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sse_index.py
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sse_index.py
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# coding=utf-8
################################################################################
#
# Copyright (c) 2016 eBay Software Foundation.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
#################################################################################
#
# @Author: Mingkuan Liu
# @Email: mingkliu@ebay.com
# @Date: 2017-10-24
#
##################################################################################
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
# from builtins import str
from builtins import str
from builtins import range
import os
import sys
import codecs
import numpy as np
import tensorflow as tf
import data_utils
import sse_model
import text_encoder
import math
tf.app.flags.DEFINE_string("idx_model_dir", 'models-classification', "Trained model directory.")
tf.app.flags.DEFINE_string("idx_rawfilename", 'targetIDs', "raw target sequence file to be indexed")
tf.app.flags.DEFINE_string("idx_encodedIndexFile", 'targetEncodingIndex.tsv', "target sequece encoding index file.")
FLAGS = tf.app.flags.FLAGS
def createIndexFile( model, encoder, rawfile, max_seq_len, encodeIndexFile, session, batchsize=10000 ):
if not os.path.exists(rawfile):
print('Error!! Could not find raw target file to be indexed!! :%s' % rawfile)
exit(-1)
# start to indexing
outFile = codecs.open(encodeIndexFile, 'w', 'utf-8')
rawdata = codecs.open(rawfile, 'r', 'utf-8').readlines()
cnt = 0
print("Start indexing whole target space entries with current model ...")
for batchId in range(math.ceil(len(rawdata) / batchsize)):
tgtInputs, tgtIds, tgtSentences = [], [], []
for line in rawdata[batchId * batchsize:(batchId + 1) * batchsize]:
cnt += 1
# Get token-ids for the raw target sequence
info = line.strip().split('\t')
if len(info) != 2:
print("Missing field with error line in raw target file: %s " % line)
continue
tgtSentence, tgtId = info[0], info[1]
tgt_tokens = encoder.encode(tgtSentence.lower())
tgtlen = len(tgt_tokens)
if tgtlen > max_seq_len - 2:
print(
'Error Detected!!! \n Target:\n %s \n Its seq length is:%d, which is longer than MAX_SEQ_LENTH of %d. Try to increase limit!!!!' % (
tgtSentence, tgtlen, max_seq_len))
tgt_tokens = [text_encoder.PAD_ID] + tgt_tokens[:max_seq_len - 2] + [text_encoder.EOS_ID]
else:
tgt_tokens = [text_encoder.PAD_ID] * (max_seq_len - tgtlen - 1) + tgt_tokens + [text_encoder.EOS_ID]
tgtInputs.append(tgt_tokens)
tgtIds.append(tgtId)
tgtSentences.append(tgtSentence)
dict = model.get_target_encoding_feed_dict(tgtInputs)
targetsEncodings = session.run([model.norm_tgt_seq_embedding], feed_dict=dict)
targetsEncodings = np.vstack(targetsEncodings)
for idx in range(len(tgtSentences)):
outFile.write(
tgtIds[idx] + '\t' + tgtSentences[idx] + '\t' + ','.join([str(n) for n in targetsEncodings[idx]]) + '\n')
print("Done of all indexing total count:%d" % cnt)
outFile.close()
def index(model_dir, rawfile, encodeIndexFile, batchsize=10000):
if not os.path.exists( model_dir ):
print('Error! Model folder does not exist!! : %s' % model_dir)
exit(-1)
if not os.path.exists( os.path.join(model_dir, 'vocabulary.txt' ) ):
print('Error!! Could not find vocabulary file for encoder in folder :%s' % model_dir)
exit(-1)
encoder = text_encoder.SubwordTextEncoder(filename=os.path.join(model_dir, 'vocabulary.txt' ))
print("Loaded vocab size is: %d" % encoder.vocab_size)
cfg = tf.ConfigProto(log_device_placement=False, allow_soft_placement=True)
with tf.Session(config=cfg) as sess:
#load model
modelConfigs = data_utils.load_model_configs(model_dir)
model = sse_model.SSEModel( modelConfigs )
ckpt = tf.train.get_checkpoint_state(model_dir)
if ckpt:
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
model.saver.restore(sess, ckpt.model_checkpoint_path)
else:
print('Error!!!Could not load any model from specified folder: %s' % model_dir)
exit(-1)
# start to indexing
createIndexFile(model, encoder, rawfile, int(modelConfigs['max_seq_length']), encodeIndexFile, sess, batchsize)
def main(_):
if not FLAGS.idx_model_dir:
print("--idx_model_dir must be specified.")
sys.exit(1)
index( FLAGS.idx_model_dir, os.path.join(FLAGS.idx_model_dir,FLAGS.idx_rawfilename) , os.path.join(FLAGS.idx_model_dir, FLAGS.idx_encodedIndexFile) )
if __name__ == "__main__":
tf.app.run()