout_keep_prob", 0.5,
                          "dropout keep cell input and output prob.")  
tf.app.flags.DEFINE_boolean("bidirectional_rnn", True,
                            "Use birectional RNN")
tf.app.flags.DEFINE_string("task", None, "Options: joint; intent; tagging")
FLAGS = tf.app.flags.FLAGS
    
if FLAGS.max_sequence_length == 0:
    print ('Please indicate max sequence length. Exit')
    exit()

if FLAGS.task is None:
    print ('Please indicate task to run. Available options: intent; tagging; joint')
    exit()

task = dict({'intent':0, 'tagging':0, 'joint':0})
if FLAGS.task == 'intent':
    task['intent'] = 1
elif FLAGS.task == 'tagging':
    task['tagging'] = 1
elif FLAGS.task == 'joint':
    task['intent'] = 1
    task['tagging'] = 1
    task['joint'] = 1
    
_buckets = [(FLAGS.max_sequence_length, FLAGS.max_sequence_length)]
#_buckets = [(3, 10), (10, 25)]

# metrics function using conlleval.pl
def conlleval(p, g, w, filename):
    '''
    INPUT:
    p :: predictions
    g :: groundtruth
    w :: corresponding words

    OUTPUT:
    filename :: name of the file where the predictions
    are written. it will be the input of conlleval.pl script
    for computing the performance in terms of precision
    recall and f1 score
    '''
    out = ''
    for sl, sp, sw in zip(g, p, w):
        out += 'BOS O O\n'
        for wl, wp, w in zip(sl, sp, sw):
            out += w + ' ' + wl + ' ' + wp + '\n'
        out += 'EOS O O\n\n'

    f = open(filename, 'w')
    f.writelines(out[:-1]) # remove the ending \n on last line
    f.close()

    return get_perf(filename)

def get_perf(filename):
    ''' run conlleval.pl perl script to obtain
    precision/recall and F1 score '''
    _conlleval = os.path.dirname(os.path.realpath(__file__)) + '/conlleval.pl'
    os.chmod(_conlleval, stat.S_IRWXU)  # give the execute permissions

    proc = subprocess.Popen(["perl",
                            _conlleval],
                            stdin=subprocess.PIPE,
                            stdout=subprocess.PIPE)

    stdout, _ = proc.communicate(''.join(open(filename).readlines()))
    for line in stdout.split('\n'):
        if 'accuracy' in line:
            out = line.split()
            break

    precision = float(out[6][:-2])
    recall = float(out[8][:-2])
    f1score = float(out[10])

    return {'p': precision, 'r': recall, 'f1': f1score}


def read_data(source_path, target_path, label_path, max_size=None):
  """Read data from source and target files and put into buckets.

  Args:
    source_path: path to the files with token-ids for the source input - word sequence.
    target_path: path to the file with token-ids for the target output - tag sequence;
      it must be aligned with the source file: n-th line contains the desired
      output for n-th line from the source_path.
    label_path: path to the file with token-ids for the sequence classification label
    max_size: maximum number of lines to read, all other will be ignored;
      if 0 or None, data files will be read completely (no limit).

  Returns:
    data_set: a list of length len(_buckets); data_set[n] contains a list of
      (source, target, label) tuple read from the provided data files that fit
      into the n-th bucket, i.e., such that len(source) < _buckets[n][0] and
      len(target) < _buckets[n][1]; source,  target, and label are lists of token-ids.
  """
  data_set = [[] for _ in _buckets]
  with tf.gfile.GFile(source_path, mode="r") as source_file:
    with tf.gfile.GFile(target_path, mode="r") as target_file:
      with tf.gfile.GFile(label_path, mode="r") as label_file:
        source, target, label = source_file.readline(), target_file.readline(), label_file.readline()
        counter = 0
        while source and target and label and (not max_size or counter < max_size):
          counter += 1
          if counter % 100000 == 0:
            print("  reading data line %d" % counter)
            sys.stdout.flush()
          source_ids = [int(x) for x in source.split()]
          target_ids = [int(x) for x in target.split()]
          label_ids = [int(x) for x in label.split()]
#          target_ids.append(data_utils.EOS_ID)
          for bucket_id, (source_size, target_size) in enumerate(_buckets):
            if len(source_ids) < source_size and len(target_ids) < target_size:
              data_set[bucket_id].append([source_ids, target_ids, label_ids])
              break
          source, target, label = source_file.readline(), target_file.readline(), label_file.readline()
  return data_set # 3 outputs in each unit: source_ids, target_ids, label_ids 

def create_model(session, source_vocab_size, target_vocab_size, label_vocab_size):
  """Create model and initialize or load parameters in session."""
  with tf.variable_scope("model", reuse=None):
    model_train = multi_task_model.MultiTaskModel(
          source_vocab_size, target_vocab_size, label_vocab_size, _buckets,
          FLAGS.word_embedding_size, FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size,
          dropout_keep_prob=FLAGS.dropout_keep_prob, use_lstm=True,
          forward_only=False, 
          use_attention=FLAGS.use_attention,
          bidirectional_rnn=FLAGS.bidirectional_rnn,
          task=task)
  with tf.variable_scope("model", reuse=True):
    model_test = multi_task_model.MultiTaskModel(
          source_vocab_size, target_vocab_size, label_vocab_size, _buckets,
          FLAGS.word_embedding_size, FLAGS.size, FLAGS.num_layers, FLAGS.max_gradient_norm, FLAGS.batch_size,
          dropout_keep_prob=FLAGS.dropout_keep_prob, use_lstm=True,
          forward_only=True, 
          use_attention=FLAGS.use_attention,
          bidirectional_rnn=FLAGS.bidirectional_rnn,
          task=task)

  ckpt = tf.train.get_checkpoint_state(FLAGS.train_dir)
  if ckpt and tf.gfile.Exists(ckpt.model_checkpoint_path):
    print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
    model_train.saver.restore(session, ckpt.model_checkpoint_path)
  else:
    print("Created model with fresh parameters.")
    session.run(tf.initialize_all_variables())
  return model_train, model_test
        
def train():
  print ('Applying Parameters:')
  for k,v in FLAGS.__dict__['__flags'].iteritems():
    print ('%s: %s' % (k, str(v)))
  print("Preparing data in %s" % FLAGS.data_dir)
  vocab_path = ''
  tag_vocab_path = ''
  label_vocab_path = ''
  in_seq_train, out_seq_train, label_train, in_seq_dev, out_seq_dev, label_dev, in_seq_test, out_seq_test, label_test, vocab_path, tag_vocab_path, label_vocab_path = data_utils.prepare_multi_task_data(
    FLAGS.data_dir, FLAGS.in_vocab_size, FLAGS.out_vocab_size)     
     
  result_dir = FLAGS.train_dir + '/test_results'
  if not os.path.isdir(result_dir):
      os.makedirs(result_dir)

  current_taging_valid_out_file = result_dir + '/tagging.valid.hyp.txt'
  current_taging_test_out_file = result_dir + '/tagging.test.hyp.txt'

  vocab, rev_vocab = data_utils.initialize_vocabulary(vocab_path)
  tag_vocab, rev_tag_vocab = data_utils.initialize_vocabulary(tag_vocab_path)
  label_vocab, rev_label_vocab = data_utils.initialize_vocabulary(label_vocab_path)
    
  with tf.Session() as sess:
    # Create model.
    print("Max sequence length: %d." % _buckets[0][0])
    print("Creating %d layers of %d units." % (FLAGS.num_layers, FLAGS.size))
    
    model, model_test = create_model(sess, len(vocab), len(tag_vocab), len(label_vocab))
    print ("Creating model with source_vocab_size=%d, target_vocab_size=%d, and label_vocab_size=%d." % (len(vocab), len(tag_vocab), len(label_vocab)))

    # Read data into buckets and compute their sizes.
    print ("Reading train/valid/test data (training set limit: %d)."
           % FLAGS.max_train_data_size)
    dev_set = read_data(in_seq_dev, out_seq_dev, label_dev)
    test_set = read_data(in_seq_test, out_seq_test, label_test)
    train_set = read_data(in_seq_train, out_seq_train, label_train)
    train_bucket_sizes = [len(train_set[b]) for b in xrange(len(_buckets))]
    train_total_size = float(sum(train_bucket_sizes))

    train_buckets_scale = [sum(train_bucket_sizes[:i + 1]) / train_total_size
                           for i in xrange(len(train_bucket_sizes))]

    # This is the training loop.
    step_time, loss = 0.0, 0.0
    current_step = 0

    best_valid_score = 0
    best_test_score = 0
    while model.global_step.eval() < FLAGS.max_training_steps:
      random_number_01 = np.random.random_sample()
      bucket_id = min([i for i in xrange(len(train_buckets_scale))
                       if train_buckets_scale[i] > random_number_01])

      # Get a batch and make a step.
      start_time = time.time()
      encoder_inputs, tags, tag_weights, batch_sequence_length, labels = model.get_batch(train_set, bucket_id)
      if task['joint'] == 1:
        _, step_loss, tagging_logits, classification_logits = model.joint_step(sess, encoder_inputs, tags, tag_weights, labels,
                                   batch_sequence_length, bucket_id, False)
      elif task['tagging'] == 1:
        _, step_loss, tagging_logits = model.tagging_step(sess, encoder_inputs, tags, tag_weights,
                                   batch_sequence_length, bucket_id, False)
      elif task['intent'] == 1:
        _, step_loss, classification_logits = model.classification_step(sess, encoder_inputs, labels,
                                   batch_sequence_length, bucket_id, False)                                   

      step_time += (time.time() - start_time) / FLAGS.steps_per_checkpoint
      loss += step_loss / FLAGS.steps_per_checkpoint
      current_step += 1

      # Once in a while, we save checkpoint, print statistics, and run evals.
      if current_step % FLAGS.steps_per_checkpoint == 0:
        perplexity = math.exp(loss) if loss < 300 else float('inf')
        print ("global step %d step-time %.2f. Training perplexity %.2f" 
            % (model.global_step.eval(), step_time, perplexity))
        sys.stdout.flush()
        # Save checkpoint and zero timer and loss.
        checkpoint_path = os.path.join(FLAGS.train_dir, "model.ckpt")
        model.saver.save(sess, checkpoint_path, global_step=model.global_step)
        step_time, loss = 0.0, 0.0 
        
        def run_valid_test(data_set, mode): # mode: Eval, Test
        # Run evals on development/test set and print the accuracy.
            word_list = list() 
            ref_tag_list = list() 
            hyp_tag_list = list()
            ref_label_list = list()
            hyp_label_list = list()
            correct_count = 0
            accuracy = 0.0
            tagging_eval_result = dict()
            for bucket_id in xrange(len(_buckets)):
              eval_loss = 0.0
              count = 0
              for i in xrange(len(data_set[bucket_id])):
                count += 1
                encoder_inputs, tags, tag_weights, sequence_length, labels = model_test.get_one(
                  data_set, bucket_id, i)
                tagging_logits = []
                classification_logits = []
                if task['joint'] == 1:
                  _, step_loss, tagging_logits, classification_logits = model_test.joint_step(sess, encoder_inputs, tags, tag_weights, labels,
                                             sequence_length, bucket_id, True)
                elif task['tagging'] == 1:
                  _, step_loss, tagging_logits = model_test.tagging_step(sess, encoder_inputs, tags, tag_weights,
                                             sequence_length, bucket_id, True)
                elif task['intent'] == 1:
                  _, step_loss, classification_logits = model_test.classification_step(sess, encoder_inputs, labels,
                                             sequence_length, bucket_id, True) 
                eval_loss += step_loss / len(data_set[bucket_id])
                hyp_label = None
                if task['intent'] == 1:
                  ref_label_list.append(rev_label_vocab[labels[0][0]])
                  hyp_label = np.argmax(classification_logits[0],0)
                  hyp_label_list.append(rev_label_vocab[hyp_label])
                  if labels[0] == hyp_label:
                    correct_count += 1
                if task['tagging'] == 1:
                  word_list.append([rev_vocab[x[0]] for x in encoder_inputs[:sequence_length[0]]])
                  ref_tag_list.append([rev_tag_vocab[x[0]] for x in tags[:sequence_length[0]]])
                  hyp_tag_list.append([rev_tag_vocab[np.argmax(x)] for x in tagging_logits[:sequence_length[0]]])

            accuracy = float(correct_count)*100/count
            if task['intent'] == 1:
              print("  %s accuracy: %.2f %d/%d" % (mode, accuracy, correct_count, count))
              sys.stdout.flush()
            if task['tagging'] == 1:
              if mode == 'Eval':
                  taging_out_file = current_taging_valid_out_file
              elif mode == 'Test':
                  taging_out_file = current_taging_test_out_file
              tagging_eval_result = conlleval(hyp_tag_list, ref_tag_list, word_list, taging_out_file)
              print("  %s f1-score: %.2f" % (mode, tagging_eval_result['f1']))
              sys.stdout.flush()
            return accuracy, tagging_eval_result
            
        # valid
        valid_accuracy, valid_tagging_result = run_valid_test(dev_set, 'Eval')        
        if task['tagging'] == 1 and valid_tagging_result['f1'] > best_valid_score:
          best_valid_score = valid_tagging_result['f1']
          # save the best output file
          subprocess.call(['mv', current_taging_valid_out_file, current_taging_valid_out_file + '.best_f1_%.2f' % best_valid_score])
        # test, run test after each validation for development purpose.
        test_accuracy, test_tagging_result = run_valid_test(test_set, 'Test')        
        if task['tagging'] == 1 and test_tagging_result['f1'] > best_test_score:
          best_test_score = test_tagging_result['f1']
          # save the best output file
          subprocess.call(['mv', current_taging_test_out_file, current_taging_test_out_file + '.best_f1_%.2f' % best_test_score])
          
def main(_):
    train()

if __name__ == "__main__":
  tf.app.run()