本文整理汇总了Python中nltk.metrics方法的典型用法代码示例。如果您正苦于以下问题：Python nltk.metrics方法的具体用法？Python nltk.metrics怎么用？Python nltk.metrics使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类nltk的用法示例。
在下文中一共展示了nltk.metrics方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: accuracy
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import metrics [as 别名]
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?
# 示例2: accuracy
# 需要导入模块: import nltk [as 别名]
# 或者: from nltk import metrics [as 别名]
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?
#