TensorFlow-Models/syntaxnet/dragnn/python/evaluation_test.py

# Copyright 2017 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.
# ==============================================================================
"""Tests for parser evaluation."""

import tensorflow as tf

from dragnn.python import evaluation
from syntaxnet import sentence_pb2


class EvaluationTest(tf.test.TestCase):

  def _add_sentence(self, tags, heads, labels, corpus):
    """Adds a sentence to the corpus."""
    sentence = sentence_pb2.Sentence()
    for tag, head, label in zip(tags, heads, labels):
      sentence.token.add(word='x', start=0, end=0,
                         tag=tag, head=head, label=label)
    corpus.append(sentence.SerializeToString())

  def setUp(self):
    self._gold_corpus = []
    self._test_corpus = []

    # A correct sentence.
    self._add_sentence(['DT'], [-1], ['ROOT'], self._gold_corpus)
    self._add_sentence(['DT'], [-1], ['ROOT'], self._test_corpus)

    # An incorrect sentence.  There is one POS mistake, two head mistakes, and
    # one label mistake.  NB: Since the label mistake occurs on the one token
    # with a correct head, this sentence has three mistakes w.r.t. LAS.
    self._add_sentence(['DT', 'JJ', 'NN'], [2, 2, -1], ['det', 'amod', 'ROOT'],
                       self._gold_corpus)
    self._add_sentence(['xx', 'JJ', 'NN'], [1, 0, -1], ['det', 'amod', 'xxxx'],
                       self._test_corpus)

  def testCalculateParseMetrics(self):
    pos, uas, las = evaluation.calculate_parse_metrics(self._gold_corpus,
                                                       self._test_corpus)
    self.assertEqual(75, pos)
    self.assertEqual(50, uas)
    self.assertEqual(25, las)

  def testCalculateSegmentationMetrics(self):
    self._gold_corpus = []
    self._test_corpus = []

    def add_sentence_for_segment_eval(starts, ends, corpus):
      """Adds a sentence to the corpus."""
      sentence = sentence_pb2.Sentence()
      for start, end in zip(starts, ends):
        sentence.token.add(word='x', start=start, end=end)
      corpus.append(sentence.SerializeToString())

    # A test case with 5 gold words, 4 test words and 3 are correct.
    #  -gold tokens: 'This is a gold sentence'
    #  -test tokens: 'Thisis  a gold sentence'
    add_sentence_for_segment_eval(
        [0, 5, 8, 10, 15], [3, 6, 8, 13, 22], self._gold_corpus)
    add_sentence_for_segment_eval(
        [0, 8, 10, 15], [6, 8, 13, 22], self._test_corpus)

    # Another test case with 3 gold words, 5 test words and 2 correct words.
    #  -gold tokens: 'another gold sentence'
    #  -test tokens: 'another gold sen tence'
    add_sentence_for_segment_eval([0, 8, 13], [6, 11, 20], self._gold_corpus)
    add_sentence_for_segment_eval([0, 8, 13, 17, 21], [6, 11, 15, 19, 22],
                                  self._test_corpus)
    prec, rec, f1 = evaluation.calculate_segmentation_metrics(self._gold_corpus,
                                                              self._test_corpus)
    self.assertEqual(55.56, prec)
    self.assertEqual(62.50, rec)
    self.assertEqual(58.82, f1)

    summaries = evaluation.segmentation_summaries(self._gold_corpus,
                                                  self._test_corpus)
    self.assertEqual({
        'precision': 55.56,
        'recall': 62.50,
        'f1': 58.82,
        'eval_metric': 58.82
    }, summaries)

  def testParserSummaries(self):
    summaries = evaluation.parser_summaries(self._gold_corpus,
                                            self._test_corpus)
    self.assertEqual({
        'POS': 75,
        'UAS': 50,
        'LAS': 25,
        'eval_metric': 25  # equals LAS
    }, summaries)


if __name__ == '__main__':
  tf.test.main()