知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
39 lines
1.4 KiB
Python
39 lines
1.4 KiB
Python
# Natural Language Toolkit: Transformation-based learning
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#
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# Copyright (C) 2001-2026 NLTK Project
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# Author: Marcus Uneson <marcus.uneson@gmail.com>
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# based on previous (nltk2) version by
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# Christopher Maloof, Edward Loper, Steven Bird
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# URL: <https://www.nltk.org/>
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# For license information, see LICENSE.TXT
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# returns a list of errors in string format
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def error_list(train_sents, test_sents):
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"""
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Returns a list of human-readable strings indicating the errors in the
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given tagging of the corpus.
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:param train_sents: The correct tagging of the corpus
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:type train_sents: list(tuple)
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:param test_sents: The tagged corpus
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:type test_sents: list(tuple)
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"""
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hdr = ("%25s | %s | %s\n" + "-" * 26 + "+" + "-" * 24 + "+" + "-" * 26) % (
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"left context",
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"word/test->gold".center(22),
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"right context",
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)
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errors = [hdr]
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for train_sent, test_sent in zip(train_sents, test_sents):
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for wordnum, (word, train_pos) in enumerate(train_sent):
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test_pos = test_sent[wordnum][1]
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if train_pos != test_pos:
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left = " ".join("%s/%s" % w for w in train_sent[:wordnum])
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right = " ".join("%s/%s" % w for w in train_sent[wordnum + 1 :])
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mid = f"{word}/{test_pos}->{train_pos}"
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errors.append(f"{left[-25:]:>25} | {mid.center(22)} | {right[:25]}")
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return errors
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