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| #!/usr/bin/env python | |
| # Copyright 2017, 2018 Google, Inc. All Rights Reserved. | |
| # | |
| # 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. | |
| # ============================================================================== | |
| """Extracts paths that are indicative of each relation.""" | |
| from __future__ import absolute_import | |
| from __future__ import division | |
| from __future__ import print_function | |
| import os | |
| import tensorflow as tf | |
| from . import path_model | |
| from . import lexnet_common | |
| tf.flags.DEFINE_string( | |
| 'dataset_dir', 'datasets', | |
| 'Dataset base directory') | |
| tf.flags.DEFINE_string( | |
| 'dataset', | |
| 'tratz/fine_grained', | |
| 'Subdirectory containing the corpus directories: ' | |
| 'subdirectory of dataset_dir') | |
| tf.flags.DEFINE_string( | |
| 'corpus', 'random/wiki', | |
| 'Subdirectory containing the corpus and split: ' | |
| 'subdirectory of dataset_dir/dataset') | |
| tf.flags.DEFINE_string( | |
| 'embeddings_base_path', 'embeddings', | |
| 'Embeddings base directory') | |
| tf.flags.DEFINE_string( | |
| 'logdir', 'logdir', | |
| 'Directory of model output files') | |
| tf.flags.DEFINE_integer( | |
| 'top_k', 20, 'Number of top paths to extract') | |
| tf.flags.DEFINE_float( | |
| 'threshold', 0.8, 'Threshold above which to consider paths as indicative') | |
| FLAGS = tf.flags.FLAGS | |
| def main(_): | |
| hparams = path_model.PathBasedModel.default_hparams() | |
| # First things first. Load the path data. | |
| path_embeddings_file = 'path_embeddings/{dataset}/{corpus}'.format( | |
| dataset=FLAGS.dataset, | |
| corpus=FLAGS.corpus) | |
| path_dim = (hparams.lemma_dim + hparams.pos_dim + | |
| hparams.dep_dim + hparams.dir_dim) | |
| path_embeddings, path_to_index = path_model.load_path_embeddings( | |
| os.path.join(FLAGS.embeddings_base_path, path_embeddings_file), | |
| path_dim) | |
| # Load and count the classes so we can correctly instantiate the model. | |
| classes_filename = os.path.join( | |
| FLAGS.dataset_dir, FLAGS.dataset, 'classes.txt') | |
| with open(classes_filename) as f_in: | |
| classes = f_in.read().splitlines() | |
| hparams.num_classes = len(classes) | |
| # We need the word embeddings to instantiate the model, too. | |
| print('Loading word embeddings...') | |
| lemma_embeddings = lexnet_common.load_word_embeddings( | |
| FLAGS.embeddings_base_path, hparams.lemma_embeddings_file) | |
| # Instantiate the model. | |
| with tf.Graph().as_default(): | |
| with tf.variable_scope('lexnet'): | |
| instance = tf.placeholder(dtype=tf.string) | |
| model = path_model.PathBasedModel( | |
| hparams, lemma_embeddings, instance) | |
| with tf.Session() as session: | |
| model_dir = '{logdir}/results/{dataset}/path/{corpus}'.format( | |
| logdir=FLAGS.logdir, | |
| dataset=FLAGS.dataset, | |
| corpus=FLAGS.corpus) | |
| saver = tf.train.Saver() | |
| saver.restore(session, os.path.join(model_dir, 'best.ckpt')) | |
| path_model.get_indicative_paths( | |
| model, session, path_to_index, path_embeddings, classes, | |
| model_dir, FLAGS.top_k, FLAGS.threshold) | |
| if __name__ == '__main__': | |
| tf.app.run() | |