Python nltk.metrics() Examples

The following are 3 code examples for showing how to use nltk.metrics(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.

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Example 1
Project: razzy-spinner   Author: rafasashi   File: util.py    License: GNU General Public License v3.0 5 votes vote down vote up
def accuracy(chunker, gold):
    """
    Score the accuracy of the chunker against the gold standard.
    Strip the chunk information from the gold standard and rechunk it using
    the chunker, then compute the accuracy score.

    :type chunker: ChunkParserI
    :param chunker: The chunker being evaluated.
    :type gold: tree
    :param gold: The chunk structures to score the chunker on.
    :rtype: float
    """

    gold_tags = []
    test_tags = []
    for gold_tree in gold:
        test_tree = chunker.parse(gold_tree.flatten())
        gold_tags += tree2conlltags(gold_tree)
        test_tags += tree2conlltags(test_tree)

#    print 'GOLD:', gold_tags[:50]
#    print 'TEST:', test_tags[:50]
    return _accuracy(gold_tags, test_tags)


# Patched for increased performance by Yoav Goldberg <yoavg@cs.bgu.ac.il>, 2006-01-13
#  -- statistics are evaluated only on demand, instead of at every sentence evaluation
#
# SB: use nltk.metrics for precision/recall scoring?
# 
Example 2
Project: luscan-devel   Author: blackye   File: util.py    License: GNU General Public License v2.0 5 votes vote down vote up
def accuracy(chunker, gold):
    """
    Score the accuracy of the chunker against the gold standard.
    Strip the chunk information from the gold standard and rechunk it using
    the chunker, then compute the accuracy score.

    :type chunker: ChunkParserI
    :param chunker: The chunker being evaluated.
    :type gold: tree
    :param gold: The chunk structures to score the chunker on.
    :rtype: float
    """

    gold_tags = []
    test_tags = []
    for gold_tree in gold:
        test_tree = chunker.parse(gold_tree.flatten())
        gold_tags += tree2conlltags(gold_tree)
        test_tags += tree2conlltags(test_tree)

#    print 'GOLD:', gold_tags[:50]
#    print 'TEST:', test_tags[:50]
    return _accuracy(gold_tags, test_tags)


# Patched for increased performance by Yoav Goldberg <yoavg@cs.bgu.ac.il>, 2006-01-13
#  -- statistics are evaluated only on demand, instead of at every sentence evaluation
#
# SB: use nltk.metrics for precision/recall scoring?
# 
Example 3
Project: V1EngineeringInc-Docs   Author: V1EngineeringInc   File: util.py    License: Creative Commons Attribution Share Alike 4.0 International 5 votes vote down vote up
def accuracy(chunker, gold):
    """
    Score the accuracy of the chunker against the gold standard.
    Strip the chunk information from the gold standard and rechunk it using
    the chunker, then compute the accuracy score.

    :type chunker: ChunkParserI
    :param chunker: The chunker being evaluated.
    :type gold: tree
    :param gold: The chunk structures to score the chunker on.
    :rtype: float
    """

    gold_tags = []
    test_tags = []
    for gold_tree in gold:
        test_tree = chunker.parse(gold_tree.flatten())
        gold_tags += tree2conlltags(gold_tree)
        test_tags += tree2conlltags(test_tree)

    #    print 'GOLD:', gold_tags[:50]
    #    print 'TEST:', test_tags[:50]
    return _accuracy(gold_tags, test_tags)


# Patched for increased performance by Yoav Goldberg <yoavg@cs.bgu.ac.il>, 2006-01-13
#  -- statistics are evaluated only on demand, instead of at every sentence evaluation
#
# SB: use nltk.metrics for precision/recall scoring?
#